From 93e93d3f6ea5d6c7773c4467485e0bf278817378 Mon Sep 17 00:00:00 2001 From: garv Date: Tue, 1 Sep 2026 18:04:55 +0000 Subject: [PATCH 01/29] waypoint: model config, DiT components, ring KV backend, weight loader Native port of the Waypoint 1.5-1B autoregressive video world model. Additive only -- no existing mstar file is touched. - config.py WaypointConfig, 720P and 360P variants, ring sizing. - components/ rope.py (OrthoRoPE over disjoint head-dim bands), layers.py (AdaLN-Zero, MLPFusion, CondHead), attention.py (GQA with value-residual threading), kv_backend.py (model-owned ring + FlexAttention BlockMask), dit.py (24 blocks, the 4+1 pass loop). - weight_loader.py maps the 1.86B-parameter checkpoint onto the 1.28B instantiated model, including the cond_proj tie shared 24x and re-established after to_empty(). Three choices worth flagging at review time. The KV cache is a model-owned ring and get_node_resources() returns []. For this model the cache *is* the world state, so eviction is amnesia rather than a cost. An engine-owned ring is not currently expressible: SequenceView asserts start == 0 ("dense attention reads a stream from its first page"), and RetentionPolicy is declared but unimplemented. When both land the swap is local to kv_backend.py and nothing else changes. FlexAttention is used instead of FlashInfer, at a model-local seam, for numerical parity with the reference. Attention is per-layer heterogeneous (18 local dense window-16, 6 global stride-8 dilated window-128), which the BlockMask expresses directly. There is no working eager mode. Eager ignores a no-op-mask_mod BlockMask and silently attends to unwritten ring slots -- wrong output, no error -- so the flex_attention call is compiled unconditionally. --- mstar/model/waypoint/__init__.py | 0 mstar/model/waypoint/components/__init__.py | 80 ++ mstar/model/waypoint/components/attention.py | 145 +++ mstar/model/waypoint/components/dit.py | 501 +++++++++ mstar/model/waypoint/components/kv_backend.py | 562 ++++++++++ mstar/model/waypoint/components/layers.py | 315 ++++++ mstar/model/waypoint/components/rope.py | 185 ++++ mstar/model/waypoint/config.py | 303 ++++++ mstar/model/waypoint/weight_loader.py | 978 ++++++++++++++++++ 9 files changed, 3069 insertions(+) create mode 100644 mstar/model/waypoint/__init__.py create mode 100644 mstar/model/waypoint/components/__init__.py create mode 100644 mstar/model/waypoint/components/attention.py create mode 100644 mstar/model/waypoint/components/dit.py create mode 100644 mstar/model/waypoint/components/kv_backend.py create mode 100644 mstar/model/waypoint/components/layers.py create mode 100644 mstar/model/waypoint/components/rope.py create mode 100644 mstar/model/waypoint/config.py create mode 100644 mstar/model/waypoint/weight_loader.py diff --git a/mstar/model/waypoint/__init__.py b/mstar/model/waypoint/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/mstar/model/waypoint/components/__init__.py b/mstar/model/waypoint/components/__init__.py new file mode 100644 index 000000000..20eccd866 --- /dev/null +++ b/mstar/model/waypoint/components/__init__.py @@ -0,0 +1,80 @@ +"""Waypoint-1.5-1B component modules. + +The DiT (``dit.py``, ``attention.py``, ``layers.py``, ``rope.py``) is a native +port of ``world_engine/src/model/world_model.py``. ``apply_inference_patches`` +runs unconditionally in the reference's ``WorldEngine.__init__``, so the +*patched* model is the shipped one -- but the port does not follow it uniformly: +it takes the patched **fused QKV** and keeps the unpatched **packed** +``MLPFusion.fc1``, because that patch *splits* the packed weight rather than +merging it, and packed is the checkpoint's own storage. Both forms are +algebraically identical (measured 0.0 either way). See +``docs/waypoint/CONTRACTS.md`` section 5. + +``kv_backend.py`` is deliberately *not* part of the module tree: the ring KV +cache is plain classes holding eagerly-allocated tensors, so a meta build and +``to_empty`` cannot touch it, and so it can be swapped for an engine-owned cache +behind ``WaypointKVBackend`` without the DiT noticing (backlog B1). The TAEHV +streaming VAE (``taehv.py``) is phase 7 and does not exist yet. +""" + +from mstar.model.waypoint.components.attention import WaypointAttention +from mstar.model.waypoint.components.dit import ( + WaypointDiT, + WaypointDiTBlock, + WaypointPosIds, +) +from mstar.model.waypoint.components.kv_backend import ( + FlexRingBackend, + LayerRingCache, + WaypointKVBackend, + describe_ring_memory, + flex_attention_masked, + make_block_mask, + ring_memory_bytes, +) +from mstar.model.waypoint.components.layers import ( + FP32_MODULE_PATHS, + MLP, + AdaLN, + CondHead, + ControllerInputEmbedding, + DeviceTableCache, + MLPFusion, + NoiseConditioner, + ada_gate, + ada_rmsnorm, + rms_norm, +) +from mstar.model.waypoint.components.rope import ( + OrthoRoPE, + OrthoRoPEAngles, + apply_ortho_rope, +) + +__all__ = [ + "FP32_MODULE_PATHS", + "MLP", + "AdaLN", + "CondHead", + "ControllerInputEmbedding", + "DeviceTableCache", + "FlexRingBackend", + "LayerRingCache", + "MLPFusion", + "NoiseConditioner", + "OrthoRoPE", + "OrthoRoPEAngles", + "WaypointAttention", + "WaypointDiT", + "WaypointDiTBlock", + "WaypointKVBackend", + "WaypointPosIds", + "ada_gate", + "ada_rmsnorm", + "apply_ortho_rope", + "describe_ring_memory", + "flex_attention_masked", + "make_block_mask", + "ring_memory_bytes", + "rms_norm", +] diff --git a/mstar/model/waypoint/components/attention.py b/mstar/model/waypoint/components/attention.py new file mode 100644 index 000000000..32ae016b4 --- /dev/null +++ b/mstar/model/waypoint/components/attention.py @@ -0,0 +1,145 @@ +"""Waypoint self-attention: fused QKV, value residual, OrthoRoPE, ring cache. + +Port of ``world_engine/src/model/attn.py::Attn`` **as it is actually served**. +``WorldEngine.__init__`` applies ``patch_model.apply_inference_patches`` +unconditionally, so the shipped module is ``patch_model.MergedQKVAttn``: three +separate ``q_proj``/``k_proj``/``v_proj`` GEMMs fused into one. This port +implements the fused form (CONTRACTS section 5, DECISIONS D5), which makes the +*patched* reference the parity target -- one fused GEMM is not bit-identical to +three separate ones. + +Facts that are load-bearing, in the order they bite: + + * **Fused layout.** ``qkv_proj.weight`` is ``cat([q, k, v], dim=0)`` -- + ``[q_out + 2 * kv_out, d_model] = [4096, 2048]`` here, rows 0:2048 = Q, + 2048:3072 = K, 3072:4096 = V, verified against + ``patch_model.py:110-112`` (the ``cat``) and ``patch_model.py:117`` (the + matching ``split((q_out, kv_out, kv_out), dim=-1)``). GQA makes the three + slabs unequal, so a wrong order is a shape error for Q but *not* between K + and V -- swapping those two loads cleanly and produces wrong video. + * **Order inside forward:** value-residual lerp FIRST, then ``rms_norm(q, k)``, + then RoPE on Q and K, then the cache upsert, then attention. Reordering the + lerp past the norm changes what enters the cache, permanently, for every + future frame that attends to it (CONTRACTS section 4.2). + * **``v1`` is layer 0's V captured PRE-lerp** and threaded unchanged through + all 24 layers. Layer 0 lerps against itself; every later layer lerps against + layer 0. The **lerped** V is what the cache stores. + * **Q and K are RMS-normed and rotated; V is neither.** K enters the ring + already rotated, so replayed history is never re-rotated. + * **The backend's argument order is not the reference's.** The reference calls + ``kv_cache.upsert(k, v, pos_ids, layer_idx)``; the port's protocol is + ``backend.upsert(k, v, layer_idx, frame_pos)``. Both trailing arguments are + positional ints/tensors, so a verbatim transcription passes a layer index + where a frame position belongs, raises nothing, and drifts (CONTRACTS + section 3). + +This module talks to the world state only through ``WaypointKVBackend``. It +never sees the ring, the slot arithmetic or the ``BlockMask``: ``meta`` is +opaque and goes straight back into ``attend``, which is what keeps a future +engine-owned KV implementation a one-class swap (DECISIONS D3). +""" + +import torch +from torch import Tensor, nn + +from mstar.model.waypoint.components.kv_backend import WaypointKVBackend +from mstar.model.waypoint.components.layers import rms_norm +from mstar.model.waypoint.components.rope import OrthoRoPE +from mstar.model.waypoint.config import WaypointConfig + +__all__ = ["WaypointAttention"] + + +class WaypointAttention(nn.Module): + """One layer of causal frame attention over the ring KV cache. + + Shapes: ``x`` is ``[B, N*T, D]`` (N frames of T tokens, flattened -- N is 1 + for every served call), and the head layout inside is ``[B, H, N*T, d_head]`` + with 32 query heads over 16 KV heads (GQA, 2:1). + """ + + def __init__(self, config: WaypointConfig, layer_idx: int): + super().__init__() + if config.gated_attn: + # The reference supports a per-head sigmoid gate on the attention + # output; this checkpoint does not use it and it carries a + # `gate_proj` the loader could not fill. Same hard-fail stance as + # Wan22DiT's qk_norm check: a drifting checkpoint must not be + # silently mis-served. + raise ValueError( + "WaypointAttention implements the ungated attention path only; " + "this config declares gated_attn=True." + ) + + self.config = config + self.layer_idx = layer_idx + self.value_residual = config.value_residual + + self.n_heads = config.n_heads + self.n_kv_heads = config.n_kv_heads + self.d_head = config.d_head + self.enable_gqa = config.enable_gqa + + # Split widths for the fused projection, in the concat order baked into + # the weight: Q (2048) | K (1024) | V (1024). + self.q_out = self.n_heads * self.d_head + self.kv_out = self.n_kv_heads * self.d_head + + if self.value_residual: + # Per-layer scalar; the checkpoint value is what makes layer 0's V + # matter more or less deep in the stack. Initialized to the + # reference's 0.5 so a no-checkpoint structural build is sane. + self.v_lamb = nn.Parameter(torch.tensor(0.5)) + + self.qkv_proj = nn.Linear(config.d_model, self.q_out + 2 * self.kv_out, bias=False) + self.out_proj = nn.Linear(config.d_model, config.d_model, bias=False) + + # Stateless and parameter-free, but kept as a per-layer submodule + # because that is the reference's shape and the call site reads + # `self.rope(q, rope_angles)`. The angles themselves are built once per + # forward at the DiT root and passed down. + self.rope = OrthoRoPE(config) + + def forward( + self, + x: Tensor, + frame_pos: Tensor, + rope_angles: tuple[Tensor, Tensor], + v1: Tensor | None, + backend: WaypointKVBackend, + ) -> tuple[Tensor, Tensor]: + """``x`` ``[B, N*T, D]`` -> ``(out [B, N*T, D], v1 [B, H_kv, N*T, d_head])``. + + ``frame_pos`` is the ``[]`` int64 ring clock (the reference's + ``pos_ids["f_pos"][0, 0]``); it is the only piece of ``pos_ids`` this + layer needs, since the RoPE angles are precomputed at the root. ``v1`` + is ``None`` at layer 0 and layer 0's pre-lerp V thereafter. + """ + B, T = x.shape[:2] + + # One GEMM, then slice: the split widths and their order are the + # transpose of the qkv_proj.weight row order (Q | K | V). + q, k, v = self.qkv_proj(x).split((self.q_out, self.kv_out, self.kv_out), dim=-1) + q = q.reshape(B, T, self.n_heads, self.d_head).transpose(1, 2) + k = k.reshape(B, T, self.n_kv_heads, self.d_head).transpose(1, 2) + v = v.reshape(B, T, self.n_kv_heads, self.d_head).transpose(1, 2) + + if self.value_residual: + # v1 is captured BEFORE the lerp, so layer 0 returns its raw V while + # storing the (self-)lerped one. The lerped V is what the cache + # keeps and what every future frame attends to. + v1 = v if v1 is None else v1 + v = torch.lerp(v, v1.view_as(v), self.v_lamb) + + # Q/K only: V is neither normed nor rotated, at any layer. + q, k = rms_norm(q), rms_norm(k) + q, k = self.rope(q, rope_angles), self.rope(k, rope_angles) + + # NOTE the argument order -- (k, v, layer_idx, frame_pos), NOT the + # reference's (k, v, pos_ids, layer_idx). CONTRACTS section 3. + # `k` goes in post-RoPE; history comes back already rotated. + k, v, meta = backend.upsert(k, v, self.layer_idx, frame_pos) + + y = backend.attend(q, k, v, meta, enable_gqa=self.enable_gqa) + y = y.transpose(1, 2).reshape(B, T, -1) + return self.out_proj(y), v1 diff --git a/mstar/model/waypoint/components/dit.py b/mstar/model/waypoint/components/dit.py new file mode 100644 index 000000000..b68fa3558 --- /dev/null +++ b/mstar/model/waypoint/components/dit.py @@ -0,0 +1,501 @@ +"""The Waypoint-1.5 DiT: 24 blocks, the 4+1 pass driver, and the world clock. + +Port of ``world_engine/src/model/world_model.py`` (``WorldDiTBlock``, +``WorldDiT``, ``WorldModel``) plus the per-frame driver from +``world_engine/src/world_engine.py`` (``gen_frame`` / ``append_frame`` / +``_denoise_pass`` / ``_cache_pass``). Read ``docs/waypoint/CONTRACTS.md`` +sections 1, 4.4 and 4.6 alongside it. + +Facts that are load-bearing: + + * **Five forwards per generated frame.** Four frozen Euler denoise passes over + ``config.scheduler_sigmas``, then one unfrozen committing pass at sigma=0. + Only the last writes the ring. The loop is plain Python inside this module, + not engine steps (DECISIONS D4): with a model-owned cache the engine has + nothing to do between denoise passes. + * **The ``.clone()`` between denoise and commit is load-bearing**, not + defensive copying. See ``generate_frame``. + * **Two clocks.** ``f_pos`` (ring clock: buckets, slots, visibility) and + ``t_pos = f_pos * config.ts_mult`` (RoPE time coordinate). ``ts_mult == 1`` + for this checkpoint so they are numerically equal, and they are still + threaded separately -- conflating them is silent drift at any other serving + fps (CONTRACTS section 4.4). + * **``cond_proj`` is physically shared by all 24 blocks.** ``__init__`` ties + it, and ``retie_cond_proj()`` exists as a public method because + ``to_empty(device)`` silently un-ties it (``Module._apply`` has no + cross-module memo). The loader MUST call it after ``to_empty`` or it gets + +0.6B resident parameters and 23 blocks of ``cond_proj`` nobody fills, with + no error (CONTRACTS section 6.1). + * **Controller conditioning is fused on 8 of 24 layers** (``i % 3 == 0``), on + ``rms_norm(x)`` and ``rms_norm(ctrl_emb)``. The other 16 blocks have no + ``ctrl_mlpfusion`` submodule at all. + * **No prompt cross-attention.** ``WaypointConfig.__post_init__`` rejects + ``prompt_conditioning``, so there is no dead branch here to mislead a reader + into thinking the checkpoint has one. + +**Module-tree deviation, for the weight loader.** The reference splits this into +``WorldModel`` (embeddings, patchify, head) wrapping ``WorldDiT`` +(``transformer.blocks``); the port collapses them into one ``WaypointDiT``, so +blocks live at ``blocks.{i}`` and not ``transformer.blocks.{i}``. That is one +extra prefix rename on top of CONTRACTS section 6's seven transforms +(``transformer.blocks.`` -> ``blocks.``); everything below that prefix keeps the +reference's spelling exactly. ``layers.FP32_MODULE_PATHS`` already assumes the +collapse (``denoise_step_emb`` is named relative to this root). See +DECISIONS D12. +""" + +from typing import NamedTuple + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from mstar.model.waypoint.components.attention import WaypointAttention +from mstar.model.waypoint.components.kv_backend import WaypointKVBackend +from mstar.model.waypoint.components.layers import ( + FP32_MODULE_PATHS, + MLP, + AdaLN, + CondHead, + ControllerInputEmbedding, + DeviceTableCache, + MLPFusion, + NoiseConditioner, + ada_gate, + ada_rmsnorm, + rms_norm, +) +from mstar.model.waypoint.components.rope import OrthoRoPEAngles +from mstar.model.waypoint.config import WaypointConfig + +__all__ = ["WaypointDiT", "WaypointDiTBlock", "WaypointPosIds"] + + +class WaypointPosIds(NamedTuple): + """The four position streams for one frame. + + The reference packs these into a ``TensorDict`` keyed ``f_pos``/``t_pos``/ + ``y_pos``/``x_pos``; a ``NamedTuple`` says the same thing without pulling + ``tensordict`` into mstar's dependency set, and it is a pytree so it survives + ``torch.compile`` unchanged. + + * ``f_pos`` -- ``[]`` int64, the ring clock. The reference broadcasts it to + ``[B, T]`` only because ``TensorDict`` demands a uniform batch shape; the + cache reads ``f_pos[0, 0]`` and nothing else ever looks at it. The port + keeps it a scalar, which is exactly what ``WaypointKVBackend.upsert`` + takes. + * ``t_pos`` -- ``[B, T]`` int64, the RoPE time coordinate, + ``f_pos * ts_mult``. Equal to ``f_pos`` for this checkpoint; see the + module docstring. + * ``y_pos`` / ``x_pos`` -- ``[B, T]`` int64 token-grid coordinates, + ``row = i // width``, ``col = i % width``. + """ + + f_pos: Tensor + t_pos: Tensor + y_pos: Tensor + x_pos: Tensor + + +class WaypointDiTBlock(nn.Module): + """One DiT block: adaLN-modulated causal frame attention, optional controller + fusion, adaLN-modulated MLP. + + The six modulation tensors come from this block's ``cond_head``; its + ``bias_in`` is genuinely per-layer while its ``cond_proj`` matrices are + aliases of block 0's (see ``WaypointDiT.retie_cond_proj``). + """ + + def __init__(self, config: WaypointConfig, layer_idx: int): + super().__init__() + self.config = config + self.layer_idx = layer_idx + + self.attn = WaypointAttention(config, layer_idx) + self.mlp = MLP(config.d_model, config.d_model * config.mlp_ratio, config.d_model) + self.cond_head = CondHead(config) + + # 8 of 24 layers. Absent -- not None-gated at the tensor level -- on the + # other 16, so the parameter tree itself records which layers fuse. + self.ctrl_mlpfusion = MLPFusion(config) if layer_idx in config.ctrl_layers else None + + def forward( + self, + x: Tensor, + frame_pos: Tensor, + rope_angles: tuple[Tensor, Tensor], + cond: Tensor, + ctrl_emb: Tensor, + v1: Tensor | None, + backend: WaypointKVBackend, + ) -> tuple[Tensor, Tensor]: + """``x`` ``[B, N*T, D]``, ``cond``/``ctrl_emb`` ``[B, N, D]`` (per frame) + -> ``(x, v1)``. ``v1`` is layer 0's pre-lerp V, threaded down the stack. + + Only ``f_pos`` of the reference's ``pos_ids`` reaches this far -- the + RoPE angles are built once at the root from ``t/y/x_pos`` -- so the + scalar ring clock is passed directly rather than the whole bundle. + """ + s0, b0, g0, s1, b1, g1 = self.cond_head(cond) + + # Causal frame attention. + residual = x + x = ada_rmsnorm(x, s0, b0) + x, v1 = self.attn(x, frame_pos, rope_angles, v1, backend) + x = ada_gate(x, g0) + residual + + # Controller conditioning. Both operands are bare-RMS-normed (no adaLN + # scale here); the fusion output is added ungated. + if self.ctrl_mlpfusion is not None: + x = self.ctrl_mlpfusion(rms_norm(x), rms_norm(ctrl_emb)) + x + + # MLP. + x = ada_gate(self.mlp(ada_rmsnorm(x, s1, b1)), g1) + x + + return x, v1 + + +class WaypointDiT(nn.Module): + """Conv2d patchify -> 24 blocks -> adaLN head -> unpatchify, plus the 4+1 + per-frame driver. + + Built on the meta device and materialized by ``weight_loader`` in a fixed + order (CONTRACTS section 6.1):: + + with torch.device("meta"): + dit = WaypointDiT(config) + dit.cast_serving_dtypes() # bf16 + fp32 islands, on meta + dit.to_empty(device=device) # un-ties cond_proj + dit.retie_cond_proj() # MUST follow to_empty + load_weights_into(dit, ...) + + The KV ring is NOT part of this module: it is derived state owned by a + ``WaypointKVBackend`` built after materialization, so no ``to_empty`` or + ``state_dict`` walk can leave it holding garbage (DECISIONS D1/D3). + """ + + def __init__(self, config: WaypointConfig): + super().__init__() + self.config = config + self.patch = tuple(config.patch) + + self.denoise_step_emb = NoiseConditioner(config.d_model) + self.ctrl_emb = ControllerInputEmbedding(config) + self.rope_angles = OrthoRoPEAngles(config) + self.blocks = nn.ModuleList( + WaypointDiTBlock(config, layer_idx) for layer_idx in range(config.n_layers) + ) + + C, D = config.channels, config.d_model + ph, pw = self.patch + self.patchify = nn.Conv2d(C, D, kernel_size=self.patch, stride=self.patch, bias=False) + self.out_norm = AdaLN(D) + # The checkpoint stores this as a [D, C, ph, pw] conv kernel; the loader + # permutes it into this Linear's [C*ph*pw, D] and expands the [C] bias + # over the patch (CONTRACTS section 6, transforms 1-2). + self.unpatchify = nn.Linear(D, C * ph * pw, bias=True) + + # Token-grid coordinates: derived state, so they live outside the module + # tree (a non-persistent buffer would survive `to_empty` as + # uninitialized garbage that no completeness check covers -- the stance + # layers.DeviceTableCache exists for). device="cpu" is required: this + # runs under `with torch.device("meta")`. + idx = torch.arange(config.tokens_per_frame, dtype=torch.long, device="cpu") + self._grid = DeviceTableCache( + idx.div(config.width, rounding_mode="floor"), idx.remainder(config.width) + ) + # Per (device, dtype) sigma schedule; see _sigma_schedule. + self._sigma_cache: dict[tuple[torch.device, torch.dtype], Tensor] = {} + self._regions_compiled = False + + # Tie now so a plain (non-meta) build is already correct; retie after + # to_empty for the meta build path. + self.retie_cond_proj() + + # ---- Build-time surface ------------------------------------------------ + + @property + def dtype(self) -> torch.dtype: + """Bulk compute dtype (the non-island weights); callers cast latents to + this, mirroring ``Wan22DiT.dtype``.""" + return self.patchify.weight.dtype + + def cast_serving_dtypes(self) -> "WaypointDiT": + """bf16 everywhere, then the fp32 islands back to fp32. + + Called on the **meta** module before ``to_empty(device)`` so storage is + allocated directly in the serving dtype. ``.to(dtype)`` on meta + preserves the ``cond_proj`` aliasing (``to_empty`` does not), so no + retie is needed here. + + The island list is ``layers.FP32_MODULE_PATHS`` rather than a literal: + the modules that must stay fp32 are a fact of the reference's + ``NoCastModule`` set, and it belongs next to the modules themselves + (DECISIONS D7). + """ + self.to(torch.bfloat16) + for path in FP32_MODULE_PATHS: + self.get_submodule(path).to(torch.float32) + return self + + def retie_cond_proj(self) -> "WaypointDiT": + """Alias blocks 1..23's six ``cond_proj`` matrices onto block 0's. + + **Public, and separate from ``__init__``, because ``to_empty(device)`` + destroys the tying.** ``Module._apply`` allocates per parameter object + with no cross-module memo, so the 24 blocks come out of ``to_empty`` + holding 24 independent copies. Nothing raises; the symptoms are +0.6B + resident parameters and 23 unfilled ``cond_proj`` sets. Once re-tied, + ``named_parameters()`` deduplicates and the loader's completeness check + sees block 0's set only (CONTRACTS section 6.1). + + ``bias_in`` is deliberately NOT tied -- it is genuinely per-layer, and + the checkpoint has 24 distinct values for it. + """ + ref_proj = self.blocks[0].cond_head.cond_proj + for block in self.blocks[1:]: + for blk_mod, ref_mod in zip(block.cond_head.cond_proj, ref_proj, strict=True): + blk_mod.weight = ref_mod.weight + return self + + def compile_regions(self, **compile_kwargs) -> "WaypointDiT": + """Compile the denoise and cache passes, one region each, matching the + reference's two ``@torch.compile(fullgraph=True, dynamic=False)`` sites. + + Not done in ``__init__``: the serving layer decides, from + ``config.compile_dit``, whether to compile at all (the eager path is the + bit-exact reference and the parity harness wants it). Idempotent. + + Run one eager frame first. The derived tables (this module's token grid, + ``OrthoRoPEAngles``' frequency tables) are copied to the device on first + use by design -- they are deliberately outside the module tree, so + nothing else materializes them -- and a first touch inside a + ``fullgraph=True`` region is at best a specialization and at worst a + graph break. + """ + if not self._regions_compiled: + options = {"fullgraph": True, "dynamic": False, **compile_kwargs} + self._denoise_pass = torch.compile(self._denoise_pass, **options) + self._cache_pass = torch.compile(self._cache_pass, **options) + self._regions_compiled = True + return self + + # ---- Positions --------------------------------------------------------- + + def _pos_ids(self, frame_pos: Tensor) -> WaypointPosIds: + """Build one frame's position streams from the ring clock.""" + if not torch.compiler.is_compiling(): + torch._check( + frame_pos.ndim == 0 and frame_pos.dtype == torch.int64, + lambda: f"frame_pos must be a [] int64 tensor; got {tuple(frame_pos.shape)} " + f"{frame_pos.dtype}", + ) + y_pos, x_pos = self._grid.get(frame_pos.device) + # The multiply is a no-op at ts_mult == 1 and is kept anyway: it is the + # only place the two clocks are related, and deleting it is how a + # different-fps checkpoint would start drifting silently. + t_pos = (frame_pos * self.config.ts_mult).reshape(1, 1).expand(1, y_pos.numel()) + return WaypointPosIds(f_pos=frame_pos, t_pos=t_pos, y_pos=y_pos[None], x_pos=x_pos[None]) + + # ---- One forward ------------------------------------------------------- + + def forward( + self, + x: Tensor, + sigma: Tensor, + frame_pos: Tensor, + *, + mouse: Tensor, + button: Tensor, + scroll: Tensor, + backend: WaypointKVBackend, + ) -> Tensor: + """One pass over one latent frame; returns the rectified-flow velocity. + + ``x`` ``[B, N, C, H, W]`` latent (B == N == 1), ``sigma`` ``[B, N]``, + ``frame_pos`` ``[]`` int64 ring clock, controller inputs ``[B, N, 2]`` / + ``[B, N, n_buttons]`` / ``[B, N, 1]``. Returns ``[B, N, C, H, W]``. + + Whether this pass commits to the ring is the *backend's* state + (``set_frozen``), not an argument here -- exactly as in the reference, + where the two compiled driver regions set it and the model is unaware. + """ + B, N, C, H, W = x.shape + ph, pw = self.patch + if H % ph or W % pw: + raise ValueError(f"latent {H}x{W} is not divisible by patch {self.patch}.") + Hp, Wp = H // ph, W // pw + torch._assert( + Hp * Wp == self.config.tokens_per_frame, + f"{Hp} * {Wp} != {self.config.tokens_per_frame}", + ) + # One frame per call, batch 1: the ring cache indexes a single frame per + # upsert and the whole driver is built on that (reference asserts the + # same thing). + torch._assert(B == 1 and N == 1, "WaypointDiT.forward supports B == 1, N == 1") + + pos_ids = self._pos_ids(frame_pos) + # Keyword arguments on purpose: (x, y, t) here vs the reference's + # dict lookup, and a silent x/y swap on a non-square grid is a + # wrong-video-no-error bug. + rope_angles = self.rope_angles( + x_pos=pos_ids.x_pos, y_pos=pos_ids.y_pos, t_pos=pos_ids.t_pos + ) + + cond = self.denoise_step_emb(sigma) # [B, N, D], fp32 island + # Positional and in this order: (mouse, button, scroll) is a checkpoint + # fact whose widths sum correctly under any permutation. + ctrl_emb = self.ctrl_emb(mouse, button, scroll) # [B, N, D] + + D = self.config.d_model + h = self.patchify(x.reshape(B * N, C, H, W)) # [B*N, D, Hp, Wp] + h = h.view(B, N, D, Hp * Wp).transpose(2, 3).flatten(1, 2) # [B, N*T, D] + + v1 = None # layer 0's pre-lerp V, threaded through all 24 blocks + for block in self.blocks: + h, v1 = block(h, pos_ids.f_pos, rope_angles, cond, ctrl_emb, v1, backend) + + # Output head: silu sits BETWEEN the adaLN norm and the unpatchify + # projection (reference world_model.py:348-352), not after it. + h = F.silu(self.out_norm(h, cond)) + h = self.unpatchify(h) # [B, N*T, C*ph*pw] + h = h.view(B, N, Hp, Wp, C, ph, pw).permute(0, 1, 4, 2, 5, 3, 6) + return h.reshape(B, N, C, Hp * ph, Wp * pw) + + # ---- The 4+1 driver ---------------------------------------------------- + + def _sigma_schedule(self, device: torch.device, dtype: torch.dtype) -> Tensor: + """The sigma table, memoized per (device, dtype). Resolved by the caller + *outside* the compiled region -- materializing a tensor from a Python + list inside ``fullgraph=True`` is a graph break, which is also why the + reference keeps this table on the engine and only reads it in + ``_denoise_pass``. + + **The dtype is load-bearing.** The reference builds this table in the + serving dtype and takes ``.diff()`` there, so the Euler step sizes are + bf16 differences of bf16 sigmas: ``bf16(0.9) - 1.0 == -0.1015625``, + whereas an fp32 diff rounded to bf16 is ``-0.10009765625`` (two of the + four steps differ). Building the table in fp32 "for precision" would + change the ODE. + """ + key = (device, dtype) + schedule = self._sigma_cache.get(key) + if schedule is None: + schedule = torch.tensor(self.config.scheduler_sigmas, dtype=dtype, device=device) + self._sigma_cache[key] = schedule + return schedule + + def _denoise_pass( + self, + x: Tensor, + frame_pos: Tensor, + sigmas: Tensor, + backend: WaypointKVBackend, + *, + mouse: Tensor, + button: Tensor, + scroll: Tensor, + ) -> Tensor: + """Four frozen Euler steps of the rectified-flow ODE. Returns the settled + latent; **does not write the ring**. + + Frozen matters: each step attends to a different noisy version of the + same frame, so none of them may commit. They still see themselves, + through the unconditional scratch write at the ring tail. + + ``sigmas`` is the ``[5]`` schedule in ``x``'s dtype, passed in rather + than built here -- see ``_sigma_schedule``. + """ + backend.set_frozen(True) + # One reused sigma buffer, filled per step (the reference's shape -- + # a fresh allocation per step would defeat cudagraph capture). + sigma = x.new_empty((x.size(0), x.size(1))) + # strict=False is deliberate: there are 5 sigmas and 4 diffs, the + # trailing 0.0 exists only to produce the last step size, and that + # truncation IS the "4 denoise passes" of the 4+1 structure. + for step_sigma, step_dsigma in zip(sigmas, sigmas.diff(), strict=False): + v = self( + x, + sigma.fill_(step_sigma), + frame_pos, + mouse=mouse, + button=button, + scroll=scroll, + backend=backend, + ) + # fp32 accumulate, back to the latent dtype -- the reference's exact + # expression; doing the add in bf16 loses the small late steps. + x = (x.float() + step_dsigma.float() * v.float()).type_as(x) + return x + + def _cache_pass( + self, + x: Tensor, + frame_pos: Tensor, + backend: WaypointKVBackend, + *, + mouse: Tensor, + button: Tensor, + scroll: Tensor, + ) -> None: + """The committing pass: one unfrozen forward at sigma=0 on the settled + latent. Its only purpose is the side effect -- the K/V it writes into + every layer's ring. The returned velocity is discarded. + """ + backend.set_frozen(False) + self( + x, + x.new_zeros((x.size(0), x.size(1))), + frame_pos, + mouse=mouse, + button=button, + scroll=scroll, + backend=backend, + ) + + def generate_frame( + self, + noise: Tensor, + frame_pos: Tensor, + backend: WaypointKVBackend, + *, + mouse: Tensor, + button: Tensor, + scroll: Tensor, + ) -> Tensor: + """Denoise one frame from ``noise`` ``[B, N, C, H, W]`` and commit it. + + Five forwards: 4 frozen + 1 committing (CONTRACTS section 1). The caller + owns the ring clock and must advance ``frame_pos`` by exactly one per + committed frame -- all five passes of a frame share the same value. + """ + # The .clone() is load-bearing, not hygiene: _denoise_pass is a compiled + # region and inductor/cudagraphs reuse its output buffer, so the cache + # pass's own allocations would stomp the latent it is supposed to be + # reading. It must stay OUTSIDE the compiled region, on the returned + # tensor, so the copy lands in caller-owned memory. + sigmas = self._sigma_schedule(noise.device, noise.dtype) + x0 = self._denoise_pass( + noise, frame_pos, sigmas, backend, mouse=mouse, button=button, scroll=scroll + ).clone() + self._cache_pass(x0, frame_pos, backend, mouse=mouse, button=button, scroll=scroll) + return x0 + + def append_frame( + self, + latent: Tensor, + frame_pos: Tensor, + backend: WaypointKVBackend, + *, + mouse: Tensor, + button: Tensor, + scroll: Tensor, + ) -> Tensor: + """Prime the world state from a real (VAE-encoded) frame: the committing + pass alone, no denoising, one forward. + + ``latent`` is already the settled x0, so there is nothing to solve and + nothing to clone. Returned unchanged for the caller to decode. + """ + self._cache_pass(latent, frame_pos, backend, mouse=mouse, button=button, scroll=scroll) + return latent diff --git a/mstar/model/waypoint/components/kv_backend.py b/mstar/model/waypoint/components/kv_backend.py new file mode 100644 index 000000000..a0a6f0092 --- /dev/null +++ b/mstar/model/waypoint/components/kv_backend.py @@ -0,0 +1,562 @@ +"""Waypoint's ring KV cache and the model-local attention backend seam. + +The cache is not an optimization here, it *is* the world state: Waypoint +denoises one latent frame at a time and everything the model knows about the +past lives in these rings. Evicting a slot is not a cache miss, it is amnesia, +and a position bug does not raise -- it produces plausible, smoothly drifting +video. See ``docs/waypoint/CONTRACTS.md`` sections 1-3, which this file is the +implementation of. + +Facts the rest of the port depends on: + + * **Per-layer heterogeneous geometry.** 18 local layers hold 16 consecutive + frames; 6 global layers hold 16 frames spaced 8 apart, spanning 128 frames + of history. Every layer carries one extra *scratch* frame at the tail, so + capacity is ``(ring_frames + 1) * tokens_per_frame``. All of it comes from + ``WaypointConfig``; nothing is re-derived here. + * **4+1 passes per frame.** The four Euler denoise passes run with + ``is_frozen=True`` and touch only the scratch frame -- they must not mutate + the ring, because each attends to a different noisy version of the same + frame. The fifth pass (sigma=0, ``is_frozen=False``) is the only writer. + * **The scratch write is unconditional**, frozen passes included. It is the + entire mechanism by which the frame being denoised attends to itself. + * **Global layers commit on one frame in eight.** + ``torch.where(write_step, ring_idx, current_idx)`` redirects a + non-committing global write back into the scratch slot it just wrote, so it + commits nothing. That redirect *is* the dilation, expressed without + data-dependent control flow. + * **The mask hides the ring slot this frame is about to overwrite**, on + frozen and unfrozen passes alike, so the current frame never attends to the + stale frame it is replacing -- and so all five passes of a frame see + byte-identical KV. + * The ``BlockMask`` is query-uniform and full-blocks-only (no partial blocks, + and a no-op ``mask_mod``), which is what makes "capacity must be a multiple + of 128" a real constraint rather than a convenience. + +Deviation from the reference (``world_engine/src/model/kv_cache.py``), argued in +CONTRACTS section 2.4: global rings are **compacted** to their 16 addressable +slots. The reference allocates 128 frame slots per global layer but +``slot = bucket % 16`` can never address past the 16th, so 7/8 of every global +ring is permanently unwritten and permanently masked off. Dropping never-written +blocks is bit-exact -- ``BlockMask.from_kv_blocks`` orders visited blocks by a +*stable* descending argsort truncated to the visited count, so trailing ``False`` +entries neither enter the visited list nor perturb the order of the ``True`` +ones, and attention accumulates over the same blocks in the same order. It saves +1.31 GiB (see ``describe_ring_memory``). Set ``WaypointConfig.full_global_ring`` +to restore the reference allocation for an A/B parity run. + +The one thing compaction changes structurally: the bucket count can no longer be +derived from the buffer length. The reference computes +``num_buckets = (L // tpf) // dilation``, which is only correct because ``L`` is +8x oversized; against a compacted ring it would yield 2 instead of 16 and shred +the history. ``LayerRingCache`` therefore takes ``ring_buckets`` as an explicit +argument, sourced from ``WaypointConfig.ring_buckets``. + +Nothing in this module is an ``nn.Module``. The rings are DERIVED state, never +checkpoint state: keeping them out of the DiT's module tree means +``to_empty(device)``, ``state_dict()`` and the weight loader have no buffer of +ours to leave holding garbage (the stance ``wan22.components.dit.Wan22RoPE3D`` +takes for its RoPE tables). Storage is therefore allocated **eagerly and +explicitly** on the device handed to the constructor rather than lazily on first +use -- the backend is built after the DiT has been materialized, so there is no +meta-device phase to defer past, and eager allocation means a rollout cannot +discover halfway through that it is 800 MiB short of VRAM. +""" + +import dataclasses +from typing import Any, Protocol, runtime_checkable + +import torch +from torch import Tensor +from torch.nn.attention.flex_attention import ( + _DEFAULT_SPARSE_BLOCK_SIZE, + BlockMask, + flex_attention, +) + +from mstar.model.waypoint.config import WaypointConfig + +__all__ = [ + "FlexRingBackend", + "LayerRingCache", + "WaypointKVBackend", + "describe_ring_memory", + "flex_attention_masked", + "make_block_mask", + "ring_memory_bytes", +] + + +# CORRECTNESS, not speed. Our BlockMask carries a NO-OP `mask_mod`: we pass +# `mask_mod=None` to `from_kv_blocks` and it substitutes `noop_mask`, so +# `bm.mask_mod` is a function returning True everywhere, not `None`. Visibility +# is therefore encoded *entirely* in the block index lists (`full_kv_indices` +# truncated to `full_kv_num_blocks`), which is what makes a ring expressible at +# all. The compiled kernel iterates exactly those blocks. The eager path does +# not -- it rebuilds the mask by evaluating `mask_mod` over the grid, and a noop +# mask_mod means "everything is visible", so eager attention silently reads +# every unwritten slot in the ring as a zero K/V and blends it in. +# +# Measured on the ported cache: eager output diverges from a masked-dense +# reference by 2.7e-01, while the compiled path matches it to 1.2e-07. Nothing +# raises. This is why the reference wraps both of its regions in +# @torch.compile(fullgraph=True) -- compilation is load-bearing for the *result* +# there too, not just the throughput. +# +# So the compile is pinned here rather than left to the caller: correctness must +# not depend on whether someone set `WaypointConfig.compile_dit`. See +# docs/waypoint/CONTRACTS.md section 2.3 and DECISIONS.md D10. +flex_attention_masked = torch.compile(flex_attention, dynamic=False) + + +@runtime_checkable +class WaypointKVBackend(Protocol): + """The seam between ``WaypointAttn`` and whatever owns the world state. + + ``mstar/engine/resources/attn/`` is deliberately not involved: Waypoint's + cache is model-owned, ``get_node_resources()`` returns ``[]``, and the + engine builds no KV resource for it. The node/walk topology is invariant + across that decision, so if engine-owned paged KV later becomes viable only + the implementation behind this protocol is replaced -- no graph reshape, no + submodule signature change. + + The opaque ``meta`` returned by ``upsert`` and consumed by ``attend`` is + what keeps the protocol independent of FlexAttention: ``FlexRingBackend`` + puts a ``BlockMask`` there, a paged backend would put a page table there, + and the attention module never has to know which. + """ + + def upsert( + self, + k: Tensor, + v: Tensor, + layer_idx: int, + frame_pos: Tensor, + ) -> tuple[Tensor, Tensor, Any]: + """Commit one frame's K/V for ``layer_idx`` and return what to attend to. + + ``k``/``v`` are ``[B, H_kv, tokens_per_frame, D]``. ``k`` is already + RoPE'd and RMS-normed and ``v`` is post value-residual lerp: the cache + stores post-RoPE keys, so replayed history is never re-rotated (see + CONTRACTS section 4.2). Returns ``(k_all, v_all, meta)`` spanning the + whole ring plus the scratch frame. + + **Why ``frame_pos`` is passed explicitly** and never derived from an + internal slot cursor: it is a ``[]`` int64 device tensor holding the + ring clock -- not a slot id -- and it alone determines both the ring + slot written and the visibility mask. Desynchronizing it from the + caller's clock does not raise; it silently rewrites history. An input + with that failure mode has to be an argument, not hidden state. + """ + ... + + def attend(self, q: Tensor, k: Tensor, v: Tensor, meta: Any, *, enable_gqa: bool) -> Tensor: + """Attend ``q`` ``[B, H_q, T, D]`` against the ``(k, v, meta)`` triple + returned by ``upsert``. Returns ``[B, H_q, T, D]``.""" + ... + + def set_frozen(self, frozen: bool) -> None: + """``True`` for the four denoise passes, ``False`` for the committing + pass. Python-level state on purpose: it gates a real branch, and + branching on it is graph-safe.""" + ... + + def reset(self) -> None: + """Drop the world state and re-freeze. A new rollout starts here.""" + ... + + def get_state(self) -> dict: ... + + def load_state(self, state: dict) -> None: ... + + +def make_block_mask(q_len: int, kv_len: int, written: Tensor) -> BlockMask: + """Build the query-uniform, full-blocks-only ``BlockMask`` over ``written``. + + ``written`` is ``[kv_len]`` bool, True where the ring holds valid KV. Both + lengths must be exact multiples of the 128-token sparse block size and + ``written`` must be block-aligned -- both hold because writes are whole + frames of 512 (or 128) tokens. + + Two properties are load-bearing. Every query block sees the same KV blocks, + so the ``[1, 1, 1, num_kv_blocks]`` row broadcasts over query blocks and + ``compute_q_blocks=False`` is safe. And every visible block is *full* + (no partial blocks at all), because frame-granular writes + mean "any token in the block is written" and "all of them are" coincide. + """ + block_size = _DEFAULT_SPARSE_BLOCK_SIZE + + if not torch.compiler.is_compiling(): + torch._check( + q_len % block_size == 0, + lambda: f"q_len ({q_len}) must be a multiple of block size ({block_size})", + ) + torch._check( + kv_len % block_size == 0, + lambda: f"kv_len ({kv_len}) must be a multiple of block size ({block_size})", + ) + + q_blocks = q_len // block_size + kv_blocks = kv_len // block_size + + written_blocks = written.view(kv_blocks, block_size) + block_any = written_blocks.any(-1) + if not torch.compiler.is_compiling(): + assert torch.equal(block_any, written_blocks.all(-1)), "written must be block-aligned" + + full_bm = block_any[None, :].expand(q_blocks, kv_blocks) + full_kv_num_blocks = full_bm.sum(dim=-1, dtype=torch.int32)[None, None].contiguous() + # Stable descending argsort: the visited list is this truncated to + # full_kv_num_blocks, so unwritten blocks sort to the tail and are never + # read. That is exactly why compacting the global ring is bit-exact. + full_kv_indices = ( + full_bm.argsort(dim=-1, descending=True, stable=True) + .to(torch.int32)[None, None] + .contiguous() + ) + + # No partial blocks at all -- these two exist only to satisfy the signature. + kv_num_blocks = torch.zeros((1, 1, q_blocks), dtype=torch.int32, device=written.device) + kv_indices = torch.zeros((1, 1, q_blocks, kv_blocks), dtype=torch.int32, device=written.device) + + return BlockMask.from_kv_blocks( + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + BLOCK_SIZE=block_size, + mask_mod=None, + seq_lengths=(q_len, kv_len), + compute_q_blocks=False, + ) + + +class LayerRingCache: + """One attention layer's ring: ``ring_frames`` frame slots of history plus + one scratch frame at the tail. + + Storage is a single ``[2, B, H_kv, capacity, D]`` tensor so that a commit is + one ``index_copy_`` for K and V together and the read is one ``unbind(0)`` + into two views -- no copy on the read path. + + ``ring_buckets`` is the number of *addressable* slots and is passed in, not + derived from ``ring_frames``: under the compacted global allocation the two + differ from the reference's relationship (16 slots over a 16-frame buffer at + stride 8, where the reference had 16 slots over a 128-frame buffer), and + re-deriving it would silently produce 2. + """ + + def __init__( + self, + batch: int, + n_kv_heads: int, + ring_frames: int, + ring_buckets: int, + d_head: int, + tokens_per_frame: int, + pinned_dilation: int, + dtype: torch.dtype, + device: torch.device | str, + ): + if pinned_dilation < 1: + raise ValueError(f"pinned_dilation must be >= 1; got {pinned_dilation}.") + if not 1 <= ring_buckets <= ring_frames: + raise ValueError( + f"ring_buckets ({ring_buckets}) must be in [1, ring_frames ({ring_frames})]; " + "the addressable slots have to fit inside the allocated ring." + ) + if tokens_per_frame % _DEFAULT_SPARSE_BLOCK_SIZE: + raise ValueError( + f"tokens_per_frame ({tokens_per_frame}) must be a multiple of the sparse " + f"block size ({_DEFAULT_SPARSE_BLOCK_SIZE}); the BlockMask has no partial blocks." + ) + + self.tokens_per_frame = tokens_per_frame + self.ring_frames = ring_frames + self.ring_buckets = ring_buckets + self.pinned_dilation = pinned_dilation + # ring_len is the reference's `L`: the history region, scratch excluded. + self.ring_len = ring_frames * tokens_per_frame + self.capacity = self.ring_len + tokens_per_frame + + self.kv = torch.zeros( + 2, batch, n_kv_heads, self.capacity, d_head, dtype=dtype, device=device + ) + + # The tail frame is permanently visible: it always holds the frame + # currently being denoised, so masking it would remove self-attention. + written = torch.zeros(self.capacity, dtype=torch.bool, device=device) + written[self.ring_len :] = True + self.written = written + # Preallocated scratch for the per-call visibility mask. Allocating it + # inside upsert would put a fresh buffer in the compiled region on every + # one of the 120 upserts per frame. + self._mask_written = torch.empty_like(written) + + self.frame_offsets = torch.arange(tokens_per_frame, dtype=torch.long, device=device) + self.current_idx = self.frame_offsets + self.ring_len + + @property + def memory_bytes(self) -> int: + """Resident bytes of KV storage (the bool/index buffers are noise).""" + return self.kv.numel() * self.kv.element_size() + + def reset(self) -> None: + self.kv.zero_() + self.written.zero_() + self.written[self.ring_len :].fill_(True) + + def upsert( + self, kv: Tensor, frame_pos: Tensor, is_frozen: bool + ) -> tuple[Tensor, Tensor, BlockMask]: + """``kv`` is ``[2, B, H_kv, tokens_per_frame, D]`` for exactly one frame; + ``frame_pos`` is a ``[]`` int64 device tensor (the ring clock). + + Ported statement for statement from the reference; CONTRACTS section 2.2 + lists the five ways to get it subtly wrong, all of which drift instead of + raising. Everything below is index arithmetic on device tensors -- + ``torch.where`` and ``index_copy_`` rather than a Python ``if`` -- because + this runs inside ``torch.compile(fullgraph=True)`` and a branch on a + tensor value would graph-break. Only ``is_frozen`` is Python-level. + """ + tokens = self.tokens_per_frame + + if not torch.compiler.is_compiling(): + torch._check( + kv.size(3) == tokens, + lambda: f"ring cache expects exactly one frame per upsert; got {kv.size(3)} tokens", + ) + torch._check( + frame_pos.ndim == 0 and frame_pos.dtype == torch.int64, + lambda: f"frame_pos must be a [] int64 tensor; got {tuple(frame_pos.shape)} " + f"{frame_pos.dtype}", + ) + + # Bucket rounds UP (+ dilation - 1), copying the reference verbatim. + # + # The rounding is in fact DEAD arithmetic here, and the comment that + # used to sit on this line -- "flooring rotates the whole history by one + # slot" -- was measured false: swapping ceil for floor changes nothing, + # 0.0 across a 26-frame rollout through two ring wraps. `ring_idx` is + # only ever *read* under `write_step` (below), and where + # `frame_pos % dilation == 0` the ceil and the floor agree. It is kept + # because the reference has it and this port does not silently + # "simplify" expressions it merely believes to be dead -- but do not + # mistake it for a live invariant. + bucket = (frame_pos + (self.pinned_dilation - 1)) // self.pinned_dilation + slot = bucket % self.ring_buckets + ring_idx = self.frame_offsets + slot * tokens + + # Unconditional, frozen passes included: this is how the frame being + # denoised attends to itself between Euler steps. + self.kv.index_copy_(3, self.current_idx, kv) + + # Hide the ring slot this frame is about to take over, so the current + # frame never attends to the stale frame it is replacing. Done on every + # pass -- the four frozen passes must see exactly the KV the committing + # pass will see -- but only where write_step, hence the `& ~write_step`. + write_step = frame_pos.remainder(self.pinned_dilation) == 0 + mask_written = self._mask_written + mask_written.copy_(self.written) + mask_written[ring_idx] = mask_written[ring_idx] & ~write_step + bm = make_block_mask(tokens, self.capacity, mask_written) + + if not is_frozen: + # On a global layer's 7-in-8 non-committing frames this redirects + # the commit onto the scratch slot that was just written with the + # same data, i.e. it commits nothing. That is the dilation, and it + # is a select rather than a branch so the graph stays whole. + # + # `is_frozen` is REDUNDANT under the shipped 4+1 schedule, measured: + # ignoring it entirely (committing on all five passes) gives 0.0 at + # the output AND a byte-identical ring, because the mask-hide above + # already blinds the current frame to this slot and the fifth pass + # rewrites the same `dst` last. That is a genuine no-op and not an + # untested patch: the same monkeypatch produces 5.8e-02 when it + # suppresses the commit instead, so it was demonstrably live. + # + # Do not delete it on that basis. It stops being redundant the + # moment anything reads the ring between the frozen passes and the + # cache pass, or the pass order changes, or two frames are in + # flight at once (B8). Redundant-under-this-schedule is not the + # same as unnecessary. + dst = torch.where(write_step, ring_idx, self.current_idx) + self.kv.index_copy_(3, dst, kv) + self.written[dst] = True + + k, v = self.kv.unbind(0) + return k, v, bm + + +class FlexRingBackend: + """``WaypointKVBackend`` over per-layer ring caches plus FlexAttention. + + **Why FlexAttention and not FlashInfer**, given that mstar has a paged + FlashInfer path: numerical parity. The reference runs ``flex_attention`` + with a full-block-only ``BlockMask``; a paged kernel changes the + accumulation order, which would make bit-exact comparison against the + reference impossible and defeat the point of the parity harness. + + Constructed after the DiT is materialized, with a real device -- see the + module docstring on why none of this rides through ``to_empty``. + """ + + def __init__( + self, + config: WaypointConfig, + device: torch.device | str, + *, + dtype: torch.dtype = torch.bfloat16, + batch_size: int = 1, + ): + self.config = config + self.dtype = dtype + self.device = torch.device(device) + self.batch_size = batch_size + # Convenience mirror of the config fact, so the attention module can + # read it off the backend; `attend` still takes it explicitly because + # the protocol says so and the caller owns its own head counts. + self.enable_gqa = config.enable_gqa + + self.layers = [ + LayerRingCache( + batch=batch_size, + n_kv_heads=config.n_kv_heads, + ring_frames=config.ring_frames(i), + ring_buckets=config.ring_buckets(i), + d_head=config.d_head, + tokens_per_frame=config.tokens_per_frame, + pinned_dilation=config.pinned_dilation(i), + dtype=dtype, + device=self.device, + ) + for i in range(config.n_layers) + ] + # A fresh backend is frozen: nothing may commit until the model's cache + # pass explicitly unfreezes it. + self._is_frozen = True + + # ---- WaypointKVBackend ------------------------------------------------ + + def upsert( + self, k: Tensor, v: Tensor, layer_idx: int, frame_pos: Tensor + ) -> tuple[Tensor, Tensor, BlockMask]: + # `layer_idx` is Python-level (it indexes a list of differently-shaped + # rings), so indexing on it is graph-safe. + kv = torch.stack([k, v], dim=0) + return self.layers[layer_idx].upsert(kv, frame_pos, self._is_frozen) + + def attend(self, q: Tensor, k: Tensor, v: Tensor, meta: BlockMask, *, enable_gqa: bool) -> Tensor: + # `flex_attention_masked`, never bare `flex_attention`: with a no-op + # `mask_mod` the eager path ignores the block mask entirely and attends + # to unwritten ring slots. See the note at its definition. + return flex_attention_masked(q, k, v, block_mask=meta, enable_gqa=enable_gqa) + + def set_frozen(self, frozen: bool) -> None: + self._is_frozen = bool(frozen) + + def reset(self) -> None: + for layer in self.layers: + layer.reset() + self._is_frozen = True + + @torch.no_grad() + def get_state(self) -> dict: + """Snapshot the world state. Cloned, so the caller can hold it across + further rollout steps that mutate the rings in place.""" + return { + "_is_frozen": self._is_frozen, + "layers": [ + (layer.kv.detach().clone(), layer.written.detach().clone()) + for layer in self.layers + ], + } + + @torch.no_grad() + def load_state(self, state: dict) -> None: + layers = state["layers"] + if len(layers) != len(self.layers): + raise ValueError( + f"state has {len(layers)} layers, backend has {len(self.layers)}." + ) + for i, (layer, (kv, written)) in enumerate(zip(self.layers, layers, strict=True)): + # Geometry mismatch (e.g. a 360p state into a 720p backend, or a + # compacted state into a full_global_ring backend) would otherwise + # surface as a copy_ broadcast error deep in the loop. + if tuple(kv.shape) != tuple(layer.kv.shape): + raise ValueError( + f"layer {i} state shape {tuple(kv.shape)} != ring shape " + f"{tuple(layer.kv.shape)}." + ) + layer.kv.copy_(kv) + layer.written.copy_(written) + self._is_frozen = bool(state.get("_is_frozen", True)) + + # ---- Introspection ---------------------------------------------------- + + def memory_bytes(self) -> int: + """Total resident ring bytes across all layers.""" + return sum(layer.memory_bytes for layer in self.layers) + + def describe(self) -> str: + """Human-readable geometry/footprint table, including what the other + setting of ``full_global_ring`` would cost.""" + return describe_ring_memory(self.config, batch_size=self.batch_size, dtype=self.dtype) + + +def ring_memory_bytes( + config: WaypointConfig, *, batch_size: int = 1, dtype: torch.dtype = torch.bfloat16 +) -> list[int]: + """Per-layer ring bytes for ``config``, computed without allocating anything + (so it can be called on a laptop while sizing a deployment).""" + per_slot = 2 * batch_size * config.n_kv_heads * config.d_head * dtype.itemsize + return [per_slot * config.kv_capacity(i) for i in range(config.n_layers)] + + +def _fmt_bytes(n: int) -> str: + return f"{n / 2**20:.1f} MiB" if n < 2**30 else f"{n / 2**30:.2f} GiB" + + +def describe_ring_memory( + config: WaypointConfig, *, batch_size: int = 1, dtype: torch.dtype = torch.bfloat16 +) -> str: + """Geometry and footprint of every ring, grouped local vs global, with the + counterfactual under the opposite ``full_global_ring`` setting. + + The compacted default is what a reviewer should see; the ``full_global_ring`` + line is the reference's allocation, 8/9ths of whose global storage is + permanently unwritten (CONTRACTS section 2.4). + """ + per_layer = ring_memory_bytes(config, batch_size=batch_size, dtype=dtype) + total = sum(per_layer) + + lines = [ + f"Waypoint ring KV variant={config.variant} batch={batch_size} dtype={dtype} " + f"full_global_ring={config.full_global_ring}" + ] + groups = ( + ("local ", [i for i in range(config.n_layers) if not config.is_global_layer(i)]), + ("global", sorted(config.global_layers)), + ) + for name, indices in groups: + if not indices: + continue + i = indices[0] + lines.append( + f" {name} x{len(indices):2d} " + f"{config.ring_frames(i):3d} ring frames @ stride {config.pinned_dilation(i)} " + f"({config.ring_buckets(i)} addressable) + 1 scratch " + f"= {config.kv_capacity(i):6d} tok " + f"= {_fmt_bytes(per_layer[i]):>9s}/layer " + f"= {_fmt_bytes(sum(per_layer[j] for j in indices)):>9s}" + ) + lines.append(f" total {_fmt_bytes(total)} ({total} bytes)") + + other = dataclasses.replace(config, full_global_ring=not config.full_global_ring) + other_total = sum(ring_memory_bytes(other, batch_size=batch_size, dtype=dtype)) + delta = other_total - total + lines.append( + f" full_global_ring={other.full_global_ring} would use {_fmt_bytes(other_total)} " + f"({'+' if delta > 0 else '-'}{_fmt_bytes(abs(delta))})" + ) + return "\n".join(lines) diff --git a/mstar/model/waypoint/components/layers.py b/mstar/model/waypoint/components/layers.py new file mode 100644 index 000000000..50d43de2c --- /dev/null +++ b/mstar/model/waypoint/components/layers.py @@ -0,0 +1,315 @@ +"""Stateless layer primitives for the Waypoint-1.5 DiT. + +Faithful port of ``world_engine/src/model/nn.py`` plus the three conditioning +modules from ``world_engine/src/model/world_model.py`` +(``ControllerInputEmbedding``, ``MLPFusion``, ``CondHead``). Attention, the +transformer block and the DiT itself live elsewhere; nothing here holds +sequence state, and nothing here touches the KV ring. + +Two things in this file are load-bearing beyond "it is the same arithmetic": + + * **Parameter names are checkpoint keys.** ``fc1``/``fc2``, ``bias_in``, + ``cond_proj``, ``mlp`` are the reference's attribute names and therefore + the names ``weight_loader`` remaps onto. mstar's shared + ``components.mlp.MLP`` spells its projections ``linear_in``/``linear_out``, + so it is deliberately NOT reused: a local two-line ``MLP`` that keeps the + checkpoint spelling is worth more than the shared class. + * **``NoiseConditioner`` is an fp32 island.** The reference marks it + ``NoCastModule``, i.e. it silently ignores ``.to(dtype)``. That trick is a + bad fit for mstar (it warns, and it fights ``to_empty``), so the port keeps + it an ordinary ``nn.Module`` that runs its own body under + ``autocast(enabled=False)`` on ``.float()`` inputs, and publishes + ``FP32_MODULE_PATHS`` for ``WaypointDiT.cast_serving_dtypes()`` to re-pin + after the global bf16 cast. See ``docs/waypoint/CONTRACTS.md`` section 4.1. + +The Fourier frequency table is derived state, not checkpoint state. The +reference registers it as a non-persistent buffer, which under mstar's meta +build would survive ``to_empty(device)`` as uninitialized garbage — a silent +wrong-numbers bug, since no loader completeness check covers buffers. It is +held outside the module tree instead, built on CPU at init and copied per +device on first use (the ``wan22.components.dit.Wan22RoPE3D`` approach). +""" + +import torch +import torch.nn.functional as F +from torch import nn + +from mstar.model.waypoint.config import WaypointConfig + +# Submodule paths, relative to the ``WaypointDiT`` root, whose parameters must +# be restored to fp32 after a global ``.to(torch.bfloat16)``. This is the +# contract ``WaypointDiT.cast_serving_dtypes()`` consumes: "everything goes +# bf16, then these paths go back to fp32", mirroring +# ``Wan22DiT.cast_serving_dtypes``. +# +# The reference's ``NoCastModule`` set has three members; only one of them +# appears here, because the other two (``OrthoRoPEAngles``, ``OrthoRoPE`` in +# ``rope.py``) carry no parameters and no buffers at all — their tables live +# outside the module tree and are always fp32 — so there is nothing for a dtype +# cast to corrupt and nothing to pin back. See rope.py's module docstring. +FP32_MODULE_PATHS: tuple[str, ...] = ("denoise_step_emb",) + + +class DeviceTableCache: + """Per-device replicas of small derived fp32 tables (RoPE frequencies, + Fourier frequencies). + + These tables are a pure function of the config, so they are neither + checkpoint state nor something a dtype cast should reach. Registering them + as non-persistent buffers would put them inside the module tree, where + ``to_empty(device)`` replaces their storage with uninitialized memory and + ``.to(bfloat16)`` would truncate their precision. Holding them here instead + keeps them out of ``state_dict``, out of ``to_empty``'s reach, and fp32 + forever. + + Callers MUST build the CPU tables with an explicit ``device="cpu"``: module + ``__init__`` runs under ``with torch.device("meta")`` in mstar's build path, + and an ambient-device ``torch.arange`` would produce data-less meta tensors. + """ + + _CPU = torch.device("cpu") + + def __init__(self, *tables: torch.Tensor): + for table in tables: + if table.device.type != "cpu": + raise ValueError( + f"DeviceTableCache expects CPU-built tables (got {table.device}); " + "pass device='cpu' explicitly so the meta build context cannot capture it." + ) + self._by_device: dict[torch.device, tuple[torch.Tensor, ...]] = {self._CPU: tables} + + def get(self, device: torch.device) -> tuple[torch.Tensor, ...]: + device = torch.device(device) + if device not in self._by_device: + self._by_device[device] = tuple(t.to(device) for t in self._by_device[self._CPU]) + return self._by_device[device] + + +def rms_norm(x: torch.Tensor) -> torch.Tensor: + """Unweighted RMS norm over the last dim (reference ``nn.rms_norm``). + + No learned gain: every use site in Waypoint either has its scale supplied + by adaLN (``ada_rmsnorm``, ``AdaLN``) or wants a bare normalization (Q/K + norm, the two ``MLPFusion`` inputs). + """ + return F.rms_norm(x, (x.size(-1),)) + + +def ada_rmsnorm(x: torch.Tensor, scale: torch.Tensor, bias: torch.Tensor) -> torch.Tensor: + """Per-frame adaLN modulation: ``rms_norm(x) * (1 + scale) + bias``. + + ``x`` is ``[B, N*T, D]`` (N frames of T tokens, flattened); ``scale`` and + ``bias`` are ``[B, N, D]``, one modulation vector per *frame*. The unflatten + exists purely so the per-frame vectors broadcast over that frame's tokens — + it is the reference's ``eo.rearrange(x, 'b (n m) d -> b n m d')``. + """ + x4 = x.unflatten(1, (scale.size(1), -1)) + y4 = rms_norm(x4) * (1 + scale.unsqueeze(2)) + bias.unsqueeze(2) + return y4.flatten(1, 2) + + +def ada_gate(x: torch.Tensor, gate: torch.Tensor) -> torch.Tensor: + """Per-frame adaLN output gate: ``x * gate``, ``gate`` broadcast over the + tokens of its frame. Same shapes as ``ada_rmsnorm``. Note there is no + ``1 +`` here — the gate multiplies the sublayer output before the residual + add, so a zero gate means "contribute nothing".""" + x4 = x.unflatten(1, (gate.size(1), -1)) + return (x4 * gate.unsqueeze(2)).flatten(1, 2) + + +class AdaLN(nn.Module): + """adaLN with the scale/shift projection folded in: one Linear produces + ``[scale | shift]`` from ``silu(cond)``. + + Used only for the DiT's output head (``out_norm``); the blocks get their + six modulation tensors from ``CondHead`` and apply them with + ``ada_rmsnorm``/``ada_gate`` instead. As there, ``cond`` is per-frame + ``[B, N, D]`` and is expanded over each frame's tokens. + """ + + def __init__(self, dim: int): + super().__init__() + self.fc = nn.Linear(dim, 2 * dim, bias=False) + + def forward(self, x: torch.Tensor, cond: torch.Tensor) -> torch.Tensor: + b, n, d = cond.shape + _, nm, _ = x.shape + m = nm // n + + ab = self.fc(F.silu(cond)) # [b, n, 2d] + ab = ab.view(b, n, 1, 2 * d).expand(-1, -1, m, -1).reshape(b, nm, 2 * d) + scale, shift = ab.chunk(2, dim=-1) + return rms_norm(x) * (1 + scale) + shift + + +class MLP(nn.Module): + """Two-layer SiLU MLP, both projections bias-free. + + ``fc1``/``fc2`` and ``bias=False`` are checkpoint facts, not style: the + reference's ``MLPFusion`` calls ``F.linear(h, self.mlp.fc2.weight)`` with no + bias argument at all, which is only correct because there is no bias to + pass. Adding one would load nothing and silently change the output. + """ + + def __init__(self, dim_in: int, dim_middle: int, dim_out: int): + super().__init__() + self.fc1 = nn.Linear(dim_in, dim_middle, bias=False) + self.fc2 = nn.Linear(dim_middle, dim_out, bias=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.fc2(F.silu(self.fc1(x))) + + +class NoiseConditioner(nn.Module): + """sigma -> Fourier features -> MLP, the DiT's only noise-level input. + + fp32 island (reference ``NoCastModule``). ``FP32_MODULE_PATHS`` pins + ``self.mlp`` back to fp32 after the serving bf16 cast, and the body runs + under ``autocast(enabled=False)`` on a ``.float()`` sigma so nothing + downcasts it again. The precision matters more than the 2 MB it costs: the + four denoise sigmas are close together (1.0, 0.9, 0.75, 0.3) and the whole + schedule is only distinguishable to the model through this embedding. + + The ``* 1000`` before the phase computation is the reference's scaling of + the [0, 1] sigma range into a range where the Fourier basis actually + rotates; ``* 2**0.5`` restores unit variance after the sin/cos concat. + """ + + def __init__(self, dim: int, fourier_dim: int = 512, base: float = 10_000.0): + super().__init__() + if fourier_dim % 2: + raise ValueError(f"NoiseConditioner needs an even fourier_dim; got {fourier_dim}.") + self.fourier_dim = fourier_dim + # Derived, not checkpoint state — see the module docstring. device="cpu" + # is required: __init__ runs under the meta-device build context. + self._freq = DeviceTableCache( + torch.logspace(0, -1, steps=fourier_dim // 2, base=base, dtype=torch.float32, device="cpu") + ) + self.mlp = MLP(fourier_dim, dim * 4, dim) + + def forward(self, s: torch.Tensor) -> torch.Tensor: + """``s`` is ``[B, N]`` sigma; returns ``[B, N, dim]`` in ``s``'s dtype.""" + orig_dtype, shape = s.dtype, s.shape + (freq,) = self._freq.get(s.device) + + with torch.amp.autocast("cuda", enabled=False): + s = s.reshape(-1).float() * 1000 # fp32 for Fourier stability; x1000 for rotation range + phase = s[:, None] * freq[None, :] + emb = torch.cat((torch.sin(phase), torch.cos(phase)), dim=-1) + emb = self.mlp(emb * 2**0.5) + + return emb.to(orig_dtype).view(*shape, -1) + + +class ControllerInputEmbedding(nn.Module): + """Controller state -> one conditioning vector per frame. + + The concat order is ``(mouse, button, scroll)``: 2 + n_buttons + 1 = 259 for + this checkpoint. **A permuted order does not raise** — the widths still sum + to 259 and every downstream shape checks out — it just reads the mouse + velocity out of button columns and produces plausible, wrong video. Treat + this ordering as part of the checkpoint, and keep callers passing the three + tensors positionally in this order. + """ + + def __init__(self, config: WaypointConfig): + super().__init__() + self.mlp = MLP(config.d_ctrl_in, config.d_model * config.mlp_ratio, config.d_model) + + def forward(self, mouse: torch.Tensor, button: torch.Tensor, scroll: torch.Tensor) -> torch.Tensor: + """``mouse`` ``[B, N, 2]``, ``button`` ``[B, N, n_buttons]``, ``scroll`` + ``[B, N, 1]`` -> ``[B, N, d_model]``.""" + x = torch.cat((mouse, button, scroll), dim=-1) + return self.mlp(x) + + +class MLPFusion(nn.Module): + """Fuses a per-frame conditioning vector into that frame's tokens. + + Nominally ``MLP(2*D, D, D)`` applied to ``cat([x, cond])``, and the + parameter tree says exactly that — ``mlp.fc1`` is one ``[D, 2D]`` matrix, so + ``weight_loader`` has a single key to fill. The *compute* splits it: + ``fc1.weight.chunk(2, dim=1)`` gives the x-half and the cond-half, each + ``[D, D]``, which lets ``cond`` broadcast over the T tokens of its frame + instead of being repeated into a ``[B, N*T, 2D]`` concat. Same arithmetic, + no materialized copy; this is the path the reference actually ships (and + what its ``SplitMLPFusion`` inference patch bakes in). + + The split is at compute time only. Do not turn it into stored ``fc1_x`` / + ``fc1_c`` parameters: the checkpoint stores them split, and the loader's job + is to ``cat(dim=1)`` them back into ``mlp.fc1`` (CONTRACTS section 6, + transform 7). + """ + + def __init__(self, config: WaypointConfig): + super().__init__() + self.mlp = MLP(2 * config.d_model, config.d_model, config.d_model) + + def forward(self, x: torch.Tensor, cond: torch.Tensor) -> torch.Tensor: + """``x`` ``[B, N*T, D]``, ``cond`` ``[B, N, D]`` -> ``[B, N*T, D]``.""" + B, _, D = x.shape + L = cond.shape[1] + + Wx, Wc = self.mlp.fc1.weight.chunk(2, dim=1) # each [D, D] + + x = x.view(B, L, -1, D) + h = F.linear(x, Wx) + F.linear(cond, Wc).unsqueeze(2) # broadcast, no repeat/cat + h = F.silu(h) + y = F.linear(h, self.mlp.fc2.weight) + return y.flatten(1, 2) + + +class CondHead(nn.Module): + """Per-layer WAN-style conditioning head: the noise embedding becomes the + block's six adaLN modulation tensors (scale/shift/gate for attention, then + the same three for the MLP). + + **The parameters here are split between per-layer and shared, and the split + is the reason the checkpoint is 1.86B on disk but 1.28B resident:** + + * ``bias_in`` — a plain ``[d_model]`` ``nn.Parameter``, genuinely + per-layer, 24 distinct copies. Present only for + ``noise_conditioning == "wan"``. + * ``cond_proj`` — a ``nn.ModuleList`` of 6 bias-free ``[D, D]`` Linears + that is **physically shared across all 24 blocks**. The DiT ties them + after construction by aliasing the ``.weight`` of blocks 1..23 onto + block 0's (reference ``WorldDiT.__init__``), and the loader loads block + 0's copies and drops the other 23 sets. That is why ``cond_proj`` is a + plain ``ModuleList`` of ``nn.Linear`` and nothing cleverer: aliasing + ``blk.cond_head.cond_proj[j].weight = ref.cond_head.cond_proj[j].weight`` + has to remain a one-line assignment. + + **Tie after ``to_empty``, not in ``__init__``.** ``Module._apply`` allocates + per parameter with no cross-module memo, so ``to_empty(device)`` silently + un-aliases tied weights (``.to(dtype)`` on meta does not). Tying only in the + constructor therefore yields 24 independent copies at serve time — no error, + just 0.6B of duplicated resident weights and 23 blocks whose ``cond_proj`` + the loader never fills. Once tied, ``named_parameters()`` deduplicates, so + the loader's completeness check sees block 0's set only, as CONTRACTS + section 6 assumes. + + The checkpoint spells this head as two half-heads, ``attn_cond_head`` + (indices 0..2) and ``mlp_cond_head`` (indices 3..5), with a ``bias_in`` on + each; the loader merges them and keeps the mlp one. See CONTRACTS section 6. + """ + + n_cond = 6 + + def __init__(self, config: WaypointConfig): + super().__init__() + if config.noise_conditioning == "wan": + self.bias_in = nn.Parameter(torch.zeros(config.d_model)) + else: + # register_parameter(None) rather than a bare attribute so the + # `is not None` branch below stays cheap and state_dict stays clean. + self.register_parameter("bias_in", None) + self.cond_proj = nn.ModuleList( + nn.Linear(config.d_model, config.d_model, bias=False) for _ in range(self.n_cond) + ) + + def forward(self, cond: torch.Tensor) -> tuple[torch.Tensor, ...]: + """``cond`` ``[B, N, D]`` -> six ``[B, N, D]`` tensors, in block order + ``(s0, b0, g0, s1, b1, g1)``.""" + cond = cond + self.bias_in if self.bias_in is not None else cond + h = F.silu(cond) + return tuple(p(h) for p in self.cond_proj) diff --git a/mstar/model/waypoint/components/rope.py b/mstar/model/waypoint/components/rope.py new file mode 100644 index 000000000..c1e731142 --- /dev/null +++ b/mstar/model/waypoint/components/rope.py @@ -0,0 +1,185 @@ +"""OrthoRoPE: Waypoint's orthogonal (x, y, t) rotary position embedding. + +Port of ``OrthoRoPEAngles`` and ``OrthoRoPE`` from +``world_engine/src/model/attn.py``. "Ortho" refers to the frequency layout: the +head dim's rotation pairs are partitioned into three disjoint bands, so the x, +y and t coordinates never share a rotation plane and their phases add +independently. + +For ``d_head == 64`` the split is ``d_xy = d_head // 8 = 8`` rotation pairs each +for x and y and ``d_t = d_head // 4 = 16`` pairs for t — **32 pairs, which is +every one of the 64 dims**. Nothing is left unrotated: x owns dims 0..15, y +dims 16..31, t dims 32..63. (Pairs, not dims, is the trap. Reading ``d_xy``/ +``d_t`` as dim counts gives "32 rotated dims, the top 32 untouched", which is +wrong — the reference builds a ``[..., d_head // 2]`` angle table and applies +it to all ``d_head // 2`` pairs. ``CONTRACTS.md`` section 4.3 and ``config.py`` +both stated the wrong reading until this port corrected them.) + +Numerics contract (``docs/waypoint/CONTRACTS.md`` section 4.1): + + * Both classes are fp32 islands — the reference marks them ``NoCastModule``, + i.e. they refuse ``.to(dtype)``. The port does not reproduce that mechanism + (it warns, and it fights ``to_empty``). Instead the arithmetic is + unconditionally fp32: the tables are fp32, the bodies run under + ``autocast(enabled=False)``, and ``OrthoRoPE`` calls ``.float()`` on its + input before rotating and ``.type_as`` on the way out. + * Consequently neither class appears in ``layers.FP32_MODULE_PATHS``: they + hold no parameters and no buffers, so ``.to(torch.bfloat16)`` has nothing to + corrupt and ``cast_serving_dtypes()`` has nothing to pin back. That is not + an accident — the frequency tables are held in a ``DeviceTableCache`` + outside the module tree precisely so that no global cast, and no + ``to_empty(device)``, can reach them. + * The cos/sin tables are DERIVED state, not checkpoint state. As non- + persistent buffers they would survive mstar's meta build as uninitialized + garbage (``to_empty`` allocates, it does not fill, and the loader's + completeness check covers parameters, not buffers). They are built on CPU + at init and copied per device on first use, matching + ``wan22.components.dit.Wan22RoPE3D``. + +The cache stores post-RoPE keys (section 4.2), so replayed history is never +re-rotated; the angles a frame is rotated with are the angles it keeps forever. +""" + +import torch +from torch import nn + +from mstar.model.waypoint.components.layers import DeviceTableCache +from mstar.model.waypoint.config import WaypointConfig + + +class OrthoRoPEAngles(nn.Module): + """Builds the shared ``(cos, sin)`` angle tables for one forward. + + Lives once under the DiT (not once per block): every block rotates with the + same angles, so the tables are computed once per forward and threaded into + each ``Attn``. + + Frequency layout, verbatim from the reference: + + * spatial — ``(linspace(1.0, max_freq / 2, (d_xy + 1) // 2) * pi)`` + ``.repeat_interleave(2)[:d_xy]``, with + ``max_freq = min(height, width) * rope_nyquist_frac``. The Nyquist cap + is what keeps the highest spatial frequency below one cycle per two + cells on the *shorter* grid axis, so nothing aliases. The + ``repeat_interleave(2)`` makes adjacent rotation pairs share a + frequency; x and y share one table. + * temporal — ``(1 / theta ** (arange(0, d_t, 2) / d_t))`` + ``.repeat_interleave(2)``, the standard NTK-style geometric ladder. + + Positions: x/y are normalized to ``[-1, 1)`` **cell centers** + (``(2 * p + 1) / extent - 1``, so the grid is symmetric about 0 and + resolution-independent), while t stays a raw frame count so that history + beyond the ring is still phase-distinguishable. + """ + + def __init__(self, config: WaypointConfig): + super().__init__() + self.config = config + + d_head = config.d_head + if d_head % 8: + raise ValueError(f"OrthoRoPE needs d_head divisible by 8 (x/y/t band split); got {d_head}.") + d_xy, d_t = d_head // 8, d_head // 4 + + # device="cpu" everywhere below is load-bearing: __init__ runs inside + # `with torch.device("meta")` in mstar's build path, and meta tensors + # carry no data to compute frequencies from. + max_freq = min(config.height, config.width) * float(config.rope_nyquist_frac) + n = (d_xy + 1) // 2 + xy = torch.linspace(1.0, max_freq / 2, n, dtype=torch.float32, device="cpu") * torch.pi + xy = xy.repeat_interleave(2)[:d_xy] # [d_xy] + + theta = float(config.rope_theta) + inv_t = 1.0 / (theta ** (torch.arange(0, d_t, 2, dtype=torch.float32, device="cpu") / d_t)) + inv_t = inv_t.repeat_interleave(2) # [d_t] + + self._tables = DeviceTableCache(xy, inv_t) + + def forward( + self, x_pos: torch.Tensor, y_pos: torch.Tensor, t_pos: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor]: + """``x_pos``/``y_pos``/``t_pos`` are ``[B, T]`` integer position grids; + returns ``(cos, sin)``, each ``[B, 1, T, d_head // 2]`` fp32 (the head + axis is a broadcast singleton). + + ``t_pos`` is the RoPE clock and is NOT the ring clock ``f_pos``; they + happen to be equal for this checkpoint (``ts_mult == 1``) and diverge + for any other serving fps. Section 4.4. + """ + xy, inv_t = self._tables.get(x_pos.device) + + if not torch.compiler.is_compiling(): + # Out-of-range positions produce wrapped phases rather than an + # index error, so this is checked rather than trusted. + torch._assert( + (y_pos.max() < self.config.height) & (x_pos.max() < self.config.width), + f"pos_ids out of bounds, {self.config.height}, {self.config.width}", + ) + + with torch.amp.autocast("cuda", enabled=False): + # Cell centers in [-1, 1); t stays a raw (unnormalized) frame count. + x = (2.0 * x_pos.float() + 1.0) / self.config.width - 1.0 + y = (2.0 * y_pos.float() + 1.0) / self.config.height - 1.0 + t = t_pos.float() + + # x and y share the `xy` table; the bands are disjoint slices of the + # angle vector, which is what makes the three axes orthogonal. + freqs = torch.cat( + (x.unsqueeze(-1) * xy, y.unsqueeze(-1) * xy, t.unsqueeze(-1) * inv_t), + dim=-1, # [B, T, d_head // 2] + ) + return freqs.cos()[:, None], freqs.sin()[:, None] + + +def apply_ortho_rope(x: torch.Tensor, rope_angles: tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor: + """Rotate ``x`` ``[B, H, T, d_head]`` by the ``(cos, sin)`` tables. + + **This is the interleaved-pair form with a concatenated output, and the + asymmetry is deliberate.** Pairs are read interleaved — ``unfold(-1, 2, 2)`` + splits the head into ``(x[..., 0::2], x[..., 1::2])`` — but the two rotated + halves are written back with ``cat``, so result ``i`` of the even stream + lands at output dim ``i`` and result ``i`` of the odd stream at + ``i + d_head // 2``. The output is therefore a *permutation* of the + conventional interleaved layout, not the interleaved layout itself. + + Rewriting this as an in-place interleave (``out[..., 0::2] = ...``) is the + natural "fix". Do not do it — but the reason is narrower than it looks, and + an earlier version of this docstring overstated it as "a scrambled head". + + Measured: the interleave rewrite changes the cached K by **5.6** and the + model output by **7.2e-07**, i.e. the noise floor. It is invisible at the + output because q and k receive the *same* head-dim permutation and V is + never rotated, so the permutation cancels inside ``q @ k.T``. What it is + NOT invisible to is anything that reads K or Q directly: a parity harness + diffing cache contents, a quantizer with per-channel scales, a tensor- + parallel head split, or any future consumer of the stored keys. The port + keeps the reference's layout so that stored state is comparable + bit-for-bit, not because attention would break. See CONTRACTS section 4.3. + + fp32 island: ``x`` is upcast before the rotation and cast back at the end, + so the trig math never runs in bf16 regardless of the ambient autocast. + """ + cos, sin = rope_angles + with torch.amp.autocast("cuda", enabled=False): + x0, x1 = x.float().unfold(-1, 2, 2).unbind(-1) + y0 = x0 * cos - x1 * sin + y1 = x1 * cos + x0 * sin + return torch.cat((y0, y1), dim=-1).type_as(x) + + +class OrthoRoPE(nn.Module): + """Module wrapper around :func:`apply_ortho_rope`. + + Stateless — no parameters, no buffers — but kept as an ``nn.Module`` and + constructed per attention layer (``self.rope = OrthoRoPE(config)``) because + that is the reference's shape and the attention port calls it as + ``self.rope(q, rope_angles)``. ``config`` is accepted and stored for that + call-site parity; the reference ignores it here too. + """ + + def __init__(self, config: WaypointConfig | None = None): + super().__init__() + self.config = config + + def forward(self, x: torch.Tensor, rope_angles: tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor: + return apply_ortho_rope(x, rope_angles) diff --git a/mstar/model/waypoint/config.py b/mstar/model/waypoint/config.py new file mode 100644 index 000000000..de84035ea --- /dev/null +++ b/mstar/model/waypoint/config.py @@ -0,0 +1,303 @@ +"""Configuration for Waypoint-1.5 (autoregressive video world model). + +The values here are facts of the ``Overworld/Waypoint-1.5-1B`` checkpoint's +``config.yaml``, hardcoded so that constructing the model never touches the +network. The reference implementation reads that YAML through OmegaConf with +``MODEL_CONFIG_DEFAULTS`` merged underneath; this dataclass is the merged +result, with the defaults that actually matter spelled out. + +Waypoint is not a diffusion pipeline that happens to run several times. It is a +*world model*: one 1.28B DiT denoises exactly one latent frame per step, and the +KV cache IS the world state rather than an optimization over it. Two facts +follow and drive most of this file: + + * Attention geometry is per-layer heterogeneous. 18 layers attend densely over + a 16-frame local window; 6 attend over a 128-frame window subsampled at + stride 8. See ``global_layers`` / ``ring_frames``. + * Every frame costs 5 forwards: 4 non-committing Euler denoise passes over + ``scheduler_sigmas``, then 1 committing pass at sigma=0 that writes the + settled K/V into the ring. See ``docs/waypoint/CONTRACTS.md``. +""" + +from dataclasses import dataclass, field + +# The 720P checkpoint is the one this port implements end to end. The 360P +# sibling differs only in the token grid (see ``waypoint_1_5_1b_360p``). +WAYPOINT_VARIANT_720P = "waypoint-1.5-1b-720p" +WAYPOINT_VARIANT_360P = "waypoint-1.5-1b-360p" + + +@dataclass +class WaypointConfig: + """Waypoint-1.5-1B model configuration. + + Field names track the reference ``config.yaml`` keys rather than mstar's + usual spellings. That is deliberate: the checkpoint's YAML is the ground + truth a reviewer will diff this against, and renaming ``d_model`` to + ``hidden_size`` buys nothing but a translation step during review. + """ + + variant: str = WAYPOINT_VARIANT_720P + + # ---- Transformer ------------------------------------------------------ + n_layers: int = 24 + n_heads: int = 32 + n_kv_heads: int = 16 # GQA: 2 query heads per kv head + d_model: int = 2048 + mlp_ratio: int = 4 + channels: int = 32 # VAE latent channels + + # ---- Token grid ------------------------------------------------------- + # height/width are POST-patch token counts, not latent pixels: the latent + # frame is (height*patch[0], width*patch[1]) and patchify collapses it to + # tokens_per_frame tokens. The reference asserts tokens_per_frame == h*w. + tokens_per_frame: int = 512 + height: int = 16 + width: int = 32 + patch: tuple[int, int] = (2, 2) + + # ---- Attention geometry ----------------------------------------------- + # Layer i is "global" iff (i - global_attn_offset % period) % period == 0. + # With offset=-1, period=4 that is {3, 7, 11, 15, 19, 23}; the other 18 are + # local. Local layers see local_window consecutive frames. Global layers see + # global_window frames subsampled at stride global_pinned_dilation, i.e. + # global_window // global_pinned_dilation == 16 retained frames spanning + # 128 frames of history. + local_window: int = 16 + global_window: int = 128 + global_pinned_dilation: int = 8 + global_attn_period: int = 4 + global_attn_offset: int = -1 + + # ---- RoPE ------------------------------------------------------------- + # OrthoRoPE splits the head into disjoint axis slices. d_head//8 rotation + # PAIRS go to x, d_head//8 to y, d_head//4 to t -- 8+8+16 = 32 pairs for + # d_head=64, i.e. the whole head. x owns dims 0-15, y 16-31, t 32-63; + # nothing is left unrotated. (The counts are pairs, not dims: the angle + # table is d_head//2 wide and unfold(-1, 2, 2) pairs the head up.) + rope_impl: str = "ortho" + rope_nyquist_frac: float = 0.8 + rope_theta: float = 10000.0 + + # ---- Conditioning ----------------------------------------------------- + noise_conditioning: str = "wan" # WAN-style CondHead with a shared cond_proj + value_residual: bool = True + gated_attn: bool = False + moe: bool = False + prompt_conditioning: str | None = None # no cross-attention in this checkpoint + + ctrl_conditioning: bool = True + ctrl_cond_dropout: float = 0.0 + # Controller conditioning is injected on layers where i % period == 0, i.e. + # 8 of 24 layers: {0, 3, 6, 9, 12, 15, 18, 21}. + ctrl_conditioning_period: int = 3 + n_buttons: int = 256 + + # ---- Sampling --------------------------------------------------------- + # 5 entries -> 4 Euler steps (zip(sigmas, sigmas.diff())) -> then one + # separate committing pass at sigma=0. Not a per-request knob: the cached + # sigma/cond tables in the reference's inference patches are keyed on it. + scheduler_sigmas: tuple[float, ...] = (1.0, 0.9, 0.75, 0.3, 0.0) + + # ---- Temporal --------------------------------------------------------- + base_fps: int = 15 # fps the RoPE time axis was trained against + inference_fps: int = 60 # raw video fps + temporal_compression: int = 4 # raw frames per latent frame (TAEHV) + max_frames: int = 512 # training-time rollout ceiling; not enforced here + + # ---- VAE -------------------------------------------------------------- + taehv_ae: bool = True + ae_uri: str = "Overworld-Models/taehv1_5" + auto_aspect_ratio: bool = True + + # ---- Port-local knobs (NOT checkpoint facts) -------------------------- + # The reference allocates global-layer ring storage as + # ``global_window * tokens_per_frame`` tokens but can only ever address + # ``global_window // global_pinned_dilation`` frame slots, so 7/8 of that + # buffer is permanently unwritten and permanently masked off. Compacting it + # is bit-exact -- unwritten blocks are absent from the BlockMask, and the + # stable argsort that orders the visited blocks is unaffected by trailing + # False entries -- and saves ~1.35 GiB. Set True to restore the reference's + # allocation for an A/B parity run. See docs/waypoint/CONTRACTS.md. + full_global_ring: bool = False + + # torch.compile the two OUTER regions (denoise pass, cache pass), matching + # the reference's two @torch.compile(fullgraph=True, dynamic=False) sites. + # + # This is a throughput knob only. It does NOT govern attention correctness: + # the BlockMask carries a no-op mask_mod, so eager flex_attention ignores it and + # attends to unwritten ring slots (measured: 2.7e-01 off a masked-dense + # reference, silently). kv_backend pins its own torch.compile around the + # flex_attention call for that reason. Unlike wan22, this model has no + # eager reference-equivalence mode -- see CONTRACTS.md section 2.3.1. + compile_dit: bool = True + + # Guard rails the ported modules assert against, kept here so a drifting + # checkpoint fails loudly at construction rather than silently mis-serving. + _supported_rope_impls: tuple[str, ...] = field( + default=("ortho",), repr=False, compare=False + ) + _supported_noise_conditioning: tuple[str, ...] = field( + default=("wan",), repr=False, compare=False + ) + + def __post_init__(self) -> None: + if self.rope_impl not in self._supported_rope_impls: + raise ValueError( + f"WaypointDiT implements rope_impl in {self._supported_rope_impls}; " + f"got {self.rope_impl!r}." + ) + if self.noise_conditioning not in self._supported_noise_conditioning: + raise ValueError( + f"WaypointDiT implements noise_conditioning in " + f"{self._supported_noise_conditioning}; got {self.noise_conditioning!r}." + ) + if self.moe: + raise ValueError("WaypointDiT does not implement the MoE variant.") + if self.prompt_conditioning is not None: + raise ValueError( + "WaypointDiT does not implement prompt cross-attention; this " + f"checkpoint declares prompt_conditioning={self.prompt_conditioning!r}." + ) + if self.tokens_per_frame != self.height * self.width: + raise ValueError( + f"tokens_per_frame ({self.tokens_per_frame}) must equal " + f"height*width ({self.height}*{self.width})." + ) + if self.d_model % self.n_heads: + raise ValueError( + f"d_model ({self.d_model}) must be divisible by n_heads ({self.n_heads})." + ) + if self.n_heads % self.n_kv_heads: + raise ValueError( + f"n_heads ({self.n_heads}) must be divisible by n_kv_heads " + f"({self.n_kv_heads}) for GQA." + ) + if self.global_window % self.global_pinned_dilation: + raise ValueError( + f"global_window ({self.global_window}) must be divisible by " + f"global_pinned_dilation ({self.global_pinned_dilation})." + ) + + # ---- Derived ---------------------------------------------------------- + + @property + def d_head(self) -> int: + """Per-head width (64). OrthoRoPE rotates exactly half of it.""" + return self.d_model // self.n_heads + + @property + def enable_gqa(self) -> bool: + return self.n_kv_heads != self.n_heads + + @property + def d_ctrl_in(self) -> int: + """Controller feature width: mouse(2) + button(n_buttons) + scroll(1). + + The concat order in ``ControllerInputEmbedding.forward`` is + ``(mouse, button, scroll)``. Getting it wrong does not raise -- the + widths still sum to 259 -- it just produces plausible wrong video. + """ + return self.n_buttons + 3 + + @property + def latent_height(self) -> int: + """Latent frame height in cells, pre-patchify (32).""" + return self.height * self.patch[0] + + @property + def latent_width(self) -> int: + """Latent frame width in cells, pre-patchify (64).""" + return self.width * self.patch[1] + + @property + def latent_shape(self) -> tuple[int, int, int]: + """One latent frame as (channels, height, width) = (32, 32, 64).""" + return (self.channels, self.latent_height, self.latent_width) + + @property + def ts_mult(self) -> int: + """RoPE time-axis stride per latent frame. + + ``base_fps // (inference_fps // temporal_compression)`` = 15 // 15 = 1 + for this checkpoint, so the RoPE clock ``t_pos`` and the ring-bucketing + clock ``f_pos`` are numerically equal. They are still threaded through + the model as two separate values: a checkpoint served at a different + inference_fps would separate them, and conflating them there is a + silent-drift bug rather than a crash. + """ + return self.base_fps // (self.inference_fps // self.temporal_compression) + + @property + def num_denoise_steps(self) -> int: + """Euler steps per frame (4). The committing pass is separate.""" + return len(self.scheduler_sigmas) - 1 + + @property + def global_layers(self) -> frozenset[int]: + """Layer indices attending over the dilated 128-frame window. + + ``{3, 7, 11, 15, 19, 23}`` -- note the offset is applied modulo the + period first, so offset=-1 means "the last layer of each period". + """ + period = self.global_attn_period + off = self.global_attn_offset % period + return frozenset(i for i in range(self.n_layers) if (i - off) % period == 0) + + @property + def ctrl_layers(self) -> frozenset[int]: + """Layer indices that fuse controller conditioning (8 of 24).""" + if not self.ctrl_conditioning: + return frozenset() + return frozenset( + i for i in range(self.n_layers) if i % self.ctrl_conditioning_period == 0 + ) + + def is_global_layer(self, layer_idx: int) -> bool: + return layer_idx in self.global_layers + + def pinned_dilation(self, layer_idx: int) -> int: + """Frame stride this layer retains history at (8 global, 1 local).""" + return self.global_pinned_dilation if self.is_global_layer(layer_idx) else 1 + + def ring_frames(self, layer_idx: int) -> int: + """Number of frame slots in this layer's ring. + + Local: 16 consecutive frames. Global: 16 slots holding frames 8 apart, + spanning 128 frames. ``full_global_ring`` restores the reference's + 8x-oversized global allocation, whose extra slots are unreachable. + """ + if not self.is_global_layer(layer_idx): + return self.local_window + if self.full_global_ring: + return self.global_window + return self.global_window // self.global_pinned_dilation + + def ring_buckets(self, layer_idx: int) -> int: + """Addressable ring slots. Equals ``ring_frames`` unless + ``full_global_ring`` is set, in which case it is 8x smaller.""" + if not self.is_global_layer(layer_idx): + return self.local_window + return self.global_window // self.global_pinned_dilation + + def kv_capacity(self, layer_idx: int) -> int: + """Total KV slots for this layer, in tokens. + + ``ring + one scratch frame``. The scratch frame at the tail is where an + uncommitted (frozen) denoise pass parks its K/V so the current frame can + attend to itself; it is permanently marked visible and is overwritten on + every forward. + """ + return (self.ring_frames(layer_idx) + 1) * self.tokens_per_frame + + +def waypoint_1_5_1b_720p() -> WaypointConfig: + """The default checkpoint: 512 tokens/frame over a 32x64 latent grid.""" + return WaypointConfig(variant=WAYPOINT_VARIANT_720P) + + +def waypoint_1_5_1b_360p() -> WaypointConfig: + """The 360P sibling. Identical weights-shape-wise except the token grid.""" + return WaypointConfig( + variant=WAYPOINT_VARIANT_360P, tokens_per_frame=128, height=8, width=16 + ) diff --git a/mstar/model/waypoint/weight_loader.py b/mstar/model/waypoint/weight_loader.py new file mode 100644 index 000000000..59df7e8cd --- /dev/null +++ b/mstar/model/waypoint/weight_loader.py @@ -0,0 +1,978 @@ +"""Weight loading for the native Waypoint-1.5-1B DiT (mstar loader pattern). + +``build_waypoint_dit`` constructs ``WaypointDiT`` on meta, casts it to the serving +dtypes while still on meta (so ``to_empty`` allocates storage in the final +dtypes), moves it to the device, **re-ties ``cond_proj``**, then streams the +safetensors shards through ``load_weights_into``. + +The order is fixed and ``retie_cond_proj()`` must follow ``to_empty``: +``Module._apply`` has no cross-module memo, so ``to_empty(device)`` silently +un-aliases the six ``cond_proj`` matrices that blocks 1..23 share with block 0. +Nothing raises; the symptoms are +0.6B resident parameters and 23 blocks of +``cond_proj`` the loader never fills (``CONTRACTS.md`` section 6.1). + +Authoritative key map: ``docs/waypoint/PARAM_TREE.md``. Thirteen transforms sit +between the checkpoint's 369 keys and this module's 174 parameters: + +=== ============================================================== =========== +T0 ``transformer.blocks.{i}.`` -> ``blocks.{i}.`` prefix +T1 ``unpatchify.weight`` ``[D,C,ph,pw]`` -> ``[C*ph*pw,D]`` reshape +T2 ``unpatchify.bias`` ``[C]`` -> ``[C*ph*pw]`` reshape +T3 ``dit_mlp.{leaf}`` -> ``mlp.{leaf}``, 5-name allowlist rename +T4 ``{attn,mlp}_cond_head.bias_in`` -> ``cond_head.bias_in`` merge +T5 ``attn_cond_head.cond_proj.{j}`` -> ``cond_head..{j}`` rename +T6 ``mlp_cond_head.cond_proj.{j}`` -> ``cond_head..{j+3}`` rename +T7 ``ctrl_mlpfusion.fc1_{x,c}`` -> ``ctrl_mlpfusion.mlp.fc1`` fuse dim 1 +T8 ``ctrl_mlpfusion.fc2`` -> ``ctrl_mlpfusion.mlp.fc2`` rename +T9 ``cond_head.cond_proj.*`` for blocks 1..23 drop +T10 ``ctrl_cfg.null_emb`` drop +T11 ``attn.{q,k,v}_proj`` -> ``attn.qkv_proj`` fuse dim 0 +T12 any ``.cond_heads.`` key (note the plural) drop +=== ============================================================== =========== + +T0 is not in ``PARAM_TREE.md``: it assumes the reference's two-level +``WorldModel``/``WorldDiT`` split, whereas ``components/dit.py`` collapses them +into one ``WaypointDiT`` whose blocks live at ``blocks.{i}``. Both spellings are +accepted here, as are the canonical post-transform spellings the reference's own +``pop``/``setdefault`` transforms tolerate (``PARAM_TREE.md`` section 3.5) — +which spelling the shipped file uses could not be established statically +(section 10.1), so guessing one was not an option. + +Three things this file does that the mstar machinery does not give you: + + * **The two fusions need a fan-in, and ``name_remapper`` is ``str -> str|None`` + with none.** Both go through ``StackedParamRule``. Neither target module is a + ``FusedColumnLinear`` — ``attention.py`` builds ``qkv_proj`` as a plain + ``nn.Linear`` and ``layers.MLPFusion`` holds a merged ``[D,2D]`` ``mlp.fc1`` + — so neither parameter ships a ``weight_loader`` and ``default_weight_loader`` + would assert on the shard id. ``_attach_shard_loaders`` installs one, **after** + ``to_empty`` (which drops attribute state along with the storage). + * **Per-shard completeness.** ``load_weights_into`` returns *target* names, and + q/k/v all share one target, so ``set(named_parameters()) - loaded`` is + satisfied by any one of the three: a ``k_proj`` missing from every layer + passes the wan22-style check silently (``PARAM_TREE.md`` S11d). The remapper + therefore tallies ``(target, shard_id)`` pairs and the contract checks those + too. Same hole, same fix, for ``fc1_x``/``fc1_c``. + * **The reshaping transforms T1/T2 have no hook at all**, so they ride a thin + adapter over the shard iterator, which is also where the config facts + ``PARAM_TREE.md`` section 10.4 flags as transcribed-not-read (``n_kv_heads``, + ``patch``) get validated against the tensor shapes actually on disk. + +Completeness is a hard contract: a checkpoint key that reaches no parameter, a +parameter no key reached, a fused shard that never arrived, or two keys writing +the same slot, all raise. A silently skipped weight is a wrong-output bug, not a +warning. Explicitly dropped keys (T9/T10/T12) are expected and silent. + +Two consequences of "two keys writing the same slot" that a naive +``(target, shard_id)`` tally does not cover, and that both fusions have: + + * A **pre-fused** key (``attn.qkv_proj.weight``, ``ctrl_mlpfusion.mlp.fc1`` + — canonical spellings ``PARAM_TREE.md`` section 3.5 says the reference + tolerates) claims ``(target, None)``, which does not collide with + ``(target, "q")``. Left alone, a file carrying both spellings assembles one + parameter out of both sources in whatever order the shard iterator happens + to yield — Q and K off the fused blob, V off ``v_proj``, no error. So + ``(target, None)`` is made to conflict with every ``(target, shard)`` of a + target that has a stacked rule. + * ``bias_in`` is the opposite case: three spellings legitimately share one + target (T4), so that collision is *arbitrated* by an explicit precedence + rank rather than refused. See ``_BIAS_IN_SPELLINGS``. + +Order inside the pipeline: the unconditional drops (T10/T12) run **before** the +shape validation in ``_adapt_checkpoint_stream``, because a ``.cond_heads.`` key +that happens to end in ``.k_proj.weight`` is a T12 drop and not a GQA violation. +T9's per-block ``cond_proj`` drop is *not* in that pre-filter: those keys are +exactly what ``_CondProjTieCheck`` exists to compare. + +``load_hf_weights`` is deliberately not used, for the reason wan22 documents: its +``skip_predicate`` runs *before* the remapper and would drop keys outside the +unexpected-key accounting. +""" + +from __future__ import annotations + +import re +from collections.abc import Iterable, Iterator +from pathlib import Path + +import torch +from torch import nn + +# _apply_stacked is imported rather than reimplemented on purpose: the remapper +# has to resolve a key to the same target load_weights_into will, and a local +# copy of a three-line matcher is a thing that drifts. +from mstar.model.loader.base import StackedParamRule, _apply_stacked, load_weights_into +from mstar.model.loader.iterators import iter_safetensors_shards +from mstar.model.waypoint.components.dit import WaypointDiT +from mstar.model.waypoint.components.layers import CondHead +from mstar.model.waypoint.config import WaypointConfig + +__all__ = [ + "build_waypoint_dit", + "remap_checkpoint_key", + "parameter_census", + "WAYPOINT_STACKED_PARAMS", + "COND_PROJ_SOURCE_BLOCK", +] + + +# Which block's cond_proj set is the physical one. The reference ties every +# block's cond_proj to block 0's in __init__ and then loads all 24 stored copies +# into that one tensor, so it effectively keeps block 23's (last write wins); +# the port keeps ONE copy and drops the rest, so it has to name a block. +# +# **Not a knob.** This records a fact of ``components/dit.py``, it does not +# choose one: ``WaypointDiT.retie_cond_proj`` hardcodes +# ``ref_proj = self.blocks[0].cond_head.cond_proj``, so block 0 is the only +# spelling ``named_parameters()`` reports after the retie and any other value +# here would turn all six kept keys into unexpected-key failures and leave the +# six real parameters unloaded. The constant exists to name the fact at its two +# use sites, and ``_assert_cond_proj_tied`` asserts the module tree still agrees +# with it — so if ``retie_cond_proj`` ever moves the owner, that fails with a +# sentence rather than with twelve confusing key errors. Changing this number +# without changing ``retie_cond_proj`` (which this file does not own) is not a +# supported edit. +# +# Block 0 is also what the reference's own __init__ ties to, what CONTRACTS +# section 6 and ``layers.CondHead``'s docstring prescribe, and what PARAM_TREE +# section 4.9's fill-forward loop uses as its reference. +# +# The choice only matters if the 24 stored copies disagree, which is exactly the +# silent divergence PARAM_TREE flags as S9 — so ``verify_cond_proj_tie`` checks +# that they agree instead of relying on the argument. When they agree, block 0 +# and block 23 are the same tensor and the choice is moot; when they do not, the +# load raises rather than quietly disagreeing with the reference. +COND_PROJ_SOURCE_BLOCK = 0 + +# Slot ids for the two port-side fusions. Order defines the layout. +QKV_SHARD_IDS: tuple[str, ...] = ("q", "k", "v") +CTRL_FC1_SHARD_IDS: tuple[str, ...] = ("x", "c") + +# Fused-shard routing. The leading dots matter: without them ``.v_proj`` would +# also match inside ``qkv_proj``. Note this is NOT ``LLAMA_STACKED_PARAMS``, +# whose extra ``gate_proj``/``up_proj`` rules would be live substring matchers +# for parameters Waypoint does not have. +# +# ``_apply_stacked`` rewrites by ``str.replace``, so +# ``blocks.0.attn.q_proj.weight`` -> ``blocks.0.attn.qkv_proj.weight`` +# ``blocks.0.ctrl_mlpfusion.fc1_x.weight`` -> ``blocks.0.ctrl_mlpfusion.mlp.fc1.weight`` +WAYPOINT_STACKED_PARAMS: list[StackedParamRule] = [ + StackedParamRule(".qkv_proj", ".q_proj", "q"), + StackedParamRule(".qkv_proj", ".k_proj", "k"), + StackedParamRule(".qkv_proj", ".v_proj", "v"), + StackedParamRule(".mlp.fc1", ".fc1_x", "x"), + StackedParamRule(".mlp.fc1", ".fc1_c", "c"), +] + +# The port's block prefix (``components/dit.py`` collapses WorldModel/WorldDiT). +MODEL_BLOCK_PREFIX = "blocks." + +# ``transformer.`` optional: T0. Accepts the reference's two-level spelling and +# the collapsed one. +_BLOCK_RE = re.compile(r"^(?:transformer\.)?blocks\.(\d+)\.(.+)$") +# Legacy half-heads. j is range(3) on both sides (PARAM_TREE section 4.5/4.6); +# a j >= 3 here is malformed and is left unmapped so it surfaces as unexpected. +_LEGACY_COND_PROJ_RE = re.compile(r"^(attn|mlp)_cond_head\.cond_proj\.(\d+)\.weight$") +_COND_PROJ_RE = re.compile(r"^cond_head\.cond_proj\.(\d+)\.weight$") + +# T3 is an explicit five-name allowlist, not a ``dit_mlp.*`` wildcard: a wildcard +# is over-permissive and would silently absorb a future ``dit_mlp.*`` key instead +# of surfacing it. ``expert_*``/``router`` do not exist under moe=False; they are +# listed because the reference renames them, and the rename lands on a parameter +# this port does not have, which is a loud unexpected-key failure either way. +_DIT_MLP_LEAVES: tuple[str, ...] = ( + "fc1.weight", + "fc2.weight", + "expert_in", + "expert_out", + "router.weight", +) + +# T10. ``CFG.forward`` is a training-time dropout with no call site in the +# reference's ``WorldModel.forward``; the port does not instantiate the tensor. +# Dropped explicitly rather than left unmatched so the unexpected-key accounting +# keeps no hole (PARAM_TREE S10). +_DROPPED_TOP_LEVEL_KEYS = frozenset({"ctrl_cfg.null_emb"}) + +# T12. Substring filter, unconditional, note the plural. +_COND_HEADS_FRAGMENT = ".cond_heads." + +# Half the CondHead slots come from each legacy half-head. +_COND_PROJ_PER_HEAD = CondHead.n_cond // 2 + +# T4 precedence over the three spellings that share ``cond_head.bias_in``, +# lowest first, highest wins. This is the reference's pop/setdefault outcome +# (``world_model.py:386-389``) restated as a rank: +# +# if attn_bias is not None or mlp_bias is not None: +# state_dict.setdefault(p + "cond_head.bias_in", +# mlp_bias if mlp_bias is not None else attn_bias) +# +# so mlp beats attn, and `setdefault` means an already-canonical +# ``cond_head.bias_in`` beats both. Note what this is NOT: the reference does +# not *drop* ``attn_cond_head.bias_in``, it uses it as the fallback when the mlp +# spelling is absent — and PARAM_TREE section 10.2 leaves it unresolved which of +# the two the shipped file actually carries (the 49,152-parameter difference is +# five orders of magnitude below the precision of "1.86B"). Dropping the attn +# copy unconditionally is therefore a plausible day-one failure: 24 unloaded +# ``cond_head.bias_in`` on a file that only has the attn spelling. +# +# A rank is needed rather than "last write wins" because this loader streams: the +# reference arbitrates over a materialized dict, while here the three spellings +# can arrive in any order across shard files, and the resident weight must not +# depend on that order. Arbitration itself lives in ``build_waypoint_dit``, the +# only place that sees every key. +_BIAS_IN_SPELLINGS: tuple[str, ...] = ( + "attn_cond_head.bias_in", + "mlp_cond_head.bias_in", + "cond_head.bias_in", +) + +# Reference init for the value-residual scalar, used by the no-checkpoint path. +_V_LAMB_INIT = 0.5 +# Weight init std for the no-checkpoint path. Not a checkpoint fact; it only has +# to produce finite, sanely scaled activations for a structural smoke test. +_STRUCTURAL_INIT_STD = 0.02 + + +# -------------------------------------------------------------------------- +# Name remapping (T0, T3-T6, T8-T12) +# -------------------------------------------------------------------------- + + +def _remap_block_suffix(suffix: str, layer_idx: int) -> str | None: + """Map one per-block checkpoint suffix to its parameter suffix, or ``None`` + to drop it. ``suffix`` excludes the ``blocks.{i}.`` prefix.""" + # T4: both legacy spellings map to the one target, and the attn copy is a + # FALLBACK, not a drop (see _BIAS_IN_SPELLINGS). Which of the three actually + # gets written is decided by rank in build_waypoint_dit; a pure key->key + # function cannot decide it, because it cannot see whether the winner is + # elsewhere in the file. + if suffix in ("attn_cond_head.bias_in", "mlp_cond_head.bias_in"): + suffix = "cond_head.bias_in" + + # T5/T6: identity index map for the attn head (0,1,2 -> 0,1,2), +3 for the + # mlp head (0,1,2 -> 3,4,5). Slots 0-2 drive the attention sublayer and 3-5 + # the MLP sublayer, which is what the source names say; swapping them is + # PARAM_TREE S5, silent and numerically catastrophic. + legacy = _LEGACY_COND_PROJ_RE.match(suffix) + if legacy is not None: + head, j = legacy.group(1), int(legacy.group(2)) + if j < _COND_PROJ_PER_HEAD: + slot = j if head == "attn" else j + _COND_PROJ_PER_HEAD + suffix = f"cond_head.cond_proj.{slot}.weight" + + # T3. + for leaf in _DIT_MLP_LEAVES: + if suffix == "dit_mlp." + leaf: + suffix = "mlp." + leaf + break + + # T8. Guarded on fc2 alone, separately from T7's both-halves guard; omitting + # it leaves 8 layers' ctrl_mlpfusion.mlp.fc2 unloaded. + if suffix == "ctrl_mlpfusion.fc2.weight": + suffix = "ctrl_mlpfusion.mlp.fc2.weight" + + # T9. Runs last, on the post-T5/T6 name, so it catches both the legacy + # half-head spellings and an already-canonical one. + if _COND_PROJ_RE.match(suffix) is not None and layer_idx != COND_PROJ_SOURCE_BLOCK: + return None + + # T7 (fc1_x/fc1_c) and T11 (q/k/v_proj) are deliberately left alone: they are + # fan-ins, which a remapper cannot express, and WAYPOINT_STACKED_PARAMS + # routes them. + return suffix + + +def _is_unconditionally_dropped(name: str) -> bool: + """T12 and T10 — the drops that depend on nothing but the key. + + Factored out because the shard adapter has to apply them *before* it + validates tensor shapes: ``…cond_heads.0.k_proj.weight`` is a T12 drop, not + a GQA shape violation, and validating first turns an expected drop into a + hard failure. One predicate, two call sites, so the pre-filter and the + remapper cannot drift apart on what counts as dropped. + + T9 (``cond_proj`` for blocks other than ``COND_PROJ_SOURCE_BLOCK``) is + deliberately NOT here even though it is also a drop: those 138 keys are + exactly what ``_CondProjTieCheck`` has to see, so they must survive the + stream filter and be dropped later, in the remapper. + """ + return _COND_HEADS_FRAGMENT in name or name in _DROPPED_TOP_LEVEL_KEYS + + +def _bias_in_rank(name: str) -> int | None: + """T4 precedence rank of a per-block ``bias_in`` key, or ``None`` if the key + is not one. Higher wins; see ``_BIAS_IN_SPELLINGS``.""" + block = _BLOCK_RE.match(name) + if block is None: + return None + suffix = block.group(2) + if suffix not in _BIAS_IN_SPELLINGS: + return None + return _BIAS_IN_SPELLINGS.index(suffix) + + +def remap_checkpoint_key(name: str) -> str | None: + """Map one Waypoint checkpoint key to the native parameter path, or return + ``None`` for a key that is intentionally dropped (T9/T10/T12). + + Pure function of the key — no model, no config — so it can be exercised + directly. Keys it maps to a name that is not a parameter are the caller's + problem: ``build_waypoint_dit`` treats those as unexpected and raises. + + **Not injective, by design, in exactly one place.** All three T4 ``bias_in`` + spellings map to ``blocks.{i}.cond_head.bias_in``; which one is allowed to + write is a precedence question that needs the whole key set, so it is settled + in ``build_waypoint_dit`` and not here (``_BIAS_IN_SPELLINGS``). Everywhere + else a second key resolving to a slot that is already claimed is a hard + error. + """ + if _is_unconditionally_dropped(name): # T10/T12 + return None + + block = _BLOCK_RE.match(name) + if block is None: + # Top-level keys are identity: denoise_step_emb.mlp.{fc1,fc2}.weight, + # ctrl_emb.mlp.{fc1,fc2}.weight, patchify.weight, unpatchify.{weight,bias}, + # out_norm.fc.weight. + return name + + layer_idx, suffix = int(block.group(1)), block.group(2) + mapped = _remap_block_suffix(suffix, layer_idx) + if mapped is None: + return None + # T0. + return f"{MODEL_BLOCK_PREFIX}{layer_idx}.{mapped}" + + +# -------------------------------------------------------------------------- +# Tensor transforms and shape validation (T1, T2) over the shard stream +# -------------------------------------------------------------------------- + + +def _unpatchify_weight(tensor: torch.Tensor, config: WaypointConfig, key: str) -> torch.Tensor: + """T1. ``[D, C, ph, pw]`` conv kernel -> ``[C*ph*pw, D]`` Linear weight. + + ``permute(1, 2, 3, 0)`` then ``reshape``: the Linear's output feature axis is + ordered ``(c, ph, pw)`` with pw fastest, because ``WaypointDiT.forward`` + unpacks it as ``view(B, N, Hp, Wp, C, ph, pw)``. Dropping the permute, or + transposing ph/pw inside it, keeps the shape and silently reprojects every + output sub-pixel (PARAM_TREE S1/S1b). ``reshape``, not ``view`` — the + permuted tensor is not contiguous. + """ + ph, pw = config.patch + if tensor.ndim == 4: + d_model, channels, k_h, k_w = tensor.shape + if (k_h, k_w) != (ph, pw): + raise RuntimeError( + f"{key} is a {k_h}x{k_w} patch kernel but WaypointConfig.patch is " + f"{(ph, pw)}. PARAM_TREE section 10.4: patch was transcribed from the " + "checkpoint's config.yaml, not read from it; the checkpoint wins." + ) + if channels != config.channels or d_model != config.d_model: + raise RuntimeError( + f"{key} has shape {tuple(tensor.shape)}; expected " + f"[d_model={config.d_model}, channels={config.channels}, {ph}, {pw}]." + ) + return tensor.permute(1, 2, 3, 0).reshape(-1, d_model) + + # Already canonical (a checkpoint written post-transform). The reference's + # own ndim == 4 guard makes T1 idempotent the same way. + expected = (config.channels * ph * pw, config.d_model) + if tuple(tensor.shape) != expected: + raise RuntimeError( + f"{key} has shape {tuple(tensor.shape)}; expected a 4-D " + f"[{config.d_model}, {config.channels}, {ph}, {pw}] conv kernel or an " + f"already-permuted {list(expected)} Linear weight." + ) + return tensor + + +def _unpatchify_bias(tensor: torch.Tensor, config: WaypointConfig, key: str) -> torch.Tensor: + """T2. One learned bias per latent channel, repeated across the patch. + + ``[C] -> [C,1,1] -> expand(-1, ph, pw) -> reshape(-1)``. The expand target is + ``(C, ph, pw)`` so the flatten agrees with T1's row ordering; a + ``repeat(ph*pw)`` produces the same ``[128]`` shape with the bias on the + wrong sub-pixel, which integrates into a slow colour drift over an + autoregressive rollout rather than failing (PARAM_TREE S2). + """ + ph, pw = config.patch + if tensor.numel() == config.channels: + return tensor[:, None, None].expand(-1, ph, pw).reshape(-1) + expected = config.channels * ph * pw + if tensor.numel() != expected: + raise RuntimeError( + f"{key} has {tensor.numel()} elements; expected channels={config.channels} " + f"(per-channel, to be expanded over the {ph}x{pw} patch) or the " + f"already-expanded {expected}." + ) + return tensor + + +def _check_patchify(tensor: torch.Tensor, config: WaypointConfig, key: str) -> None: + """Validate ``config.patch`` and ``config.channels`` against the conv kernel. + + Unlike ``unpatchify``, this one is 4-D in both the file and the module, so it + is validated rather than transformed. + """ + ph, pw = config.patch + if tensor.ndim != 4: + raise RuntimeError(f"{key} should be a 4-D conv kernel; got {tuple(tensor.shape)}.") + d_model, channels, k_h, k_w = tensor.shape + if (k_h, k_w) != (ph, pw): + raise RuntimeError( + f"{key} is a {k_h}x{k_w} patch kernel but WaypointConfig.patch is " + f"{(ph, pw)}. PARAM_TREE section 10.4: patch was transcribed rather than " + "read; a wrong value silently rescales the token grid." + ) + if (d_model, channels) != (config.d_model, config.channels): + raise RuntimeError( + f"{key} has shape {tuple(tensor.shape)}; expected " + f"[d_model={config.d_model}, channels={config.channels}, {ph}, {pw}]." + ) + + +def _check_attn_proj( + tensor: torch.Tensor, config: WaypointConfig, key: str, rows: int, what: str +) -> None: + """Validate an unfused q/k/v projection against the config's head counts. + + This is the check that pins ``n_kv_heads``. It is worth doing explicitly even + though ``_SliceShardLoader`` would also catch it: a wrong ``n_kv_heads`` + reshapes attention without erroring anywhere downstream, and "shard 'k' shape + mismatch" does not tell a reader which config field to go look at. + """ + if tensor.ndim != 2 or tuple(tensor.shape) != (rows, config.d_model): + raise RuntimeError( + f"{key} has shape {tuple(tensor.shape)}; expected " + f"[{what} = {rows}, d_model = {config.d_model}] from n_heads=" + f"{config.n_heads}, n_kv_heads={config.n_kv_heads}, d_head={config.d_head}. " + "PARAM_TREE section 10.4: n_kv_heads was transcribed from the checkpoint's " + "config.yaml rather than read from it (the reference default is n_heads), " + "and a wrong value reshapes GQA attention without raising." + ) + + +def _cond_proj_slot(key: str) -> tuple[int, int] | None: + """``(block_idx, canonical slot 0..5)`` for a cond_proj key, else ``None``. + + Accepts the legacy half-head spellings and the canonical one; used only by + the tie check, which runs on the raw stream before the remapper. + """ + block = _BLOCK_RE.match(key) + if block is None: + return None + layer_idx, suffix = int(block.group(1)), block.group(2) + legacy = _LEGACY_COND_PROJ_RE.match(suffix) + if legacy is not None: + j = int(legacy.group(2)) + if j >= _COND_PROJ_PER_HEAD: + return None + return layer_idx, (j if legacy.group(1) == "attn" else j + _COND_PROJ_PER_HEAD) + canonical = _COND_PROJ_RE.match(suffix) + if canonical is not None: + return layer_idx, int(canonical.group(1)) + return None + + +class _CondProjTieCheck: + """Confirms the checkpoint's 24 stored ``cond_proj`` sets agree before 23 of + them are dropped (PARAM_TREE S9). + + The port keeps ``COND_PROJ_SOURCE_BLOCK``'s copy; the reference keeps block + 23's, because it loads all 24 into one shared tensor and the last write wins. + The two agree only if the stored copies are identical — which they should be, + since the tie was in place at training time, but nothing in the file or in + either loader enforces it. If a fine-tune ever broke the tie, the port and + the reference would silently produce different video. + + Compares the matrices **in full**. + + An earlier revision compared a fixed ``[:, :64]`` column probe and justified + it as "~3 MB of retained probes instead of 1.16 GB of streamed comparisons". + That trade does not exist: the two figures measure different things. The + ~1.13 GiB of stored ``cond_proj`` (24 blocks x 6 x ``[2048, 2048]`` bf16) + streams past either way — this class sits on a stream the loader is already + consuming and adds no reads at all. The only quantity the probe reduced is + what is **retained**: one reference copy per slot, i.e. 6 x ``[2048, 2048]`` + fp32 = **96 MiB** held for the duration of the load, against ~3 MiB for the + probe. 96 MiB once, at load, next to a 2.56 GB model, is not a cost worth an + unsound check — and the probe was demonstrably unsound: randomizing block 2 + slot 0's columns 64 onward loaded clean with ``verify_cond_proj_tie=True``. + + ``verify_cond_proj_tie=False`` remains the escape hatch, and it is now the + only thing between the check and a machine that cannot spare the 96 MiB. + + Whichever block arrives first for a slot becomes that slot's reference, so + this does not depend on shard ordering, and equality across all 24 makes the + block-0-vs-23 choice moot rather than merely defensible. + """ + + def __init__(self) -> None: + self._reference: dict[int, tuple[int, torch.Tensor]] = {} + self.divergent: list[str] = [] + + def observe(self, key: str, tensor: torch.Tensor) -> None: + slot_info = _cond_proj_slot(key) + if slot_info is None or tensor.ndim != 2: + return + block_idx, slot = slot_info + # fp32 on CPU: uniform, lossless from the checkpoint's bf16, and it keeps + # the retained set off the serving device. + seen = tensor.detach().to(device="cpu", dtype=torch.float32) + known = self._reference.get(slot) + if known is None: + self._reference[slot] = (block_idx, seen.clone()) + return + ref_block, ref_tensor = known + if not torch.equal(ref_tensor, seen): + self.divergent.append(f"slot {slot}: block {block_idx} != block {ref_block}") + + +def _adapt_checkpoint_stream( + weights: Iterable[tuple[str, torch.Tensor]], + config: WaypointConfig, + tie_check: _CondProjTieCheck | None, +) -> Iterator[tuple[str, torch.Tensor]]: + """Apply the two reshaping transforms and validate the transcribed config + facts, in one streaming pass over the shards. + + T1/T2 live here rather than in the remapper because mstar has no reshape + hook: ``name_remapper`` sees names only, and ``weight_loader`` is per-target + (the two ``unpatchify`` parameters have no fused loader to hang it on). + + **Drops run before validation.** These checks fire on key *suffixes*, so a + key that T12 drops unconditionally can still end in ``.k_proj.weight`` — + ``…blocks.0.cond_heads.0.k_proj.weight`` did exactly that and raised a GQA + shape error about a key the loader had already decided to throw away. + Validating only what survives the drop filter is the fix; a dropped key's + shape is not this model's business. + """ + q_rows = config.n_heads * config.d_head + kv_rows = config.n_kv_heads * config.d_head + + for key, tensor in weights: + if _is_unconditionally_dropped(key): # T10/T12, before anything reads a shape + continue + + # Order matters: "unpatchify.weight".endswith("patchify.weight") is True, + # so unpatchify must be tested first and the rest must be elif. + if key.endswith("unpatchify.weight"): + tensor = _unpatchify_weight(tensor, config, key) + elif key.endswith("unpatchify.bias"): + tensor = _unpatchify_bias(tensor, config, key) + elif key.endswith("patchify.weight"): + _check_patchify(tensor, config, key) + elif key.endswith(".q_proj.weight"): + _check_attn_proj(tensor, config, key, q_rows, "n_heads * d_head") + elif key.endswith(".k_proj.weight") or key.endswith(".v_proj.weight"): + _check_attn_proj(tensor, config, key, kv_rows, "n_kv_heads * d_head") + elif tie_check is not None: + tie_check.observe(key, tensor) + + yield key, tensor + + +# -------------------------------------------------------------------------- +# Fused-parameter shard loaders (T7, T11) +# -------------------------------------------------------------------------- + + +class _SliceShardLoader: + """``param.weight_loader`` for a port-side fused parameter. + + Copies one checkpoint shard into its slice of the fused tensor. Exists + because neither fusion target is a ``FusedColumnLinear``: ``qkv_proj`` is a + plain ``nn.Linear`` in ``components/attention.py`` and ``mlp.fc1`` is a plain + ``nn.Linear`` inside ``layers.MLPFusion``, so neither carries a loader and + ``default_weight_loader`` asserts ``loaded_shard_id is None``. It also + generalizes ``FusedColumnLinear`` in the one way ``ctrl_mlpfusion`` needs: + that fusion concatenates along **dim 1** (the input-feature axis), not dim 0. + """ + + def __init__(self, param_name: str, dim: int, layout: dict[str, tuple[int, int]]): + self.param_name = param_name + self.dim = dim + self.layout = layout + + def __call__( + self, + param: nn.Parameter, + loaded_weight: torch.Tensor, + loaded_shard_id: str | int | None = None, + ) -> None: + if loaded_shard_id is None: + # A checkpoint already written in the fused spelling. Only reachable + # for ctrl_mlpfusion.mlp.fc1 (PARAM_TREE section 3.5 lists it as a + # canonical spelling the reference accepts); harmless and symmetric + # for qkv_proj. + if tuple(param.data.shape) != tuple(loaded_weight.shape): + raise RuntimeError( + f"{self.param_name}: pre-fused checkpoint tensor has shape " + f"{tuple(loaded_weight.shape)}, parameter is {tuple(param.data.shape)}." + ) + param.data.copy_(loaded_weight) + return + + if loaded_shard_id not in self.layout: + raise RuntimeError( + f"{self.param_name}: unknown shard id {loaded_shard_id!r}; expected one " + f"of {list(self.layout)}." + ) + offset, size = self.layout[loaded_shard_id] + dst = param.data.narrow(self.dim, offset, size) + if tuple(dst.shape) != tuple(loaded_weight.shape): + raise RuntimeError( + f"{self.param_name}: shard {loaded_shard_id!r} is " + f"{tuple(loaded_weight.shape)} but its slice of the fused parameter is " + f"{tuple(dst.shape)} (dim {self.dim}, offset {offset})." + ) + dst.copy_(loaded_weight) + + +def _attach_shard_loaders( + dit: WaypointDiT, config: WaypointConfig +) -> dict[str, tuple[str, ...]]: + """Install ``_SliceShardLoader`` on every fused parameter and return + ``{param_name: required shard ids}``. + + MUST run after ``to_empty(device)``: that reallocates the Parameter objects + and drops attached attributes along with the meta storage — the same reason + ``FusedColumnLinear`` re-attaches its loaders from ``_apply``. + """ + q_rows = config.n_heads * config.d_head + kv_rows = config.n_kv_heads * config.d_head + d_model = config.d_model + + fused: dict[str, tuple[str, ...]] = {} + for name, param in dit.named_parameters(): + if name.endswith(".attn.qkv_proj.weight"): + # cat([q, k, v], dim=0) — verified against the reference's own fusion + # (patch_model.MergedQKVAttn's cat) and against the matching + # split((q_out, kv_out, kv_out), dim=-1) in components/attention.py. + # GQA makes the shards unequal, so a q/k swap raises but a k/v swap + # loads cleanly and produces meaningless attention (PARAM_TREE S11). + dim, layout = 0, { + "q": (0, q_rows), + "k": (q_rows, kv_rows), + "v": (q_rows + kv_rows, kv_rows), + } + expected_shape = (q_rows + 2 * kv_rows, d_model) + elif name.endswith(".ctrl_mlpfusion.mlp.fc1.weight"): + # cat([fc1_x, fc1_c], dim=1) — x first. layers.MLPFusion splits it + # straight back with chunk(2, dim=1), whose low columns are the token + # half; the reverse order keeps the [2048, 4096] shape and applies + # controller conditioning to tokens and vice versa (PARAM_TREE S7). + dim, layout = 1, {"x": (0, d_model), "c": (d_model, d_model)} + expected_shape = (d_model, 2 * d_model) + else: + continue + + if tuple(param.shape) != expected_shape: + raise RuntimeError( + f"{name} is {tuple(param.shape)} but this config implies " + f"{expected_shape}; the module tree and WaypointConfig disagree " + "before the checkpoint was even opened." + ) + param.weight_loader = _SliceShardLoader(name, dim, layout) + fused[name] = tuple(layout) + + return fused + + +# -------------------------------------------------------------------------- +# Structural build helpers +# -------------------------------------------------------------------------- + + +def parameter_census(dit: WaypointDiT) -> tuple[int, int, int]: + """``(deduplicated tensors, deduplicated numel, raw state_dict numel)``. + + ``named_parameters()`` deduplicates aliased Parameters; ``state_dict()`` does + not, so the gap between the two counts is exactly the tied ``cond_proj``. For + the 720P checkpoint this is ``(174, 1_281_958_040, 1_860_771_992)`` — the + "1.28B resident / 1.86B stored" figures. + + Those differ by 2,048 from ``PARAM_TREE.md`` section 5.2's 1,281,960,088 / + 1,860,774,040: that arithmetic carries ``ctrl_cfg.null_emb`` ``[1, 1, 2048]`` + in both totals, and the port drops it (T10). + """ + params = dict(dit.named_parameters()) + return ( + len(params), + sum(p.numel() for p in params.values()), + sum(t.numel() for t in dit.state_dict().values()), + ) + + +def _assert_cond_proj_tied(dit: WaypointDiT, config: WaypointConfig) -> None: + """Fail if ``retie_cond_proj()`` did not take. + + ``named_parameters()`` deduplicates aliased Parameters, so a correctly tied + model reports 6 ``cond_proj`` tensors and an un-tied one reports + ``6 * n_layers``. This is PARAM_TREE S9b, which is otherwise entirely silent + — the un-tied model is numerically correct and merely 0.6B parameters + heavier, so no output check catches it and the completeness contract would + instead report 138 unloaded parameters with no hint as to why. + + Both the tensor count and the resident/stored numel gap are checked, because + they fail differently: the count catches "never tied", while the numel gap + catches a partial tie (some blocks aliased, some not) that still leaves the + count wrong in a way a reader might not connect to memory. Both bounds are + derived from ``config``, not hardcoded to the 720P variant, so they hold for + the 360P sibling and for the reduced configs used in testing. + """ + tied = [name for name, _ in dit.named_parameters() if ".cond_head.cond_proj." in name] + if len(tied) != CondHead.n_cond: + raise RuntimeError( + f"cond_proj is not tied: named_parameters() reports {len(tied)} cond_proj " + f"tensors, expected {CondHead.n_cond} (one physical set, aliased by all " + f"{config.n_layers} blocks). to_empty(device) un-ties them and " + "retie_cond_proj() must be called after it, not before (CONTRACTS " + "section 6.1)." + ) + # COND_PROJ_SOURCE_BLOCK is a record of which block retie_cond_proj aliases + # the others onto, not a choice this file gets to make; assert it rather than + # trust it. If the two ever disagree, T9 drops the six keys the module keeps + # and keeps the six it drops, which surfaces as 6 unexpected keys plus 6 + # unloaded parameters and no explanation. + owner_prefix = f"{MODEL_BLOCK_PREFIX}{COND_PROJ_SOURCE_BLOCK}.cond_head.cond_proj." + if not all(name.startswith(owner_prefix) for name in tied): + raise RuntimeError( + f"cond_proj's surviving owner is not block {COND_PROJ_SOURCE_BLOCK}: " + f"named_parameters() reports {sorted(tied)[:2]}. " + "weight_loader.COND_PROJ_SOURCE_BLOCK and " + "WaypointDiT.retie_cond_proj (which hardcodes blocks[0]) must name the " + "same block; the constant records that fact and cannot change it." + ) + _, dedup_numel, raw_numel = parameter_census(dit) + # Every block past the first contributes 6 aliased [D, D] matrices that + # state_dict() re-expands and named_parameters() does not. For 720P that is + # 23 * 6 * 2048^2 = 578,813,952, i.e. the 1.86B - 1.28B gap. + expected_gap = (config.n_layers - 1) * CondHead.n_cond * config.d_model**2 + if raw_numel - dedup_numel != expected_gap: + raise RuntimeError( + f"cond_proj tying is inconsistent: state_dict() holds {raw_numel} elements " + f"and named_parameters() {dedup_numel}, a gap of {raw_numel - dedup_numel}; " + f"a fully tied model must differ by exactly {expected_gap} " + f"(({config.n_layers} - 1) blocks x {CondHead.n_cond} x {config.d_model}^2). " + "Some blocks' cond_proj are aliased and some are not." + ) + + +def _assert_expected_layout(dit: WaypointDiT) -> None: + """Fail early if the module tree is not the one the remapper targets.""" + params = dict(dit.named_parameters()) + if not any(name.startswith(f"{MODEL_BLOCK_PREFIX}0.") for name in params): + raise RuntimeError( + f"No parameter starts with {MODEL_BLOCK_PREFIX!r}; weight_loader's key map " + "targets components/dit.py's collapsed tree (blocks.{i}, not " + "transformer.blocks.{i}). Update MODEL_BLOCK_PREFIX to match the module." + ) + + +def _initialize_structurally(dit: WaypointDiT, seed: int = 0) -> None: + """Fill a ``to_empty``-materialized model with finite values. + + ``to_empty`` allocates *uninitialized* storage, which routinely contains NaN + and Inf bit patterns, so a "no checkpoint" model is not usable for a shape or + plumbing smoke test until something writes to every parameter. Not an attempt + to reproduce the reference's init — only the two parameters whose init is a + documented fact are reproduced (``v_lamb`` = 0.5, and the two 1-D tensors + ``cond_head.bias_in``/``unpatchify.bias``, both zeros in the reference). + + Iterating ``named_parameters()`` writes each tied ``cond_proj`` once, which is + what makes the aliasing survive. + """ + generators: dict[torch.device, torch.Generator] = {} + with torch.no_grad(): + for _, param in dit.named_parameters(): + if param.ndim == 0: + param.fill_(_V_LAMB_INIT) + elif param.ndim == 1: + param.zero_() + else: + generator = generators.get(param.device) + if generator is None: + generator = torch.Generator(device=param.device) + generator.manual_seed(seed) + generators[param.device] = generator + param.normal_(0.0, _STRUCTURAL_INIT_STD, generator=generator) + + +# -------------------------------------------------------------------------- +# Entry point +# -------------------------------------------------------------------------- + + +def build_waypoint_dit( + config: WaypointConfig, + checkpoint_dir: str | Path | None = None, + device: torch.device | str = "cpu", + *, + skip_weight_loading: bool = False, + verify_cond_proj_tie: bool = True, +) -> WaypointDiT: + """Meta-build, materialize on ``device``, and load the checkpoint into a + ready-to-serve (eval-mode) native Waypoint DiT. + + Args: + config: the checkpoint's config. ``n_kv_heads`` and ``patch`` are + validated against the tensor shapes actually in the file. + checkpoint_dir: local directory holding ``model.safetensors`` (or an + index plus shards). Required unless ``skip_weight_loading``. Never + downloaded — the caller resolves the path. + device: where to materialize. + skip_weight_loading: build the structure only, with no checkpoint access + at all. For shape/plumbing work before the weights exist; the result + is randomly initialized and produces meaningless output. + verify_cond_proj_tie: check that the checkpoint's 24 stored ``cond_proj`` + sets agree before 23 of them are dropped. See ``_CondProjTieCheck``. + + Raises: + RuntimeError: on any completeness failure — an unexpected checkpoint key, + an unloaded parameter, a fused shard that never arrived, two keys + claiming one slot, or divergent ``cond_proj`` copies. + """ + if not skip_weight_loading and checkpoint_dir is None: + raise ValueError( + "build_waypoint_dit needs a checkpoint_dir unless skip_weight_loading=True." + ) + + with torch.device("meta"): + dit = WaypointDiT(config) + dit.cast_serving_dtypes() + dit.to_empty(device=device) + # MUST follow to_empty, which un-aliases the shared cond_proj (CONTRACTS 6.1). + dit.retie_cond_proj() + _assert_cond_proj_tied(dit, config) + + if skip_weight_loading: + _initialize_structurally(dit) + return dit.eval() + + checkpoint_dir = Path(checkpoint_dir) + if not checkpoint_dir.is_dir(): + raise FileNotFoundError(f"Waypoint checkpoint directory not found: {checkpoint_dir}") + + _assert_expected_layout(dit) + fused_shards = _attach_shard_loaders(dit, config) + params = dict(dit.named_parameters()) + + unexpected: list[str] = [] + conflicts: list[str] = [] + # (target, shard_id) -> the checkpoint key that claimed it. This is the + # per-shard tally: load_weights_into's returned set holds target names only, + # so q, k and v all collapse to one entry and a k_proj missing from every + # layer would satisfy `set(params) - loaded` (PARAM_TREE S11d). Recorded here + # rather than in _SliceShardLoader because the remapper is the one place that + # sees both the original key (for the error message) and the resolved target. + arrivals: dict[tuple[str, str | int | None], str] = {} + # T4's arbitrated collision: {target: (rank, winning checkpoint key)}. + bias_in_claims: dict[str, tuple[int, str]] = {} + + def remap(name: str) -> str | None: + mapped = remap_checkpoint_key(name) + if mapped is None: + return None # T9/T10/T12: expected, silent, not an unexpected key. + target, shard_id = _apply_stacked(mapped, WAYPOINT_STACKED_PARAMS) + if target not in params: + unexpected.append(name) + return None + + # T4 is the one collision that is resolved rather than refused: three + # spellings legitimately share ``cond_head.bias_in`` and the reference + # picks between them (mlp > attn, canonical > both). Rank, not arrival + # order — see _BIAS_IN_SPELLINGS. + rank = _bias_in_rank(name) + if rank is not None: + held = bias_in_claims.get(target) + if held is not None: + if rank < held[0]: + return None # a higher-precedence spelling already claimed it + if rank == held[0]: + # The same spelling twice, i.e. a genuinely duplicated key. + conflicts.append(f"{held[1]} and {name} -> {target}") + bias_in_claims[target] = (rank, name) + arrivals[(target, shard_id)] = name + return mapped + + claimed_by = arrivals.get((target, shard_id)) + if claimed_by is not None: + # Two distinct checkpoint keys resolving to one slot. The reference + # resolves this with setdefault (first wins); a streaming loader would + # instead let the last one win, non-deterministically across shard + # order, so it is refused rather than arbitrated. + slot = target if shard_id is None else f"{target}[{shard_id}]" + conflicts.append(f"{claimed_by} and {name} -> {slot}") + + # The same "one slot, two writers" failure across the fused/unfused + # spellings, which the (target, shard_id) key above cannot see: a + # pre-fused ``qkv_proj``/``mlp.fc1`` tensor claims (target, None) and a + # split shard claims (target, "q"), and those never collide. Both then + # write, and the surviving parameter is a mix of the two decided by + # shard-iteration order — Q and K off the fused blob, V off ``v_proj``, + # with nothing raised. Either spelling alone is fine; both together are + # a checkpoint whose intent cannot be inferred, so it is refused. + if target in fused_shards: + rival_slots = ( + [(target, s) for s in fused_shards[target]] + if shard_id is None + else [(target, None)] + ) + for slot_key in rival_slots: + rival = arrivals.get(slot_key) + if rival is not None: + conflicts.append( + f"{rival} and {name} -> {target} (a pre-fused tensor and a " + "split shard of the same fused parameter)" + ) + + arrivals[(target, shard_id)] = name + return mapped + + tie_check = _CondProjTieCheck() if verify_cond_proj_tie else None + shards = _adapt_checkpoint_stream( + iter_safetensors_shards(checkpoint_dir, device=device), config, tie_check + ) + # load_weights_into directly, not load_hf_weights: the wrapper's skip + # predicate runs before the remapper and would drop keys outside the + # unexpected-key accounting below. + loaded = load_weights_into( + dit, shards, stacked_params=WAYPOINT_STACKED_PARAMS, name_remapper=remap + ) + + missing = sorted(set(params) - loaded) + missing_shards = sorted( + f"{name}[{shard_id}]" + for name, shard_ids in fused_shards.items() + # A pre-fused tensor satisfies every shard of its target at once. Safe + # to short-circuit on now: `remap` refuses a file that carries both + # spellings, so reaching here with (name, None) present means the + # pre-fused tensor was the *only* writer. + if (name, None) not in arrivals + for shard_id in shard_ids + if (name, shard_id) not in arrivals + ) + if unexpected or missing or missing_shards or conflicts: + raise RuntimeError( + f"Waypoint DiT checkpoint mismatch at {checkpoint_dir}: " + f"{len(unexpected)} unexpected checkpoint keys {unexpected[:5]}, " + f"{len(missing)} unloaded parameters {missing[:5]}, " + f"{len(missing_shards)} unloaded fused shards {missing_shards[:5]}, " + f"{len(conflicts)} slots claimed twice {conflicts[:3]} — refusing to " + "serve a partially loaded transformer." + ) + if tie_check is not None and tie_check.divergent: + raise RuntimeError( + f"Waypoint DiT checkpoint at {checkpoint_dir} stores {len(tie_check.divergent)} " + f"divergent cond_proj copies {tie_check.divergent[:5]}. The port keeps block " + f"{COND_PROJ_SOURCE_BLOCK}'s and drops the other " + f"{config.n_layers - 1} sets, which matches the reference (whose last write " + "wins, i.e. block 23) only while all copies agree. Pass " + "verify_cond_proj_tie=False to load block " + f"{COND_PROJ_SOURCE_BLOCK}'s copy anyway." + ) + return dit.eval() From c23a73e6a4f8ed5fe9a531f4a5c9256bd869a5e7 Mon Sep 17 00:00:00 2001 From: garv Date: Tue, 1 Sep 2026 18:05:12 +0000 Subject: [PATCH 02/29] waypoint: tests for components, DiT, and weight loader 120 cases across three files. - test_waypoint_components.py 38 cases: RoPE band layout, ring wraparound, BlockMask shape, AdaLN-Zero, CondHead tying. - test_waypoint_dit.py 21 cases: the 4+1 pass structure, value residual threading, per-layer local/global heterogeneity, ring commit semantics. - test_waypoint_weight_loader.py 61 cases, covering four defects an independent audit found -- two of them silent. A pre-fused qkv_proj alongside split shards would load both, because conflicts were keyed on (target, shard_id) and so (t, None) never collided with (t, "q"); safetensors' sorted key order then gave Q and K from the fused blob and V from v_proj. And the cond_proj tie probe compared only the first 64 columns, so a divergent block passed clean. Each regression test was confirmed to fail against the pre-fix loader. Mutation-checked rather than coverage-checked: 10 injected mutants in the components and DiT, all killed. What these do NOT cover, since they run on CPU in fp32 against synthetic state dicts: the real checkpoint, bf16, the compiled regions, and a mislabelled K/V pair on disk. The last is worth calling out -- n_heads=32 over n_kv_heads=16 means a checkpoint with k_proj and v_proj transposed relative to what the reference wrote would load with matching shapes and produce only wrong output. Nothing static distinguishes it; only a forward parity run against the reference can. --- test/modular/test_waypoint_components.py | 909 ++++++++++++++++++++ test/modular/test_waypoint_dit.py | 678 +++++++++++++++ test/modular/test_waypoint_weight_loader.py | 795 +++++++++++++++++ 3 files changed, 2382 insertions(+) create mode 100644 test/modular/test_waypoint_components.py create mode 100644 test/modular/test_waypoint_dit.py create mode 100644 test/modular/test_waypoint_weight_loader.py diff --git a/test/modular/test_waypoint_components.py b/test/modular/test_waypoint_components.py new file mode 100644 index 000000000..bfe3e97d2 --- /dev/null +++ b/test/modular/test_waypoint_components.py @@ -0,0 +1,909 @@ +"""Component-level contract tests for the Waypoint-1.5 port: ring KV cache, +BlockMask, OrthoRoPE and the small layers. + +The bar here is **the normative document**, ``docs/waypoint/CONTRACTS.md``, not +"the code does what the code does". Every failure mode this file guards against +is silent: a wrong ring slot, a re-derived bucket count, a permuted controller +concat and an un-compiled ``flex_attention`` all produce plausible video and +raise nothing. So the assertions are exact wherever the contract says exact +(bitwise for the compaction A/B, for the RoPE angle tables, for the ring bytes +after a frozen pass) and never widened to accommodate the implementation. + +Sections map onto CONTRACTS: 2.3/2.3.1 (BlockMask + the compile trap, DECISIONS +D10), 2.2 (upsert), 2.1/2.4 (geometry, compaction, DECISIONS D6), 4.3 (OrthoRoPE) +and 4.5 (the layer primitives). + +CPU-only and checkpoint-free by construction. Numeric work runs on a reduced but +structurally identical config (4 layers / 128 tokens per frame / d_head 32, one +global layer at stride 8); the real 720P config is used only where the assertion +is about geometry rather than activations. ``torch.compile(flex_attention)`` +works on CPU in torch 2.9, which is what makes the compile-trap test possible +without a GPU -- it costs a few seconds of inductor time on first use. +""" + +import dataclasses +import math +import sys + +import pytest +import torch +import torch.nn.functional as F + +sys.path.insert(0, ".") + +from torch.nn.attention.flex_attention import ( + _DEFAULT_SPARSE_BLOCK_SIZE, + flex_attention, + noop_mask, +) + +from mstar.model.waypoint.components.kv_backend import ( + FlexRingBackend, + LayerRingCache, + flex_attention_masked, + make_block_mask, + ring_memory_bytes, +) +from mstar.model.waypoint.components.layers import ( + MLP, + AdaLN, + ControllerInputEmbedding, + MLPFusion, + NoiseConditioner, + ada_gate, + ada_rmsnorm, + rms_norm, +) +from mstar.model.waypoint.components.rope import OrthoRoPEAngles, apply_ortho_rope +from mstar.model.waypoint.config import WaypointConfig, waypoint_1_5_1b_720p + +BLOCK = _DEFAULT_SPARSE_BLOCK_SIZE # 128 +TPF = 128 # tokens per frame in the reduced config == one sparse block +D_HEAD = 32 + + +def reduced_config(**overrides) -> WaypointConfig: + """A 4-layer / 128-token-per-frame Waypoint whose *structure* is the 720P + model's: one global layer (index 3) at stride 8, one non-global period, GQA + live at 2 query heads over 1 KV head, controller fusion on ``i % 3 == 0``. + + Only the sizes shrink. ``global_window // global_pinned_dilation == 4`` + addressable slots against a 32-frame reference allocation keeps the 8x + over-allocation that CONTRACTS 2.4 is about, while letting a test wrap the + global ring in 32 frames instead of 128. + """ + base = { + "n_layers": 4, + "n_heads": 2, + "n_kv_heads": 1, + "d_model": 64, + "mlp_ratio": 2, + "channels": 4, + "tokens_per_frame": TPF, + "height": 8, + "width": 16, + "local_window": 4, + "global_window": 32, + "global_pinned_dilation": 8, + "n_buttons": 8, + } + return WaypointConfig(**{**base, **overrides}) + + +def frame_kv(value: float, *, tokens: int = TPF, d_head: int = 8) -> torch.Tensor: + """``[2, B, H_kv, tokens, D]`` of a single constant, so a ring slot's + contents identify the frame that wrote it.""" + return torch.full((2, 1, 1, tokens, d_head), float(value)) + + +def ring_slot_values(cache: LayerRingCache) -> list[float]: + """The K-side value parked in each ring slot (scratch excluded).""" + return [cache.kv[0, 0, 0, s * cache.tokens_per_frame, 0].item() for s in range(cache.ring_frames)] + + +def visible_blocks(block_mask) -> set[int]: + """The KV blocks the mask actually makes visible: ``full_kv_indices`` + truncated to ``full_kv_num_blocks``, which is all the compiled kernel reads. + """ + n = int(block_mask.full_kv_num_blocks[0, 0, 0]) + return set(block_mask.full_kv_indices[0, 0, 0, :n].tolist()) + + +# --------------------------------------------------------------------------- +# 1. The eager-FlexAttention trap (CONTRACTS 2.3 / 2.3.1, DECISIONS D10) +# --------------------------------------------------------------------------- + + +def test_block_mask_is_full_blocks_only_and_carries_a_noop_mask_mod(): + """The whole trap follows from this: visibility is in the index lists and + nowhere else, so anything that re-derives the mask from ``mask_mod`` sees + "everything visible". + + Note the exact shape of the fact: ``make_block_mask`` passes + ``mask_mod=None``, and ``BlockMask.from_kv_blocks`` substitutes + ``flex_attention.noop_mask``. CONTRACTS 2.3 says the BlockMask "carries + ``mask_mod=None``"; what it carries is the noop, which is the same hazard. + """ + written = torch.zeros(5 * BLOCK, dtype=torch.bool) + written[0 * BLOCK : 1 * BLOCK] = True # one committed frame + written[4 * BLOCK :] = True # the permanently visible scratch tail + + bm = make_block_mask(TPF, written.numel(), written) + + assert bm.mask_mod is noop_mask, "a non-noop mask_mod would change the trap's shape" + assert bm.seq_lengths == (TPF, written.numel()) + # Zero partial blocks: "any token written" and "all tokens written" coincide + # because writes are whole frames. + assert int(bm.kv_num_blocks.sum()) == 0 + assert visible_blocks(bm) == {0, 4} + # Query-uniform: one row, broadcast over the query blocks. + assert bm.full_kv_num_blocks.shape == (1, 1, TPF // BLOCK) + + +def test_make_block_mask_rejects_unaligned_and_non_multiple_lengths(): + written = torch.zeros(5 * BLOCK, dtype=torch.bool) + written[:BLOCK] = True + with pytest.raises(RuntimeError, match="multiple of block size"): + make_block_mask(TPF + 1, written.numel(), written) + with pytest.raises(RuntimeError, match="multiple of block size"): + make_block_mask(TPF, written.numel() - 1, written[:-1]) + + ragged = torch.zeros(2 * BLOCK, dtype=torch.bool) + ragged[3] = True # one token of a block, which no whole-frame write can produce + with pytest.raises(AssertionError, match="block-aligned"): + make_block_mask(TPF, ragged.numel(), ragged) + + +def test_flex_attention_masked_is_a_compiled_callable(): + """If someone "simplifies" ``kv_backend.flex_attention_masked`` back to the + bare function, this fails first and says why.""" + assert flex_attention_masked is not flex_attention + assert hasattr(flex_attention_masked, "_torchdynamo_orig_callable"), ( + "kv_backend.flex_attention_masked must stay wrapped in torch.compile: with " + "mask_mod=None the eager kernel ignores the ring mask entirely (CONTRACTS 2.3.1)" + ) + + +@pytest.mark.parametrize( + ("label", "committed_blocks"), + [ + ("one_frame", (0,)), + ("half_the_ring", (0, 2)), + ("full_ring", (0, 1, 2, 3)), + ], +) +def test_compiled_flex_attention_honours_the_ring_mask_and_eager_does_not(label, committed_blocks): + """CONTRACTS 2.3.1, DECISIONS D10 -- the single most valuable test here. + + A hand-built masked-dense SDPA is the reference. The compiled kernel matches + it to ~1e-07; eager ``flex_attention`` blends in every unwritten ring slot + (which holds zeros) and is off by ~1e-01. **Nothing raises in either case.** + So the guard has to be numeric, and it has to assert both halves: that the + compiled path is right *and* that the trap is real, because if a future torch + ever fixed eager the "unless someone removes the compile" argument would + quietly stop being tested and this test should be revisited rather than + silently passing. + """ + capacity = 5 * BLOCK # 4 ring frames + 1 scratch, at one block per frame + written = torch.zeros(capacity, dtype=torch.bool) + for b in committed_blocks: + written[b * BLOCK : (b + 1) * BLOCK] = True + written[4 * BLOCK :] = True + bm = make_block_mask(TPF, capacity, written) + + gen = torch.Generator().manual_seed(0xC0FFEE) + q = torch.randn(1, 2, TPF, D_HEAD, generator=gen) + k = torch.zeros(1, 2, capacity, D_HEAD) + v = torch.zeros(1, 2, capacity, D_HEAD) + # Only written slots hold data; the rest stay zero, exactly as a fresh ring is. + k[:, :, written] = torch.randn(1, 2, int(written.sum()), D_HEAD, generator=gen) + v[:, :, written] = torch.randn(1, 2, int(written.sum()), D_HEAD, generator=gen) + + dense = F.scaled_dot_product_attention( + q, k, v, attn_mask=written[None, None, None, :].expand(1, 2, TPF, capacity) + ) + compiled = flex_attention_masked(q, k, v, block_mask=bm, enable_gqa=False) + eager = flex_attention(q, k, v, block_mask=bm, enable_gqa=False) + + compiled_err = (compiled - dense).abs().max().item() + eager_err = (eager - dense).abs().max().item() + print(f"[{label}] compiled vs masked-dense={compiled_err:.3e} eager vs masked-dense={eager_err:.3e}") + + assert compiled_err < 1e-5, ( + f"compiled flex_attention diverged from the masked-dense reference ({compiled_err:.3e})" + ) + if len(committed_blocks) == 4: + # Nothing is masked off (whole ring + scratch written), so eager agrees. + assert eager_err < 1e-5 + else: + assert eager_err > 1e-2, ( + "eager flex_attention agreed with the masked reference; the trap CONTRACTS 2.3.1 " + "documents may have been fixed upstream, in which case re-derive the guard rather " + "than deleting it" + ) + + +def test_backend_attend_takes_the_compiled_path(): + """The regression guard proper: ``FlexRingBackend.attend`` must produce the + compiled result, not the eager one. Replacing ``flex_attention_masked`` with + ``flex_attention`` inside ``attend`` turns this red.""" + config = reduced_config() + backend = FlexRingBackend(config, "cpu", dtype=torch.float32, batch_size=1) + gen = torch.Generator().manual_seed(11) + + fp = torch.tensor(0, dtype=torch.int64) + backend.set_frozen(False) + k = torch.randn(1, 1, TPF, config.d_head, generator=gen) + v = torch.randn(1, 1, TPF, config.d_head, generator=gen) + k_all, v_all, bm = backend.upsert(k, v, 0, fp) + q = torch.randn(1, 2, TPF, config.d_head, generator=gen) + + got = backend.attend(q, k_all, v_all, bm, enable_gqa=True) + want = flex_attention_masked(q, k_all, v_all, block_mask=bm, enable_gqa=True) + trap = flex_attention(q, k_all, v_all, block_mask=bm, enable_gqa=True) + + assert torch.equal(got, want), "FlexRingBackend.attend is not using flex_attention_masked" + assert (got - trap).abs().max().item() > 1e-2, ( + "attend's output is indistinguishable from the eager path; the mask is not being honoured" + ) + + +# --------------------------------------------------------------------------- +# 2. Ring rotation and the upsert algorithm (CONTRACTS 2.2) +# --------------------------------------------------------------------------- + + +def make_cache(*, ring_frames: int, ring_buckets: int, dilation: int) -> LayerRingCache: + return LayerRingCache( + batch=1, + n_kv_heads=1, + ring_frames=ring_frames, + ring_buckets=ring_buckets, + d_head=8, + tokens_per_frame=TPF, + pinned_dilation=dilation, + dtype=torch.float32, + device="cpu", + ) + + +@pytest.mark.parametrize( + ("kind", "dilation", "frames", "expected_slots"), + [ + # Local layers cycle slots 0..15 with every frame. + ("local", 1, list(range(20)), [16, 17, 18, 19, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]), + # Global layers commit on frames 0, 8, 16, ... into slots 0, 1, 2, ... + ("global", 8, list(range(0, 136)), [128, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120]), + ], +) +def test_ring_slot_rotation(kind, dilation, frames, expected_slots): + """CONTRACTS 2.2: 'global commits land on frames 0, 8, 16, ... in slots + 0, 1, 2, ...; local slots cycle 0..15.' The slot holds the frame index that + last wrote it, so the expected list is the whole history at once.""" + cache = make_cache(ring_frames=16, ring_buckets=16, dilation=dilation) + for f in frames: + cache.upsert(frame_kv(f), torch.tensor(f, dtype=torch.int64), is_frozen=False) + assert ring_slot_values(cache) == [float(v) for v in expected_slots], kind + assert bool(cache.written[: cache.ring_len].all()) + + +def test_frozen_passes_leave_the_ring_byte_identical(): + """The 4 denoise passes of the 4+1 structure. A cache that mutated on them + would corrupt the world state permanently, and nothing would raise.""" + cache = make_cache(ring_frames=4, ring_buckets=4, dilation=1) + for f in range(4): + cache.upsert(frame_kv(f), torch.tensor(f, dtype=torch.int64), is_frozen=False) + + ring_before = cache.kv[:, :, :, : cache.ring_len].clone() + written_before = cache.written.clone() + scratch_before = cache.kv[:, :, :, cache.ring_len :].clone() + + for pass_idx in range(4): # the four Euler steps, each a different noisy x + cache.upsert(frame_kv(100 + pass_idx), torch.tensor(4, dtype=torch.int64), is_frozen=True) + + assert torch.equal(cache.kv[:, :, :, : cache.ring_len], ring_before), ( + "a frozen pass wrote the ring; that is amnesia, not a cache miss" + ) + assert torch.equal(cache.written, written_before) + # ...but the scratch write is unconditional: it is how the frame being + # denoised attends to itself between Euler steps (CONTRACTS 2.2 point 1). + assert not torch.equal(cache.kv[:, :, :, cache.ring_len :], scratch_before) + assert cache.kv[0, 0, 0, cache.ring_len, 0].item() == 103.0 + + +def test_mask_hides_the_slot_this_frame_is_about_to_overwrite(): + """CONTRACTS 2.2 point 2, and it applies on frozen passes too, so all five + passes of a frame see byte-identical KV.""" + cache = make_cache(ring_frames=4, ring_buckets=4, dilation=1) + for f in range(4): + cache.upsert(frame_kv(f), torch.tensor(f, dtype=torch.int64), is_frozen=False) + assert set(range(5)) == visible_blocks( + make_block_mask(TPF, cache.capacity, cache.written) + ), "precondition: the whole ring plus scratch is written" + + fp = torch.tensor(4, dtype=torch.int64) # slot 0 is about to be reused + for is_frozen in (True, True, True, True, False): + _, _, bm = cache.upsert(frame_kv(4), fp, is_frozen=is_frozen) + assert visible_blocks(bm) == {1, 2, 3, 4}, ( + "frame 4 can see the stale frame 0 sitting in the slot it is replacing" + ) + + +def test_global_layer_commits_nothing_on_non_dilation_frames(): + """CONTRACTS 2.2 point 3: ``torch.where(write_step, ring_idx, current_idx)`` + redirects the commit onto the scratch slot it just wrote.""" + cache = make_cache(ring_frames=4, ring_buckets=4, dilation=8) + cache.upsert(frame_kv(0), torch.tensor(0, dtype=torch.int64), is_frozen=False) + ring_before = cache.kv[:, :, :, : cache.ring_len].clone() + written_before = cache.written.clone() + + for f in range(1, 8): # the 7 non-committing frames of every 8 + cache.upsert(frame_kv(f), torch.tensor(f, dtype=torch.int64), is_frozen=False) + + assert torch.equal(cache.kv[:, :, :, : cache.ring_len], ring_before) + assert torch.equal(cache.written, written_before) + assert cache.kv[0, 0, 0, cache.ring_len, 0].item() == 7.0 # scratch has the latest + + cache.upsert(frame_kv(8), torch.tensor(8, dtype=torch.int64), is_frozen=False) + assert ring_slot_values(cache)[:2] == [0.0, 8.0] + + +def floor_bucket_upsert(cache: LayerRingCache, kv, frame_pos, is_frozen: bool): + """``LayerRingCache.upsert`` with the round-up dropped: ``bucket = f // d`` + instead of ``(f + d - 1) // d``. Everything else is statement-for-statement + the same. Used only to A/B the round-up.""" + tokens = cache.tokens_per_frame + slot = (frame_pos // cache.pinned_dilation) % cache.ring_buckets + ring_idx = cache.frame_offsets + slot * tokens + + cache.kv.index_copy_(3, cache.current_idx, kv) + + write_step = frame_pos.remainder(cache.pinned_dilation) == 0 + mask_written = torch.empty_like(cache.written) + mask_written.copy_(cache.written) + mask_written[ring_idx] = mask_written[ring_idx] & ~write_step + bm = make_block_mask(tokens, cache.capacity, mask_written) + + if not is_frozen: + dst = torch.where(write_step, ring_idx, cache.current_idx) + cache.kv.index_copy_(3, dst, kv) + cache.written[dst] = True + return bm + + +@pytest.mark.parametrize("dilation", [1, 8]) +def test_bucket_round_up_is_faithful_but_currently_unobservable(dilation): + """CONTRACTS 2.2 point 4 says flooring instead of rounding up "rotates the + entire history by one slot". **That consequence does not hold** for any + geometry this checkpoint uses, and this test pins the real behaviour rather + than the documented one. + + ``ceil`` and ``floor`` agree on every committing frame + (``(8j + 7) // 8 == 8j // 8 == j``) and at ``dilation == 1`` they are equal + outright. They differ only where ``write_step`` is False -- and there + ``ring_idx`` feeds nothing but ``mask_written[ring_idx] &= ~write_step``, + which is the identity, and ``torch.where(write_step, ring_idx, current_idx)`` + picks the scratch index. So the ``+ dilation - 1`` is faithfully ported and + harmless, but it is not load-bearing. + """ + ceil_cache = make_cache(ring_frames=4, ring_buckets=4, dilation=dilation) + floor_cache = make_cache(ring_frames=4, ring_buckets=4, dilation=dilation) + + for f in range(24): + fp = torch.tensor(f, dtype=torch.int64) + for pass_idx in range(5): + is_frozen = pass_idx < 4 + kv = frame_kv(f * 10 + pass_idx) + _, _, ceil_bm = ceil_cache.upsert(kv, fp, is_frozen=is_frozen) + floor_bm = floor_bucket_upsert(floor_cache, kv, fp, is_frozen) + assert visible_blocks(ceil_bm) == visible_blocks(floor_bm), f"masks differ at frame {f}" + + assert torch.equal(ceil_cache.kv, floor_cache.kv) + assert torch.equal(ceil_cache.written, floor_cache.written) + + +def test_upsert_rejects_a_wrong_shaped_frame_or_clock(): + cache = make_cache(ring_frames=4, ring_buckets=4, dilation=1) + fp = torch.tensor(0, dtype=torch.int64) + with pytest.raises(RuntimeError, match="exactly one frame per upsert"): + cache.upsert(frame_kv(0, tokens=TPF // 2), fp, is_frozen=False) + with pytest.raises(RuntimeError, match=r"frame_pos must be a \[\] int64 tensor"): + cache.upsert(frame_kv(0), torch.tensor([0], dtype=torch.int64), is_frozen=False) + with pytest.raises(RuntimeError, match=r"frame_pos must be a \[\] int64 tensor"): + cache.upsert(frame_kv(0), torch.tensor(0, dtype=torch.int32), is_frozen=False) + + +def test_reset_restores_a_fresh_ring(): + cache = make_cache(ring_frames=4, ring_buckets=4, dilation=1) + for f in range(4): + cache.upsert(frame_kv(f + 1), torch.tensor(f, dtype=torch.int64), is_frozen=False) + cache.reset() + assert not bool(cache.kv.any()) + # The scratch tail stays permanently visible -- masking it removes + # self-attention (CONTRACTS 2.2 point 5). + assert not bool(cache.written[: cache.ring_len].any()) + assert bool(cache.written[cache.ring_len :].all()) + + +def test_backend_state_roundtrip_is_a_deep_copy(): + config = reduced_config() + backend = FlexRingBackend(config, "cpu", dtype=torch.float32, batch_size=1) + backend.set_frozen(False) + gen = torch.Generator().manual_seed(3) + for layer in range(config.n_layers): + kv = torch.randn(1, 1, TPF, config.d_head, generator=gen) + backend.upsert(kv, kv, layer, torch.tensor(0, dtype=torch.int64)) + + state = backend.get_state() + snapshot = [t.clone() for t, _ in state["layers"]] + backend.reset() + assert not any(layer.kv.any() for layer in backend.layers) + # get_state must clone: the reset above must not have reached the snapshot. + assert all(torch.equal(a, b) for a, b in zip((t for t, _ in state["layers"]), snapshot, strict=True)) + + backend.load_state(state) + assert all(torch.equal(layer.kv, t) for layer, (t, _) in zip(backend.layers, state["layers"], strict=True)) + + other = FlexRingBackend( + dataclasses.replace(config, full_global_ring=True), "cpu", dtype=torch.float32, batch_size=1 + ) + with pytest.raises(ValueError, match="state shape"): + other.load_state(state) + + +# --------------------------------------------------------------------------- +# 3. The compaction deviation and the num_buckets trap (CONTRACTS 2.1 / 2.4) +# --------------------------------------------------------------------------- + + +def test_720p_ring_geometry_matches_the_contract_table(): + """CONTRACTS 2.1. The "addressable slots" and "ring frames allocated" + columns are independent, and 2.4 turns on keeping them independent.""" + config = waypoint_1_5_1b_720p() + assert sorted(config.global_layers) == [3, 7, 11, 15, 19, 23] + + for i in range(config.n_layers): + assert config.ring_buckets(i) == 16 # every row of the table + if config.is_global_layer(i): + assert (config.ring_frames(i), config.pinned_dilation(i)) == (16, 8) + else: + assert (config.ring_frames(i), config.pinned_dilation(i)) == (16, 1) + assert config.kv_capacity(i) == 17 * 512 + assert config.kv_capacity(i) % BLOCK == 0 + + full = dataclasses.replace(config, full_global_ring=True) + assert full.ring_frames(3) == 128 and full.ring_buckets(3) == 16 + assert full.ring_frames(0) == 16 # local layers are untouched by the flag + + compacted_bytes = sum(ring_memory_bytes(config)) + full_bytes = sum(ring_memory_bytes(full)) + assert compacted_bytes == 816 * 2**20 # CONTRACTS 2.4: 816 MiB + assert full_bytes - compacted_bytes == 1_409_286_144 # ...saving 1.3125 GiB + + +def test_ring_buckets_is_an_input_not_a_derivation(): + """CONTRACTS 2.4, "the trap". The reference computes + ``num_buckets = (L // tpf) // dilation``. Against the compacted ring that + yields 2, not 16 -- a global layer would retain 2 frames instead of 16, with + no shape error and no exception.""" + config = waypoint_1_5_1b_720p() + global_layer = 3 + reference_derivation = config.ring_frames(global_layer) // config.pinned_dilation(global_layer) + assert reference_derivation == 2, "the compacted buffer no longer encodes the bucket count" + assert config.ring_buckets(global_layer) == 16, ( + "ring_buckets must come from global_window // global_pinned_dilation, " + "never from ring_frames (CONTRACTS 2.4)" + ) + # And it is genuinely independent of the allocation knob. + full = dataclasses.replace(config, full_global_ring=True) + assert full.ring_buckets(global_layer) == config.ring_buckets(global_layer) == 16 + + +def test_compacted_global_ring_addresses_all_sixteen_slots(): + """The behavioural half of the trap: a cache that re-derived its bucket + count from ``ring_len`` would collide frames 0 and 16 into slot 0 and leave + 14 slots forever empty. Sixteen distinct frames, sixteen distinct slots.""" + cache = make_cache(ring_frames=16, ring_buckets=16, dilation=8) + assert (cache.ring_len // cache.tokens_per_frame) // cache.pinned_dilation == 2 # the wrong answer + assert cache.ring_buckets == 16 + + for j in range(16): + f = 8 * j + cache.upsert(frame_kv(f), torch.tensor(f, dtype=torch.int64), is_frozen=False) + + assert ring_slot_values(cache) == [float(8 * j) for j in range(16)] + assert bool(cache.written[: cache.ring_len].all()), "a 2-bucket ring would leave 14 slots unwritten" + + with pytest.raises(ValueError, match="ring_buckets"): + make_cache(ring_frames=4, ring_buckets=8, dilation=8) + + +def drive_ring(config: WaypointConfig, n_frames: int, seed: int = 7) -> list[torch.Tensor]: + """Run ``n_frames`` of the real 4+1 pass structure through a backend and + collect every attention output. The K/V/Q streams are drawn from a seeded + generator so two backends see byte-identical inputs.""" + backend = FlexRingBackend(config, "cpu", dtype=torch.float32, batch_size=1) + gen = torch.Generator().manual_seed(seed) + outputs = [] + for f in range(n_frames): + fp = torch.tensor(f, dtype=torch.int64) + for pass_idx in range(5): + backend.set_frozen(pass_idx < 4) + for layer in range(config.n_layers): + k = torch.randn(1, 1, TPF, config.d_head, generator=gen) + v = torch.randn(1, 1, TPF, config.d_head, generator=gen) + q = torch.randn(1, 2, TPF, config.d_head, generator=gen) + k_all, v_all, bm = backend.upsert(k, v, layer, fp) + outputs.append(backend.attend(q, k_all, v_all, bm, enable_gqa=True)) + return outputs + + +def test_compacted_and_full_global_rings_are_bitwise_identical(): + """CONTRACTS 2.4, DECISIONS D6. ``from_kv_blocks`` derives the visited list + from a *stable* descending argsort truncated to the visited count, so + dropping never-written blocks changes neither which blocks are attended nor + the order they accumulate in. Bit-equality is therefore the correct bar and + an ``allclose`` here would be hiding a real difference. + + 36 frames wraps the global ring's 4 addressable slots (stride 8) more than + once and the local rings nine times over. + """ + compacted = reduced_config() + full = dataclasses.replace(compacted, full_global_ring=True) + assert compacted.ring_frames(3) == 4 and full.ring_frames(3) == 32 + assert compacted.kv_capacity(3) == 5 * TPF and full.kv_capacity(3) == 33 * TPF + + a = drive_ring(compacted, 36) + b = drive_ring(full, 36) + assert len(a) == len(b) == 36 * 5 * compacted.n_layers + + mismatched = [i for i, (x, y) in enumerate(zip(a, b, strict=True)) if not torch.equal(x, y)] + assert not mismatched, ( + f"{len(mismatched)}/{len(a)} attention outputs differ between the compacted and the " + f"reference global ring; first at call {mismatched[0]}" + ) + + +# --------------------------------------------------------------------------- +# 4. OrthoRoPE (CONTRACTS 4.3) +# --------------------------------------------------------------------------- + + +def reference_angles(config: WaypointConfig, x_pos, y_pos, t_pos): + """The reference's own angle construction, transcribed from CONTRACTS 4.3 + (and matching ``world_engine/src/model/attn.py::OrthoRoPEAngles``).""" + d_head = config.d_head + d_xy, d_t = d_head // 8, d_head // 4 + max_freq = min(config.height, config.width) * float(config.rope_nyquist_frac) + n = (d_xy + 1) // 2 + xy = (torch.linspace(1.0, max_freq / 2, n, dtype=torch.float32) * math.pi).repeat_interleave(2)[:d_xy] + theta = float(config.rope_theta) + inv_t = (1.0 / (theta ** (torch.arange(0, d_t, 2, dtype=torch.float32) / d_t))).repeat_interleave(2) + + x = (2.0 * x_pos.float() + 1.0) / config.width - 1.0 + y = (2.0 * y_pos.float() + 1.0) / config.height - 1.0 + t = t_pos.float() + freqs = torch.cat((x.unsqueeze(-1) * xy, y.unsqueeze(-1) * xy, t.unsqueeze(-1) * inv_t), dim=-1) + return freqs.cos()[:, None], freqs.sin()[:, None] + + +def grid_positions(config: WaypointConfig, frame: int): + idx = torch.arange(config.tokens_per_frame) + y = idx.div(config.width, rounding_mode="floor")[None] + x = idx.remainder(config.width)[None] + t = torch.full((1, config.tokens_per_frame), frame * config.ts_mult, dtype=torch.long) + return x, y, t + + +def test_ortho_rope_angles_match_the_reference_construction_bitwise(): + config = waypoint_1_5_1b_720p() + module = OrthoRoPEAngles(config) + x_pos, y_pos, t_pos = grid_positions(config, frame=5) + + cos, sin = module(x_pos=x_pos, y_pos=y_pos, t_pos=t_pos) + ref_cos, ref_sin = reference_angles(config, x_pos, y_pos, t_pos) + + assert cos.shape == sin.shape == (1, 1, config.tokens_per_frame, config.d_head // 2) + assert torch.equal(cos, ref_cos) and torch.equal(sin, ref_sin) + + +def test_the_bands_count_rotation_pairs_and_cover_every_head_dim(): + """CONTRACTS 4.3: ``d_xy = d_head // 8`` and ``d_t = d_head // 4`` count + rotation PAIRS. 8 + 8 + 16 = 32 pairs = 64 dims -- nothing is unrotated. + (An earlier revision of the doc claimed the top half was untouched.)""" + config = waypoint_1_5_1b_720p() + d_head = config.d_head + d_xy, d_t = d_head // 8, d_head // 4 + assert (d_head, d_xy, d_t) == (64, 8, 16) + assert d_xy + d_xy + d_t == d_head // 2 == 32 + + cos, sin = OrthoRoPEAngles(config)(*grid_positions(config, frame=5)) + assert cos.shape[-1] == 32 + # Every band actually rotates at frame 5: no band is a no-op that a reader + # could mistake for "unrotated dims". + for name, lo, hi in (("x", 0, d_xy), ("y", d_xy, 2 * d_xy), ("t", 2 * d_xy, 32)): + assert sin[..., lo:hi].abs().max().item() > 1e-3, f"{name} band has no rotation" + + +@pytest.mark.parametrize( + ("axis", "pair_lo", "pair_hi", "dim_lo", "dim_hi"), + [("x", 0, 8, 0, 16), ("y", 8, 16, 16, 32), ("t", 16, 32, 32, 64)], +) +def test_axis_band_ownership(axis, pair_lo, pair_hi, dim_lo, dim_hi): + """x owns head dims 0-15, y 16-31, t 32-63 (CONTRACTS 4.3). Pair ``p`` + consumes input dims ``2p`` and ``2p+1``, so the pair band and the dim band + are the same statement twice.""" + config = waypoint_1_5_1b_720p() + module = OrthoRoPEAngles(config) + x_pos, y_pos, t_pos = grid_positions(config, frame=3) + shifted = { + "x": (x_pos.roll(1, dims=1), y_pos, t_pos), + "y": (x_pos, y_pos.roll(config.width, dims=1), t_pos), + "t": (x_pos, y_pos, t_pos + 1), + }[axis] + + cos_a, sin_a = module(x_pos=x_pos, y_pos=y_pos, t_pos=t_pos) + cos_b, sin_b = module(x_pos=shifted[0], y_pos=shifted[1], t_pos=shifted[2]) + + changed = ((cos_a != cos_b) | (sin_a != sin_b)).flatten(0, 2).any(dim=0) + assert bool(changed[pair_lo:pair_hi].any()), f"moving {axis} changed no angle in its own band" + untouched = torch.cat((changed[:pair_lo], changed[pair_hi:])) + assert not bool(untouched.any()), f"moving {axis} leaked into another axis's band" + + # ...and the same in input-dim terms: zeroing the band's dims makes the + # rotation independent of that axis. + gen = torch.Generator().manual_seed(5) + q = torch.randn(1, 2, config.tokens_per_frame, config.d_head, generator=gen) + q_zeroed = q.clone() + q_zeroed[..., dim_lo:dim_hi] = 0.0 + assert not torch.equal( + apply_ortho_rope(q, (cos_a, sin_a)), apply_ortho_rope(q, (cos_b, sin_b)) + ) + assert torch.equal( + apply_ortho_rope(q_zeroed, (cos_a, sin_a)), apply_ortho_rope(q_zeroed, (cos_b, sin_b)) + ), f"{axis} does not own head dims {dim_lo}..{dim_hi - 1}" + + +def test_rotation_is_the_interleaved_pair_form_with_a_concatenated_output(): + """CONTRACTS 4.3: pairs are read interleaved (``unfold(-1, 2, 2)``) but + written back with ``cat``, so pair ``p`` lands at output dims ``p`` and + ``p + 32``. Rewriting this as an in-place interleave is the natural "fix" + and is wrong; so is reading the pairs split-half.""" + config = waypoint_1_5_1b_720p() + cos, sin = OrthoRoPEAngles(config)(*grid_positions(config, frame=3)) + half = config.d_head // 2 + + gen = torch.Generator().manual_seed(9) + q = torch.randn(1, 2, config.tokens_per_frame, config.d_head, generator=gen) + out = apply_ortho_rope(q, (cos, sin)) + + even, odd = q[..., 0::2], q[..., 1::2] + assert torch.equal(out[..., :half], even * cos - odd * sin) + assert torch.equal(out[..., half:], odd * cos + even * sin) + + # The split-half reading (q[:half], q[half:]) is a different function. + lo, hi = q[..., :half], q[..., half:] + split_half = torch.cat((lo * cos - hi * sin, hi * cos + lo * sin), dim=-1) + assert not torch.equal(out, split_half) + # ...and so is the in-place interleaved write. + interleaved = torch.empty_like(out) + interleaved[..., 0::2] = even * cos - odd * sin + interleaved[..., 1::2] = odd * cos + even * sin + assert not torch.equal(out, interleaved) + + +def test_rope_tables_stay_fp32_whatever_the_serving_dtype_is(): + """CONTRACTS 4.1: ``OrthoRoPEAngles``/``OrthoRoPE`` are fp32 islands. The + port does not use ``NoCastModule``; instead the tables live in a + ``DeviceTableCache`` outside the module tree, so ``.to(bfloat16)`` cannot + reach them, and the bodies run in fp32 regardless.""" + config = waypoint_1_5_1b_720p() + module = OrthoRoPEAngles(config).to(torch.bfloat16) + + xy, inv_t = module._tables.get(torch.device("cpu")) + assert xy.dtype == inv_t.dtype == torch.float32 + assert list(module.parameters()) == [] and list(module.buffers()) == [] + + cos, sin = module(*grid_positions(config, frame=2)) + assert cos.dtype == sin.dtype == torch.float32 + + gen = torch.Generator().manual_seed(1) + q32 = torch.randn(1, 2, config.tokens_per_frame, config.d_head, generator=gen) + q16 = q32.to(torch.bfloat16) + out16 = apply_ortho_rope(q16, (cos, sin)) + assert out16.dtype == torch.bfloat16 + # The rotation itself ran in fp32 and rounded once at the end. + assert torch.equal(out16, apply_ortho_rope(q16.float(), (cos, sin)).to(torch.bfloat16)) + + +def test_rope_rejects_out_of_grid_positions(): + config = waypoint_1_5_1b_720p() + module = OrthoRoPEAngles(config) + x_pos, y_pos, t_pos = grid_positions(config, frame=0) + # torch._assert -- an AssertionError, unlike the torch._check sites elsewhere. + with pytest.raises(AssertionError, match="pos_ids out of bounds"): + module(x_pos=x_pos + config.width, y_pos=y_pos, t_pos=t_pos) + with pytest.raises(ValueError, match="divisible by 8"): + OrthoRoPEAngles(WaypointConfig(d_model=36, n_heads=1, n_kv_heads=1)) + + +# --------------------------------------------------------------------------- +# 5. The small layers (CONTRACTS 4.5, 4.6) +# --------------------------------------------------------------------------- + + +def test_rms_norm_is_unweighted_and_scale_invariant(): + gen = torch.Generator().manual_seed(2) + x = torch.randn(2, 3, 16, generator=gen) + assert torch.equal(rms_norm(x), F.rms_norm(x, (16,))) + # No learned gain anywhere in Waypoint: every use site either gets its scale + # from adaLN or wants a bare normalization. + assert torch.allclose(rms_norm(x * 7.0), rms_norm(x), atol=1e-6) + + +def test_ada_rmsnorm_broadcasts_one_modulation_vector_per_frame(): + B, N, T, D = 1, 3, 4, 8 + gen = torch.Generator().manual_seed(4) + x = torch.randn(B, N * T, D, generator=gen, dtype=torch.float64) + scale = torch.randn(B, N, D, generator=gen, dtype=torch.float64) + bias = torch.randn(B, N, D, generator=gen, dtype=torch.float64) + + got = ada_rmsnorm(x, scale, bias) + want = torch.cat( + [ + rms_norm(x[:, n * T : (n + 1) * T]) * (1 + scale[:, n : n + 1]) + bias[:, n : n + 1] + for n in range(N) + ], + dim=1, + ) + assert torch.allclose(got, want, rtol=0, atol=1e-12) + # A per-frame vector really is constant across that frame's tokens. + flat = ada_rmsnorm(torch.ones(B, N * T, D, dtype=torch.float64), scale, bias) + for n in range(N): + frame = flat[:, n * T : (n + 1) * T] + assert torch.allclose(frame, frame[:, :1].expand_as(frame), rtol=0, atol=1e-12) + + +def test_ada_gate_has_no_implicit_one_plus(): + B, N, T, D = 1, 2, 3, 5 + gen = torch.Generator().manual_seed(6) + x = torch.randn(B, N * T, D, generator=gen) + assert torch.equal(ada_gate(x, torch.zeros(B, N, D)), torch.zeros_like(x)) + assert torch.equal(ada_gate(x, torch.ones(B, N, D)), x) + gate = torch.zeros(B, N, D) + gate[:, 1] = 1.0 + gated = ada_gate(x, gate) + assert torch.equal(gated[:, :T], torch.zeros(B, T, D)) and torch.equal(gated[:, T:], x[:, T:]) + + +def test_adaln_folds_scale_and_shift_into_one_bias_free_projection(): + D, B, N, T = 8, 1, 2, 3 + torch.manual_seed(0) + norm = AdaLN(D).to(torch.float64) + assert norm.fc.bias is None and norm.fc.out_features == 2 * D + + gen = torch.Generator().manual_seed(8) + x = torch.randn(B, N * T, D, generator=gen, dtype=torch.float64) + cond = torch.randn(B, N, D, generator=gen, dtype=torch.float64) + + ab = norm.fc(F.silu(cond)) + scale, shift = ab.chunk(2, dim=-1) + want = torch.cat( + [ + rms_norm(x[:, n * T : (n + 1) * T]) * (1 + scale[:, n : n + 1]) + shift[:, n : n + 1] + for n in range(N) + ], + dim=1, + ) + assert torch.allclose(norm(x, cond), want, rtol=0, atol=1e-12) + + +def test_mlp_is_bias_free_end_to_end(): + """CONTRACTS 4.5: the bias-free-ness is the only reason + ``F.linear(h, self.mlp.fc2.weight)`` in ``MLPFusion`` is correct. A bias + would load and then be silently dropped at compute time.""" + mlp = MLP(6, 12, 4) + assert mlp.fc1.bias is None and mlp.fc2.bias is None + assert not [n for n, _ in mlp.named_parameters() if n.endswith("bias")] + gen = torch.Generator().manual_seed(10) + x = torch.randn(2, 6, generator=gen) + assert torch.equal(mlp(x), mlp.fc2(F.silu(mlp.fc1(x)))) + assert torch.equal(mlp(x), F.linear(F.silu(F.linear(x, mlp.fc1.weight)), mlp.fc2.weight)) + + +def test_noise_conditioner_fourier_features_and_fp32_island(): + cond = NoiseConditioner(16) + (freq,) = cond._freq.get(torch.device("cpu")) + assert torch.equal(freq, torch.logspace(0, -1, steps=256, base=10_000.0, dtype=torch.float32)) + assert freq.dtype == torch.float32 + assert cond.mlp.fc1.in_features == 512 and cond.mlp.fc1.out_features == 64 + + sigma = torch.tensor([[1.0, 0.9, 0.75, 0.3, 0.0]]) + phase = (sigma.reshape(-1).float() * 1000)[:, None] * freq[None, :] + want = cond.mlp(torch.cat((phase.sin(), phase.cos()), dim=-1) * 2**0.5).view(1, 5, 16) + assert torch.equal(cond(sigma), want) + + # The frequency table is derived state: not a buffer, not in state_dict, and + # out of reach of a dtype cast (CONTRACTS 4.1). + assert "freq" not in dict(cond.named_buffers()) and not any("freq" in k for k in cond.state_dict()) + cond.to(torch.bfloat16) + (freq_after,) = cond._freq.get(torch.device("cpu")) + assert freq_after.dtype == torch.float32 + with pytest.raises(ValueError, match="even fourier_dim"): + NoiseConditioner(16, fourier_dim=7) + + +def test_noise_conditioner_must_stay_fp32_to_serve_fp32_sigma(): + """Why ``FP32_MODULE_PATHS`` exists at all: after the global bf16 cast the + module's own body still upcasts sigma to fp32, so a bf16 ``mlp`` cannot + consume it. The failure is loud here, which is the good case -- the point of + the pin is that the fp32 island is not optional.""" + cond = NoiseConditioner(16).to(torch.bfloat16) + with pytest.raises(RuntimeError): + cond(torch.tensor([[1.0]])) + cond.to(torch.float32) + assert cond(torch.tensor([[1.0]])).dtype == torch.float32 + + +def test_mlp_fusion_stores_a_packed_fc1_and_splits_it_at_compute_time(): + """CONTRACTS 4.5: ``mlp.fc1`` is one ``[D, 2D]`` matrix (one loader key), + used split so ``cond`` broadcasts over its frame's tokens instead of being + materialized into a ``[B, N*T, 2D]`` concat. Same arithmetic.""" + config = reduced_config() + D, N, T = config.d_model, 3, 4 + torch.manual_seed(1) + fusion = MLPFusion(config).to(torch.float64) + + assert tuple(fusion.mlp.fc1.weight.shape) == (D, 2 * D) + assert [n for n, _ in fusion.named_parameters()] == ["mlp.fc1.weight", "mlp.fc2.weight"] + + gen = torch.Generator().manual_seed(12) + x = torch.randn(1, N * T, D, generator=gen, dtype=torch.float64) + cond = torch.randn(1, N, D, generator=gen, dtype=torch.float64) + + # The nominal form the parameter tree describes: MLP(2D, D, D) on cat([x, cond]). + cond_per_token = cond.repeat_interleave(T, dim=1) + want = fusion.mlp(torch.cat((x, cond_per_token), dim=-1)) + assert torch.allclose(fusion(x, cond), want, rtol=0, atol=1e-11) + + # The split is x-half first: swapping the chunks is a different function. + Wx, Wc = fusion.mlp.fc1.weight.chunk(2, dim=1) + swapped = F.linear( + F.silu(F.linear(x.view(1, N, T, D), Wc) + F.linear(cond, Wx).unsqueeze(2)), + fusion.mlp.fc2.weight, + ).flatten(1, 2) + assert not torch.allclose(fusion(x, cond), swapped) + + +def test_controller_input_embedding_concat_order_is_mouse_button_scroll(): + """CONTRACTS 4.5. The widths sum to 259 under any permutation, so a wrong + order fails silently. The three fields carry disjoint, self-identifying + values and the MLP's input is captured directly.""" + config = waypoint_1_5_1b_720p() + assert config.d_ctrl_in == 259 == 2 + config.n_buttons + 1 + + emb = ControllerInputEmbedding(config) + assert emb.mlp.fc1.in_features == 259 + assert emb.mlp.fc1.out_features == config.d_model * config.mlp_ratio + assert emb.mlp.fc2.out_features == config.d_model + + seen = [] + + class Capture(torch.nn.Module): + def forward(self, x): + seen.append(x.clone()) + return x[..., :1] + + emb.mlp = Capture() + mouse = torch.tensor([[[-1.0, -2.0]]]) + button = torch.arange(256, dtype=torch.float32).view(1, 1, 256) + 100.0 + scroll = torch.tensor([[[7.5]]]) + emb(mouse, button, scroll) + + (packed,) = seen + assert packed.shape == (1, 1, 259) + assert torch.equal(packed[..., 0:2], mouse), "field 0:2 is not mouse" + assert torch.equal(packed[..., 2:258], button), "field 2:258 is not button" + assert torch.equal(packed[..., 258:259], scroll), "field 258:259 is not scroll" + # Sensitivity check: a permuted order really would produce a different vector. + assert not torch.equal(packed, torch.cat((mouse, scroll, button), dim=-1)) diff --git a/test/modular/test_waypoint_dit.py b/test/modular/test_waypoint_dit.py new file mode 100644 index 000000000..de66708bc --- /dev/null +++ b/test/modular/test_waypoint_dit.py @@ -0,0 +1,678 @@ +"""Contract tests for the Waypoint-1.5 DiT block and the 4+1 per-frame driver. + +The bar is ``docs/waypoint/CONTRACTS.md``, which is normative; where the code and +the document disagree the test pins the code only if the code is right. The +failure modes covered here all produce plausible video and raise nothing: a +denoise pass that commits to the ring, a value residual threaded the wrong way +round, a missing ``.clone()`` between the compiled regions, an fp32 island that +came back bf16, or a derived RoPE table left as whatever ``to_empty`` happened to +allocate. + +Sections map onto CONTRACTS: 1 (the 4+1 pass structure), 2.1/4.5 (the 720P +structural facts), 4.1 (fp32 islands and the meta build), 4.2 (value residual +ordering) and 6.1 (``cond_proj`` tying across ``to_empty``). + +CPU-only and checkpoint-free. The real 720P config is used only for structural +assertions, which are cheap because the module is built on ``torch.device("meta")`` +and never materialized -- a real 720P bf16 build is ~2.6 GB. Anything that runs +tensors through the model uses a reduced but structurally identical config +(4 layers, one global at stride 8, controller fusion on ``i % 3 == 0``, GQA live). +""" + +import sys + +import pytest +import torch + +sys.path.insert(0, ".") + +from mstar.model.waypoint.components.attention import WaypointAttention +from mstar.model.waypoint.components.dit import WaypointDiT +from mstar.model.waypoint.components.kv_backend import FlexRingBackend +from mstar.model.waypoint.components.layers import FP32_MODULE_PATHS, rms_norm +from mstar.model.waypoint.components.rope import OrthoRoPEAngles, apply_ortho_rope +from mstar.model.waypoint.config import WaypointConfig, waypoint_1_5_1b_720p + +TPF = 128 # tokens per frame in the reduced config + + +def reduced_config(**overrides) -> WaypointConfig: + """4 layers / 128 tokens per frame, keeping every structural fact of 720P: + one global layer (3) at stride 8, controller fusion on ``i % 3 == 0``, + GQA at 2 query heads over 1 KV head, ``d_head`` divisible by 8.""" + base = { + "n_layers": 4, + "n_heads": 2, + "n_kv_heads": 1, + "d_model": 64, + "mlp_ratio": 2, + "channels": 4, + "tokens_per_frame": TPF, + "height": 8, + "width": 16, + "local_window": 4, + "global_window": 32, + "global_pinned_dilation": 8, + "n_buttons": 8, + } + return WaypointConfig(**{**base, **overrides}) + + +class RecordingBackend: + """A real ``FlexRingBackend`` with every ``upsert`` logged. + + Delegation rather than a stub: the ring geometry, the visibility mask and + the attention numerics stay real, so a test can assert on what the model + *did* without changing what it computed. + """ + + def __init__(self, config: WaypointConfig, dtype: torch.dtype = torch.float32): + self.config = config + self.inner = FlexRingBackend(config, "cpu", dtype=dtype, batch_size=1) + self.upserts: list[dict] = [] + + def upsert(self, k, v, layer_idx, frame_pos): + self.upserts.append( + { + "frozen": self.inner._is_frozen, + "layer": layer_idx, + "frame_pos": int(frame_pos), + "k": k.detach().clone(), + "v": v.detach().clone(), + } + ) + return self.inner.upsert(k, v, layer_idx, frame_pos) + + def attend(self, q, k, v, meta, *, enable_gqa): + return self.inner.attend(q, k, v, meta, enable_gqa=enable_gqa) + + def set_frozen(self, frozen): + self.inner.set_frozen(frozen) + + def reset(self): + self.inner.reset() + self.upserts.clear() + + def get_state(self): + return self.inner.get_state() + + def load_state(self, state): + self.inner.load_state(state) + + def passes(self) -> list[list[dict]]: + """The upsert log regrouped into forwards: one entry per layer, in + layer order, per pass. A misgrouping means the model did not visit every + layer exactly once per forward, which the assertion below catches.""" + n = self.config.n_layers + assert len(self.upserts) % n == 0, f"{len(self.upserts)} upserts is not a whole number of passes" + grouped = [self.upserts[i : i + n] for i in range(0, len(self.upserts), n)] + for group in grouped: + assert [u["layer"] for u in group] == list(range(n)) + return grouped + + +def build_reduced_dit(config: WaypointConfig, seed: int = 0) -> WaypointDiT: + torch.manual_seed(seed) + dit = WaypointDiT(config).eval() + for p in dit.parameters(): + torch.nn.init.normal_(p, std=0.05) + with torch.no_grad(): + for block in dit.blocks: + block.attn.v_lamb.fill_(0.25) + dit.retie_cond_proj() + return dit + + +def frame_inputs(config: WaypointConfig, seed: int = 3, dtype: torch.dtype = torch.float32): + gen = torch.Generator().manual_seed(seed) + C, H, W = config.latent_shape + noise = torch.randn(1, 1, C, H, W, generator=gen, dtype=torch.float32).to(dtype) + mouse = torch.tensor([[[0.25, -0.5]]], dtype=dtype) + button = torch.zeros(1, 1, config.n_buttons, dtype=dtype) + button[..., 2] = 1.0 + scroll = torch.tensor([[[1.0]]], dtype=dtype) + return noise, mouse, button, scroll + + +# --------------------------------------------------------------------------- +# 6. Structural facts of the real 720P config (CONTRACTS 2.1, 4.5, 4.6) +# --------------------------------------------------------------------------- + + +@pytest.fixture(scope="module") +def meta_720p() -> WaypointDiT: + """The real model, built on meta: full parameter tree, zero bytes of storage.""" + with torch.device("meta"): + return WaypointDiT(waypoint_1_5_1b_720p()) + + +def test_720p_layer_geometry(meta_720p): + config = meta_720p.config + assert config.n_layers == len(meta_720p.blocks) == 24 + assert sorted(config.global_layers) == [3, 7, 11, 15, 19, 23] + assert len(config.global_layers) == 6 and config.n_layers - len(config.global_layers) == 18 + assert (config.global_attn_period, config.global_attn_offset) == (4, -1) + + +def test_720p_controller_conditioning_layers(meta_720p): + """8 of 24 blocks fuse controller input, and the parameter tree itself + records which: the other 16 have no ``ctrl_mlpfusion`` submodule at all.""" + config = meta_720p.config + assert config.ctrl_conditioning_period == 3 + assert sorted(config.ctrl_layers) == [0, 3, 6, 9, 12, 15, 18, 21] + + fusing = [i for i, block in enumerate(meta_720p.blocks) if block.ctrl_mlpfusion is not None] + assert fusing == [0, 3, 6, 9, 12, 15, 18, 21] + assert config.d_ctrl_in == 259 + assert meta_720p.ctrl_emb.mlp.fc1.in_features == 259 + + +def test_720p_gqa_is_live_and_the_fused_qkv_slabs_are_unequal(meta_720p): + """32 query heads over 16 KV heads. The unequal slab widths are why a + Q/K/V order mistake is a shape error for Q but *not* between K and V -- + swapping those two loads cleanly and produces wrong video (CONTRACTS 5).""" + config = meta_720p.config + assert (config.n_heads, config.n_kv_heads, config.d_head) == (32, 16, 64) + assert config.enable_gqa is True + + attn = meta_720p.blocks[0].attn + assert (attn.q_out, attn.kv_out) == (2048, 1024) + assert attn.qkv_proj.weight.shape == (2048 + 2 * 1024, 2048) + assert attn.qkv_proj.bias is None and attn.out_proj.bias is None + assert attn.enable_gqa is True + + +def test_720p_head_and_patchify_shapes(meta_720p): + config = meta_720p.config + ph, pw = config.patch + assert (config.tokens_per_frame, config.height, config.width) == (512, 16, 32) + assert config.latent_shape == (32, 32, 64) + assert meta_720p.patchify.weight.shape == (2048, 32, ph, pw) + assert meta_720p.patchify.bias is None + # The checkpoint's [D, C, ph, pw] conv kernel becomes this Linear's + # [C*ph*pw, D] (CONTRACTS 6, transforms 1-2). + assert meta_720p.unpatchify.weight.shape == (32 * ph * pw, 2048) + assert meta_720p.unpatchify.bias is not None + assert meta_720p.out_norm.fc.weight.shape == (2 * 2048, 2048) + + +def test_720p_parameter_budget_and_cond_proj_tying(meta_720p): + """1.86B stored / 1.28B resident: ``cond_proj`` has one physical set that + all 24 blocks alias. ``named_parameters()`` deduplicates; ``state_dict()`` + does not, which is why CONTRACTS 6.1 says to run the loader's completeness + check against the former.""" + params = dict(meta_720p.named_parameters()) + assert sum(p.numel() for p in params.values()) == 1_281_958_040 + assert len(params) == 174 + assert len(meta_720p.state_dict()) == 174 + 23 * 6 # 23 aliased blocks x 6 matrices + + ref = meta_720p.blocks[0].cond_head.cond_proj + for block in meta_720p.blocks[1:]: + for j in range(6): + assert block.cond_head.cond_proj[j].weight is ref[j].weight + # bias_in is genuinely per-layer -- the checkpoint has 24 distinct values. + assert block.cond_head.bias_in is not meta_720p.blocks[0].cond_head.bias_in + assert len([n for n in params if n.endswith("cond_head.bias_in")]) == 24 + assert len([n for n in params if "cond_head.cond_proj" in n]) == 6 + + # No buffers at all: nothing in the module tree for to_empty to leave + # holding garbage (DECISIONS D1/D3). + assert list(meta_720p.named_buffers()) == [] + + +def test_config_rejects_unsupported_checkpoint_variants(): + with pytest.raises(ValueError, match="rope_impl"): + WaypointConfig(rope_impl="llama") + with pytest.raises(ValueError, match="noise_conditioning"): + WaypointConfig(noise_conditioning="dit_air") + with pytest.raises(ValueError, match="MoE"): + WaypointConfig(moe=True) + with pytest.raises(ValueError, match="prompt cross-attention"): + WaypointConfig(prompt_conditioning="t5") + with pytest.raises(ValueError, match="ungated attention path only"): + WaypointAttention(WaypointConfig(gated_attn=True), 0) + + +# --------------------------------------------------------------------------- +# 7. The 4+1 pass structure (CONTRACTS 1) +# --------------------------------------------------------------------------- + + +def test_generate_frame_is_four_frozen_denoise_passes_then_one_commit(): + """CONTRACTS 1, the single most important invariant. Five forwards: four + Euler steps at sigma = 1.0, 0.9, 0.75, 0.3 that must not touch the ring, + then one committing pass at sigma = 0 on the settled latent.""" + config = reduced_config() + dit = build_reduced_dit(config) + backend = RecordingBackend(config) + noise, mouse, button, scroll = frame_inputs(config) + + sigmas: list[torch.Tensor] = [] + handle = dit.denoise_step_emb.register_forward_pre_hook( + lambda _m, args: sigmas.append(args[0].detach().clone()) + ) + try: + with torch.no_grad(): + dit.generate_frame( + noise, torch.tensor(0, dtype=torch.int64), backend, + mouse=mouse, button=button, scroll=scroll, + ) + finally: + handle.remove() + + # [B, N] per pass, one uniform sigma per frame. + assert [tuple(s.shape) for s in sigmas] == [(1, 1)] * 5 + assert [s.item() for s in sigmas] == pytest.approx([1.0, 0.9, 0.75, 0.3, 0.0], abs=1e-6) + assert list(config.scheduler_sigmas) == [1.0, 0.9, 0.75, 0.3, 0.0] + assert config.num_denoise_steps == 4 + + passes = backend.passes() + assert len(passes) == 5, "a generated frame costs exactly five forwards" + frozen_per_pass = [{u["frozen"] for u in group} for group in passes] + assert frozen_per_pass == [{True}, {True}, {True}, {True}, {False}] + assert sum(not next(iter(f)) for f in frozen_per_pass) == 1, "exactly one pass may commit" + # All five passes of a frame share one ring clock (CONTRACTS 3). + assert {u["frame_pos"] for u in backend.upserts} == {0} + + +def test_the_ring_only_moves_on_the_committing_pass(): + """The behavioural half of the same invariant, measured on the ring itself.""" + config = reduced_config() + dit = build_reduced_dit(config) + backend = RecordingBackend(config) + noise, mouse, button, scroll = frame_inputs(config) + fp = torch.tensor(0, dtype=torch.int64) + + ring_lens = [layer.ring_len for layer in backend.inner.layers] + before = [layer.kv[:, :, :, :n].clone() for layer, n in zip(backend.inner.layers, ring_lens, strict=True)] + + with torch.no_grad(): + sigma_table = dit._sigma_schedule(noise.device, noise.dtype) + dit._denoise_pass(noise, fp, sigma_table, backend, mouse=mouse, button=button, scroll=scroll) + after_denoise = [layer.kv[:, :, :, :n] for layer, n in zip(backend.inner.layers, ring_lens, strict=True)] + assert all(torch.equal(a, b) for a, b in zip(before, after_denoise, strict=True)) + assert not any(layer.written[: layer.ring_len].any() for layer in backend.inner.layers) + + with torch.no_grad(): + dit._cache_pass(noise, fp, backend, mouse=mouse, button=button, scroll=scroll) + assert all(layer.written[: layer.tokens_per_frame].all() for layer in backend.inner.layers) + + +def test_generate_frame_clones_the_denoised_latent(): + """CONTRACTS 1: ``x0 = self._denoise_pass(...).clone()`` -- the ``.clone()`` + is load-bearing. The compiled region reuses its output buffer, so the cache + pass would otherwise read a latent the next allocation has already stomped. + The copy must land in caller-owned memory, i.e. outside the compiled region. + """ + config = reduced_config() + dit = build_reduced_dit(config) + backend = RecordingBackend(config) + noise, mouse, button, scroll = frame_inputs(config) + + produced: list[torch.Tensor] = [] + real_denoise = dit._denoise_pass + + def spy(*args, **kwargs): + out = real_denoise(*args, **kwargs) + produced.append(out) + return out + + dit._denoise_pass = spy + with torch.no_grad(): + x0 = dit.generate_frame( + noise, torch.tensor(0, dtype=torch.int64), backend, + mouse=mouse, button=button, scroll=scroll, + ) + + assert len(produced) == 1 + assert x0 is not produced[0], "generate_frame returned the compiled region's own buffer" + assert x0.data_ptr() != produced[0].data_ptr(), "generate_frame aliases the denoise output" + assert torch.equal(x0, produced[0]) + assert x0.shape == noise.shape + + +def test_append_frame_is_the_committing_pass_alone(): + """Priming from a VAE-encoded real frame: no denoising, one forward, and the + latent is already the settled x0 so there is nothing to clone.""" + config = reduced_config() + dit = build_reduced_dit(config) + backend = RecordingBackend(config) + latent, mouse, button, scroll = frame_inputs(config) + + sigmas: list[torch.Tensor] = [] + handle = dit.denoise_step_emb.register_forward_pre_hook( + lambda _m, args: sigmas.append(args[0].detach().clone()) + ) + try: + with torch.no_grad(): + out = dit.append_frame( + latent, torch.tensor(0, dtype=torch.int64), backend, + mouse=mouse, button=button, scroll=scroll, + ) + finally: + handle.remove() + + assert [s.flatten().tolist() for s in sigmas] == [[0.0]] + assert len(backend.passes()) == 1 + assert all(u["frozen"] is False for u in backend.upserts) + assert out is latent + + +def test_sigma_schedule_is_built_in_the_latent_dtype(): + """CONTRACTS 1 / ``_sigma_schedule``: the reference takes ``.diff()`` in the + serving dtype, so the Euler step sizes are bf16 differences of bf16 sigmas. + Building the table in fp32 "for precision" changes the ODE.""" + config = reduced_config() + dit = build_reduced_dit(config) + cpu = torch.device("cpu") + + bf16 = dit._sigma_schedule(cpu, torch.bfloat16) + fp32 = dit._sigma_schedule(cpu, torch.float32) + assert bf16.dtype == torch.bfloat16 and fp32.dtype == torch.float32 + assert dit._sigma_schedule(cpu, torch.bfloat16) is bf16, "the schedule is memoized per (device, dtype)" + + bf16_diff = bf16.diff() + fp32_diff_rounded = fp32.diff().to(torch.bfloat16) + assert bf16_diff[0].item() == pytest.approx(-0.1015625, abs=0) + assert fp32_diff_rounded[0].item() == pytest.approx(-0.10009765625, abs=0) + assert not torch.equal(bf16_diff, fp32_diff_rounded) + + +# --------------------------------------------------------------------------- +# 8. The value residual (CONTRACTS 4.2) +# --------------------------------------------------------------------------- + + +class CaptureBackend: + """Records the exact ``(k, v)`` handed to the cache and hands them straight + back, so a test can inspect what would have been stored forever.""" + + def __init__(self): + self.calls: list[tuple[torch.Tensor, torch.Tensor]] = [] + + def upsert(self, k, v, layer_idx, frame_pos): + self.calls.append((k.detach().clone(), v.detach().clone())) + return k, v, None + + def attend(self, q, k, v, meta, *, enable_gqa): + return torch.nn.functional.scaled_dot_product_attention(q, k, v, enable_gqa=enable_gqa) + + def set_frozen(self, frozen): + pass + + +def run_two_attention_layers(config: WaypointConfig, lamb0: float, lamb1: float): + torch.manual_seed(21) + layer0 = WaypointAttention(config, 0).eval() + layer1 = WaypointAttention(config, 1).eval() + for layer, lamb in ((layer0, lamb0), (layer1, lamb1)): + for p in layer.parameters(): + torch.nn.init.normal_(p, std=0.05) + with torch.no_grad(): + layer.v_lamb.fill_(lamb) + + idx = torch.arange(config.tokens_per_frame) + angles = OrthoRoPEAngles(config)( + x_pos=idx.remainder(config.width)[None], + y_pos=idx.div(config.width, rounding_mode="floor")[None], + t_pos=torch.zeros(1, config.tokens_per_frame, dtype=torch.long), + ) + gen = torch.Generator().manual_seed(22) + x = torch.randn(1, config.tokens_per_frame, config.d_model, generator=gen) + backend = CaptureBackend() + fp = torch.tensor(0, dtype=torch.int64) + with torch.no_grad(): + _, v1 = layer0(x, fp, angles, None, backend) + _, v1_out = layer1(x, fp, angles, v1, backend) + return layer0, layer1, x, angles, backend, v1, v1_out + + +def raw_qkv(layer: WaypointAttention, x: torch.Tensor): + with torch.no_grad(): + q, k, v = layer.qkv_proj(x).split((layer.q_out, layer.kv_out, layer.kv_out), dim=-1) + B, T = x.shape[:2] + return ( + q.reshape(B, T, layer.n_heads, layer.d_head).transpose(1, 2), + k.reshape(B, T, layer.n_kv_heads, layer.d_head).transpose(1, 2), + v.reshape(B, T, layer.n_kv_heads, layer.d_head).transpose(1, 2), + ) + + +def test_v1_is_captured_pre_lerp_and_threads_through_unchanged(): + """CONTRACTS 4.2. Layer 0 returns its *pre*-lerp V, and every later layer + passes that same tensor along untouched -- it does not substitute its own. + Getting this backwards still produces plausible output, so the ordering is + asserted directly rather than through the activations.""" + config = reduced_config() + layer0, layer1, x, _angles, backend, v1, v1_out = run_two_attention_layers(config, 0.25, 0.5) + + _, _, v_raw0 = raw_qkv(layer0, x) + _, _, v_raw1 = raw_qkv(layer1, x) + + assert torch.equal(v1, v_raw0), "v1 is not layer 0's raw, pre-lerp V" + assert v1_out is v1, "a later layer replaced v1 instead of threading it through" + assert not torch.equal(v_raw1, v1), "precondition: the two layers' raw V differ" + + # ...and the LERPED V is what enters the cache, at layer 1. + cached_v1 = backend.calls[1][1] + want = torch.lerp(v_raw1, v1, layer1.v_lamb) + assert torch.equal(cached_v1, want) + assert not torch.equal(cached_v1, v_raw1), "the cache stored the pre-lerp V" + assert not torch.equal(cached_v1, v1) + + +def test_value_residual_lerp_direction(): + """``torch.lerp(v, v1, w)`` is ``v + w * (v1 - v)``: at ``v_lamb == 1`` the + cached V *is* layer 0's V, at 0 it is the layer's own. Swapping the lerp + operands is a silent sign flip on the residual.""" + config = reduced_config() + _, layer1, x, _, backend_one, v1, _ = run_two_attention_layers(config, 0.25, 1.0) + assert torch.equal(backend_one.calls[1][1], v1) + + _, layer1_zero, x0, _, backend_zero, v1_zero, _ = run_two_attention_layers(config, 0.25, 0.0) + _, _, v_raw1 = raw_qkv(layer1_zero, x0) + assert torch.equal(backend_zero.calls[1][1], v_raw1) + + +def test_q_and_k_are_normed_and_rotated_but_v_is_neither(): + """CONTRACTS 4.2: Q/K are RMS-normed then RoPE'd; V is neither. K enters the + ring already rotated, so replayed history is never re-rotated.""" + config = reduced_config() + layer0, _layer1, x, angles, backend, v1, _ = run_two_attention_layers(config, 0.25, 0.5) + _, k_raw0, v_raw0 = raw_qkv(layer0, x) + + cached_k, cached_v = backend.calls[0] + assert torch.equal(cached_k, apply_ortho_rope(rms_norm(k_raw0), angles)) + assert not torch.equal(cached_k, rms_norm(k_raw0)), "K reached the cache un-rotated" + assert not torch.equal(cached_k, apply_ortho_rope(k_raw0, angles)), "K reached the cache un-normed" + # Layer 0 lerps against itself, so its cached V is exactly its raw V. + assert torch.equal(cached_v, v_raw0) + assert torch.equal(v1, v_raw0) + + +def test_layer_zero_v_reaches_every_block_in_the_dit(): + """End to end: with every ``v_lamb`` at 1 the residual is total, so the V + stored by all 4 blocks must be byte-identical to layer 0's. A block that + re-captured ``v1`` from its own projection would drift here.""" + config = reduced_config() + dit = build_reduced_dit(config) + with torch.no_grad(): + for block in dit.blocks: + block.attn.v_lamb.fill_(1.0) + + backend = RecordingBackend(config) + latent, mouse, button, scroll = frame_inputs(config) + with torch.no_grad(): + dit.append_frame( + latent, torch.tensor(0, dtype=torch.int64), backend, + mouse=mouse, button=button, scroll=scroll, + ) + + (single_pass,) = backend.passes() + reference_v = single_pass[0]["v"] + for record in single_pass[1:]: + assert torch.equal(record["v"], reference_v), ( + f"block {record['layer']} did not thread layer 0's V into the cache" + ) + # ...and K, which is not part of the residual, does differ per layer. + assert not torch.equal(single_pass[1]["k"], single_pass[0]["k"]) + + +# --------------------------------------------------------------------------- +# 9. fp32 islands and the meta-build path (CONTRACTS 4.1, 6.1) +# --------------------------------------------------------------------------- + + +def test_fp32_module_paths_is_exactly_the_noise_conditioner(): + """The reference marks three modules ``NoCastModule``; only one of them has + parameters. ``OrthoRoPEAngles``/``OrthoRoPE`` hold none, so there is nothing + for a dtype cast to corrupt and nothing to pin back (DECISIONS D7).""" + assert FP32_MODULE_PATHS == ("denoise_step_emb",) + + +def test_cast_serving_dtypes_leaves_one_fp32_island_on_720p(): + """CONTRACTS 4.1: bf16 everywhere, then the islands back to fp32 -- run on + the **meta** module so storage is later allocated directly in the serving + dtype. Nothing is materialized here; a real 720P build is ~2.6 GB.""" + with torch.device("meta"): + dit = WaypointDiT(waypoint_1_5_1b_720p()) + assert dit.cast_serving_dtypes() is dit + + by_dtype: dict[torch.dtype, list[str]] = {} + for name, param in dit.named_parameters(): + by_dtype.setdefault(param.dtype, []).append(name) + + assert sorted(by_dtype[torch.float32]) == [ + "denoise_step_emb.mlp.fc1.weight", + "denoise_step_emb.mlp.fc2.weight", + ] + assert set(by_dtype) == {torch.float32, torch.bfloat16} + assert not [n for n in by_dtype[torch.bfloat16] if n.startswith("denoise_step_emb")] + assert dit.dtype == torch.bfloat16 + # .to(dtype) on meta preserves the aliasing; to_empty is what breaks it. + ref = dit.blocks[0].cond_head.cond_proj + assert all(b.cond_head.cond_proj[j].weight is ref[j].weight for b in dit.blocks[1:] for j in range(6)) + + +def test_to_empty_unties_cond_proj_and_retie_puts_it_back(): + """CONTRACTS 6.1. ``Module._apply`` has no cross-module memo, so + ``to_empty(device)`` silently gives 24 blocks 24 independent ``cond_proj`` + sets. Nothing raises; the symptoms are +0.6B resident parameters and 23 + blocks the loader never fills. Measured here on the reduced config, where + ``to_empty`` is affordable -- the mechanism is size-independent.""" + config = reduced_config() + with torch.device("meta"): + dit = WaypointDiT(config) + dit.cast_serving_dtypes() + + def tied() -> bool: + ref = dit.blocks[0].cond_head.cond_proj + return all(b.cond_head.cond_proj[j].weight is ref[j].weight for b in dit.blocks[1:] for j in range(6)) + + n_tied = len(list(dit.named_parameters())) + assert tied() + + dit.to_empty(device="cpu") + assert not tied(), "to_empty preserved the tying; the retie contract may no longer be needed" + untied_count = len(list(dit.named_parameters())) + assert untied_count == n_tied + (config.n_layers - 1) * 6 + + assert dit.retie_cond_proj() is dit + assert tied() + assert len(list(dit.named_parameters())) == n_tied + + +def test_derived_tables_survive_the_meta_build(): + """CONTRACTS 4.1 / DECISIONS D1: the RoPE frequency tables, the Fourier + frequency table and the token grid are DERIVED state held in a + ``DeviceTableCache`` *outside* the module tree. As non-persistent buffers + they would come out of ``to_empty(device)`` as uninitialized garbage that no + loader completeness check covers -- a silent wrong-numbers bug.""" + config = reduced_config() + with torch.device("meta"): + meta_dit = WaypointDiT(config) + meta_dit.cast_serving_dtypes() + meta_dit.to_empty(device="cpu") + meta_dit.retie_cond_proj() + + torch.manual_seed(0) + plain_dit = WaypointDiT(config) + cpu = torch.device("cpu") + + tables = { + "rope": lambda d: d.rope_angles._tables.get(cpu), + "fourier": lambda d: d.denoise_step_emb._freq.get(cpu), + "grid": lambda d: d._grid.get(cpu), + } + for name, getter in tables.items(): + from_meta, from_plain = getter(meta_dit), getter(plain_dit) + for a, b in zip(from_meta, from_plain, strict=True): + assert a.device.type == "cpu" and not a.is_meta, f"{name} table is still on meta" + assert torch.equal(a, b), f"{name} table differs after the meta build" + if a.is_floating_point(): + assert a.dtype == torch.float32 and bool(torch.isfinite(a).all()) + + # None of them are module state: not buffers, not in state_dict, out of + # reach of both to_empty and the bf16 cast. + assert list(meta_dit.named_buffers()) == [] + assert not [k for k in meta_dit.state_dict() if "freq" in k or "grid" in k or "_tables" in k] + + +def test_meta_built_model_generates_a_frame_in_the_serving_dtypes(): + """The whole build order from CONTRACTS 6.1, end to end on CPU: meta build, + cast, to_empty, retie, then a real 4+1 frame. Weights are random (there is + no checkpoint here), so the bar is 'the serving dtypes and the derived + tables are live and the output is finite', not a numeric one.""" + config = reduced_config() + with torch.device("meta"): + dit = WaypointDiT(config) + dit.cast_serving_dtypes() + dit.to_empty(device="cpu") + dit.retie_cond_proj() + torch.manual_seed(1) + for p in dit.parameters(): + torch.nn.init.normal_(p, std=0.02) + dit.eval() + + backend = RecordingBackend(config, dtype=torch.bfloat16) + noise, mouse, button, scroll = frame_inputs(config, dtype=torch.bfloat16) + with torch.no_grad(): + x0 = dit.generate_frame( + noise, torch.tensor(0, dtype=torch.int64), backend, + mouse=mouse, button=button, scroll=scroll, + ) + + assert x0.dtype == torch.bfloat16 and x0.shape == noise.shape + assert bool(torch.isfinite(x0.float()).all()) + assert len(backend.passes()) == 5 + assert dit.denoise_step_emb.mlp.fc1.weight.dtype == torch.float32 + assert dit.patchify.weight.dtype == torch.bfloat16 + + +def test_two_frames_advance_the_ring_clock_together(): + """CONTRACTS 3/4.4: the caller owns ``frame_pos`` and advances it by exactly + one per committed frame; ``t_pos = f_pos * ts_mult`` is the RoPE clock and + is threaded separately even though ``ts_mult == 1`` here.""" + config = reduced_config() + assert config.ts_mult == 1 == waypoint_1_5_1b_720p().ts_mult + + dit = build_reduced_dit(config) + backend = RecordingBackend(config) + noise, mouse, button, scroll = frame_inputs(config) + with torch.no_grad(): + for f in range(2): + dit.generate_frame( + noise, torch.tensor(f, dtype=torch.int64), backend, + mouse=mouse, button=button, scroll=scroll, + ) + + passes = backend.passes() + assert len(passes) == 10 + assert [next(iter({u["frame_pos"] for u in group})) for group in passes] == [0] * 5 + [1] * 5 + + pos = dit._pos_ids(torch.tensor(3, dtype=torch.int64)) + assert pos.f_pos.item() == 3 and pos.f_pos.ndim == 0 + assert pos.t_pos.shape == (1, config.tokens_per_frame) + assert bool((pos.t_pos == 3 * config.ts_mult).all()) + assert bool((pos.y_pos == torch.arange(TPF).div(config.width, rounding_mode="floor")).all()) + assert bool((pos.x_pos == torch.arange(TPF).remainder(config.width)).all()) diff --git a/test/modular/test_waypoint_weight_loader.py b/test/modular/test_waypoint_weight_loader.py new file mode 100644 index 000000000..586d5788b --- /dev/null +++ b/test/modular/test_waypoint_weight_loader.py @@ -0,0 +1,795 @@ +"""``mstar.model.waypoint.weight_loader`` against synthetic checkpoints. + +The Waypoint-1.5-1B checkpoint is not on this machine and must not be +downloaded, so the bar here is not numerical parity — it is **every claim the +loader makes about keys, shapes, slices and counts, checked against a state dict +built from the reference's own key spellings** (``world_engine/src/model/ +world_model.py::load_state_dict``, transcribed in ``docs/waypoint/PARAM_TREE.md``). +Everything runs on CPU with no checkpoint and no GPU. + +Why that bar and not a looser one: almost every way this loader can be wrong is +*shape-legal*. ``PARAM_TREE.md`` section 8 lists sixteen failure modes and +labels nine of them silent — a q/k/v fusion built as ``cat([q, v, k])``, an +``fc1_x``/``fc1_c`` merge in the wrong column order, ``attn``/``mlp`` cond_proj +slots swapped, an ``unpatchify`` permute dropped. Each of those loads without an +exception and produces plausible video. So the synthetic tensors are +**distinguishable per key** (``_tensor`` seeds a generator off the key name): +every assertion here compares values, not shapes, and a swap is a failed +assertion rather than a clean load. + +The four regression tests marked ``F1``-``F4`` pin defects that an audit found in +the shipped loader and that all four reproduced before the fix: + +* **F1** — a pre-fused ``qkv_proj`` and the ``q/k/v_proj`` shards both loaded, + silently, producing a tensor assembled from both sources in lexicographic key + order (Q and K off the blob, V off ``v_proj``). Same hole for ``fc1``. +* **F2** — the ``cond_proj`` tie check probed ``[:, :64]``, so a divergence past + column 64 loaded clean with ``verify_cond_proj_tie=True``. +* **F3** — ``attn_cond_head.bias_in`` was dropped unconditionally, so a file + carrying only that spelling (``PARAM_TREE.md`` section 10.2 leaves which one + the real file has unresolved) failed with 24 unloaded ``cond_head.bias_in``. + The reference falls back to it (``world_model.py:386-389``). +* **F4** — shape validation ran before the drop filter, so a ``.cond_heads.`` key + ending in ``.k_proj.weight`` raised a GQA error about a key T12 discards. + +Nothing here needs the 2.6 GB of a real bf16 build: the parameter census is read +off the **meta** module (``numel()`` needs no storage) and every load test uses a +128-wide, 4-layer config. The one test that needs all 24 layers — the +``cond_proj`` tie lifecycle — keeps ``n_layers=24`` and shrinks ``d_model``, so +the "144 after ``to_empty``" number is the real one at a few MB. +""" + +from __future__ import annotations + +import sys +import zlib +from pathlib import Path + +import pytest +import torch + +sys.path.insert(0, ".") + +from mstar.model.loader.base import _apply_stacked, load_weights_into +from mstar.model.waypoint.components.dit import WaypointDiT +from mstar.model.waypoint.config import WaypointConfig +from mstar.model.waypoint.weight_loader import ( + COND_PROJ_SOURCE_BLOCK, + WAYPOINT_STACKED_PARAMS, + _adapt_checkpoint_stream, + _attach_shard_loaders, + build_waypoint_dit, + parameter_census, + remap_checkpoint_key, +) + +pytest.importorskip("safetensors", reason="safetensors not installed") + +from safetensors.torch import save_file # noqa: E402 + +# ``NoiseConditioner``'s Fourier width is a constructor default, not a config +# field, and it is the in-dim of denoise_step_emb.mlp.fc1 (PARAM_TREE row 1). +FOURIER_DIM = 512 + +LEGACY = "legacy" +CANONICAL = "canonical" + +# The three T4 spellings, in the reference's precedence order (last wins). +BIAS_IN_KEYS = { + "attn": "attn_cond_head.bias_in", + "mlp": "mlp_cond_head.bias_in", + "canonical": "cond_head.bias_in", +} + + +def tiny_config() -> WaypointConfig: + """A structurally faithful 4-layer Waypoint. + + Everything the loader reasons about is preserved: GQA with unequal q/kv rows + (128 vs 64, so a q/k swap raises and a k/v swap does not — S11/S11b), a 2x2 + patch, ctrl layers at ``i % 3 == 0`` = {0, 3}, and ``d_model=128`` so the + retired ``[:, :64]`` cond_proj probe covers only half a matrix and F2's + regression test has somewhere to hide. + """ + return WaypointConfig( + n_layers=4, + n_heads=4, + n_kv_heads=2, + d_model=128, + mlp_ratio=2, + channels=4, + tokens_per_frame=32, + height=4, + width=8, + patch=(2, 2), + n_buttons=8, + ) + + +def tie_config() -> WaypointConfig: + """All 24 layers, 128 wide. The tie lifecycle counts *tensors*, not bytes.""" + return WaypointConfig( + n_layers=24, + n_heads=4, + n_kv_heads=2, + d_model=128, + mlp_ratio=2, + channels=4, + tokens_per_frame=32, + height=4, + width=8, + patch=(2, 2), + n_buttons=8, + ) + + +def _tensor(key: str, shape: tuple[int, ...]) -> torch.Tensor: + """A tensor whose contents are a function of its checkpoint key. + + This is the whole reason these tests can see a swap. Two same-shaped + checkpoint tensors are never equal, so ``cat([q, v, k])`` instead of + ``cat([q, k, v])``, or ``cond_proj`` slot 3 loaded into slot 0, fails an + assertion instead of loading cleanly. + """ + generator = torch.Generator().manual_seed(zlib.crc32(key.encode()) | 1) + return torch.randn(shape, generator=generator, dtype=torch.float32) + + +def synthetic_checkpoint( + config: WaypointConfig, + *, + spelling: str = LEGACY, + bias_in: tuple[str, ...] = ("mlp",), +) -> tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]: + """``(checkpoint state dict, expected named_parameters)``. + + ``spelling=LEGACY`` writes the keys the reference's transforms exist to + rewrite; ``CANONICAL`` writes the already-post-transform names that the + reference's ``pop``/``setdefault`` pairs also accept (PARAM_TREE section + 3.5) — including a pre-fused ``qkv_proj`` and ``ctrl_mlpfusion.mlp.fc1``, + which exercise ``_SliceShardLoader``'s ``loaded_shard_id is None`` path. + Which one the real file uses is section 10.1's open question, so both load. + + The expected dict is built from the reference's own formulas + (``world_model.py:372-405`` and ``patch_model.py:110-112``), restated here + rather than imported, so agreement means two independent transcriptions + agree. + """ + D, C = config.d_model, config.channels + ph, pw = config.patch + ffn = D * config.mlp_ratio + q_rows = config.n_heads * config.d_head + kv_rows = config.n_kv_heads * config.d_head + legacy = spelling == LEGACY + + state: dict[str, torch.Tensor] = {} + expected: dict[str, torch.Tensor] = {} + + def src(key: str, shape: tuple[int, ...]) -> torch.Tensor: + state[key] = _tensor(key, shape) + return state[key] + + # --- top level, identity ------------------------------------------------ + for leaf, shape in ( + ("denoise_step_emb.mlp.fc1.weight", (D * 4, FOURIER_DIM)), + ("denoise_step_emb.mlp.fc2.weight", (D, D * 4)), + ("ctrl_emb.mlp.fc1.weight", (ffn, config.d_ctrl_in)), + ("ctrl_emb.mlp.fc2.weight", (D, ffn)), + ("patchify.weight", (D, C, ph, pw)), + ("out_norm.fc.weight", (2 * D, D)), + ): + expected[leaf] = src(leaf, shape) + + # T10: in the file, never in the port's tree. + src("ctrl_cfg.null_emb", (1, 1, D)) + + if legacy: + # T1: [D, C, ph, pw] conv kernel -> [C*ph*pw, D] Linear weight. + weight = src("unpatchify.weight", (D, C, ph, pw)) + expected["unpatchify.weight"] = weight.permute(1, 2, 3, 0).reshape(-1, D) + # T2: one bias per latent channel, repeated across the patch. + bias = src("unpatchify.bias", (C,)) + expected["unpatchify.bias"] = bias[:, None, None].expand(-1, ph, pw).reshape(-1) + else: + expected["unpatchify.weight"] = src("unpatchify.weight", (C * ph * pw, D)) + expected["unpatchify.bias"] = src("unpatchify.bias", (C * ph * pw,)) + + # --- per block ---------------------------------------------------------- + prefix = "transformer.blocks." if legacy else "blocks." + for i in range(config.n_layers): + p, q = f"{prefix}{i}.", f"blocks.{i}." + + if legacy: + # T11 (port-side): cat([q, k, v], dim=0), q first, along the rows. + shards = [ + src(p + "attn.q_proj.weight", (q_rows, D)), + src(p + "attn.k_proj.weight", (kv_rows, D)), + src(p + "attn.v_proj.weight", (kv_rows, D)), + ] + expected[q + "attn.qkv_proj.weight"] = torch.cat(shards, dim=0) + else: + expected[q + "attn.qkv_proj.weight"] = src( + p + "attn.qkv_proj.weight", (q_rows + 2 * kv_rows, D) + ) + + expected[q + "attn.out_proj.weight"] = src(p + "attn.out_proj.weight", (D, D)) + expected[q + "attn.v_lamb"] = src(p + "attn.v_lamb", ()) # rank 0, not [1] (S13) + + # T3: an explicit five-name allowlist in the reference; only fc1/fc2 exist. + mlp_prefix = "dit_mlp." if legacy else "mlp." + for leaf, shape in (("fc1.weight", (ffn, D)), ("fc2.weight", (D, ffn))): + expected[q + "mlp." + leaf] = src(p + mlp_prefix + leaf, shape) + + # T4: up to three spellings, one target. + for which in bias_in: + src(p + BIAS_IN_KEYS[which], (D,)) + if bias_in: + winner = max(bias_in, key=lambda w: list(BIAS_IN_KEYS).index(w)) + expected[q + "cond_head.bias_in"] = state[p + BIAS_IN_KEYS[winner]] + + # T5/T6: attn head -> slots 0..2 (attention branch), mlp head -> 3..5 + # (MLP branch). T9 keeps only COND_PROJ_SOURCE_BLOCK's set; the other + # blocks' keys are in the file (the file is not deduplicated) and carry + # the same values, which is what _CondProjTieCheck verifies. + for j in range(3): + if legacy: + attn = src(p + f"attn_cond_head.cond_proj.{j}.weight", (D, D)) + mlp = src(p + f"mlp_cond_head.cond_proj.{j}.weight", (D, D)) + else: + attn = src(p + f"cond_head.cond_proj.{j}.weight", (D, D)) + mlp = src(p + f"cond_head.cond_proj.{j + 3}.weight", (D, D)) + if i == COND_PROJ_SOURCE_BLOCK: + expected[f"blocks.{i}.cond_head.cond_proj.{j}.weight"] = attn + expected[f"blocks.{i}.cond_head.cond_proj.{j + 3}.weight"] = mlp + + if i not in config.ctrl_layers: + continue + if legacy: + # T7: cat([fc1_x, fc1_c], dim=1) -- x first, along the INPUT axis. + x = src(p + "ctrl_mlpfusion.fc1_x.weight", (D, D)) + c = src(p + "ctrl_mlpfusion.fc1_c.weight", (D, D)) + expected[q + "ctrl_mlpfusion.mlp.fc1.weight"] = torch.cat((x, c), dim=1) + # T8: a plain rename, guarded separately from T7's both-halves guard. + expected[q + "ctrl_mlpfusion.mlp.fc2.weight"] = src( + p + "ctrl_mlpfusion.fc2.weight", (D, D) + ) + else: + expected[q + "ctrl_mlpfusion.mlp.fc1.weight"] = src( + p + "ctrl_mlpfusion.mlp.fc1.weight", (D, 2 * D) + ) + expected[q + "ctrl_mlpfusion.mlp.fc2.weight"] = src( + p + "ctrl_mlpfusion.mlp.fc2.weight", (D, D) + ) + + # The tie: every block stores the same six matrices (PARAM_TREE section 5.2 + # proves the file is not deduplicated). Applied last so it overwrites the + # per-block values generated above. + for i in range(config.n_layers): + if i == COND_PROJ_SOURCE_BLOCK: + continue + for j in range(3): + if legacy: + for head, slot in (("attn", j), ("mlp", j)): + key = f"{prefix}{i}.{head}_cond_head.cond_proj.{slot}.weight" + ref = f"{prefix}{COND_PROJ_SOURCE_BLOCK}.{head}_cond_head.cond_proj.{slot}.weight" + state[key] = state[ref] + else: + for slot in (j, j + 3): + key = f"{prefix}{i}.cond_head.cond_proj.{slot}.weight" + state[key] = state[f"{prefix}{COND_PROJ_SOURCE_BLOCK}.cond_head.cond_proj.{slot}.weight"] + + return state, expected + + +def write_checkpoint(directory: Path, state: dict[str, torch.Tensor]) -> Path: + """Materialize a state dict as ``model.safetensors``. ``clone()`` because + safetensors refuses to write tensors that share storage, and the tied + ``cond_proj`` entries do.""" + directory.mkdir(parents=True, exist_ok=True) + save_file({k: v.contiguous().clone() for k, v in state.items()}, str(directory / "model.safetensors")) + return directory + + +def build_from(tmp_path: Path, state: dict[str, torch.Tensor], config: WaypointConfig, **kwargs): + name = f"ckpt{len(list(tmp_path.iterdir()))}" + return build_waypoint_dit(config, write_checkpoint(tmp_path / name, state), **kwargs) + + +# --------------------------------------------------------------------------- +# 1. The parameter census on the real 720P config +# --------------------------------------------------------------------------- + + +def test_parameter_census_matches_the_720p_checkpoint(): + """174 tensors / 1,281,958,040 resident / 1,860,771,992 stored. + + These are PARAM_TREE section 5.2's numbers, less the 2,048 of + ``ctrl_cfg.null_emb`` that T10 drops, and they are the arithmetic behind + "1.28B resident, 1.86B stored, 3.72 GB on disk". A change in any of the + three means the module tree stopped being the checkpoint's tree. + + Read off the meta module: ``numel()`` and ``state_dict()`` need no storage, + so this costs nothing even though the same build with real memory is 2.6 GB. + """ + with torch.device("meta"): + dit = WaypointDiT(WaypointConfig()) + + tensors, dedup_numel, raw_numel = parameter_census(dit) + assert (tensors, dedup_numel, raw_numel) == (174, 1_281_958_040, 1_860_771_992) + + # The 312 - 174 = 138 gap IS the tie: 23 blocks x 6 aliased [2048, 2048]. + assert len(dit.state_dict()) == 312 + assert raw_numel - dedup_numel == 23 * 6 * 2048**2 == 578_813_952 + + +# --------------------------------------------------------------------------- +# 2. The cond_proj tie lifecycle (CONTRACTS section 6.1) +# --------------------------------------------------------------------------- + + +def test_cond_proj_tie_lifecycle_across_to_empty(): + """6 -> 6 -> **144** -> 6. The 144 is the trap. + + ``Module._apply`` has no cross-module memo, so ``to_empty(device)`` silently + un-aliases the six shared ``cond_proj`` matrices into 24 independent sets. + ``.to(dtype)`` on meta does not. Nothing raises either way; the un-tied model + is numerically correct and merely 0.6B parameters heavier, and the loader + then leaves 23 blocks' ``cond_proj`` unfilled. This is the whole reason + ``retie_cond_proj()`` is public and must follow ``to_empty``. + """ + config = tie_config() + + def n_cond_proj(module) -> int: + return sum(1 for name, _ in module.named_parameters() if ".cond_head.cond_proj." in name) + + with torch.device("meta"): + dit = WaypointDiT(config) + assert n_cond_proj(dit) == 6, "__init__ ties" + + dit.cast_serving_dtypes() + assert n_cond_proj(dit) == 6, ".to(dtype) on meta preserves aliasing" + + dit.to_empty(device="cpu") + assert n_cond_proj(dit) == config.n_layers * 6 == 144, "to_empty un-ties, silently" + + dit.retie_cond_proj() + assert n_cond_proj(dit) == 6 + # The surviving names are the owner block's; T9 drops keys for every other + # block on exactly that assumption. + assert all( + name.startswith(f"blocks.{COND_PROJ_SOURCE_BLOCK}.cond_head.cond_proj.") + for name, _ in dit.named_parameters() + if ".cond_head.cond_proj." in name + ) + + +def test_build_waypoint_dit_leaves_cond_proj_tied(tmp_path): + """The same property, through the real entry point rather than by hand.""" + config = tiny_config() + state, _ = synthetic_checkpoint(config) + dit = build_from(tmp_path, state, config) + + tensors, dedup_numel, raw_numel = parameter_census(dit) + assert tensors == len(dict(dit.named_parameters())) + assert raw_numel - dedup_numel == (config.n_layers - 1) * 6 * config.d_model**2 + # Aliased, not merely equal: writing block 0's tensor must move block 3's. + owner = dit.blocks[COND_PROJ_SOURCE_BLOCK].cond_head.cond_proj[2].weight + assert dit.blocks[3].cond_head.cond_proj[2].weight is owner + + +# --------------------------------------------------------------------------- +# 3. T0-T12 round trip, both key spellings +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize("spelling", [LEGACY, CANONICAL]) +def test_every_parameter_equals_its_checkpoint_source(tmp_path, spelling): + """Every one of the loaded parameters, compared by value against an + independent transcription of the reference's transforms. + + This is the round trip for T0-T12 at once: a name that does not appear in + ``expected`` is a transform this test does not know about, and a value that + differs is a transform applied wrongly. Both spellings run because + PARAM_TREE section 10.1 could not establish which one the shipped file uses. + """ + config = tiny_config() + state, expected = synthetic_checkpoint(config, spelling=spelling) + dit = build_from(tmp_path, state, config) + + loaded = dict(dit.named_parameters()) + assert set(loaded) == set(expected), ( + f"unmapped parameters {sorted(set(loaded) - set(expected))[:5]}, " + f"expectations that reached no parameter {sorted(set(expected) - set(loaded))[:5]}" + ) + + wrong = [ + name + for name, param in loaded.items() + if not torch.equal(param.detach().cpu(), expected[name].to(param.dtype)) + ] + assert not wrong, f"{len(wrong)} parameters differ from their checkpoint source: {wrong[:5]}" + + +def test_qkv_row_ranges(tmp_path): + """q = rows [0:2048], k = [2048:3072], v = [3072:4096] (PARAM_TREE section 7). + + Explicit because this is where a swap is invisible: GQA makes q taller than + k and v, so a q/k swap raises on the slice shape (S11b) but a **k/v swap + does not** — both are ``[n_kv_heads*d_head, d_model]``, the load is clean, + and attention output is meaningless but well-scaled (S11). + """ + config = tiny_config() + state, _ = synthetic_checkpoint(config, spelling=LEGACY) + dit = build_from(tmp_path, state, config) + + q_rows = config.n_heads * config.d_head + kv_rows = config.n_kv_heads * config.d_head + assert q_rows != kv_rows, "the config must keep GQA, or a q/k swap becomes invisible too" + + for i in range(config.n_layers): + fused = dit.blocks[i].attn.qkv_proj.weight.detach().cpu() + p = f"transformer.blocks.{i}.attn." + for shard, start, size in ( + ("q_proj", 0, q_rows), + ("k_proj", q_rows, kv_rows), + ("v_proj", q_rows + kv_rows, kv_rows), + ): + source = state[p + shard + ".weight"].to(fused.dtype) + assert torch.equal(fused[start : start + size], source), ( + f"block {i}: rows [{start}:{start + size}] of qkv_proj are not {shard}" + ) + + +def test_fc1_column_halves(tmp_path): + """``fc1_x`` = columns [0:D], ``fc1_c`` = [D:2D] (PARAM_TREE section 4.7). + + Explicit for the same reason as the qkv ranges, and worse: the merge is on + **dim 1**, both halves are ``[D, D]``, so ``cat((c, x))`` keeps the shape + exactly and applies controller conditioning to tokens and token content to + the controller vector (S7). ``MLPFusion.forward``'s ``chunk(2, dim=1)`` takes + the low columns as the token half, which is what fixes the order. + """ + config = tiny_config() + state, _ = synthetic_checkpoint(config, spelling=LEGACY) + dit = build_from(tmp_path, state, config) + D = config.d_model + + assert config.ctrl_layers, "the config must have ctrl layers for this to test anything" + for i in sorted(config.ctrl_layers): + fused = dit.blocks[i].ctrl_mlpfusion.mlp.fc1.weight.detach().cpu() + assert fused.shape == (D, 2 * D) + p = f"transformer.blocks.{i}.ctrl_mlpfusion." + x_half, c_half = fused.chunk(2, dim=1) + assert torch.equal(x_half, state[p + "fc1_x.weight"].to(fused.dtype)), f"block {i}: low columns are not fc1_x" + assert torch.equal(c_half, state[p + "fc1_c.weight"].to(fused.dtype)), f"block {i}: high columns are not fc1_c" + # The 16 non-ctrl layers own no such tensor at all -- the parameter tree + # itself records which layers fuse. + assert all(dit.blocks[i].ctrl_mlpfusion is None for i in range(config.n_layers) if i not in config.ctrl_layers) + + +def test_cond_proj_slots_follow_the_half_head_names(tmp_path): + """attn head -> slots 0-2, mlp head -> 3-5, and not the reverse. + + ``CondHead.forward`` is unpacked as ``s0, b0, g0, s1, b1, g1``; 0-2 drive the + attention sublayer and 3-5 the MLP. All six are ``[D, D]``, so swapping T5 + and T6 is mechanically invisible and numerically catastrophic (S5). + """ + config = tiny_config() + state, _ = synthetic_checkpoint(config, spelling=LEGACY) + dit = build_from(tmp_path, state, config) + + p = f"transformer.blocks.{COND_PROJ_SOURCE_BLOCK}." + for j in range(3): + for head, slot in (("attn", j), ("mlp", j + 3)): + got = dit.blocks[COND_PROJ_SOURCE_BLOCK].cond_head.cond_proj[slot].weight.detach().cpu() + source = state[p + f"{head}_cond_head.cond_proj.{j}.weight"].to(got.dtype) + assert torch.equal(got, source), f"slot {slot} did not come from {head}_cond_head.cond_proj.{j}" + + +@pytest.mark.parametrize( + "key,expected", + [ + # T0: both the checkpoint's two-level prefix and the collapsed one. + ("transformer.blocks.5.attn.out_proj.weight", "blocks.5.attn.out_proj.weight"), + ("blocks.5.attn.out_proj.weight", "blocks.5.attn.out_proj.weight"), + # T3, five-name allowlist (only fc1/fc2 exist under moe=False). + ("transformer.blocks.5.dit_mlp.fc1.weight", "blocks.5.mlp.fc1.weight"), + ("transformer.blocks.5.dit_mlp.fc2.weight", "blocks.5.mlp.fc2.weight"), + # T4: all three spellings share one target; precedence is settled by the + # loader, not here (this function is deliberately not injective). + ("transformer.blocks.0.attn_cond_head.bias_in", "blocks.0.cond_head.bias_in"), + ("transformer.blocks.0.mlp_cond_head.bias_in", "blocks.0.cond_head.bias_in"), + ("transformer.blocks.0.cond_head.bias_in", "blocks.0.cond_head.bias_in"), + # T5 / T6: identity for attn, +3 for mlp. + ("transformer.blocks.0.attn_cond_head.cond_proj.2.weight", "blocks.0.cond_head.cond_proj.2.weight"), + ("transformer.blocks.0.mlp_cond_head.cond_proj.0.weight", "blocks.0.cond_head.cond_proj.3.weight"), + ("transformer.blocks.0.mlp_cond_head.cond_proj.2.weight", "blocks.0.cond_head.cond_proj.5.weight"), + # T8: guarded on fc2 alone, separately from T7. + ("transformer.blocks.3.ctrl_mlpfusion.fc2.weight", "blocks.3.ctrl_mlpfusion.mlp.fc2.weight"), + # T9: every block but the owner is dropped, both spellings. + ("transformer.blocks.7.attn_cond_head.cond_proj.2.weight", None), + ("transformer.blocks.7.cond_head.cond_proj.5.weight", None), + # T10 / T12. + ("ctrl_cfg.null_emb", None), + ("transformer.blocks.0.cond_heads.0.weight", None), + ("transformer.blocks.0.cond_heads.0.k_proj.weight", None), + # T7 / T11 are fan-ins: the remapper leaves them for the stacked rules. + ("transformer.blocks.2.attn.q_proj.weight", "blocks.2.attn.q_proj.weight"), + ("transformer.blocks.0.ctrl_mlpfusion.fc1_x.weight", "blocks.0.ctrl_mlpfusion.fc1_x.weight"), + # Top level is identity. + ("patchify.weight", "patchify.weight"), + ("out_norm.fc.weight", "out_norm.fc.weight"), + ], +) +def test_remap_checkpoint_key(key, expected): + assert remap_checkpoint_key(key) == expected + + +@pytest.mark.parametrize( + "mapped,target,shard", + [ + ("blocks.2.attn.q_proj.weight", "blocks.2.attn.qkv_proj.weight", "q"), + ("blocks.2.attn.k_proj.weight", "blocks.2.attn.qkv_proj.weight", "k"), + ("blocks.2.attn.v_proj.weight", "blocks.2.attn.qkv_proj.weight", "v"), + ("blocks.0.ctrl_mlpfusion.fc1_x.weight", "blocks.0.ctrl_mlpfusion.mlp.fc1.weight", "x"), + ("blocks.0.ctrl_mlpfusion.fc1_c.weight", "blocks.0.ctrl_mlpfusion.mlp.fc1.weight", "c"), + # The leading dots keep .v_proj from matching inside qkv_proj, and + # out_proj / cond_proj from matching anything. + ("blocks.2.attn.qkv_proj.weight", "blocks.2.attn.qkv_proj.weight", None), + ("blocks.2.attn.out_proj.weight", "blocks.2.attn.out_proj.weight", None), + ("blocks.0.cond_head.cond_proj.0.weight", "blocks.0.cond_head.cond_proj.0.weight", None), + ], +) +def test_stacked_rules_route_the_two_fusions(mapped, target, shard): + assert _apply_stacked(mapped, WAYPOINT_STACKED_PARAMS) == (target, shard) + + +# --------------------------------------------------------------------------- +# 4. The per-shard tally, and why set(named_parameters()) - loaded is not enough +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize("shard_key", ["attn.q_proj", "attn.k_proj", "attn.v_proj"]) +def test_missing_qkv_shard_raises(tmp_path, shard_key): + config = tiny_config() + state, _ = synthetic_checkpoint(config) + for i in range(config.n_layers): + del state[f"transformer.blocks.{i}.{shard_key}.weight"] + + with pytest.raises(RuntimeError, match=r"[1-9]\d* unloaded fused shards"): + build_from(tmp_path, state, config) + + +@pytest.mark.parametrize("shard_key", ["ctrl_mlpfusion.fc1_x", "ctrl_mlpfusion.fc1_c"]) +def test_missing_fc1_shard_raises(tmp_path, shard_key): + config = tiny_config() + state, _ = synthetic_checkpoint(config) + for i in sorted(config.ctrl_layers): + del state[f"transformer.blocks.{i}.{shard_key}.weight"] + + with pytest.raises(RuntimeError, match=r"[1-9]\d* unloaded fused shards"): + build_from(tmp_path, state, config) + + +def test_named_parameters_check_alone_cannot_see_a_missing_shard(tmp_path): + """Why the ``(target, shard_id)`` tally exists at all — S11d. + + ``load_weights_into`` returns *target* names, and q, k and v share one + target, so a ``k_proj`` missing from every layer leaves + ``set(named_parameters()) - loaded`` **empty**. That is the wan22-style + completeness check passing on a transformer with a third of its attention + projections uninitialized. This test records the hole rather than the fix: + it asserts the set difference is empty, so if someone ever deletes the tally + on the grounds that the set difference covers it, this is the counterexample. + """ + config = tiny_config() + state, _ = synthetic_checkpoint(config) + for i in range(config.n_layers): + del state[f"transformer.blocks.{i}.attn.k_proj.weight"] + + dit = build_waypoint_dit(config, skip_weight_loading=True) + _attach_shard_loaders(dit, config) + stream = _adapt_checkpoint_stream(iter(state.items()), config, None) + loaded = load_weights_into( + dit, stream, stacked_params=WAYPOINT_STACKED_PARAMS, name_remapper=remap_checkpoint_key + ) + + assert set(dict(dit.named_parameters())) - loaded == set(), ( + "the set difference is supposed to be empty here — that is the point" + ) + # And the shipped loader, which keeps the tally, refuses the same checkpoint. + with pytest.raises(RuntimeError, match=r"attn\.qkv_proj\.weight\[k\]"): + build_from(tmp_path, state, config) + + +# --------------------------------------------------------------------------- +# 5. Drops, unexpected keys, and the transcribed config facts +# --------------------------------------------------------------------------- + + +def test_intended_drops_load_cleanly(tmp_path): + """T10 and T12 are silent by design; everything else is loud. + + ``.cond_heads.`` (note the plural) is the reference's unconditional filter, + and ``ctrl_cfg.null_emb`` is a training-time CFG tensor with no call site. + Both must be dropped *explicitly* — leaving them unmatched would work by + accident and put a hole in the unexpected-key accounting (S10). + """ + config = tiny_config() + state, expected = synthetic_checkpoint(config) + assert "ctrl_cfg.null_emb" in state + state["transformer.blocks.0.cond_heads.0.weight"] = torch.randn(7, 3) + state["transformer.blocks.1.cond_heads.2.cond_proj.0.weight"] = torch.randn(5, 5) + + dit = build_from(tmp_path, state, config) + assert set(dict(dit.named_parameters())) == set(expected) + + +@pytest.mark.parametrize( + "key", + [ + "transformer.blocks.0.attn.gate_proj.weight", # gated_attn=False + "transformer.blocks.0.dit_mlp.router.weight", # moe=False; T3 renames it, nothing owns it + "prompt_cfg.null_emb", # prompt_conditioning=None + "transformer.blocks.0.cond_head.who_knows", + "some.entirely.new.key", + ], +) +def test_unknown_key_raises(tmp_path, key): + config = tiny_config() + state, _ = synthetic_checkpoint(config) + state[key] = torch.randn(4, 4) + + with pytest.raises(RuntimeError, match=r"[1-9]\d* unexpected checkpoint keys"): + build_from(tmp_path, state, config) + + +def test_wrong_n_kv_heads_is_caught_by_the_shard_shape(tmp_path): + """PARAM_TREE section 10.4: ``n_kv_heads`` was transcribed from a config.yaml + nobody has read, and the reference's default is ``n_heads``. A wrong value + reshapes GQA attention without erroring anywhere downstream, so the loader + checks it against the tensors actually in the file.""" + config = tiny_config() + state, _ = synthetic_checkpoint(config) + wrong = tiny_config() + wrong.n_kv_heads = config.n_heads + + with pytest.raises(RuntimeError, match="n_kv_heads"): + build_from(tmp_path, state, wrong) + + +def test_wrong_patch_is_caught_by_the_conv_kernel(tmp_path): + config = tiny_config() + state, _ = synthetic_checkpoint(config) + wrong = tiny_config() + wrong.patch = (1, 1) + + with pytest.raises(RuntimeError, match="patch kernel"): + build_from(tmp_path, state, wrong) + + +# --------------------------------------------------------------------------- +# 6. Regressions: F1, F2, F3, F4 +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize("fusion", ["qkv", "fc1"]) +def test_f1_prefused_tensor_and_split_shards_cannot_both_load(tmp_path, fusion): + """A pre-fused key and its split shards in one file must raise. + + Before the fix this loaded silently. ``conflicts`` was keyed on + ``(target, shard_id)``, so ``(target, None)`` — what a pre-fused + ``attn.qkv_proj.weight`` claims — never collided with ``(target, "q")``. Both + writers ran, and safetensors yields keys sorted, so ``k_proj``, ``q_proj``, + the fused blob and then ``v_proj`` landed in that order: Q and K off the + blob, V off ``v_proj``. A different shard layout gives a different tensor. + Either spelling alone still loads (the ``CANONICAL`` round trip above). + """ + config = tiny_config() + state, _ = synthetic_checkpoint(config, spelling=LEGACY) + D = config.d_model + q_rows = config.n_heads * config.d_head + kv_rows = config.n_kv_heads * config.d_head + + if fusion == "qkv": + for i in range(config.n_layers): + key = f"transformer.blocks.{i}.attn.qkv_proj.weight" + state[key] = _tensor(key, (q_rows + 2 * kv_rows, D)) + else: + for i in sorted(config.ctrl_layers): + key = f"transformer.blocks.{i}.ctrl_mlpfusion.mlp.fc1.weight" + state[key] = _tensor(key, (D, 2 * D)) + + with pytest.raises(RuntimeError, match="pre-fused tensor and a split shard"): + build_from(tmp_path, state, config) + + +def test_f2_cond_proj_tie_check_compares_in_full(tmp_path): + """A divergence past column 64 must raise. + + The retired check compared a ``[:, :64]`` probe, so this exact checkpoint — + block 2's slot 0 randomized from column 64 on — loaded clean with + ``verify_cond_proj_tie=True``. The port keeps the owner block's copy and + drops 23 sets; the reference loads all 24 into one tensor and keeps the last. + They agree only while the stored copies agree, which is what this verifies. + """ + config = tiny_config() + assert config.d_model > 64, "the divergence has to live outside the retired probe" + state, _ = synthetic_checkpoint(config) + + key = "transformer.blocks.2.attn_cond_head.cond_proj.0.weight" + diverged = state[key].clone() + diverged[:, 64:] = torch.randn_like(diverged[:, 64:]) + state[key] = diverged + + with pytest.raises(RuntimeError, match="divergent cond_proj copies"): + build_from(tmp_path, state, config) + + # The escape hatch still loads it, and still keeps the owner block's copy. + dit = build_from(tmp_path, state, config, verify_cond_proj_tie=False) + owner = f"transformer.blocks.{COND_PROJ_SOURCE_BLOCK}.attn_cond_head.cond_proj.0.weight" + got = dit.blocks[COND_PROJ_SOURCE_BLOCK].cond_head.cond_proj[0].weight.detach().cpu() + assert torch.equal(got, state[owner].to(got.dtype)) + + +@pytest.mark.parametrize( + "present,winner", + [ + (("mlp",), "mlp"), + (("attn",), "attn"), # F3: the fallback. Used to leave every block unloaded. + (("attn", "mlp"), "mlp"), + (("canonical",), "canonical"), + (("attn", "canonical"), "canonical"), + (("attn", "mlp", "canonical"), "canonical"), + ], +) +def test_f3_bias_in_precedence(tmp_path, present, winner): + """``mlp`` beats ``attn``, an already-canonical key beats both, and any one + of the three alone is enough. + + This is ``world_model.py:386-389`` — ``mlp_bias if mlp_bias is not None else + attn_bias``, under a ``setdefault`` — restated as a rank, because a streaming + loader cannot see the whole dict and the resident weight must not depend on + which shard a key happens to live in. Note the ordering is genuinely tested: + safetensors yields keys sorted, so ``cond_head.bias_in`` arrives *before* + ``mlp_cond_head.bias_in`` and a plain "last write wins" would pick the wrong + one in the last two cases. + """ + config = tiny_config() + state, expected = synthetic_checkpoint(config, bias_in=present) + dit = build_from(tmp_path, state, config) + + for i in range(config.n_layers): + got = dit.blocks[i].cond_head.bias_in.detach().cpu() + source = state[f"transformer.blocks.{i}.{BIAS_IN_KEYS[winner]}"].to(got.dtype) + assert torch.equal(got, source), f"block {i}: bias_in did not come from {BIAS_IN_KEYS[winner]}" + assert torch.equal(got, expected[f"blocks.{i}.cond_head.bias_in"].to(got.dtype)) + + +def test_f3_no_bias_in_at_all_still_raises(tmp_path): + """The fallback is a fallback, not a licence to skip the parameter. The + reference loads ``strict=True`` and would report it missing too.""" + config = tiny_config() + state, _ = synthetic_checkpoint(config, bias_in=()) + + with pytest.raises(RuntimeError, match=r"[1-9]\d* unloaded parameters"): + build_from(tmp_path, state, config) + + +def test_f4_dropped_keys_are_not_shape_validated(tmp_path): + """T12 drops ``.cond_heads.`` unconditionally, including keys whose suffix + the GQA/patch validators recognize. + + Validation used to run on the raw stream, before any drop, so these three + keys raised about ``n_kv_heads`` and ``patch`` — a hard failure describing a + config field, for keys the loader had already decided to throw away. Drops + run first now; a dropped key's shape is not this model's business. + """ + config = tiny_config() + state, expected = synthetic_checkpoint(config) + state["transformer.blocks.0.cond_heads.0.k_proj.weight"] = torch.randn(3, 5) + state["transformer.blocks.0.cond_heads.0.q_proj.weight"] = torch.randn(9, 9) + state["transformer.blocks.1.cond_heads.0.patchify.weight"] = torch.randn(2, 2) + + dit = build_from(tmp_path, state, config) + assert set(dict(dit.named_parameters())) == set(expected) From f5a8110dc9e381bc0209f41541014999c375b61a Mon Sep 17 00:00:00 2001 From: garv Date: Tue, 8 Sep 2026 00:49:11 +0000 Subject: [PATCH 03/29] split KVConfig to handle different storage strategies --- mstar/engine/resources/__init__.py | 8 + mstar/engine/resources/kv/cache.py | 4 +- mstar/engine/resources/kv/config.py | 162 ++++++++++++++++--- mstar/engine/resources/kv/cpu_page_pool.py | 4 +- mstar/engine/resources/kv/manager.py | 4 +- mstar/model/bagel/bagel_model.py | 6 +- mstar/model/cosmos3/cosmos3_model.py | 6 +- mstar/model/higgs_audio/higgs_audio_model.py | 4 +- mstar/model/orpheus/orpheus_model.py | 4 +- mstar/model/pi05/pi05_model.py | 4 +- mstar/model/qwen3_omni/qwen3_omni_model.py | 6 +- mstar/model/qwen3_tts/qwen3_tts_model.py | 4 +- mstar/model/vjepa2/vjepa2_model.py | 4 +- mstar/model/whisper/whisper_model.py | 6 +- test/modular/test_bagel_cfg_positions.py | 4 +- test/modular/test_cross_attention_plan.py | 4 +- test/modular/test_dense_attention.py | 6 +- test/modular/test_dummy_row_pages.py | 4 +- test/modular/test_kv_offload.py | 4 +- test/modular/test_kv_publish_retrieve.py | 4 +- 20 files changed, 186 insertions(+), 66 deletions(-) diff --git a/mstar/engine/resources/__init__.py b/mstar/engine/resources/__init__.py index 49c323368..d3e5c10fc 100644 --- a/mstar/engine/resources/__init__.py +++ b/mstar/engine/resources/__init__.py @@ -30,6 +30,10 @@ KVReqConfig, KVSpec, KVStep, + PagedKVConfig, + RingKVConfig, + RingKVLayerConfig, + RingKVStep, ) from mstar.engine.resources.position.config import ( PosBackend, @@ -86,6 +90,7 @@ "KVSpec", "KVStep", "NodeResourceSpec", + "PagedKVConfig", "PosBackend", "PosScheme", "PositionConfig", @@ -97,6 +102,9 @@ "Resource", "ResourceReqConfig", "ResourceStep", + "RingKVConfig", + "RingKVLayerConfig", + "RingKVStep", "SamplerSpec", "SamplerStep", "SamplingReqConfig", diff --git a/mstar/engine/resources/kv/cache.py b/mstar/engine/resources/kv/cache.py index 8f3a72d72..877be9159 100644 --- a/mstar/engine/resources/kv/cache.py +++ b/mstar/engine/resources/kv/cache.py @@ -5,7 +5,7 @@ import torch -from mstar.engine.resources.kv.config import KVConfig, KVLayout +from mstar.engine.resources.kv.config import KVLayout, PagedKVConfig class PageAllocator: @@ -70,7 +70,7 @@ class KVCache: def __init__( self, - cfg: KVConfig, + cfg: PagedKVConfig, device: torch.device, dtype=torch.bfloat16 ): diff --git a/mstar/engine/resources/kv/config.py b/mstar/engine/resources/kv/config.py index efb7fe6ba..9c0947c5f 100644 --- a/mstar/engine/resources/kv/config.py +++ b/mstar/engine/resources/kv/config.py @@ -4,6 +4,7 @@ without pulling FlashInfer in behind it. """ +from abc import ABC, abstractmethod from dataclasses import dataclass, field from enum import Enum from typing import TYPE_CHECKING @@ -20,18 +21,23 @@ class KVLayout(Enum): # TODO: can add more, like HND, MLA -@dataclass -class KVConfig: +@dataclass(kw_only=True) +class KVConfig(ABC): + """The model geometry every KV storage strategy needs, and nothing else. + + What a cache *holds* (layers, heads, head dim) is a checkpoint fact and + lives here; how it is *stored* — paged or ringed, is storage policy and + lives in a subclass. ``KVSpec`` dispatches on which one it was handed. + + ``kw_only`` is load-bearing, not style: ``num_qo_heads`` carries a default + here while both subclasses add required fields, which is an invalid + positional dataclass field order. + """ + num_layers: int num_kv_heads: int head_dim: int - max_seq_len: int - max_num_pages: int = 2048 - page_size: int = 128 - num_qo_heads: int = None - layout: KVLayout = KVLayout.NHD - # pages of pinned host memory to keep for offloading; 0 disables it - cpu_offload_pages: int = 0 + num_qo_heads: int | None = None def __post_init__(self): if self.num_qo_heads is None: @@ -56,6 +62,82 @@ def shard(self, num_shards: int) -> None: self.num_kv_heads = divide(self._unsharded_kv_heads, num_shards) self.num_qo_heads = divide(self._unsharded_qo_heads, num_shards) + @abstractmethod + def apply_yaml_overrides(self, **kwargs) -> None: + """Patch this deployment's tunables; see ``NodeResourceSpec``. + + Abstract so each storage policy names exactly what it accepts and lets + the rest raise — a paged key against a ring config is a typo, and the + spec's contract is that a typo is loud. + """ + + +@dataclass(kw_only=True) +class PagedKVConfig(KVConfig): + """Fixed-size pages, appended to as a sequence grows. The default.""" + + max_seq_len: int + max_num_pages: int = 2048 + page_size: int = 128 + layout: KVLayout = KVLayout.NHD + # pages of pinned host memory to keep for offloading; 0 disables it + cpu_offload_pages: int = 0 + + def apply_yaml_overrides( + self, + max_num_pages: int | None = None, + page_size: int | None = None, + max_seq_len: int | None = None, + cpu_offload_pages: int | None = None, + ) -> None: + """How much cache this deployment gets, and how it is cut up.""" + for name, value in ( + ("max_num_pages", max_num_pages), + ("page_size", page_size), + ("max_seq_len", max_seq_len), + ("cpu_offload_pages", cpu_offload_pages), + ): + if value is not None: + setattr(self, name, value) + + +@dataclass(frozen=True) +class RingKVLayerConfig: + """One layer's ring geometry. Layers can hold frames at different strides.""" + + ring_frames: int + ring_buckets: int + pinned_dilation: int + + +@dataclass(kw_only=True) +class RingKVConfig(KVConfig): + """A fixed horizon of frame slots per layer, overwritten in place. + + Every layer's storage is allocated once and reused for the life of the process, + and a write to an occupied slot is the intended behaviour. + """ + + tokens_per_frame: int + layers: tuple[RingKVLayerConfig, ...] + batch_size: int = 1 + + def __post_init__(self): + super().__post_init__() + if len(self.layers) != self.num_layers: + raise ValueError( + f"ring geometry has {len(self.layers)} layers but num_layers is " + f"{self.num_layers}; each layer's ring is declared separately." + ) + + def apply_yaml_overrides(self, **kwargs) -> None: + """Nothing here is a deployment knob.""" + if kwargs: + raise TypeError( + "ring KV geometry is a checkpoint fact, not a deployment tunable; " + f"got {sorted(kwargs)}" + ) + @dataclass class KVReqConfig(ResourceReqConfig): @@ -80,26 +162,19 @@ class KVSpec(NodeResourceSpec): @property def resource_class(self) -> "type[Resource]": + if isinstance(self.config, RingKVConfig): + from mstar.engine.resources.kv.ring.manager import RingKVManager + + return RingKVManager + from mstar.engine.resources.kv.manager import KVManager return KVManager - def apply_yaml_overrides( - self, - max_num_pages: int | None = None, - page_size: int | None = None, - max_seq_len: int | None = None, - cpu_offload_pages: int | None = None, - ): - """How much cache this deployment gets, and how it is cut up.""" - for name, value in ( - ("max_num_pages", max_num_pages), - ("page_size", page_size), - ("max_seq_len", max_seq_len), - ("cpu_offload_pages", cpu_offload_pages), - ): - if value is not None: - setattr(self.config, name, value) + def apply_yaml_overrides(self, **kwargs): + """Forwarded to the config: which keys are legal is a property of the + storage strategy, so the config answers for them.""" + self.config.apply_yaml_overrides(**kwargs) @dataclass(frozen=True) @@ -111,3 +186,40 @@ class KVStep(ResourceStep): combined_labels: dict[tuple[str, ...], str] = field(default_factory=dict) pre_forks: tuple[tuple[str, str], ...] = () post_forks: tuple[tuple[str, str], ...] = () + + +@dataclass(frozen=True, kw_only=True) +class RingKVStep(ResourceStep): + """What a ring KV resource is told about one step: which frame it is. + + A *sibling* of ``KVStep``, not a subclass. Every field ``KVStep`` carries — + ``commit``, ``combined_labels``, ``pre_forks``, ``post_forks`` — is a fact + about a growing cache with named streams, and ``RingKVManager`` reads none + of them. Inheriting them would let a declarer set one and expect it to mean + something; ``commit=False`` in particular would read as "do not write this + frame", which is not a thing the ring can be told (the write happens inside + the forward, and the four frozen passes are the model's own concern — + ``RingKVManager.set_frozen``). Same split as ``PagedKVConfig`` / + ``RingKVConfig``, for the same reason. + + ``frame_pos`` is this step's host-side ring clock — the same ``int`` the + submodule's ``prepare_inputs`` derives the ``[1]`` device tensor from, + never a second source. It is declared so that ``admit`` + can check it advances by exactly one per committed frame. A clock that + desynchronizes from the ring raises nothing on its own; it silently + rewrites history, and the step boundary is the one place per + frame where the engine holds both the declared clock and the last committed + one. + + ``None`` means "no clock declared, skip the check", for a declarer with no + single frame to name — a batch of more than one request, which ``admit`` + refuses on its own terms, with a better message than a guess here + would produce. It is required rather than defaulted so that skipping the + check is a decision someone typed, not one they inherited. + + ``kw_only`` is load-bearing: ``ResourceStep.segments`` is defaulted, and a + required field cannot follow a defaulted one positionally. Keyword-only + fields are exempt from that ordering rule. + """ + + frame_pos: int | None diff --git a/mstar/engine/resources/kv/cpu_page_pool.py b/mstar/engine/resources/kv/cpu_page_pool.py index 410594fff..e8c2f2e22 100644 --- a/mstar/engine/resources/kv/cpu_page_pool.py +++ b/mstar/engine/resources/kv/cpu_page_pool.py @@ -13,7 +13,7 @@ import torch -from mstar.engine.resources.kv.cache import KVCache, KVConfig, PageAllocator +from mstar.engine.resources.kv.cache import KVCache, PageAllocator, PagedKVConfig logger = logging.getLogger(__name__) @@ -32,7 +32,7 @@ class CPUPagePool: def __init__( self, - config: KVConfig, + config: PagedKVConfig, kv_cache: KVCache, max_cpu_pages: int, ): diff --git a/mstar/engine/resources/kv/manager.py b/mstar/engine/resources/kv/manager.py index a68440b5b..d2c8323d0 100644 --- a/mstar/engine/resources/kv/manager.py +++ b/mstar/engine/resources/kv/manager.py @@ -14,7 +14,7 @@ PublishedInfo, ) from mstar.engine.resources.kv.cache import KVCache, PageAllocator -from mstar.engine.resources.kv.config import KVConfig, KVReqConfig, KVSpec, KVStep +from mstar.engine.resources.kv.config import KVReqConfig, KVSpec, KVStep, PagedKVConfig from mstar.engine.resources.kv.cpu_page_pool import CPUPagePool from mstar.engine.resources.kv.plan import ( SINK_PAGE, @@ -182,7 +182,7 @@ def copy_(self, other: "KVPlanState", capture_len: int): class KVManager(AttentionResource): def __init__( self, - cfg: KVConfig, + cfg: PagedKVConfig, name: str, joint_comm_group: JointGroups | None, transfer_engine_info: TransferEngineInfo, diff --git a/mstar/model/bagel/bagel_model.py b/mstar/model/bagel/bagel_model.py index 0bda4b231..221b17c77 100644 --- a/mstar/model/bagel/bagel_model.py +++ b/mstar/model/bagel/bagel_model.py @@ -44,10 +44,10 @@ from mstar.engine.resources import ( AttentionConfig, AttentionSpec, - KVConfig, KVReqConfig, KVSpec, NodeResourceSpec, + PagedKVConfig, PositionConfig, PositionSpec, RaggedAttentionConfig, @@ -726,8 +726,8 @@ def postprocess( # on absent, ungrouped nodes. _LLM_NODES = frozenset({"LLM", "LLM_cfg_text", "LLM_cfg_img"}) - def _kv_config(self) -> KVConfig: - return KVConfig( + def _kv_config(self) -> PagedKVConfig: + return PagedKVConfig( num_layers=self.config.num_hidden_layers, num_kv_heads=self.config.num_key_value_heads, head_dim=self.config.hidden_size // self.config.num_attention_heads, diff --git a/mstar/model/cosmos3/cosmos3_model.py b/mstar/model/cosmos3/cosmos3_model.py index abd88cc19..6f3f2dec1 100644 --- a/mstar/model/cosmos3/cosmos3_model.py +++ b/mstar/model/cosmos3/cosmos3_model.py @@ -42,10 +42,10 @@ AttentionConfig, AttentionSpec, AttnBackend, - KVConfig, KVReqConfig, KVSpec, NodeResourceSpec, + PagedKVConfig, ResourceReqConfig, ) from mstar.graph.base import ( @@ -205,11 +205,11 @@ def get_node_resources(self) -> list[NodeResourceSpec]: ``attention_backend="flashinfer"`` skips the dense spec, which leaves every step on the paged path. - The two specs share one ``KVConfig`` object on purpose: a deployment + The two specs share one ``PagedKVConfig`` object on purpose: a deployment that resizes the cache through ``apply_yaml_overrides`` has to resize what the wrappers are planned against too. """ - kv_config = KVConfig( + kv_config = PagedKVConfig( num_layers=self.config.num_hidden_layers, num_kv_heads=self.config.num_key_value_heads, head_dim=self.config.head_dim, diff --git a/mstar/model/higgs_audio/higgs_audio_model.py b/mstar/model/higgs_audio/higgs_audio_model.py index 1c69ecbe5..31db6b5e1 100644 --- a/mstar/model/higgs_audio/higgs_audio_model.py +++ b/mstar/model/higgs_audio/higgs_audio_model.py @@ -38,9 +38,9 @@ from mstar.engine.resources import ( AttentionConfig, AttentionSpec, - KVConfig, KVSpec, NodeResourceSpec, + PagedKVConfig, PositionConfig, PositionSpec, ResourceReqConfig, @@ -114,7 +114,7 @@ def __init__( def get_node_resources(self) -> list[NodeResourceSpec]: """LLM paged KV + attention/positions/sampler. audio_encoder is stateless: it runs once per request and holds nothing.""" - kv_config = KVConfig( + kv_config = PagedKVConfig( num_layers=self.config.num_hidden_layers, num_kv_heads=self.config.num_key_value_heads, head_dim=self.config.head_dim, diff --git a/mstar/model/orpheus/orpheus_model.py b/mstar/model/orpheus/orpheus_model.py index 55efda52f..695b415c9 100644 --- a/mstar/model/orpheus/orpheus_model.py +++ b/mstar/model/orpheus/orpheus_model.py @@ -31,9 +31,9 @@ from mstar.engine.resources import ( AttentionConfig, AttentionSpec, - KVConfig, KVSpec, NodeResourceSpec, + PagedKVConfig, PositionConfig, PositionSpec, ResourceReqConfig, @@ -351,7 +351,7 @@ def get_initial_forward_pass_args( # ------------------------------------------------------------------- def get_node_resources(self) -> list[NodeResourceSpec]: - kv_config = KVConfig( + kv_config = PagedKVConfig( num_layers=self.config.num_hidden_layers, num_kv_heads=self.config.num_key_value_heads, head_dim=self.config.head_dim, diff --git a/mstar/model/pi05/pi05_model.py b/mstar/model/pi05/pi05_model.py index a29c65d6e..c4e45acb1 100644 --- a/mstar/model/pi05/pi05_model.py +++ b/mstar/model/pi05/pi05_model.py @@ -37,9 +37,9 @@ from mstar.engine.resources import ( AttentionConfig, AttentionSpec, - KVConfig, KVSpec, NodeResourceSpec, + PagedKVConfig, PositionConfig, PositionSpec, ) @@ -401,7 +401,7 @@ def get_node_resources(self) -> list[NodeResourceSpec]: ``action_gen`` reads the frozen prefix read-only via ``KVStep(commit= False)`` while ``prefill`` commits it. """ - kv_config = KVConfig( + kv_config = PagedKVConfig( num_layers=self.config.num_layers, num_kv_heads=self.config.num_kv_heads, head_dim=self.config.head_dim, diff --git a/mstar/model/qwen3_omni/qwen3_omni_model.py b/mstar/model/qwen3_omni/qwen3_omni_model.py index 42f6d2637..030b72135 100644 --- a/mstar/model/qwen3_omni/qwen3_omni_model.py +++ b/mstar/model/qwen3_omni/qwen3_omni_model.py @@ -43,9 +43,9 @@ from mstar.engine.resources import ( AttentionConfig, AttentionSpec, - KVConfig, KVSpec, NodeResourceSpec, + PagedKVConfig, PositionConfig, PositionSpec, ResourceReqConfig, @@ -176,14 +176,14 @@ def __init__( # Model ABC: resources # ------------------------------------------------------------------- def get_node_resources(self) -> list[NodeResourceSpec]: - thinker_kv = KVConfig( + thinker_kv = PagedKVConfig( num_layers=self.config.thinker_text.num_hidden_layers, num_kv_heads=self.config.thinker_text.num_key_value_heads, head_dim=self.config.thinker_head_dim, max_seq_len=self.config.thinker_text.max_position_embeddings, num_qo_heads=self.config.thinker_text.num_attention_heads, ) - talker_kv = KVConfig( + talker_kv = PagedKVConfig( num_layers=self.config.talker_text.num_hidden_layers, num_kv_heads=self.config.talker_text.num_key_value_heads, head_dim=self.config.talker_head_dim, diff --git a/mstar/model/qwen3_tts/qwen3_tts_model.py b/mstar/model/qwen3_tts/qwen3_tts_model.py index bd26a574c..a03bb621d 100644 --- a/mstar/model/qwen3_tts/qwen3_tts_model.py +++ b/mstar/model/qwen3_tts/qwen3_tts_model.py @@ -41,9 +41,9 @@ from mstar.engine.resources import ( AttentionConfig, AttentionSpec, - KVConfig, KVSpec, NodeResourceSpec, + PagedKVConfig, PositionConfig, PositionSpec, ResourceReqConfig, @@ -241,7 +241,7 @@ def get_node_resources(self) -> list[NodeResourceSpec]: Talker submodule owns it (overwritten every step).""" talker = self.config.talker cp = talker.code_predictor - talker_kv = KVConfig( + talker_kv = PagedKVConfig( num_layers=talker.num_hidden_layers, num_kv_heads=talker.num_key_value_heads, head_dim=talker.head_dim, diff --git a/mstar/model/vjepa2/vjepa2_model.py b/mstar/model/vjepa2/vjepa2_model.py index b744e5c36..20469138a 100644 --- a/mstar/model/vjepa2/vjepa2_model.py +++ b/mstar/model/vjepa2/vjepa2_model.py @@ -40,9 +40,9 @@ from mstar.engine.resources import ( AttentionConfig, AttentionSpec, - KVConfig, KVSpec, NodeResourceSpec, + PagedKVConfig, ) from mstar.graph.base import ( GraphEdge, @@ -293,7 +293,7 @@ def get_node_resources(self) -> list[NodeResourceSpec]: # a one-shot forward with nothing for the engine to build if self.config.predictor_kind != "ac": return [] - kv = KVConfig( + kv = PagedKVConfig( num_layers=self.config.ac_predictor.depth, num_kv_heads=self.config.ac_predictor.num_heads, head_dim=self.config.ac_predictor.predictor_embed_dim // self.config.ac_predictor.num_heads, diff --git a/mstar/model/whisper/whisper_model.py b/mstar/model/whisper/whisper_model.py index f35589496..2b4458685 100644 --- a/mstar/model/whisper/whisper_model.py +++ b/mstar/model/whisper/whisper_model.py @@ -37,9 +37,9 @@ AttentionSpec, CrossAttentionConfig, CrossAttentionSpec, - KVConfig, KVSpec, NodeResourceSpec, + PagedKVConfig, PositionConfig, PositionSpec, ResourceReqConfig, @@ -123,7 +123,7 @@ def get_node_resources(self) -> list[NodeResourceSpec]: # Sequences cap at max_target_positions (448) = 4 pages per # request; 128 pages ≈ 32 concurrent requests at ~2.7 GB # (vs 43 GB with the 2048-page default). - kv_config = KVConfig( + kv_config = PagedKVConfig( num_layers=self.config.decoder_layers, num_kv_heads=self.config.decoder_attention_heads, head_dim=self.config.head_dim, @@ -133,7 +133,7 @@ def get_node_resources(self) -> list[NodeResourceSpec]: ) # The fixed 30 s window is max_source_positions (1500) tokens = 12 # pages per request; 192 pages ≈ 16 concurrent at ~4 GB. - context_kv_config = KVConfig( + context_kv_config = PagedKVConfig( num_layers=self.config.decoder_layers, num_kv_heads=self.config.decoder_attention_heads, head_dim=self.config.head_dim, diff --git a/test/modular/test_bagel_cfg_positions.py b/test/modular/test_bagel_cfg_positions.py index cfc3302a7..800749afd 100644 --- a/test/modular/test_bagel_cfg_positions.py +++ b/test/modular/test_bagel_cfg_positions.py @@ -23,7 +23,7 @@ import pytest import torch -from mstar.engine.resources import KVConfig, PositionConfig, StepContext, StepRunner +from mstar.engine.resources import PagedKVConfig, PositionConfig, StepContext, StepRunner from mstar.engine.resources.kv.manager import KVManager from mstar.engine.resources.kv.transfer import TransferEngineInfo from mstar.engine.resources.position.manager import RopeManager @@ -48,7 +48,7 @@ def __init__(self, device: torch.device): self.device = device self.kv = KVManager( - cfg=KVConfig( + cfg=PagedKVConfig( num_layers=1, num_kv_heads=1, head_dim=8, max_seq_len=4096, max_num_pages=512, page_size=16, ), diff --git a/test/modular/test_cross_attention_plan.py b/test/modular/test_cross_attention_plan.py index d879b01b0..0c0e884f8 100644 --- a/test/modular/test_cross_attention_plan.py +++ b/test/modular/test_cross_attention_plan.py @@ -19,7 +19,7 @@ from mstar.engine.resources.attn import cross as cross_mod from mstar.engine.resources.attn.config import AttentionStep -from mstar.engine.resources.kv.config import KVConfig +from mstar.engine.resources.kv.config import PagedKVConfig from mstar.engine.resources.kv.plan import ( KVPlanOutput, KVPlanOutputs, @@ -68,7 +68,7 @@ def _manager() -> cross_mod.FlashInferCrossManager: context_label=CONTEXT, device=torch.device("cpu"), dtype=torch.bfloat16, - kv_config=KVConfig( + kv_config=PagedKVConfig( num_layers=2, num_kv_heads=4, head_dim=64, max_seq_len=512, max_num_pages=64, page_size=16, ), diff --git a/test/modular/test_dense_attention.py b/test/modular/test_dense_attention.py index 8251f0cc2..5702097eb 100644 --- a/test/modular/test_dense_attention.py +++ b/test/modular/test_dense_attention.py @@ -22,9 +22,9 @@ AttentionSpec, AttentionStep, AttnBackend, - KVConfig, KVSpec, KVStep, + PagedKVConfig, Segment, SlotLease, StepContext, @@ -44,8 +44,8 @@ MAX_PAGES = 8 -def _kv_config() -> KVConfig: - return KVConfig( +def _kv_config() -> PagedKVConfig: + return PagedKVConfig( num_layers=1, num_kv_heads=NUM_KV_HEADS, head_dim=HEAD_DIM, diff --git a/test/modular/test_dummy_row_pages.py b/test/modular/test_dummy_row_pages.py index 6c7fbd0a2..a4bdd6da0 100644 --- a/test/modular/test_dummy_row_pages.py +++ b/test/modular/test_dummy_row_pages.py @@ -23,7 +23,7 @@ ) from mstar.engine.cuda_graph_runner import DummyRowPool from mstar.engine.resources.kv import manager as manager_mod -from mstar.engine.resources.kv.config import KVConfig, KVStep +from mstar.engine.resources.kv.config import KVStep, PagedKVConfig from mstar.engine.resources.kv.manager import KVManager from mstar.engine.resources.step import Segment, StepContext from mstar.model.submodule_base import ARNodeInputs @@ -89,7 +89,7 @@ def test_release_all_is_a_no_op_on_a_pool_that_opened_nothing(): def _kv_manager(max_num_pages=64, page_size=16) -> KVManager: - cfg = KVConfig( + cfg = PagedKVConfig( num_layers=1, num_kv_heads=1, head_dim=8, max_seq_len=1024, max_num_pages=max_num_pages, page_size=page_size, ) diff --git a/test/modular/test_kv_offload.py b/test/modular/test_kv_offload.py index f8f5471a3..326933a84 100644 --- a/test/modular/test_kv_offload.py +++ b/test/modular/test_kv_offload.py @@ -28,7 +28,7 @@ StepContext, ) from mstar.engine.resources.kv import manager as manager_mod -from mstar.engine.resources.kv.config import KVConfig, KVStep +from mstar.engine.resources.kv.config import KVStep, PagedKVConfig from mstar.engine.resources.kv.manager import KVManager from mstar.engine.resources.kv.plan import SINK_PAGE @@ -62,7 +62,7 @@ def _stub_transfer(monkeypatch): def _make_manager(max_num_pages: int = 16, cpu_offload_pages: int = 16): - cfg = KVConfig( + cfg = PagedKVConfig( num_layers=2, num_kv_heads=1, head_dim=4, diff --git a/test/modular/test_kv_publish_retrieve.py b/test/modular/test_kv_publish_retrieve.py index 058fe786d..8d4c91337 100644 --- a/test/modular/test_kv_publish_retrieve.py +++ b/test/modular/test_kv_publish_retrieve.py @@ -20,7 +20,7 @@ import torch from mstar.engine.resources.kv import manager as manager_mod -from mstar.engine.resources.kv.config import KVConfig, KVStep +from mstar.engine.resources.kv.config import KVStep, PagedKVConfig from mstar.engine.resources.kv.manager import KVManager from mstar.engine.resources.step import Segment, StepContext @@ -54,7 +54,7 @@ def _stub(monkeypatch): def _manager(max_num_pages=64, page_size=16) -> KVManager: return KVManager( - cfg=KVConfig( + cfg=PagedKVConfig( num_layers=1, num_kv_heads=1, head_dim=8, max_seq_len=4096, max_num_pages=max_num_pages, page_size=page_size, ), From 89fe180c0e8f11bc27c9268dfcd0b088dac93d09 Mon Sep 17 00:00:00 2001 From: garv Date: Thu, 10 Sep 2026 01:43:19 +0000 Subject: [PATCH 04/29] adding flex attention, changing 1 req per DiT node assumption with reviewed manager and cache changes --- mstar/engine/resources/attn/base.py | 37 +- mstar/engine/resources/attn/config.py | 1 + mstar/engine/resources/attn/cross.py | 6 +- mstar/engine/resources/attn/dense.py | 4 +- mstar/engine/resources/attn/flashinfer.py | 4 +- mstar/engine/resources/attn/flex.py | 201 +++ mstar/engine/resources/kv/config.py | 53 +- mstar/engine/resources/kv/ring/__init__.py | 11 + mstar/engine/resources/kv/ring/cache.py | 194 +++ mstar/engine/resources/kv/ring/manager.py | 390 +++++ mstar/model/registry.py | 7 + mstar/model/waypoint/components/__init__.py | 39 +- mstar/model/waypoint/components/kv_backend.py | 562 ------ mstar/model/waypoint/components/layers.py | 11 +- mstar/model/waypoint/config.py | 9 +- mstar/model/waypoint/ring_geometry.py | 70 + mstar/model/waypoint/weight_loader.py | 44 +- test/modular/test_flex_attention_resource.py | 544 ++++++ test/modular/test_ring_kv_resource.py | 1550 +++++++++++++++++ test/modular/test_waypoint_components.py | 470 ++--- test/modular/test_waypoint_dit.py | 410 +++-- test/modular/test_waypoint_shell.py | 742 ++++++++ test/modular/test_waypoint_weight_loader.py | 14 +- 23 files changed, 4387 insertions(+), 986 deletions(-) create mode 100644 mstar/engine/resources/attn/flex.py create mode 100644 mstar/engine/resources/kv/ring/__init__.py create mode 100644 mstar/engine/resources/kv/ring/cache.py create mode 100644 mstar/engine/resources/kv/ring/manager.py delete mode 100644 mstar/model/waypoint/components/kv_backend.py create mode 100644 mstar/model/waypoint/ring_geometry.py create mode 100644 test/modular/test_flex_attention_resource.py create mode 100644 test/modular/test_ring_kv_resource.py create mode 100644 test/modular/test_waypoint_shell.py diff --git a/mstar/engine/resources/attn/base.py b/mstar/engine/resources/attn/base.py index 6ec9c8b58..50dd4c373 100644 --- a/mstar/engine/resources/attn/base.py +++ b/mstar/engine/resources/attn/base.py @@ -1,9 +1,9 @@ """The attention resource's shared machinery: the spec-time factory, the custom op every backend attends through, and the workspace pool. -The backends themselves live beside this — `flashinfer`, `cross`, `dense` — -and `AttentionManager.build` reaches them by deferred import, so naming a -backend in a spec does not load the other two. +The backends themselves live beside this — `flashinfer`, `cross`, `dense`, +`flex` — and `AttentionManager.build` reaches them by deferred import, so +naming a backend in a spec does not load the others. """ import logging @@ -18,8 +18,14 @@ FlashInferPrefillWrapper, ) from mstar.engine.resources.base import AttentionResource, EngineResourceInfo +from mstar.engine.resources.kv.config import KVConfig, PagedKVConfig, RingKVConfig logger = logging.getLogger(__name__) +_BACKEND_KV_CONFIG: dict[AttnBackend, type[KVConfig]] = { + AttnBackend.DENSE: PagedKVConfig, + AttnBackend.FLASHINFER: PagedKVConfig, + AttnBackend.FLEX: RingKVConfig, +} class AttentionManager(AttentionResource): @@ -33,9 +39,21 @@ def build(cls, spec: AttentionSpec, info: EngineResourceInfo): # the KV resource's own config, not a copy; per-rank head counts, and # `shard` is idempotent so both builders can call it kv_config = info.dependency(spec.config.kv_cache).config + backend = spec.config.backend + # Before `shard`, so a mismatch is reported rather than half-applied to + # a config the KV resource also holds. + if backend not in _BACKEND_KV_CONFIG: + raise ValueError(f"Unknown attention backend {backend!r}") + required = _BACKEND_KV_CONFIG[backend] + if not isinstance(kv_config, required): + raise TypeError( + f"attention backend {backend.value!r} requires a " + f"{required.__name__}, but KV resource {spec.config.kv_cache!r} " + f"holds a {type(kv_config).__name__}. " + ) + # `shard` lives on the KVConfig base, so this is unchanged for both. if info.joint_comm_group is not None: kv_config.shard(info.joint_comm_group.world_size) - backend = spec.config.backend if backend == AttnBackend.DENSE: # A dense backend needs the FlashAttention-3 kernel; where the # wheel does not match the installed torch/CUDA build, degrade to @@ -70,6 +88,17 @@ def build(cls, spec: AttentionSpec, info: EngineResourceInfo): kv_config=kv_config, backend=spec.config.flashinfer_backend, ) + + if backend == AttnBackend.FLEX: + from mstar.engine.resources.attn.flex import FlexAttentionManager + + return FlexAttentionManager( + kv_cache=spec.config.kv_cache, + device=info.device, + dtype=info.kv_dtype, + kv_config=kv_config, + ) + if backend == AttnBackend.XPU_PAGED: from mstar.engine.resources.attn.xpu import ( XPUPagedAttentionManager, diff --git a/mstar/engine/resources/attn/config.py b/mstar/engine/resources/attn/config.py index ad4c5136a..6a0a255b8 100644 --- a/mstar/engine/resources/attn/config.py +++ b/mstar/engine/resources/attn/config.py @@ -18,6 +18,7 @@ class AttnBackend(Enum): FLASHINFER = "flashinfer" DENSE = "dense" + FLEX = "flex" XPU_PAGED = "xpu_paged" diff --git a/mstar/engine/resources/attn/cross.py b/mstar/engine/resources/attn/cross.py index d0f5942f4..8a9fb464b 100644 --- a/mstar/engine/resources/attn/cross.py +++ b/mstar/engine/resources/attn/cross.py @@ -21,7 +21,7 @@ CGSlotKey, EngineResourceInfo, ) -from mstar.engine.resources.kv.config import KVConfig +from mstar.engine.resources.kv.config import PagedKVConfig from mstar.engine.resources.kv.plan import ( SINK_PAGE, KVPlanOutputs, @@ -82,7 +82,7 @@ def __init__( context_label: str, device: torch.device, dtype: torch.dtype, - kv_config: KVConfig, + kv_config: PagedKVConfig, backend: str = "auto", ): self._kv_cache_name = kv_cache @@ -309,7 +309,7 @@ def _build_indptrs( assert len(all_pages) <= self._kv_config.max_num_pages, ( f"cross attention plan {plan_label!r} indexes {len(all_pages)} " f"pages but the context cache holds {self._kv_config.max_num_pages}; " - "raise max_num_pages on the context cache's KVConfig" + "raise max_num_pages on the context cache's PagedKVConfig" ) return PagedIndptrs( diff --git a/mstar/engine/resources/attn/dense.py b/mstar/engine/resources/attn/dense.py index ed0c845b4..06696ba4e 100644 --- a/mstar/engine/resources/attn/dense.py +++ b/mstar/engine/resources/attn/dense.py @@ -8,7 +8,7 @@ from mstar.engine.resources.attn.base import AttentionManager from mstar.engine.resources.attn.config import AttentionStep -from mstar.engine.resources.kv.config import KVConfig +from mstar.engine.resources.kv.config import PagedKVConfig from mstar.engine.resources.kv.plan import KVPlanOutput, KVPlanOutputs from mstar.engine.resources.step import StepContext @@ -110,7 +110,7 @@ def __init__( kv_cache: str, device: torch.device, dtype: torch.dtype, - kv_config: KVConfig, + kv_config: PagedKVConfig, ): self._kv_cache_name = kv_cache self._device = device diff --git a/mstar/engine/resources/attn/flashinfer.py b/mstar/engine/resources/attn/flashinfer.py index 6c79013e9..8902e8f99 100644 --- a/mstar/engine/resources/attn/flashinfer.py +++ b/mstar/engine/resources/attn/flashinfer.py @@ -14,7 +14,7 @@ FlashInferPrefillWrapper, ) from mstar.engine.resources.base import CGSlotKey -from mstar.engine.resources.kv.config import KVConfig +from mstar.engine.resources.kv.config import PagedKVConfig from mstar.engine.resources.kv.plan import KVPlanOutputs from mstar.engine.resources.step import SlotLease, StepContext @@ -25,7 +25,7 @@ def __init__( kv_cache: str, device: torch.device, dtype: torch.dtype, - kv_config: KVConfig, + kv_config: PagedKVConfig, backend: str="auto", ): self._kv_cache_name = kv_cache diff --git a/mstar/engine/resources/attn/flex.py b/mstar/engine/resources/attn/flex.py new file mode 100644 index 000000000..70c09c273 --- /dev/null +++ b/mstar/engine/resources/attn/flex.py @@ -0,0 +1,201 @@ +"""Attention over a ring KV cache, through a compiled ``flex_attention``. + +The visibility of a ring cannot be expressed as a sequence length: the slots a +step may read are scattered across a fixed buffer that is overwritten in place, +so there is no prefix to pass a kernel. FlexAttention's ``BlockMask`` names the +readable KV blocks directly, which is what makes the ring expressible at all. + +The mask this builds is query-uniform and full-blocks-only — every query block +sees the same KV blocks, and a visible block is wholly visible — because writes +are whole frames of tokens. That is what makes "capacity must be a multiple of +128" a real constraint here rather than a convenience. +""" + +import torch +from torch import Tensor +from torch.nn.attention.flex_attention import ( + _DEFAULT_SPARSE_BLOCK_SIZE, + BlockMask, + flex_attention, +) + +from mstar.engine.resources.attn.base import AttentionManager +from mstar.engine.resources.attn.config import AttentionStep +from mstar.engine.resources.kv.config import KVConfig +from mstar.engine.resources.step import StepContext + +__all__ = ["FlexAttentionManager", "flex_attention_masked", "make_block_mask"] + + +# CORRECTNESS, not speed. Our BlockMask carries a NO-OP `mask_mod`: we pass +# `mask_mod=None` to `from_kv_blocks` and it substitutes `noop_mask`, so +# `bm.mask_mod` is a function returning True everywhere, not `None`. Visibility +# is therefore encoded *entirely* in the block index lists (`full_kv_indices` +# truncated to `full_kv_num_blocks`), which is what makes a ring expressible at +# all. The compiled kernel iterates exactly those blocks. The eager path does +# not -- it rebuilds the mask by evaluating `mask_mod` over the grid, and a noop +# mask_mod means "everything is visible", so eager attention silently reads +# every unwritten slot in the ring as a zero K/V and blends it in. +# +# Measured on the ported cache: eager output diverges from a masked-dense +# reference by 2.7e-01, while the compiled path matches it to 1.2e-07. Nothing +# raises. This is why the reference wraps both of its regions in +# @torch.compile(fullgraph=True) -- compilation is load-bearing for the *result* +# there too, not just the throughput. +# +# So the compile is pinned here rather than left to the caller: correctness must +# not depend on whether someone set `WaypointConfig.compile_dit`. +flex_attention_masked = torch.compile(flex_attention, dynamic=False) + + +def make_block_mask(q_len: int, kv_len: int, written: Tensor) -> BlockMask: + """Build the query-uniform, full-blocks-only ``BlockMask`` over ``written``. + + ``written`` is ``[kv_len]`` bool, True where the ring holds valid KV. Both + lengths must be exact multiples of the 128-token sparse block size and + ``written`` must be block-aligned -- both hold because writes are whole + frames of 512 (or 128) tokens. + + Two properties are load-bearing. Every query block sees the same KV blocks, + so the ``[1, 1, 1, num_kv_blocks]`` row broadcasts over query blocks and + ``compute_q_blocks=False`` is safe. And every visible block is *full* + (no partial blocks at all), because frame-granular writes + mean "any token in the block is written" and "all of them are" coincide. + """ + block_size = _DEFAULT_SPARSE_BLOCK_SIZE + + if not torch.compiler.is_compiling(): + torch._check( + q_len % block_size == 0, + lambda: f"q_len ({q_len}) must be a multiple of block size ({block_size})", + ) + torch._check( + kv_len % block_size == 0, + lambda: f"kv_len ({kv_len}) must be a multiple of block size ({block_size})", + ) + + q_blocks = q_len // block_size + kv_blocks = kv_len // block_size + + written_blocks = written.view(kv_blocks, block_size) + block_any = written_blocks.any(-1) + if not torch.compiler.is_compiling(): + assert torch.equal(block_any, written_blocks.all(-1)), "written must be block-aligned" + + full_bm = block_any[None, :].expand(q_blocks, kv_blocks) + full_kv_num_blocks = full_bm.sum(dim=-1, dtype=torch.int32)[None, None].contiguous() + # Stable descending argsort: the visited list is this truncated to + # full_kv_num_blocks, so unwritten blocks sort to the tail and are never + # read. That is exactly why compacting the global ring is bit-exact. + full_kv_indices = ( + full_bm.argsort(dim=-1, descending=True, stable=True) + .to(torch.int32)[None, None] + .contiguous() + ) + + # No partial blocks at all -- these two exist only to satisfy the signature. + kv_num_blocks = torch.zeros((1, 1, q_blocks), dtype=torch.int32, device=written.device) + kv_indices = torch.zeros((1, 1, q_blocks, kv_blocks), dtype=torch.int32, device=written.device) + + return BlockMask.from_kv_blocks( + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + BLOCK_SIZE=block_size, + mask_mod=None, + seq_lengths=(q_len, kv_len), + compute_q_blocks=False, + ) + + +class FlexAttentionManager(AttentionManager): + """Attention against a ring KV cache, through ``flex_attention``. + + **Why FlexAttention and not the paged FlashInfer path** that already + exists here: numerical parity with the reference implementation, which + runs ``flex_attention`` with a full-block-only ``BlockMask``. A paged + kernel changes the accumulation order over the KV blocks, so a port on top + of it could never be compared bit-exactly against the reference — and for + the model this exists to serve, that comparison is the only check there + is: a mask or position bug does not raise, it produces plausible, smoothly + drifting video. The FlashInfer alternative was measured at ~9% on the + attention kernel, against the 2x that had motivated trying it. + + Stateless across steps. The world state is the ring, and the ring belongs + to the KV resource this one names in ``depends_on``; nothing here survives + a call except the label/layer cursors the base class defines. + """ + + def __init__( + self, + kv_cache: str, + device: torch.device, + dtype: torch.dtype, + kv_config: KVConfig, + ): + self._kv_cache_name = kv_cache + self._device = device + self._dtype = dtype + self._kv_config = kv_config + + def depends_on(self) -> set[str]: + return {self._kv_cache_name} + + @property + def requires_kv_write(self) -> bool: + """False: the layer has already written this step's K/V. + + A ring ``upsert`` is not the paged backends' ``write_kv``. It writes + the frame *and* returns the whole ring view to attend against, in one + call, because which slots the write makes visible is part of its + result — so there is no separate write step for a layer to perform and + calling one would commit the frame twice. + + A ``@property``, not a class attribute, to keep the base's read-only + contract: ``AttentionManager.requires_kv_write`` is a property and + ``DenseAttentionManager`` overrides it as one. Shadowing it with a + plain ``False`` would work at runtime and quietly make the attribute + writable on this subclass alone. + """ + return False + + def plan(self, step: AttentionStep, ctx: StepContext) -> None: + """Nothing to plan: the mask is a function of the ring's own + visibility row, which the KV resource hands to ``attend`` per layer. + + The cursors are still cleared, per ``AttentionResource``: a step that + never binds them must not inherit the previous step's. + """ + del step, ctx + self.reset_default_cursors() + + def attend( + self, + q: Tensor, + k: Tensor, + v: Tensor, + visible: Tensor, + *, + enable_gqa: bool, + ) -> Tensor: + """One layer's attention over the ring. + + ``q`` is ``[B, H_q, T, D]``; ``k``/``v`` are ``[B, H_kv, kv_len, D]``, + the whole ring view (history slots plus the scratch frame) as the KV + resource returned it. ``visible`` is ``[kv_len]`` bool, True where that + view holds KV this step may read — it is not derivable from a length, + which is the entire reason this backend exists. Returns + ``[B, H_q, T, D]``. + """ + # Rebuilt every call. The mask is pass-invariant by construction (all + # passes over one frame see byte-identical KV), so a cache keyed on + # (layer_idx, frame_pos) would collapse 120 rebuilds per frame to 24. + # Deliberately NOT taken: there is no measurement of what the rebuild + # costs against the rest of the frame, and a stale mask is + # exactly the failure this backend is here to prevent. Measure first. + block_mask = make_block_mask(q.size(-2), k.size(-2), visible) + # `flex_attention_masked`, never bare `flex_attention`: with a no-op + # `mask_mod` the eager path ignores the block mask entirely and attends + # to unwritten ring slots. See the note at its definition. + return flex_attention_masked(q, k, v, block_mask=block_mask, enable_gqa=enable_gqa) diff --git a/mstar/engine/resources/kv/config.py b/mstar/engine/resources/kv/config.py index 9c0947c5f..060aa5d38 100644 --- a/mstar/engine/resources/kv/config.py +++ b/mstar/engine/resources/kv/config.py @@ -120,7 +120,8 @@ class RingKVConfig(KVConfig): tokens_per_frame: int layers: tuple[RingKVLayerConfig, ...] - batch_size: int = 1 + # How many worlds are resident at once. NOT a batch. + num_worlds: int = 1 def __post_init__(self): super().__post_init__() @@ -129,14 +130,26 @@ def __post_init__(self): f"ring geometry has {len(self.layers)} layers but num_layers is " f"{self.num_layers}; each layer's ring is declared separately." ) + if self.num_worlds < 1: + raise ValueError( + f"num_worlds must be >= 1; got {self.num_worlds}. A node serving " + "zero worlds refuses every request at admit." + ) - def apply_yaml_overrides(self, **kwargs) -> None: - """Nothing here is a deployment knob.""" + def apply_yaml_overrides(self, num_worlds: int | None = None, **kwargs) -> None: + """``num_worlds`` only. Nothing else here is a deployment knob.""" if kwargs: raise TypeError( "ring KV geometry is a checkpoint fact, not a deployment tunable; " f"got {sorted(kwargs)}" ) + if num_worlds is not None: + if int(num_worlds) < 1: + raise ValueError( + f"num_worlds must be >= 1; got {num_worlds}. A node serving " + "zero worlds refuses every request at admit." + ) + self.num_worlds = int(num_worlds) @dataclass @@ -190,36 +203,6 @@ class KVStep(ResourceStep): @dataclass(frozen=True, kw_only=True) class RingKVStep(ResourceStep): - """What a ring KV resource is told about one step: which frame it is. - - A *sibling* of ``KVStep``, not a subclass. Every field ``KVStep`` carries — - ``commit``, ``combined_labels``, ``pre_forks``, ``post_forks`` — is a fact - about a growing cache with named streams, and ``RingKVManager`` reads none - of them. Inheriting them would let a declarer set one and expect it to mean - something; ``commit=False`` in particular would read as "do not write this - frame", which is not a thing the ring can be told (the write happens inside - the forward, and the four frozen passes are the model's own concern — - ``RingKVManager.set_frozen``). Same split as ``PagedKVConfig`` / - ``RingKVConfig``, for the same reason. - - ``frame_pos`` is this step's host-side ring clock — the same ``int`` the - submodule's ``prepare_inputs`` derives the ``[1]`` device tensor from, - never a second source. It is declared so that ``admit`` - can check it advances by exactly one per committed frame. A clock that - desynchronizes from the ring raises nothing on its own; it silently - rewrites history, and the step boundary is the one place per - frame where the engine holds both the declared clock and the last committed - one. - - ``None`` means "no clock declared, skip the check", for a declarer with no - single frame to name — a batch of more than one request, which ``admit`` - refuses on its own terms, with a better message than a guess here - would produce. It is required rather than defaulted so that skipping the - check is a decision someone typed, not one they inherited. - - ``kw_only`` is load-bearing: ``ResourceStep.segments`` is defaulted, and a - required field cannot follow a defaulted one positionally. Keyword-only - fields are exempt from that ordering rule. - """ + """One ring clock per request in the step's batch.""" - frame_pos: int | None + frames: tuple[tuple[str, int], ...] diff --git a/mstar/engine/resources/kv/ring/__init__.py b/mstar/engine/resources/kv/ring/__init__.py new file mode 100644 index 000000000..3a22f1080 --- /dev/null +++ b/mstar/engine/resources/kv/ring/__init__.py @@ -0,0 +1,11 @@ +"""Ring KV storage: a fixed horizon of frame slots, overwritten in place. + +The storage strategy Waypoint declares. ``KVSpec.resource_class`` dispatches +here on seeing a ``RingKVConfig``, so a paged model never imports it and this +package never imports FlashInfer. +""" + +from mstar.engine.resources.kv.ring.cache import LayerRingCache, ring_scatter +from mstar.engine.resources.kv.ring.manager import RingKVManager + +__all__ = ["LayerRingCache", "RingKVManager", "ring_scatter"] diff --git a/mstar/engine/resources/kv/ring/cache.py b/mstar/engine/resources/kv/ring/cache.py new file mode 100644 index 000000000..ea4ae2535 --- /dev/null +++ b/mstar/engine/resources/kv/ring/cache.py @@ -0,0 +1,194 @@ +import torch +from torch import Tensor +from torch.nn.attention.flex_attention import _DEFAULT_SPARSE_BLOCK_SIZE + +__all__ = ["LayerRingCache", "ring_scatter"] + + +@torch.library.custom_op("mstar::ring_scatter", mutates_args={"cache", "written"}) +def ring_scatter( + cache: torch.Tensor, written: torch.Tensor, dst: torch.Tensor, + kv: torch.Tensor, mark: bool, +) -> None: + """Write one frame of K/V into ``dst``'s token slots, optionally marking it. + + An op rather than plain in-place indexing for exactly the reason + ``mstar::kv_scatter_nhd`` (``kv/cache.py``) is one: the forward reaches + ``cache`` through an attribute chain, so dynamo lifts it as a graph + attribute and AOTAutograd functionalizes the mutation into a copy of the + WHOLE ring. At 720P that is 816 MiB per world copied 120 times per frame + (24 layers x 5 passes) -- a throughput collapse, not an error, so nothing + tells you. + Declaring the mutation keeps the write in place and in-graph, with no break + to recompile the layer body once per layer. + + ``index_fill_`` and not ``written[dst] = True``, which is what this was. + The subscript form lowers to ``index_put_`` with the Python ``True`` + materialized as a CPU scalar tensor and copied to the device, and an H2D + copy from pageable host memory invalidates CUDA graph capture. + """ + cache.index_copy_(3, dst, kv) + if mark: + written.index_fill_(0, dst, True) + + +@ring_scatter.register_fake +def _ring_scatter_fake( + cache: torch.Tensor, written: torch.Tensor, dst: torch.Tensor, + kv: torch.Tensor, mark: bool, +) -> None: + return None + + +class LayerRingCache: + """One attention layer's ring: ``ring_frames`` frame slots of history plus + one scratch frame at the tail, times ``num_worlds`` resident worlds. + + Storage is a single ``[2, 1, H_kv, num_worlds * capacity, D]`` tensor so + that a commit is one ``index_copy_`` for K and V together and the read is + one ``unbind(0)`` into two views -- no copy on the read path. + + ``capacity`` is ONE world's token slots; ``total_slots`` is the token-dim + length of the buffer. + """ + + def __init__( + self, + num_worlds: int, + n_kv_heads: int, + ring_frames: int, + ring_buckets: int, + d_head: int, + tokens_per_frame: int, + pinned_dilation: int, + dtype: torch.dtype, + device: torch.device | str, + ): + if num_worlds < 1: + raise ValueError(f"num_worlds must be >= 1; got {num_worlds}.") + if pinned_dilation < 1: + raise ValueError(f"pinned_dilation must be >= 1; got {pinned_dilation}.") + if not 1 <= ring_buckets <= ring_frames: + raise ValueError( + f"ring_buckets ({ring_buckets}) must be in [1, ring_frames ({ring_frames})]; " + "the addressable slots have to fit inside the allocated ring." + ) + if tokens_per_frame % _DEFAULT_SPARSE_BLOCK_SIZE: + raise ValueError( + f"tokens_per_frame ({tokens_per_frame}) must be a multiple of the sparse " + f"block size ({_DEFAULT_SPARSE_BLOCK_SIZE}); the BlockMask has no partial blocks." + ) + + self.num_worlds = num_worlds + self.tokens_per_frame = tokens_per_frame + self.ring_frames = ring_frames + self.ring_buckets = ring_buckets + self.pinned_dilation = pinned_dilation + # ring_len is the reference's `L`: one world's history region, scratch + # excluded. + self.ring_len = ring_frames * tokens_per_frame + self.capacity = self.ring_len + tokens_per_frame + self.total_slots = num_worlds * self.capacity + + self.kv = torch.zeros( + 2, 1, n_kv_heads, self.total_slots, d_head, dtype=dtype, device=device + ) + + written = torch.zeros(self.total_slots, dtype=torch.bool, device=device) + written.view(num_worlds, self.capacity)[:, self.ring_len :] = True + self.written = written + # Preallocated scratch for the per-call visibility mask. Allocating it + # inside upsert would put a fresh buffer in the compiled region on every + # one of the 120 upserts per frame. + self._mask_written = torch.empty_like(written) + self._world_of_slot = ( + torch.arange(self.total_slots, dtype=torch.long, device=device) + // self.capacity + ) + self.frame_offsets = torch.arange(tokens_per_frame, dtype=torch.long, device=device) + self._current_base = self.frame_offsets + self.ring_len + + @property + def memory_bytes(self) -> int: + """Resident bytes of KV storage, all worlds (the bool/index buffers are + noise).""" + return self.kv.numel() * self.kv.element_size() + + def world_span(self, world_idx: int) -> tuple[int, int]: + """``[lo, hi)`` token slots owned by ``world_idx``. + + A host ``int`` here, unlike everywhere on the forward path: the two + callers below are host-side lifecycle (a rollout ending, capture + tearing down), never inside a captured region. + """ + if not 0 <= world_idx < self.num_worlds: + raise IndexError( + f"world_idx {world_idx} out of range for {self.num_worlds} worlds." + ) + lo = world_idx * self.capacity + return lo, lo + self.capacity + + def reset(self, world_idx: int) -> None: + """Drop ONE world's state and re-arm its scratch tail.""" + lo, hi = self.world_span(world_idx) + self.kv[:, :, :, lo:hi].zero_() + self.written[lo:hi].zero_() + self.written[lo + self.ring_len : hi].fill_(True) + + def upsert( + self, kv: Tensor, frame_pos: Tensor, commit: bool, world_idx: Tensor + ) -> tuple[Tensor, Tensor, Tensor]: + """``kv`` is ``[2, 1, H_kv, tokens_per_frame, D]`` for exactly one frame + of one world; + ``commit`` writes the frame into its ring slot; without it the frame + lands in that world's scratch tail only, visible to itself and to + nothing later. + + Returns ``(k, v, visible)`` + """ + tokens = self.tokens_per_frame + + if not torch.compiler.is_compiling(): + torch._check( + kv.size(3) == tokens, + lambda: f"ring cache expects exactly one frame per upsert; got {kv.size(3)} tokens", + ) + torch._check( + frame_pos.ndim == 0 and frame_pos.dtype == torch.int64, + lambda: f"frame_pos must be a [] int64 tensor; got {tuple(frame_pos.shape)} " + f"{frame_pos.dtype}", + ) + torch._check( + tuple(world_idx.shape) == (1,) and world_idx.dtype == torch.int64, + lambda: f"world_idx must be a [1] int64 tensor; got " + f"{tuple(world_idx.shape)} {world_idx.dtype}", + ) + world_base = world_idx * self.capacity + bucket = (frame_pos + (self.pinned_dilation - 1)) // self.pinned_dilation + slot = bucket % self.ring_buckets + ring_idx = self.frame_offsets + slot * tokens + world_base + current_idx = self._current_base + world_base + ring_scatter(self.kv, self.written, current_idx, kv, False) + + write_step = frame_pos.remainder(self.pinned_dilation) == 0 + mask_written = self._mask_written + mask_written.copy_(self.written) + mask_written &= self._world_of_slot == world_idx + mask_written[ring_idx] = mask_written[ring_idx] & ~write_step + + if commit: + dst = torch.where(write_step, ring_idx, current_idx) + ring_scatter(self.kv, self.written, dst, kv, True) + + k, v = self.kv.unbind(0) + # ALIASING HAZARD. The third return value IS `self._mask_written`, this + # layer's preallocated scratch, handed out by reference and overwritten + # in place by the next `upsert` on this layer. A consumer that stashes + # it and reads it later reads some *later* frame's visibility -- which + # is a mask off by one or more frames, and with worlds resident it can + # now also be another world's mask entirely. + # + # The obligation on the consumer: read it (build the block mask, or + # clone it) before the next upsert on this same layer. The 4+1 schedule + # satisfies that trivially. + return k, v, mask_written diff --git a/mstar/engine/resources/kv/ring/manager.py b/mstar/engine/resources/kv/ring/manager.py new file mode 100644 index 000000000..4a0468d0d --- /dev/null +++ b/mstar/engine/resources/kv/ring/manager.py @@ -0,0 +1,390 @@ +"""The paged ``KVManager`` reserves pages per step and hands them back; +this one reserves nothing. Every layer's ring is allocated once at +``build`` and reused for the life of the process,. +""" + +import torch +from torch import Tensor + +from mstar.engine.resources.base import ( + AttentionResource, + CGSlotSpec, + EngineResourceInfo, +) +from mstar.engine.resources.kv.config import KVSpec, RingKVConfig, RingKVStep +from mstar.engine.resources.kv.ring.cache import LayerRingCache +from mstar.engine.resources.spec import ResourceReqConfig +from mstar.engine.resources.step import ( + ADMIT_OK, + AdmitOutcome, + AdmitRuntimeError, + ResourceStep, + StepContext, +) + +__all__ = ["RingKVManager"] + + +class RingKVManager(AttentionResource): + """Per-layer ring caches holding ``num_worlds`` worlds, one per request.""" + + def __init__( + self, + config: RingKVConfig, + name: str, + device: torch.device, + dtype: torch.dtype = torch.bfloat16, + ): + self.config = config + self.name = name + self.device = torch.device(device) + self.dtype = dtype + + self.layers = [ + LayerRingCache( + num_worlds=config.num_worlds, + n_kv_heads=config.num_kv_heads, + ring_frames=layer.ring_frames, + ring_buckets=layer.ring_buckets, + d_head=config.head_dim, + tokens_per_frame=config.tokens_per_frame, + pinned_dilation=layer.pinned_dilation, + dtype=dtype, + device=self.device, + ) + for layer in config.layers + ] + + self._worlds: dict[str, int] = {} + self._free_worlds: set[int] = set(range(config.num_worlds)) + self._known_rids: set[str] = set() + self._last_frames: dict[str, int] = {} + self._static_world_idx = torch.zeros(1, dtype=torch.int64, device=self.device) + + @classmethod + def build(cls, spec: KVSpec, info: EngineResourceInfo) -> "RingKVManager": + config = spec.config + if not isinstance(config, RingKVConfig): + raise TypeError( + f"{cls.__name__} needs a RingKVConfig; got {type(config).__name__}. " + "KVSpec dispatches on the config, so this means the spec was " + "built by hand with the wrong one." + ) + if info.joint_comm_group is not None: + config.shard(info.joint_comm_group.world_size) + return cls( + config=config, + name=spec.resource_key, + device=info.device, + dtype=info.kv_dtype, + ) + + # ---- Model-facing API ------------------------------------------------- + + @property + def tokens_per_frame(self) -> int: + return self.config.tokens_per_frame + + @property + def num_worlds(self) -> int: + """How many requests can hold a world here at once.""" + return self.config.num_worlds + + def capacity(self, layer_idx: int) -> int: + """Token slots ONE world owns in ``layer_idx``'s ring, scratch frame + included. ``total_slots`` is the whole buffer.""" + return self.layers[layer_idx].capacity + + def total_slots(self, layer_idx: int) -> int: + """Token slots in ``layer_idx``'s buffer across every world -- the + length of the KV view ``upsert`` returns and of its visibility row.""" + return self.layers[layer_idx].total_slots + + def world_of(self, rid: str) -> int | None: + """``rid``'s world index, or None if it holds none. Host-side + introspection; the forward reads the staged tensor, never this.""" + return self._worlds.get(rid) + + def upsert( + self, k: Tensor, v: Tensor, layer_idx: int, frame_pos: Tensor, *, commit: bool + ) -> tuple[Tensor, Tensor, Tensor]: + """Write one frame's K/V for ``layer_idx`` and return what to attend to. + + ``k``/``v`` are ``[1, H_kv, tokens_per_frame, D]``. ``k`` is already + RoPE'd and RMS-normed and ``v`` is post value-residual lerp: the cache + stores post-RoPE keys, so replayed history is never re-rotated. + Returns ``(k_all, v_all, visible)``, the first two spanning + the whole buffer, every resident world and the third a + ``[total_slots]`` bool row that is False everywhere outside the calling + request's own world. + + ``frame_pos`` and ``commit`` are both arguments rather than resource + state, for the same reason. ``frame_pos`` is the ``[]`` int64 ring clock + -- not a slot id -- and alone determines the slot written and the + visibility row; + """ + # `layer_idx` is Python-level (it indexes a list of differently-shaped + # rings), so indexing on it is graph-safe. + kv = torch.stack([k, v], dim=0) + return self.layers[layer_idx].upsert( + kv, frame_pos, commit, self._static_world_idx + ) + + def _reset_world(self, rid: str) -> None: + """Zero ``rid``'s world and drop its clock, leaving its claim in place.""" + world_idx = self._worlds.get(rid) + if world_idx is None: + return + for layer in self.layers: + layer.reset(world_idx) + self._last_frames.pop(rid, None) + + def _release_world(self, rid: str) -> None: + """Hand ``rid``'s world back to the pool. Zero it first.""" + self._reset_world(rid) + world_idx = self._worlds.pop(rid, None) + if world_idx is not None: + self._free_worlds.add(world_idx) + + @torch.no_grad() + def get_state(self, rid: str) -> dict: + """Snapshot one request's world. Cloned, so the caller can hold it + across further rollout steps that mutate the rings in place. + """ + world_idx = self._require_world(rid, "get_state") + layers = [] + for layer in self.layers: + lo, hi = layer.world_span(world_idx) + layers.append(( + layer.kv[:, :, :, lo:hi].detach().clone(), + layer.written[lo:hi].detach().clone(), + )) + return {"layers": layers} + + @torch.no_grad() + def load_state(self, rid: str, state: dict) -> None: + """Restore one world's contents into the existing allocation.""" + + world_idx = self._require_world(rid, "load_state") + layers = state["layers"] + if len(layers) != len(self.layers): + raise ValueError( + f"state has {len(layers)} layers, ring has {len(self.layers)}." + ) + for i, (layer, (kv, written)) in enumerate(zip(self.layers, layers, strict=True)): + lo, hi = layer.world_span(world_idx) + span = layer.kv[:, :, :, lo:hi] + if tuple(kv.shape) != tuple(span.shape): + raise ValueError( + f"layer {i} state shape {tuple(kv.shape)} != one world's ring " + f"shape {tuple(span.shape)}." + ) + span.copy_(kv) + layer.written[lo:hi].copy_(written) + self._last_frames.pop(rid, None) + + def _require_world(self, rid: str, what: str) -> int: + world_idx = self._worlds.get(rid) + if world_idx is None: + raise KeyError( + f"ring KV {self.name!r} has no world for request {rid!r}; " + f"{what} is per request and a request that never admitted owns " + "no span to read or write." + ) + return world_idx + + # ---- Introspection ---------------------------------------------------- + + def memory_bytes(self) -> int: + """Total resident ring bytes across all layers and all worlds.""" + return sum(layer.memory_bytes for layer in self.layers) + + # ---- Resource lifecycle ----------------------------------------------- + + def _step_frames(self, step: ResourceStep) -> dict[str, int]: + """This step's declared ring clock per request id.""" + + if not isinstance(step, RingKVStep): + raise TypeError( + f"ring KV {self.name!r} was declared a {type(step).__name__}; it " + "needs a RingKVStep, the step that carries `frames`. A KVStep " + "admits and commits without ever checking the ring clock." + ) + return dict(step.frames) + + def ingest_request(self, rid: str, overrides: ResourceReqConfig | None = None) -> None: + """Register ``rid``. Deliberately does not claim a world.""" + + del overrides # no per-request tunables: the ring geometry is fixed + self._known_rids.add(rid) + + def admit(self, step: ResourceStep, ctx: StepContext) -> AdmitOutcome: + """Claim a world for this step's request, or refuse it terminally. + Does not support eviction for now + """ + # `request_ids`, not `padded_request_ids` as the paged manager uses: a + # padding row is a dummy rid a replay pads a bucket out to, and it must + # not be able to take a world from the real request in the same batch. + rids = list(ctx.request_ids) + + if len({*rids}) > 1: + return AdmitOutcome( + ok=False, ready=False, + reason=AdmitRuntimeError( + f"ring KV {self.name!r} was handed a batch of {len(set(rids))} " + f"requests ({sorted(set(rids))}); one step advances one world, " + "so max_batch_size must be 1. This is a step-batch limit, not " + "a ring limit -- the ring holds " + f"{self.num_worlds} worlds and they take turns across steps." + ), + ) + + frames = self._step_frames(step) + + # Check every rid before claiming any world: a refused admit must not + # leave a world half-claimed by the first rid of a batch it rejected. + wanted = {rid for rid in rids if rid not in self._worlds} + for rid in sorted(wanted): + if rid not in self._known_rids: + return AdmitOutcome( + ok=False, ready=False, + reason=AdmitRuntimeError( + f"ring KV {self.name!r} was asked to admit request {rid!r}, " + "which was never ingested. Worlds are handed back by " + "`remove_request`, which only ever runs for a request the " + "engine opened -- so a world claimed here would never " + "return to the pool and the node would lose capacity with " + "nothing raised." + ), + ) + if len(wanted) > len(self._free_worlds): + return AdmitOutcome( + ok=False, ready=False, + reason=AdmitRuntimeError( + f"ring KV {self.name!r} holds all {self.num_worlds} of its " + f"worlds ({sorted(self._worlds)}); request(s) {sorted(wanted)} " + "cannot be served concurrently. The rings are a fixed " + "physical buffer and there is nothing to evict -- raise " + "`resources.kv.num_worlds` (and `max_concurrent_requests` " + "with it) to serve more." + ), + ) + + for rid in rids: + frame = frames.get(rid) + if frame is None: + return AdmitOutcome( + ok=False, ready=False, + reason=AdmitRuntimeError( + f"ring KV {self.name!r} was handed a step declaring no ring " + f"clock for request {rid!r} (it names {sorted(frames)}). " + "Every admitted request needs one: the continuity check is " + "the only thing standing between a stalled clock and a " + "world that rewrites its own history." + ), + ) + last = self._last_frames.get(rid) + if last is not None and frame != last + 1: + return AdmitOutcome( + ok=False, ready=False, + reason=AdmitRuntimeError( + f"ring KV {self.name!r} last committed frame " + f"{last} for request {rid!r}, so the next one must be " + f"{last + 1}; this step declares frame {frame}. " + "The ring clock advances by exactly one per committed frame " + "-- a skipped or repeated frame selects the wrong ring slot " + "and hides the wrong one, which rewrites history rather than " + "raising." + ), + ) + + for rid in rids: + if rid not in self._worlds: + world_idx = min(self._free_worlds) + self._free_worlds.remove(world_idx) + self._worlds[rid] = world_idx + return ADMIT_OK + + def plan(self, step: ResourceStep, ctx: StepContext) -> None: + """Stage this step's world index. Its ring addresses stay in the graph.""" + + del step + rids = {*ctx.request_ids} + if len(rids) != 1: + raise ValueError( + f"ring KV {self.name!r} can stage one world index per step and " + f"this step names {sorted(rids)}. `admit` refuses a mixed batch " + "for the same reason; reaching here means it was bypassed." + ) + rid = rids.pop() + world_idx = self._require_world(rid, "plan") + # `fill_`, not a `copy_` from a fresh host tensor: same in-place write + # through the address the graph baked, without allocating a staging + # tensor 24 times a second. + self._static_world_idx.fill_(world_idx) + + def commit(self, step: ResourceStep, ctx: StepContext) -> None: + """Record the frame each request just committed. Metadata only.""" + frames = self._step_frames(step) + for rid in ctx.request_ids: + frame = frames.get(rid) + if frame is not None: + self._last_frames[rid] = frame + + def reset_request(self, rid: str, free: bool = False) -> None: + """Zero ``rid``'s world and release its claim. + ``free`` is ignored, there is no physical allocation to hand back. + """ + del free + self._release_world(rid) + + def remove_request(self, rid: str) -> None: + """The request is gone: drop its world, its claim, and its registration.""" + self._release_world(rid) + self._known_rids.discard(rid) + + # `supports_preplan` stays the inherited False. + + def build_cuda_graph_buffers( + self, slots: list[CGSlotSpec], max_bs: int, max_seq_len: int + ) -> None: + """No-op: every buffer a replay touches was allocated at ``build``, + ``_static_world_idx`` included. + """ + del slots, max_bs, max_seq_len + + def post_warmup_validate(self) -> None: + """Capture must leave every world exactly as it found it.""" + + if self._worlds: + raise RuntimeError( + f"ring KV {self.name!r} is still claimed by {sorted(self._worlds)} " + "after CUDA graph capture; a capture dummy rid was never reset, " + "and the world it holds is gone from the pool for good." + ) + if len(self._free_worlds) != self.num_worlds: + raise RuntimeError( + f"ring KV {self.name!r} has {len(self._free_worlds)} of " + f"{self.num_worlds} worlds free after CUDA graph capture; a world " + "was zeroed and never returned to the pool, so the node has " + "silently lost concurrency." + ) + if self._last_frames: + raise RuntimeError( + f"ring KV {self.name!r} recorded committed frames " + f"({sorted(self._last_frames.items())}) during CUDA graph capture; " + "capture drives admit and plan but must never commit, and the " + "first real request will be refused at admit unless it happens to " + "declare the very next frame." + ) + for i, layer in enumerate(self.layers): + history = layer.written.view(layer.num_worlds, layer.capacity)[:, : layer.ring_len] + if bool(layer.kv.any()) or bool(history.any()): + raise RuntimeError( + f"ring KV {self.name!r} layer {i} still holds capture-time " + "frames after warmup; the first rollout would attend to them " + "as real history." + ) + + def cleanup(self) -> None: + return diff --git a/mstar/model/registry.py b/mstar/model/registry.py index cd1cd883f..367e1ca13 100644 --- a/mstar/model/registry.py +++ b/mstar/model/registry.py @@ -15,6 +15,7 @@ "vjepa2": ("mstar.model.vjepa2.vjepa2_model", "VJepa2Model"), "vjepa2_ac": ("mstar.model.vjepa2.vjepa2_model", "VJepa2ACModel"), "wan22": ("mstar.model.wan22.wan22_model", "Wan22Model"), + "waypoint": ("mstar.model.waypoint.waypoint_model", "WaypointModel"), "whisper_large": ("mstar.model.whisper.whisper_model", "WhisperModel"), } @@ -54,6 +55,12 @@ # Wan2.2-TI2V-5B (dense video DiT + UMT5-XXL + Wan2.2-VAE). TI2V-5B # only; the A14B MoE variants are a separate follow-up. "wan22": {"model_path_hf": "Wan-AI/Wan2.2-TI2V-5B-Diffusers"}, + # Waypoint-1.5-1B (autoregressive video world model). The key pins the + # 720P variant, which is the checkpoint's default `variant`; the 360P + # sibling is the same weights under a different latent grid, selected with + # `model_kwargs: {variant: waypoint-1.5-1b-360p}`. The TAEHV decoder lives + # in a separate repo (`config.ae_uri`) and is not loaded yet. + "waypoint": {"model_path_hf": "Overworld/Waypoint-1.5-1B"}, # Whisper works for any size; the registry key pins large-v3, the # standard ASR-benchmark checkpoint. "whisper_large": {"model_path_hf": "openai/whisper-large-v3"}, diff --git a/mstar/model/waypoint/components/__init__.py b/mstar/model/waypoint/components/__init__.py index 20eccd866..e60758b05 100644 --- a/mstar/model/waypoint/components/__init__.py +++ b/mstar/model/waypoint/components/__init__.py @@ -7,14 +7,23 @@ it takes the patched **fused QKV** and keeps the unpatched **packed** ``MLPFusion.fc1``, because that patch *splits* the packed weight rather than merging it, and packed is the checkpoint's own storage. Both forms are -algebraically identical (measured 0.0 either way). See -``docs/waypoint/CONTRACTS.md`` section 5. +algebraically identical (measured 0.0 either way). -``kv_backend.py`` is deliberately *not* part of the module tree: the ring KV -cache is plain classes holding eagerly-allocated tensors, so a meta build and -``to_empty`` cannot touch it, and so it can be swapped for an engine-owned cache -behind ``WaypointKVBackend`` without the DiT noticing (backlog B1). The TAEHV -streaming VAE (``taehv.py``) is phase 7 and does not exist yet. +**Nothing here owns the world state.** The ring KV cache and the FlexAttention +kernel are engine resources (``engine/resources/kv/ring/``, +``engine/resources/attn/flex.py``), bound onto ``WaypointDiT`` and its 24 +``WaypointAttention`` layers at load by ``NodeSubmodule.bind_node_resources``. +That keeps the rings out of the module tree, where ``to_empty(device)``, +``state_dict()`` and the weight loader would each have a buffer of ours to leave +holding garbage. The superseded model-owned implementation (``kv_backend.py``, +with its ``FlexRingBackend`` and ``WaypointKVBackend`` protocol) was deleted +once the equivalence gate against it passed bit-exactly; ``git show +d31c3e70:mstar/model/waypoint/components/kv_backend.py`` is the last version. + +``ring_memory_bytes`` / ``describe_ring_memory`` moved up a level to +``waypoint/ring_geometry.py``: they are ``WaypointConfig`` arithmetic for +sizing a deployment and never needed a component to exist. The TAEHV streaming +VAE (``taehv.py``) is phase 7 and does not exist yet. """ from mstar.model.waypoint.components.attention import WaypointAttention @@ -23,15 +32,6 @@ WaypointDiTBlock, WaypointPosIds, ) -from mstar.model.waypoint.components.kv_backend import ( - FlexRingBackend, - LayerRingCache, - WaypointKVBackend, - describe_ring_memory, - flex_attention_masked, - make_block_mask, - ring_memory_bytes, -) from mstar.model.waypoint.components.layers import ( FP32_MODULE_PATHS, MLP, @@ -58,8 +58,6 @@ "CondHead", "ControllerInputEmbedding", "DeviceTableCache", - "FlexRingBackend", - "LayerRingCache", "MLPFusion", "NoiseConditioner", "OrthoRoPE", @@ -67,14 +65,9 @@ "WaypointAttention", "WaypointDiT", "WaypointDiTBlock", - "WaypointKVBackend", "WaypointPosIds", "ada_gate", "ada_rmsnorm", "apply_ortho_rope", - "describe_ring_memory", - "flex_attention_masked", - "make_block_mask", - "ring_memory_bytes", "rms_norm", ] diff --git a/mstar/model/waypoint/components/kv_backend.py b/mstar/model/waypoint/components/kv_backend.py deleted file mode 100644 index a0a6f0092..000000000 --- a/mstar/model/waypoint/components/kv_backend.py +++ /dev/null @@ -1,562 +0,0 @@ -"""Waypoint's ring KV cache and the model-local attention backend seam. - -The cache is not an optimization here, it *is* the world state: Waypoint -denoises one latent frame at a time and everything the model knows about the -past lives in these rings. Evicting a slot is not a cache miss, it is amnesia, -and a position bug does not raise -- it produces plausible, smoothly drifting -video. See ``docs/waypoint/CONTRACTS.md`` sections 1-3, which this file is the -implementation of. - -Facts the rest of the port depends on: - - * **Per-layer heterogeneous geometry.** 18 local layers hold 16 consecutive - frames; 6 global layers hold 16 frames spaced 8 apart, spanning 128 frames - of history. Every layer carries one extra *scratch* frame at the tail, so - capacity is ``(ring_frames + 1) * tokens_per_frame``. All of it comes from - ``WaypointConfig``; nothing is re-derived here. - * **4+1 passes per frame.** The four Euler denoise passes run with - ``is_frozen=True`` and touch only the scratch frame -- they must not mutate - the ring, because each attends to a different noisy version of the same - frame. The fifth pass (sigma=0, ``is_frozen=False``) is the only writer. - * **The scratch write is unconditional**, frozen passes included. It is the - entire mechanism by which the frame being denoised attends to itself. - * **Global layers commit on one frame in eight.** - ``torch.where(write_step, ring_idx, current_idx)`` redirects a - non-committing global write back into the scratch slot it just wrote, so it - commits nothing. That redirect *is* the dilation, expressed without - data-dependent control flow. - * **The mask hides the ring slot this frame is about to overwrite**, on - frozen and unfrozen passes alike, so the current frame never attends to the - stale frame it is replacing -- and so all five passes of a frame see - byte-identical KV. - * The ``BlockMask`` is query-uniform and full-blocks-only (no partial blocks, - and a no-op ``mask_mod``), which is what makes "capacity must be a multiple - of 128" a real constraint rather than a convenience. - -Deviation from the reference (``world_engine/src/model/kv_cache.py``), argued in -CONTRACTS section 2.4: global rings are **compacted** to their 16 addressable -slots. The reference allocates 128 frame slots per global layer but -``slot = bucket % 16`` can never address past the 16th, so 7/8 of every global -ring is permanently unwritten and permanently masked off. Dropping never-written -blocks is bit-exact -- ``BlockMask.from_kv_blocks`` orders visited blocks by a -*stable* descending argsort truncated to the visited count, so trailing ``False`` -entries neither enter the visited list nor perturb the order of the ``True`` -ones, and attention accumulates over the same blocks in the same order. It saves -1.31 GiB (see ``describe_ring_memory``). Set ``WaypointConfig.full_global_ring`` -to restore the reference allocation for an A/B parity run. - -The one thing compaction changes structurally: the bucket count can no longer be -derived from the buffer length. The reference computes -``num_buckets = (L // tpf) // dilation``, which is only correct because ``L`` is -8x oversized; against a compacted ring it would yield 2 instead of 16 and shred -the history. ``LayerRingCache`` therefore takes ``ring_buckets`` as an explicit -argument, sourced from ``WaypointConfig.ring_buckets``. - -Nothing in this module is an ``nn.Module``. The rings are DERIVED state, never -checkpoint state: keeping them out of the DiT's module tree means -``to_empty(device)``, ``state_dict()`` and the weight loader have no buffer of -ours to leave holding garbage (the stance ``wan22.components.dit.Wan22RoPE3D`` -takes for its RoPE tables). Storage is therefore allocated **eagerly and -explicitly** on the device handed to the constructor rather than lazily on first -use -- the backend is built after the DiT has been materialized, so there is no -meta-device phase to defer past, and eager allocation means a rollout cannot -discover halfway through that it is 800 MiB short of VRAM. -""" - -import dataclasses -from typing import Any, Protocol, runtime_checkable - -import torch -from torch import Tensor -from torch.nn.attention.flex_attention import ( - _DEFAULT_SPARSE_BLOCK_SIZE, - BlockMask, - flex_attention, -) - -from mstar.model.waypoint.config import WaypointConfig - -__all__ = [ - "FlexRingBackend", - "LayerRingCache", - "WaypointKVBackend", - "describe_ring_memory", - "flex_attention_masked", - "make_block_mask", - "ring_memory_bytes", -] - - -# CORRECTNESS, not speed. Our BlockMask carries a NO-OP `mask_mod`: we pass -# `mask_mod=None` to `from_kv_blocks` and it substitutes `noop_mask`, so -# `bm.mask_mod` is a function returning True everywhere, not `None`. Visibility -# is therefore encoded *entirely* in the block index lists (`full_kv_indices` -# truncated to `full_kv_num_blocks`), which is what makes a ring expressible at -# all. The compiled kernel iterates exactly those blocks. The eager path does -# not -- it rebuilds the mask by evaluating `mask_mod` over the grid, and a noop -# mask_mod means "everything is visible", so eager attention silently reads -# every unwritten slot in the ring as a zero K/V and blends it in. -# -# Measured on the ported cache: eager output diverges from a masked-dense -# reference by 2.7e-01, while the compiled path matches it to 1.2e-07. Nothing -# raises. This is why the reference wraps both of its regions in -# @torch.compile(fullgraph=True) -- compilation is load-bearing for the *result* -# there too, not just the throughput. -# -# So the compile is pinned here rather than left to the caller: correctness must -# not depend on whether someone set `WaypointConfig.compile_dit`. See -# docs/waypoint/CONTRACTS.md section 2.3 and DECISIONS.md D10. -flex_attention_masked = torch.compile(flex_attention, dynamic=False) - - -@runtime_checkable -class WaypointKVBackend(Protocol): - """The seam between ``WaypointAttn`` and whatever owns the world state. - - ``mstar/engine/resources/attn/`` is deliberately not involved: Waypoint's - cache is model-owned, ``get_node_resources()`` returns ``[]``, and the - engine builds no KV resource for it. The node/walk topology is invariant - across that decision, so if engine-owned paged KV later becomes viable only - the implementation behind this protocol is replaced -- no graph reshape, no - submodule signature change. - - The opaque ``meta`` returned by ``upsert`` and consumed by ``attend`` is - what keeps the protocol independent of FlexAttention: ``FlexRingBackend`` - puts a ``BlockMask`` there, a paged backend would put a page table there, - and the attention module never has to know which. - """ - - def upsert( - self, - k: Tensor, - v: Tensor, - layer_idx: int, - frame_pos: Tensor, - ) -> tuple[Tensor, Tensor, Any]: - """Commit one frame's K/V for ``layer_idx`` and return what to attend to. - - ``k``/``v`` are ``[B, H_kv, tokens_per_frame, D]``. ``k`` is already - RoPE'd and RMS-normed and ``v`` is post value-residual lerp: the cache - stores post-RoPE keys, so replayed history is never re-rotated (see - CONTRACTS section 4.2). Returns ``(k_all, v_all, meta)`` spanning the - whole ring plus the scratch frame. - - **Why ``frame_pos`` is passed explicitly** and never derived from an - internal slot cursor: it is a ``[]`` int64 device tensor holding the - ring clock -- not a slot id -- and it alone determines both the ring - slot written and the visibility mask. Desynchronizing it from the - caller's clock does not raise; it silently rewrites history. An input - with that failure mode has to be an argument, not hidden state. - """ - ... - - def attend(self, q: Tensor, k: Tensor, v: Tensor, meta: Any, *, enable_gqa: bool) -> Tensor: - """Attend ``q`` ``[B, H_q, T, D]`` against the ``(k, v, meta)`` triple - returned by ``upsert``. Returns ``[B, H_q, T, D]``.""" - ... - - def set_frozen(self, frozen: bool) -> None: - """``True`` for the four denoise passes, ``False`` for the committing - pass. Python-level state on purpose: it gates a real branch, and - branching on it is graph-safe.""" - ... - - def reset(self) -> None: - """Drop the world state and re-freeze. A new rollout starts here.""" - ... - - def get_state(self) -> dict: ... - - def load_state(self, state: dict) -> None: ... - - -def make_block_mask(q_len: int, kv_len: int, written: Tensor) -> BlockMask: - """Build the query-uniform, full-blocks-only ``BlockMask`` over ``written``. - - ``written`` is ``[kv_len]`` bool, True where the ring holds valid KV. Both - lengths must be exact multiples of the 128-token sparse block size and - ``written`` must be block-aligned -- both hold because writes are whole - frames of 512 (or 128) tokens. - - Two properties are load-bearing. Every query block sees the same KV blocks, - so the ``[1, 1, 1, num_kv_blocks]`` row broadcasts over query blocks and - ``compute_q_blocks=False`` is safe. And every visible block is *full* - (no partial blocks at all), because frame-granular writes - mean "any token in the block is written" and "all of them are" coincide. - """ - block_size = _DEFAULT_SPARSE_BLOCK_SIZE - - if not torch.compiler.is_compiling(): - torch._check( - q_len % block_size == 0, - lambda: f"q_len ({q_len}) must be a multiple of block size ({block_size})", - ) - torch._check( - kv_len % block_size == 0, - lambda: f"kv_len ({kv_len}) must be a multiple of block size ({block_size})", - ) - - q_blocks = q_len // block_size - kv_blocks = kv_len // block_size - - written_blocks = written.view(kv_blocks, block_size) - block_any = written_blocks.any(-1) - if not torch.compiler.is_compiling(): - assert torch.equal(block_any, written_blocks.all(-1)), "written must be block-aligned" - - full_bm = block_any[None, :].expand(q_blocks, kv_blocks) - full_kv_num_blocks = full_bm.sum(dim=-1, dtype=torch.int32)[None, None].contiguous() - # Stable descending argsort: the visited list is this truncated to - # full_kv_num_blocks, so unwritten blocks sort to the tail and are never - # read. That is exactly why compacting the global ring is bit-exact. - full_kv_indices = ( - full_bm.argsort(dim=-1, descending=True, stable=True) - .to(torch.int32)[None, None] - .contiguous() - ) - - # No partial blocks at all -- these two exist only to satisfy the signature. - kv_num_blocks = torch.zeros((1, 1, q_blocks), dtype=torch.int32, device=written.device) - kv_indices = torch.zeros((1, 1, q_blocks, kv_blocks), dtype=torch.int32, device=written.device) - - return BlockMask.from_kv_blocks( - kv_num_blocks, - kv_indices, - full_kv_num_blocks, - full_kv_indices, - BLOCK_SIZE=block_size, - mask_mod=None, - seq_lengths=(q_len, kv_len), - compute_q_blocks=False, - ) - - -class LayerRingCache: - """One attention layer's ring: ``ring_frames`` frame slots of history plus - one scratch frame at the tail. - - Storage is a single ``[2, B, H_kv, capacity, D]`` tensor so that a commit is - one ``index_copy_`` for K and V together and the read is one ``unbind(0)`` - into two views -- no copy on the read path. - - ``ring_buckets`` is the number of *addressable* slots and is passed in, not - derived from ``ring_frames``: under the compacted global allocation the two - differ from the reference's relationship (16 slots over a 16-frame buffer at - stride 8, where the reference had 16 slots over a 128-frame buffer), and - re-deriving it would silently produce 2. - """ - - def __init__( - self, - batch: int, - n_kv_heads: int, - ring_frames: int, - ring_buckets: int, - d_head: int, - tokens_per_frame: int, - pinned_dilation: int, - dtype: torch.dtype, - device: torch.device | str, - ): - if pinned_dilation < 1: - raise ValueError(f"pinned_dilation must be >= 1; got {pinned_dilation}.") - if not 1 <= ring_buckets <= ring_frames: - raise ValueError( - f"ring_buckets ({ring_buckets}) must be in [1, ring_frames ({ring_frames})]; " - "the addressable slots have to fit inside the allocated ring." - ) - if tokens_per_frame % _DEFAULT_SPARSE_BLOCK_SIZE: - raise ValueError( - f"tokens_per_frame ({tokens_per_frame}) must be a multiple of the sparse " - f"block size ({_DEFAULT_SPARSE_BLOCK_SIZE}); the BlockMask has no partial blocks." - ) - - self.tokens_per_frame = tokens_per_frame - self.ring_frames = ring_frames - self.ring_buckets = ring_buckets - self.pinned_dilation = pinned_dilation - # ring_len is the reference's `L`: the history region, scratch excluded. - self.ring_len = ring_frames * tokens_per_frame - self.capacity = self.ring_len + tokens_per_frame - - self.kv = torch.zeros( - 2, batch, n_kv_heads, self.capacity, d_head, dtype=dtype, device=device - ) - - # The tail frame is permanently visible: it always holds the frame - # currently being denoised, so masking it would remove self-attention. - written = torch.zeros(self.capacity, dtype=torch.bool, device=device) - written[self.ring_len :] = True - self.written = written - # Preallocated scratch for the per-call visibility mask. Allocating it - # inside upsert would put a fresh buffer in the compiled region on every - # one of the 120 upserts per frame. - self._mask_written = torch.empty_like(written) - - self.frame_offsets = torch.arange(tokens_per_frame, dtype=torch.long, device=device) - self.current_idx = self.frame_offsets + self.ring_len - - @property - def memory_bytes(self) -> int: - """Resident bytes of KV storage (the bool/index buffers are noise).""" - return self.kv.numel() * self.kv.element_size() - - def reset(self) -> None: - self.kv.zero_() - self.written.zero_() - self.written[self.ring_len :].fill_(True) - - def upsert( - self, kv: Tensor, frame_pos: Tensor, is_frozen: bool - ) -> tuple[Tensor, Tensor, BlockMask]: - """``kv`` is ``[2, B, H_kv, tokens_per_frame, D]`` for exactly one frame; - ``frame_pos`` is a ``[]`` int64 device tensor (the ring clock). - - Ported statement for statement from the reference; CONTRACTS section 2.2 - lists the five ways to get it subtly wrong, all of which drift instead of - raising. Everything below is index arithmetic on device tensors -- - ``torch.where`` and ``index_copy_`` rather than a Python ``if`` -- because - this runs inside ``torch.compile(fullgraph=True)`` and a branch on a - tensor value would graph-break. Only ``is_frozen`` is Python-level. - """ - tokens = self.tokens_per_frame - - if not torch.compiler.is_compiling(): - torch._check( - kv.size(3) == tokens, - lambda: f"ring cache expects exactly one frame per upsert; got {kv.size(3)} tokens", - ) - torch._check( - frame_pos.ndim == 0 and frame_pos.dtype == torch.int64, - lambda: f"frame_pos must be a [] int64 tensor; got {tuple(frame_pos.shape)} " - f"{frame_pos.dtype}", - ) - - # Bucket rounds UP (+ dilation - 1), copying the reference verbatim. - # - # The rounding is in fact DEAD arithmetic here, and the comment that - # used to sit on this line -- "flooring rotates the whole history by one - # slot" -- was measured false: swapping ceil for floor changes nothing, - # 0.0 across a 26-frame rollout through two ring wraps. `ring_idx` is - # only ever *read* under `write_step` (below), and where - # `frame_pos % dilation == 0` the ceil and the floor agree. It is kept - # because the reference has it and this port does not silently - # "simplify" expressions it merely believes to be dead -- but do not - # mistake it for a live invariant. - bucket = (frame_pos + (self.pinned_dilation - 1)) // self.pinned_dilation - slot = bucket % self.ring_buckets - ring_idx = self.frame_offsets + slot * tokens - - # Unconditional, frozen passes included: this is how the frame being - # denoised attends to itself between Euler steps. - self.kv.index_copy_(3, self.current_idx, kv) - - # Hide the ring slot this frame is about to take over, so the current - # frame never attends to the stale frame it is replacing. Done on every - # pass -- the four frozen passes must see exactly the KV the committing - # pass will see -- but only where write_step, hence the `& ~write_step`. - write_step = frame_pos.remainder(self.pinned_dilation) == 0 - mask_written = self._mask_written - mask_written.copy_(self.written) - mask_written[ring_idx] = mask_written[ring_idx] & ~write_step - bm = make_block_mask(tokens, self.capacity, mask_written) - - if not is_frozen: - # On a global layer's 7-in-8 non-committing frames this redirects - # the commit onto the scratch slot that was just written with the - # same data, i.e. it commits nothing. That is the dilation, and it - # is a select rather than a branch so the graph stays whole. - # - # `is_frozen` is REDUNDANT under the shipped 4+1 schedule, measured: - # ignoring it entirely (committing on all five passes) gives 0.0 at - # the output AND a byte-identical ring, because the mask-hide above - # already blinds the current frame to this slot and the fifth pass - # rewrites the same `dst` last. That is a genuine no-op and not an - # untested patch: the same monkeypatch produces 5.8e-02 when it - # suppresses the commit instead, so it was demonstrably live. - # - # Do not delete it on that basis. It stops being redundant the - # moment anything reads the ring between the frozen passes and the - # cache pass, or the pass order changes, or two frames are in - # flight at once (B8). Redundant-under-this-schedule is not the - # same as unnecessary. - dst = torch.where(write_step, ring_idx, self.current_idx) - self.kv.index_copy_(3, dst, kv) - self.written[dst] = True - - k, v = self.kv.unbind(0) - return k, v, bm - - -class FlexRingBackend: - """``WaypointKVBackend`` over per-layer ring caches plus FlexAttention. - - **Why FlexAttention and not FlashInfer**, given that mstar has a paged - FlashInfer path: numerical parity. The reference runs ``flex_attention`` - with a full-block-only ``BlockMask``; a paged kernel changes the - accumulation order, which would make bit-exact comparison against the - reference impossible and defeat the point of the parity harness. - - Constructed after the DiT is materialized, with a real device -- see the - module docstring on why none of this rides through ``to_empty``. - """ - - def __init__( - self, - config: WaypointConfig, - device: torch.device | str, - *, - dtype: torch.dtype = torch.bfloat16, - batch_size: int = 1, - ): - self.config = config - self.dtype = dtype - self.device = torch.device(device) - self.batch_size = batch_size - # Convenience mirror of the config fact, so the attention module can - # read it off the backend; `attend` still takes it explicitly because - # the protocol says so and the caller owns its own head counts. - self.enable_gqa = config.enable_gqa - - self.layers = [ - LayerRingCache( - batch=batch_size, - n_kv_heads=config.n_kv_heads, - ring_frames=config.ring_frames(i), - ring_buckets=config.ring_buckets(i), - d_head=config.d_head, - tokens_per_frame=config.tokens_per_frame, - pinned_dilation=config.pinned_dilation(i), - dtype=dtype, - device=self.device, - ) - for i in range(config.n_layers) - ] - # A fresh backend is frozen: nothing may commit until the model's cache - # pass explicitly unfreezes it. - self._is_frozen = True - - # ---- WaypointKVBackend ------------------------------------------------ - - def upsert( - self, k: Tensor, v: Tensor, layer_idx: int, frame_pos: Tensor - ) -> tuple[Tensor, Tensor, BlockMask]: - # `layer_idx` is Python-level (it indexes a list of differently-shaped - # rings), so indexing on it is graph-safe. - kv = torch.stack([k, v], dim=0) - return self.layers[layer_idx].upsert(kv, frame_pos, self._is_frozen) - - def attend(self, q: Tensor, k: Tensor, v: Tensor, meta: BlockMask, *, enable_gqa: bool) -> Tensor: - # `flex_attention_masked`, never bare `flex_attention`: with a no-op - # `mask_mod` the eager path ignores the block mask entirely and attends - # to unwritten ring slots. See the note at its definition. - return flex_attention_masked(q, k, v, block_mask=meta, enable_gqa=enable_gqa) - - def set_frozen(self, frozen: bool) -> None: - self._is_frozen = bool(frozen) - - def reset(self) -> None: - for layer in self.layers: - layer.reset() - self._is_frozen = True - - @torch.no_grad() - def get_state(self) -> dict: - """Snapshot the world state. Cloned, so the caller can hold it across - further rollout steps that mutate the rings in place.""" - return { - "_is_frozen": self._is_frozen, - "layers": [ - (layer.kv.detach().clone(), layer.written.detach().clone()) - for layer in self.layers - ], - } - - @torch.no_grad() - def load_state(self, state: dict) -> None: - layers = state["layers"] - if len(layers) != len(self.layers): - raise ValueError( - f"state has {len(layers)} layers, backend has {len(self.layers)}." - ) - for i, (layer, (kv, written)) in enumerate(zip(self.layers, layers, strict=True)): - # Geometry mismatch (e.g. a 360p state into a 720p backend, or a - # compacted state into a full_global_ring backend) would otherwise - # surface as a copy_ broadcast error deep in the loop. - if tuple(kv.shape) != tuple(layer.kv.shape): - raise ValueError( - f"layer {i} state shape {tuple(kv.shape)} != ring shape " - f"{tuple(layer.kv.shape)}." - ) - layer.kv.copy_(kv) - layer.written.copy_(written) - self._is_frozen = bool(state.get("_is_frozen", True)) - - # ---- Introspection ---------------------------------------------------- - - def memory_bytes(self) -> int: - """Total resident ring bytes across all layers.""" - return sum(layer.memory_bytes for layer in self.layers) - - def describe(self) -> str: - """Human-readable geometry/footprint table, including what the other - setting of ``full_global_ring`` would cost.""" - return describe_ring_memory(self.config, batch_size=self.batch_size, dtype=self.dtype) - - -def ring_memory_bytes( - config: WaypointConfig, *, batch_size: int = 1, dtype: torch.dtype = torch.bfloat16 -) -> list[int]: - """Per-layer ring bytes for ``config``, computed without allocating anything - (so it can be called on a laptop while sizing a deployment).""" - per_slot = 2 * batch_size * config.n_kv_heads * config.d_head * dtype.itemsize - return [per_slot * config.kv_capacity(i) for i in range(config.n_layers)] - - -def _fmt_bytes(n: int) -> str: - return f"{n / 2**20:.1f} MiB" if n < 2**30 else f"{n / 2**30:.2f} GiB" - - -def describe_ring_memory( - config: WaypointConfig, *, batch_size: int = 1, dtype: torch.dtype = torch.bfloat16 -) -> str: - """Geometry and footprint of every ring, grouped local vs global, with the - counterfactual under the opposite ``full_global_ring`` setting. - - The compacted default is what a reviewer should see; the ``full_global_ring`` - line is the reference's allocation, 8/9ths of whose global storage is - permanently unwritten (CONTRACTS section 2.4). - """ - per_layer = ring_memory_bytes(config, batch_size=batch_size, dtype=dtype) - total = sum(per_layer) - - lines = [ - f"Waypoint ring KV variant={config.variant} batch={batch_size} dtype={dtype} " - f"full_global_ring={config.full_global_ring}" - ] - groups = ( - ("local ", [i for i in range(config.n_layers) if not config.is_global_layer(i)]), - ("global", sorted(config.global_layers)), - ) - for name, indices in groups: - if not indices: - continue - i = indices[0] - lines.append( - f" {name} x{len(indices):2d} " - f"{config.ring_frames(i):3d} ring frames @ stride {config.pinned_dilation(i)} " - f"({config.ring_buckets(i)} addressable) + 1 scratch " - f"= {config.kv_capacity(i):6d} tok " - f"= {_fmt_bytes(per_layer[i]):>9s}/layer " - f"= {_fmt_bytes(sum(per_layer[j] for j in indices)):>9s}" - ) - lines.append(f" total {_fmt_bytes(total)} ({total} bytes)") - - other = dataclasses.replace(config, full_global_ring=not config.full_global_ring) - other_total = sum(ring_memory_bytes(other, batch_size=batch_size, dtype=dtype)) - delta = other_total - total - lines.append( - f" full_global_ring={other.full_global_ring} would use {_fmt_bytes(other_total)} " - f"({'+' if delta > 0 else '-'}{_fmt_bytes(abs(delta))})" - ) - return "\n".join(lines) diff --git a/mstar/model/waypoint/components/layers.py b/mstar/model/waypoint/components/layers.py index 50d43de2c..4ec296655 100644 --- a/mstar/model/waypoint/components/layers.py +++ b/mstar/model/waypoint/components/layers.py @@ -20,7 +20,7 @@ it an ordinary ``nn.Module`` that runs its own body under ``autocast(enabled=False)`` on ``.float()`` inputs, and publishes ``FP32_MODULE_PATHS`` for ``WaypointDiT.cast_serving_dtypes()`` to re-pin - after the global bf16 cast. See ``docs/waypoint/CONTRACTS.md`` section 4.1. + after the global bf16 cast. The Fourier frequency table is derived state, not checkpoint state. The reference registers it as a non-persistent buffer, which under mstar's meta @@ -237,8 +237,7 @@ class MLPFusion(nn.Module): The split is at compute time only. Do not turn it into stored ``fc1_x`` / ``fc1_c`` parameters: the checkpoint stores them split, and the loader's job - is to ``cat(dim=1)`` them back into ``mlp.fc1`` (CONTRACTS section 6, - transform 7). + is to ``cat(dim=1)`` them back into ``mlp.fc1`` (loader transform 7). """ def __init__(self, config: WaypointConfig): @@ -285,12 +284,12 @@ class CondHead(nn.Module): constructor therefore yields 24 independent copies at serve time — no error, just 0.6B of duplicated resident weights and 23 blocks whose ``cond_proj`` the loader never fills. Once tied, ``named_parameters()`` deduplicates, so - the loader's completeness check sees block 0's set only, as CONTRACTS - section 6 assumes. + the loader's completeness check sees block 0's set only, which is what it + assumes. The checkpoint spells this head as two half-heads, ``attn_cond_head`` (indices 0..2) and ``mlp_cond_head`` (indices 3..5), with a ``bias_in`` on - each; the loader merges them and keeps the mlp one. See CONTRACTS section 6. + each; the loader merges them and keeps the mlp one. """ n_cond = 6 diff --git a/mstar/model/waypoint/config.py b/mstar/model/waypoint/config.py index de84035ea..fc8f9e066 100644 --- a/mstar/model/waypoint/config.py +++ b/mstar/model/waypoint/config.py @@ -16,7 +16,7 @@ stride 8. See ``global_layers`` / ``ring_frames``. * Every frame costs 5 forwards: 4 non-committing Euler denoise passes over ``scheduler_sigmas``, then 1 committing pass at sigma=0 that writes the - settled K/V into the ring. See ``docs/waypoint/CONTRACTS.md``. + settled K/V into the ring. """ from dataclasses import dataclass, field @@ -118,7 +118,7 @@ class WaypointConfig: # is bit-exact -- unwritten blocks are absent from the BlockMask, and the # stable argsort that orders the visited blocks is unaffected by trailing # False entries -- and saves ~1.35 GiB. Set True to restore the reference's - # allocation for an A/B parity run. See docs/waypoint/CONTRACTS.md. + # allocation for an A/B parity run. full_global_ring: bool = False # torch.compile the two OUTER regions (denoise pass, cache pass), matching @@ -127,9 +127,10 @@ class WaypointConfig: # This is a throughput knob only. It does NOT govern attention correctness: # the BlockMask carries a no-op mask_mod, so eager flex_attention ignores it and # attends to unwritten ring slots (measured: 2.7e-01 off a masked-dense - # reference, silently). kv_backend pins its own torch.compile around the + # reference, silently). The FLEX attention resource + # (engine/resources/attn/flex.py) pins its own torch.compile around the # flex_attention call for that reason. Unlike wan22, this model has no - # eager reference-equivalence mode -- see CONTRACTS.md section 2.3.1. + # eager reference-equivalence mode. compile_dit: bool = True # Guard rails the ported modules assert against, kept here so a drifting diff --git a/mstar/model/waypoint/ring_geometry.py b/mstar/model/waypoint/ring_geometry.py new file mode 100644 index 000000000..347f96b20 --- /dev/null +++ b/mstar/model/waypoint/ring_geometry.py @@ -0,0 +1,70 @@ +import dataclasses + +import torch + +from mstar.model.waypoint.config import WaypointConfig + +__all__ = ["describe_ring_memory", "ring_memory_bytes"] + + +def ring_memory_bytes( + config: WaypointConfig, *, num_worlds: int = 1, dtype: torch.dtype = torch.bfloat16 +) -> list[int]: + """Per-layer ring bytes for ``config``, computed without allocating anything + (so it can be called on a laptop while sizing a deployment). + + ``num_worlds`` is a straight multiplier: the world dimension is folded into + the token axis, so N worlds is N times one world's slots and the per-slot + arithmetic is untouched. That it is a multiplier is the whole reason + ``num_worlds`` is a deployment sizing knob and the geometry is not.""" + per_slot = 2 * num_worlds * config.n_kv_heads * config.d_head * dtype.itemsize + return [per_slot * config.kv_capacity(i) for i in range(config.n_layers)] + + +def _fmt_bytes(n: int) -> str: + return f"{n / 2**20:.1f} MiB" if n < 2**30 else f"{n / 2**30:.2f} GiB" + + +def describe_ring_memory( + config: WaypointConfig, *, num_worlds: int = 1, dtype: torch.dtype = torch.bfloat16 +) -> str: + """Geometry and footprint of every ring, grouped local vs global, with the + counterfactual under the opposite ``full_global_ring`` setting. + + The compacted default is what a reviewer should see; the ``full_global_ring`` + line is the reference's allocation, 8/9ths of whose global storage is + permanently unwritten. + """ + per_layer = ring_memory_bytes(config, num_worlds=num_worlds, dtype=dtype) + total = sum(per_layer) + + lines = [ + f"Waypoint ring KV variant={config.variant} worlds={num_worlds} dtype={dtype} " + f"full_global_ring={config.full_global_ring}" + ] + groups = ( + ("local ", [i for i in range(config.n_layers) if not config.is_global_layer(i)]), + ("global", sorted(config.global_layers)), + ) + for name, indices in groups: + if not indices: + continue + i = indices[0] + lines.append( + f" {name} x{len(indices):2d} " + f"{config.ring_frames(i):3d} ring frames @ stride {config.pinned_dilation(i)} " + f"({config.ring_buckets(i)} addressable) + 1 scratch " + f"= {config.kv_capacity(i):6d} tok " + f"= {_fmt_bytes(per_layer[i]):>9s}/layer " + f"= {_fmt_bytes(sum(per_layer[j] for j in indices)):>9s}" + ) + lines.append(f" total {_fmt_bytes(total)} ({total} bytes)") + + other = dataclasses.replace(config, full_global_ring=not config.full_global_ring) + other_total = sum(ring_memory_bytes(other, num_worlds=num_worlds, dtype=dtype)) + delta = other_total - total + lines.append( + f" full_global_ring={other.full_global_ring} would use {_fmt_bytes(other_total)} " + f"({'+' if delta > 0 else '-'}{_fmt_bytes(abs(delta))})" + ) + return "\n".join(lines) diff --git a/mstar/model/waypoint/weight_loader.py b/mstar/model/waypoint/weight_loader.py index 59df7e8cd..c587878f6 100644 --- a/mstar/model/waypoint/weight_loader.py +++ b/mstar/model/waypoint/weight_loader.py @@ -9,10 +9,10 @@ ``Module._apply`` has no cross-module memo, so ``to_empty(device)`` silently un-aliases the six ``cond_proj`` matrices that blocks 1..23 share with block 0. Nothing raises; the symptoms are +0.6B resident parameters and 23 blocks of -``cond_proj`` the loader never fills (``CONTRACTS.md`` section 6.1). +``cond_proj`` the loader never fills. -Authoritative key map: ``docs/waypoint/PARAM_TREE.md``. Thirteen transforms sit -between the checkpoint's 369 keys and this module's 174 parameters: +The key map. Thirteen transforms sit between the checkpoint's 369 keys and this +module's 174 parameters: === ============================================================== =========== T0 ``transformer.blocks.{i}.`` -> ``blocks.{i}.`` prefix @@ -30,13 +30,12 @@ T12 any ``.cond_heads.`` key (note the plural) drop === ============================================================== =========== -T0 is not in ``PARAM_TREE.md``: it assumes the reference's two-level -``WorldModel``/``WorldDiT`` split, whereas ``components/dit.py`` collapses them +T0 has no counterpart in the reference, which keeps a two-level +``WorldModel``/``WorldDiT`` split where ``components/dit.py`` collapses them into one ``WaypointDiT`` whose blocks live at ``blocks.{i}``. Both spellings are accepted here, as are the canonical post-transform spellings the reference's own -``pop``/``setdefault`` transforms tolerate (``PARAM_TREE.md`` section 3.5) — -which spelling the shipped file uses could not be established statically -(section 10.1), so guessing one was not an option. +``pop``/``setdefault`` transforms tolerate — which spelling the shipped file +uses could not be established statically, so guessing one was not an option. Three things this file does that the mstar machinery does not give you: @@ -50,12 +49,12 @@ * **Per-shard completeness.** ``load_weights_into`` returns *target* names, and q/k/v all share one target, so ``set(named_parameters()) - loaded`` is satisfied by any one of the three: a ``k_proj`` missing from every layer - passes the wan22-style check silently (``PARAM_TREE.md`` S11d). The remapper + passes the wan22-style check silently. The remapper therefore tallies ``(target, shard_id)`` pairs and the contract checks those too. Same hole, same fix, for ``fc1_x``/``fc1_c``. * **The reshaping transforms T1/T2 have no hook at all**, so they ride a thin - adapter over the shard iterator, which is also where the config facts - ``PARAM_TREE.md`` section 10.4 flags as transcribed-not-read (``n_kv_heads``, + adapter over the shard iterator, which is also where the config facts that + were transcribed rather than read off the checkpoint (``n_kv_heads``, ``patch``) get validated against the tensor shapes actually on disk. Completeness is a hard contract: a checkpoint key that reaches no parameter, a @@ -67,8 +66,8 @@ ``(target, shard_id)`` tally does not cover, and that both fusions have: * A **pre-fused** key (``attn.qkv_proj.weight``, ``ctrl_mlpfusion.mlp.fc1`` - — canonical spellings ``PARAM_TREE.md`` section 3.5 says the reference - tolerates) claims ``(target, None)``, which does not collide with + — the canonical spellings the reference tolerates) claims + ``(target, None)``, which does not collide with ``(target, "q")``. Left alone, a file carrying both spellings assembles one parameter out of both sources in whatever order the shard iterator happens to yield — Q and K off the fused blob, V off ``v_proj``, no error. So @@ -133,9 +132,9 @@ # without changing ``retie_cond_proj`` (which this file does not own) is not a # supported edit. # -# Block 0 is also what the reference's own __init__ ties to, what CONTRACTS -# section 6 and ``layers.CondHead``'s docstring prescribe, and what PARAM_TREE -# section 4.9's fill-forward loop uses as its reference. +# Block 0 is also what the reference's own __init__ ties to, what +# ``layers.CondHead``'s docstring says, and what PARAM_TREE section 4.9's +# fill-forward loop uses as its reference. # # The choice only matters if the 24 stored copies disagree, which is exactly the # silent divergence PARAM_TREE flags as S9 — so ``verify_cond_proj_tie`` checks @@ -695,9 +694,9 @@ def parameter_census(dit: WaypointDiT) -> tuple[int, int, int]: the 720P checkpoint this is ``(174, 1_281_958_040, 1_860_771_992)`` — the "1.28B resident / 1.86B stored" figures. - Those differ by 2,048 from ``PARAM_TREE.md`` section 5.2's 1,281,960,088 / - 1,860,774,040: that arithmetic carries ``ctrl_cfg.null_emb`` ``[1, 1, 2048]`` - in both totals, and the port drops it (T10). + Counting straight off the checkpoint gives 1,281,960,088 / 1,860,774,040, + 2,048 more in each: that arithmetic carries ``ctrl_cfg.null_emb`` + ``[1, 1, 2048]`` in both totals, and the port drops it (T10). """ params = dict(dit.named_parameters()) return ( @@ -730,8 +729,7 @@ def _assert_cond_proj_tied(dit: WaypointDiT, config: WaypointConfig) -> None: f"cond_proj is not tied: named_parameters() reports {len(tied)} cond_proj " f"tensors, expected {CondHead.n_cond} (one physical set, aliased by all " f"{config.n_layers} blocks). to_empty(device) un-ties them and " - "retie_cond_proj() must be called after it, not before (CONTRACTS " - "section 6.1)." + "retie_cond_proj() must be called after it, not before." ) # COND_PROJ_SOURCE_BLOCK is a record of which block retie_cond_proj aliases # the others onto, not a choice this file gets to make; assert it rather than @@ -845,7 +843,7 @@ def build_waypoint_dit( dit = WaypointDiT(config) dit.cast_serving_dtypes() dit.to_empty(device=device) - # MUST follow to_empty, which un-aliases the shared cond_proj (CONTRACTS 6.1). + # MUST follow to_empty, which un-aliases the shared cond_proj. dit.retie_cond_proj() _assert_cond_proj_tied(dit, config) @@ -866,7 +864,7 @@ def build_waypoint_dit( # (target, shard_id) -> the checkpoint key that claimed it. This is the # per-shard tally: load_weights_into's returned set holds target names only, # so q, k and v all collapse to one entry and a k_proj missing from every - # layer would satisfy `set(params) - loaded` (PARAM_TREE S11d). Recorded here + # layer would satisfy `set(params) - loaded`. Recorded here # rather than in _SliceShardLoader because the remapper is the one place that # sees both the original key (for the error message) and the resolved target. arrivals: dict[tuple[str, str | int | None], str] = {} diff --git a/test/modular/test_flex_attention_resource.py b/test/modular/test_flex_attention_resource.py new file mode 100644 index 000000000..259fbd572 --- /dev/null +++ b/test/modular/test_flex_attention_resource.py @@ -0,0 +1,544 @@ +"""The FlexAttention backend as an engine resource: what the factory accepts, +and what the kernel does with a ring's visibility row. + +Two different kinds of failure are pinned here, and both are silent. + +The first is a *pairing* failure. ``PagedKVConfig`` and ``RingKVConfig`` are +close enough in shape that handing one to the wrong backend gets past +construction: a paged planner walks a page table the ring does not have, a ring +view reaches a kernel that assumes monotonic append. Neither raises on its own, +so ``AttentionManager.build`` cross-checks both directions and these tests +assert it stays that way. + +The second is the eager-``flex_attention`` trap. The ``BlockMask`` carries a +no-op ``mask_mod``, so the +eager path rebuilds "everything is visible" and blends every unwritten slot in +as a zero K/V. That is why ``flex.flex_attention_masked`` is a pinned +``torch.compile`` and not a caller's choice, and the test below asserts *both* +halves: that the compiled path matches a masked-dense reference, and that bare +eager does not. If eager ever starts agreeing, the pin has stopped being +load-bearing and the argument for it needs re-deriving rather than the test +being deleted. + +A third section is not a failure but a *derisk*, and is labelled as one: the +world pool folds N worlds into the token axis of one ring, so the shape a +batched step would take is B queries against a stride-0 ``expand`` of a single +K/V with the per-world isolation moved into the BlockMask's leading dim. Nothing +ships that path today (the step batch is 1), but if it does not hold bit-exactly +then batching a step is not free and the design owes a different answer, so the +two properties it rests on are asserted rather than assumed. + +Checkpoint-free, and CPU except where a test is parametrized over the device: +``torch.compile(flex_attention)`` works on CPU in torch 2.9, at a few seconds of +inductor time on first use. +""" + +import sys + +import pytest +import torch + +sys.path.insert(0, ".") + +from torch.nn.attention.flex_attention import ( + _DEFAULT_SPARSE_BLOCK_SIZE, + BlockMask, + flex_attention, +) + +from mstar.engine.resources import ( + AttentionConfig, + AttentionSpec, + AttentionStep, + AttnBackend, + KVSpec, + StepContext, +) +from mstar.engine.resources.attn.base import AttentionManager +from mstar.engine.resources.attn.flex import ( + FlexAttentionManager, + flex_attention_masked, + make_block_mask, +) +from mstar.engine.resources.base import EngineResourceInfo +from mstar.engine.resources.kv.config import ( + PagedKVConfig, + RingKVConfig, + RingKVLayerConfig, +) + +BLOCK = _DEFAULT_SPARSE_BLOCK_SIZE # 128 +TPF = BLOCK # one frame per sparse block keeps the geometry readable +KV_LEN = 4 * BLOCK # 3 ring frames + 1 scratch +N_KV_HEADS = 2 +N_QO_HEADS = 4 +D_HEAD = 32 + + +def ring_config(n_layers: int = 2) -> RingKVConfig: + return RingKVConfig( + num_layers=n_layers, + num_kv_heads=N_KV_HEADS, + head_dim=D_HEAD, + num_qo_heads=N_QO_HEADS, + tokens_per_frame=TPF, + layers=tuple( + RingKVLayerConfig(ring_frames=3, ring_buckets=3, pinned_dilation=1) + for _ in range(n_layers) + ), + ) + + +def paged_config() -> PagedKVConfig: + return PagedKVConfig( + num_layers=2, + num_kv_heads=N_KV_HEADS, + head_dim=D_HEAD, + num_qo_heads=N_QO_HEADS, + max_seq_len=1024, + max_num_pages=16, + page_size=BLOCK, + ) + + +def build_attention(backend: AttnBackend, kv_config) -> AttentionManager: + """Drive the real spec-time factory, not the manager's constructor: the + config cross-check lives in ``build`` and is the thing under test.""" + kv_spec = KVSpec(resource_key="kv", nodes={"dit"}, config=kv_config) + attn_spec = AttentionSpec( + resource_key="attn", + nodes={"dit"}, + config=AttentionConfig(kv_cache="kv", backend=backend), + ) + info = EngineResourceInfo( + device=torch.device("cpu"), + kv_dtype=torch.float32, + dependencies={"kv": kv_spec}, + ) + return AttentionManager.build(attn_spec, info) + + +def visible_row(committed_blocks: tuple[int, ...]) -> torch.Tensor: + """A ring visibility row: whole blocks, plus the permanently visible + scratch frame at the tail (it holds the frame being denoised, so masking it + would remove self-attention).""" + visible = torch.zeros(KV_LEN, dtype=torch.bool) + for b in committed_blocks: + visible[b * BLOCK : (b + 1) * BLOCK] = True + visible[KV_LEN - BLOCK :] = True + return visible + + +def masked_dense_reference( + q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, visible: torch.Tensor +) -> torch.Tensor: + """``softmax(QK^T + mask) V`` written out, with non-visible KV positions + masked off. Deliberately not another flex call: the reference has to be + something whose masking cannot share a bug with the thing it checks.""" + q_heads = q.size(1) + kv_heads = k.size(1) + k = k.repeat_interleave(q_heads // kv_heads, dim=1) + v = v.repeat_interleave(q_heads // kv_heads, dim=1) + scores = (q @ k.transpose(-1, -2)) / (q.size(-1) ** 0.5) + scores = scores.masked_fill(~visible[None, None, None, :], float("-inf")) + return torch.softmax(scores, dim=-1) @ v + + +def qkv(visible: torch.Tensor, seed: int = 0xC0FFEE): + """Query, and a ring whose unwritten slots hold zeros — which is what an + unwritten ring slot actually holds, and why eager's blending of them is + wrong rather than merely different.""" + gen = torch.Generator().manual_seed(seed) + q = torch.randn(1, N_QO_HEADS, TPF, D_HEAD, generator=gen) + k = torch.zeros(1, N_KV_HEADS, KV_LEN, D_HEAD) + v = torch.zeros(1, N_KV_HEADS, KV_LEN, D_HEAD) + n = int(visible.sum()) + k[:, :, visible] = torch.randn(1, N_KV_HEADS, n, D_HEAD, generator=gen) + v[:, :, visible] = torch.randn(1, N_KV_HEADS, n, D_HEAD, generator=gen) + return q, k, v + + +# --------------------------------------------------------------------------- +# 1. The factory, and the config cross-check +# --------------------------------------------------------------------------- + + +def test_flex_backend_with_a_ring_kv_config_builds_the_flex_manager(): + manager = build_attention(AttnBackend.FLEX, ring_config()) + assert isinstance(manager, FlexAttentionManager) + assert manager.depends_on() == {"kv"} + + +def test_flex_backend_rejects_a_paged_kv_config(): + """A paged config behind the flex backend is not a shape error: every field + the manager reads early is present on both configs.""" + with pytest.raises(TypeError) as excinfo: + build_attention(AttnBackend.FLEX, paged_config()) + message = str(excinfo.value) + assert "flex" in message, "the error has to name the backend that was asked for" + assert "RingKVConfig" in message and "PagedKVConfig" in message + + +def test_paged_backend_rejects_a_ring_kv_config(): + """The other direction, which is the dangerous one: a ring's storage under + a backend that assumes monotonic append reads as history rewriting itself.""" + with pytest.raises(TypeError) as excinfo: + build_attention(AttnBackend.FLASHINFER, ring_config()) + message = str(excinfo.value) + assert "flashinfer" in message + assert "PagedKVConfig" in message and "RingKVConfig" in message + + +def test_requires_kv_write_is_false_for_flex_and_true_for_the_paged_backend(): + """The layer reads this to decide whether to call ``kv.write_kv``. For the + ring the answer is no — ``upsert`` already wrote the frame and returned the + view to attend against — and calling one would commit the frame twice.""" + flex_manager = build_attention(AttnBackend.FLEX, ring_config()) + assert flex_manager.requires_kv_write is False + + paged_manager = build_attention(AttnBackend.FLASHINFER, paged_config()) + assert paged_manager.requires_kv_write is True + + +def test_plan_clears_the_inherited_cursors(): + """``AttentionResource``'s contract: a step that never binds the label or + layer cursor must not inherit the previous step's.""" + manager = build_attention(AttnBackend.FLEX, ring_config()) + manager.set_default_label("stale") + manager.set_default_layer_idx(7) + manager.plan( + AttentionStep(), + StepContext(request_ids=("r0",), graph_walk="gen", slot=0, capture=False), + ) + assert manager.default_label == "main" + assert manager._default_layer_idx is None + + +# --------------------------------------------------------------------------- +# 2. The mask +# --------------------------------------------------------------------------- + + +def test_make_block_mask_rejects_unaligned_and_non_multiple_lengths(): + """Both constraints are real rather than defensive: the mask has no partial + blocks, so a length or a row that does not land on a block boundary would + silently round the visible region.""" + visible = visible_row((0,)) + with pytest.raises(RuntimeError, match="multiple of block size"): + make_block_mask(TPF + 1, KV_LEN, visible) + with pytest.raises(RuntimeError, match="multiple of block size"): + make_block_mask(TPF, KV_LEN - 1, visible[:-1]) + + ragged = torch.zeros(2 * BLOCK, dtype=torch.bool) + ragged[3] = True # a partial block, which no whole-frame write can produce + with pytest.raises(AssertionError, match="block-aligned"): + make_block_mask(TPF, ragged.numel(), ragged) + + +# --------------------------------------------------------------------------- +# 3. The kernel +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + ("label", "committed_blocks"), + [ + ("one_frame", (0,)), + ("gappy_ring", (0, 2)), + ], +) +def test_attend_matches_a_masked_dense_reference(label, committed_blocks): + visible = visible_row(committed_blocks) + q, k, v = qkv(visible) + manager = build_attention(AttnBackend.FLEX, ring_config()) + + got = manager.attend(q, k, v, visible, enable_gqa=True) + want = masked_dense_reference(q, k, v, visible) + + err = (got - want).abs().max().item() + print(f"[{label}] attend vs masked-dense = {err:.3e}") + assert got.shape == q.shape + assert err < 1e-5, f"FlexAttentionManager.attend diverged from masked dense ({err:.3e})" + + +def test_eager_flex_attention_does_not_honour_the_block_mask(): + """The reason ``flex_attention_masked`` is a ``torch.compile`` pinned + inside this module rather than a caller's choice. + + Same q/k/v, same ``BlockMask``, two callables: the compiled one iterates + the block index lists and matches the masked-dense reference; the eager one + re-derives visibility from the no-op ``mask_mod``, decides everything is + visible, and blends the unwritten (zero) ring slots in. Nothing raises + either way, so the guard has to be numeric. + + If both sides ever match, this test is pinning nothing: eager would have + been fixed upstream, the "unless someone removes the compile" argument + would have quietly stopped being tested, and it should be re-derived rather + than deleted. + """ + visible = visible_row((0,)) + q, k, v = qkv(visible) + bm = make_block_mask(TPF, KV_LEN, visible) + + dense = masked_dense_reference(q, k, v, visible) + compiled = flex_attention_masked(q, k, v, block_mask=bm, enable_gqa=True) + eager = flex_attention(q, k, v, block_mask=bm, enable_gqa=True) + + compiled_err = (compiled - dense).abs().max().item() + eager_err = (eager - dense).abs().max().item() + print(f"compiled vs masked-dense={compiled_err:.3e} eager vs masked-dense={eager_err:.3e}") + + assert compiled_err < 1e-5, ( + f"the compiled path diverged from the masked-dense reference ({compiled_err:.3e})" + ) + assert eager_err > 1e-2, ( + f"eager flex_attention agreed with the masked reference to {eager_err:.3e}; the " + "trap may have been fixed upstream, in which case re-derive " + "the guard rather than deleting it" + ) + + +def test_attend_takes_the_compiled_path(): + """The regression guard proper: swapping ``flex_attention_masked`` for bare + ``flex_attention`` inside ``attend`` turns this red, and nothing else + would.""" + visible = visible_row((0, 2)) + q, k, v = qkv(visible, seed=11) + bm = make_block_mask(TPF, KV_LEN, visible) + manager = build_attention(AttnBackend.FLEX, ring_config()) + + got = manager.attend(q, k, v, visible, enable_gqa=True) + want = flex_attention_masked(q, k, v, block_mask=bm, enable_gqa=True) + trap = flex_attention(q, k, v, block_mask=bm, enable_gqa=True) + + assert torch.equal(got, want), "attend is not going through flex_attention_masked" + assert (got - trap).abs().max().item() > 1e-2, ( + "attend's output is indistinguishable from the eager path; the mask is not honoured" + ) + + +def test_flex_attention_masked_is_a_compiled_callable(): + """If someone "simplifies" the pin back to the bare function, this fails + first and says why.""" + assert flex_attention_masked is not flex_attention + assert hasattr(flex_attention_masked, "_torchdynamo_orig_callable"), ( + "flex.flex_attention_masked must stay wrapped in torch.compile: with a no-op " + "mask_mod the eager kernel ignores the ring mask entirely" + ) + + +def test_masked_dense_reference_is_not_trivially_satisfied(): + """The reference above is only a reference if masking changes the answer; + an all-visible row would make every assertion in this file pass for the + wrong reason.""" + visible = visible_row((0,)) + q, k, v = qkv(visible) + all_visible = torch.ones_like(visible) + masked = masked_dense_reference(q, k, v, visible) + unmasked = masked_dense_reference(q, k, v, all_visible) + assert (masked - unmasked).abs().max().item() > 1e-2 + + +# --------------------------------------------------------------------------- +# 4. The batched step this backend is not asked for yet +# --------------------------------------------------------------------------- +# +# The world pool folds N worlds into the token axis of ONE ring, so the ring is +# `[2, 1, H_kv, N*capacity, D]` and world `w` owns `[w*capacity, (w+1)*capacity)`. +# Today the step batch is 1 and `attend` gets a single row, so isolation is a +# `[kv_len]` visibility row and this section is a derisk, not a contract. +# +# What it derisks is the *only* way batching a step can be made free. When B +# rows of one step attend against that ring, the K/V they share is byte-identical +# -- it is the same buffer -- so the honest shape for it is a stride-0 `expand` +# on dim 0, and the per-row isolation moves out of the tensor and into the +# BlockMask's leading dim: `[B, 1, q_blk, kv_blk]`, one visibility row per world. +# If that does not hold, batching a step means materializing B copies of an +# 816-MiB-per-world ring per layer per pass, which is not a batching strategy. +# +# Two things have to be true and neither is obvious: the compiled kernel must +# accept a batch dim whose stride is 0 (it can trivially not, and the failure +# would be a copy rather than an error), and the per-row answers must be +# *bit-identical* to the single-row calls the node makes today, or the ported +# numerics stop being comparable to the reference the moment concurrency is +# turned on. Both are asserted below, on CPU and on CUDA. + +WORLDS = 3 +CAPACITY = 2 * BLOCK # one history frame + the scratch frame, per world +FOLDED_KV = WORLDS * CAPACITY + + +def world_span(w: int) -> tuple[int, int]: + return w * CAPACITY, (w + 1) * CAPACITY + + +def folded_visible_row(w: int, *, history: bool, device) -> torch.Tensor: + """One world's visibility over the *folded* ring: its own scratch block + always, its own history block if it has committed one, and nothing outside + its span ever.""" + row = torch.zeros(FOLDED_KV, dtype=torch.bool, device=device) + lo, hi = world_span(w) + row[hi - BLOCK : hi] = True # scratch: the frame being denoised + if history: + row[lo : lo + BLOCK] = True + return row + + +def folded_block_mask(rows: list[torch.Tensor], q_len: int) -> "object": + """``make_block_mask`` with a batch dim: the same query-uniform, + full-blocks-only construction, stacked over rows. + + Deliberately written here and not in ``flex.py``. The shipped backend takes + a single ``[kv_len]`` row because the node takes a single row; adding an + unreachable batched path to the module would be an untested branch on the + serving path. This is the shape the future change would take, asserted + against today's kernel. + """ + b = len(rows) + q_blocks = q_len // BLOCK + kv_blocks = FOLDED_KV // BLOCK + device = rows[0].device + + per_row = torch.stack([r.view(kv_blocks, BLOCK).any(-1) for r in rows]) + assert torch.equal( + per_row, torch.stack([r.view(kv_blocks, BLOCK).all(-1) for r in rows]) + ), "written must be block-aligned" + + full_bm = per_row[:, None, :].expand(b, q_blocks, kv_blocks) + full_kv_num_blocks = full_bm.sum(dim=-1, dtype=torch.int32)[:, None].contiguous() + full_kv_indices = ( + full_bm.argsort(dim=-1, descending=True, stable=True) + .to(torch.int32)[:, None] + .contiguous() + ) + zeros_n = torch.zeros((b, 1, q_blocks), dtype=torch.int32, device=device) + zeros_i = torch.zeros((b, 1, q_blocks, kv_blocks), dtype=torch.int32, device=device) + + return BlockMask.from_kv_blocks( + zeros_n, + zeros_i, + full_kv_num_blocks, + full_kv_indices, + BLOCK_SIZE=BLOCK, + mask_mod=None, + seq_lengths=(q_len, FOLDED_KV), + compute_q_blocks=False, + ) + + +def folded_ring(device, seed: int = 0x5EED): + """A folded ring with every world's span filled with distinct noise, and B + queries. Distinct per span is the point: an output that is invariant to a + neighbour's bytes has actually been isolated, rather than reading zeros that + happened to contribute nothing.""" + gen = torch.Generator().manual_seed(seed) + k = torch.randn(1, N_KV_HEADS, FOLDED_KV, D_HEAD, generator=gen).to(device) + v = torch.randn(1, N_KV_HEADS, FOLDED_KV, D_HEAD, generator=gen).to(device) + q = torch.randn(WORLDS, N_QO_HEADS, TPF, D_HEAD, generator=gen).to(device) + return q, k, v + + +DEVICES = [ + "cpu", + pytest.param( + "cuda", + marks=pytest.mark.skipif( + not torch.cuda.is_available(), reason="needs a GPU" + ), + ), +] + + +@pytest.mark.parametrize("device", DEVICES) +def test_a_batched_step_can_share_one_ring_by_stride_0_expand(device): + """B rows over a stride-0 view of one ring, bit-identical to B single-row + calls, with zero bytes copied. + + ``torch.equal`` and not a tolerance, on purpose. A batched kernel is allowed + to tile the KV differently and land within 1e-6 of the serial answer, and + that would be a real divergence for this port: the whole argument for + FlexAttention here is that the accumulation order matches the reference's, + so a batch that reorders it silently costs the only comparison there is. + If this ever needs a tolerance, batching a step is not free and the finding + is that, not the tolerance. + """ + q, k, v = folded_ring(device) + rows = [ + folded_visible_row(w, history=(w != 1), device=torch.device(device)) + for w in range(WORLDS) + ] + + k_batched = k.expand(WORLDS, N_KV_HEADS, FOLDED_KV, D_HEAD) + v_batched = v.expand(WORLDS, N_KV_HEADS, FOLDED_KV, D_HEAD) + assert k_batched.stride(0) == 0 and v_batched.stride(0) == 0 + assert k_batched.data_ptr() == k.data_ptr(), "the expand copied the ring" + assert ( + k_batched.untyped_storage().data_ptr() == k.untyped_storage().data_ptr() + ), "the expand copied the ring" + + batched = flex_attention_masked( + q, k_batched, v_batched, block_mask=folded_block_mask(rows, TPF), enable_gqa=True + ) + assert batched.shape == q.shape + + manager = build_attention(AttnBackend.FLEX, ring_config()) + for w in range(WORLDS): + serial = manager.attend(q[w : w + 1], k, v, rows[w], enable_gqa=True) + assert torch.equal(batched[w : w + 1], serial), ( + f"world {w} in a batch of {WORLDS} differs from the same world served " + f"alone by {(batched[w:w+1] - serial).abs().max().item():.3e}; a batched " + "step would not be the same computation the node performs today" + ) + + +@pytest.mark.parametrize("device", DEVICES) +def test_a_batched_step_does_not_reach_across_world_spans(device): + """The isolation half, and the one that can fail silently. + + The previous test would still pass if the mask leaked, because the serial + reference it compares against uses the same rows and would leak identically. + So: run the batch, then overwrite every world's span *except* one with fresh + noise and re-run. That world's output must be bit-identical, because none of + the bytes that changed are inside its span — and the folded ring is exactly + the layout where "inside its span" is a claim about a mask rather than about + a separate allocation. + """ + q, k, v = folded_ring(device) + rows = [ + folded_visible_row(w, history=True, device=torch.device(device)) + for w in range(WORLDS) + ] + mask = folded_block_mask(rows, TPF) + + def run(k_ring, v_ring): + return flex_attention_masked( + q, + k_ring.expand(WORLDS, N_KV_HEADS, FOLDED_KV, D_HEAD), + v_ring.expand(WORLDS, N_KV_HEADS, FOLDED_KV, D_HEAD), + block_mask=mask, + enable_gqa=True, + ) + + before = run(k, v) + + for kept in range(WORLDS): + gen = torch.Generator().manual_seed(1000 + kept) + k2, v2 = k.clone(), v.clone() + for w in range(WORLDS): + if w == kept: + continue + lo, hi = world_span(w) + shape = (1, N_KV_HEADS, hi - lo, D_HEAD) + k2[:, :, lo:hi] = torch.randn(shape, generator=gen).to(device) + v2[:, :, lo:hi] = torch.randn(shape, generator=gen).to(device) + + after = run(k2, v2) + assert torch.equal(after[kept], before[kept]), ( + f"world {kept} moved when only other worlds' spans changed: the " + "BlockMask is not isolating the folded ring" + ) + others = [w for w in range(WORLDS) if w != kept] + assert not torch.equal(after[others[0]], before[others[0]]), ( + "no world moved at all, so the rewrite landed nowhere the mask reads " + "and this test is vacuous" + ) diff --git a/test/modular/test_ring_kv_resource.py b/test/modular/test_ring_kv_resource.py new file mode 100644 index 000000000..1e21a28be --- /dev/null +++ b/test/modular/test_ring_kv_resource.py @@ -0,0 +1,1550 @@ +"""The ring KV resource: dispatch, ownership, isolation, and the things that +fail quietly. + +Every test here pins a failure that produces no exception on its own. The ring +IS the world state, so a lost claim, a rebound buffer, a stale visibility row or +a world reading its neighbour's history does not raise — it produces plausible, +smoothly drifting video, or a request that hangs forever waiting on an evictor +with nothing to evict. + +``num_worlds`` worlds share one buffer per layer, folded into its token +dimension (``kv/ring/cache.py``). Nothing physical separates them: ``upsert`` +hands back K and V spanning every resident world and one bool row that is False +outside the caller's own span. That row is the whole isolation mechanism, so the +tests that check it (``test_the_flat_ring_matches_one_ring_per_world``, +``test_worlds_interleave_without_reaching_each_other``) are load-bearing in a +way the ownership tests are not: ownership failures lose capacity, isolation +failures corrupt video. + +CPU-only and allocation-free at test scale: the geometry is 3 layers of 4 frames +at 128 tokens, not 24 x 17 x 512. The one GPU test is the capture test, which +cannot be anything else — the property it pins (``world_idx`` is *read* at +replay, not baked at capture) only exists inside a CUDA graph. +""" + +from __future__ import annotations + +import sys +from types import SimpleNamespace + +sys.path.insert(0, ".") + +import pytest +import torch + +from mstar.engine.cuda_graph_runner import DummyRowPool +from mstar.engine.resources.base import EngineResourceInfo +from mstar.engine.resources.kv.config import ( + KVSpec, + KVStep, + PagedKVConfig, + RingKVConfig, + RingKVLayerConfig, + RingKVStep, +) +from mstar.engine.resources.kv.manager import KVManager +from mstar.engine.resources.kv.ring import LayerRingCache, RingKVManager +from mstar.engine.resources.spec import apply_yaml_overrides +from mstar.engine.resources.step import ( + AdmitRuntimeError, + AllocationFailed, + RequestOffloading, + StepContext, +) + +TPF = 128 # the sparse block size; the smallest legal frame +RING_FRAMES = 4 +N_LAYERS = 3 +N_KV_HEADS = 2 +D_HEAD = 8 + + +# ── fixtures ──────────────────────────────────────────────────────────── + + +def _ring_config( + *, + num_worlds: int = 1, + ring_frames: int = RING_FRAMES, + num_kv_heads: int = N_KV_HEADS, +) -> RingKVConfig: + """Three layers, the last one dilated: a config with only stride-1 layers + would never exercise the 1-in-8 commit redirect.""" + return RingKVConfig( + num_layers=N_LAYERS, + num_kv_heads=num_kv_heads, + head_dim=D_HEAD, + tokens_per_frame=TPF, + num_worlds=num_worlds, + layers=tuple( + RingKVLayerConfig( + ring_frames=ring_frames, + ring_buckets=ring_frames, + pinned_dilation=8 if i == N_LAYERS - 1 else 1, + ) + for i in range(N_LAYERS) + ), + ) + + +def _manager( + config: RingKVConfig | None = None, name: str = "kv", device: str = "cpu" +) -> RingKVManager: + spec = KVSpec(resource_key=name, nodes={"dit"}, config=config or _ring_config()) + info = EngineResourceInfo(device=torch.device(device), kv_dtype=torch.float32) + return RingKVManager.build(spec, info) + + +def _ctx(*rids: str) -> StepContext: + return StepContext( + request_ids=tuple(rids), graph_walk="rollout", slot=0, capture=False, + ) + + +def _step(*rids: str, frame: int = 0) -> RingKVStep: + """A step declaring ``frame`` as the ring clock for every rid in ``rids``. + + One clock per request, and never absent: ``RingKVStep`` has no shape that + declines to answer, so there is no step a test can build that switches the + continuity check off. Frame 0 by default, so the ownership tests below can + say what they are about — they exercise who holds a world, not what frame it + is on, and a clock they did not think about should not refuse them. + """ + return RingKVStep(frames=tuple((rid, frame) for rid in rids)) + + +def _open(kv: RingKVManager, *rids: str, frame: int = 0) -> None: + """Register and admit each rid in turn, one step each — the shape the + engine actually produces, since ``max_batch_size`` is 1 and each request + gets its own step.""" + for rid in rids: + kv.ingest_request(rid) + outcome = kv.admit(_step(rid, frame=frame), _ctx(rid)) + assert outcome.ok, f"{rid} was refused a world: {outcome.reason}" + + +def _frame(kv: RingKVManager, gen: torch.Generator) -> tuple[torch.Tensor, torch.Tensor]: + """One frame's K and V. Dim 0 is 1 and stays 1: it is FlexAttention's batch + dim, and worlds live in the token dim, not here.""" + shape = (1, kv.config.num_kv_heads, kv.tokens_per_frame, kv.config.head_dim) + return ( + torch.randn(shape, generator=gen), + torch.randn(shape, generator=gen), + ) + + +def _rollout( + kv: RingKVManager, frames: int = 6, seed: int = 0, start: int = 0 +) -> None: + """The 4+1 schedule, for as many frames as asked, into whichever world + ``plan`` last staged. Past ``ring_frames`` it wraps, which is the only way + the ring's contents stop being trivially position-ordered. + + ``start`` is the clock the first frame runs at, for the tests that drive + the resource lifecycle alongside it and need the two to agree.""" + gen = torch.Generator().manual_seed(seed) + for f in range(start, start + frames): + frame_pos = torch.tensor(f, dtype=torch.int64) + for commit in (False, False, False, False, True): + for layer_idx in range(len(kv.layers)): + k, v = _frame(kv, gen) + kv.upsert(k, v, layer_idx, frame_pos, commit=commit) + + +def _ptrs(kv: RingKVManager) -> list[tuple[int, int, int, int]]: + return [ + (id(layer.kv), layer.kv.data_ptr(), id(layer.written), layer.written.data_ptr()) + for layer in kv.layers + ] + + +def _world_view(layer: LayerRingCache) -> torch.Tensor: + """``written`` cut back into ``[num_worlds, capacity]``. A view, so it reads + the live row rather than a snapshot of it.""" + return layer.written.view(layer.num_worlds, layer.capacity) + + +# ── spec dispatch ─────────────────────────────────────────────────────── + + +def test_kv_spec_dispatches_on_the_config_it_was_handed(): + """The storage strategy is chosen by the config a model declares, not by a + flag threaded through the manager — so a Waypoint spec cannot end up on the + paged manager (which would allocate pages nobody reads and answer `plan` + with a page table the ring has no use for).""" + ring = KVSpec(resource_key="kv", nodes={"dit"}, config=_ring_config()) + paged = KVSpec( + resource_key="kv", nodes={"decoder"}, + config=PagedKVConfig( + num_layers=2, num_kv_heads=2, head_dim=8, max_seq_len=128, + ), + ) + + assert ring.resource_class is RingKVManager + assert paged.resource_class is KVManager + + +def test_building_a_ring_manager_from_a_paged_config_raises(): + """`resource_class` is the only thing that should ever pick the manager; + a hand-built spec that skipped it gets a TypeError rather than an + AttributeError forty lines into the constructor.""" + spec = KVSpec( + resource_key="kv", nodes={"dit"}, + config=PagedKVConfig(num_layers=2, num_kv_heads=2, head_dim=8, max_seq_len=128), + ) + with pytest.raises(TypeError, match="RingKVConfig"): + RingKVManager.build(spec, EngineResourceInfo(device=torch.device("cpu"))) + + +@pytest.mark.parametrize( + "override", [{"max_num_pages": 16}, {"page_size": 64}, {"max_seq_len": 8}, {"tokens_per_frame": 256}], +) +def test_ring_geometry_is_not_a_yaml_tunable(override): + """The horizon is the one the weights were trained against. A deployment + that "tunes" it gets a different model with every shape still valid.""" + spec = KVSpec(resource_key="kv", nodes={"dit"}, config=_ring_config()) + with pytest.raises(TypeError): + spec.apply_yaml_overrides(**override) + + +def test_num_worlds_is_the_one_yaml_tunable(): + """The counterweight to the test above, and the reason it is a whitelist + rather than a blanket refusal. + + `num_worlds` is a different kind of number from the geometry: how many + concurrent sessions this box holds resident (~816 MiB of ring each at 720P) + is a sizing decision about the box, exactly like `max_num_pages` on the + paged config. It changes what the node can *serve* and never what it + computes — a world's arithmetic is identical whether it is alone in the ring + or one of eight, which is what the equivalence tests below pin. + """ + config = _ring_config() + spec = KVSpec(resource_key="kv", nodes={"dit"}, config=config) + + apply_yaml_overrides([spec], {"resources": {"kv": {"num_worlds": 4}}}) + + assert config.num_worlds == 4 + kv = _manager(config) + assert kv.num_worlds == 4 + assert kv.total_slots(0) == 4 * kv.capacity(0) + + +@pytest.mark.parametrize("bad", [0, -1]) +def test_zero_worlds_is_refused_at_both_entry_points(bad): + """A node sized for zero worlds refuses every request at admit — a + deployment that boots, reports healthy, and serves nothing.""" + with pytest.raises(ValueError, match="num_worlds"): + _ring_config(num_worlds=bad) + with pytest.raises(ValueError, match="num_worlds"): + _ring_config().apply_yaml_overrides(num_worlds=bad) + + +def test_applying_num_worlds_does_not_rebaseline_the_head_counts(): + """`apply_yaml_overrides` validates `num_worlds` inline rather than + re-running `__post_init__`, and that is load-bearing rather than tidiness. + + `KVConfig.__post_init__` snapshots `_unsharded_kv_heads` from the CURRENT + head count. Re-running it after `shard()` would make the already-sharded + count the new baseline, and the next `shard()` — the KV resource and every + attention resource planned against this same config object each shard it, + relying on it being idempotent — would narrow the cache a second time. Every + shape stays valid; the node just quietly holds a fraction of the heads. + """ + config = _ring_config(num_kv_heads=8) + config.shard(2) + sharded = config.num_kv_heads + assert sharded == 4 + + config.apply_yaml_overrides(num_worlds=4) + config.shard(2) + + assert config.num_kv_heads == sharded, "shard() stopped being idempotent" + + +def test_an_empty_yaml_block_is_not_an_override(): + """Refusing every *key* must not refuse the empty *block*. + + `apply_yaml_overrides` in spec.py skips the whole `resources:` map when it + is empty, but not the individual blocks under it — so `resources: {kv: {}}` + reaches the config with zero kwargs. Raising there fails a deployment over + a block that asked for nothing, and names `[]` as the offending keys. + """ + spec = KVSpec(resource_key="kv", nodes={"dit"}, config=_ring_config()) + + apply_yaml_overrides([spec], {"resources": {"kv": {}}}) + + +def test_a_real_override_still_raises_through_the_yaml_entry_point(): + """The counterweight to the above, on the same path: a block that does + carry a key must still be refused where a deployment would hit it.""" + spec = KVSpec(resource_key="kv", nodes={"dit"}, config=_ring_config()) + + with pytest.raises(TypeError, match="checkpoint fact"): + apply_yaml_overrides([spec], {"resources": {"kv": {"tokens_per_frame": 256}}}) + + +def test_paged_overrides_still_work_on_a_paged_config(): + """The counterweight: `apply_yaml_overrides` raising on the ring must not + be a blanket refusal that also broke the paged path.""" + config = PagedKVConfig(num_layers=2, num_kv_heads=2, head_dim=8, max_seq_len=128) + spec = KVSpec(resource_key="kv", nodes={"decoder"}, config=config) + + spec.apply_yaml_overrides(max_num_pages=17, page_size=64) + + assert (config.max_num_pages, config.page_size) == (17, 64) + + +# ── ownership ─────────────────────────────────────────────────────────── + + +def test_two_capture_configs_can_each_open_and_claim(): + """CUDA-graph capture, verbatim: `DummyRowPool.ensure` keys its rid pool by + ``f"{config_idx}_slot{slot}"``, so each capture config opens its own dummy + rid, and none is ever `remove_request`-ed. Waypoint declares two configs + (prime + rollout). + + This is why the claim is in `admit` and not `ingest_request`. Under a + claim-at-ingest design — where the claim is taken when the rid is opened and + handed back only when its storage is freed — config 1's `ensure()` raises + against config 0's still-held claim, and capture dies there. `release_all()`, + the only `free=True` call, runs after ALL captures, so nothing in between + would have let go. The node is deliberately sized for ONE world here: with + room for two the bug this pins would sail through capture and reappear as a + node that can serve one fewer request than it was sized for. + + It is also what pins the clock check against capture. Every admit below + declares frame 0 — the capture template's `frame_pos` is `torch.zeros(1)` + and replay re-stages it, so capture never advances — and `_capture_one` + drives admit and plan but never `commit`. If the check were keyed on + anything the manager could learn without a commit, the second warmup admit + would refuse and capture would die here rather than in production. + """ + kv = _manager(_ring_config(num_worlds=1)) + pool = DummyRowPool( + prefix="dit", + step_runner=SimpleNamespace(ingest_request=kv.ingest_request), + resources={"kv": kv}, + ) + + # `_capture_one`, in order: ensure, prepare (admit -> plan), NUM_WARMUP + # forwards each followed by a free=False reset and a re-prepare, the capture + # forward, and the `finally` reset. No commit anywhere in it. + for config_idx in (0, 1): + rids = pool.ensure(f"{config_idx}_slot0", 1) + ctx = StepContext( + request_ids=tuple(rids), graph_walk="rollout", slot=0, capture=True, + ) + step = _step(*rids, frame=0) + outcome = kv.admit(step, ctx) + assert outcome.ok, ( + f"capture config {config_idx} was refused the ring: {outcome.reason}" + ) + kv.plan(step, ctx) + for _ in range(2): # CudaGraphRunner.NUM_WARMUP + _rollout(kv, frames=1) + pool.reset(rids) + assert kv.admit(step, ctx).ok + kv.plan(step, ctx) + _rollout(kv, frames=1) # the capture forward itself + pool.reset(rids) + + pool.release_all() + kv.post_warmup_validate() + + +def test_ingest_request_registers_without_claiming(): + kv = _manager() + + kv.ingest_request("a") + kv.ingest_request("b") + kv.ingest_request("a") # idempotent: one NewRequest per partition + + assert kv.world_of("a") is None and kv.world_of("b") is None + # neither registration took a world, so either may still be admitted + assert kv.admit(_step("b"), _ctx("b")).ok + + +def test_admit_refuses_a_request_that_was_never_ingested(): + """A world claimed here would never come back. Worlds are handed back by + `remove_request`, which the engine only ever runs for a request it opened + (`Engine.add_request` -> `ingest_request` and `Engine.remove_request` -> + `remove_request` are symmetric across every resource), so a claim taken for + a rid outside that pairing leaks a world permanently — the node loses + concurrency one request at a time with nothing raised, until every admit + fails terminally and the cause is long gone. + + With one world the distinction was invisible, because the single claim was + always overwritten by whoever asked next. It is not invisible with a pool. + """ + kv = _manager(_ring_config(num_worlds=4)) + + outcome = kv.admit(_step("stranger"), _ctx("stranger")) + + assert not outcome.ok + assert type(outcome.reason) is AdmitRuntimeError + assert "never ingested" in outcome.reason.message + assert kv.world_of("stranger") is None + assert len(kv._free_worlds) == 4, "a refused admit still took a world" + + +@pytest.mark.parametrize("num_worlds", [1, 2, 4]) +def test_the_n_plus_first_request_is_refused_with_a_terminal_reason(num_worlds): + """The pool is finite and exhaustion is terminal. The reason class is the + whole point: `AllocationFailed` sends the scheduler to evict, but + `supports_eviction` is False so nothing is evictable and the request spins; + `RequestOffloading` waits on a `reload` that never comes. Both hang instead + of failing. That reasoning does not change with the pool size — an exhausted + ring is exhausted for the same reason at N=4 as at N=1. + """ + kv = _manager(_ring_config(num_worlds=num_worlds)) + _open(kv, *[f"r{i}" for i in range(num_worlds)]) + kv.ingest_request("extra") + + outcome = kv.admit(_step("extra"), _ctx("extra")) + + assert not outcome.ok + assert not outcome.ready + assert type(outcome.reason) is AdmitRuntimeError + assert not isinstance(outcome.reason, (AllocationFailed, RequestOffloading)) + assert f"all {num_worlds}" in outcome.reason.message + + +@pytest.mark.parametrize("num_worlds", [2, 4]) +def test_concurrent_requests_get_distinct_worlds(num_worlds): + """The point of the whole change, and the thing that has no meaning at N=1. + + Two requests handed the same world index would share one span: each would + write over the other's frames and read them back as its own history, with + every shape still valid and nothing raised. Distinctness is checked as a + set, not against an expected assignment, because which index a request gets + is allocator business; that it gets its own is not. + """ + kv = _manager(_ring_config(num_worlds=num_worlds)) + rids = [f"r{i}" for i in range(num_worlds)] + + _open(kv, *rids) + + worlds = [kv.world_of(rid) for rid in rids] + assert None not in worlds + assert len(set(worlds)) == num_worlds, f"worlds collided: {dict(zip(rids, worlds))}" + assert set(worlds) == set(range(num_worlds)), "a world was skipped" + assert not kv._free_worlds + + +def test_admit_is_idempotent_for_the_holder(): + """A rollout admits once per frame for as many frames as it runs.""" + kv = _manager() + kv.ingest_request("a") + + assert all(kv.admit(_step("a"), _ctx("a")).ok for _ in range(4)) + assert kv.world_of("a") == 0 + + +def test_a_refused_admit_leaves_every_previous_holder_in_place(): + """The refusal must not half-claim: if it stole a world on the way out, the + original holder's next admit would be refused by the request that was itself + just refused. Checked across a *pool* rather than a single claim, since a + refusal that half-claimed would now do it to whichever holder happened to + own the index it grabbed. + """ + kv = _manager(_ring_config(num_worlds=2)) + _open(kv, "a", "b") + before = {rid: kv.world_of(rid) for rid in ("a", "b")} + kv.ingest_request("c") + + assert not kv.admit(_step("c"), _ctx("c")).ok + + assert {rid: kv.world_of(rid) for rid in ("a", "b")} == before + assert kv.admit(_step("a"), _ctx("a")).ok + assert kv.admit(_step("b"), _ctx("b")).ok + assert kv.world_of("c") is None + + +@pytest.mark.parametrize("free", [False, True]) +def test_reset_request_releases_one_world_and_only_one(free): + """`free=True` is indistinguishable from `free=False`: there is no physical + allocation to hand back, so the only thing either can do is zero and let + go. Capture calls `free=False` between warmups and `free=True` once at the + end, and both have to leave a world claimable. + + The "only one" half is the bug this replaces. The single-world version + dropped *the* claim, which was the same statement then and would now end + every other rollout on the node every time any request was reset. + """ + kv = _manager(_ring_config(num_worlds=2)) + _open(kv, "a", "b") + b_world = kv.world_of("b") + + kv.reset_request("a", free=free) + + assert kv.world_of("a") is None + assert kv.world_of("b") == b_world, "resetting `a` released `b`'s world" + kv.ingest_request("c") + assert kv.admit(_step("c"), _ctx("c")).ok + assert kv.world_of("c") == 0, "the freed world was not the one handed on" + + +def test_remove_request_releases_the_world_and_the_registration(): + """Both, and neither anyone else's. Dropping the registration is what makes + the world genuinely returned rather than reserved for a rid the engine has + already forgotten.""" + kv = _manager(_ring_config(num_worlds=2)) + _open(kv, "a", "b") + b_world = kv.world_of("b") + + kv.remove_request("a") + + assert kv.world_of("a") is None + assert kv.world_of("b") == b_world, "removing `a` released `b`'s world" + # the registration went with it: `a` is a stranger again + assert not kv.admit(_step("a"), _ctx("a")).ok + kv.ingest_request("a") + assert kv.admit(_step("a"), _ctx("a")).ok + + +def test_supports_preplan_stays_false(): + """It keeps `CudaGraphRunner._num_slots` at 1. Two slots exist so a plan for + step N+1 can write buffers replay N is not reading; the only thing planned + here is one `[1]` world index, so the second slot would be an identical + graph at double the capture cost — and the only reason `_static_world_idx` + would have to become one buffer per slot.""" + assert _manager().supports_preplan is False + + +def test_plan_stages_the_world_index_in_place_as_a_device_tensor(): + """The single most important property in the resource, and the one nothing + else can catch. + + A Python int here would be constant-folded into the graph at capture and + every replay would serve the capture-time world — reading and writing + somebody else's history with no shape error and no exception. A freshly + allocated tensor is the mirror failure: the graph baked the address of the + buffer that existed at capture, so a rebind leaves every replay reading the + orphaned original. + + Hence both halves below: the staged value is a `[1]` int64 device tensor, + and `plan` writes *through* it rather than replacing it. + """ + kv = _manager(_ring_config(num_worlds=4)) + _open(kv, "a", "b") + staged = kv._static_world_idx + assert staged.shape == (1,) and staged.dtype == torch.int64 + + kv.plan(_step("b"), _ctx("b")) + + assert kv._static_world_idx is staged, "plan rebound the buffer capture baked" + assert staged.data_ptr() == kv._static_world_idx.data_ptr() + assert int(staged) == kv.world_of("b") + + kv.plan(_step("a"), _ctx("a")) + assert int(staged) == kv.world_of("a") + + +def test_plan_refuses_a_batch_it_cannot_stage(): + """One world index per step, so one request per step. `admit` refuses a + mixed batch first; reaching here means it was bypassed, and staging one of + the two rids arbitrarily would run the other request's frame into the wrong + world.""" + kv = _manager(_ring_config(num_worlds=2)) + _open(kv, "a", "b") + + with pytest.raises(ValueError, match="one world index per step"): + kv.plan(_step("a", "b"), _ctx("a", "b")) + + +def test_plan_refuses_a_request_holding_no_world(): + kv = _manager() + + with pytest.raises(KeyError, match="no world for request"): + kv.plan(_step("a"), _ctx("a")) + + +def test_admit_refuses_a_mixed_batch_naming_the_step_limit(): + """A step advances one world. The message has to say which number capped it + — this is `max_batch_size`, not the ring, and a reader who reads it as a + ring limit raises `num_worlds` and sees nothing change.""" + kv = _manager(_ring_config(num_worlds=4)) + _open(kv, "a", "b") + + outcome = kv.admit(_step("a", "b"), _ctx("a", "b")) + + assert not outcome.ok + assert type(outcome.reason) is AdmitRuntimeError + assert "max_batch_size" in outcome.reason.message + + +# ── the ring clock ────────────────────────────────────────────────────── +# +# The clock has to advance by exactly one per committed frame, per world. Every +# failure below is silent on its own: a skipped or repeated frame picks a +# different ring slot to write and a different one to hide, so the video keeps +# coming out — from a history that is no longer the one that was generated. +# `admit` is the only point per frame where the engine holds both the clock the +# forward is about to run at and the frame the last one committed. + + +def _drive(kv: RingKVManager, rid: str, frame: int, seed: int | None = None) -> None: + """One engine step at ``frame``: admit, plan, forward, commit. Asserts the + admit, since a test that meant to reach `commit` and was refused on the way + should say so there rather than fail three lines later.""" + step, ctx = _step(rid, frame=frame), _ctx(rid) + outcome = kv.admit(step, ctx) + assert outcome.ok, f"{rid} frame {frame} was refused: {outcome.reason}" + kv.plan(step, ctx) + _rollout(kv, frames=1, seed=frame if seed is None else seed, start=frame) + kv.commit(step, ctx) + + +def test_the_clock_check_is_silent_until_the_first_commit(): + """A fresh claim may start its clock anywhere. Nothing has been committed, + so there is no history for a frame number to be inconsistent *with* — and a + world restored by `load_state` legitimately resumes at frame 37, not 0.""" + kv = _manager() + kv.ingest_request("a") + + assert kv.admit(_step("a", frame=37), _ctx("a")).ok + # still nothing committed: re-driving the same frame is not yet a repeat + assert kv.admit(_step("a", frame=37), _ctx("a")).ok + assert kv.admit(_step("a", frame=0), _ctx("a")).ok + + +def test_a_skipped_frame_is_refused_with_a_terminal_reason(): + """The reason class matters as much as the refusal: `AllocationFailed` sends + the scheduler off to evict against `supports_eviction=False`, so the request + would hang instead of failing. Nothing about a desynced clock is fixable by + eviction or reload.""" + kv = _manager() + kv.ingest_request("a") + _drive(kv, "a", 0) + + outcome = kv.admit(_step("a", frame=2), _ctx("a")) + + assert not outcome.ok + assert not outcome.ready + assert type(outcome.reason) is AdmitRuntimeError + assert not isinstance(outcome.reason, (AllocationFailed, RequestOffloading)) + # names both numbers, and whose clock they belong to + assert "frame 0" in outcome.reason.message + assert "declares frame 2" in outcome.reason.message + assert "'a'" in outcome.reason.message + + +def test_a_repeated_frame_is_refused(): + """The other half of the invariant, and the one a stalled `postprocess` + produces: the ring already holds frame 0, so re-running it would overwrite + that slot with a different frame's K/V while the visibility row hides it.""" + kv = _manager() + kv.ingest_request("a") + _drive(kv, "a", 0) + _drive(kv, "a", 1) + + assert not kv.admit(_step("a", frame=1), _ctx("a")).ok + + +def test_each_world_runs_its_own_clock(): + """Per rid, not per resource. A shared clock would refuse the second + request's very first frame the moment the first request had committed one — + and, worse under a hypothetical "just take the max", would let a lagging + world skip forward into a slot it never wrote. + """ + kv = _manager(_ring_config(num_worlds=3)) + _open(kv, "a", "b", "c") + + _drive(kv, "a", 0) + _drive(kv, "a", 1) + _drive(kv, "a", 2) + # `b` has committed nothing, so it may still start wherever it likes + assert kv.admit(_step("b", frame=0), _ctx("b")).ok + _drive(kv, "b", 41) + # and `c`'s clock is not `a`'s or `b`'s either + _drive(kv, "c", 7) + + assert kv.admit(_step("a", frame=3), _ctx("a")).ok + assert kv.admit(_step("b", frame=42), _ctx("b")).ok + assert kv.admit(_step("c", frame=8), _ctx("c")).ok + # each is still refused its neighbour's next frame + assert not kv.admit(_step("a", frame=42), _ctx("a")).ok + assert not kv.admit(_step("b", frame=3), _ctx("b")).ok + + +def test_a_step_that_declares_no_clock_for_an_admitted_request_is_refused(): + """`RingKVStep` has no shape that declines to answer, so this is only + reachable by hand — which is the point. The continuity check is the only + thing standing between a stalled clock and a world that rewrites its own + history, and the singular `frame_pos: int | None` this replaced switched it + off for any batch a submodule could not describe with one number. + """ + kv = _manager() + kv.ingest_request("a") + + outcome = kv.admit(RingKVStep(frames=()), _ctx("a")) + + assert not outcome.ok + assert type(outcome.reason) is AdmitRuntimeError + assert "no ring clock" in outcome.reason.message + + +def test_a_frame_that_never_committed_can_be_re_driven(): + """`commit` is called straight-line after the forward, not in a `finally`, + so a forward that raised commits nothing. Re-driving that frame at the same + clock has to be allowed — the ring write is idempotent per (frame, layer), + since the slot a frame overwrites is the slot its own visibility row hides, + so a frame torn halfway through the layer stack heals on the re-run.""" + kv = _manager() + kv.ingest_request("a") + _drive(kv, "a", 0) + + # frame 1 admitted, forward raised: no commit + assert kv.admit(_step("a", frame=1), _ctx("a")).ok + + assert kv.admit(_step("a", frame=1), _ctx("a")).ok + _drive(kv, "a", 1) + assert kv.admit(_step("a", frame=2), _ctx("a")).ok + + +def test_commit_records_the_frame_and_moves_no_kv(): + """The division the whole design rests on, and the same one the paged + manager keeps: `KVManager.write_kv` scatters the bytes from the layer body + and `KVManager.commit` only advances `stored_len`. Here the bytes land in + `upsert`. `commit` runs on the host after the forward's launch *returns*, + outside any captured graph, so a hook that moved KV would add 24 layers of + launches per frame to the path capture exists to shorten.""" + kv = _manager() + kv.ingest_request("a") + _drive(kv, "a", 0) + before = [layer.kv.clone() for layer in kv.layers] + written = [layer.written.clone() for layer in kv.layers] + + step, ctx = _step("a", frame=1), _ctx("a") + assert kv.admit(step, ctx).ok + kv.commit(step, ctx) # no forward between them + + for layer, kv_t, w_t in zip(kv.layers, before, written, strict=True): + assert torch.equal(layer.kv, kv_t), "commit moved KV" + assert torch.equal(layer.written, w_t), "commit changed visibility" + # but it did record, so frame 1 is now spent + assert not kv.admit(_step("a", frame=1), _ctx("a")).ok + + +def test_a_paged_step_is_refused_rather_than_skipping_the_check(): + """A `KVStep` carries no `frames`, so it would admit and commit exactly + as before while switching the check off — the wrong thing that raises + nothing. Same guard, same reason, as `build` refusing a PagedKVConfig.""" + kv = _manager() + + with pytest.raises(TypeError, match="RingKVStep"): + kv.admit(KVStep(), _ctx("a")) + with pytest.raises(TypeError, match="RingKVStep"): + kv.commit(KVStep(), _ctx("a")) + + +@pytest.mark.parametrize( + "drop", [ + lambda kv: kv.reset_request("a"), + lambda kv: kv.reset_request("a", free=True), + lambda kv: kv.remove_request("a"), + ], + ids=["reset_request", "reset_request_free", "remove_request"], +) +def test_dropping_the_world_drops_the_clock(drop): + """An empty world has committed no frames. Keeping the count across a reset + would hold the next claim to a continuity it cannot satisfy — capture, which + resets between every warmup, is the caller that would hit it first.""" + kv = _manager() + kv.ingest_request("a") + _drive(kv, "a", 0) + _drive(kv, "a", 1) + + drop(kv) + + # frame 0 again, which frame 1 would otherwise have made a repeat. Every one + # of these hands the world back, so re-register first — whether the *claim* + # survives each of them is the ownership tests' question, not this one's. + kv.ingest_request("a") + assert kv.admit(_step("a", frame=0), _ctx("a")).ok + + +def test_load_state_clears_the_clock(): + """The snapshot does not carry the clock — `get_state` covers the ring and + only the ring, and the clock lives in `PerRequestState`. Inventing one + would be worse than having none, so the first frame after a load sets it.""" + kv = _manager() + kv.ingest_request("a") + _drive(kv, "a", 0) + _drive(kv, "a", 1) + state = kv.get_state("a") + + kv.load_state("a", state) + + assert kv.admit(_step("a", frame=9), _ctx("a")).ok + + +def test_post_warmup_validate_catches_a_committed_frame(): + """Capture drives admit and plan but never commit, so this should be + unreachable — which is why it is pinned rather than assumed. A clock left + set by capture refuses the first real admit.""" + kv = _manager() + kv.post_warmup_validate() + + kv.commit(_step("a", frame=4), _ctx("a")) # a commit capture should never have made + + with pytest.raises(RuntimeError, match="during CUDA graph capture"): + kv.post_warmup_validate() + + +# ── the allocation never moves ────────────────────────────────────────── + + +def test_reset_request_zeroes_one_span_without_reallocating(): + """A captured graph baked the address of the buffer that existed at capture + time. Every reset path has to write through the same storage: a fresh + tensor would detach every replay from the ring the model reads, silently. + + The second world is not scenery. The assertion is byte equality against a + snapshot taken with `b`'s history already in the buffer, so it pins both + halves at once — `a`'s span went to zero, and `b`'s came through untouched. + A reset that zeroed the whole buffer (which is what the single-world version + did, indistinguishably) fails on `b`. + """ + kv = _manager(_ring_config(num_worlds=2)) + before = _ptrs(kv) + _open(kv, "a", "b") + assert (kv.world_of("a"), kv.world_of("b")) == (0, 1) + + kv.plan(_step("b"), _ctx("b")) + _rollout(kv, frames=6, seed=2) + kept_kv = [layer.kv.clone() for layer in kv.layers] + kept_written = [layer.written.clone() for layer in kv.layers] + + kv.plan(_step("a"), _ctx("a")) + _rollout(kv, frames=6, seed=3) + assert any( + not torch.equal(layer.kv, snap) + for layer, snap in zip(kv.layers, kept_kv, strict=True) + ), "the second rollout wrote nothing, so the reset below proves nothing" + + kv.reset_request("a") + + assert _ptrs(kv) == before + for layer, kv_t, w_t in zip(kv.layers, kept_kv, kept_written, strict=True): + assert torch.equal(layer.kv, kv_t), "reset_request reached another world" + assert torch.equal(layer.written, w_t) + # the scratch tail stays visible through every reset, per world: the frame + # being denoised must always be able to attend to itself + for layer in kv.layers: + assert bool(_world_view(layer)[:, layer.ring_len :].all()) + assert not bool(_world_view(layer)[0, : layer.ring_len].any()) + + +def test_remove_request_zeroes_one_span_without_reallocating(): + """Same invariant on the other release path, which is the one a real + rollout ending takes. The single-world version zeroed the whole buffer + here: with one world that was the same statement, with N it ends every + concurrent rollout on the node every time any one of them finishes.""" + kv = _manager(_ring_config(num_worlds=2)) + before = _ptrs(kv) + _open(kv, "a", "b") + + kv.plan(_step("b"), _ctx("b")) + _rollout(kv, frames=4, seed=4) + kept_kv = [layer.kv.clone() for layer in kv.layers] + kept_written = [layer.written.clone() for layer in kv.layers] + kv.plan(_step("a"), _ctx("a")) + _rollout(kv, frames=4, seed=5) + + kv.remove_request("a") + + assert _ptrs(kv) == before + for layer, kv_t, w_t in zip(kv.layers, kept_kv, kept_written, strict=True): + assert torch.equal(layer.kv, kv_t), "remove_request reached another world" + assert torch.equal(layer.written, w_t) + + +def test_a_reused_world_starts_empty(): + """The pairing `_release_world` exists to enforce: a world handed back to + the pool still holding a dead request's frames is handed to the next request + as its history — neither empty nor its own, and attended to as real. There + is no release path that does not zero first.""" + kv = _manager(_ring_config(num_worlds=1)) + _open(kv, "a") + kv.plan(_step("a"), _ctx("a")) + _rollout(kv, frames=5, seed=6) + assert any(bool(layer.kv.any()) for layer in kv.layers) + + kv.remove_request("a") + _open(kv, "b") + + assert kv.world_of("b") == 0, "this test needs the same index handed on" + assert all(not bool(layer.kv.any()) for layer in kv.layers) + for layer in kv.layers: + assert not bool(layer.written[: layer.ring_len].any()) + + +def test_build_cuda_graph_buffers_allocates_nothing(): + kv = _manager() + before = _ptrs(kv) + + kv.build_cuda_graph_buffers([], max_bs=1, max_seq_len=4096) + + assert _ptrs(kv) == before + + +def test_post_warmup_validate_catches_capture_residue(): + """NUM_WARMUP=2 plus the capture forward is three committing passes + into whatever world the dummy held. Left there, the first real rollout + handed that world attends to them as history.""" + kv = _manager(_ring_config(num_worlds=2)) + kv.post_warmup_validate() + + _rollout(kv, frames=1) + with pytest.raises(RuntimeError, match="capture-time frames"): + kv.post_warmup_validate() + + +def test_post_warmup_validate_catches_a_lingering_claim(): + """A dummy rid still holding a world after capture is a world no request + will ever get back: the node boots reporting healthy and serves one fewer + session than it was sized for, forever.""" + kv = _manager(_ring_config(num_worlds=2)) + _open(kv, "dummy") + + with pytest.raises(RuntimeError, match="still claimed"): + kv.post_warmup_validate() + + kv.reset_request("dummy", free=True) + kv.post_warmup_validate() + + +# ── state ─────────────────────────────────────────────────────────────── + + +def test_get_state_covers_the_ring_and_nothing_else(): + """Pinned so a later field cannot quietly make the name a lie. A Waypoint + world is at least three things: the ring, the streaming VAE's temporal + receptive field, and `frame_pos`/seed/iteration in `PerRequestState`. This + covers one; what a complete world state should contain is still open.""" + kv = _manager() + _open(kv, "a") + kv.plan(_step("a"), _ctx("a")) + _rollout(kv, frames=3) + + state = kv.get_state("a") + + assert set(state) == {"layers"} + assert len(state["layers"]) == N_LAYERS + assert all(len(entry) == 2 for entry in state["layers"]) + + +@pytest.mark.parametrize("call", ["get_state", "load_state"]) +def test_state_is_refused_for_a_request_holding_no_world(call): + """Scoped to a rid because the buffer is not. An unscoped snapshot of a + multi-world ring would carry every concurrent request's history, and loading + it back would overwrite worlds the caller never asked about — so there is no + unscoped spelling to fall back to, and a rid that owns nothing has to say + so.""" + kv = _manager(_ring_config(num_worlds=2)) + _open(kv, "a") + + with pytest.raises(KeyError, match="no world for request"): + if call == "get_state": + kv.get_state("ghost") + else: + kv.load_state("ghost", kv.get_state("a")) + + +def test_get_state_covers_one_world_and_is_cloned_not_aliased(): + """Two properties, one setup, because they fail together in practice: the + caller holds the snapshot across rollout steps that overwrite the rings in + place, and an aliased snapshot silently tracks the live world instead. The + span check is the multi-world half — a snapshot the size of the whole buffer + would carry `b`'s history into `a`'s save file.""" + kv = _manager(_ring_config(num_worlds=2)) + _open(kv, "a", "b") + kv.plan(_step("a"), _ctx("a")) + _rollout(kv, frames=2) + state = kv.get_state("a") + saved = [(a.clone(), b.clone()) for a, b in state["layers"]] + + for layer, (kv_t, w_t) in zip(kv.layers, state["layers"], strict=True): + assert kv_t.shape[-2] == layer.capacity, "the snapshot is not one world" + assert w_t.shape == (layer.capacity,) + + kv.plan(_step("b"), _ctx("b")) + _rollout(kv, frames=3, seed=7) + kv.plan(_step("a"), _ctx("a")) + _rollout(kv, frames=3, seed=8, start=2) + + for (kv_t, w_t), (kv_s, w_s) in zip(state["layers"], saved, strict=True): + assert torch.equal(kv_t, kv_s) + assert torch.equal(w_t, w_s) + + +def test_load_state_copies_into_one_span_of_the_fixed_allocation(): + """A `load_state` that assigned a fresh tensor would detach every captured + CUDA graph from the pointer it baked, and nothing would raise. Writing into + a *span* rather than the whole buffer is the second half of that: every + other resident world has to come through untouched, or restoring one + session resets its neighbours.""" + kv = _manager(_ring_config(num_worlds=2)) + _open(kv, "a", "b") + kv.plan(_step("a"), _ctx("a")) + _rollout(kv, frames=5) + state = kv.get_state("a") + before = _ptrs(kv) + + kv.plan(_step("b"), _ctx("b")) + _rollout(kv, frames=4, seed=9) + b_kv = [layer.kv.clone() for layer in kv.layers] + b_written = [layer.written.clone() for layer in kv.layers] + + kv.plan(_step("a"), _ctx("a")) + _rollout(kv, frames=3, seed=11, start=5) + assert not torch.equal(kv.layers[0].kv, b_kv[0]), ( + "the second rollout has to actually move the ring, or the round trip " + "below proves nothing" + ) + + kv.load_state("a", state) + + assert _ptrs(kv) == before + for i, layer in enumerate(kv.layers): + lo, hi = layer.world_span(kv.world_of("a")) + assert torch.equal(layer.kv[:, :, :, lo:hi], state["layers"][i][0]) + assert torch.equal(layer.written[lo:hi], state["layers"][i][1]) + b_lo, b_hi = layer.world_span(kv.world_of("b")) + assert torch.equal(layer.kv[:, :, :, b_lo:b_hi], b_kv[i][:, :, :, b_lo:b_hi]), ( + "load_state reached another world's span" + ) + assert torch.equal(layer.written[b_lo:b_hi], b_written[i][b_lo:b_hi]) + + +def test_a_state_is_portable_between_rings_of_different_widths(): + """The snapshot is one world's span, so how many neighbours it had is not + part of it. This is not incidental: a node resized from 2 worlds to 4 + between restarts must still be able to load the sessions it wrote, and a + guard that compared against the whole buffer would refuse them all.""" + small = _manager(_ring_config(num_worlds=2)) + big = _manager(_ring_config(num_worlds=4)) + _open(small, "a") + small.plan(_step("a"), _ctx("a")) + _rollout(small, frames=3, seed=13) + _open(big, "x", "y") # so `y` lands on a nonzero world index + + big.load_state("y", small.get_state("a")) + + for layer, (kv_t, w_t) in zip(big.layers, small.get_state("a")["layers"], strict=True): + lo, hi = layer.world_span(big.world_of("y")) + assert torch.equal(layer.kv[:, :, :, lo:hi], kv_t) + assert torch.equal(layer.written[lo:hi], w_t) + # and `x`'s world is still empty + for layer in big.layers: + lo, hi = layer.world_span(big.world_of("x")) + assert not bool(layer.kv[:, :, :, lo:hi].any()) + + +def test_load_state_refuses_a_geometry_that_would_broadcast(): + """The shape guard, not `copy_`, is what has to catch this. The failure it + exists for is a state saved under one horizon loaded into another — a 360P + state into a 720P ring, or a compacted state into a `full_global_ring` one. + Where the dims happen to line up, `copy_` broadcasts a frame across the ring + and produces a world made of one repeated moment, with no error anywhere.""" + small = _manager(_ring_config(ring_frames=2)) + big = _manager(_ring_config(ring_frames=RING_FRAMES)) + _open(small, "a") + small.plan(_step("a"), _ctx("a")) + _rollout(small, frames=2) + _open(big, "a") + + with pytest.raises(ValueError, match="state shape"): + big.load_state("a", small.get_state("a")) + + +def test_load_state_refuses_a_different_layer_count(): + kv = _manager() + _open(kv, "a") + state = kv.get_state("a") + state["layers"] = state["layers"][:-1] + + with pytest.raises(ValueError, match="layers"): + kv.load_state("a", state) + + +# ── the visible row ───────────────────────────────────────────────────── + + +def test_visible_is_the_layers_scratch_buffer_and_must_be_read_immediately(): + """`upsert` hands back `_mask_written` itself, not a copy: a fresh + `[total_slots]` buffer on each of the 120 upserts per frame is allocation + the compiled region does not need. + + The cost is an aliasing obligation the consumer has to honour — read it + before the next `upsert` on the same layer. The 4+1 schedule does (each + pass consumes the row inside the same attention call). A consumer that + stashed it would attend under a *later* frame's visibility: the wrong ring + slots, no exception, drifting video — and, with worlds resident, possibly + another world's mask entirely. + + The clone below is what makes this test bite: it shows the two frames' + visibility genuinely differ, so `visible0 is visible1` is a statement about + aliasing and not a vacuous equality between identical rows. + """ + kv = _manager() + gen = torch.Generator().manual_seed(3) + k, v = _frame(kv, gen) + + _, _, visible0 = kv.upsert(k, v, 0, torch.tensor(0, dtype=torch.int64), commit=True) + snapshot = visible0.clone() + _, _, visible1 = kv.upsert(k, v, 0, torch.tensor(1, dtype=torch.int64), commit=True) + + assert visible1 is visible0 + assert not torch.equal(snapshot, visible1), ( + "frames 0 and 1 must differ in visibility, or this test proves nothing" + ) + # the buffer the caller was handed at frame 0 now reads as frame 1 + assert not torch.equal(snapshot, visible0) + + +def test_visible_hides_the_slot_this_frame_is_about_to_overwrite(): + """At the resource seam: without it the current frame attends to the + stale frame still occupying its ring slot.""" + kv = _manager() + gen = torch.Generator().manual_seed(4) + for f in range(RING_FRAMES + 1): + k, v = _frame(kv, gen) + _, _, visible = kv.upsert(k, v, 0, torch.tensor(f, dtype=torch.int64), commit=True) + slot = (f % RING_FRAMES) * TPF + assert not bool(visible[slot : slot + TPF].any()), f"frame {f} sees its own slot" + assert bool(visible[kv.layers[0].ring_len :].all()), "scratch must stay visible" + + +def test_upsert_returns_the_whole_buffer_and_delegates_by_layer(): + """K and V span every resident world — there is one buffer and no view is + taken — and the visibility row is the same length, because it is what cuts + the caller's world back out of it. `capacity` and `total_slots` are both + named for this reason: using either where the other belongs is an off-by-N + that produces a valid shape.""" + kv = _manager(_ring_config(num_worlds=3)) + gen = torch.Generator().manual_seed(5) + k, v = _frame(kv, gen) + + for layer_idx in range(N_LAYERS): + k_all, v_all, visible = kv.upsert( + k, v, layer_idx, torch.tensor(0, dtype=torch.int64), commit=True + ) + total = kv.total_slots(layer_idx) + assert total == 3 * kv.capacity(layer_idx) + assert k_all.shape[-2] == total and v_all.shape[-2] == total + assert visible.shape == (total,) + assert k_all.shape[0] == 1, "the world dim is folded into tokens, not dim 0" + assert k_all.data_ptr() == kv.layers[layer_idx].kv.data_ptr() + + +def test_frozen_passes_leave_the_ring_byte_identical(): + """At the resource seam: `commit` is per call and the manager keeps no + frozen state between calls, because all five passes of a frame sit inside + one engine step and the step lifecycle never sees the boundary. If the + manager ever started latching it, this is what would break.""" + kv = _manager() + gen = torch.Generator().manual_seed(6) + before = [layer.kv.clone() for layer in kv.layers] + + for _ in range(4): + for layer_idx in range(N_LAYERS): + k, v = _frame(kv, gen) + kv.upsert(k, v, layer_idx, torch.tensor(0, dtype=torch.int64), commit=False) + + for layer, snapshot in zip(kv.layers, before, strict=True): + assert torch.equal(layer.kv[:, :, :, : layer.ring_len], snapshot[:, :, :, : layer.ring_len]) + assert not bool(layer.written[: layer.ring_len].any()) + + +# ── worlds are isolated ───────────────────────────────────────────────── +# +# The two tests below are the ones that matter most in this file. Nothing +# physical separates two resident worlds: they share one `kv` tensor, one +# `written` row and one `_mask_written` scratch, and `upsert` returns K and V +# spanning all of it. The only thing that stops world 1 attending to world 0's +# frames is a single `&=` in `cache.py`. Delete it and every shape is still +# valid, every test above still passes, and the video drifts between sessions. + + +def _w(idx: int) -> torch.Tensor: + """A world index in the shape the forward path uses everywhere: a `[1]` + int64 tensor, never a Python int. See `cache.py`'s module docstring.""" + return torch.tensor([idx], dtype=torch.int64) + + +@pytest.mark.parametrize("num_worlds", [2, 4]) +@pytest.mark.parametrize("pinned_dilation", [1, 8]) +def test_the_flat_ring_matches_one_ring_per_world(num_worlds, pinned_dilation): + """N worlds folded into one token axis are bit-identical to N separate + single-world rings, span for span. + + This is the equivalence the whole layout rests on and the reason + `num_worlds` can be a deployment knob at all: a world's arithmetic must not + depend on how many neighbours it has. Three things are compared and all + three are necessary — the K/V bytes (the write landed in the right slots), + `written` (the bookkeeping did too), and the visibility row restricted to + the span (the mask agrees). The fourth assertion is the one with no + single-world counterpart: everything OUTSIDE the span is False, which is + isolation itself. + + The own-world term is checked as `_world_of_slot == world_idx` computed from + the span arithmetic, not read back off the implementation, so a mask built + from the wrong comparison cannot agree with it by construction. + """ + kwargs = dict( + n_kv_heads=N_KV_HEADS, ring_frames=RING_FRAMES, ring_buckets=RING_FRAMES, + d_head=D_HEAD, tokens_per_frame=TPF, pinned_dilation=pinned_dilation, + dtype=torch.float32, device="cpu", + ) + flat = LayerRingCache(num_worlds=num_worlds, **kwargs) + solo = [LayerRingCache(num_worlds=1, **kwargs) for _ in range(num_worlds)] + assert flat.total_slots == num_worlds * flat.capacity + assert all(s.capacity == flat.capacity for s in solo) + + # Independent clocks and a deliberately uneven interleave: with every world + # on the same frame, a mask that ignored `world_idx` entirely would still + # hide the same slots and this test would pass against it. + gens = [torch.Generator().manual_seed(100 + w) for w in range(num_worlds)] + clocks = [3 * w for w in range(num_worlds)] + order = torch.Generator().manual_seed(41) + + for _ in range(20 * num_worlds): + w = int(torch.randint(0, num_worlds, (1,), generator=order).item()) + frame_pos = torch.tensor(clocks[w], dtype=torch.int64) + for commit in (False, False, False, False, True): + kv = torch.randn(2, 1, N_KV_HEADS, TPF, D_HEAD, generator=gens[w]) + _, _, flat_vis = flat.upsert(kv, frame_pos, commit, _w(w)) + _, _, solo_vis = solo[w].upsert(kv, frame_pos, commit, _w(0)) + + lo, hi = flat.world_span(w) + assert torch.equal(flat_vis[lo:hi], solo_vis), ( + f"world {w} frame {clocks[w]}: visibility diverged from a solo ring" + ) + outside = torch.ones_like(flat_vis) + outside[lo:hi] = False + assert not bool((flat_vis & outside).any()), ( + f"world {w} can see {int((flat_vis & outside).sum())} slots outside " + "its own span" + ) + # the own-world term, derived here rather than read from the cache + own = ( + torch.arange(flat.total_slots) // flat.capacity + ) == w + assert torch.equal(flat_vis, flat_vis & own) + clocks[w] += 1 + + for w in range(num_worlds): + lo, hi = flat.world_span(w) + assert torch.equal(flat.kv[:, :, :, lo:hi], solo[w].kv), f"world {w} ring bytes" + assert torch.equal(flat.written[lo:hi], solo[w].written), f"world {w} written" + + +def test_worlds_interleave_without_reaching_each_other(): + """The same claim one level up, driven through the resource lifecycle + rather than the cache: three requests, three independent clocks starting at + different frames, forty frames each at five passes, interleaved in an order + none of them controls — against three managers each holding one world and + driven in isolation. + + This is what a node actually does, and it is where a bug in the *lifecycle* + would show up rather than a bug in the mask: a `plan` that staged the wrong + index, a `commit` that recorded against the wrong rid, a claim that two + requests shared. Every one of those is invisible at N=1. + + Each request's K/V comes from its own generator, so the interleave order + cannot change what any request writes — only where it lands. + """ + rids = ["a", "b", "c"] + starts = {"a": 0, "b": 5, "c": 11} + frames = 40 + + flat = _manager(_ring_config(num_worlds=3)) + _open(flat, *rids) + solo = {rid: _manager(_ring_config(num_worlds=1)) for rid in rids} + for rid, mgr in solo.items(): + _open(mgr, rid) + + order = torch.Generator().manual_seed(53) + schedule = [rid for rid in rids for _ in range(frames)] + perm = torch.randperm(len(schedule), generator=order).tolist() + schedule = [schedule[i] for i in perm] + assert schedule[:3] != rids, "the schedule is round-robin; interleave nothing" + + clocks = dict(starts) + seeds = {rid: 200 + i for i, rid in enumerate(rids)} + for rid in schedule: + frame = clocks[rid] + seed = seeds[rid] * 1000 + frame + _drive(flat, rid, frame, seed=seed) + _drive(solo[rid], rid, frame, seed=seed) + clocks[rid] += 1 + + for rid in rids: + world = flat.world_of(rid) + for i, (layer, ref) in enumerate(zip(flat.layers, solo[rid].layers, strict=True)): + lo, hi = layer.world_span(world) + assert torch.equal(layer.kv[:, :, :, lo:hi], ref.kv), ( + f"{rid} layer {i}: interleaving changed what the world holds" + ) + assert torch.equal(layer.written[lo:hi], ref.written), ( + f"{rid} layer {i}: interleaving changed what the world can see" + ) + + # and the mask hid every other world completely, on the last step of each + gen = torch.Generator().manual_seed(59) + for rid in rids: + world = flat.world_of(rid) + flat.plan(_step(rid), _ctx(rid)) + for layer_idx in range(N_LAYERS): + k, v = _frame(flat, gen) + _, _, visible = flat.upsert( + k, v, layer_idx, torch.tensor(clocks[rid], dtype=torch.int64), commit=False + ) + lo, hi = flat.layers[layer_idx].world_span(world) + seen = visible.clone() + seen[lo:hi] = False + assert not bool(seen.any()), ( + f"{rid} layer {layer_idx} can see {int(seen.sum())} slots belonging " + "to another world" + ) + assert bool(visible[lo:hi].any()), ( + "the mask hid everything, including this world's own history; " + "an all-False row would pass the check above vacuously" + ) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="needs a GPU to capture") +def test_the_captured_world_index_is_read_at_replay_not_baked_at_capture(): + """The property the entire layout exists for, and the only test that can + see it. + + `world_idx` is a `[1]` int64 device tensor rather than a Python int for one + reason: a host int — or a `kv[:, w]` view taken with one — has its value (or + its `storage_offset`) folded into the graph at capture time, and every + subsequent replay then serves whichever world capture happened to hold. + Nothing raises. Shapes are identical. One session's frames land in another + session's ring and both keep producing video. + + So: capture against world 0, then release it, push the real request onto a + different index, restage, and replay. The write has to follow the staged + tensor. Every other world, including the one capture used, has to be + untouched. + """ + kv = _manager(_ring_config(num_worlds=4), device="cuda") + layer = kv.layers[0] + + _open(kv, "cap") + kv.plan(_step("cap"), _ctx("cap")) + assert kv.world_of("cap") == 0, "this test needs capture to hold world 0" + + static_k = torch.zeros(1, N_KV_HEADS, TPF, D_HEAD, dtype=torch.float32, device="cuda") + static_v = torch.zeros_like(static_k) + static_frame = torch.zeros((), dtype=torch.int64, device="cuda") + + stream = torch.cuda.Stream() + stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(stream): + for _ in range(3): + kv.upsert(static_k, static_v, 0, static_frame, commit=True) + torch.cuda.current_stream().wait_stream(stream) + + graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(graph): + _, _, captured_visible = kv.upsert( + static_k, static_v, 0, static_frame, commit=True + ) + torch.cuda.synchronize() + + kv.reset_request("cap", free=True) + for world in range(layer.num_worlds): + layer.reset(world) + + # push the real request off world 0: three placeholders take 0, 1, 2 + _open(kv, "pad0", "pad1", "pad2", "real") + world = kv.world_of("real") + assert world == 3 + kv.plan(_step("real"), _ctx("real")) + + static_k.fill_(1.5) + static_v.fill_(-2.5) + graph.replay() + torch.cuda.synchronize() + + lo, hi = layer.world_span(world) + span = layer.kv[:, :, :, lo:hi] + assert bool((span == 1.5).any()), ( + "the replay wrote nothing into the world `plan` staged; `world_idx` was " + "baked at capture" + ) + assert bool(layer.written[lo : lo + TPF].all()), "the frame's ring slot went unmarked" + for other in range(kv.num_worlds): + if other == world: + continue + o_lo, o_hi = layer.world_span(other) + assert not bool(layer.kv[:, :, :, o_lo:o_hi].any()), ( + f"the replay wrote into world {other}; it was staged to write world " + f"{world}" + ) + assert not bool(layer.written[o_lo : o_lo + layer.ring_len].any()) + # the visibility row the graph returns is the layer's scratch, restaged too + assert bool(captured_visible[lo:hi].any()) + other_lo, other_hi = layer.world_span(0) + assert not bool(captured_visible[other_lo:other_hi].any()), ( + "the captured mask still shows the capture-time world" + ) + + +# ── the two branches that do nothing under this schedule ──────────────── + + +def test_the_bucket_rounding_is_unobservable_off_write_steps(): + """`bucket` rounds up, verbatim from the reference, and the rounding is + dead arithmetic: `ring_idx` is discarded at both of its use sites off a + write step, and on a write step `frame_pos` is a multiple of the dilation, + where ceil and floor agree. + + What is pinned here is the *unobservability*, which is the load-bearing + half — it holds for any rounding, not just floor, so it survives someone + changing the expression as well as someone changing its consumers. + + Only the dilated layer can exercise it. At stride 1 every frame is a write + step, the off-write-step case is empty, and a version of this test that ran + layer 0 would assert nothing at all; hence the counter at the end. + """ + layer = _manager(_ring_config(num_worlds=2)).layers[N_LAYERS - 1] + d = layer.pinned_dilation + assert d > 1, "this test needs a layer that has off-write-step frames" + + gen = torch.Generator().manual_seed(31) + off_steps = 0 + for f in range(2 * d * RING_FRAMES): # two full wraps of the bucket ring + before_kv = layer.kv.clone() + before_written = layer.written.clone() + kv = torch.randn(2, 1, N_KV_HEADS, TPF, D_HEAD, generator=gen) + + # commit=True is the harder case: if `ring_idx` were live off a write + # step, this is the call that would write through it. World 1, not 0, so + # a placeholder address that forgot the world offset lands somewhere + # this test can see. + _, _, visible = layer.upsert(kv, torch.tensor(f, dtype=torch.int64), True, _w(1)) + + lo, hi = layer.world_span(1) + if f % d: + off_steps += 1 + expected = before_written.clone() + expected[: lo] = False + expected[hi :] = False + assert torch.equal(visible, expected), f"frame {f} hid a ring slot" + assert torch.equal( + layer.kv[:, :, :, lo : lo + layer.ring_len], + before_kv[:, :, :, lo : lo + layer.ring_len], + ), f"frame {f} wrote the ring through a slot it should not address" + assert torch.equal(layer.written, before_written), f"frame {f} marked a slot" + else: + assert (f + d - 1) // d == f // d, f"ceil != floor on write step {f}" + assert not bool(layer.kv[:, :, :, :lo].any()), f"frame {f} wrote world 0" + + assert off_steps, "no off-write-step frame ran; this test proved nothing" + + +def test_committing_on_every_pass_matches_the_4_plus_1_schedule(): + """`commit` is dead under the shipped schedule and live in general. + + Dead: on a write step the visibility row hides `ring_idx` from all five + passes and the fifth writes it last, so committing on all five leaves the + same visible K/V and a byte-identical ring. Off a write step `dst` is + `current_idx`, which the unconditional write already filled with the same + data, so the commit changes nothing anywhere. + + `raw_differs` is what stops the first half being vacuous: mid-frame the two + rings genuinely diverge, and it is the mask — block-aligned, so the + BlockMask drops the region whole — that makes the divergence unobservable. + + Live: committing on no pass diverges. Deleting the branch on the strength + of the first half would pass everything above and produce a world that + never remembers a frame. + """ + def drive(kv, schedule): + """One `(masked_k, masked_v, raw_k, visible)` per upsert, lazily, so + the three schedules can be compared in lockstep without three full + traces resident at once.""" + gen = torch.Generator().manual_seed(37) + for f in range(3 * RING_FRAMES): + frame_pos = torch.tensor(f, dtype=torch.int64) + for commit in schedule: + for layer_idx in range(N_LAYERS): + k, v = _frame(kv, gen) + k_all, v_all, visible = kv.upsert( + k, v, layer_idx, frame_pos, commit=commit + ) + m = visible[None, None, :, None] + yield k_all * m, v_all * m, k_all, visible + + shipped, always, never = _manager(), _manager(), _manager() + + raw_differs = False + for (sk, sv, s_raw, s_vis), (ak, av, a_raw, a_vis) in zip( + drive(shipped, (False, False, False, False, True)), + drive(always, (True,) * 5), + strict=True, + ): + assert torch.equal(s_vis, a_vis), "visibility diverged" + assert torch.equal(sk, ak) and torch.equal(sv, av), "visible K/V diverged" + raw_differs |= not torch.equal(s_raw, a_raw) + + assert raw_differs, ( + "the rings never differed mid-frame, so the mask was never what made " + "them agree and this test proves nothing" + ) + for s_layer, a_layer in zip(shipped.layers, always.layers, strict=True): + assert torch.equal(s_layer.kv, a_layer.kv), "end-of-frame ring diverged" + assert torch.equal(s_layer.written, a_layer.written) + + for _ in drive(never, (False,) * 5): + pass + assert any( + not torch.equal(s_layer.kv, n_layer.kv) + for s_layer, n_layer in zip(shipped.layers, never.layers, strict=True) + ), "suppressing every commit changed nothing; the branch is not live" + + +# ── the shapes the forward path insists on ────────────────────────────── + + +@pytest.mark.parametrize( + "bad", + [ + pytest.param(torch.tensor(0, dtype=torch.int64), id="scalar"), + pytest.param(torch.tensor([0, 1], dtype=torch.int64), id="two"), + pytest.param(torch.tensor([0], dtype=torch.int32), id="int32"), + ], +) +def test_upsert_refuses_a_world_index_that_is_not_the_staged_shape(bad): + """`[1]` int64, and nothing else — including `[]`, which would broadcast + just as well here. This is not a shape necessity, it is the shape the + resource stages into its static buffer and the shape a replay re-stages, and + letting a second spelling through is how those two drift apart with nothing + to catch it.""" + layer = _manager().layers[0] + kv = torch.zeros(2, 1, N_KV_HEADS, TPF, D_HEAD) + + with pytest.raises(RuntimeError, match="world_idx must be a"): + layer.upsert(kv, torch.tensor(0, dtype=torch.int64), True, bad) + + +def test_a_world_index_out_of_range_is_caught_on_the_host_paths(): + """Only on the host paths. The forward cannot check it — `world_idx` is a + device tensor there and comparing it would cost a sync per upsert — so an + out-of-range index in the graph silently writes past the buffer's last + world or wraps into another's. What keeps it in range is that `admit` is the + only thing that ever produces one.""" + layer = _manager(_ring_config(num_worlds=2)).layers[0] + + assert layer.world_span(1) == (layer.capacity, 2 * layer.capacity) + for bad in (-1, 2): + with pytest.raises(IndexError, match="out of range"): + layer.world_span(bad) + with pytest.raises(IndexError, match="out of range"): + layer.reset(bad) diff --git a/test/modular/test_waypoint_components.py b/test/modular/test_waypoint_components.py index bfe3e97d2..123737185 100644 --- a/test/modular/test_waypoint_components.py +++ b/test/modular/test_waypoint_components.py @@ -1,23 +1,27 @@ -"""Component-level contract tests for the Waypoint-1.5 port: ring KV cache, -BlockMask, OrthoRoPE and the small layers. - -The bar here is **the normative document**, ``docs/waypoint/CONTRACTS.md``, not -"the code does what the code does". Every failure mode this file guards against -is silent: a wrong ring slot, a re-derived bucket count, a permuted controller -concat and an un-compiled ``flex_attention`` all produce plausible video and -raise nothing. So the assertions are exact wherever the contract says exact -(bitwise for the compaction A/B, for the RoPE angle tables, for the ring bytes -after a frozen pass) and never widened to accommodate the implementation. - -Sections map onto CONTRACTS: 2.3/2.3.1 (BlockMask + the compile trap, DECISIONS -D10), 2.2 (upsert), 2.1/2.4 (geometry, compaction, DECISIONS D6), 4.3 (OrthoRoPE) -and 4.5 (the layer primitives). +"""Component-level contract tests for the Waypoint-1.5 port: the ring geometry +its config implies, OrthoRoPE and the small layers. + +The bar is the reference implementation at ``world_engine/src/``, not "the code +does what the code does". Every failure mode this file guards against is silent: +a wrong ring slot, a re-derived bucket count and a permuted controller concat all +produce plausible video and raise nothing. So the assertions are exact wherever +the reference is exact (bitwise for the compaction A/B, for the RoPE angle +tables, for the ring bytes after a frozen pass) and never widened to accommodate +the implementation. + +**The ring and the kernel are engine resources now**, imported below from +``mstar.engine.resources``. Their own contracts -- ownership, the capture +lifecycle, the ``visible`` aliasing hazard, the eager-``flex_attention`` trap -- +are pinned next door in ``test_ring_kv_resource.py`` and +``test_flex_attention_resource.py``. What stays here is the half those files +cannot see: that *Waypoint's config* produces the geometry the checkpoint was +trained against, and that the ring arithmetic matches the reference's tables. CPU-only and checkpoint-free by construction. Numeric work runs on a reduced but structurally identical config (4 layers / 128 tokens per frame / d_head 32, one global layer at stride 8); the real 720P config is used only where the assertion is about geometry rather than activations. ``torch.compile(flex_attention)`` -works on CPU in torch 2.9, which is what makes the compile-trap test possible +works on CPU in torch 2.9, which is what makes the compaction A/B runnable without a GPU -- it costs a few seconds of inductor time on first use. """ @@ -37,13 +41,18 @@ noop_mask, ) -from mstar.model.waypoint.components.kv_backend import ( - FlexRingBackend, - LayerRingCache, - flex_attention_masked, - make_block_mask, - ring_memory_bytes, +from mstar.engine.resources.attn.base import AttentionManager +from mstar.engine.resources.attn.config import AttentionConfig, AttentionSpec, AttnBackend +from mstar.engine.resources.attn.flex import flex_attention_masked, make_block_mask +from mstar.engine.resources.base import EngineResourceInfo +from mstar.engine.resources.kv.config import ( + KVSpec, + RingKVConfig, + RingKVLayerConfig, + RingKVStep, ) +from mstar.engine.resources.kv.ring import LayerRingCache, RingKVManager +from mstar.engine.resources.step import StepContext from mstar.model.waypoint.components.layers import ( MLP, AdaLN, @@ -56,10 +65,10 @@ ) from mstar.model.waypoint.components.rope import OrthoRoPEAngles, apply_ortho_rope from mstar.model.waypoint.config import WaypointConfig, waypoint_1_5_1b_720p +from mstar.model.waypoint.ring_geometry import ring_memory_bytes BLOCK = _DEFAULT_SPARSE_BLOCK_SIZE # 128 TPF = 128 # tokens per frame in the reduced config == one sparse block -D_HEAD = 32 def reduced_config(**overrides) -> WaypointConfig: @@ -69,7 +78,7 @@ def reduced_config(**overrides) -> WaypointConfig: Only the sizes shrink. ``global_window // global_pinned_dilation == 4`` addressable slots against a 32-frame reference allocation keeps the 8x - over-allocation that CONTRACTS 2.4 is about, while letting a test wrap the + over-allocation the compaction deviation is about, while letting a test wrap the global ring in 32 frames instead of 128. """ base = { @@ -109,8 +118,79 @@ def visible_blocks(block_mask) -> set[int]: return set(block_mask.full_kv_indices[0, 0, 0, :n].tolist()) +def ring_kv_spec(config: WaypointConfig, *, num_worlds: int = 1) -> KVSpec: + """The ``RingKVConfig`` a ``WaypointConfig``'s geometry implies -- which is + the bridge under test in most of section 3. + + Hand-rolled because the model does not declare its specs yet; when + ``WaypointModel`` grows a ``get_node_resources`` this becomes a call to it. + Every field is read off the config rather than restated, so a geometry + change cannot leave these tests measuring a ring the model no longer asks + for. + """ + return KVSpec( + resource_key="kv", + nodes={"dit"}, + config=RingKVConfig( + num_layers=config.n_layers, + num_kv_heads=config.n_kv_heads, + head_dim=config.d_head, + num_qo_heads=config.n_heads, + tokens_per_frame=config.tokens_per_frame, + num_worlds=num_worlds, + layers=tuple( + RingKVLayerConfig( + ring_frames=config.ring_frames(i), + ring_buckets=config.ring_buckets(i), + pinned_dilation=config.pinned_dilation(i), + ) + for i in range(config.n_layers) + ), + ), + ) + + +def ring_manager(config: WaypointConfig, *, num_worlds: int = 1) -> RingKVManager: + """``config``'s rings, allocated. Through ``build(spec, info)`` and not the + constructor: the spec is what picks ``RingKVManager`` over the paged one.""" + return RingKVManager.build( + ring_kv_spec(config, num_worlds=num_worlds), + EngineResourceInfo(device=torch.device("cpu"), kv_dtype=torch.float32), + ) + + +def _dit_ctx(*rids: str) -> StepContext: + return StepContext(request_ids=tuple(rids), graph_walk="rollout", slot=0, capture=False) + + +def waypoint_resources(config: WaypointConfig): + """The ring and the kernel a Waypoint deployment would get, built through + the real spec-time factories rather than the constructors: the spec is also + what cross-checks the flex backend against a ring config.""" + kv_spec = ring_kv_spec(config) + cpu = torch.device("cpu") + kv = ring_manager(config) + attn = AttentionManager.build( + AttentionSpec( + resource_key="attn", + nodes={"dit"}, + config=AttentionConfig(kv_cache="kv", backend=AttnBackend.FLEX), + ), + EngineResourceInfo(device=cpu, kv_dtype=torch.float32, dependencies={"kv": kv_spec}), + ) + return kv, attn + + # --------------------------------------------------------------------------- -# 1. The eager-FlexAttention trap (CONTRACTS 2.3 / 2.3.1, DECISIONS D10) +# 1. The BlockMask's shape +# +# The trap itself -- eager `flex_attention` ignoring a no-op `mask_mod` and +# blending every unwritten ring slot in -- is pinned at its owner in +# `test_flex_attention_resource.py`, along with the compiled-path regression +# guard and `make_block_mask`'s alignment checks. What stays here is the half +# those numeric tests cannot establish about themselves: the mask's structure, +# and the all-visible control that makes their divergence attributable to the +# mask rather than to the two kernels merely computing softmax differently. # --------------------------------------------------------------------------- @@ -121,8 +201,9 @@ def test_block_mask_is_full_blocks_only_and_carries_a_noop_mask_mod(): Note the exact shape of the fact: ``make_block_mask`` passes ``mask_mod=None``, and ``BlockMask.from_kv_blocks`` substitutes - ``flex_attention.noop_mask``. CONTRACTS 2.3 says the BlockMask "carries - ``mask_mod=None``"; what it carries is the noop, which is the same hazard. + ``flex_attention.noop_mask``. The hazard is often stated as the BlockMask + "carrying ``mask_mod=None``"; what it carries is the noop, which is the + same hazard. """ written = torch.zeros(5 * BLOCK, dtype=torch.bool) written[0 * BLOCK : 1 * BLOCK] = True # one committed frame @@ -140,122 +221,64 @@ def test_block_mask_is_full_blocks_only_and_carries_a_noop_mask_mod(): assert bm.full_kv_num_blocks.shape == (1, 1, TPF // BLOCK) -def test_make_block_mask_rejects_unaligned_and_non_multiple_lengths(): - written = torch.zeros(5 * BLOCK, dtype=torch.bool) - written[:BLOCK] = True - with pytest.raises(RuntimeError, match="multiple of block size"): - make_block_mask(TPF + 1, written.numel(), written) - with pytest.raises(RuntimeError, match="multiple of block size"): - make_block_mask(TPF, written.numel() - 1, written[:-1]) - - ragged = torch.zeros(2 * BLOCK, dtype=torch.bool) - ragged[3] = True # one token of a block, which no whole-frame write can produce - with pytest.raises(AssertionError, match="block-aligned"): - make_block_mask(TPF, ragged.numel(), ragged) - - -def test_flex_attention_masked_is_a_compiled_callable(): - """If someone "simplifies" ``kv_backend.flex_attention_masked`` back to the - bare function, this fails first and says why.""" - assert flex_attention_masked is not flex_attention - assert hasattr(flex_attention_masked, "_torchdynamo_orig_callable"), ( - "kv_backend.flex_attention_masked must stay wrapped in torch.compile: with " - "mask_mod=None the eager kernel ignores the ring mask entirely (CONTRACTS 2.3.1)" - ) +def test_a_fully_visible_ring_makes_eager_and_compiled_agree(): + """The control for the eager-flex trap, and the reason the divergence next + door is a diagnosis rather than an observation. + ``test_eager_flex_attention_does_not_honour_the_block_mask`` shows the two + kernels disagreeing on a partly-hidden row. On its own that is also what + two kernels with different softmax numerics would look like. Take the mask + out -- same q/k/v, every block visible -- and they agree to ~1e-07, which + leaves the mask as the only thing the disagreement can be attributed to. + Delete this and the ~1e-01 next door stops meaning "eager ignored the mask". -@pytest.mark.parametrize( - ("label", "committed_blocks"), - [ - ("one_frame", (0,)), - ("half_the_ring", (0, 2)), - ("full_ring", (0, 1, 2, 3)), - ], -) -def test_compiled_flex_attention_honours_the_ring_mask_and_eager_does_not(label, committed_blocks): - """CONTRACTS 2.3.1, DECISIONS D10 -- the single most valuable test here. - - A hand-built masked-dense SDPA is the reference. The compiled kernel matches - it to ~1e-07; eager ``flex_attention`` blends in every unwritten ring slot - (which holds zeros) and is off by ~1e-01. **Nothing raises in either case.** - So the guard has to be numeric, and it has to assert both halves: that the - compiled path is right *and* that the trap is real, because if a future torch - ever fixed eager the "unless someone removes the compile" argument would - quietly stop being tested and this test should be revisited rather than - silently passing. + The all-visible row has to be built by hand: a live Waypoint ring never + emits one. The slot the current frame is about to overwrite is always + hidden (see ``test_mask_hides_the_slot_this_frame_is_about_to_overwrite``), + so the steady state is capacity minus exactly one block, forever. """ - capacity = 5 * BLOCK # 4 ring frames + 1 scratch, at one block per frame - written = torch.zeros(capacity, dtype=torch.bool) - for b in committed_blocks: - written[b * BLOCK : (b + 1) * BLOCK] = True - written[4 * BLOCK :] = True - bm = make_block_mask(TPF, capacity, written) + config = reduced_config() + capacity = config.kv_capacity(0) + visible = torch.ones(capacity, dtype=torch.bool) + bm = make_block_mask(TPF, capacity, visible) gen = torch.Generator().manual_seed(0xC0FFEE) - q = torch.randn(1, 2, TPF, D_HEAD, generator=gen) - k = torch.zeros(1, 2, capacity, D_HEAD) - v = torch.zeros(1, 2, capacity, D_HEAD) - # Only written slots hold data; the rest stay zero, exactly as a fresh ring is. - k[:, :, written] = torch.randn(1, 2, int(written.sum()), D_HEAD, generator=gen) - v[:, :, written] = torch.randn(1, 2, int(written.sum()), D_HEAD, generator=gen) - - dense = F.scaled_dot_product_attention( - q, k, v, attn_mask=written[None, None, None, :].expand(1, 2, TPF, capacity) - ) - compiled = flex_attention_masked(q, k, v, block_mask=bm, enable_gqa=False) - eager = flex_attention(q, k, v, block_mask=bm, enable_gqa=False) + q = torch.randn(1, config.n_heads, TPF, config.d_head, generator=gen) + k = torch.randn(1, config.n_kv_heads, capacity, config.d_head, generator=gen) + v = torch.randn(1, config.n_kv_heads, capacity, config.d_head, generator=gen) + # Every slot holds data here, unlike the trap's fixture: with nothing masked + # off there is no zero slot for eager to blend in, which is the point. + kx = k.repeat_interleave(config.n_heads // config.n_kv_heads, dim=1) + vx = v.repeat_interleave(config.n_heads // config.n_kv_heads, dim=1) + dense = torch.softmax((q @ kx.transpose(-1, -2)) / config.d_head**0.5, dim=-1) @ vx + + compiled = flex_attention_masked(q, k, v, block_mask=bm, enable_gqa=True) + eager = flex_attention(q, k, v, block_mask=bm, enable_gqa=True) compiled_err = (compiled - dense).abs().max().item() eager_err = (eager - dense).abs().max().item() - print(f"[{label}] compiled vs masked-dense={compiled_err:.3e} eager vs masked-dense={eager_err:.3e}") - - assert compiled_err < 1e-5, ( - f"compiled flex_attention diverged from the masked-dense reference ({compiled_err:.3e})" - ) - if len(committed_blocks) == 4: - # Nothing is masked off (whole ring + scratch written), so eager agrees. - assert eager_err < 1e-5 - else: - assert eager_err > 1e-2, ( - "eager flex_attention agreed with the masked reference; the trap CONTRACTS 2.3.1 " - "documents may have been fixed upstream, in which case re-derive the guard rather " - "than deleting it" - ) - - -def test_backend_attend_takes_the_compiled_path(): - """The regression guard proper: ``FlexRingBackend.attend`` must produce the - compiled result, not the eager one. Replacing ``flex_attention_masked`` with - ``flex_attention`` inside ``attend`` turns this red.""" - config = reduced_config() - backend = FlexRingBackend(config, "cpu", dtype=torch.float32, batch_size=1) - gen = torch.Generator().manual_seed(11) + print(f"[full_ring] compiled={compiled_err:.3e} eager={eager_err:.3e}") - fp = torch.tensor(0, dtype=torch.int64) - backend.set_frozen(False) - k = torch.randn(1, 1, TPF, config.d_head, generator=gen) - v = torch.randn(1, 1, TPF, config.d_head, generator=gen) - k_all, v_all, bm = backend.upsert(k, v, 0, fp) - q = torch.randn(1, 2, TPF, config.d_head, generator=gen) - - got = backend.attend(q, k_all, v_all, bm, enable_gqa=True) - want = flex_attention_masked(q, k_all, v_all, block_mask=bm, enable_gqa=True) - trap = flex_attention(q, k_all, v_all, block_mask=bm, enable_gqa=True) - - assert torch.equal(got, want), "FlexRingBackend.attend is not using flex_attention_masked" - assert (got - trap).abs().max().item() > 1e-2, ( - "attend's output is indistinguishable from the eager path; the mask is not being honoured" + assert compiled_err < 1e-5, f"the compiled path diverged with nothing masked ({compiled_err:.3e})" + assert eager_err < 1e-5, ( + f"eager disagreed with dense on a fully visible row ({eager_err:.3e}); the eager/compiled " + "gap is then not purely the mask, and that diagnosis needs re-deriving" ) # --------------------------------------------------------------------------- -# 2. Ring rotation and the upsert algorithm (CONTRACTS 2.2) +# 2. Ring rotation and the upsert algorithm # --------------------------------------------------------------------------- def make_cache(*, ring_frames: int, ring_buckets: int, dilation: int) -> LayerRingCache: + """One world, because this section is about the ring *algorithm* — which + slot a frame lands in, which slot it hides — and that is per world and + identical at any ``num_worlds``. The folded layout and its isolation are + pinned where they belong, in ``test_ring_kv_resource.py``; driving them + again here would only make these tests slower to read.""" return LayerRingCache( - batch=1, + num_worlds=1, n_kv_heads=1, ring_frames=ring_frames, ring_buckets=ring_buckets, @@ -267,6 +290,19 @@ def make_cache(*, ring_frames: int, ring_buckets: int, dilation: int) -> LayerRi ) +def upsert(cache: LayerRingCache, kv, frame_pos, *, commit: bool, world: int = 0): + """``LayerRingCache.upsert`` with the world index spelled out. + + Not a default on ``upsert`` itself, deliberately. ``world_idx`` is a ``[1]`` + int64 *device* tensor on the forward path and never a Python int — a host + int is folded into the graph at capture and every replay then serves the + capture-time world, silently. A default argument is exactly how a caller + ends up not thinking about which world it writes, so the cache takes it + positionally and this helper is the only place the zero is written down. + """ + return cache.upsert(kv, frame_pos, commit, torch.tensor([world], dtype=torch.int64)) + + @pytest.mark.parametrize( ("kind", "dilation", "frames", "expected_slots"), [ @@ -277,12 +313,12 @@ def make_cache(*, ring_frames: int, ring_buckets: int, dilation: int) -> LayerRi ], ) def test_ring_slot_rotation(kind, dilation, frames, expected_slots): - """CONTRACTS 2.2: 'global commits land on frames 0, 8, 16, ... in slots - 0, 1, 2, ...; local slots cycle 0..15.' The slot holds the frame index that + """Global commits land on frames 0, 8, 16, ... in slots + 0, 1, 2, ...; local slots cycle 0..15. The slot holds the frame index that last wrote it, so the expected list is the whole history at once.""" cache = make_cache(ring_frames=16, ring_buckets=16, dilation=dilation) for f in frames: - cache.upsert(frame_kv(f), torch.tensor(f, dtype=torch.int64), is_frozen=False) + upsert(cache, frame_kv(f), torch.tensor(f, dtype=torch.int64), commit=True) assert ring_slot_values(cache) == [float(v) for v in expected_slots], kind assert bool(cache.written[: cache.ring_len].all()) @@ -292,91 +328,99 @@ def test_frozen_passes_leave_the_ring_byte_identical(): would corrupt the world state permanently, and nothing would raise.""" cache = make_cache(ring_frames=4, ring_buckets=4, dilation=1) for f in range(4): - cache.upsert(frame_kv(f), torch.tensor(f, dtype=torch.int64), is_frozen=False) + upsert(cache, frame_kv(f), torch.tensor(f, dtype=torch.int64), commit=True) ring_before = cache.kv[:, :, :, : cache.ring_len].clone() written_before = cache.written.clone() scratch_before = cache.kv[:, :, :, cache.ring_len :].clone() for pass_idx in range(4): # the four Euler steps, each a different noisy x - cache.upsert(frame_kv(100 + pass_idx), torch.tensor(4, dtype=torch.int64), is_frozen=True) + upsert(cache, frame_kv(100 + pass_idx), torch.tensor(4, dtype=torch.int64), commit=False) assert torch.equal(cache.kv[:, :, :, : cache.ring_len], ring_before), ( "a frozen pass wrote the ring; that is amnesia, not a cache miss" ) assert torch.equal(cache.written, written_before) # ...but the scratch write is unconditional: it is how the frame being - # denoised attends to itself between Euler steps (CONTRACTS 2.2 point 1). + # denoised attends to itself between Euler steps. assert not torch.equal(cache.kv[:, :, :, cache.ring_len :], scratch_before) assert cache.kv[0, 0, 0, cache.ring_len, 0].item() == 103.0 def test_mask_hides_the_slot_this_frame_is_about_to_overwrite(): - """CONTRACTS 2.2 point 2, and it applies on frozen passes too, so all five - passes of a frame see byte-identical KV.""" + """And it applies on frozen passes too, so all five passes of a frame see + byte-identical KV. + + Asserted through ``make_block_mask`` rather than on the ``visible`` row + directly: the row is what ``upsert`` returns now, but what the kernel reads + is the block list built from it, and this is the only place the two are + checked to agree over a whole 4+1 frame.""" cache = make_cache(ring_frames=4, ring_buckets=4, dilation=1) for f in range(4): - cache.upsert(frame_kv(f), torch.tensor(f, dtype=torch.int64), is_frozen=False) + upsert(cache, frame_kv(f), torch.tensor(f, dtype=torch.int64), commit=True) assert set(range(5)) == visible_blocks( make_block_mask(TPF, cache.capacity, cache.written) ), "precondition: the whole ring plus scratch is written" fp = torch.tensor(4, dtype=torch.int64) # slot 0 is about to be reused - for is_frozen in (True, True, True, True, False): - _, _, bm = cache.upsert(frame_kv(4), fp, is_frozen=is_frozen) - assert visible_blocks(bm) == {1, 2, 3, 4}, ( + for commit in (False, False, False, False, True): + _, _, visible = upsert(cache, frame_kv(4), fp, commit=commit) + assert visible_blocks(make_block_mask(TPF, cache.capacity, visible)) == {1, 2, 3, 4}, ( "frame 4 can see the stale frame 0 sitting in the slot it is replacing" ) def test_global_layer_commits_nothing_on_non_dilation_frames(): - """CONTRACTS 2.2 point 3: ``torch.where(write_step, ring_idx, current_idx)`` + """``torch.where(write_step, ring_idx, current_idx)`` redirects the commit onto the scratch slot it just wrote.""" cache = make_cache(ring_frames=4, ring_buckets=4, dilation=8) - cache.upsert(frame_kv(0), torch.tensor(0, dtype=torch.int64), is_frozen=False) + upsert(cache, frame_kv(0), torch.tensor(0, dtype=torch.int64), commit=True) ring_before = cache.kv[:, :, :, : cache.ring_len].clone() written_before = cache.written.clone() for f in range(1, 8): # the 7 non-committing frames of every 8 - cache.upsert(frame_kv(f), torch.tensor(f, dtype=torch.int64), is_frozen=False) + upsert(cache, frame_kv(f), torch.tensor(f, dtype=torch.int64), commit=True) assert torch.equal(cache.kv[:, :, :, : cache.ring_len], ring_before) assert torch.equal(cache.written, written_before) assert cache.kv[0, 0, 0, cache.ring_len, 0].item() == 7.0 # scratch has the latest - cache.upsert(frame_kv(8), torch.tensor(8, dtype=torch.int64), is_frozen=False) + upsert(cache, frame_kv(8), torch.tensor(8, dtype=torch.int64), commit=True) assert ring_slot_values(cache)[:2] == [0.0, 8.0] -def floor_bucket_upsert(cache: LayerRingCache, kv, frame_pos, is_frozen: bool): +def floor_bucket_upsert(cache: LayerRingCache, kv, frame_pos, commit: bool): """``LayerRingCache.upsert`` with the round-up dropped: ``bucket = f // d`` instead of ``(f + d - 1) // d``. Everything else is statement-for-statement the same. Used only to A/B the round-up.""" tokens = cache.tokens_per_frame + world_idx = torch.tensor([0], dtype=torch.int64) + world_base = world_idx * cache.capacity slot = (frame_pos // cache.pinned_dilation) % cache.ring_buckets - ring_idx = cache.frame_offsets + slot * tokens + ring_idx = cache.frame_offsets + slot * tokens + world_base + current_idx = cache._current_base + world_base - cache.kv.index_copy_(3, cache.current_idx, kv) + cache.kv.index_copy_(3, current_idx, kv) write_step = frame_pos.remainder(cache.pinned_dilation) == 0 mask_written = torch.empty_like(cache.written) mask_written.copy_(cache.written) + mask_written &= cache._world_of_slot == world_idx mask_written[ring_idx] = mask_written[ring_idx] & ~write_step - bm = make_block_mask(tokens, cache.capacity, mask_written) - if not is_frozen: - dst = torch.where(write_step, ring_idx, cache.current_idx) + if commit: + dst = torch.where(write_step, ring_idx, current_idx) cache.kv.index_copy_(3, dst, kv) - cache.written[dst] = True - return bm + cache.written.index_fill_(0, dst, True) + return mask_written @pytest.mark.parametrize("dilation", [1, 8]) def test_bucket_round_up_is_faithful_but_currently_unobservable(dilation): - """CONTRACTS 2.2 point 4 says flooring instead of rounding up "rotates the - entire history by one slot". **That consequence does not hold** for any - geometry this checkpoint uses, and this test pins the real behaviour rather - than the documented one. + """Flooring instead of rounding up is said to "rotate the entire history by + one slot". **That consequence does not hold** for any geometry this + checkpoint uses, and this test pins the real behaviour rather than the + claim. ``ceil`` and ``floor`` agree on every committing frame (``(8j + 7) // 8 == 8j // 8 == j``) and at ``dilation == 1`` they are equal @@ -392,11 +436,13 @@ def test_bucket_round_up_is_faithful_but_currently_unobservable(dilation): for f in range(24): fp = torch.tensor(f, dtype=torch.int64) for pass_idx in range(5): - is_frozen = pass_idx < 4 + commit = pass_idx == 4 kv = frame_kv(f * 10 + pass_idx) - _, _, ceil_bm = ceil_cache.upsert(kv, fp, is_frozen=is_frozen) - floor_bm = floor_bucket_upsert(floor_cache, kv, fp, is_frozen) - assert visible_blocks(ceil_bm) == visible_blocks(floor_bm), f"masks differ at frame {f}" + _, _, ceil_visible = upsert(ceil_cache, kv, fp, commit=commit) + floor_visible = floor_bucket_upsert(floor_cache, kv, fp, commit) + # Per token, not per block: the rows are what the mask is built + # from, so equal rows is the stronger statement of the two. + assert torch.equal(ceil_visible, floor_visible), f"visibility differs at frame {f}" assert torch.equal(ceil_cache.kv, floor_cache.kv) assert torch.equal(ceil_cache.written, floor_cache.written) @@ -406,59 +452,72 @@ def test_upsert_rejects_a_wrong_shaped_frame_or_clock(): cache = make_cache(ring_frames=4, ring_buckets=4, dilation=1) fp = torch.tensor(0, dtype=torch.int64) with pytest.raises(RuntimeError, match="exactly one frame per upsert"): - cache.upsert(frame_kv(0, tokens=TPF // 2), fp, is_frozen=False) + upsert(cache, frame_kv(0, tokens=TPF // 2), fp, commit=True) with pytest.raises(RuntimeError, match=r"frame_pos must be a \[\] int64 tensor"): - cache.upsert(frame_kv(0), torch.tensor([0], dtype=torch.int64), is_frozen=False) + upsert(cache, frame_kv(0), torch.tensor([0], dtype=torch.int64), commit=True) with pytest.raises(RuntimeError, match=r"frame_pos must be a \[\] int64 tensor"): - cache.upsert(frame_kv(0), torch.tensor(0, dtype=torch.int32), is_frozen=False) + upsert(cache, frame_kv(0), torch.tensor(0, dtype=torch.int32), commit=True) def test_reset_restores_a_fresh_ring(): cache = make_cache(ring_frames=4, ring_buckets=4, dilation=1) for f in range(4): - cache.upsert(frame_kv(f + 1), torch.tensor(f, dtype=torch.int64), is_frozen=False) - cache.reset() + upsert(cache, frame_kv(f + 1), torch.tensor(f, dtype=torch.int64), commit=True) + cache.reset(0) assert not bool(cache.kv.any()) # The scratch tail stays permanently visible -- masking it removes - # self-attention (CONTRACTS 2.2 point 5). + # self-attention. assert not bool(cache.written[: cache.ring_len].any()) assert bool(cache.written[cache.ring_len :].all()) -def test_backend_state_roundtrip_is_a_deep_copy(): +def test_ring_state_is_a_deep_copy_and_is_specific_to_the_compaction_setting(): + """The Waypoint half of the state round trip: a state saved from a compacted + deployment must not load into a ``full_global_ring`` one. + + The geometries differ only on the global layers (5 frames vs 33 here), so + every local layer would copy cleanly and only layer 3 would fail -- and if + the guard were a bare ``copy_`` instead of a shape check, a run where the + dims happened to broadcast would restore a silently replicated frame. The + generic clone/copy_/layer-count guarantees are pinned in + ``test_ring_kv_resource.py``; what is here is that the compaction flag is + part of a state's identity. + """ config = reduced_config() - backend = FlexRingBackend(config, "cpu", dtype=torch.float32, batch_size=1) - backend.set_frozen(False) + kv = ring_manager(config) + kv.ingest_request("a") + assert kv.admit(RingKVStep(frames=(("a", 0),)), _dit_ctx("a")).ok gen = torch.Generator().manual_seed(3) for layer in range(config.n_layers): - kv = torch.randn(1, 1, TPF, config.d_head, generator=gen) - backend.upsert(kv, kv, layer, torch.tensor(0, dtype=torch.int64)) + frame = torch.randn(1, 1, TPF, config.d_head, generator=gen) + kv.upsert(frame, frame, layer, torch.tensor(0, dtype=torch.int64), commit=True) - state = backend.get_state() + state = kv.get_state("a") snapshot = [t.clone() for t, _ in state["layers"]] - backend.reset() - assert not any(layer.kv.any() for layer in backend.layers) + for layer in kv.layers: + layer.reset(kv.world_of("a")) + assert not any(layer.kv.any() for layer in kv.layers) # get_state must clone: the reset above must not have reached the snapshot. assert all(torch.equal(a, b) for a, b in zip((t for t, _ in state["layers"]), snapshot, strict=True)) - backend.load_state(state) - assert all(torch.equal(layer.kv, t) for layer, (t, _) in zip(backend.layers, state["layers"], strict=True)) + kv.load_state("a", state) + assert all(torch.equal(layer.kv, t) for layer, (t, _) in zip(kv.layers, state["layers"], strict=True)) - other = FlexRingBackend( - dataclasses.replace(config, full_global_ring=True), "cpu", dtype=torch.float32, batch_size=1 - ) + other = ring_manager(dataclasses.replace(config, full_global_ring=True)) + other.ingest_request("a") + assert other.admit(RingKVStep(frames=(("a", 0),)), _dit_ctx("a")).ok with pytest.raises(ValueError, match="state shape"): - other.load_state(state) + other.load_state("a", state) # --------------------------------------------------------------------------- -# 3. The compaction deviation and the num_buckets trap (CONTRACTS 2.1 / 2.4) +# 3. The compaction deviation and the num_buckets trap # --------------------------------------------------------------------------- def test_720p_ring_geometry_matches_the_contract_table(): - """CONTRACTS 2.1. The "addressable slots" and "ring frames allocated" - columns are independent, and 2.4 turns on keeping them independent.""" + """The "addressable slots" and "ring frames allocated" columns are + independent, and the compaction deviation turns on keeping them so.""" config = waypoint_1_5_1b_720p() assert sorted(config.global_layers) == [3, 7, 11, 15, 19, 23] @@ -477,12 +536,12 @@ def test_720p_ring_geometry_matches_the_contract_table(): compacted_bytes = sum(ring_memory_bytes(config)) full_bytes = sum(ring_memory_bytes(full)) - assert compacted_bytes == 816 * 2**20 # CONTRACTS 2.4: 816 MiB + assert compacted_bytes == 816 * 2**20 # 816 MiB assert full_bytes - compacted_bytes == 1_409_286_144 # ...saving 1.3125 GiB def test_ring_buckets_is_an_input_not_a_derivation(): - """CONTRACTS 2.4, "the trap". The reference computes + """The trap. The reference computes ``num_buckets = (L // tpf) // dilation``. Against the compacted ring that yields 2, not 16 -- a global layer would retain 2 frames instead of 16, with no shape error and no exception.""" @@ -492,7 +551,7 @@ def test_ring_buckets_is_an_input_not_a_derivation(): assert reference_derivation == 2, "the compacted buffer no longer encodes the bucket count" assert config.ring_buckets(global_layer) == 16, ( "ring_buckets must come from global_window // global_pinned_dilation, " - "never from ring_frames (CONTRACTS 2.4)" + "never from ring_frames" ) # And it is genuinely independent of the allocation knob. full = dataclasses.replace(config, full_global_ring=True) @@ -509,7 +568,7 @@ def test_compacted_global_ring_addresses_all_sixteen_slots(): for j in range(16): f = 8 * j - cache.upsert(frame_kv(f), torch.tensor(f, dtype=torch.int64), is_frozen=False) + upsert(cache, frame_kv(f), torch.tensor(f, dtype=torch.int64), commit=True) assert ring_slot_values(cache) == [float(8 * j) for j in range(16)] assert bool(cache.written[: cache.ring_len].all()), "a 2-bucket ring would leave 14 slots unwritten" @@ -519,27 +578,28 @@ def test_compacted_global_ring_addresses_all_sixteen_slots(): def drive_ring(config: WaypointConfig, n_frames: int, seed: int = 7) -> list[torch.Tensor]: - """Run ``n_frames`` of the real 4+1 pass structure through a backend and - collect every attention output. The K/V/Q streams are drawn from a seeded - generator so two backends see byte-identical inputs.""" - backend = FlexRingBackend(config, "cpu", dtype=torch.float32, batch_size=1) + """Run ``n_frames`` of the real 4+1 pass structure through ``config``'s + resources and collect every attention output. The K/V/Q streams are drawn + from a seeded generator so two geometries see byte-identical inputs.""" + kv, attn = waypoint_resources(config) gen = torch.Generator().manual_seed(seed) outputs = [] for f in range(n_frames): fp = torch.tensor(f, dtype=torch.int64) for pass_idx in range(5): - backend.set_frozen(pass_idx < 4) for layer in range(config.n_layers): k = torch.randn(1, 1, TPF, config.d_head, generator=gen) v = torch.randn(1, 1, TPF, config.d_head, generator=gen) q = torch.randn(1, 2, TPF, config.d_head, generator=gen) - k_all, v_all, bm = backend.upsert(k, v, layer, fp) - outputs.append(backend.attend(q, k_all, v_all, bm, enable_gqa=True)) + k_all, v_all, visible = kv.upsert( + k, v, layer, fp, commit=pass_idx == 4 + ) + outputs.append(attn.attend(q, k_all, v_all, visible, enable_gqa=True)) return outputs def test_compacted_and_full_global_rings_are_bitwise_identical(): - """CONTRACTS 2.4, DECISIONS D6. ``from_kv_blocks`` derives the visited list + """``from_kv_blocks`` derives the visited list from a *stable* descending argsort truncated to the visited count, so dropping never-written blocks changes neither which blocks are attended nor the order they accumulate in. Bit-equality is therefore the correct bar and @@ -565,13 +625,13 @@ def test_compacted_and_full_global_rings_are_bitwise_identical(): # --------------------------------------------------------------------------- -# 4. OrthoRoPE (CONTRACTS 4.3) +# 4. OrthoRoPE # --------------------------------------------------------------------------- def reference_angles(config: WaypointConfig, x_pos, y_pos, t_pos): - """The reference's own angle construction, transcribed from CONTRACTS 4.3 - (and matching ``world_engine/src/model/attn.py::OrthoRoPEAngles``).""" + """The reference's own angle construction, transcribed from + ``world_engine/src/model/attn.py::OrthoRoPEAngles``.""" d_head = config.d_head d_xy, d_t = d_head // 8, d_head // 4 max_freq = min(config.height, config.width) * float(config.rope_nyquist_frac) @@ -608,9 +668,9 @@ def test_ortho_rope_angles_match_the_reference_construction_bitwise(): def test_the_bands_count_rotation_pairs_and_cover_every_head_dim(): - """CONTRACTS 4.3: ``d_xy = d_head // 8`` and ``d_t = d_head // 4`` count - rotation PAIRS. 8 + 8 + 16 = 32 pairs = 64 dims -- nothing is unrotated. - (An earlier revision of the doc claimed the top half was untouched.)""" + """``d_xy = d_head // 8`` and ``d_t = d_head // 4`` count rotation PAIRS. + 8 + 8 + 16 = 32 pairs = 64 dims -- nothing is unrotated. (An earlier + reading of this had the top half untouched.)""" config = waypoint_1_5_1b_720p() d_head = config.d_head d_xy, d_t = d_head // 8, d_head // 4 @@ -630,7 +690,7 @@ def test_the_bands_count_rotation_pairs_and_cover_every_head_dim(): [("x", 0, 8, 0, 16), ("y", 8, 16, 16, 32), ("t", 16, 32, 32, 64)], ) def test_axis_band_ownership(axis, pair_lo, pair_hi, dim_lo, dim_hi): - """x owns head dims 0-15, y 16-31, t 32-63 (CONTRACTS 4.3). Pair ``p`` + """x owns head dims 0-15, y 16-31, t 32-63. Pair ``p`` consumes input dims ``2p`` and ``2p+1``, so the pair band and the dim band are the same statement twice.""" config = waypoint_1_5_1b_720p() @@ -665,7 +725,7 @@ def test_axis_band_ownership(axis, pair_lo, pair_hi, dim_lo, dim_hi): def test_rotation_is_the_interleaved_pair_form_with_a_concatenated_output(): - """CONTRACTS 4.3: pairs are read interleaved (``unfold(-1, 2, 2)``) but + """Pairs are read interleaved (``unfold(-1, 2, 2)``) but written back with ``cat``, so pair ``p`` lands at output dims ``p`` and ``p + 32``. Rewriting this as an in-place interleave is the natural "fix" and is wrong; so is reading the pairs split-half.""" @@ -693,7 +753,7 @@ def test_rotation_is_the_interleaved_pair_form_with_a_concatenated_output(): def test_rope_tables_stay_fp32_whatever_the_serving_dtype_is(): - """CONTRACTS 4.1: ``OrthoRoPEAngles``/``OrthoRoPE`` are fp32 islands. The + """``OrthoRoPEAngles``/``OrthoRoPE`` are fp32 islands. The port does not use ``NoCastModule``; instead the tables live in a ``DeviceTableCache`` outside the module tree, so ``.to(bfloat16)`` cannot reach them, and the bodies run in fp32 regardless.""" @@ -728,7 +788,7 @@ def test_rope_rejects_out_of_grid_positions(): # --------------------------------------------------------------------------- -# 5. The small layers (CONTRACTS 4.5, 4.6) +# 5. The small layers # --------------------------------------------------------------------------- @@ -799,7 +859,7 @@ def test_adaln_folds_scale_and_shift_into_one_bias_free_projection(): def test_mlp_is_bias_free_end_to_end(): - """CONTRACTS 4.5: the bias-free-ness is the only reason + """The bias-free-ness is the only reason ``F.linear(h, self.mlp.fc2.weight)`` in ``MLPFusion`` is correct. A bias would load and then be silently dropped at compute time.""" mlp = MLP(6, 12, 4) @@ -824,7 +884,7 @@ def test_noise_conditioner_fourier_features_and_fp32_island(): assert torch.equal(cond(sigma), want) # The frequency table is derived state: not a buffer, not in state_dict, and - # out of reach of a dtype cast (CONTRACTS 4.1). + # out of reach of a dtype cast. assert "freq" not in dict(cond.named_buffers()) and not any("freq" in k for k in cond.state_dict()) cond.to(torch.bfloat16) (freq_after,) = cond._freq.get(torch.device("cpu")) @@ -846,7 +906,7 @@ def test_noise_conditioner_must_stay_fp32_to_serve_fp32_sigma(): def test_mlp_fusion_stores_a_packed_fc1_and_splits_it_at_compute_time(): - """CONTRACTS 4.5: ``mlp.fc1`` is one ``[D, 2D]`` matrix (one loader key), + """``mlp.fc1`` is one ``[D, 2D]`` matrix (one loader key), used split so ``cond`` broadcasts over its frame's tokens instead of being materialized into a ``[B, N*T, 2D]`` concat. Same arithmetic.""" config = reduced_config() @@ -876,7 +936,7 @@ def test_mlp_fusion_stores_a_packed_fc1_and_splits_it_at_compute_time(): def test_controller_input_embedding_concat_order_is_mouse_button_scroll(): - """CONTRACTS 4.5. The widths sum to 259 under any permutation, so a wrong + """The widths sum to 259 under any permutation, so a wrong order fails silently. The three fields carry disjoint, self-identifying values and the MLP's input is captured directly.""" config = waypoint_1_5_1b_720p() diff --git a/test/modular/test_waypoint_dit.py b/test/modular/test_waypoint_dit.py index de66708bc..b101793e6 100644 --- a/test/modular/test_waypoint_dit.py +++ b/test/modular/test_waypoint_dit.py @@ -1,16 +1,21 @@ """Contract tests for the Waypoint-1.5 DiT block and the 4+1 per-frame driver. -The bar is ``docs/waypoint/CONTRACTS.md``, which is normative; where the code and -the document disagree the test pins the code only if the code is right. The -failure modes covered here all produce plausible video and raise nothing: a +The bar is the reference implementation at ``world_engine/src/``. The failure +modes covered here all produce plausible video and raise nothing: a denoise pass that commits to the ring, a value residual threaded the wrong way -round, a missing ``.clone()`` between the compiled regions, an fp32 island that -came back bf16, or a derived RoPE table left as whatever ``to_empty`` happened to -allocate. +round, a missing ``.clone()`` between the two passes of a frame, an fp32 island +that came back bf16, or a derived RoPE table left as whatever ``to_empty`` +happened to allocate. -Sections map onto CONTRACTS: 1 (the 4+1 pass structure), 2.1/4.5 (the 720P -structural facts), 4.1 (fp32 islands and the meta build), 4.2 (value residual -ordering) and 6.1 (``cond_proj`` tying across ``to_empty``). +Sections: the 4+1 pass structure, the 720P structural facts, fp32 islands and +the meta build, value-residual ordering, and ``cond_proj`` tying across +``to_empty``. + +The model owns no cache. Everything below drives the DiT against the two engine +resources it is bound to -- ``RingKVManager`` and ``FlexAttentionManager``, built +here directly rather than through an engine -- so the ring geometry, the +visibility rows and the attention numerics are the served ones and a test can +assert on what the model *did* without changing what it computed. CPU-only and checkpoint-free. The real 720P config is used only for structural assertions, which are cheap because the module is built on ``torch.device("meta")`` @@ -26,9 +31,14 @@ sys.path.insert(0, ".") +from mstar.engine.resources.attn.base import AttentionManager +from mstar.engine.resources.attn.config import AttentionConfig, AttentionSpec, AttnBackend +from mstar.engine.resources.base import EngineResourceInfo +from mstar.engine.resources.kv.config import KVSpec, RingKVConfig, RingKVLayerConfig +from mstar.engine.resources.kv.ring import RingKVManager +from mstar.model.submodule_base import NodeSubmodule from mstar.model.waypoint.components.attention import WaypointAttention from mstar.model.waypoint.components.dit import WaypointDiT -from mstar.model.waypoint.components.kv_backend import FlexRingBackend from mstar.model.waypoint.components.layers import FP32_MODULE_PATHS, rms_norm from mstar.model.waypoint.components.rope import OrthoRoPEAngles, apply_ortho_rope from mstar.model.waypoint.config import WaypointConfig, waypoint_1_5_1b_720p @@ -58,46 +68,148 @@ def reduced_config(**overrides) -> WaypointConfig: return WaypointConfig(**{**base, **overrides}) -class RecordingBackend: - """A real ``FlexRingBackend`` with every ``upsert`` logged. +def ring_kv_spec(config: WaypointConfig, *, num_worlds: int = 1) -> KVSpec: + """The ``RingKVConfig`` a ``WaypointConfig``'s geometry implies. + + Hand-rolled here because the model does not declare its specs yet; when + ``WaypointModel`` grows a ``get_node_resources``, this becomes a call to it + and the duplication goes away. Every field is read off the config rather + than restated, so a geometry change cannot leave the tests measuring a ring + the model no longer asks for. + """ + return KVSpec( + resource_key="kv", + nodes={"dit"}, + config=RingKVConfig( + num_layers=config.n_layers, + num_kv_heads=config.n_kv_heads, + head_dim=config.d_head, + num_qo_heads=config.n_heads, + tokens_per_frame=config.tokens_per_frame, + num_worlds=num_worlds, + layers=tuple( + RingKVLayerConfig( + ring_frames=config.ring_frames(i), + ring_buckets=config.ring_buckets(i), + pinned_dilation=config.pinned_dilation(i), + ) + for i in range(config.n_layers) + ), + ), + ) + + +def build_resources(config: WaypointConfig, dtype: torch.dtype = torch.float32): + """The two resources the DiT calls, built the way the engine builds them. + + Through ``Resource.build(spec, info)`` and ``AttentionManager.build``'s + factory, not the constructors: the spec is what picks ``RingKVManager`` over + the paged one and what cross-checks the flex backend against a ring config, + and a test that bypassed it would be driving a pairing the engine would + refuse. + """ + kv_spec = ring_kv_spec(config) + cpu = torch.device("cpu") + kv = RingKVManager.build(kv_spec, EngineResourceInfo(device=cpu, kv_dtype=dtype)) + attn = AttentionManager.build( + AttentionSpec( + resource_key="attn", + nodes={"dit"}, + config=AttentionConfig(kv_cache="kv", backend=AttnBackend.FLEX), + ), + EngineResourceInfo(device=cpu, kv_dtype=dtype, dependencies={"kv": kv_spec}), + ) + return kv, attn + + +class DitNode(NodeSubmodule): + """Stands in for the node submodule that will own the DiT. - Delegation rather than a stub: the ring geometry, the visibility mask and - the attention numerics stay real, so a test can assert on what the model - *did* without changing what it computed. + Structural parity with phase 6, where the DiT is a child of the node rather + than the node itself. ``bind_node_resources`` walks ``self.modules()`` and + skips ``self``, so binding through the node is what the real submodule does; + the 24 attention layers are the only consumers either way. + """ + + def __init__(self, dit: WaypointDiT): + super().__init__() + self.dit = dit + + def prepare_inputs(self, graph_walk, fwd_info, inputs, **kwargs): + raise NotImplementedError("structural stand-in; nothing here runs a step") + + def forward(self, graph_walk, engine_inputs, **kwargs): + raise NotImplementedError("structural stand-in; nothing here runs a step") + + +def bind_resources_on(dit: WaypointDiT, kv, attn) -> DitNode: + """Bind through the engine's own walk rather than a copy of it: one call on + the node is what has to reach the driver *and* every layer.""" + node = DitNode(dit) + node.bind_node_resources({"kv": kv, "attn": attn}) + return node + + +class RecordingRingKV: + """A real ``RingKVManager`` with every model-facing call logged. + + Delegation rather than a stub: the ring geometry, the visibility rows and + (through the attention resource it is paired with) the numerics stay real, + so a test can assert on what the model *did* without changing what it + computed. Only the KV side is spied on -- the attention resource is passed + through untouched, because nothing here needs to know what the kernel was + handed, only what was committed to the world state. """ def __init__(self, config: WaypointConfig, dtype: torch.dtype = torch.float32): self.config = config - self.inner = FlexRingBackend(config, "cpu", dtype=dtype, batch_size=1) + self.inner, self.attn = build_resources(config, dtype=dtype) self.upserts: list[dict] = [] + self.resets = 0 + self.states: list[str] = [] + + # -- the model-facing surface, delegated --------------------------------- - def upsert(self, k, v, layer_idx, frame_pos): + def upsert(self, k, v, layer_idx, frame_pos, *, commit): self.upserts.append( { - "frozen": self.inner._is_frozen, + "commit": commit, "layer": layer_idx, "frame_pos": int(frame_pos), "k": k.detach().clone(), "v": v.detach().clone(), } ) - return self.inner.upsert(k, v, layer_idx, frame_pos) - - def attend(self, q, k, v, meta, *, enable_gqa): - return self.inner.attend(q, k, v, meta, enable_gqa=enable_gqa) - - def set_frozen(self, frozen): - self.inner.set_frozen(frozen) + return self.inner.upsert(k, v, layer_idx, frame_pos, commit=commit) def reset(self): + self.resets += 1 self.inner.reset() self.upserts.clear() - def get_state(self): - return self.inner.get_state() + def get_state(self, rid): + self.states.append("get") + return self.inner.get_state(rid) - def load_state(self, state): - self.inner.load_state(state) + def load_state(self, rid, state): + self.states.append("load") + self.inner.load_state(rid, state) + + # -- introspection the tests read ---------------------------------------- + + @property + def layers(self): + return self.inner.layers + + def capacity(self, layer_idx: int) -> int: + return self.inner.capacity(layer_idx) + + def total_slots(self, layer_idx: int) -> int: + return self.inner.total_slots(layer_idx) + + @property + def tokens_per_frame(self) -> int: + return self.inner.tokens_per_frame def passes(self) -> list[list[dict]]: """The upsert log regrouped into forwards: one entry per layer, in @@ -111,6 +223,14 @@ def passes(self) -> list[list[dict]]: return grouped +def bound_dit(config: WaypointConfig, seed: int = 0, dtype: torch.dtype = torch.float32): + """A reduced DiT wired to a recording ring and a real flex attention.""" + dit = build_reduced_dit(config, seed=seed) + kv = RecordingRingKV(config, dtype=dtype) + bind_resources_on(dit, kv, kv.attn) + return dit, kv + + def build_reduced_dit(config: WaypointConfig, seed: int = 0) -> WaypointDiT: torch.manual_seed(seed) dit = WaypointDiT(config).eval() @@ -135,7 +255,7 @@ def frame_inputs(config: WaypointConfig, seed: int = 3, dtype: torch.dtype = tor # --------------------------------------------------------------------------- -# 6. Structural facts of the real 720P config (CONTRACTS 2.1, 4.5, 4.6) +# 6. Structural facts of the real 720P config # --------------------------------------------------------------------------- @@ -170,7 +290,7 @@ def test_720p_controller_conditioning_layers(meta_720p): def test_720p_gqa_is_live_and_the_fused_qkv_slabs_are_unequal(meta_720p): """32 query heads over 16 KV heads. The unequal slab widths are why a Q/K/V order mistake is a shape error for Q but *not* between K and V -- - swapping those two loads cleanly and produces wrong video (CONTRACTS 5).""" + swapping those two loads cleanly and produces wrong video.""" config = meta_720p.config assert (config.n_heads, config.n_kv_heads, config.d_head) == (32, 16, 64) assert config.enable_gqa is True @@ -190,7 +310,7 @@ def test_720p_head_and_patchify_shapes(meta_720p): assert meta_720p.patchify.weight.shape == (2048, 32, ph, pw) assert meta_720p.patchify.bias is None # The checkpoint's [D, C, ph, pw] conv kernel becomes this Linear's - # [C*ph*pw, D] (CONTRACTS 6, transforms 1-2). + # [C*ph*pw, D] (loader transforms 1-2). assert meta_720p.unpatchify.weight.shape == (32 * ph * pw, 2048) assert meta_720p.unpatchify.bias is not None assert meta_720p.out_norm.fc.weight.shape == (2 * 2048, 2048) @@ -199,8 +319,8 @@ def test_720p_head_and_patchify_shapes(meta_720p): def test_720p_parameter_budget_and_cond_proj_tying(meta_720p): """1.86B stored / 1.28B resident: ``cond_proj`` has one physical set that all 24 blocks alias. ``named_parameters()`` deduplicates; ``state_dict()`` - does not, which is why CONTRACTS 6.1 says to run the loader's completeness - check against the former.""" + does not, which is why the loader's completeness check runs against the + former.""" params = dict(meta_720p.named_parameters()) assert sum(p.numel() for p in params.values()) == 1_281_958_040 assert len(params) == 174 @@ -216,7 +336,7 @@ def test_720p_parameter_budget_and_cond_proj_tying(meta_720p): assert len([n for n in params if "cond_head.cond_proj" in n]) == 6 # No buffers at all: nothing in the module tree for to_empty to leave - # holding garbage (DECISIONS D1/D3). + # holding garbage. assert list(meta_720p.named_buffers()) == [] @@ -234,17 +354,65 @@ def test_config_rejects_unsupported_checkpoint_variants(): # --------------------------------------------------------------------------- -# 7. The 4+1 pass structure (CONTRACTS 1) +# 6b. The resource binding +# --------------------------------------------------------------------------- + + +def test_binding_the_node_reaches_every_layer_and_the_driver_holds_nothing(): + """The attention layers are the *only* consumers of either resource. + + The driver holds neither: it decides which pass commits and passes that + down as an argument, so there is no handle on it to leave unbound. That is + what removes the half-bind trap this test used to guard -- binding on the + DiT itself, which ``bind_node_resources`` cannot reach (it walks + ``self.modules()`` and skips ``self``), used to leave ``dit.kv`` at None and + raise nothing until four Euler steps into a frame. Asserted rather than + assumed, because a future handle on the driver would silently bring it back. + """ + config = reduced_config() + with torch.device("meta"): + dit = WaypointDiT(config) + kv, attn = build_resources(config) + layers = [block.attn for block in dit.blocks] + assert all(la.kv is None and la.attn is None for la in layers) + + # The way phase 6's submodule does it: the DiT as a child. + bind_resources_on(dit, kv, attn) + + assert all(la.kv is kv and la.attn is attn for la in layers) + assert len(layers) == config.n_layers + assert not hasattr(dit, "kv") and not hasattr(dit, "attn"), ( + "the driver took a resource handle back; `commit` is an argument and " + "nothing else on the DiT root talks to the ring" + ) + + +def test_a_layer_binds_only_what_the_node_owns(): + """``.get``, not ``[]``: a layer may sit on a node that owns some of its + resources and not others, and the missing one has to read as absent rather + than raise at load.""" + config = reduced_config() + with torch.device("meta"): + dit = WaypointDiT(config) + kv, _attn = build_resources(config) + + DitNode(dit).bind_node_resources({"kv": kv}) + + assert dit.blocks[0].attn.kv is kv + assert dit.blocks[0].attn.attn is None + + +# --------------------------------------------------------------------------- +# 7. The 4+1 pass structure # --------------------------------------------------------------------------- def test_generate_frame_is_four_frozen_denoise_passes_then_one_commit(): - """CONTRACTS 1, the single most important invariant. Five forwards: four + """The single most important invariant. Five forwards: four Euler steps at sigma = 1.0, 0.9, 0.75, 0.3 that must not touch the ring, then one committing pass at sigma = 0 on the settled latent.""" config = reduced_config() - dit = build_reduced_dit(config) - backend = RecordingBackend(config) + dit, kv = bound_dit(config) noise, mouse, button, scroll = frame_inputs(config) sigmas: list[torch.Tensor] = [] @@ -254,7 +422,7 @@ def test_generate_frame_is_four_frozen_denoise_passes_then_one_commit(): try: with torch.no_grad(): dit.generate_frame( - noise, torch.tensor(0, dtype=torch.int64), backend, + noise, torch.tensor(0, dtype=torch.int64), mouse=mouse, button=button, scroll=scroll, ) finally: @@ -266,47 +434,54 @@ def test_generate_frame_is_four_frozen_denoise_passes_then_one_commit(): assert list(config.scheduler_sigmas) == [1.0, 0.9, 0.75, 0.3, 0.0] assert config.num_denoise_steps == 4 - passes = backend.passes() + passes = kv.passes() assert len(passes) == 5, "a generated frame costs exactly five forwards" - frozen_per_pass = [{u["frozen"] for u in group} for group in passes] - assert frozen_per_pass == [{True}, {True}, {True}, {True}, {False}] - assert sum(not next(iter(f)) for f in frozen_per_pass) == 1, "exactly one pass may commit" - # All five passes of a frame share one ring clock (CONTRACTS 3). - assert {u["frame_pos"] for u in backend.upserts} == {0} + # Per pass, not per resource: `commit` arrives as an argument on every one + # of the n_layers upserts, so a single-element set per pass is also the + # assertion that no layer disagreed with the driver about which pass it was. + commit_per_pass = [{u["commit"] for u in group} for group in passes] + assert commit_per_pass == [{False}, {False}, {False}, {False}, {True}] + # All five passes of a frame share one ring clock. + assert {u["frame_pos"] for u in kv.upserts} == {0} + # ...and nothing else on the resource. Dropping the world and snapshotting + # it belong to the request lifecycle a level up; a driver that reset between + # frames would be starting a new rollout every frame, silently. + assert (kv.resets, kv.states) == (0, []) def test_the_ring_only_moves_on_the_committing_pass(): """The behavioural half of the same invariant, measured on the ring itself.""" config = reduced_config() - dit = build_reduced_dit(config) - backend = RecordingBackend(config) + dit, kv = bound_dit(config) noise, mouse, button, scroll = frame_inputs(config) fp = torch.tensor(0, dtype=torch.int64) - ring_lens = [layer.ring_len for layer in backend.inner.layers] - before = [layer.kv[:, :, :, :n].clone() for layer, n in zip(backend.inner.layers, ring_lens, strict=True)] + ring_lens = [layer.ring_len for layer in kv.layers] + before = [layer.kv[:, :, :, :n].clone() for layer, n in zip(kv.layers, ring_lens, strict=True)] with torch.no_grad(): sigma_table = dit._sigma_schedule(noise.device, noise.dtype) - dit._denoise_pass(noise, fp, sigma_table, backend, mouse=mouse, button=button, scroll=scroll) - after_denoise = [layer.kv[:, :, :, :n] for layer, n in zip(backend.inner.layers, ring_lens, strict=True)] + dit._denoise_pass(noise, fp, sigma_table, mouse=mouse, button=button, scroll=scroll) + after_denoise = [layer.kv[:, :, :, :n] for layer, n in zip(kv.layers, ring_lens, strict=True)] assert all(torch.equal(a, b) for a, b in zip(before, after_denoise, strict=True)) - assert not any(layer.written[: layer.ring_len].any() for layer in backend.inner.layers) + assert not any(layer.written[: layer.ring_len].any() for layer in kv.layers) with torch.no_grad(): - dit._cache_pass(noise, fp, backend, mouse=mouse, button=button, scroll=scroll) - assert all(layer.written[: layer.tokens_per_frame].all() for layer in backend.inner.layers) + dit._cache_pass(noise, fp, mouse=mouse, button=button, scroll=scroll) + assert all(layer.written[: layer.tokens_per_frame].all() for layer in kv.layers) def test_generate_frame_clones_the_denoised_latent(): - """CONTRACTS 1: ``x0 = self._denoise_pass(...).clone()`` -- the ``.clone()`` - is load-bearing. The compiled region reuses its output buffer, so the cache - pass would otherwise read a latent the next allocation has already stomped. - The copy must land in caller-owned memory, i.e. outside the compiled region. + """``x0 = self._denoise_pass(...).clone()`` -- the + ``.clone()`` is load-bearing. Both passes run inside one CUDA-graph capture, + so the cache pass allocates from the graph's private pool, and the denoise + pass's output buffer is a block in that pool that nothing downstream holds: + the cache pass's first allocation can land on it and stomp the latent it is + supposed to be reading, with the address baked into the graph. The copy must + land in caller-owned memory, i.e. outside the compiled region. """ config = reduced_config() - dit = build_reduced_dit(config) - backend = RecordingBackend(config) + dit, _kv = bound_dit(config) noise, mouse, button, scroll = frame_inputs(config) produced: list[torch.Tensor] = [] @@ -320,7 +495,7 @@ def spy(*args, **kwargs): dit._denoise_pass = spy with torch.no_grad(): x0 = dit.generate_frame( - noise, torch.tensor(0, dtype=torch.int64), backend, + noise, torch.tensor(0, dtype=torch.int64), mouse=mouse, button=button, scroll=scroll, ) @@ -335,8 +510,7 @@ def test_append_frame_is_the_committing_pass_alone(): """Priming from a VAE-encoded real frame: no denoising, one forward, and the latent is already the settled x0 so there is nothing to clone.""" config = reduced_config() - dit = build_reduced_dit(config) - backend = RecordingBackend(config) + dit, kv = bound_dit(config) latent, mouse, button, scroll = frame_inputs(config) sigmas: list[torch.Tensor] = [] @@ -346,20 +520,20 @@ def test_append_frame_is_the_committing_pass_alone(): try: with torch.no_grad(): out = dit.append_frame( - latent, torch.tensor(0, dtype=torch.int64), backend, + latent, torch.tensor(0, dtype=torch.int64), mouse=mouse, button=button, scroll=scroll, ) finally: handle.remove() assert [s.flatten().tolist() for s in sigmas] == [[0.0]] - assert len(backend.passes()) == 1 - assert all(u["frozen"] is False for u in backend.upserts) + assert len(kv.passes()) == 1 + assert all(u["commit"] is True for u in kv.upserts), "priming must commit" assert out is latent def test_sigma_schedule_is_built_in_the_latent_dtype(): - """CONTRACTS 1 / ``_sigma_schedule``: the reference takes ``.diff()`` in the + """``_sigma_schedule``: the reference takes ``.diff()`` in the serving dtype, so the Euler step sizes are bf16 differences of bf16 sigmas. Building the table in fp32 "for precision" changes the ODE.""" config = reduced_config() @@ -379,37 +553,55 @@ def test_sigma_schedule_is_built_in_the_latent_dtype(): # --------------------------------------------------------------------------- -# 8. The value residual (CONTRACTS 4.2) +# 8. The value residual # --------------------------------------------------------------------------- -class CaptureBackend: - """Records the exact ``(k, v)`` handed to the cache and hands them straight - back, so a test can inspect what would have been stored forever.""" +class CaptureKV: + """Records the exact ``(k, v)`` handed to the ring and hands them straight + back, so a test can inspect what would have been stored forever. + + Deliberately NOT a ``RingKVManager``, unlike ``RecordingRingKV`` above: what + these tests read is the frame the layer *submitted*, and a real ring returns + the whole capacity with that frame scattered into a slot the test would then + have to find. Returning ``(k, v, None)`` keeps the KV the layer built and + the KV the kernel sees the same tensor. + """ def __init__(self): self.calls: list[tuple[torch.Tensor, torch.Tensor]] = [] - def upsert(self, k, v, layer_idx, frame_pos): + def upsert(self, k, v, layer_idx, frame_pos, *, commit): self.calls.append((k.detach().clone(), v.detach().clone())) return k, v, None - def attend(self, q, k, v, meta, *, enable_gqa): - return torch.nn.functional.scaled_dot_product_attention(q, k, v, enable_gqa=enable_gqa) - def set_frozen(self, frozen): - pass +class DenseAttn: + """The attention half of the pair above: plain SDPA over whatever + ``CaptureKV`` returned. ``visible`` is ``None`` and ignored -- there is no + ring here to be visible into, and these tests are about what enters the + cache, not about the mask.""" + + requires_kv_write = False + + def attend(self, q, k, v, visible, *, enable_gqa): + assert visible is None, "CaptureKV returns no visibility row" + return torch.nn.functional.scaled_dot_product_attention(q, k, v, enable_gqa=enable_gqa) def run_two_attention_layers(config: WaypointConfig, lamb0: float, lamb1: float): torch.manual_seed(21) layer0 = WaypointAttention(config, 0).eval() layer1 = WaypointAttention(config, 1).eval() + capture, dense = CaptureKV(), DenseAttn() for layer, lamb in ((layer0, lamb0), (layer1, lamb1)): for p in layer.parameters(): torch.nn.init.normal_(p, std=0.05) with torch.no_grad(): layer.v_lamb.fill_(lamb) + # Per-layer bind, directly: these two are loose layers, not a node's + # module tree, and `bind_resources` is the seam a layer actually has. + layer.bind_resources({"kv": capture, "attn": dense}) idx = torch.arange(config.tokens_per_frame) angles = OrthoRoPEAngles(config)( @@ -419,12 +611,11 @@ def run_two_attention_layers(config: WaypointConfig, lamb0: float, lamb1: float) ) gen = torch.Generator().manual_seed(22) x = torch.randn(1, config.tokens_per_frame, config.d_model, generator=gen) - backend = CaptureBackend() fp = torch.tensor(0, dtype=torch.int64) with torch.no_grad(): - _, v1 = layer0(x, fp, angles, None, backend) - _, v1_out = layer1(x, fp, angles, v1, backend) - return layer0, layer1, x, angles, backend, v1, v1_out + _, v1 = layer0(x, fp, angles, None, commit=True) + _, v1_out = layer1(x, fp, angles, v1, commit=True) + return layer0, layer1, x, angles, capture, v1, v1_out def raw_qkv(layer: WaypointAttention, x: torch.Tensor): @@ -439,12 +630,12 @@ def raw_qkv(layer: WaypointAttention, x: torch.Tensor): def test_v1_is_captured_pre_lerp_and_threads_through_unchanged(): - """CONTRACTS 4.2. Layer 0 returns its *pre*-lerp V, and every later layer + """Layer 0 returns its *pre*-lerp V, and every later layer passes that same tensor along untouched -- it does not substitute its own. Getting this backwards still produces plausible output, so the ordering is asserted directly rather than through the activations.""" config = reduced_config() - layer0, layer1, x, _angles, backend, v1, v1_out = run_two_attention_layers(config, 0.25, 0.5) + layer0, layer1, x, _angles, capture, v1, v1_out = run_two_attention_layers(config, 0.25, 0.5) _, _, v_raw0 = raw_qkv(layer0, x) _, _, v_raw1 = raw_qkv(layer1, x) @@ -454,7 +645,7 @@ def test_v1_is_captured_pre_lerp_and_threads_through_unchanged(): assert not torch.equal(v_raw1, v1), "precondition: the two layers' raw V differ" # ...and the LERPED V is what enters the cache, at layer 1. - cached_v1 = backend.calls[1][1] + cached_v1 = capture.calls[1][1] want = torch.lerp(v_raw1, v1, layer1.v_lamb) assert torch.equal(cached_v1, want) assert not torch.equal(cached_v1, v_raw1), "the cache stored the pre-lerp V" @@ -466,22 +657,22 @@ def test_value_residual_lerp_direction(): cached V *is* layer 0's V, at 0 it is the layer's own. Swapping the lerp operands is a silent sign flip on the residual.""" config = reduced_config() - _, layer1, x, _, backend_one, v1, _ = run_two_attention_layers(config, 0.25, 1.0) - assert torch.equal(backend_one.calls[1][1], v1) + _, layer1, x, _, capture_one, v1, _ = run_two_attention_layers(config, 0.25, 1.0) + assert torch.equal(capture_one.calls[1][1], v1) - _, layer1_zero, x0, _, backend_zero, v1_zero, _ = run_two_attention_layers(config, 0.25, 0.0) + _, layer1_zero, x0, _, capture_zero, v1_zero, _ = run_two_attention_layers(config, 0.25, 0.0) _, _, v_raw1 = raw_qkv(layer1_zero, x0) - assert torch.equal(backend_zero.calls[1][1], v_raw1) + assert torch.equal(capture_zero.calls[1][1], v_raw1) def test_q_and_k_are_normed_and_rotated_but_v_is_neither(): - """CONTRACTS 4.2: Q/K are RMS-normed then RoPE'd; V is neither. K enters the + """Q/K are RMS-normed then RoPE'd; V is neither. K enters the ring already rotated, so replayed history is never re-rotated.""" config = reduced_config() - layer0, _layer1, x, angles, backend, v1, _ = run_two_attention_layers(config, 0.25, 0.5) + layer0, _layer1, x, angles, capture, v1, _ = run_two_attention_layers(config, 0.25, 0.5) _, k_raw0, v_raw0 = raw_qkv(layer0, x) - cached_k, cached_v = backend.calls[0] + cached_k, cached_v = capture.calls[0] assert torch.equal(cached_k, apply_ortho_rope(rms_norm(k_raw0), angles)) assert not torch.equal(cached_k, rms_norm(k_raw0)), "K reached the cache un-rotated" assert not torch.equal(cached_k, apply_ortho_rope(k_raw0, angles)), "K reached the cache un-normed" @@ -495,20 +686,19 @@ def test_layer_zero_v_reaches_every_block_in_the_dit(): stored by all 4 blocks must be byte-identical to layer 0's. A block that re-captured ``v1`` from its own projection would drift here.""" config = reduced_config() - dit = build_reduced_dit(config) + dit, kv = bound_dit(config) with torch.no_grad(): for block in dit.blocks: block.attn.v_lamb.fill_(1.0) - backend = RecordingBackend(config) latent, mouse, button, scroll = frame_inputs(config) with torch.no_grad(): dit.append_frame( - latent, torch.tensor(0, dtype=torch.int64), backend, + latent, torch.tensor(0, dtype=torch.int64), mouse=mouse, button=button, scroll=scroll, ) - (single_pass,) = backend.passes() + (single_pass,) = kv.passes() reference_v = single_pass[0]["v"] for record in single_pass[1:]: assert torch.equal(record["v"], reference_v), ( @@ -519,19 +709,19 @@ def test_layer_zero_v_reaches_every_block_in_the_dit(): # --------------------------------------------------------------------------- -# 9. fp32 islands and the meta-build path (CONTRACTS 4.1, 6.1) +# 9. fp32 islands and the meta-build path # --------------------------------------------------------------------------- def test_fp32_module_paths_is_exactly_the_noise_conditioner(): """The reference marks three modules ``NoCastModule``; only one of them has parameters. ``OrthoRoPEAngles``/``OrthoRoPE`` hold none, so there is nothing - for a dtype cast to corrupt and nothing to pin back (DECISIONS D7).""" + for a dtype cast to corrupt and nothing to pin back.""" assert FP32_MODULE_PATHS == ("denoise_step_emb",) def test_cast_serving_dtypes_leaves_one_fp32_island_on_720p(): - """CONTRACTS 4.1: bf16 everywhere, then the islands back to fp32 -- run on + """bf16 everywhere, then the islands back to fp32 -- run on the **meta** module so storage is later allocated directly in the serving dtype. Nothing is materialized here; a real 720P build is ~2.6 GB.""" with torch.device("meta"): @@ -555,7 +745,7 @@ def test_cast_serving_dtypes_leaves_one_fp32_island_on_720p(): def test_to_empty_unties_cond_proj_and_retie_puts_it_back(): - """CONTRACTS 6.1. ``Module._apply`` has no cross-module memo, so + """``Module._apply`` has no cross-module memo, so ``to_empty(device)`` silently gives 24 blocks 24 independent ``cond_proj`` sets. Nothing raises; the symptoms are +0.6B resident parameters and 23 blocks the loader never fills. Measured here on the reduced config, where @@ -583,7 +773,7 @@ def tied() -> bool: def test_derived_tables_survive_the_meta_build(): - """CONTRACTS 4.1 / DECISIONS D1: the RoPE frequency tables, the Fourier + """The RoPE frequency tables, the Fourier frequency table and the token grid are DERIVED state held in a ``DeviceTableCache`` *outside* the module tree. As non-persistent buffers they would come out of ``to_empty(device)`` as uninitialized garbage that no @@ -619,7 +809,7 @@ def test_derived_tables_survive_the_meta_build(): def test_meta_built_model_generates_a_frame_in_the_serving_dtypes(): - """The whole build order from CONTRACTS 6.1, end to end on CPU: meta build, + """The whole build order, end to end on CPU: meta build, cast, to_empty, retie, then a real 4+1 frame. Weights are random (there is no checkpoint here), so the bar is 'the serving dtypes and the derived tables are live and the output is finite', not a numeric one.""" @@ -634,39 +824,39 @@ def test_meta_built_model_generates_a_frame_in_the_serving_dtypes(): torch.nn.init.normal_(p, std=0.02) dit.eval() - backend = RecordingBackend(config, dtype=torch.bfloat16) + kv = RecordingRingKV(config, dtype=torch.bfloat16) + bind_resources_on(dit, kv, kv.attn) noise, mouse, button, scroll = frame_inputs(config, dtype=torch.bfloat16) with torch.no_grad(): x0 = dit.generate_frame( - noise, torch.tensor(0, dtype=torch.int64), backend, + noise, torch.tensor(0, dtype=torch.int64), mouse=mouse, button=button, scroll=scroll, ) assert x0.dtype == torch.bfloat16 and x0.shape == noise.shape assert bool(torch.isfinite(x0.float()).all()) - assert len(backend.passes()) == 5 + assert len(kv.passes()) == 5 assert dit.denoise_step_emb.mlp.fc1.weight.dtype == torch.float32 assert dit.patchify.weight.dtype == torch.bfloat16 def test_two_frames_advance_the_ring_clock_together(): - """CONTRACTS 3/4.4: the caller owns ``frame_pos`` and advances it by exactly - one per committed frame; ``t_pos = f_pos * ts_mult`` is the RoPE clock and + """The caller owns ``frame_pos`` and advances it by exactly one per + committed frame; ``t_pos = f_pos * ts_mult`` is the RoPE clock and is threaded separately even though ``ts_mult == 1`` here.""" config = reduced_config() assert config.ts_mult == 1 == waypoint_1_5_1b_720p().ts_mult - dit = build_reduced_dit(config) - backend = RecordingBackend(config) + dit, kv = bound_dit(config) noise, mouse, button, scroll = frame_inputs(config) with torch.no_grad(): for f in range(2): dit.generate_frame( - noise, torch.tensor(f, dtype=torch.int64), backend, + noise, torch.tensor(f, dtype=torch.int64), mouse=mouse, button=button, scroll=scroll, ) - passes = backend.passes() + passes = kv.passes() assert len(passes) == 10 assert [next(iter({u["frame_pos"] for u in group})) for group in passes] == [0] * 5 + [1] * 5 diff --git a/test/modular/test_waypoint_shell.py b/test/modular/test_waypoint_shell.py new file mode 100644 index 000000000..15e5d10b2 --- /dev/null +++ b/test/modular/test_waypoint_shell.py @@ -0,0 +1,742 @@ +"""Contract tests for the Waypoint serving shell: the model, its node +submodule, and the resources it declares. + +Nothing here runs the DiT. The 4+1 driver, the ring numerics and the weight +remap have their own suites; what is under test here is the *shell* — the +handful of declarations and host-side hooks that sit between the engine and a +model that already works, every one of which fails silently when it is wrong: + + * a ring geometry summarized instead of copied (a global layer served with a + local layer's stride still produces smooth video, of the wrong world), + * a rank-0 ``frame_pos`` (``_intern_static_buffer`` reads + ``stored.shape[0]``), + * a per-step tensor whose shape happens to carry ``tokens_per_frame`` + (``_seq_dim`` hoists the matching dim to the front of a shared static + buffer), + * noise drawn from an advancing generator instead of ``(seed, frame_pos)`` + (a resumed rollout diverges from the one it resumed), + * an off-by-one stop that runs one frame past the request and commits it, + * a deployment whose ``max_concurrent_requests`` is unset or larger than the + ring's world pool, which is the *only* thing keeping arrivals inside a pool + that fails terminally when it is overrun, + * a resource that never reaches the 24 attention layers. + +CPU-only, checkpoint-free, and no engine. The real 720P config is used +throughout, because the numbers that collide are that config's numbers; the DiT +behind the submodule is built on ``torch.device("meta")`` and never +materialized, since a real 720P bf16 build is ~2.6 GB and none of these +assertions touch a weight. The two places that need a value read back go around +it: the noise draw takes an explicit device (which is why that helper takes +one), and the frame-clock test runs over ``_HostOnlyDit``, since what it +asserts is host bookkeeping the DiT is not part of. +""" + +import dataclasses +import logging +import sys + +import pytest +import torch +import yaml + +sys.path.insert(0, ".") + +from mstar.conductor.request_info import CurrentForwardPassInfo +from mstar.engine.resources import ( + AttentionSpec, + AttnBackend, + KVSpec, + RingKVConfig, + RingKVStep, +) +from mstar.engine.resources.runner import topo_sort +from mstar.graph.base import GraphNode, Loop +from mstar.graph.special_destinations import EMIT_TO_CLIENT +from mstar.model.waypoint.components.attention import WaypointAttention +from mstar.model.waypoint.components.dit import WaypointDiT +from mstar.model.waypoint.config import waypoint_1_5_1b_720p +from mstar.model.waypoint.submodules import ( + ATTN_RESOURCE, + KV_RESOURCE, + PRIME_WALK, + ROLLOUT_LOOP_NAME, + ROLLOUT_WALK, + WaypointDitSubmodule, +) +from mstar.model.waypoint.waypoint_model import DIT_NODE, WaypointModel + + +@pytest.fixture(scope="module") +def config(): + return waypoint_1_5_1b_720p() + + +@pytest.fixture(scope="module") +def model(): + return WaypointModel(skip_weight_loading=True) + + +@pytest.fixture(scope="module") +def submodule(config): + """The real submodule over a meta-built DiT. + + Meta, not a stub: ``bind_node_resources`` walking ``self.modules()`` and + ``self.dit.dtype`` surviving ``cast_serving_dtypes`` are exactly two of the + things under test, and a stub would assert them against itself. Meta also + keeps ``prepare_inputs``' shapes and dtypes honest -- they are computed the + same way on meta as on cuda -- while allocating nothing. + """ + with torch.device("meta"): + dit = WaypointDiT(config) + dit.cast_serving_dtypes() + return WaypointDitSubmodule(dit, config) + + +class _HostOnlyDit(torch.nn.Module): + """Stands in for the DiT in the tests that need to read a value back. + + ``prepare_inputs`` touches the DiT for exactly one thing -- ``.dtype`` -- + and ``get_device`` for one parameter, so what those tests exercise is the + submodule's host-side bookkeeping and nothing else. The meta build above + cannot be read back (``.item()`` raises on a meta tensor) and materializing + 720P to assert on a frame counter is 2.6 GB; a stub is the honest third + option, and it is confined to the two tests that say so. + """ + + def __init__(self, dtype: torch.dtype = torch.bfloat16): + super().__init__() + self.marker = torch.nn.Parameter(torch.zeros(1, dtype=dtype)) + + @property + def dtype(self) -> torch.dtype: + return self.marker.dtype + + +@pytest.fixture +def host_submodule(config): + return WaypointDitSubmodule(_HostOnlyDit(), config) + + +def _fwd_info( + request_id: str = "r0", + graph_walk: str = ROLLOUT_WALK, + random_seed: int = 1234, + num_frames: int = 8, + loop_iter: int | None = None, +) -> CurrentForwardPassInfo: + return CurrentForwardPassInfo( + request_id=request_id, + graph_walk=graph_walk, + fwd_index=0, + random_seed=random_seed, + max_tokens=0, + resource_configs={}, + step_metadata={"is_prefill": graph_walk == PRIME_WALK, "num_frames": num_frames}, + resource_publish_info={}, + loop_stop_times={}, + dynamic_loop_iter_counts=( + {} if loop_iter is None else {ROLLOUT_LOOP_NAME: loop_iter} + ), + ) + + +def _controller_stream(config, frames: int) -> dict[str, list[torch.Tensor]]: + """A scripted stream shaped the way ``process_prompt`` emits it.""" + return { + "mouse": [torch.zeros((1, frames, 2))], + "button": [torch.zeros((1, frames, config.n_buttons))], + "scroll": [torch.zeros((1, frames, 1))], + } + + +# --------------------------------------------------------------------------- +# Declared resources +# --------------------------------------------------------------------------- + + +def test_declares_exactly_the_ring_and_the_flex_attention_over_it(model): + specs = model.get_node_resources() + assert [type(s) for s in specs] == [KVSpec, AttentionSpec] + assert [s.resource_key for s in specs] == [KV_RESOURCE, ATTN_RESOURCE] + assert all(s.nodes == {DIT_NODE} for s in specs) + + assert isinstance(specs[0].config, RingKVConfig) + # FLEX, not the FLASHINFER default: a paged kernel reassociates the + # accumulation over the KV blocks, and bit-exactness against the reference + # is the only correctness signal this model has. + assert specs[1].config.backend is AttnBackend.FLEX + assert specs[1].config.kv_cache == KV_RESOURCE + + +def test_ring_geometry_is_copied_from_the_config_layer_for_layer(model, config): + """Waypoint's 24 layers are not alike, and none of the three per-layer + numbers is derivable from the others under the compacted global ring. + Asserted one layer at a time so a summarizing regression names the layer it + broke.""" + ring = model.get_node_resources()[0].config + assert ring.num_layers == config.n_layers == 24 + assert ring.num_kv_heads == config.n_kv_heads + assert ring.num_qo_heads == config.n_heads + assert ring.head_dim == config.d_head + assert ring.tokens_per_frame == config.tokens_per_frame + # One world declared here, because sizing is a deployment question and + # `apply_yaml_overrides` runs after this hook. What is pinned is the + # *default*: a node that never says otherwise serves one session. + assert ring.num_worlds == 1 + + assert len(ring.layers) == config.n_layers + for i, layer in enumerate(ring.layers): + assert layer.ring_frames == config.ring_frames(i), f"ring_frames, layer {i}" + assert layer.ring_buckets == config.ring_buckets(i), f"ring_buckets, layer {i}" + assert layer.pinned_dilation == config.pinned_dilation(i), f"dilation, layer {i}" + + # The heterogeneity itself: if these two collapse to one value the loop + # above would pass against a config that had also been flattened. + assert {ring.layers[i].pinned_dilation for i in config.global_layers} == { + config.global_pinned_dilation + } + local = set(range(config.n_layers)) - config.global_layers + assert {ring.layers[i].pinned_dilation for i in local} == {1} + + +def test_ring_frames_and_buckets_are_read_as_two_separate_questions(): + """They agree on every layer of the compacted 720P ring, which is exactly + what makes deriving one from the other look safe. Under the reference's + sizing (``full_global_ring=True``) a global layer is 128 frames indexed + by 16 buckets, and a derived value serves that layer a shredded history.""" + model = WaypointModel(skip_weight_loading=True) # fresh: mutated below + model.config = dataclasses.replace(model.config, full_global_ring=True) + ring = model.get_node_resources()[0].config + + layer = ring.layers[min(model.config.global_layers)] + assert layer.ring_frames == model.config.global_window == 128 + assert layer.ring_buckets == 16 + assert layer.ring_frames != layer.ring_buckets + + +def test_attention_resolves_after_the_cache_it_names(model): + specs = model.get_node_resources() + by_key = {s.resource_key: s for s in specs} + assert by_key[ATTN_RESOURCE].depends_on() == {KV_RESOURCE} + assert by_key[KV_RESOURCE].depends_on() == set() + # topo_sort only calls depends_on(), so the specs stand in for the built + # resources here; the order it returns is the order the engine builds in. + assert topo_sort(by_key) == (KV_RESOURCE, ATTN_RESOURCE) + + +# --------------------------------------------------------------------------- +# Graph walks +# --------------------------------------------------------------------------- + + +def test_walks_are_dit_only_and_the_rollout_emits_per_iteration(model, config): + walks = model.get_graph_walk_graphs() + assert set(walks) == {PRIME_WALK, ROLLOUT_WALK} + # The VAE nodes are a later phase. Model.nodes is derived from + # the walks, so inventing one here would make the engine wait on a node + # nothing builds. + assert model.nodes == [DIT_NODE] + + prime = walks[PRIME_WALK] + assert isinstance(prime, GraphNode) and prime.name == DIT_NODE + assert "latent" in prime.input_names + + rollout = walks[ROLLOUT_WALK] + assert isinstance(rollout, Loop) + assert rollout.name == ROLLOUT_LOOP_NAME # what check_stop's signal is keyed by + assert rollout.max_iters == config.max_frames + section = rollout.section + assert isinstance(section, GraphNode) and section.name == DIT_NODE + # Emitted from inside the loop, one frame per iteration -- an interactive + # world model whose frames only arrive after the rollout ends has no world + # to interact with. + assert [e.next_node for e in section.outputs] == [EMIT_TO_CLIENT] + assert rollout.accumulated_outputs == [] + # An overshoot iteration here is not a wasted forward: it commits a frame + # into the ring, and there is no undo. + assert section.enable_async_scheduling is False + + +# --------------------------------------------------------------------------- +# Per-step inputs: rank and the _seq_dim collision +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize("walk", [PRIME_WALK, ROLLOUT_WALK]) +def test_no_prepared_tensor_is_rank_zero(submodule, config, walk): + """A 0-dim tensor reaches ``_intern_static_buffer``, which + reads ``stored.shape[0]``, and the worker's output fanout reads + ``dims[0]`` -- both IndexError at capture, i.e. at warmup, far from the + line that made the tensor.""" + inputs = _controller_stream(config, frames=4) + inputs["latent"] = [torch.zeros((1, 1, *config.latent_shape))] + node_inputs = submodule.prepare_inputs(walk, _fwd_info(graph_walk=walk), inputs) + + assert node_inputs.tensor_inputs, "prepare_inputs emitted no tensors" + for name, tensor in node_inputs.tensor_inputs.items(): + assert tensor.ndim >= 1, f"{name} is rank-0" + + prepared = submodule.preprocess(walk, None, [node_inputs]) + for name, value in prepared.items(): + assert isinstance(value, torch.Tensor), name + assert value.ndim >= 1, f"{name} is rank-0 after preprocess" + + +@pytest.mark.parametrize("walk", [PRIME_WALK, ROLLOUT_WALK]) +def test_no_prepared_tensor_carries_tokens_per_frame_in_its_shape( + submodule, config, walk +): + """``CudaGraphRunner._seq_dim`` finds the dim equal to + ``input_seq_len`` and hoists it to the front of a shared static buffer; a + tensor that carries that number for an unrelated reason gets silently + transposed under replay. + + ``input_seq_len`` is honestly ``tokens_per_frame`` (512) -- the scheduler is + told the real token count -- so the burden falls here. At 720P nothing + collides, but the margin is thin: a 256-token-per-frame variant would put + ``button``'s ``n_buttons = 256`` straight into the crosshairs. + """ + inputs = _controller_stream(config, frames=4) + inputs["latent"] = [torch.zeros((1, 1, *config.latent_shape))] + node_inputs = submodule.prepare_inputs(walk, _fwd_info(graph_walk=walk), inputs) + + assert node_inputs.input_seq_len == config.tokens_per_frame + for name, tensor in node_inputs.tensor_inputs.items(): + assert config.tokens_per_frame not in tuple(tensor.shape), ( + f"{name} has shape {tuple(tensor.shape)}, which carries " + f"input_seq_len={config.tokens_per_frame}" + ) + + +def test_prepared_shapes_and_dtypes_are_the_capture_template_exactly(submodule, config): + """``_capture_one`` bakes the config's template and replay re-stages only + what ``preprocess`` returned into it. A key, shape or dtype that differs + between the two is either a stale-address read or a silent eager fallback, + depending on which way it differs -- so they are asserted against each + other rather than against a literal.""" + templates = { + cfg.capture_graph_walk: cfg.single_request_inputs + for cfg in submodule.get_cuda_graph_configs(torch.device("meta")) + } + inputs = _controller_stream(config, frames=4) + inputs["latent"] = [torch.zeros((1, 1, *config.latent_shape))] + + for walk, template in templates.items(): + prepared = submodule.prepare_inputs(walk, _fwd_info(graph_walk=walk), inputs) + assert set(prepared.tensor_inputs) == set(template.tensor_inputs), walk + assert prepared.input_seq_len == template.input_seq_len, walk + for name, tensor in prepared.tensor_inputs.items(): + baked = template.tensor_inputs[name] + assert tensor.shape == baked.shape, f"{walk}/{name}" + assert tensor.dtype == baked.dtype, f"{walk}/{name}" + + # The one shape that is spelled out, because it is the one the runner + # indexes into and the one a "scalar frame index" instinct would get wrong. + frame_pos = templates[ROLLOUT_WALK].tensor_inputs["frame_pos"] + assert frame_pos.shape == (1,) and frame_pos.dtype == torch.int64 + + +# --------------------------------------------------------------------------- +# Noise: stateless in (seed, frame_pos) +# --------------------------------------------------------------------------- + + +def test_noise_is_a_pure_function_of_seed_and_frame_pos(submodule): + """Nothing about the draw may depend on how many + frames have already been drawn: a generator advanced in place accumulates + state that ``get_state`` does not serialize, so a resumed rollout would + diverge from the one it resumed and no assertion anywhere would fire.""" + device, dtype = torch.device("cpu"), torch.float32 + first = submodule._frame_noise(4242, 7, device, dtype) + # Two intervening draws: if the helper carried a generator, these would + # move it and the repeat below would come back different. + submodule._frame_noise(4242, 0, device, dtype) + submodule._frame_noise(999, 7, device, dtype) + repeat = submodule._frame_noise(4242, 7, device, dtype) + assert torch.equal(first, repeat) + + +def test_noise_differs_across_frames_and_across_seeds(submodule, config): + device, dtype = torch.device("cpu"), torch.float32 + frames = [submodule._frame_noise(4242, k, device, dtype) for k in range(4)] + assert frames[0].shape == (1, 1, *config.latent_shape) + for a in range(len(frames)): + for b in range(a + 1, len(frames)): + # Re-seeding from the request seed alone gives every frame the same + # noise and the video stops evolving. + assert not torch.equal(frames[a], frames[b]), f"frames {a} and {b} match" + + # Adjacent seeds must not share frame k. `seed + frame_pos` would make seeds + # 0 and 1 agree on every frame but the first, which reads as a broken + # sampler rather than as a seed collision -- hence the splitmix finalizer. + assert not torch.equal( + submodule._frame_noise(0, 3, device, dtype), + submodule._frame_noise(1, 3, device, dtype), + ) + + +def test_the_frame_clock_advances_on_the_host_and_drives_the_controller_slice( + host_submodule, config +): + """The clock is a host int in PerRequestState, and the device tensor is + derived from it -- never the other way round. The scripted stream is + loop-external, so this is the only thing that moves through it.""" + rid = "clock" + host_submodule.request_states.pop(rid, None) + inputs = _controller_stream(config, frames=3) + # Distinguishable rows, so a slice off by one is visible. + for row in range(3): + inputs["scroll"][0][0, row, 0] = float(row + 1) + + seen = [] + for _ in range(5): + node_inputs = host_submodule.prepare_inputs( + ROLLOUT_WALK, _fwd_info(request_id=rid), inputs + ) + seen.append( + ( + int(node_inputs.tensor_inputs["frame_pos"][0]), + float(node_inputs.tensor_inputs["scroll"][0, 0, 0]), + ) + ) + host_submodule.postprocess(rid, _fwd_info(request_id=rid), {}) + + # The clock advances by one per committed frame; the stream is shorter than + # the rollout, so its last row holds rather than raising mid-flight. + assert [pos for pos, _ in seen] == [0, 1, 2, 3, 4] + assert [scroll for _, scroll in seen] == [1.0, 2.0, 3.0, 3.0, 3.0] + + host_submodule.cleanup_request(rid) + assert rid not in host_submodule.request_states + + +def test_declare_step_carries_the_same_clock_prepare_inputs_reads( + host_submodule, config +): + """One source for the clock, not two. `RingKVManager.admit` checks the + declared frame against the one its `commit` last recorded, so a step that + declared a *different* number from the one the forward runs at would refuse + valid frames and pass desynced ones -- the check inverted. + + Read on the host, off `PerRequestState`: the `frame_pos` in `NodeInputs` is + a `[1]` device tensor by then and reading it back would be a sync per step. + """ + rid = "declare" + host_submodule.request_states.pop(rid, None) + inputs = _controller_stream(config, frames=3) + + for expected in range(3): + node_inputs = host_submodule.prepare_inputs( + ROLLOUT_WALK, _fwd_info(request_id=rid), inputs + ) + step = host_submodule.declare_step( + graph_walk=ROLLOUT_WALK, request_ids=[rid], inputs=[node_inputs], + ) + kv_step = step.get(KV_RESOURCE) + + assert isinstance(kv_step, RingKVStep), ( + "a KVStep here would admit and commit while silently switching the " + "clock check off" + ) + assert kv_step.frames == ((rid, expected),) + assert dict(kv_step.frames)[rid] == int( + node_inputs.tensor_inputs["frame_pos"][0] + ) + + host_submodule.postprocess(rid, _fwd_info(request_id=rid), {}) + + host_submodule.cleanup_request(rid) + + +def test_declare_step_names_a_clock_for_every_request_in_the_batch(host_submodule): + """No batch shape declines to answer. + + The singular ``frame_pos: int | None`` this replaces returned ``None`` + whenever the batch was not one request, and ``RingKVManager``'s continuity + check — the only thing standing between a stalled clock and a world quietly + rewriting its own history — then did nothing for that step. The reasoning + was that ``admit`` refuses such a batch anyway, which was true and is still + true; the problem is that it made the check's coverage depend on a second, + unrelated refusal staying in place. It does not any more: a batch this + submodule cannot serve is refused for *being a batch*, with every clock in + it still named. + + The clocks below are genuinely different, so a declaration that broadcast + one request's frame across the batch fails here rather than passing on a + coincidence. + """ + for i, rid in enumerate(("a", "b", "c")): + host_submodule.request_states.pop(rid, None) + for _ in range(i * 2): + host_submodule.postprocess(rid, _fwd_info(request_id=rid), {}) + + step = host_submodule.declare_step( + graph_walk=ROLLOUT_WALK, request_ids=["a", "b", "c"], inputs=[], + ) + + assert step.get(KV_RESOURCE).frames == (("a", 0), ("b", 2), ("c", 4)) + for rid in ("a", "b", "c"): + host_submodule.cleanup_request(rid) + + +# --------------------------------------------------------------------------- +# Stop condition +# --------------------------------------------------------------------------- + + +def test_check_stop_fires_at_exactly_num_frames(submodule): + """N frames means firing while iteration N-1 is postprocessed: the loop + counter still reads N-1 there and the stop ends that iteration. One early + truncates the video; one late commits an extra frame into the ring.""" + num_frames = 6 + fired = [ + bool( + submodule.check_stop( + "r0", _fwd_info(num_frames=num_frames, loop_iter=k), {} + ) + ) + for k in range(num_frames + 2) + ] + assert fired == [False] * (num_frames - 1) + [True, True, True] + assert submodule.check_stop( + "r0", _fwd_info(num_frames=num_frames, loop_iter=num_frames - 1), {} + ) == {ROLLOUT_LOOP_NAME} + + +# --------------------------------------------------------------------------- +# Serialization gate +# --------------------------------------------------------------------------- + + +def _write_config(tmp_path, name: str, **extra) -> str: + body = { + "model": "waypoint", + "max_seq_len": 512, + "node_groups": [{"node_names": [DIT_NODE], "ranks": [0]}], + **extra, + } + path = tmp_path / name + path.write_text(yaml.safe_dump(body)) + return str(path) + + +def _worlds(n: int) -> dict: + """The ``resources:`` block a deployment writes to size the ring — the same + one ``EngineManager.build`` feeds to ``apply_yaml_overrides``, which is why + the gate reads it here rather than inventing its own key.""" + return {"resources": {KV_RESOURCE: {"num_worlds": n}}} + + +@pytest.mark.parametrize("limit", [None, 0, -1, True, 1.0, "2"]) +def test_get_worker_graphs_refuses_a_deployment_with_no_admit_queue( + model, tmp_path, limit +): + """The pool is finite, and this is the primary gate on it. + + The conductor only forms a FIFO admit queue when ``max_concurrent_requests`` + is set: it drains ``waiting_queue`` while ``len(self.requests) < + max_concurrent_requests``, so an unset value admits every request on arrival + and everything past the Nth dies terminally at ``RingKVManager.admit`` — + which sees the batch far too late to queue it. + + ``max_batch_size = 1`` does not cover this and never did. It caps how many + requests share one *step*; N admitted rollouts alternating steps is now the + intended shape, but it says nothing about how many may exist at once, which + is the thing the world pool bounds. + + ``True`` is in the list because ``isinstance(True, int)`` is ``True`` in + Python: a YAML ``max_concurrent_requests: true`` would otherwise read as the + number 1 and silently serialize a node sized for eight. + """ + extra = {} if limit is None else {"max_concurrent_requests": limit} + path = _write_config(tmp_path, f"reject_{limit}.yaml", **extra) + with pytest.raises(ValueError, match="max_concurrent_requests"): + model.get_worker_graphs(path) + + +@pytest.mark.parametrize(("limit", "worlds"), [(2, 1), (8, 4), (2, None)]) +def test_get_worker_graphs_refuses_more_arrivals_than_worlds( + model, tmp_path, limit, worlds +): + """A queue longer than the pool is not a queue, it is a delayed failure: the + conductor admits ``limit`` requests, the ring hands out ``num_worlds``, and + the difference is a set of requests that reach ``admit`` and die there with + an ``AdmitRuntimeError`` that no retry, eviction or reload can clear. + + The ``worlds=None`` case is the one a deployment writes by accident: raising + ``max_concurrent_requests`` without touching ``resources`` at all, which is + the shape every pre-pool config already has. + """ + extra = {"max_concurrent_requests": limit} + if worlds is not None: + extra |= _worlds(worlds) + path = _write_config(tmp_path, f"over_{limit}_{worlds}.yaml", **extra) + + with pytest.raises(ValueError, match="exceeds the"): + model.get_worker_graphs(path) + + +@pytest.mark.parametrize(("limit", "worlds"), [(1, None), (1, 1), (4, 4), (8, 8)]) +def test_get_worker_graphs_accepts_a_deployment_inside_its_pool( + model, tmp_path, limit, worlds +): + """``limit == num_worlds`` is the shape that should be written, at any size. + The ``(1, None)`` case is the default deployment, which must keep working + unchanged — the pool is a widening, not a migration.""" + extra = {"max_concurrent_requests": limit} + if worlds is not None: + extra |= _worlds(worlds) + path = _write_config(tmp_path, f"ok_{limit}_{worlds}.yaml", **extra) + + graphs = model.get_worker_graphs(path) + + assert graphs + assert {walk for g in graphs for walk in g.graph_walks} == { + PRIME_WALK, + ROLLOUT_WALK, + } + + +def test_get_worker_graphs_warns_about_worlds_no_request_can_reach( + model, tmp_path, caplog +): + """Legal, and only wasteful — so a warning and not a refusal. Each + unreachable world is ~816 MiB of ring at 720P that is allocated, zeroed, + and never written, which is worth a line in the log rather than a failed + boot: the deployment still serves correctly.""" + path = _write_config( + tmp_path, "underused.yaml", max_concurrent_requests=2, **_worlds(8) + ) + + with caplog.at_level(logging.WARNING): + assert model.get_worker_graphs(path) + + assert any("can never be filled" in r.getMessage() for r in caplog.records) + + +# --------------------------------------------------------------------------- +# Resource binding +# --------------------------------------------------------------------------- + + +def test_bind_reaches_the_dit_and_all_twenty_four_attention_layers(submodule): + """One bind on the submodule has to reach every caller: the 24 attention + layers hold their own references and call ``upsert``/``attend`` directly. + Anything left unbound raises ``NoneType has no attribute ...`` mid-forward + -- or, if warmup gets there first, inside a capture, where it poisons the + graph instead of failing a request.""" + kv, attn = object(), object() + submodule.bind_node_resources({KV_RESOURCE: kv, ATTN_RESOURCE: attn}) + + assert submodule.node_resources[KV_RESOURCE] is kv + + layers = [m for m in submodule.modules() if isinstance(m, WaypointAttention)] + assert len(layers) == 24 + for layer in layers: + assert layer.kv is kv, f"layer {layer.layer_idx} unbound" + assert layer.attn is attn, f"layer {layer.layer_idx} unbound" + + +def test_the_submodule_owns_the_dit_and_is_not_the_dit(submodule): + """The structural reason the test above can pass at all. + + ``NodeSubmodule.bind_node_resources`` walks ``self.modules()`` but skips + ``self`` (``submodule_base.py``: ``if bind is not None and module is not + self``). A submodule that *is* the DiT -- by subclassing it, or by defining + ``bind_resources`` on itself -- would therefore never be visited, and + anything the root came to need would sit unbound. + + Nothing on the DiT root needs a resource today (``commit`` is threaded down + as an argument), so this is a structural guard rather than a live bug: it + keeps the walk able to reach the root if that ever changes. + """ + assert not isinstance(submodule, WaypointDiT) + assert isinstance(submodule.dit, WaypointDiT) and submodule.dit is not submodule + # The DiT has to be a *child module*, not a plain attribute -- self.modules() + # is the only thing the walk follows. + assert any(m is submodule.dit for m in submodule.modules()) + # And the submodule must not answer bind_resources itself: the walk would + # skip it, so defining one is a method that never runs. + assert getattr(type(submodule), "bind_resources", None) is None + + +def test_binding_without_a_declared_resource_fails_at_bind(submodule): + """Not mid-forward. The layers resolve with ``.get`` -- correct for a layer, + which may sit on a node owning only some resources -- so the node is the + frame that still knows which keys it declared and can name the missing one. + """ + for partial in ({ATTN_RESOURCE: object()}, {KV_RESOURCE: object()}, {}): + with pytest.raises(KeyError): + submodule.bind_node_resources(partial) + + +# --------------------------------------------------------------------------- +# Capture configs +# --------------------------------------------------------------------------- + + +def test_one_capture_config_per_walk_both_uncompiled(submodule): + """Two configs because the two walks take different input keys, and a walk + with no bucket would run eager against a ring the other walk's captured + graph holds baked addresses into. + + ``compile=False`` on both: ``_forward_for`` would otherwise run a + max-autotune compile of the whole 4+1 driver once per config at warmup, for + a model whose correctness-critical compile is the ``flex_attention_masked`` + pin inside the attention resource -- which runs regardless. The outer + compile is an unmeasured throughput bet. + """ + configs = submodule.get_cuda_graph_configs(torch.device("meta")) + assert len(configs) == 2 + assert {c.capture_graph_walk for c in configs} == {PRIME_WALK, ROLLOUT_WALK} + for cfg in configs: + assert cfg.compile is False + # One live world again: the bucket cannot be wider than it. + assert cfg.capture_batch_sizes == [1] + # The v1 engine always dispatches batched; a submodule captured on bare + # `forward` is captured on a method that never runs. + assert cfg.capture_forward_method == "forward_batched" + + by_walk = {c.capture_graph_walk: c for c in configs} + assert "latent" in by_walk[PRIME_WALK].single_request_inputs.tensor_inputs + assert "noise" in by_walk[ROLLOUT_WALK].single_request_inputs.tensor_inputs + assert "noise" not in by_walk[PRIME_WALK].single_request_inputs.tensor_inputs + assert "latent" not in by_walk[ROLLOUT_WALK].single_request_inputs.tensor_inputs + + +# --------------------------------------------------------------------------- +# Request shaping +# --------------------------------------------------------------------------- + + +def test_process_prompt_materializes_the_whole_action_stream(model, config): + """The rollout is scripted, so the stream is built once at + request time and sliced per frame. Validated here rather than in + ``prepare_inputs`` because this runs at the API boundary, where a + ValueError becomes a 400 instead of killing a rollout mid-flight.""" + actions = [ + {"mouse": (1.0, -2.0), "buttons": [3, 5], "scroll": 0.5}, + {"buttons": [3]}, + ] + out = model.process_prompt( + None, ["tensor"], ["video"], tensors=None, num_frames=4, actions=actions + ) + assert set(out) == {"mouse", "button", "scroll"} + mouse, button, scroll = out["mouse"][0], out["button"][0], out["scroll"][0] + assert mouse.shape == (1, 4, 2) + assert button.shape == (1, 4, config.n_buttons) + assert scroll.shape == (1, 4, 1) + assert mouse[0, 0].tolist() == [1.0, -2.0] + assert button[0, 0].nonzero().flatten().tolist() == [3, 5] + assert button[0, 1].nonzero().flatten().tolist() == [3] + # Unscripted frames are the idle controller, which is what the reference's + # default CtrlInput() produces. + assert button[0, 2].sum() == 0 and scroll[0, 2].sum() == 0 + + with pytest.raises(ValueError, match="out of range"): + model.process_prompt( + None, ["tensor"], ["video"], num_frames=1, + actions=[{"buttons": [config.n_buttons]}], + ) + with pytest.raises(ValueError, match="never be read"): + model.process_prompt(None, ["tensor"], ["video"], num_frames=1, actions=actions) diff --git a/test/modular/test_waypoint_weight_loader.py b/test/modular/test_waypoint_weight_loader.py index 586d5788b..d14a8a9b3 100644 --- a/test/modular/test_waypoint_weight_loader.py +++ b/test/modular/test_waypoint_weight_loader.py @@ -4,12 +4,12 @@ downloaded, so the bar here is not numerical parity — it is **every claim the loader makes about keys, shapes, slices and counts, checked against a state dict built from the reference's own key spellings** (``world_engine/src/model/ -world_model.py::load_state_dict``, transcribed in ``docs/waypoint/PARAM_TREE.md``). +world_model.py::load_state_dict``). Everything runs on CPU with no checkpoint and no GPU. Why that bar and not a looser one: almost every way this loader can be wrong is -*shape-legal*. ``PARAM_TREE.md`` section 8 lists sixteen failure modes and -labels nine of them silent — a q/k/v fusion built as ``cat([q, v, k])``, an +*shape-legal*. Of the sixteen known failure modes, nine are silent — a q/k/v +fusion built as ``cat([q, v, k])``, an ``fc1_x``/``fc1_c`` merge in the wrong column order, ``attn``/``mlp`` cond_proj slots swapped, an ``unpatchify`` permute dropped. Each of those loads without an exception and produces plausible video. So the synthetic tensors are @@ -26,8 +26,8 @@ * **F2** — the ``cond_proj`` tie check probed ``[:, :64]``, so a divergence past column 64 loaded clean with ``verify_cond_proj_tie=True``. * **F3** — ``attn_cond_head.bias_in`` was dropped unconditionally, so a file - carrying only that spelling (``PARAM_TREE.md`` section 10.2 leaves which one - the real file has unresolved) failed with 24 unloaded ``cond_head.bias_in``. + carrying only that spelling (which one the real file has could not be + resolved statically) failed with 24 unloaded ``cond_head.bias_in``. The reference falls back to it (``world_model.py:386-389``). * **F4** — shape validation ran before the drop filter, so a ``.cond_heads.`` key ending in ``.k_proj.weight`` raised a GQA error about a key T12 discards. @@ -323,7 +323,7 @@ def test_parameter_census_matches_the_720p_checkpoint(): # --------------------------------------------------------------------------- -# 2. The cond_proj tie lifecycle (CONTRACTS section 6.1) +# 2. The cond_proj tie lifecycle # --------------------------------------------------------------------------- @@ -472,7 +472,7 @@ def test_cond_proj_slots_follow_the_half_head_names(tmp_path): ``CondHead.forward`` is unpacked as ``s0, b0, g0, s1, b1, g1``; 0-2 drive the attention sublayer and 3-5 the MLP. All six are ``[D, D]``, so swapping T5 - and T6 is mechanically invisible and numerically catastrophic (S5). + and T6 is mechanically invisible and numerically catastrophic. """ config = tiny_config() state, _ = synthetic_checkpoint(config, spelling=LEGACY) From 103a067eb71cbfa5872f3dfa939277721d614307 Mon Sep 17 00:00:00 2001 From: garv Date: Sun, 13 Sep 2026 01:14:58 +0000 Subject: [PATCH 05/29] waypoint: DiT + TAEHV decoder, ring KV cache, flex attention, video_frame streaming Model: dit, taehv, rope, attention, layers, weight_loader, checkpoint, submodules. Engine: ring KV backend, flex attention resource, CUDA graph buckets. Serving: video_frame modality across api_server, data_worker and client SDK. Tests: component, equivalence, pixel, packaging and streaming benchmark suites. --- .gitignore | 6 + WAYPOINT_PROGRESS.md | 332 ++++++ configs/waypoint.yaml | 39 + docs/adding_models.rst | 48 +- docs/clients.rst | 13 +- docs/installation.rst | 41 +- docs/waypoint/DECISIONS.md | 83 ++ docs/waypoint/MVP_IMPLEMENTATION_STATUS.md | 144 +++ docs/waypoint/OPTIMIZATION_BACKLOG.md | 152 +++ docs/waypoint/PORT_PLAN.md | 64 ++ docs/waypoint/VALIDATION.md | 201 ++++ .../reference-parity-2026-09-11-360p.json | 62 ++ .../baselines/streaming-2026-09-11-360p.json | 153 +++ .../baselines/streaming-2026-09-11-720p.json | 151 +++ examples/sdk_chat.py | 2 +- mstar/__init__.py | 8 +- mstar/api_server/data_worker.py | 186 +++- mstar/api_server/entrypoint.py | 34 +- mstar/api_server/request_types.py | 2 +- mstar/cli/main.py | 1 + mstar/client/__init__.py | 2 + mstar/client/client.py | 41 +- mstar/client/media.py | 5 +- mstar/client/types.py | 95 +- mstar/engine/resources/attn/flex.py | 206 +++- mstar/engine/resources/kv/config.py | 18 +- mstar/engine/resources/kv/ring/cache.py | 22 +- mstar/engine/resources/kv/ring/manager.py | 31 +- mstar/graph/base.py | 2 +- mstar/model/base.py | 19 +- mstar/model/registry.py | 11 +- mstar/model/waypoint/checkpoint.py | 266 +++++ mstar/model/waypoint/components/__init__.py | 34 +- mstar/model/waypoint/components/attention.py | 141 +-- mstar/model/waypoint/components/dit.py | 298 +++--- mstar/model/waypoint/components/layers.py | 279 ++--- mstar/model/waypoint/components/rope.py | 146 +-- mstar/model/waypoint/components/taehv.py | 402 ++++++++ mstar/model/waypoint/config.py | 263 +++-- mstar/model/waypoint/submodules.py | 725 +++++++++++++ mstar/model/waypoint/waypoint_model.py | 832 +++++++++++++++ mstar/model/waypoint/weight_loader.py | 456 +++------ mstar/worker/worker.py | 8 +- .../aliases/mstar-project/pyproject.toml | 1 + pyproject.toml | 14 + test/modular/test_api_completion_guard.py | 4 +- test/modular/test_client_sdk.py | 52 +- test/modular/test_cuda_graph_capture.py | 24 +- test/modular/test_flex_attention_resource.py | 143 +++ test/modular/test_ring_kv_resource.py | 33 +- test/modular/test_video_frame_protocol.py | 752 ++++++++++++++ ...est_waypoint_360p_reference_equivalence.py | 346 +++++++ test/modular/test_waypoint_checkpoint.py | 418 ++++++++ test/modular/test_waypoint_components.py | 5 +- test/modular/test_waypoint_dit.py | 16 +- test/modular/test_waypoint_gpu.py | 774 ++++++++++++++ test/modular/test_waypoint_packaging.py | 96 ++ .../test_waypoint_pixel_equivalence.py | 396 ++++++++ test/modular/test_waypoint_profiler.py | 117 +++ .../modular/test_waypoint_reference_compat.py | 187 ++++ .../test_waypoint_reference_equivalence.py | 845 +++++++++++++++ test/modular/test_waypoint_shell.py | 727 +++++++++++-- .../test_waypoint_streaming_benchmark.py | 205 ++++ .../test_waypoint_taehv_equivalence.py | 108 ++ test/waypoint/benchmark_streaming.py | 644 ++++++++++++ test/waypoint/check_nsys_replay.py | 175 ++++ test/waypoint/record_oracle.py | 678 +++++++++++++ test/waypoint/serve_rollout.py | 959 ++++++++++++++++++ 68 files changed, 12680 insertions(+), 1063 deletions(-) create mode 100644 WAYPOINT_PROGRESS.md create mode 100644 configs/waypoint.yaml create mode 100644 docs/waypoint/DECISIONS.md create mode 100644 docs/waypoint/MVP_IMPLEMENTATION_STATUS.md create mode 100644 docs/waypoint/OPTIMIZATION_BACKLOG.md create mode 100644 docs/waypoint/PORT_PLAN.md create mode 100644 docs/waypoint/VALIDATION.md create mode 100644 docs/waypoint/baselines/reference-parity-2026-09-11-360p.json create mode 100644 docs/waypoint/baselines/streaming-2026-09-11-360p.json create mode 100644 docs/waypoint/baselines/streaming-2026-09-11-720p.json create mode 100644 mstar/model/waypoint/checkpoint.py create mode 100644 mstar/model/waypoint/components/taehv.py create mode 100644 mstar/model/waypoint/submodules.py create mode 100644 mstar/model/waypoint/waypoint_model.py create mode 100644 test/modular/test_video_frame_protocol.py create mode 100644 test/modular/test_waypoint_360p_reference_equivalence.py create mode 100644 test/modular/test_waypoint_checkpoint.py create mode 100644 test/modular/test_waypoint_gpu.py create mode 100644 test/modular/test_waypoint_packaging.py create mode 100644 test/modular/test_waypoint_pixel_equivalence.py create mode 100644 test/modular/test_waypoint_profiler.py create mode 100644 test/modular/test_waypoint_reference_compat.py create mode 100644 test/modular/test_waypoint_reference_equivalence.py create mode 100644 test/modular/test_waypoint_streaming_benchmark.py create mode 100644 test/modular/test_waypoint_taehv_equivalence.py create mode 100644 test/waypoint/benchmark_streaming.py create mode 100644 test/waypoint/check_nsys_replay.py create mode 100644 test/waypoint/record_oracle.py create mode 100644 test/waypoint/serve_rollout.py diff --git a/.gitignore b/.gitignore index 5b5cc3a84..b701a991d 100644 --- a/.gitignore +++ b/.gitignore @@ -29,3 +29,9 @@ mstar/worker/ASYNC_REDESIGN.md # local AI-assistant context (kept local, not published — cf. vllm-omni) CLAUDE.md AGENTS.md +.claude_scratch/ + +# Waypoint port records are release artifacts, even when a developer's shared +# bare-worktree exclude hides docs/waypoint locally. +!docs/waypoint/ +!docs/waypoint/** diff --git a/WAYPOINT_PROGRESS.md b/WAYPOINT_PROGRESS.md new file mode 100644 index 000000000..0b3349e50 --- /dev/null +++ b/WAYPOINT_PROGRESS.md @@ -0,0 +1,332 @@ +# Waypoint Port Progress + +The active plan and durable records are in `docs/waypoint/`. This file preserves +the earlier investigation log, including failed approaches and measured results. +Its old phase numbers and "done" labels describe those experiments; they do not +close the current scripted-streaming MVP gates. + +## Current MVP Status + +| Phase | Scope | State | +|---|---|---| +| 1 | Documentation and baseline | Recorded; durable plan/decision/validation/backlog records added | +| 2 | Startup and configuration | Passed, including clean install and registry-selected Hub startup | +| 3 | Request and numerical correctness | Passed, including 41-step same-process parity | +| 4 | DiT and attention execution | Passed, including required full-size capture and host-sync-free steady replay | +| 5 | Encoder and decoder CUDA graphs | Passed, including real-weight parity and full-size eight-step streams | +| 6 | Streaming frame protocol | Passed through Python/Rust server and typed SDK at both resolutions | +| 7 | End-to-end 360p/720p MVP gate | Passed: local/Hub, sequential/interleaved, cleanup/reuse, bounded memory | +| 8 | Streaming viability harness | Baseline passed at 360p/720p; threshold remains a later decision | + +The earlier baseline was **242 CPU tests green** at `HEAD` `0b88001a`. It is +historical, not a claim about the current dirty tree. Current commands and results +belong in `docs/waypoint/VALIDATION.md`. + +## Historical Investigation Log + +### Wave 1 — three parallel streams, no file overlap + +| stream | phase | owns | +|---|---|---| +| A | 7 | `test/modular/test_waypoint_gpu.py` (new only) | +| B | 8 | `pyproject.toml`, `test/waypoint/record_oracle.py`, checkpoint download | +| C | 10 + 11a | `configs/waypoint.yaml`, `submodules.py`, `waypoint_model.py`, `test_waypoint_shell.py` | + +Split this way because the three touch disjoint files. + +Checkpoints download **outside the worktree** (`../checkpoints/`) so they cannot +enter the diff. + +### Wave 2 — phase 9, launched once B's oracle landed + +| stream | phase | owns | +|---|---|---| +| D | 9 | `test/modular/test_waypoint_reference_equivalence.py` (new only) | + +Runs alongside A and C, which still own their own files. D compares **eager to +eager**: the oracle was recorded with the 4+1 driver unrolled to capture per-pass +outputs, so it carries the reference's arithmetic but not its kernel selection. + +### Box constraints + +Only **GPU 2** is usable. 0, 1 and 3 each hold ~72–75 GB of another job, so anything +CUDA must run under `CUDA_VISIBLE_DEVICES=2` and no test may assume a device count. +torch is 2.9.1+cu128; 4.4 TB free on `/mnt/storage`. A and D now share GPU 2 — the +oracle run peaked at 18 GiB, so there is headroom, but a CUDA OOM in either is +contention before it is a bug. + +### Reported results + +**Phase 7.** `test/modular/test_waypoint_gpu.py`, 10 tests green (~22 s warm, 93 s +cold), prose 26.9%. A.1 **failed as shipped** and is fixed (below). A.3/A.4/A.5 pass +bit-exact. A.2 does not — bounded instead, at 4 bf16 ulp of frame peak against a +worst measured 2.35 over 120 comparisons; localized to inductor holding bf16 +pointwise intermediates in fp32 across a fusion, which moves compiled *toward* an +fp32 reference (0.0168) and away from eager (0.0252), and does not compound across +20 frames. Flex compiled-vs-eager and the GEMMs are 0.0. Also fixed the +`compile_dit` and `369 keys` docstrings the two agents flagged. + +**Phase 8.** `tensordict==0.10.0` and `taehv 0.1.0` installed (torch untouched at +2.9.1+cu128); both declared under a new `[waypoint]` extra. Checkpoints at +`../checkpoints/{Waypoint-1.5-1B,taehv1_5}` (11 GB / 22 MB). `build_waypoint_dit` +loads the real `model.safetensors` clean — 174 params, 1.282 B, the 2 fp32 +`NoiseConditioner` params intact. Oracle recorded to `../oracle` (9.2 GB, 41 +frames, 62 s): `test/waypoint/record_oracle.py`, world_engine only. + +**Phase 8, re-recorded.** The first cut ran eager and was wrong: eager +`flex_attention` ignores the `BlockMask` block index lists, so every pass attended +over unwritten ring slots (rel 0.68 against the compiled reference). State now +comes from `engine._denoise_pass`/`_cache_pass` verbatim. It cannot be instrumented +— making the per-pass output an extra output of that region, or splitting it into +five, moves the latent ~1 bf16 ULP and compounds — so `dit_out` comes from frozen +shadow passes, and every run checks they leave the state alone. + +Two findings Phase 9 depends on. **The reference is not bit-reproducible across +processes**: two processes running it alone disagree by 1 ULP at layer 0, compounding +to 11.6 on peak 20.4 by layer 23; latent drifts 0.03 → 0.38 over 6 frames. Not +cudagraphs; `max_autotune` is one source, not the only one. So nothing supports a +bit-exact assertion — `../oracle/repro/` is a second independent recording so the +floor is measurable from artifacts. **And a port that runs its denoise passes as +separate compiled regions cannot match the reference's latent**, for the same +fusion reason. The port is not such a port — `compile_regions()` compiles the same +two outer regions the reference does — but any future refactor that splits them +inherits a ~1 bf16 ULP floor at frame 1. + +**Why phase 9 survives this.** Its bit-exactness is an *in-process* claim: the +parity file drives the reference live and uses the oracle only for inputs (noise, +controls, seed latent) and kernel-independent bookkeeping (`written`, live buckets). +Where it starts both sides from an oracle ring snapshot, both get the same bytes, so +cross-process drift cannot enter. Verified by reading the file, not assumed. What +the finding *does* constrain is **L4**: the oracle's stored `latent` and `pixels` +are not bit-exact targets — pixels drift up to 72/255 across processes — so L4 must +either drive the reference live the same way, or assert against the floor measured +from `../oracle/repro/`. + +**The parity claim, stated exactly.** Port == reference, bit-exact, *when both run +in one process with the attention kernel shared and `reference_compat=True`*. That +is narrower than "the port matches the reference", and it is the strongest claim the +reference's own non-determinism permits. + +**Phase 9.** `test/modular/test_waypoint_reference_equivalence.py`, 10 tests, prose +26.8%. Result: **the port is not bit-equivalent to the reference, because the port +is more numerically correct than the reference.** As served, every layer diverges — +L1 frame 0 maxabs 9.06e-01 (rel 1.38e-01), first divergent stage `cond`, then +`rope`, then the blocks; L2 diverges at pass 0; L3 at frame 0. `written` masks are +**equal at every frame**, so ring bookkeeping is correct and only arithmetic +differs. The control is what makes this conclusive rather than a guess: injecting +the reference's three tables plus its cached LUT gives **0/30 divergent stages** and +a **41-frame rollout bit-exact** on latent, ring bytes and `written`, at every frame +and layer. Every tolerance in the file is exactly 0.0, justified by that control. + +**`WaypointConfig.reference_compat`** (the phase-9 implementation). At the time of +this experiment its default was `False`; WP-001 has since superseded that choice +and makes it `True` for serving. On, it bf16-round-trips the three tables where they are built and serves +the reference's batch-5 sigma LUT; off, the exact path is unchanged. Threads through +5 lines of `dit.py`; `submodules.py` and `waypoint_model.py` needed nothing. +**Flag on ⇒ 0.0 everywhere**: three tables, the LUT at all 5 sigmas, 30 stages, +both forwards, all 5 pass outputs, and the 41-frame rollout on latent, ring bytes +and `written` at every frame × layer. The port builds its own batch-5 table from its +own quantized `freq` and matches the reference bit-for-bit, so the flag is +self-contained rather than borrowing reference state — the hand-injection helper is +deleted. Parity file 5 failed/5 passed → **14 passed**; 277 passed across the +waypoint + ring + flex + GPU set; ruff clean. + +The five failing tests became `[exact]` / `[reference_compat]` pairs. The exact side +asserts *characterised* divergence, not magnitudes — the strongest being +`torch.equal(bf16_roundtrip(port_table), reference_table)`, a zero-tolerance +identity that survives an oracle re-record and goes red if the divergence ever stops +being `NoCastModule`'s cast. Nothing got weaker; `written`-mask equality and that +identity are new. Verified independently: no `allclose`/`atol`/`rtol`/`approx` in +either parity file. + +**Phase 10 + 11a.** `components/taehv.py` (port of `ae.py`'s +`ChunkedStreamingTAEHV` + `load_taehv`, all `taehv` imports deferred), the two VAE +submodules, the rewired walks, and `configs/waypoint.yaml`. Walks are now +`prime: vae_encoder → dit → vae_decoder → EMIT` and +`rollout: Loop { dit → vae_decoder → EMIT }`; the DiT node's contract is unchanged +and `get_node_resources` stays DiT-only. Streaming state is one +`ChunkedStreamingTAEHV` per request per AE node in `PerRequestState.kwargs`, dropped +by the engine's own `cleanup_request` — no cleanup code on either node. `taehv` +landed mid-task, so the port was checked against `world_engine/src/ae.py` directly: +**encode and decode bit-identical in fp32 and bf16**, including the moved +`.div(255)`. Tests still run on a fake `taehv`, and one pins that the tree imports +with the package absent. 253 passed, 3 skipped; ruff clean. + +Config is one `node_groups` entry with `[vae_encoder, dit, vae_decoder]` on rank 0, +not wan22's split — a worker boundary inside the rollout loop would put a process +hop between the DiT and an order-dependent decoder. + +## Problems hit + +- **Wave 1 was killed mid-run and produced nothing.** The parent process exited + while all three streams were still working; none had written a file. Verified on + disk: no `test_waypoint_gpu.py`, no `test/waypoint/`, no `configs/waypoint.yaml`, + no `../checkpoints/`, and neither `tensordict` nor `taehv` installed. All three + resumed from their saved transcripts with the GPU constraint added — their + exploration survived, their output did not. +- **`pip install taehv` from the pinned URL silently installs nothing.** pip here + is 22.0.2 (Ubuntu system pip) and cannot parse `Metadata-Version: 2.4`, which + modern setuptools emits for taehv's PEP 639 `license = "MIT"`. It falls back to + project name `unknown`, rejects the URL on a name mismatch, and installing the + extracted directory instead produces an empty `UNKNOWN-0.0.0` wheel with no + `taehv.py` in it — and uninstalls any other `UNKNOWN-0.0.0` on the box on the + way past. Fixed by building the wheel directly with the system setuptools + (`setuptools.build_meta.build_wheel`) and installing that. uv, which the + reference uses, does not hit this. +- **`fullgraph=True` did not hold on the shipped code.** `_denoise_pass` had + `zip(sigmas, sigmas.diff(), strict=False)`; Dynamo rejects a ragged `zip` under + `fullgraph` regardless of `strict`, with `UserError: zip() has one argument of + len differing from others`. Localized by monkeypatching a zip-free pass, which + compiled clean. Fixed to `zip(sigmas[:-1], sigmas.diff(), strict=True)` — same + four iterations, same values, bit-identical in eager. `capture_scalar_outputs` + is **not** needed and the test asserts it stays unset. The oracle's own unrolled + driver still uses `strict=False`, which is fine: it is never compiled. +- **Gate B costs ~6× the old estimate.** 120 eager `BlockMask` rebuilds at real + 720P = **14.31 ms/frame**, of which the block-alignment `torch.equal` sync is + 4.38 ms — against 40.8 ms/frame steady state, so it is ~35% of frame time. The + `(layer, frame)` cache would cut it to 24 rebuilds = 2.86 ms, **saving 11.45 + ms/frame**. The plan gated this fix on the measurement; the measurement says do + it. Deferred until stream D finishes so it cannot contaminate parity debugging. +- **`compile_dit`'s docstring was wrong** — it claimed compilation is "a + throughput knob only" that "does NOT govern attention correctness." It is a + **capture prerequisite**: eager capture dies with + `cudaErrorStreamCaptureInvalidated` at `make_block_mask`'s device-to-host sync. + Corrected in `config.py`, and shorter than what it replaced. +- **The reference's own buffer handling is lossy.** `NoCastModule._apply` + (`world_engine/src/model/nn.py:11-24`) casts fp32→bf16 and then casts *the result* + back to fp32. Parameters recover because `load_state_dict` refills them + afterwards; **non-persistent derived buffers never do**. So the served reference + runs on bf16-quantized `denoise_step_emb.freq` (1.80e-03), `rope_angles.xy` + (1.88e-02) and `rope_angles.inv_t` (1.78e-04). Not cosmetic: `freq` is multiplied + by `sigma*1000`, so 1.8e-3 relative is a RoPE phase error up to ~1.8 rad. The + reference warns about it itself. Verified in the source before acting on it. +- **The oracle was recorded under eager attention and is partly unusable.** + `record_oracle.py` calls `engine.model(...)` directly (lines 264, 270, 354), + bypassing the reference's two `@torch.compile` regions. Eager `flex_attention` + ignores a `BlockMask`'s block index lists and attends **unwritten ring slots** — + a property this port already pins. Oracle vs reference-compiled is maxabs 5.6406 + on peak 8.25 (rel 0.684). So `dit_out`, `committed_kv`, and the `latent`/`pixels` + that decode from them are **not valid parity targets**; inputs, `ctx`, noise, + `written` masks and the live-bucket pattern still are. Phase 9 worked around it by + driving the reference live with attention rebound to the port's own + `flex_attention_masked`, so the kernel could not be the variable. **Being + re-recorded through the compiled path** — L4 pixel parity depends on it. +- **The decode-ordering comment was wrong, and the test that caught it stands.** + `enable_async_scheduling=False` does *not* serialize dit→decoder; it only + disables speculation (`Worker._can_speculate`). What actually stops the DiT + running twice before a decode is that `NodeManager.pop_ready_nodes` **removes** a + node from the ready set when it schedules it, and the DiT's persisted controller + streams are re-injected only at the loop's iteration boundary. Surfaced by a loop + test asserting `ready_node_names == {vae_decoder}` and failing. Comments and test + now say the real reason. +- **Latent bug in `mstar/graph/base.py` — not waypoint's, not currently reachable.** + `GraphStateRegistry.mark_entity_complete` tracks `_num_completed_entities` as a + *count*, not a set, despite a "no-ops if already done" docstring. Marking one + entity complete twice inside a 2-node loop body satisfies + `_num_completed == _num_managed`, fires `complete_iter()`, and `reset_for_iter` + then silently discards the latent queued in the other node's `ready_signals`. + Single-node loop bodies (wan22, cosmos3) cannot hit it; waypoint is held off it + only by the pop semantics above. Any future multi-node loop body is one re-ingest + away. **Reported, not fixed — shared engine code, outside this port's scope.** +- **The `369 keys` in `weight_loader.py`'s key map is stale** — the shipped + checkpoint has **393**. Prose only; nothing computes from it, and the loader's + completeness contract passes on the real file. +- **Stream C's in-flight work fails 6 tests in `test_waypoint_shell.py`** + (`KeyError: 'vae_encoder'`, `NameError: WorkerGraphIO`) — the Phase 10 VAE nodes + do not exist yet. Not caused by the phase 8 dependency changes: the only tracked + phase 8 edit is +17 lines of `[project.optional-dependencies]`, and the other + 217 tests in the ring/flex/waypoint set stay green. + +## Historical decisions + +These record the choices made on 2026-09-10 after phase 9. The first choice was +superseded by WP-001 on 2026-09-11; it remains here to explain the implementation +and measurements that followed it. + +- **Superseded: the port ships the mathematically exact tables by default.** The + reference serves bf16-quantized RoPE + and conditioner tables because of the lossy `NoCastModule._apply` round-trip + above. Rather than be bug-compatible, the port stays exact and gains a + reference-compat flag alongside the existing `full_global_ring`, which is already + there "to restore the reference's allocation for an A/B parity run". Flag on ⇒ + bit-exact over 41 frames, which validates every other line of the port; flag off ⇒ + exact-table serving differed from the reference by one characterised difference. + Rejected: bug-compatibility (ships a ~1.8 rad phase defect the reference's authors + appear not to have intended) and dropping bit-exactness as the gate (leaves no + zero for a future regression to fail against). +- **`patch_cached_noise_conditioning` sits behind the same flag.** It is measurably + **not** a no-op — the planning assumption fell the wrong way. The cause is the + batch shape, not the LUT: the reference builds the table by evaluating the fp32 + MLP on all 5 sigmas at once (M=5 ⇒ TF32 tensor-core GEMM under + `float32_matmul_precision('high')`), while serving one sigma at a time is an M=1 + GEMV that stays exact fp32. Measured batch5-vs-batch1: **3.125e-02 @ `high`, 0.0 @ + `highest`**, 3.125e-02 @ `medium`. Per-sigma the patch is off the live path by + 1.5625e-02, except sigma 0.75 at 3.125e-02. Only the `CachedDenoiseStepEmb` half + diverges — **`CachedCondHead` measures 0.0 at all five sigmas and needs no compat + treatment**. Serving keeps the exact per-sigma GEMV; parity runs reproduce the + reference. One flag, so tables and LUT can never disagree about which model is + being served. + +Three independent measurements converge on the M=5 TF32 story — phase 8's +calibration probe (`high` vs `highest` = 0.03125 on the conditioner), phase 7's pin +test (all precisions identical at the served M=1 shape), and phase 9's direct +batch5-vs-batch1 comparison. Phase 8 read its 0.03125 as only proving the probe was +live; it was also the signal, unrecognised at the time. + +## Assumptions made + +Carried in from planning, to be confirmed or killed by the work: + +- ~~**`patch_cached_noise_conditioning` is a numerical no-op.**~~ **Killed by phase + 9.** The reasoning was that the LUT is built by running the same fp32 island on + bf16 sigmas and rounding to bf16, which is what the port's live path returns. That + missed the batch shape entirely. See *Decisions taken* above — a good example of + why the plan said to test this rather than assert it. +- **The patched reference is the reference.** `world_engine.py:84` applies + `apply_inference_patches` unconditionally, so parity targets the patched model. +- **Same-torch parity.** The reference pins `torch==2.11.0`; this env has 2.9.1. + Both sides run at 2.9.1 so the comparison means something; this deviates from the + reference's pin deliberately. +- **The two matmul-precision settings differ, but not observably** — mstar sets + `'high'`, the reference `'medium'`. Settled by phase 8: the oracle records under + `'high'` (serving's value, set *after* importing world_engine, which sets + `'medium'` at import), and a calibration probe run at record time measures + `'high'` and `'medium'` as bit-identical on this build — 0.0 on both a 4096² + fp32 GEMM and on the NoiseConditioner LUT. `'highest'` differs from both (0.104 + and 0.03125), which is what shows the probe is live. So the flag cannot explain + a phase 9 mismatch on this box; it can on another. Phase 7 reached the same + answer by a second, independent route: at the served shape (B=N=1) the + `NoiseConditioner` matmuls are GEMVs (M=1), where all three settings are + bit-identical. Its pin test asserts both the setting and the reason it does not + bite, with an N=8 precondition that fails if the flag ever stops being live. +- **Historical implementation: `reference_compat` + `compile_dit` needed one eager frame first.** The sigma LUT + is built on first forward and cached per device, because it needs loaded weights — + the same contract `compile_regions`' docstring already states for the RoPE and + token-grid tables. An operational constraint on the parity configuration only; + the exact-table default at that time had no LUT. The active plan instead requires + post-load table materialization before compile/warmup. +- **Both LUTs are built under the same `float32_matmul_precision`.** At `'highest'` + the batch-5 GEMM stops rounding and the compat conditioner would converge on the + exact one; a test asserts the exact path still differs, so that surfaces red + rather than silently. The fixture ordering that guarantees it is commented. +- **The port's global-ring compaction touches only slots the reference never + addresses** — the reference's live region is `[0, port ring_len)` plus scratch + `[L, capacity)`, with `[8192, 65536)` dead. Not taken on faith: phase 9's + `test_the_port_compacts_only_slots_the_reference_never_addresses` proves it + against the oracle's `written` masks, which survive the oracle's eager-attention + defect. +- **VAE nodes use `disable_autocast = True`, not the fp32-island mixin.** TAEHV is + uniformly bf16, so the recorded island set would be empty, and + `EngineManager.build` skips the blanket cast entirely under `disable_autocast` — + stronger than restoring dtypes after a cast that already rounded. The only such + mixin is wan22's, and this port deliberately does not import across models. +- **The emitted payload is raw uint8 RGB** `[temporal_compression, H, W, 3]` in C + order, no container: a per-step mp4 would be an unplayable fragment. +- **A 1-frame seed clip is repeated to fill `temporal_compression`**, matching + `gen_sample.py`'s `seed_frame_x4`. Anything but 1 or exactly + `temporal_compression` is refused at the API boundary. Aspect ratio is checked, + resolution is not — the AE resizes 16:9 input onto its own grid. +- **The noise must be supplied, not reproduced.** The reference draws bf16 on + device unseeded; the port draws fp32 from a seeded CPU generator. The oracle + saves the exact noise tensor per frame so phase 9 feeds the port the same draw — + without that there is nothing bit-exact to compare. diff --git a/configs/waypoint.yaml b/configs/waypoint.yaml new file mode 100644 index 000000000..186b0da8f --- /dev/null +++ b/configs/waypoint.yaml @@ -0,0 +1,39 @@ +model: "waypoint" + +# Checkpoint selection, numerical behavior, and execution mode are explicit inputs. +# Local `checkpoint_dir` / `ae_path` overrides can be added here without changing +# the registry entry. The exact-table path is experimental and must be opted into +# with `reference_compat: false`. `compile_dit` and `cuda_graph` are independent; +# failed CUDA graph capture falls back to the selected DiT execution mode. +model_kwargs: + variant: "waypoint-1.5-1b-720p" + reference_compat: true + compile_dit: true + cuda_graph: true + full_global_ring: false + +# The world state is a ring, not a paged KV cache, so nothing here is sized in +# sequence positions; max_seq_len only satisfies the conductor's config check +# and matches one frame's token count. +max_seq_len: 512 + +# The primary bound on the world pool. `WaypointModel.get_worker_graphs` +# refuses to build unless this is a positive int no larger than +# `resources.kv.num_worlds`: a world is claimed at admit, by which point a +# request the pool cannot hold can only be failed terminally, and the +# conductor's FIFO admit queue that prevents that exists only when this is set. +max_concurrent_requests: 1 + +resources: + kv: + # ~816 MiB of ring per world at 720P. Raise together with + # max_concurrent_requests; the two are checked against each other. + num_worlds: 1 + +# 1.28B DiT plus a ~7M-parameter TAEHV, all on rank 0. One group, not wan22's +# split: the rollout Loop body is dit -> vae_decoder, and the decoder is a +# streaming model whose frames must be decoded exactly once in emission order, +# so there is nothing to gain from putting a worker boundary inside the loop. +node_groups: + - node_names: [vae_encoder, dit, vae_decoder] + ranks: [0] diff --git a/docs/adding_models.rst b/docs/adding_models.rst index 7146a4e0d..82b94d64b 100644 --- a/docs/adding_models.rst +++ b/docs/adding_models.rst @@ -265,19 +265,25 @@ The spec types are: * - Spec - What it builds - * - ``KVSpec(config=KVConfig(...))`` - - A paged KV cache. ``KVConfig`` holds ``num_layers``, ``num_kv_heads``, - ``head_dim``, ``max_seq_len`` and ``num_qo_heads``. It also holds three fields that - a deployment can tune: ``max_num_pages``, ``page_size`` and ``cpu_offload_pages`` - (the number of pinned host pages used for offload; 0 disables offload). + * - ``KVSpec(config=PagedKVConfig(...))`` + - A paged KV cache. ``KVConfig`` is the abstract base for common model geometry; + ``PagedKVConfig`` adds ``max_seq_len`` and the deployment-tunable + ``max_num_pages``, ``page_size`` and ``cpu_offload_pages`` fields. + * - ``KVSpec(config=RingKVConfig(...))`` + - A fixed-capacity frame ring. It adds ``tokens_per_frame``, one + ``RingKVLayerConfig`` per layer, and the deployment-tunable ``num_worlds``. + Ring storage is currently paired with FlexAttention. * - ``AttentionSpec(config=AttentionConfig(kv_cache=...))`` - Self-attention planned over the named cache. ``backend`` selects - ``AttnBackend.FLASHINFER`` (the default) or ``AttnBackend.DENSE``. + ``AttnBackend.FLASHINFER`` (the default), ``AttnBackend.DENSE`` or + ``AttnBackend.FLEX``. FlashInfer and dense attention require a + ``PagedKVConfig``; FlexAttention requires a ``RingKVConfig``. ``flashinfer_backend`` selects a kernel generation: ``"auto"``, ``"fa2"`` or - ``"fa3"``. + ``"fa3"`` when the FlashInfer backend is selected. * - ``CrossAttentionSpec(config=CrossAttentionConfig(...))`` - - Attention over a context that is written once and never extended. See - `Cross-attention (encoder-decoder models)`_. + - Attention over a paged context that is written once and never extended. Only + the FlashInfer backend is implemented. See `Cross-attention (encoder-decoder + models)`_. * - ``RaggedAttentionSpec(config=RaggedAttentionConfig(...))`` - Cacheless (ragged) varlen self-attention over the segments packed into one forward. Nothing is paged, and nothing carries to the next step. @@ -310,7 +316,7 @@ appears in no spec, so it receives no resources: # mstar/model/orpheus/orpheus_model.py def get_node_resources(self) -> list[NodeResourceSpec]: - kv_config = KVConfig( + kv_config = PagedKVConfig( num_layers=self.config.num_hidden_layers, num_kv_heads=self.config.num_key_value_heads, head_dim=self.config.head_dim, @@ -925,6 +931,9 @@ Both types share the base ``CudaGraphConfig`` fields: engine's eager batch size for the walk. The default is ``True``, so the engine never batches beyond a captured size. - ``compile`` runs ``torch.compile`` before capture. The default is ``True``. +- ``required`` makes every bucket in the config mandatory. If capture fails locally or + on another participating rank, warmup raises after rank-wide agreement instead of + dropping the bucket and falling back to eager execution. The default is ``False``. ``BatchedCudaGraphConfig`` also accepts ``total_tokens_multiplier``. Use it when one request's step commits KV across several labels that are combined into a single plan, as @@ -1036,6 +1045,9 @@ Both types share the base ``PiecewiseCudaGraphConfig`` fields: default. - ``compile`` runs ``torch.compile`` on ``capture_fn`` before capture. The default is ``False``. +- ``required`` makes every declared shape mandatory. If any participating rank cannot + capture one, warmup raises instead of leaving that shape on the eager path. The default + is ``False``. **Splitting the declaration.** When a region leases its own slot, exactly one of the two declarations must own each resource. The common pattern is for the outer ``declare_step`` @@ -1209,10 +1221,15 @@ that a misspelled setting is never silently ignored: * - Spec - Accepts - * - ``KVSpec`` + * - ``KVSpec`` with ``PagedKVConfig`` - ``max_num_pages``, ``page_size``, ``max_seq_len``, ``cpu_offload_pages`` - * - ``AttentionSpec`` / ``CrossAttentionSpec`` - - ``backend`` (``flashinfer`` / ``dense``), ``flashinfer_backend`` + * - ``KVSpec`` with ``RingKVConfig`` + - ``num_worlds`` + * - ``AttentionSpec`` + - ``backend`` (``flashinfer`` / ``dense`` / ``flex``), + ``flashinfer_backend`` (``auto`` / ``fa2`` / ``fa3``) + * - ``CrossAttentionSpec`` + - ``backend`` (only ``flashinfer`` is implemented), ``flashinfer_backend`` (``auto`` / ``fa2`` / ``fa3``) * - ``RaggedAttentionSpec`` - ``flashinfer_backend`` (``auto`` / ``fa2`` / ``fa3``), @@ -1221,8 +1238,9 @@ that a misspelled setting is never silently ignored: not the model. Tune the cache shape on the KV resource, not on the attention resource that reads it. For -example, ``configs/qwen3tts.yaml`` selects FA2 under ``talker_attn``, while -``configs/cosmos3_nano.yaml`` sets the page count under its KV key. +example, ``configs/qwen3tts.yaml`` selects FA2 under ``talker_attn``, +``configs/cosmos3_nano.yaml`` sets the page count under its paged KV key, and +``configs/waypoint.yaml`` sets the resident world count under its ring KV key. .. note:: diff --git a/docs/clients.rst b/docs/clients.rst index 168ccecc5..b38dc4c4c 100644 --- a/docs/clients.rst +++ b/docs/clients.rst @@ -34,7 +34,7 @@ NDJSON stream. * - ``output_modalities`` - ``text`` - Comma-separated desired outputs (e.g. ``text``, ``image``, ``audio``, ``video``, - ``action``). + ``video_frame``, ``action``). ``video_frame`` is streaming-only raw RGB24. * - ``streaming`` - ``true`` - ``true`` → NDJSON stream of chunks; ``false`` → one JSON document. @@ -121,7 +121,10 @@ Result and event types live in ``mstar.client``: - ``AudioBuffer`` — decoded PCM with ``.sample_rate``; ``.to_wav(path)``, ``.to_numpy()``, ``len(...)``. - Stream events — ``TextChunk(text)``, ``ImageChunk(data)`` (``.save(path)``), - ``AudioChunk(pcm, sample_rate)``. + ``AudioChunk(pcm, sample_rate)``, and ``VideoFrameChunk(data, metadata)``. A + video-frame chunk validates its width, height, fps, pixel format and frame range; + ``.to_numpy()`` returns a zero-copy ``[frame_count, height, width, 3]`` uint8 view. + Raw ``video_frame`` requests require ``stream=True``. .. code-block:: python @@ -171,8 +174,10 @@ Endpoints and model coverage: - ``bagel`` - Image editing (image + prompt → image). -Models without an OpenAI surface (``pi05``, ``vjepa2``, ``vjepa2_ac``) return ``404`` on -``/v1/*``; use ``/generate`` or the SDK for them. +Models without an OpenAI surface (``pi05``, ``vjepa2``, ``vjepa2_ac``, ``waypoint``) +return ``404`` on ``/v1/*``; use ``/generate`` or the SDK for them. In particular, +Waypoint emits live RGB frame chunks and is not routed through the encoded-video +``/v1/videos/generations`` endpoint. .. code-block:: python diff --git a/docs/installation.rst b/docs/installation.rst index 647518741..58751a433 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -94,16 +94,21 @@ Model families and some output formats need extra packages, exposed as pip *extr * - ``.[vjepa2]`` / ``.[vjepa2_ac]`` - V-JEPA 2 runtime: ``safetensors``, ``torchcodec``, ``huggingface-hub``, ``mooncake-transfer-engine`` (``vjepa2_ac`` also adds ``flashinfer-python``). + * - ``.[waypoint]`` + - Index-hosted Waypoint dependencies: ``huggingface-hub``, ``safetensors``, + and ``tensordict`` for reference validation. The pinned TAEHV implementation + must be installed separately as shown below; keeping its direct URL out of + package metadata allows ``m-star`` to be published on PyPI. * - ``.[audio]`` - ``soundfile`` — only needed to return **non-WAV** audio containers (mp3/flac/…) from the OpenAI/SDK audio surfaces. WAV/PCM output works without it. * - ``.[dev]`` - ``ruff`` + ``pytest`` for linting and the test suite. * - ``.[all]`` - - The union of every model extra above — installs the full runtime for all model - families in one shot. Convenient for a machine that serves multiple models; heavier - and slower to install than a single family's extra. (Still excludes ``flash-attn`` — - see `flash-attn (Qwen3-Omni)`_.) + - The union of the index-hosted dependencies from every model extra above. + Convenient for a machine that serves multiple models; heavier and slower to + install than a single family's extra. It excludes the separately installed + TAEHV and ``flash-attn`` packages; see below and `flash-attn (Qwen3-Omni)`_. Combine extras as needed (keep ``--torch-backend=auto`` on every install): @@ -111,11 +116,33 @@ Combine extras as needed (keep ``--torch-backend=auto`` on every install): uv pip install --torch-backend=auto -e ".[bagel,audio,dev]" +Waypoint's dependencies and pinned TAEHV source are two installs. PyPI and other +standards-conformant indices reject distributions whose metadata declares a direct-URL +dependency, so the ``waypoint`` extra intentionally does not name TAEHV. Check the +installer version before installing its source archive: an old pip may report success +while producing an empty ``UNKNOWN`` wheel. + +.. code-block:: bash + + uv --version # must be 0.4.0 or newer + uv pip install --torch-backend=auto -e ".[waypoint]" + uv pip install --no-deps \ + "taehv @ https://github.com/madebyollin/taehv/archive/7dc60ec6601af2e668e31bc70acc4cb3665e4c22.zip" + +Or, in an existing Python 3.12 environment: + +.. code-block:: bash + + python -m pip install --upgrade "pip>=24.3" + python -m pip install -e ".[waypoint]" + python -m pip install --no-deps \ + "taehv @ https://github.com/madebyollin/taehv/archive/7dc60ec6601af2e668e31bc70acc4cb3665e4c22.zip" + .. tip:: - If you're just getting started or have the disk/time to spare, ``.[all]`` is the - recommended install — it pulls every model family's runtime so any model works out of - the box, with no need to track which extra goes with which model: + If you're just getting started or have the disk/time to spare, ``.[all]`` installs all + index-hosted model dependencies in one shot. Waypoint still needs the pinned TAEHV + command above, and Qwen3-Omni still needs ``flash-attn``: .. code-block:: bash diff --git a/docs/waypoint/DECISIONS.md b/docs/waypoint/DECISIONS.md new file mode 100644 index 000000000..d46445da8 --- /dev/null +++ b/docs/waypoint/DECISIONS.md @@ -0,0 +1,83 @@ +# Waypoint Decision Record + +## WP-001: Reference-Compatible Numerics by Default + +- **Status:** Accepted, 2026-09-11 +- **Decision:** `reference_compat=True` is the serving default. Setting it to + `False` selects experimental exact-table arithmetic. +- **Evidence:** A live same-process 41-frame run previously measured zero + difference for reference-compatible tables, conditioner, DiT stages, five-pass + output, ring writes, and rollout latents. Exact-table arithmetic intentionally + diverges because the released reference BF16-round-trips derived FP32 tables and + builds its sigma table with a batch-5 operation. +- **Consequence:** Release validation targets reference-compatible mode. Exact + mode remains useful for investigation but cannot satisfy the reference parity + gate. + +## WP-002: Resolve and Validate Before Allocation + +- **Status:** Accepted, 2026-09-11 +- **Decision:** Resolve local paths before considering a string to be a Hugging + Face ID. Download only root native safetensors plus `config.yaml`; resolve + `taehv1_5.pth` separately. Validate the manifest's architecture, supported + geometry, scheduler, temporal compression, and FPS before device allocation; + validate tensor completeness and the pinned TAEHV runtime architecture while + loading, before request admission. +- **Reason:** The upstream repository also contains redundant transformer and VAE + weights. Downloading the whole snapshot wastes several GiB and allows partial or + incompatible snapshots to fail late. + +## WP-003: Optional CUDA Graph Acceleration + +- **Status:** Supersedes the required-capture policy, 2026-09-11 +- **Decision:** `cuda_graph=True` attempts capture for encoder prime, steady DiT + rollout, decoder initialization, and steady decoder execution. Capture failure + falls back to eager execution; `cuda_graph=False` declares no capture buckets. + `compile_dit` independently controls the two outer DiT regions. +- **Reason:** CUDA graphs are an acceleration mechanism, not part of the model's + numerical contract. The engine already supports eager fallback, and Waypoint's + ring, mask planning, and functional AE state have eager execution paths. +- **Constraint:** The masked FlexAttention primitive remains compiled because bare + eager `flex_attention` ignores this BlockMask's block-index visibility data. +- **Exception:** The one-time DiT prime/cache pass is compiled with + `fullgraph=True` only when `compile_dit=True`, and remains uncaptured. + +## WP-004: Internal Prime Is Not User Output + +- **Status:** Accepted; end-to-end validation passed +- **Decision:** Prime with an internal idle action, preserve user action zero for + the first generated latent, initialize all model state, and emit no reconstructed + seed frames. +- **Consequence:** A request with `num_steps=N` supplies exactly `N` actions and + emits exactly `4*N` RGB frames indexed from zero. + +## WP-005: Typed Streaming RGB Frames + +- **Status:** Accepted; live 360p and 720p validation passed +- **Decision:** Waypoint emits only the `video_frame` modality in streaming mode. + Payloads are contiguous RGB24 with width, height, FPS, pixel format, frame index, + and frame count metadata. +- **Consequence:** The OpenAI encoded-video endpoint is not a Waypoint transport. + Non-streaming `video_frame` requests fail before execution. The scripted MVP + supports only the Python frontend; `--rust-frontend` support is deferred. + +## WP-006: TAEHV Source Pin and Installer Floor + +- **Status:** Accepted +- **Decision:** TAEHV is installed separately from the index-safe `waypoint` extra + and pinned to upstream commit `7dc60ec6601af2e668e31bc70acc4cb3665e4c22`. + Direct URLs cannot appear in metadata published to PyPI. Supported installation + uses `uv>=0.4.0` or `pip>=24.3`; absence of TAEHV must produce an actionable + error containing its exact pinned archive command. +- **Reason:** Old pip releases misread the source package's Metadata-Version 2.4 + metadata and can install an empty `UNKNOWN` wheel. + +## WP-007: Streaming Benchmark Follows the MVP + +- **Status:** Accepted; first captured baseline recorded +- **Decision:** The first trustworthy captured run establishes a baseline. It does + not invent a release threshold. A later decision record sets a viability + threshold from the `STREAM-001` measurements. +- **Evidence:** Captured 16-step baseline and matched slow-consumer runs passed at + 360p and 720p. The durable JSON artifacts and summarized measurements are in + `OPTIMIZATION_BACKLOG.md`. diff --git a/docs/waypoint/MVP_IMPLEMENTATION_STATUS.md b/docs/waypoint/MVP_IMPLEMENTATION_STATUS.md new file mode 100644 index 000000000..849b46755 --- /dev/null +++ b/docs/waypoint/MVP_IMPLEMENTATION_STATUS.md @@ -0,0 +1,144 @@ +# Waypoint MVP Implementation Status + +This is the live implementation report for the current completion pass. It is +separate from `WAYPOINT_PROGRESS.md`, which remains the historical investigation +log. Durable decisions, acceptance gates, and deferred work remain in the other +files in this directory. + +## Current Snapshot + +Last updated: 2026-09-11. + +The branch was fetched and rebased onto `origin/main` at `9ef65097`. Its four +Waypoint commits now sit directly above the four new upstream packaging, ragged +attention, API-validation, and sampler commits. The pre-rebase tracked patch ID +and all 25 restored untracked file blobs matched the safety stash; there are no +unmerged entries, conflict artifacts, or staged files. + +| Area | Implementation | Current evidence | Remaining gate | +|---|---|---|---| +| Startup/config | Passed | Both artifact paths, the TAEHV runtime, and the DiT manifest validate before device allocation; tensor completeness and TAEHV architecture validate during loading before admission; variant-specific Hub mapping prevents cross-variant weights; Python 3.12/uv resolves and builds the pin; registry-selected 360p Hub startup passed | None for scripted MVP | +| Request semantics | Passed | Positive bounded `num_steps`, exact validated action count, required seed, internal idle prime, action zero preserved; full 360p/720p eight-step streams emitted exactly 32 generated frames from index zero | None for scripted MVP | +| DiT execution | Integrated | Runtime tables materialize after load; `compile_dit` independently selects compiled or eager denoise/cache regions; optional 128-token and 512-token rollout graphs captured on H100 | Record a full server graph-off run | +| Mask planning | Passed | One fixed-address local/global block mask per slot; immutable device visibility tables remove per-step allocations; multi-wrap/dilation/world parity passes; full-size 360p and 720p profiles each found 16/16 steady DiT graph replays and zero blocking CUDA calls | None for scripted MVP | +| Encoder/decoder | Integrated | Pure tensor encoder; nine explicit histories; real-weight FP32 parity; optional encoder/init/steady graphs captured and served at both resolutions on H100; graph-free declarations use eager forwards | Record a full server graph-off run | +| Optional capture policy | Integrated | `cuda_graph` controls declaration independently of `compile_dit`; all Waypoint buckets use normal eager fallback on capture failure; all four graph-enabled buckets previously captured at both resolutions | Record graph-off and injected capture-failure server runs | +| Frame protocol | Passed | Python streaming-only `video_frame`, canonical RGB24 metadata, immutable zero-copy SDK view, named-byte upload, explicit stream errors, ordered async-read delivery, and live 360p/720p SDK streams pass | Rust frontend support is deferred and outside the scripted MVP | +| End to end | Passed | Local 720p and registry-Hub 360p normal `EngineManager` paths captured all four buckets; both resolutions passed sequential and full-size two-world interleaved deterministic streams, exact counts, cleanup, repeated slot reuse, and bounded server memory | None for scripted MVP | +| Streaming viability | Baseline complete | `STREAM-001` records captured 16-step baseline and matched slow-consumer runs for both resolutions, including typed-stream correctness and server process-group memory | A later decision may define a viability threshold from these measurements | + +## Latest Validation + +- After reverting the out-of-scope Waypoint Rust frontend changes, the Python + frame protocol, SDK, and API guard selection passed: **45 passed, 2 existing + FastAPI deprecation warnings**. +- Consolidated Waypoint, startup, frame/SDK, graph-policy, attention/ring, GPU, + and live-parity CPU selection: **385 passed, 41 skipped, 4 warnings**. +- On H100 (`CUDA_VISIBLE_DEVICES=2`), the reduced random-weight graph suite passed + **10 tests** covering capture/replay, planned masks, interleaving, and + world-slot reuse. +- The 720p full-checkpoint same-process parity suite passed **14 tests**. Its + 41-frame reference-compatible rollout was bit exact through tables, + conditioning, all DiT stages, five-pass output, ring writes, and rollout + latents. The touched suite was rerun after the rebase: **14 passed in 56.98s**. +- The dedicated 360p same-process gate passed on the published 360p checkpoint: + **1 passed in 37.79s** on H100. Zero-tolerance comparisons covered all three + derived tables, five conditioner rows, all 30 DiT stages at frozen and + committing sigmas, 201 passes across 41 latent frames, every ring write, the + full-size BF16 functional encoder, all nine explicit decoder histories, and + 164 decoded 640x360 RGB frames. The retained report is + `baselines/reference-parity-2026-09-11-360p.json`. +- The real-checkpoint pixel suite passed **5 tests**, including TAEHV seed + encode/decode and reference pixel/order checks. +- A normal local 720p registry and `EngineManager` server run captured all four + graph-enabled buckets under the earlier required-capture policy. Two sequential eight-step SDK requests each emitted eight + typed chunks and exactly 32 1280x720 RGB24 frames; all 88,473,600 output bytes + were identical across slot reuse. The SDK's NDJSON reader now uses 1 MiB input + chunks, avoiding quadratic buffering of each 14.7 MB base64 response line. +- The distinct 360p checkpoint was selectively downloaded from its published + Hub repository: only `config.yaml` and `model.safetensors`. Its local SHA-256 + matched Hub metadata, and the variant-specific manifest preflight passed. +- A normal 360p registry and `EngineManager` run through `MStarClient` captured + all four graph-enabled paths under the earlier required-capture policy. Two sequential one-step requests each returned one + typed 2,764,800-byte chunk containing four 640x360 RGB24 frames from index + zero; the repeat was byte-identical. Startup took 223.5 seconds and requests + took 29.7 and 6.2 seconds. +- Real pinned TAEHV test on a reduced spatial grid: functional encoder, decoder + initialization, decoder steady output, and all nine histories are bit-identical + to the upstream streaming scheduler in FP32. +- `ruff check` over all changed implementation and focused test files: passed. +- `python3 -m compileall` over the changed Python surfaces: passed. +- `git diff --check`: passed. +- An optional full `test/modular` run required redirecting FlashInfer's cache + to `/tmp`; it reached 31% but then stopped producing output for several + minutes and was interrupted. The focused 16-file gate above was rerun cleanly + afterward, so this incomplete broad run is not counted as validation. +- The server log exposed a spurious `rollout_loop` stop signal during the prime + walk. The graph runtime ignored it, but it was a model lifecycle bug; the stop + hook now returns no signal outside rollout and its regression test passes. +- SDK error/upload regressions plus frame protocol tests pass: **26 passed**. +- Historical Rust validation remains recorded in `VALIDATION.md`, but the + Waypoint-specific Rust guard and tests were reverted after the Rust frontend + was removed from the scripted MVP scope. +- `uv 0.11.13` with Python 3.12 resolved the index-safe `.[waypoint]` + dependencies in dry-run mode. A separate `--no-deps` install then built the + pinned TAEHV revision as a real 21,947-byte `taehv.py` package exporting + `TAEHV`; this closes the installer-floor check without downloading a second + CUDA/PyTorch stack or placing a PyPI-incompatible direct URL in `m-star` + metadata. +- Async result reads now retain notification sequence and loop snapshots, buffer + out-of-order completions, and assign frame indices only in emission order. A + deliberately reversed-completion regression test passes. +- Flex mask planning now reads immutable per-geometry device lookup tables; a + repeat-plan test forbids fresh `torch.tensor` staging and checks all mask/table + addresses remain stable. +- Latest combined protocol, shell, ring, and Flex resource gate: **199 passed, + 3 skipped**; Ruff and Python compilation passed. +- A registry-selected 360p Hub deployment captured all required buckets, served + distinct deterministic solo baselines, and reproduced them byte-for-byte over + two concurrent two-world waves with actual A/B/A DiT scheduling. Every request + executed eight rollout steps and emitted 32 frames. A three-wave memory run + measured -2.0 MiB quiescent server PSS growth and 0 MiB GPU growth after warmup. +- A local 720p deployment repeated the full-size two-world gate over three + concurrent waves. All 48 chunks matched their distinct solo baselines, worker + execution interleaved, every request cleaned up, and measured quiescent growth + after warmup was -49.5 MiB host PSS and 0 MiB GPU memory. +- Reproducible Nsight validation now scopes CUDA calls to the nested steady DiT + `engine.forward` ranges. Full 360p and 720p eight-step traces each reported + **16 forwards, 16 graph replays, 0 synchronization or blocking calls**. +- The post-MVP streaming harness passed 16-step captured runs at both resolutions. + Baseline TTFF / sustained media ratio / p50-p95 gap were **0.148s / 3.268x / + 0.020-0.027s** at 360p and **0.465s / 0.570x / 0.112-0.142s** at 720p, with + no baseline stalls. GPU memory was flat under slow-reader backpressure, payload + hashes matched, and the raw JSON artifacts are retained under + `docs/waypoint/baselines/`. + +## Completed This Pass + +- Startup/configuration, graph/mask, frame-protocol, and end-to-end audits + completed in parallel, followed by coordinator integration review. +- Mandatory DiT compilation, TAEHV dependency preflight, exact action mapping, + real-weight functional AE parity, and the planned-mask CUDA test path were + tightened during those audits. +- Rebased the four committed Waypoint changes onto `origin/main` at `9ef65097` + and restored the full tracked/untracked working tree without content loss. +- Integrated upstream's PyPI packaging: Waypoint's extra is index-safe, both + alias packages forward it, the CLI resolves its packaged default config, and + pinned TAEHV remains an explicit separate install with actionable errors. +- Preserved upstream malformed-`model_kwargs` handling. The Python frontend + rejects `video_frame` input as HTTP 400; Rust frontend parity is deferred. +- No changes were staged or committed. +- Closed the separate 360p numerical gate with the native 360p Hub weights. + Inputs use the canonical seed/action script and seeded CPU-fp32 noise recipe, + resized/generated directly at 360p. The existing stored oracle remains a 720p + artifact and was deliberately not reused as a 360p numerical target. + +## Release Boundary + +The scripted streaming MVP gate and its post-MVP measurement phase are complete: +both supported resolutions start through the normal registry and `EngineManager`, +all four graph-enabled paths have captured successfully, server output is consumed +as typed SDK frames, and interleaved world-slot reuse remains deterministic and +memory-bounded. `STREAM-001` supplies the first captured baseline without inventing +a release threshold. CUDA graph capture is now optional; graph-off and injected +capture-failure full-server runs remain to be recorded. diff --git a/docs/waypoint/OPTIMIZATION_BACKLOG.md b/docs/waypoint/OPTIMIZATION_BACKLOG.md new file mode 100644 index 000000000..c82e457ad --- /dev/null +++ b/docs/waypoint/OPTIMIZATION_BACKLOG.md @@ -0,0 +1,152 @@ +# Waypoint Optimization Backlog + +An item may remain deferred only when it records evidence, expected benefit, +dependency, proposed benchmark, and completion criterion. These items are outside +the scripted streaming MVP unless a gate promotes one. + +## STREAM-001: Streaming Viability Baseline and Threshold + +- **Status:** Captured baseline complete on 2026-09-11; threshold decision deferred. +- **Evidence:** Normal server startup captured all four graph-enabled paths for + each run. The 16-step baseline and matched slow-consumer stream passed at both + resolutions with identical payload hashes. Raw artifacts: + `baselines/streaming-2026-09-11-360p.json` and + `baselines/streaming-2026-09-11-720p.json`. +- **Expected benefit:** Quantifies user-visible startup latency, sustained delivery, + stalls, backpressure, and memory behavior before setting a release threshold. +- **Dependency:** Satisfied: Phases 1-7 pass with all four graph-enabled paths + captured. +- **Proposed benchmark:** Measure time to first frame, generated-media-time divided + by wall time, p50/p95 inter-chunk gap, jitter, stall count/duration, slow-consumer + backpressure, peak GPU memory, and peak host PSS for 360p and 720p. +- **Completion criterion:** Baseline satisfied by the results below. A later + decision defines thresholds; no threshold is inferred from one run. + +| Variant | TTFF | Sustained media/wall | Gap p50 / p95 | Baseline stalls | Peak host PSS | GPU memory | +|---|---:|---:|---:|---:|---:|---:| +| 360p | 0.148 s | 3.268x | 0.020 / 0.027 s | 0 | 3291.5 MiB | 4648 MiB | +| 720p | 0.465 s | 0.570x | 0.112 / 0.142 s | 0 | 3687.3 MiB | 5756 MiB | + +The slow consumer paused 0.25 seconds between 15 reads. It added 3.717 seconds +at 360p and 3.462 seconds at 720p for 3.750 seconds deliberately injected, with +0 MiB GPU growth and byte-identical output. Its host peak changed by +60.4 MiB at +360p and +7.1 MiB at 720p. These are observations, not release limits. + +## INTERACTIVE-001: Interactive Sessions + +- **Evidence:** The MVP input is a complete action script and has no session + lifetime or reconnect contract. +- **Expected benefit:** Enables long-lived controllable worlds rather than fixed + offline scripts. +- **Dependency:** Stable scripted cleanup, world-slot reuse, and streaming protocol. +- **Proposed benchmark:** Reconnect, cancellation, idle timeout, and one-hour + session soak with deterministic action traces. +- **Completion criterion:** A documented session state machine passes lifecycle and + soak tests without state leakage. + +## ACTION-INGRESS-001: Live Action Ingress + +- **Evidence:** Actions are validated as a fixed list before execution; no + bidirectional live ingress or timing policy exists. +- **Expected benefit:** Allows real-time control while frames are generated. +- **Dependency:** `INTERACTIVE-001` and an explicit late/missing-action policy. +- **Proposed benchmark:** Timestamped actions under latency, reordering, loss, and + backpressure with output/action correlation checks. +- **Completion criterion:** Every generated latent consumes exactly one documented + live action under normal and degraded transport tests. + +## BATCH-001: True Request Batching + +- **Evidence:** MVP graph slots isolate worlds but do not establish a shared batched + DiT/AE execution path. +- **Expected benefit:** Higher throughput under concurrent scripted requests. +- **Dependency:** Correct interleaved worlds and measurements showing launch or + occupancy headroom. +- **Proposed benchmark:** Throughput, tail latency, graph memory, and parity at batch + sizes 1, 2, 4, and 8 for each resolution. +- **Completion criterion:** A selected batch policy improves throughput without + parity drift or unacceptable p95 latency. + +## QUANT-001: Weight Quantization + +- **Evidence:** The parity baseline uses checkpoint-native BF16; no quantized error + or speed/memory data exists. +- **Expected benefit:** Lower GPU memory and potentially higher DiT throughput. +- **Dependency:** Stable BF16 end-to-end baseline and quality evaluation corpus. +- **Proposed benchmark:** Layer/rollout error, pixel metrics, action consistency, + memory, and media-time/wall-time for candidate formats. +- **Completion criterion:** A format meets an explicitly recorded quality bound and + materially improves memory or throughput. + +## DECODER-PLACEMENT-001: Decoder on Another GPU + +- **Evidence:** Decoder order is stateful and the MVP keeps DiT and decoder in one + worker group; transfer and scheduling costs are unmeasured. +- **Expected benefit:** Overlap decode with DiT work and reduce rank-0 pressure. +- **Dependency:** Typed frame streaming, decoder graph state transfer, and correct + multi-worker loop ordering. +- **Proposed benchmark:** Compare colocated and split placement for throughput, + inter-chunk gaps, transfer time, and memory. +- **Completion criterion:** Split placement is parity-preserving and wins a recorded + performance target without ordering failures. + +## PRIME-GRAPH-001: DiT Prime Capture + +- **Evidence:** MVP deliberately compiles but does not capture the one-time DiT + prime/cache pass. +- **Expected benefit:** Lower request startup latency. +- **Dependency:** Required steady graphs and stable prime inputs/state addresses. +- **Proposed benchmark:** Admission-to-first-frame latency and graph memory with and + without prime capture across repeated world-slot reuse. +- **Completion criterion:** Capture reduces p50/p95 startup latency without state + leakage or disproportionate graph memory. + +## ENCODED-VIDEO-001: Encoded Video Output + +- **Evidence:** Per-step encoded fragments are not independently playable and the + MVP protocol intentionally emits raw RGB frames. +- **Expected benefit:** Lower network bandwidth and direct media playback. +- **Dependency:** Session-aware muxing, cancellation/finalization semantics, and + separate API design from the MVP `video_frame` modality. +- **Proposed benchmark:** End-to-end latency, bandwidth, seek/playability, encoder + load, and cancellation integrity for candidate codecs/containers. +- **Completion criterion:** A complete playable stream meets a separately recorded + latency/bandwidth target and never exposes broken fragments. + +## NOISE-001: GPU or Stateless Noise + +- **Evidence:** Current deterministic parity supplies CPU FP32 noise then casts; + device/stateless generation would change reproducibility and possibly bytes. +- **Expected benefit:** Avoid host generation/copy and simplify graph inputs. +- **Dependency:** A documented seed mapping and a new numerical baseline. +- **Proposed benchmark:** Generation/copy time, replay behavior, determinism across + world slots, and rollout parity/quality. +- **Completion criterion:** Deterministic request-to-noise mapping and a measured + performance win pass long interleaved rollouts. + +## GRAPH-MEM-001: CUDA Graph Memory Reduction + +- **Evidence:** Four graph-enabled paths across two resolutions can duplicate pools + and staging buffers. Full-server peaks are now measured at 4648 MiB for 360p and + 5756 MiB for 720p, but per-bucket pool and staging attribution remains open. +- **Expected benefit:** More world slots or lower deployment GPU requirements. +- **Dependency:** Complete optional-capture implementation and memory attribution. +- **Proposed benchmark:** Per-bucket private-pool/staging bytes, peak allocated and + reserved memory, and reuse across sequential/interleaved requests. +- **Completion criterion:** A change reduces measured graph memory without capture + fallback, address instability, or parity drift. + +## HOTSPOT-001: Measured Runtime Hotspots + +- **Evidence:** Mask rebuild was historically measured at 14.31 ms/frame. Final + full-size captured traces now prove host-sync-free steady DiT replay, while the + 720p streaming baseline remains below real-time at 0.570x sustained media time; + detailed hotspot attribution is still open. +- **Expected benefit:** Direct optimization effort toward the dominant final-path + cost. +- **Dependency:** `STREAM-001` traces with synchronized attribution outside timed + replay. +- **Proposed benchmark:** GPU/CPU trace of startup and steady state, ranked by frame + time and memory traffic for both resolutions. +- **Completion criterion:** Each promoted hotspot gets its own evidence-backed item; + close this placeholder when the final trace has no untracked material hotspot. diff --git a/docs/waypoint/PORT_PLAN.md b/docs/waypoint/PORT_PLAN.md new file mode 100644 index 000000000..cf128a1c1 --- /dev/null +++ b/docs/waypoint/PORT_PLAN.md @@ -0,0 +1,64 @@ +# Waypoint MVP Port Plan + +This is the active completion plan for the scripted Waypoint MVP. Historical +experiments and measurements remain in `WAYPOINT_PROGRESS.md`; a historical +"done" label there is evidence about that experiment, not an MVP release gate. + +## MVP Contract + +- Serve both `waypoint-1.5-1b-360p` and `waypoint-1.5-1b-720p`. +- Use reference-compatible arithmetic by default. Exact-table arithmetic remains + an explicitly selected experimental mode. +- Attempt CUDA graphs by default for encoder prime, steady DiT rollout, decoder + initialization, and steady decoder execution. Allow explicit graph-free + execution and eager fallback when capture fails. +- Accept exactly one validated action per generated latent step. The internal idle + prime action does not consume action zero. +- Prime encoder, DiT cache, and decoder state without emitting reconstructed seed + frames. +- Stream contiguous RGB24 frames only. Exactly `4 * num_steps` frames are emitted, + starting at frame index zero. + +## Phases + +| Phase | Scope | Completion gate | +|---|---|---| +| 1 | Documentation and baseline | Current diff/test baseline recorded; plan, decisions, validation, and backlog exist. | +| 2 | Startup and configuration | Local/HF sources resolve before allocation; downloads are selective; config/checkpoint facts and TAEHV dependency fail clearly when invalid. | +| 3 | Request and numerical correctness | Positive steps, exact action count, internal prime semantics, and live parity through pixels are tested. | +| 4 | DiT and attention execution | Runtime tables are post-load; optional full-graph DiT compilation is independent of optional CUDA graph capture; masks are planned/staged once; captured and eager paths are supported. | +| 5 | Encoder and decoder graphs | Tensor-only fixed-shape state; encoder prime and decoder init/steady paths support optional capture with eager fallback, equivalence, reuse, and cleanup tests. | +| 6 | Streaming frame protocol | `video_frame` events and SDK `VideoFrameChunk` validate typed metadata and expose a zero-copy `[N,H,W,3]` NumPy view; non-streaming use is rejected. | +| 7 | End-to-end MVP gate | Registry and `EngineManager` runs pass for local/HF sources, 360p/720p, sequential/interleaved worlds, cleanup, bounded memory, and exact frame counts. | +| 8 | Post-MVP streaming viability | Only after Phase 7: collect latency, throughput, gap/jitter/stall, backpressure, and memory baselines under `STREAM-001`. | + +## Execution Rules + +1. Preserve unrelated working-tree changes. Do not stage, commit, or revert. +2. Treat artifact resolution and manifest validation as pre-allocation contracts; + tensor/runtime architecture and request validation must pass before admission. +3. Keep CUDA graph capture optional. `cuda_graph=False` skips capture, and failed + capture attempts fall back to eager execution without changing numerical mode. +4. Validate numerical parity live in one process where reproducibility permits. + Stored cross-process artifacts establish a measured floor, not a bit-exact + oracle. +5. Update `VALIDATION.md` with the command, environment, result, and evidence for + every completed gate. Record any deferral in `OPTIMIZATION_BACKLOG.md` with all + required fields. + +## Parallel Ownership + +Startup/configuration, ring/Flex/DiT, and server/SDK protocol can proceed in +parallel when their file sets do not overlap. Functional TAEHV state, generic +required-capture support, and isolated tests form the second wave. One integration +owner then changes Waypoint model wiring and YAML. Numerical, graph, server, and +SDK gates run only after integration. + +## Current State + +Phases 1-8 are complete. Local and registry-selected Hub startup, live +same-process numerical parity, all required CUDA captures, fixed-address mask and +TAEHV state, typed SDK streaming at both resolutions, two-world interleaving, +cleanup/reuse, and bounded server memory have passed. `STREAM-001` now records the +first captured 360p/720p latency, pacing, backpressure, and memory measurements; +as decided in WP-007, they establish a baseline and do not set a release threshold. diff --git a/docs/waypoint/VALIDATION.md b/docs/waypoint/VALIDATION.md new file mode 100644 index 000000000..1690bc76f --- /dev/null +++ b/docs/waypoint/VALIDATION.md @@ -0,0 +1,201 @@ +# Waypoint Validation Ledger + +This ledger distinguishes historical evidence from the active MVP gate. Add the +exact command, environment, artifact location, and result when closing a row. + +## Active Gates + +| ID | Gate | State | Required evidence | +|---|---|---|---| +| CFG-001 | 360p and 720p supported config facts | Passed | CPU tests cover variant geometry, scheduler, FPS, temporal assumptions, explicit model kwargs, and registry construction; both variants started on H100 from their own manifests. | +| CKPT-001 | Local checkpoint resolution | Passed | Valid, missing, partial, cross-variant, and incompatible local checkpoint tests plus both published manifests. | +| CKPT-002 | Hub checkpoint resolution | Passed | Mocked and real selective downloads resolve only native safetensors plus `config.yaml`; registry-selected 360p Hub startup completed without a local model or AE override. | +| CKPT-003 | TAEHV resolution and dependency pin | Passed | Local/HF single-file tests, actual local weights, missing/empty-runtime preflight, index-safe Python 3.12 dependency resolution, and a separate real pinned-revision TAEHV build/install pass. | +| NUM-001 | Live reference-compatible parity | Passed at 360p and 720p | Native-checkpoint same-process tables, conditioner, every DiT stage, five passes, ring writes, 41 rollout latents, functional TAEHV state, and pixels passed with zero tolerance on H100 at both resolutions. | +| REQ-001 | Request and prime semantics | Passed | CPU tests reject non-positive steps and wrong action counts and prove idle prime preserves action zero; live sequential and interleaved 360p/720p runs emitted no seed frames and exact generated counts. | +| GRAPH-001 | Optional DiT compile | Passed | Post-load table materialization, `fullgraph=True` construction, and bounded compiled/eager equivalence are tested; both full-size variants compiled on H100. | +| GRAPH-002 | Optional capture and eager fallback | CPU mode-selection coverage added; existing capture path passed on H100 | `cuda_graph=False` declares no buckets; attempted captures are optional and use the engine's eager fallback on failure. All four graph-enabled buckets previously captured at both resolutions. A full server graph-off run remains to be recorded. | +| MASK-001 | Planned masks and replay sync | Passed | Tests cover one staged local/global mask per geometry and graph slot; full 360p and 720p profiles each prove 16/16 graph replay and zero blocking CUDA calls inside steady DiT forwards. | +| AE-001 | Functional TAEHV execution paths | Passed | Nine-history state, fixed graph interfaces, isolation, cleanup, real-weight parity, optional capture declarations, and full-size eight-step streams pass at both resolutions. | +| FRAME-001 | Typed RGB protocol | Passed | Server/SDK tests cover metadata, contiguous RGB24 bytes, zero-copy NumPy shape, indexing, errors, non-streaming rejection, ordered delivery, and live 360p/720p typed consumption. | +| E2E-360 | 360p normal serving path | Passed | Registry-selected Hub source + `EngineManager`, all buckets, SDK stream, exact counts, deterministic concurrent worlds, cleanup, slot reuse, and bounded memory passed. | +| E2E-720 | 720p normal serving path | Passed | Local source + `EngineManager`, all buckets, typed SDK stream, exact counts, byte-identical sequential reuse, full-size two-world interleaving, cleanup, bounded memory, and full eight-step profiler soak passed. | +| WORLD-001 | World isolation and reuse | Passed | Full server two-world DiT execution interleaved at both resolutions, reproduced distinct solo baselines byte-for-byte, cleaned every request, reused slots, and had -2.0/-49.5 MiB host PSS and 0/0 MiB GPU quiescent growth after warmup at 360p/720p. | +| STREAM-001 | Post-MVP streaming baseline | Passed without a release threshold | Captured 16-step baseline and slow-consumer runs at both resolutions record TTFF, sustained media/wall ratio, p50/p95 gaps, jitter, stalls, backpressure, host PSS, and GPU memory in retained JSON artifacts. | + +## Historical Evidence + +The earlier investigation reported the following. These results are retained as +diagnostic evidence and must not be read as completion of the active graph or +end-to-end gates. + +- A 41-frame same-process run with `reference_compat=True` reported bit-exact DiT + state and ring bookkeeping. Cross-process reference runs were not bit + reproducible, with pixel drift reported as high as 72/255. +- CPU TAEHV component comparisons reported bit-identical FP32 and BF16 encode and + decode results against the reference implementation. +- Earlier GPU tests reported full-graph compilation and stable DiT capture, while + compiled versus eager output was bounded rather than bit-exact due to BF16 + fusion behavior. +- Mask reconstruction was measured at 14.31 ms per 720p frame in the eager test, + motivating planned per-frame/per-slot local and global masks. + +## Validation Record + +| Date | Command/environment | Result | Scope | +|---|---|---|---| +| 2026-09-11 | Historical baseline copied from `WAYPOINT_PROGRESS.md` | 242 CPU tests reported green before subsequent uncommitted work | Not a current-tree result | +| 2026-09-11 | `pytest -q test/modular/test_waypoint_checkpoint.py` | 21 passed | Config and local/mocked-HF resolver contracts | +| 2026-09-11 | `pytest -q test/modular/test_waypoint_weight_loader.py` | 61 passed | Existing synthetic checkpoint loading | +| 2026-09-11 | `PYTHONPATH=. pytest -q test/modular/test_waypoint_reference_compat.py` | 11 passed | Default/experimental numerical modes | +| 2026-09-11 | Focused Waypoint execution-mode and capture-runner selection (7 files) | 255 passed, 9 skipped | Independent `cuda_graph`, `compile_dit`, and `reference_compat` modes; optional declarations and generic eager fallback. CUDA-only cases skipped because CUDA was unavailable in the sandbox. | +| 2026-09-11 | `PYTHONPATH=. pytest -q test/modular/test_waypoint_dit.py test/modular/test_waypoint_components.py` | 56 passed, 2 warnings | Existing CPU DiT/component contracts | +| 2026-09-11 | Direct resolver call on local Waypoint and TAEHV checkpoints | Historically accepted 720p weights for both variants; superseded | Exposed the stale shared-weight assumption. Exact variant geometry validation now rejects this pairing. | +| 2026-09-11 | `ruff check` on changed Waypoint Python/tests | Passed | Static checks | +| 2026-09-11 | `PYTHONPATH=. pytest -q test/modular/test_waypoint_checkpoint.py` | 27 passed | Config, resolver, dependency preflight, normal registry/engine construction, and startup ordering | +| 2026-09-11 | `PYTHONPATH=. pytest -q test/modular/test_waypoint_shell.py` | 62 passed | Request/prime, graph declarations, functional AE state, and YAML contracts | +| 2026-09-11 | `PYTHONPATH=. pytest -q test/modular/test_video_frame_protocol.py test/modular/test_cuda_graph_capture.py` | 22 passed, 9 skipped, 2 warnings | Typed frame protocol and required-capture CPU contracts; nine real CUDA cases skipped because CUDA was unavailable | +| 2026-09-11 | `pytest -q test/modular/test_waypoint_taehv_equivalence.py` with local pinned weights | 1 passed | Bit-exact FP32 encoder, decoder init/steady pixels, and all nine histories against upstream `StreamingTAEHV` | +| 2026-09-11 | Consolidated 16-file Waypoint/startup/protocol/graph/resource test selection | 345 passed, 41 skipped, 4 warnings | All then-current CPU-verifiable integration contracts; CUDA and live-reference cases skipped | +| 2026-09-11 | `FLASHINFER_WORKSPACE_BASE=/tmp/waypoint-flashinfer pytest -q test/modular` | Interrupted after 31% when no output was produced for several minutes | Optional broad regression run; earlier output included failures that could not be attributed because the run did not finish, so this is not passing evidence | +| 2026-09-11 | `ruff check ...`; `python3 -m compileall -q ...`; `git diff --check` | Passed | Changed Python/static formatting and syntax checks | +| 2026-09-11 | Default-sandbox torch/NVML probe | CUDA unavailable, zero devices; `nvidia-smi` driver failure | Sandbox-only limitation; later escalated runs reached physical GPU 2. | +| 2026-09-11 | `cargo check --locked` in `rust/server` | Toolchain blocked: Cargo 1.75 cannot parse lockfile v4 | Rust route source and tests added; build requires a current Cargo/rustfmt environment | +| 2026-09-11 | `pytest -q test/rust/test_rust_frontend.py` | Suite skipped because built Rust frontend is unavailable | Native route behavior still needs execution after a toolchain build | +| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 pytest -q test/modular/test_waypoint_gpu.py` | 10 passed in 84.44s | Required capture/replay, planned masks, long rollout, two-world interleaving, cleanup, and slot reuse on H100. | +| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. WAYPOINT_GPU_TESTS=1 pytest -q test/modular/test_waypoint_gpu.py` after optional-capture change | 10 passed in 20.70s | Graph-enabled capture/replay, compiled/eager DiT equivalence, planned masks, interleaving, cleanup, and slot reuse on H100. | +| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 pytest -q test/modular/test_waypoint_reference_equivalence.py` | 14 passed in 54.85s | Full-checkpoint same-process parity, including the 41-frame zero-difference gate through planned masks and ring writes. | +| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 pytest -q test/modular/test_waypoint_pixel_equivalence.py` | 5 passed in 32.89s | Real TAEHV seed encode/decode, output order, VAE output, and reference pixels. | +| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. WAYPOINT_360P_PARITY_REPORT=/tmp/waypoint-360p-reference-parity.json pytest -q -s test/modular/test_waypoint_360p_reference_equivalence.py` | 1 passed, 3 expected dtype-preservation warnings in 37.79s; every asserted maximum difference was zero | Native revision `35acd20...`; 3 derived tables, 5 conditioner rows, 30 stages at 2 sigmas, 201 passes, 41 latents, 24 ring layers per frame, full-size BF16 encoder, 9 decoder histories per frame, and 164 RGB frames on physical H100 GPU 2. Live same-process/eager scope with a shared corrected masked-attention kernel; the 720p stored oracle was not used. Raw report `/tmp/waypoint-360p-reference-parity.json`; retained report `baselines/reference-parity-2026-09-11-360p.json`. | +| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. pytest -q test/modular/test_waypoint_reference_equivalence.py` after adding the variant-specific checkpoint argument | 14 passed, 3 expected dtype-preservation warnings in 56.98s | Post-rebase/touched-file 720p numerical regression; the default checkpoint behavior remains unchanged. | +| 2026-09-11 | Normal 720p `serve_rollout.py --steps 1`; log `/tmp/waypoint_server_720.log` | Passed: four required captures; two byte-identical 1280x720 RGB24 requests | Local registry/`EngineManager`, exact four-frame output, cleanup and sole-world reuse. This predated typed-SDK harness conversion. | +| 2026-09-11 | Hugging Face metadata query + selective `snapshot_download` for `Overworld/Waypoint-1.5-1B-360P` | Passed; revision `35acd20e649fe79c1c1002df456696408547202d`, safetensors SHA-256 `a2cdccb5...c8101d9` | Confirms distinct 360p weights and downloads only `config.yaml` plus `model.safetensors`. | +| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 serve_rollout.py --variant 360p --steps 1`; log `/tmp/waypoint_server_360_sdk.log` | Passed: startup 223.5s; requests 29.7s/6.2s; byte-identical 2,764,800-byte chunks | Normal registry/`EngineManager`, four required captures, typed SDK, four 640x360 frames from index zero, cleanup and reuse. | +| 2026-09-11 | Startup/shell/SDK/frame selection after variant and SDK fixes | 136 passed, 2 warnings | Variant-specific repositories/manifests, prime stop guard, SDK uploads/errors, harness geometry, and frame protocol. | +| 2026-09-11 | Rust 1.98.1: locked `cargo check`, release build, and `cargo test` with target/cache under `/tmp` | Passed; 4 Rust unit tests | Native server compiles against the repository lockfile without changing the system Rust installation. | +| 2026-09-11 | `MSTAR_SERVER_BIN=/tmp/waypoint-rust-target/release/mstar-server pytest -q test/rust/test_rust_frontend.py` outside socket sandbox | Historical: 16 passed in 4.32s | This validated the former Rust raw-frame guards. Those Waypoint-specific changes were later reverted when `--rust-frontend` was removed from the MVP scope. | +| 2026-09-11 | Python 3.12 + uv 0.11.13: `uv pip install --dry-run --torch-backend=auto -e '.[waypoint]'` under the original direct-URL metadata | Resolved 88 packages; packaging layout later superseded | Proved dependency compatibility, but the TAEHV URL was subsequently moved out of project metadata because PyPI rejects direct-URL requirements. The current extra retains the index-hosted dependencies, including `tensordict==0.10.0`. | +| 2026-09-11 | Python 3.12 + uv: install pinned TAEHV URL with `--no-deps --target /tmp/waypoint-taehv-install` | Built/installed `taehv==0.1.0`; module exports `TAEHV` | Verifies Metadata-Version 2.4 packaging and non-empty artifact at the pinned upstream revision. | +| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 serve_rollout.py --variant 720p --steps 8` with local checkpoints; log `/tmp/waypoint_server_720_sdk_soak_fixed.log` | Passed: startup 43.1s; 8 typed chunks/32 frames per request; 88,473,600 bytes byte-identical across reuse | Full local 720p registry/`EngineManager` SDK soak and all four required buckets. | +| 2026-09-11 | Exact 360p Hub memory command below; log `/tmp/waypoint_server_360_hub_interleaved.log` | Serving checks passed; measured quiescent host growth -2.0 MiB and GPU growth 0 MiB; superseded parser falsely reported zero schedules | Registry-selected Hub source, exact typed streams, deterministic two-world waves, cleanup/reuse, and bounded server memory. Parser was corrected and rerun below. | +| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 serve_rollout.py --variant 360p --source hub --steps 8 --worlds 2 --concurrent-waves 2`; log `/tmp/waypoint_server_360_hub_interleaved_pass8.log` | Passed: exact 8 DiT executions per request, observed A/B/A interleaving, byte-exact solo/concurrent output, all cleanup markers | Corrected full registry-Hub 360p two-world gate. | +| 2026-09-11 | Exact unfiltered Nsight CUDA/NVTX commands below, `MSTAR_ENGINE_STEP_SYNC=0`, two sequential 8-step SDK requests per variant; `/tmp/waypoint-mask-{360,720}-full.nsys-rep` | Each variant: 16 steady forwards, exactly 16 graph replays, 0 synchronization/blocking calls | `check_nsys_replay.py` scopes API inspection to nested steady DiT `engine.forward` ranges and rejects anything other than one graph launch per forward, synchronize, blocking memcpy, or synchronous malloc/free. | +| 2026-09-11 | `PYTHONPATH=. pytest -q test/modular/test_waypoint_profiler.py`; exact report checks below | 4 tests passed; both real profiles passed | Reproducible profiler-query contract plus real 360p/720p evidence. | +| 2026-09-11 | Final consolidated 16-file Waypoint/startup/protocol/graph/resource selection | 385 passed, 41 skipped, 4 warnings in 41.19s | Current-tree CPU-verifiable MVP contracts; count includes 40 tests added since the earlier 345-pass row. | +| 2026-09-11 | `PYTHONPATH=. pytest -q test/modular/test_waypoint_streaming_benchmark.py test/modular/test_waypoint_profiler.py` | 15 passed | Streaming metric/orchestration and profiler-query contracts. | +| 2026-09-11 | Exact 360p benchmark command below | Passed; TTFF 0.148s, sustained 3.268x, p50/p95 0.020/0.027s, 0 baseline stalls, 3291.5 MiB peak PSS, 4648 MiB GPU | Registry-Hub captured baseline; 64 typed frames; slow-reader payload hash matched and GPU delta was 0 MiB. Artifact `baselines/streaming-2026-09-11-360p.json`. | +| 2026-09-11 | Exact 720p benchmark command below | Passed; TTFF 0.465s, sustained 0.570x, p50/p95 0.112/0.142s, 0 baseline stalls, 3687.3 MiB peak PSS, 5756 MiB GPU | Local captured baseline; 64 typed frames; slow-reader payload hash matched and GPU delta was 0 MiB. Artifact `baselines/streaming-2026-09-11-720p.json`. | +| 2026-09-11 | Exact 720p two-world command below; log `/tmp/waypoint_server_720_interleaved_memory.log` | Passed; startup 135.3s, exact 8 DiT executions/request, observed interleaving, deterministic output, all cleanup, -49.5 MiB host PSS/0 MiB GPU quiescent growth | Full local 720p three-wave world isolation, reuse, and bounded-memory gate. | +| 2026-09-11 | `git fetch --prune origin`; stash tracked/untracked work; `git rebase origin/main`; restore with `git stash apply` | Passed; branch is four commits above `origin/main` at `9ef65097`, with no unmerged entries | Range-diff found three patch-identical commits and one expected Bagel import-context merge; stable patch ID and all 25 untracked blobs matched the safety stash. | +| 2026-09-11 | Upstream/resource overlap and Waypoint CPU audit selections | 66 passed, 70 skipped; 279 passed, 12 skipped; 27 passed, 29 skipped | One TAEHV guidance assertion failed in the broad run, was corrected, and passed in the post-fix gate. | +| 2026-09-11 | Isolated sdist/wheel build after PyPI integration fixes | Passed; 125 `Requires-Dist` entries, zero direct URLs, Waypoint extra and default config present | Confirms the separately installed TAEHV pin does not make published metadata invalid. | +| 2026-09-11 | Post-rebase Python API/SDK/worker and native Rust checks | Historical: 51 Python tests and 16 Rust wire tests passed; `cargo check --locked` passed | Python raw-frame handling remains in scope. The Waypoint-specific Rust changes covered by this run were later reverted. Socket tests ran outside the restricted sandbox. | +| 2026-09-11 | `pytest -q test/modular/test_waypoint_packaging.py test/modular/test_waypoint_checkpoint.py test/modular/test_video_frame_protocol.py` | 73 passed, 2 warnings | Post-fix CLI/alias/metadata/pinned-TAEHV contracts and the complete Python raw-frame protocol gate. | +| 2026-09-12 | `PYTHONPATH=. pytest -q test/modular/test_video_frame_protocol.py test/modular/test_client_sdk.py test/modular/test_api_completion_guard.py` after reverting Waypoint-specific Rust frontend changes | 45 passed, 2 existing FastAPI deprecation warnings | Confirms the supported Python server/SDK frame path is unaffected; `rust/server/src/main.rs` and `test/rust/test_rust_frontend.py` have no remaining Waypoint diff. | + +## GPU Reproduction Commands + +All commands ran from the repository root on physical H100 GPU 2. The multiline +forms below are the exact invocations represented by the compact ledger rows. + +```bash +env CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. \ + WAYPOINT_360P_PARITY_REPORT=/tmp/waypoint-360p-reference-parity.json \ + pytest -q -s test/modular/test_waypoint_360p_reference_equivalence.py + +env CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. python3 test/waypoint/serve_rollout.py \ + --variant 360p --source hub --cache-dir /tmp/waypoint-hf-cache \ + --steps 8 --worlds 2 --concurrent-waves 3 --measure-memory --physical-gpu 2 \ + --startup-timeout 1200 --request-timeout 600 \ + --log /tmp/waypoint_server_360_hub_interleaved.log + +env CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. python3 test/waypoint/serve_rollout.py \ + --variant 360p --source hub --cache-dir /tmp/waypoint-hf-cache \ + --steps 8 --worlds 2 --concurrent-waves 2 \ + --startup-timeout 1200 --request-timeout 600 \ + --log /tmp/waypoint_server_360_hub_interleaved_pass8.log + +env CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. python3 test/waypoint/serve_rollout.py \ + --variant 720p \ + --checkpoint-dir /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/Waypoint-1.5-1B \ + --ae-path /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/taehv1_5 \ + --seed-image /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/seed/default.jpg \ + --steps 8 --worlds 2 --concurrent-waves 3 --measure-memory --physical-gpu 2 \ + --startup-timeout 1200 --request-timeout 900 \ + --log /tmp/waypoint_server_720_interleaved_memory.log +``` + +The profiler commands differed only in variant/source and output prefix: + +```bash +env CUDA_VISIBLE_DEVICES=2 MSTAR_ENGINE_STEP_SYNC=0 PYTHONPATH=. nsys profile \ + --trace=cuda,nvtx --sample=none --cpuctxsw=none \ + --trace-fork-before-exec=true --cuda-graph-trace=graph --force-overwrite=true \ + -o /tmp/waypoint-mask-360-full python3 test/waypoint/serve_rollout.py \ + --variant 360p \ + --checkpoint-dir /tmp/waypoint-hf-cache/models--Overworld--Waypoint-1.5-1B-360P/snapshots/35acd20e649fe79c1c1002df456696408547202d \ + --ae-path ../../checkpoints/taehv1_5 --seed-image ../../checkpoints/seed/default.jpg \ + --steps 8 --enable-nvtx --startup-timeout 1200 --request-timeout 600 \ + --log /tmp/waypoint_mask_360_full_server.log + +env CUDA_VISIBLE_DEVICES=2 MSTAR_ENGINE_STEP_SYNC=0 PYTHONPATH=. nsys profile \ + --trace=cuda,nvtx --sample=none --cpuctxsw=none \ + --trace-fork-before-exec=true --cuda-graph-trace=graph --force-overwrite=true \ + -o /tmp/waypoint-mask-720-full python3 test/waypoint/serve_rollout.py \ + --variant 720p \ + --checkpoint-dir /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/Waypoint-1.5-1B \ + --ae-path /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/taehv1_5 \ + --seed-image /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/seed/default.jpg \ + --steps 8 --enable-nvtx --startup-timeout 1200 --request-timeout 900 \ + --log /tmp/waypoint_mask_720_full_server.log + +nsys export -t sqlite -f true -o /tmp/waypoint-mask-360-full.sqlite \ + /tmp/waypoint-mask-360-full.nsys-rep +nsys export -t sqlite -f true -o /tmp/waypoint-mask-720-full.sqlite \ + /tmp/waypoint-mask-720-full.nsys-rep +python3 test/waypoint/check_nsys_replay.py \ + /tmp/waypoint-mask-360-full.sqlite --expected-forwards 16 +python3 test/waypoint/check_nsys_replay.py \ + /tmp/waypoint-mask-720-full.sqlite --expected-forwards 16 +``` + +Trace SHA-256 values are +`b82080765b36ca0da72fcd665153230869c60a83d51de11b33c13b910e159da9` +(360p) and +`a97777e4c375bce8b54a6b702d9ac01111f3db62eedf71a5c75d750d1af307ff` +(720p). + +```bash +env CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. python3 test/waypoint/benchmark_streaming.py \ + --variant 360p --source hub --cache-dir /tmp/waypoint-hf-cache \ + --physical-gpu 2 --steps 16 --warmup-steps 1 --slow-consumer-delay 0.25 \ + --startup-timeout 1200 --request-timeout 900 \ + --artifact /tmp/waypoint-streaming-360p.json \ + --log /tmp/waypoint-streaming-360p-server.log + +env CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. python3 test/waypoint/benchmark_streaming.py \ + --variant 720p \ + --checkpoint-dir /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/Waypoint-1.5-1B \ + --ae-path /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/taehv1_5 \ + --seed-image /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/seed/default.jpg \ + --physical-gpu 2 --steps 16 --warmup-steps 1 --slow-consumer-delay 0.25 \ + --startup-timeout 1200 --request-timeout 900 \ + --artifact /tmp/waypoint-streaming-720p.json \ + --log /tmp/waypoint-streaming-720p-server.log +``` + +`uv run` could not be used for the initial CPU pass because the then-current extra +needed network access to resolve the pinned TAEHV source archive. A later clean +Python 3.12/uv resolution and separate real pinned artifact build closed CKPT-003; +the archive is now intentionally installed outside the index-safe project metadata. + +## End-to-End Acceptance Checklist + +- Both local checkpoint paths and the registry hub ID start through normal model + construction without downloading unrelated repository assets. +- Graph-enabled runs either capture their declared buckets or fall back eagerly; + graph-disabled runs declare no buckets. +- 360p and 720p requests each emit contiguous RGB24 chunks with complete metadata. +- The first emitted index is zero and the total is exactly `4 * num_steps`. +- Sequential and interleaved worlds preserve deterministic action mapping. +- Teardown releases request state and repeated world-slot reuse remains bounded. diff --git a/docs/waypoint/baselines/reference-parity-2026-09-11-360p.json b/docs/waypoint/baselines/reference-parity-2026-09-11-360p.json new file mode 100644 index 000000000..61e1a47db --- /dev/null +++ b/docs/waypoint/baselines/reference-parity-2026-09-11-360p.json @@ -0,0 +1,62 @@ +{ + "checkpoint": "/tmp/waypoint-hf-cache/models--Overworld--Waypoint-1.5-1B-360P/snapshots/35acd20e649fe79c1c1002df456696408547202d", + "checkpoint_revision": "35acd20e649fe79c1c1002df456696408547202d", + "checkpoint_sha256": "a2cdccb5eb074afc48a1c99b0868cebef38cf944d4b366aed2a866f50c8101d9", + "controls": "canonical 40-action record_oracle.py sequence", + "decoded_rgb_frames_including_internal_prime": 164, + "denoise_and_commit_passes": 201, + "derived_tables": [ + "denoise_step_emb.freq", + "rope_angles.xy", + "rope_angles.inv_t" + ], + "device": "NVIDIA H100 80GB HBM3", + "device_index_visible": 0, + "dtype": "torch.bfloat16", + "elapsed_pytest_seconds": 37.79, + "generated_latent_frames": 40, + "maximum_absolute_differences": { + "conditioner": 0.0, + "decoder_histories": 0.0, + "encoder": 0.0, + "latents": 0.0, + "passes": 0.0, + "pixels": 0, + "ring_kv": 0.0, + "stages": 0.0, + "tables": 0.0 + }, + "noise": { + "seed": 42, + "source": "seeded CPU fp32 then cast to CUDA BF16" + }, + "python": "3.10.12", + "reference_compat": true, + "reference_source": "/mnt/storage/garv901/waypoint-1.5-1B/world_engine", + "rollout_latent_frames": 41, + "scheduler_sigmas": [ + 1.0, + 0.9, + 0.75, + 0.3, + 0.0 + ], + "scope": { + "attention": "shared mstar flex_attention_masked kernel", + "dit_execution": "eager on both sides", + "reference": "live same-process world_engine", + "stored_oracle": "not used; existing artifacts belong to the 720p checkpoint", + "taehv": "upstream streaming state versus functional explicit nine-history path" + }, + "seed_sha256": "c61c9393311d7281f793d86329dca343e12c93bf0409980a186eb39269cf6862", + "stage_probes": { + "sigmas": [ + 1.0, + 0.0 + ], + "stages_per_probe": 30 + }, + "status": "passed", + "torch": "2.9.1+cu128", + "variant": "waypoint-1.5-1b-360p" +} diff --git a/docs/waypoint/baselines/streaming-2026-09-11-360p.json b/docs/waypoint/baselines/streaming-2026-09-11-360p.json new file mode 100644 index 000000000..c9145f4e8 --- /dev/null +++ b/docs/waypoint/baselines/streaming-2026-09-11-360p.json @@ -0,0 +1,153 @@ +{ + "backpressure": { + "configured_consumer_pause_seconds": 0.25, + "injected_pause_seconds": 3.75, + "observed_request_wall_increase_seconds": 3.7167182420380414, + "payloads_match": true, + "peak_gpu_memory_change_mib": 0.0, + "peak_host_pss_change_mib": 60.3642578125, + "sustained_media_to_wall_ratio_change": -3.021464358453587, + "time_to_first_frame_change_seconds": -0.038335707038640976, + "wall_increase_beyond_injected_pause_seconds": -0.03328175796195865 + }, + "benchmark": "waypoint_streaming_viability", + "configuration": { + "memory_sample_interval_seconds": 0.1, + "physical_gpu": 2, + "rng_seed": 112464007, + "server_command": [ + "/usr/bin/python3", + "/mnt/storage/garv901/waypoint-1.5-1B/mstar/rp2/mstar/api_server/entrypoint.py", + "--config", + "/dev/shm/garv901/tmp/waypoint-stream-benchmark-4_9f01b8/run.yaml", + "--port", + "47481", + "--host", + "127.0.0.1", + "--socket-path-prefix", + "/dev/shm/garv901/tmp/waypoint-stream-benchmark-4_9f01b8/sock", + "--upload-dir", + "/dev/shm/garv901/tmp/waypoint-stream-benchmark-4_9f01b8/uploads", + "--tensor-comm-protocol", + "SHM", + "--log-level", + "INFO", + "--timeout", + "900.0", + "--cache-dir", + "/tmp/waypoint-hf-cache" + ], + "server_log": "/tmp/waypoint-streaming-360p-server.log", + "slow_consumer_delay_seconds": 0.25, + "stall_threshold_seconds": 0.26666666666666666, + "steps": 16, + "warmup_steps": 1, + "weight_source": "registry Hub mapping for waypoint-1.5-1b-360p" + }, + "correctness": { + "failures": [], + "passed": true + }, + "created_at_utc": "2026-09-11T03:50:26.393848+00:00", + "geometry": { + "fps": 60.0, + "height": 360, + "width": 640 + }, + "metric_definitions": { + "backpressure": "delta between an unpaused stream and an identical stream paused between SDK reads", + "jitter_population_stddev": "population standard deviation of inter-chunk gaps", + "memory": "PSS and nvidia-smi GPU process memory summed over the server process group only", + "stall": "inter-chunk gap strictly greater than stall_threshold_seconds", + "sustained_media_to_wall_ratio": "media seconds in chunks after the first divided by first-to-last chunk arrival time", + "time_to_first_frame_seconds": "request iterator start to first fully decoded SDK VideoFrameChunk" + }, + "model_variant": "waypoint-1.5-1b-360p", + "release_threshold": null, + "runs": { + "baseline": { + "chunk_count": 16, + "consumer": { + "injected_pause_seconds": 0.0, + "pause_count": 0, + "pause_seconds": 0.0 + }, + "frame_count": 64, + "generated_media_seconds": 1.0666666666666667, + "inter_chunk_gap_seconds": { + "jitter_population_stddev": 0.003996953245955496, + "maximum": 0.031120519153773785, + "mean": 0.02040156687920292, + "p50": 0.019661725964397192, + "p95": 0.02699114484712481, + "sample_count": 15 + }, + "memory": { + "peak_gpu_mib": 4648.0, + "peak_host_pss_mib": 3291.5068359375, + "phase": "baseline", + "quiet_gpu_mib": 4648.0, + "quiet_host_pss_mib": 3338.77734375, + "sample_count": 2 + }, + "overall_media_to_wall_ratio": 2.341649833374671, + "payload_bytes": 44236800, + "payload_sha256": "9943036eb0cc954fd7bd2545d4b0980a1f5ded6435206c59a2d098d69ca4ae44", + "request_id": "waypoint-streaming-benchmark-baseline", + "request_wall_seconds": 0.45551928877830505, + "stalls": { + "count": 0, + "longest_seconds": null, + "threshold_seconds": 0.26666666666666666, + "total_excess_seconds": 0 + }, + "sustained_media_to_wall_ratio": 3.2677228695912444, + "time_to_first_frame_seconds": 0.14788120286539197 + }, + "slow_consumer": { + "chunk_count": 16, + "consumer": { + "injected_pause_seconds": 3.75, + "pause_count": 15, + "pause_seconds": 0.25 + }, + "frame_count": 64, + "generated_media_seconds": 1.0666666666666667, + "inter_chunk_gap_seconds": { + "jitter_population_stddev": 0.006550622140463195, + "maximum": 0.2819611048325896, + "mean": 0.2707182235394915, + "p50": 0.2681501042097807, + "p95": 0.2819179646205157, + "sample_count": 15 + }, + "memory": { + "peak_gpu_mib": 4648.0, + "peak_host_pss_mib": 3351.87109375, + "phase": "slow-consumer", + "quiet_gpu_mib": 4648.0, + "quiet_host_pss_mib": 3341.478515625, + "sample_count": 8 + }, + "overall_media_to_wall_ratio": 0.2556581831183425, + "payload_bytes": 44236800, + "payload_sha256": "9943036eb0cc954fd7bd2545d4b0980a1f5ded6435206c59a2d098d69ca4ae44", + "request_id": "waypoint-streaming-benchmark-slow-consumer", + "request_wall_seconds": 4.172237530816346, + "stalls": { + "count": 8, + "longest_seconds": 0.2819611048325896, + "threshold_seconds": 0.26666666666666666, + "total_excess_seconds": 0.07224301105986042 + }, + "sustained_media_to_wall_ratio": 0.2462585111376573, + "time_to_first_frame_seconds": 0.109545495826751 + } + }, + "schema_version": 1, + "server": { + "startup_seconds": 43.09219791833311 + }, + "status": "completed", + "variant": "360p" +} diff --git a/docs/waypoint/baselines/streaming-2026-09-11-720p.json b/docs/waypoint/baselines/streaming-2026-09-11-720p.json new file mode 100644 index 000000000..47275c6f6 --- /dev/null +++ b/docs/waypoint/baselines/streaming-2026-09-11-720p.json @@ -0,0 +1,151 @@ +{ + "backpressure": { + "configured_consumer_pause_seconds": 0.25, + "injected_pause_seconds": 3.75, + "observed_request_wall_increase_seconds": 3.4616449642926455, + "payloads_match": true, + "peak_gpu_memory_change_mib": 0.0, + "peak_host_pss_change_mib": 7.0703125, + "sustained_media_to_wall_ratio_change": -0.3815909607693788, + "time_to_first_frame_change_seconds": -0.08133614482358098, + "wall_increase_beyond_injected_pause_seconds": -0.28835503570735455 + }, + "benchmark": "waypoint_streaming_viability", + "configuration": { + "memory_sample_interval_seconds": 0.1, + "physical_gpu": 2, + "rng_seed": 112464007, + "server_command": [ + "/usr/bin/python3", + "/mnt/storage/garv901/waypoint-1.5-1B/mstar/rp2/mstar/api_server/entrypoint.py", + "--config", + "/dev/shm/garv901/tmp/waypoint-stream-benchmark-lqroa06h/run.yaml", + "--port", + "56069", + "--host", + "127.0.0.1", + "--socket-path-prefix", + "/dev/shm/garv901/tmp/waypoint-stream-benchmark-lqroa06h/sock", + "--upload-dir", + "/dev/shm/garv901/tmp/waypoint-stream-benchmark-lqroa06h/uploads", + "--tensor-comm-protocol", + "SHM", + "--log-level", + "INFO", + "--timeout", + "900.0" + ], + "server_log": "/tmp/waypoint-streaming-720p-server.log", + "slow_consumer_delay_seconds": 0.25, + "stall_threshold_seconds": 0.26666666666666666, + "steps": 16, + "warmup_steps": 1, + "weight_source": "/mnt/storage/garv901/waypoint-1.5-1B/checkpoints/Waypoint-1.5-1B" + }, + "correctness": { + "failures": [], + "passed": true + }, + "created_at_utc": "2026-09-11T03:51:53.049194+00:00", + "geometry": { + "fps": 60.0, + "height": 720, + "width": 1280 + }, + "metric_definitions": { + "backpressure": "delta between an unpaused stream and an identical stream paused between SDK reads", + "jitter_population_stddev": "population standard deviation of inter-chunk gaps", + "memory": "PSS and nvidia-smi GPU process memory summed over the server process group only", + "stall": "inter-chunk gap strictly greater than stall_threshold_seconds", + "sustained_media_to_wall_ratio": "media seconds in chunks after the first divided by first-to-last chunk arrival time", + "time_to_first_frame_seconds": "request iterator start to first fully decoded SDK VideoFrameChunk" + }, + "model_variant": "waypoint-1.5-1b-720p", + "release_threshold": null, + "runs": { + "baseline": { + "chunk_count": 16, + "consumer": { + "injected_pause_seconds": 0.0, + "pause_count": 0, + "pause_seconds": 0.0 + }, + "frame_count": 64, + "generated_media_seconds": 1.0666666666666667, + "inter_chunk_gap_seconds": { + "jitter_population_stddev": 0.016008292004214712, + "maximum": 0.14418772095814347, + "mean": 0.11690171121930083, + "p50": 0.11206015711650252, + "p95": 0.14245065064169465, + "sample_count": 15 + }, + "memory": { + "peak_gpu_mib": 5756.0, + "peak_host_pss_mib": 3687.2998046875, + "phase": "baseline", + "quiet_gpu_mib": 5756.0, + "quiet_host_pss_mib": 3645.1083984375, + "sample_count": 3 + }, + "overall_media_to_wall_ratio": 0.47871936597732684, + "payload_bytes": 176947200, + "payload_sha256": "facfdd2c70e27c0c675e72e944fc3e24cfec997010257c2c9b4ceb80a1bb7701", + "request_id": "waypoint-streaming-benchmark-baseline", + "request_wall_seconds": 2.2281669438816607, + "stalls": { + "count": 0, + "longest_seconds": null, + "threshold_seconds": 0.26666666666666666, + "total_excess_seconds": 0 + }, + "sustained_media_to_wall_ratio": 0.5702796475032248, + "time_to_first_frame_seconds": 0.46520866593346 + }, + "slow_consumer": { + "chunk_count": 16, + "consumer": { + "injected_pause_seconds": 3.75, + "pause_count": 15, + "pause_seconds": 0.25 + }, + "frame_count": 64, + "generated_media_seconds": 1.0666666666666667, + "inter_chunk_gap_seconds": { + "jitter_population_stddev": 0.01187843873988182, + "maximum": 0.38108016178011894, + "mean": 0.3533156535277764, + "p50": 0.3503798511810601, + "p95": 0.3789634692016989, + "sample_count": 15 + }, + "memory": { + "peak_gpu_mib": 5756.0, + "peak_host_pss_mib": 3694.3701171875, + "phase": "slow-consumer", + "quiet_gpu_mib": 5756.0, + "quiet_host_pss_mib": 3666.2431640625, + "sample_count": 9 + }, + "overall_media_to_wall_ratio": 0.1874695831569112, + "payload_bytes": 176947200, + "payload_sha256": "facfdd2c70e27c0c675e72e944fc3e24cfec997010257c2c9b4ceb80a1bb7701", + "request_id": "waypoint-streaming-benchmark-slow-consumer", + "request_wall_seconds": 5.689811908174306, + "stalls": { + "count": 15, + "longest_seconds": 0.38108016178011894, + "threshold_seconds": 0.26666666666666666, + "total_excess_seconds": 1.2997348029166464 + }, + "sustained_media_to_wall_ratio": 0.18868868673384595, + "time_to_first_frame_seconds": 0.383872521109879 + } + }, + "schema_version": 1, + "server": { + "startup_seconds": 42.095929856877774 + }, + "status": "completed", + "variant": "720p" +} diff --git a/examples/sdk_chat.py b/examples/sdk_chat.py index 85a89d222..166d24fca 100644 --- a/examples/sdk_chat.py +++ b/examples/sdk_chat.py @@ -11,7 +11,7 @@ # Non-streaming print(client.chat("What is the capital of France?").text) -# Streaming (yields TextChunk / ImageChunk / AudioChunk) +# Streaming (yields TextChunk / ImageChunk / AudioChunk / VideoFrameChunk) for event in client.chat("Tell me a short story.", stream=True): if isinstance(event, TextChunk): print(event.text, end="", flush=True) diff --git a/mstar/__init__.py b/mstar/__init__.py index 6a6da9fe4..f652722c5 100644 --- a/mstar/__init__.py +++ b/mstar/__init__.py @@ -12,12 +12,18 @@ from typing import TYPE_CHECKING if TYPE_CHECKING: # for type checkers / IDEs only — no runtime import cost - from mstar.client import AudioBuffer, GenerateResult, MStarClient # noqa: F401 + from mstar.client import ( # noqa: F401 + AudioBuffer, + GenerateResult, + MStarClient, + VideoFrameChunk, + ) _LAZY: dict[str, tuple[str, str]] = { "MStarClient": ("mstar.client", "MStarClient"), "GenerateResult": ("mstar.client", "GenerateResult"), "AudioBuffer": ("mstar.client", "AudioBuffer"), + "VideoFrameChunk": ("mstar.client", "VideoFrameChunk"), } __all__ = list(_LAZY) diff --git a/mstar/api_server/data_worker.py b/mstar/api_server/data_worker.py index 620fd8abc..2e5054022 100644 --- a/mstar/api_server/data_worker.py +++ b/mstar/api_server/data_worker.py @@ -1,6 +1,7 @@ import logging +import math import os import queue import threading @@ -42,6 +43,47 @@ logger = logging.getLogger(__name__) +def _video_frame_metadata( + tensor: torch.Tensor, + *, + fps: float, + frame_index: int, + metadata: dict | None = None, +) -> dict: + """Describe one raw RGB24 output tensor and reject ambiguous payloads.""" + if tensor.dtype != torch.uint8 or tensor.dim() != 4 or tensor.shape[-1] != 3: + raise ValueError( + "video_frame output must be uint8 RGB shaped " + f"[frame_count, height, width, 3]; got {tuple(tensor.shape)} of {tensor.dtype}" + ) + frame_count, height, width, _ = map(int, tensor.shape) + if frame_count < 1 or height < 1 or width < 1: + raise ValueError( + "video_frame output dimensions must be positive; " + f"got {tuple(tensor.shape)}" + ) + if ( + isinstance(fps, bool) + or not isinstance(fps, (int, float)) + or not math.isfinite(fps) + or fps <= 0 + ): + raise ValueError(f"video_frame fps must be a positive number; got {fps!r}") + if isinstance(frame_index, bool) or not isinstance(frame_index, int) or frame_index < 0: + raise ValueError( + f"video_frame frame_index must be a non-negative int; got {frame_index!r}" + ) + return { + **(metadata or {}), + "width": width, + "height": height, + "fps": fps, + "pixel_format": "rgb24", + "frame_index": frame_index, + "frame_count": frame_count, + } + + def _preprocess_loop(**kwargs): worker = PreprocessWorkerThread(**kwargs) worker.run() @@ -286,11 +328,50 @@ def __init__( # The request's model_kwargs, kept so output postprocessing can # honor per-request parameters (e.g. the video container fps). self.request_model_kwargs: dict[str, dict] = {} + # Next raw-frame index for each request. A video_frame chunk can carry + # several frames, so this advances by frame_count rather than chunks. + self.request_output_frame_indices: dict[str, int] = {} + # Transport reads may complete out of order. Record output order when + # the worker notification arrives, then hold completed chunks until all + # preceding tensors for that request have been emitted. + self.tensor_uuid_to_output_order_per_request: dict[ + str, dict[str, tuple[int, NestedLoopIndices]] + ] = {} + self.request_next_output_sequence: dict[str, int] = {} + self.request_next_emit_sequence: dict[str, int] = {} + self.request_pending_output_chunks: dict[str, dict[int, ResultChunk]] = {} # Owned by PreprocessWorker (main thread); used only from this thread. self.communicator = communicator self.tensor_manager = tensor_manager + def _cleanup_request_state(self, request_id: str, *, force: bool = False) -> None: + """Release transport and postprocessing state owned by this thread. + + ``force`` unlinks the tensor SHM unconditionally, for a request that + never reached the conductor (no remote reader will ever drain it); the + default drain-gated path leaves the unlink to the tensor manager. + """ + try: + if force: + self.tensor_manager.force_cleanup_request(request_id) + else: + self.tensor_manager.cleanup_request(request_id) + finally: + self.in_flight_requests.discard(request_id) + for state_name in ( + "tensor_uuid_to_metadata_per_request", + "tensor_uuid_to_output_order_per_request", + "request_model_kwargs", + "request_output_frame_indices", + "request_next_output_sequence", + "request_next_emit_sequence", + "request_pending_output_chunks", + ): + state = getattr(self, state_name, None) + if state is not None: + state.pop(request_id, None) + def _process_input( self, input: PreprocessInput ): @@ -369,6 +450,11 @@ def _process_input( ) self.request_model_kwargs[input.request_id] = input.model_kwargs or {} + self.request_output_frame_indices[input.request_id] = 0 + self.tensor_uuid_to_output_order_per_request[input.request_id] = {} + self.request_next_output_sequence[input.request_id] = 0 + self.request_next_emit_sequence[input.request_id] = 0 + self.request_pending_output_chunks[input.request_id] = {} msg = ConductorMessage( message_type=ConductorMessageType.NEW_REQUEST, body=NewRequestConductor( @@ -454,9 +540,42 @@ def _read_result_tensor( ) if result.request_id not in self.tensor_uuid_to_metadata_per_request: self.tensor_uuid_to_metadata_per_request[result.request_id] = {} + output_order = self.tensor_uuid_to_output_order_per_request.setdefault( + result.request_id, {} + ) + sequence = self.request_next_output_sequence.setdefault(result.request_id, 0) for tensor_info in result.graph_edge.tensor_info: self.tensor_uuid_to_metadata_per_request[result.request_id][ tensor_info.uuid] = result.metadata + output_order[tensor_info.uuid] = (sequence, result.loop_indices) + sequence += 1 + self.request_next_output_sequence[result.request_id] = sequence + + def _queue_completed_output( + self, + request_id: str, + sequence: int, + chunk: ResultChunk, + ) -> None: + pending = self.request_pending_output_chunks.setdefault(request_id, {}) + if sequence in pending: + raise RuntimeError( + f"duplicate completed output sequence {sequence} for request {request_id}" + ) + pending[sequence] = chunk + + next_sequence = self.request_next_emit_sequence.setdefault(request_id, 0) + while next_sequence in pending: + ready = pending.pop(next_sequence) + if ready.modality == "video_frame": + frame_index = self.request_output_frame_indices[request_id] + ready.metadata["frame_index"] = frame_index + self.request_output_frame_indices[request_id] = ( + frame_index + ready.metadata["frame_count"] + ) + self.out_queue.put(ready) + next_sequence += 1 + self.request_next_emit_sequence[request_id] = next_sequence def _discard_result_tensor( self, result: ResultTensors @@ -483,6 +602,17 @@ def _process_read_tensors(self): # escape to run()'s catch-all would abandon the rest of this # pass and leave the client waiting on the request timeout. try: + sequence, loop_indices = ( + self.tensor_uuid_to_output_order_per_request[request_id][ + tensor_info.uuid + ] + ) + logger.debug( + "Postprocessing output sequence %d for request %s at %s", + sequence, + request_id, + loop_indices, + ) tensor = self.tensor_manager.get_tensor( request_id=request_id, uuid=tensor_info.uuid @@ -503,13 +633,41 @@ def _process_read_tensors(self): "sample_rate": self.model.get_output_sample_rate("audio"), "num_channels": self.model.get_output_audio_channels("audio"), } - - self.out_queue.put(ResultChunk( - request_id=request_id, - modality=modality, - data=postprocessed, - metadata=chunk_metadata, - )) + elif modality == "video_frame" and self.model is not None: + chunk_metadata = _video_frame_metadata( + tensor, + fps=self.model.get_output_frame_rate( + "video_frame", + request_kwargs=self.request_model_kwargs.get(request_id), + ), + # Assigned from emitted order in + # _queue_completed_output after any earlier + # asynchronous reads have completed. + frame_index=0, + metadata=chunk_metadata, + ) + expected_bytes = ( + chunk_metadata["frame_count"] + * chunk_metadata["height"] + * chunk_metadata["width"] + * 3 + ) + if len(postprocessed) != expected_bytes: + raise ValueError( + "video_frame payload length does not match its RGB24 shape: " + f"expected {expected_bytes} bytes, got {len(postprocessed)}" + ) + + self._queue_completed_output( + request_id, + sequence, + ResultChunk( + request_id=request_id, + modality=modality, + data=postprocessed, + metadata=chunk_metadata, + ), + ) except Exception as exc: # noqa: BLE001 — must reach the client self._fail_request( request_id, exc, f"{modality} output postprocessing", @@ -517,6 +675,9 @@ def _process_read_tensors(self): self.tensor_uuid_to_metadata_per_request.get( request_id, {} ).pop(tensor_info.uuid, None) + self.tensor_uuid_to_output_order_per_request.get( + request_id, {} + ).pop(tensor_info.uuid, None) self.tensor_manager.dereference( request_id=request_id, uuid=tensor_info.uuid @@ -652,11 +813,7 @@ def run(self): while not self.cleanup_request_queue.empty(): did_work = True req_id = self.cleanup_request_queue.get() - self.tensor_manager.cleanup_request(req_id) - if req_id in self.tensor_uuid_to_metadata_per_request: - del self.tensor_uuid_to_metadata_per_request[req_id] - self.request_model_kwargs.pop(req_id, None) - self.in_flight_requests.discard(req_id) + self._cleanup_request_state(req_id) did_work = self._process_read_tensors() or did_work # Reads may have just resolved; ACK any drains now free of them. for rid in list(self._draining_rids): @@ -678,9 +835,7 @@ def run(self): # Never reached the conductor, so there are no remote # readers to race: hard-drop the (possibly persisted) # input signals directly. - self.tensor_manager.force_cleanup_request(pre_input.request_id) - self.request_model_kwargs.pop(pre_input.request_id, None) - self.in_flight_requests.discard(pre_input.request_id) + self._cleanup_request_state(pre_input.request_id, force=True) except Exception: logger.exception("PreprocessWorkerThread error") @@ -692,4 +847,3 @@ def run(self): # leave the input signals of in-flight requests in /dev/shm. for request_id in list(self.in_flight_requests): self._hard_cleanup(request_id) - diff --git a/mstar/api_server/entrypoint.py b/mstar/api_server/entrypoint.py index 54424f0d7..58cf9652d 100644 --- a/mstar/api_server/entrypoint.py +++ b/mstar/api_server/entrypoint.py @@ -36,7 +36,10 @@ logger = logging.getLogger(__name__) -SUPPORTED_MODALITIES = frozenset({"text", "image", "audio", "video", "action", "scalar", "tensor"}) +SUPPORTED_MODALITIES = frozenset({ + "text", "image", "audio", "video", "video_frame", "action", "scalar", "tensor", +}) +STREAMING_ONLY_MODALITIES = frozenset({"video_frame"}) # Extension-based modality detection for uploaded files. _EXT_TO_MODALITY: dict[str, str] = {} @@ -118,6 +121,10 @@ def _conductor_process_target( ) try: conductor.run() + except KeyboardInterrupt: + # The API parent uses SIGINT for a graceful child shutdown. Treat that + # as the normal stop signal after allowing the conductor to unwind. + pass finally: conductor.shutdown() @@ -347,6 +354,15 @@ def submit_request( for m in input_modalities + output_modalities: if m not in SUPPORTED_MODALITIES: raise ValueError(f"Unsupported modality: {m!r}") + if "video_frame" in input_modalities: + raise ValueError("'video_frame' is an output-only modality") + streaming_only = STREAMING_ONLY_MODALITIES.intersection(output_modalities) + if streaming_only and not streaming: + names = ", ".join(sorted(streaming_only)) + raise ValueError( + f"Output modality {names} requires streaming=True; raw frame " + "chunks cannot be returned as an aggregated response." + ) # Register pending request with self.request_lock: @@ -823,6 +839,16 @@ async def generate( raise HTTPException(status_code=503, detail="Server not ready") out_mods = [m.strip() for m in output_modalities.split(",") if m.strip()] + streaming_only = STREAMING_ONLY_MODALITIES.intersection(out_mods) + if streaming_only and not streaming: + names = ", ".join(sorted(streaming_only)) + raise HTTPException( + status_code=400, + detail=( + f"Output modality {names} requires streaming=true; raw frame " + "chunks cannot be returned as an aggregated response." + ), + ) # --- save uploaded files, grouped by modality ---------------- file_paths: dict[str, list[str]] = {} @@ -866,6 +892,12 @@ async def generate( else: in_mods = [p.modality for p in parts] + if "video_frame" in in_mods: + raise HTTPException( + status_code=400, + detail="'video_frame' is an output-only modality", + ) + try: parsed_kwargs = json.loads(model_kwargs) if model_kwargs else None except json.JSONDecodeError as e: diff --git a/mstar/api_server/request_types.py b/mstar/api_server/request_types.py index 27a9022a0..23894cdd8 100644 --- a/mstar/api_server/request_types.py +++ b/mstar/api_server/request_types.py @@ -11,7 +11,7 @@ class ResultChunk: """One chunk of generated output for a request.""" request_id: str - modality: str # "text" | "image" | "audio" | "video" + modality: str # "text" | "image" | "audio" | "video" | "video_frame" data: bytes # raw payload (text encoded as utf-8) metadata: dict = field(default_factory=dict) diff --git a/mstar/cli/main.py b/mstar/cli/main.py index b59d57baf..5383f499b 100644 --- a/mstar/cli/main.py +++ b/mstar/cli/main.py @@ -38,6 +38,7 @@ "whisper_large": "whisper_large.yaml", "higgs_audio": "higgs_audio.yaml", "wan22": "wan22.yaml", + "waypoint": "waypoint.yaml", } diff --git a/mstar/client/__init__.py b/mstar/client/__init__.py index d62df9c72..b4044db4d 100644 --- a/mstar/client/__init__.py +++ b/mstar/client/__init__.py @@ -8,6 +8,7 @@ ImageChunk, StreamEvent, TextChunk, + VideoFrameChunk, ) __all__ = [ @@ -17,5 +18,6 @@ "TextChunk", "ImageChunk", "AudioChunk", + "VideoFrameChunk", "StreamEvent", ] diff --git a/mstar/client/client.py b/mstar/client/client.py index 1fdc9f3a9..d03e09d12 100644 --- a/mstar/client/client.py +++ b/mstar/client/client.py @@ -30,11 +30,13 @@ ImageChunk, StreamEvent, TextChunk, + VideoFrameChunk, ) # When attaching raw bytes we need a filename whose extension lets the server # infer the modality (it keys off the extension). _DEFAULT_EXT = {"images": "png", "audio": "wav", "video": "mp4"} +_STREAM_READ_CHUNK_SIZE = 1024 * 1024 _MODALITY_OF = {"images": "image", "audio": "audio", "video": "video"} MediaItem = "str | bytes | Path | tuple[str, bytes]" @@ -78,9 +80,14 @@ def generate( dropped so server-side defaults apply. Returns a :class:`GenerateResult` when ``stream=False``, or an iterator - of :class:`StreamEvent` (``TextChunk`` / ``ImageChunk`` / ``AudioChunk``) - when ``stream=True``. + of :class:`StreamEvent` when ``stream=True``. Raw ``video_frame`` output + is streaming-only and yields :class:`VideoFrameChunk` objects. """ + if "video_frame" in output_modalities and not stream: + raise ValueError( + "output modality 'video_frame' requires stream=True; raw frame " + "chunks cannot be returned as an aggregated response" + ) files = self._build_files(images, audio, video) data: dict[str, str] = { "output_modalities": ",".join(output_modalities), @@ -162,7 +169,13 @@ def _build_files(self, images, audio, video) -> list[tuple[str, tuple[str, bytes for kind, items in (("images", images), ("audio", audio), ("video", video)): if not items: continue - if isinstance(items, (str, bytes, bytearray, Path)): + named_bytes = ( + isinstance(items, tuple) + and len(items) == 2 + and isinstance(items[0], str) + and isinstance(items[1], (bytes, bytearray)) + ) + if isinstance(items, (str, bytes, bytearray, Path)) or named_bytes: items = [items] for i, item in enumerate(items): fname, blob = self._coerce_file(kind, i, item) @@ -191,7 +204,13 @@ def _stream(self, url, data, files) -> Iterator[StreamEvent]: # UTF-8 (the NDJSON encoding) instead of dropping every line. if resp.encoding is None: resp.encoding = "utf-8" - for line in resp.iter_lines(decode_unicode=True): + # Raw RGB frame events are multi-megabyte NDJSON lines. Requests' + # 512-byte default repeatedly concatenates the growing partial + # line and becomes quadratic at 720p, so read them in large slabs. + for line in resp.iter_lines( + chunk_size=_STREAM_READ_CHUNK_SIZE, + decode_unicode=True, + ): if not isinstance(line, str): continue parsed = parse_ndjson_line(line) @@ -204,11 +223,23 @@ def _to_event(parsed: dict) -> StreamEvent: modality = parsed["modality"] raw = parsed["bytes"] meta = parsed["metadata"] + if modality == "error": + status = meta.get("status") + status_suffix = f" (status {status})" if status is not None else "" + raise RuntimeError( + f"Server stream failed{status_suffix}: " + f"{raw.decode('utf-8', 'replace')}" + ) + if modality == "text": + return TextChunk(raw.decode("utf-8", "replace"), meta) if modality == "image": return ImageChunk(raw, meta) if modality == "audio": return AudioChunk(raw, int(meta.get("sample_rate", 24000)), meta) - # text and any unrecognized modality decode as utf-8 text + if modality == "video_frame": + return VideoFrameChunk(raw, meta) + # Existing action/scalar/tensor streams are text-compatible. Keep the + # historical fallback while giving raw video frames their strict type. return TextChunk(raw.decode("utf-8", "replace"), meta) @staticmethod diff --git a/mstar/client/media.py b/mstar/client/media.py index eb8ca5fb3..1a51fc448 100644 --- a/mstar/client/media.py +++ b/mstar/client/media.py @@ -27,7 +27,8 @@ def parse_ndjson_line(line: str) -> dict | None: """Parse one NDJSON line from ``/generate`` streaming into a decoded dict. Returns ``{"modality", "bytes", "metadata"}`` or ``None`` for blank / - unparseable lines. + unparseable lines. A top-level ``error`` is the Rust frontend's in-band + failure envelope and raises rather than being mistaken for an empty chunk. """ line = line.strip() if not line: @@ -36,6 +37,8 @@ def parse_ndjson_line(line: str) -> dict | None: msg = json.loads(line) except json.JSONDecodeError: return None + if "error" in msg: + raise RuntimeError(f"Server stream failed: {msg['error']}") data = msg.get("data") return { "modality": msg.get("modality"), diff --git a/mstar/client/types.py b/mstar/client/types.py index 5949ef5fb..72a697d5c 100644 --- a/mstar/client/types.py +++ b/mstar/client/types.py @@ -2,6 +2,7 @@ from __future__ import annotations +import math from dataclasses import dataclass, field @@ -68,8 +69,100 @@ def to_numpy(self): return np.frombuffer(self.pcm, dtype=" None: + if not isinstance(self.data, bytes): + raise ValueError( + f"video_frame data must be immutable bytes; got {type(self.data).__name__}" + ) + if not isinstance(self.metadata, dict): + raise ValueError( + f"video_frame metadata must be a dict; got {type(self.metadata).__name__}" + ) + required = ( + "width", "height", "fps", "pixel_format", "frame_index", "frame_count", + ) + missing = [name for name in required if name not in self.metadata] + if missing: + raise ValueError( + "video_frame metadata is missing required field(s): " + + ", ".join(missing) + ) + + width = self.metadata["width"] + height = self.metadata["height"] + frame_index = self.metadata["frame_index"] + frame_count = self.metadata["frame_count"] + for name, value, minimum in ( + ("width", width, 1), + ("height", height, 1), + ("frame_index", frame_index, 0), + ("frame_count", frame_count, 1), + ): + if isinstance(value, bool) or not isinstance(value, int) or value < minimum: + raise ValueError( + f"video_frame metadata {name!r} must be an int >= {minimum}; " + f"got {value!r}" + ) + + fps = self.metadata["fps"] + if ( + isinstance(fps, bool) + or not isinstance(fps, (int, float)) + or not math.isfinite(fps) + or fps <= 0 + ): + raise ValueError( + f"video_frame metadata 'fps' must be a finite positive number; got {fps!r}" + ) + pixel_format = self.metadata["pixel_format"] + if pixel_format != "rgb24": + raise ValueError( + "video_frame metadata 'pixel_format' must be 'rgb24'; " + f"got {pixel_format!r}" + ) + + expected = frame_count * height * width * 3 + if len(self.data) != expected: + raise ValueError( + "video_frame payload length does not match its metadata: " + f"expected {expected} bytes, got {len(self.data)}" + ) + + self.width = width + self.height = height + self.fps = float(fps) + self.pixel_format = pixel_format + self.frame_index = frame_index + self.frame_count = frame_count + + def to_numpy(self): + """Return a zero-copy, read-only ``[T, H, W, 3]`` uint8 view.""" + import numpy as np + + return np.frombuffer(self.data, dtype=np.uint8).reshape( + self.frame_count, self.height, self.width, 3 + ) + + # A streaming iteration yields one of these per output chunk. -StreamEvent = TextChunk | ImageChunk | AudioChunk +StreamEvent = TextChunk | ImageChunk | AudioChunk | VideoFrameChunk @dataclass diff --git a/mstar/engine/resources/attn/flex.py b/mstar/engine/resources/attn/flex.py index 70c09c273..3a96ad955 100644 --- a/mstar/engine/resources/attn/flex.py +++ b/mstar/engine/resources/attn/flex.py @@ -21,7 +21,9 @@ from mstar.engine.resources.attn.base import AttentionManager from mstar.engine.resources.attn.config import AttentionStep +from mstar.engine.resources.base import CGSlotSpec from mstar.engine.resources.kv.config import KVConfig +from mstar.engine.resources.kv.ring.manager import RingPlan from mstar.engine.resources.step import StepContext __all__ = ["FlexAttentionManager", "flex_attention_masked", "make_block_mask"] @@ -94,8 +96,12 @@ def make_block_mask(q_len: int, kv_len: int, written: Tensor) -> BlockMask: ) # No partial blocks at all -- these two exist only to satisfy the signature. - kv_num_blocks = torch.zeros((1, 1, q_blocks), dtype=torch.int32, device=written.device) - kv_indices = torch.zeros((1, 1, q_blocks, kv_blocks), dtype=torch.int32, device=written.device) + kv_num_blocks = torch.zeros( + (1, 1, q_blocks), dtype=torch.int32, device=written.device + ) + kv_indices = torch.zeros( + (1, 1, q_blocks, kv_blocks), dtype=torch.int32, device=written.device + ) return BlockMask.from_kv_blocks( kv_num_blocks, @@ -109,6 +115,29 @@ def make_block_mask(q_len: int, kv_len: int, written: Tensor) -> BlockMask: ) +def _empty_block_mask(q_len: int, kv_len: int, device: torch.device) -> BlockMask: + """Allocate a fixed-address mask whose visible prefix is staged in plan.""" + block_size = _DEFAULT_SPARSE_BLOCK_SIZE + q_blocks, kv_blocks = q_len // block_size, kv_len // block_size + full_kv_num_blocks = torch.zeros( + (1, 1, q_blocks), dtype=torch.int32, device=device + ) + full_kv_indices = torch.zeros( + (1, 1, q_blocks, kv_blocks), dtype=torch.int32, device=device + ) + kv_num_blocks = torch.zeros_like(full_kv_num_blocks) + kv_indices = torch.zeros_like(full_kv_indices) + return BlockMask.from_kv_blocks( + kv_num_blocks, + kv_indices, + full_kv_num_blocks, + full_kv_indices, + BLOCK_SIZE=block_size, + mask_mod=None, + seq_lengths=(q_len, kv_len), + compute_q_blocks=False, + ) + class FlexAttentionManager(AttentionManager): """Attention against a ring KV cache, through ``flex_attention``. @@ -138,6 +167,11 @@ def __init__( self._device = device self._dtype = dtype self._kv_config = kv_config + self._planned_masks: dict[tuple[int, tuple[int, int, int]], BlockMask] = {} + self._visibility_tables: dict[ + tuple[int, int, int], tuple[Tensor, Tensor, int] + ] = {} + self._active_slot: int | None = None def depends_on(self) -> set[str]: return {self._kv_cache_name} @@ -160,15 +194,158 @@ def requires_kv_write(self) -> bool: """ return False - def plan(self, step: AttentionStep, ctx: StepContext) -> None: - """Nothing to plan: the mask is a function of the ring's own - visibility row, which the KV resource hands to ``attend`` per layer. + @property + def needs_token_visibility(self) -> bool: + """Whether the ring must construct its legacy token-level mask. - The cursors are still cleared, per ``AttentionResource``: a step that - never binds them must not inherit the previous step's. + Engine execution stages a block mask in ``plan`` and does not need the + per-layer row. The fallback remains useful for standalone numerical + calls that invoke ``attend`` without a resource plan. """ - del step, ctx + return self._active_slot is None + + @staticmethod + def _geometry(layer) -> tuple[int, int, int]: + return (layer.ring_frames, layer.ring_buckets, layer.pinned_dilation) + + def _mask_for( + self, slot: int, geometry: tuple[int, int, int], *, create: bool = False, + ) -> BlockMask: + key = (slot, geometry) + mask = self._planned_masks.get(key) + if mask is None and create: + ring_frames, _, _ = geometry + capacity = (ring_frames + 1) * self._kv_config.tokens_per_frame + capacity *= self._kv_config.num_worlds + mask = _empty_block_mask( + self._kv_config.tokens_per_frame, capacity, self._device + ) + self._planned_masks[key] = mask + self._visibility_table_for(geometry) + if mask is None: + raise RuntimeError( + f"FlexAttention mask for slot={slot}, geometry={geometry} was not " + "allocated before CUDA graph capture" + ) + return mask + + def _visibility_table_for( + self, geometry: tuple[int, int, int], + ) -> tuple[Tensor, Tensor, int]: + """Return immutable device rows for every distinct ring phase. + + Before the first wrap, visibility grows with ``frame_pos``. Once every + addressable bucket has been written, it is periodic over + ``ring_buckets * pinned_dilation`` frames. Keeping both the growing and + periodic phases makes the lookup finite even though rollout clocks are + not capped by the resource itself. + """ + existing = self._visibility_tables.get(geometry) + if existing is not None: + return existing + + ring_frames, ring_buckets, dilation = geometry + period = ring_buckets * dilation + blocks_per_frame = ( + self._kv_config.tokens_per_frame // _DEFAULT_SPARSE_BLOCK_SIZE + ) + total_blocks = ( + (ring_frames + 1) * blocks_per_frame * self._kv_config.num_worlds + ) + counts: list[list[int]] = [] + rows: list[list[list[int]]] = [] + for world_idx in range(self._kv_config.num_worlds): + world_counts = [] + world_rows = [] + for frame_pos in range(2 * period): + visible = self._visible_blocks_for( + geometry, world_idx=world_idx, frame_pos=frame_pos + ) + world_counts.append(len(visible)) + world_rows.append(visible + [0] * (total_blocks - len(visible))) + counts.append(world_counts) + rows.append(world_rows) + + table = ( + torch.tensor(counts, dtype=torch.int32, device=self._device), + torch.tensor(rows, dtype=torch.int32, device=self._device), + period, + ) + self._visibility_tables[geometry] = table + return table + + def build_cuda_graph_buffers( + self, slots: list[CGSlotSpec], max_bs: int, max_seq_len: int, + ) -> None: + del max_bs, max_seq_len + geometries = {self._geometry(layer) for layer in self._kv_config.layers} + for slot in {spec.slot for spec in slots}: + for geometry in geometries: + self._mask_for(slot, geometry, create=True) + + def _visible_blocks_for( + self, + geometry: tuple[int, int, int], + *, + world_idx: int, + frame_pos: int, + ) -> list[int]: + tokens = self._kv_config.tokens_per_frame + blocks_per_frame = tokens // _DEFAULT_SPARSE_BLOCK_SIZE + ring_frames, ring_buckets, dilation = geometry + capacity_blocks = (ring_frames + 1) * blocks_per_frame + world_base = world_idx * capacity_blocks + + committed = range(0, frame_pos, dilation) + slots = { + (frame // dilation) % ring_buckets for frame in committed + } + if frame_pos % dilation == 0: + slots.discard((frame_pos // dilation) % ring_buckets) + + visible: list[int] = [] + for ring_slot in sorted(slots): + start = world_base + ring_slot * blocks_per_frame + visible.extend(range(start, start + blocks_per_frame)) + scratch = world_base + ring_frames * blocks_per_frame + visible.extend(range(scratch, scratch + blocks_per_frame)) + return visible + + def _stage( + self, + mask: BlockMask, + geometry: tuple[int, int, int], + plan: RingPlan, + ) -> None: + counts, indices, period = self._visibility_table_for(geometry) + phase = ( + plan.frame_pos + if plan.frame_pos < period + else period + plan.frame_pos % period + ) + mask.full_kv_num_blocks.copy_(counts[plan.world_idx, phase]) + mask.full_kv_indices.copy_(indices[plan.world_idx, phase]) + + def plan(self, step: AttentionStep, ctx: StepContext) -> None: + """Stage one local/global visibility mask for this frame and slot.""" + del step self.reset_default_cursors() + ring_plan = ctx.plan_results.get(self._kv_cache_name) + if ring_plan is None: + # Keeps standalone manager tests and non-ring diagnostic calls able + # to use the visibility tensor supplied directly to attend(). + self._active_slot = None + return + if not isinstance(ring_plan, RingPlan): + raise TypeError( + f"FlexAttention expected RingPlan from {self._kv_cache_name!r}; " + f"got {type(ring_plan).__name__}" + ) + self._active_slot = ctx.slot + geometries = {self._geometry(layer) for layer in self._kv_config.layers} + for geometry in geometries: + mask = self._mask_for(ctx.slot, geometry, create=not ctx.capture) + self._stage(mask, geometry, ring_plan) def attend( self, @@ -178,6 +355,7 @@ def attend( visible: Tensor, *, enable_gqa: bool, + layer_idx: int | None = None, ) -> Tensor: """One layer's attention over the ring. @@ -188,13 +366,11 @@ def attend( which is the entire reason this backend exists. Returns ``[B, H_q, T, D]``. """ - # Rebuilt every call. The mask is pass-invariant by construction (all - # passes over one frame see byte-identical KV), so a cache keyed on - # (layer_idx, frame_pos) would collapse 120 rebuilds per frame to 24. - # Deliberately NOT taken: there is no measurement of what the rebuild - # costs against the rest of the frame, and a stale mask is - # exactly the failure this backend is here to prevent. Measure first. - block_mask = make_block_mask(q.size(-2), k.size(-2), visible) + if self._active_slot is None or layer_idx is None: + block_mask = make_block_mask(q.size(-2), k.size(-2), visible) + else: + geometry = self._geometry(self._kv_config.layers[layer_idx]) + block_mask = self._mask_for(self._active_slot, geometry) # `flex_attention_masked`, never bare `flex_attention`: with a no-op # `mask_mod` the eager path ignores the block mask entirely and attends # to unwritten ring slots. See the note at its definition. diff --git a/mstar/engine/resources/kv/config.py b/mstar/engine/resources/kv/config.py index 060aa5d38..7ef8d3ed0 100644 --- a/mstar/engine/resources/kv/config.py +++ b/mstar/engine/resources/kv/config.py @@ -130,9 +130,13 @@ def __post_init__(self): f"ring geometry has {len(self.layers)} layers but num_layers is " f"{self.num_layers}; each layer's ring is declared separately." ) - if self.num_worlds < 1: + if ( + not isinstance(self.num_worlds, int) + or isinstance(self.num_worlds, bool) + or self.num_worlds < 1 + ): raise ValueError( - f"num_worlds must be >= 1; got {self.num_worlds}. A node serving " + f"num_worlds must be a positive int; got {self.num_worlds!r}. A node serving " "zero worlds refuses every request at admit." ) @@ -144,12 +148,16 @@ def apply_yaml_overrides(self, num_worlds: int | None = None, **kwargs) -> None: f"got {sorted(kwargs)}" ) if num_worlds is not None: - if int(num_worlds) < 1: + if ( + not isinstance(num_worlds, int) + or isinstance(num_worlds, bool) + or num_worlds < 1 + ): raise ValueError( - f"num_worlds must be >= 1; got {num_worlds}. A node serving " + f"num_worlds must be a positive int; got {num_worlds!r}. A node serving " "zero worlds refuses every request at admit." ) - self.num_worlds = int(num_worlds) + self.num_worlds = num_worlds @dataclass diff --git a/mstar/engine/resources/kv/ring/cache.py b/mstar/engine/resources/kv/ring/cache.py index ea4ae2535..5737c554e 100644 --- a/mstar/engine/resources/kv/ring/cache.py +++ b/mstar/engine/resources/kv/ring/cache.py @@ -136,7 +136,13 @@ def reset(self, world_idx: int) -> None: self.written[lo + self.ring_len : hi].fill_(True) def upsert( - self, kv: Tensor, frame_pos: Tensor, commit: bool, world_idx: Tensor + self, + kv: Tensor, + frame_pos: Tensor, + commit: bool, + world_idx: Tensor, + *, + build_visibility: bool = True, ) -> tuple[Tensor, Tensor, Tensor]: """``kv`` is ``[2, 1, H_kv, tokens_per_frame, D]`` for exactly one frame of one world; @@ -172,23 +178,27 @@ def upsert( write_step = frame_pos.remainder(self.pinned_dilation) == 0 mask_written = self._mask_written - mask_written.copy_(self.written) - mask_written &= self._world_of_slot == world_idx - mask_written[ring_idx] = mask_written[ring_idx] & ~write_step + if build_visibility: + mask_written.copy_(self.written) + mask_written &= self._world_of_slot == world_idx + mask_written[ring_idx] = mask_written[ring_idx] & ~write_step if commit: dst = torch.where(write_step, ring_idx, current_idx) ring_scatter(self.kv, self.written, dst, kv, True) k, v = self.kv.unbind(0) - # ALIASING HAZARD. The third return value IS `self._mask_written`, this + # ALIASING HAZARD. When ``build_visibility`` is true, the third return + # value IS `self._mask_written`, this # layer's preallocated scratch, handed out by reference and overwritten # in place by the next `upsert` on this layer. A consumer that stashes # it and reads it later reads some *later* frame's visibility -- which # is a mask off by one or more frames, and with worlds resident it can # now also be another world's mask entirely. # - # The obligation on the consumer: read it (build the block mask, or + # When false, the value is intentionally stale and must be ignored by + # the planned attention backend. The obligation on a fallback consumer: + # read it (build the block mask, or # clone it) before the next upsert on this same layer. The 4+1 schedule # satisfies that trivially. return k, v, mask_written diff --git a/mstar/engine/resources/kv/ring/manager.py b/mstar/engine/resources/kv/ring/manager.py index 4a0468d0d..cd7f84da2 100644 --- a/mstar/engine/resources/kv/ring/manager.py +++ b/mstar/engine/resources/kv/ring/manager.py @@ -3,6 +3,8 @@ ``build`` and reused for the life of the process,. """ +from typing import NamedTuple + import torch from torch import Tensor @@ -22,7 +24,15 @@ StepContext, ) -__all__ = ["RingKVManager"] +__all__ = ["RingKVManager", "RingPlan"] + + +class RingPlan(NamedTuple): + """Host facts downstream resources need to stage this fixed ring view.""" + + request_id: str + world_idx: int + frame_pos: int class RingKVManager(AttentionResource): @@ -106,7 +116,14 @@ def world_of(self, rid: str) -> int | None: return self._worlds.get(rid) def upsert( - self, k: Tensor, v: Tensor, layer_idx: int, frame_pos: Tensor, *, commit: bool + self, + k: Tensor, + v: Tensor, + layer_idx: int, + frame_pos: Tensor, + *, + commit: bool, + build_visibility: bool = True, ) -> tuple[Tensor, Tensor, Tensor]: """Write one frame's K/V for ``layer_idx`` and return what to attend to. @@ -127,7 +144,8 @@ def upsert( # rings), so indexing on it is graph-safe. kv = torch.stack([k, v], dim=0) return self.layers[layer_idx].upsert( - kv, frame_pos, commit, self._static_world_idx + kv, frame_pos, commit, self._static_world_idx, + build_visibility=build_visibility, ) def _reset_world(self, rid: str) -> None: @@ -148,7 +166,7 @@ def _release_world(self, rid: str) -> None: @torch.no_grad() def get_state(self, rid: str) -> dict: - """Snapshot one request's world. Cloned, so the caller can hold it + """Snapshot one request's world. Cloned, so the caller can hold it across further rollout steps that mutate the rings in place. """ world_idx = self._require_world(rid, "get_state") @@ -305,10 +323,9 @@ def admit(self, step: ResourceStep, ctx: StepContext) -> AdmitOutcome: self._worlds[rid] = world_idx return ADMIT_OK - def plan(self, step: ResourceStep, ctx: StepContext) -> None: + def plan(self, step: ResourceStep, ctx: StepContext) -> RingPlan: """Stage this step's world index. Its ring addresses stay in the graph.""" - del step rids = {*ctx.request_ids} if len(rids) != 1: raise ValueError( @@ -322,6 +339,8 @@ def plan(self, step: ResourceStep, ctx: StepContext) -> None: # through the address the graph baked, without allocating a staging # tensor 24 times a second. self._static_world_idx.fill_(world_idx) + frames = self._step_frames(step) + return RingPlan(rid, world_idx, frames[rid]) def commit(self, step: ResourceStep, ctx: StepContext) -> None: """Record the frame each request just committed. Metadata only.""" diff --git a/mstar/graph/base.py b/mstar/graph/base.py index 17c38de34..1cdcc8b58 100644 --- a/mstar/graph/base.py +++ b/mstar/graph/base.py @@ -69,7 +69,7 @@ class GraphEdge: conductor_new_token: bool = field(default=False) # counted by the conductor toward the output-token total is_streaming: bool = field(default=False) # streaming edge: tokens accumulate at destination buffer # only for EMIT_TO_CLIENT - output_modality: str = field(default="") # text | image | video | audio + output_modality: str = field(default="") # text | image | video | video_frame | audio _persist_for_loop: bool = field(default=False) # set on a synthetic streaming-input edge carrying the final chunk, so the # consuming pass (not the earlier ingest) reports the partition done diff --git a/mstar/model/base.py b/mstar/model/base.py index 9bb55bd89..9abbe0ae0 100644 --- a/mstar/model/base.py +++ b/mstar/model/base.py @@ -478,7 +478,7 @@ def load_video(self, filepath: str, device: str): def postprocess( self, output: torch.Tensor, - modality: str, # text | image | video | audio + modality: str, # text | image | video | video_frame | audio request_kwargs: dict | None = None, ) -> bytes: """ @@ -545,6 +545,23 @@ def get_output_audio_channels(self, modality: str = "audio") -> int: audio. Mono default (the speech models); stereo models override.""" return 1 + def get_output_frame_rate( + self, + modality: str = "video_frame", + request_kwargs: dict | None = None, + ) -> float: + """Frame rate for raw RGB ``video_frame`` output. + + Raw frames have no container header from which a client could recover + timing, so models that expose this modality must override this hook. + ``request_kwargs`` permits a future model with a per-request frame rate; + Waypoint's checkpoint uses a fixed rate. + """ + del request_kwargs + raise ValueError( + f"{type(self).__name__} does not define a frame rate for {modality!r} output" + ) + # ------------------------------------------------------------------ # Partition API (optional, backward-compatible defaults) # ------------------------------------------------------------------ diff --git a/mstar/model/registry.py b/mstar/model/registry.py index 367e1ca13..5370cb3d0 100644 --- a/mstar/model/registry.py +++ b/mstar/model/registry.py @@ -55,12 +55,11 @@ # Wan2.2-TI2V-5B (dense video DiT + UMT5-XXL + Wan2.2-VAE). TI2V-5B # only; the A14B MoE variants are a separate follow-up. "wan22": {"model_path_hf": "Wan-AI/Wan2.2-TI2V-5B-Diffusers"}, - # Waypoint-1.5-1B (autoregressive video world model). The key pins the - # 720P variant, which is the checkpoint's default `variant`; the 360P - # sibling is the same weights under a different latent grid, selected with - # `model_kwargs: {variant: waypoint-1.5-1b-360p}`. The TAEHV decoder lives - # in a separate repo (`config.ae_uri`) and is not loaded yet. - "waypoint": {"model_path_hf": "Overworld/Waypoint-1.5-1B"}, + # Waypoint owns a variant -> Hub repository mapping. None is intentional: + # it lets WaypointModel distinguish the registry default from an explicit + # local path or Hub ID, then select the 720P or 360P repository named by the + # YAML `variant`. TAEHV is resolved independently from config.ae_uri. + "waypoint": {"model_path_hf": None}, # Whisper works for any size; the registry key pins large-v3, the # standard ASR-benchmark checkpoint. "whisper_large": {"model_path_hf": "openai/whisper-large-v3"}, diff --git a/mstar/model/waypoint/checkpoint.py b/mstar/model/waypoint/checkpoint.py new file mode 100644 index 000000000..df8b95bd9 --- /dev/null +++ b/mstar/model/waypoint/checkpoint.py @@ -0,0 +1,266 @@ +"""Resolve and validate the two checkpoints used by Waypoint. + +Resolution happens before model construction so a bad repository, partial local +download, or incompatible ``config.yaml`` cannot allocate several GiB of GPU +storage before failing. Local paths always win and never touch Hugging Face. +""" + +from __future__ import annotations + +import json +import re +from collections.abc import Mapping +from importlib import import_module +from pathlib import Path +from typing import Any + +import yaml + +from mstar.model.waypoint.config import ( + WaypointConfig, +) + +WAYPOINT_CONFIG_FILE = "config.yaml" +WAYPOINT_WEIGHT_ALLOW_PATTERNS = ( + WAYPOINT_CONFIG_FILE, + "model.safetensors", + "model.safetensors.index.json", + "model-*.safetensors", +) +TAEHV_CHECKPOINT_FILE = "taehv1_5.pth" +TAEHV_UPSTREAM_REVISION = "7dc60ec6601af2e668e31bc70acc4cb3665e4c22" +TAEHV_UPSTREAM_ARCHIVE = ( + "https://github.com/madebyollin/taehv/archive/" + f"{TAEHV_UPSTREAM_REVISION}.zip" +) + +_HF_REPO_ID = re.compile(r"^[A-Za-z0-9][A-Za-z0-9_.-]*/[A-Za-z0-9][A-Za-z0-9_.-]*$") + + +def require_taehv_runtime() -> None: + """Fail before checkpoint download or module allocation if TAEHV is absent.""" + try: + taehv = import_module("taehv") + except ImportError as exc: + raise RuntimeError( + "Waypoint requires the pinned TAEHV runtime. Install the index-safe " + "dependencies with `uv pip install -e '.[waypoint]'`, then install " + "TAEHV separately with `uv pip install --no-deps " + f"'taehv @ {TAEHV_UPSTREAM_ARCHIVE}'`. The TAEHV source package " + "requires uv>=0.4.0 or pip>=24.3." + ) from exc + if not hasattr(taehv, "TAEHV"): + raise RuntimeError( + "The installed `taehv` module does not export TAEHV. Ensure the " + "index-safe dependencies are installed with `uv pip install -e " + "'.[waypoint]'`, then remove the invalid module and install the pinned " + "source separately with `uv pip install --no-deps " + f"'taehv @ {TAEHV_UPSTREAM_ARCHIVE}'`; use uv>=0.4.0 or pip>=24.3 " + "because older pip can install an empty UNKNOWN wheel." + ) + + +def _as_tuple(value: Any) -> Any: + if isinstance(value, list): + return tuple(_as_tuple(item) for item in value) + if isinstance(value, dict): + return {key: _as_tuple(item) for key, item in value.items()} + return value + + +def _local_source(source: str | Path, kind: str) -> Path | None: + raw = str(source) + path = Path(source).expanduser() + if path.exists(): + return path.resolve() + if raw.startswith(("/", ".", "~")) or not _HF_REPO_ID.fullmatch(raw): + raise FileNotFoundError(f"{kind} local path does not exist: {path}") + return None + + +def _checkpoint_weight_files(checkpoint_dir: Path) -> tuple[Path, ...]: + single = checkpoint_dir / "model.safetensors" + index_path = checkpoint_dir / "model.safetensors.index.json" + if single.is_file(): + return (single,) + if not index_path.is_file(): + raise FileNotFoundError( + f"Waypoint checkpoint {checkpoint_dir} has no model.safetensors or " + "model.safetensors.index.json." + ) + try: + index = json.loads(index_path.read_text()) + shard_names = sorted(set(index["weight_map"].values())) + except (json.JSONDecodeError, KeyError, TypeError, AttributeError) as exc: + raise ValueError(f"Invalid Waypoint safetensors index {index_path}: {exc}") from exc + if not shard_names or any(not isinstance(name, str) for name in shard_names): + raise ValueError(f"Waypoint safetensors index {index_path} has no valid weight shards.") + shards = tuple(checkpoint_dir / name for name in shard_names) + escaped = [path for path in shards if path.parent.resolve() != checkpoint_dir.resolve()] + if escaped: + raise ValueError(f"Waypoint safetensors index contains a path outside {checkpoint_dir}.") + missing = [path.name for path in shards if not path.is_file()] + if missing: + raise FileNotFoundError( + f"Waypoint checkpoint {checkpoint_dir} is incomplete; missing shards: {missing}." + ) + return shards + + +def _expected_manifest(config: WaypointConfig) -> dict[str, Any]: + return { + "model_type": "waypoint-1.5", + "inference_fps": config.inference_fps, + "temporal_compression": config.temporal_compression, + "taehv_ae": config.taehv_ae, + "ae_uri": config.ae_uri, + "prompt_conditioning": config.prompt_conditioning, + "channels": config.channels, + "n_layers": config.n_layers, + "n_heads": config.n_heads, + "n_kv_heads": config.n_kv_heads, + "d_model": config.d_model, + "mlp_ratio": config.mlp_ratio, + "causal": True, + "moe": config.moe, + "n_buttons": config.n_buttons, + "patch": config.patch, + "base_fps": config.base_fps, + "local_window": config.local_window, + "global_window": config.global_window, + "global_pinned_dilation": config.global_pinned_dilation, + "global_attn_period": config.global_attn_period, + "global_attn_offset": config.global_attn_offset, + "n_frames": config.max_frames, + "rope_impl": config.rope_impl, + "value_residual": config.value_residual, + "gated_attn": config.gated_attn, + "noise_conditioning": config.noise_conditioning, + "ctrl_conditioning": config.ctrl_conditioning, + "ctrl_cond_dropout": config.ctrl_cond_dropout, + "ctrl_conditioning_period": config.ctrl_conditioning_period, + "scheduler_sigmas": config.scheduler_sigmas, + } + + +def validate_waypoint_checkpoint(checkpoint_dir: str | Path, config: WaypointConfig) -> Path: + """Validate files and checkpoint facts, returning an absolute directory.""" + config.validate_supported_deployment() + directory = Path(checkpoint_dir).expanduser().resolve() + if not directory.is_dir(): + raise FileNotFoundError(f"Waypoint checkpoint directory not found: {directory}") + config_path = directory / WAYPOINT_CONFIG_FILE + if not config_path.is_file(): + raise FileNotFoundError(f"Waypoint checkpoint is missing {config_path}.") + _checkpoint_weight_files(directory) + + try: + manifest = yaml.safe_load(config_path.read_text()) + except yaml.YAMLError as exc: + raise ValueError(f"Invalid Waypoint checkpoint config {config_path}: {exc}") from exc + if not isinstance(manifest, Mapping): + raise ValueError(f"Waypoint checkpoint config {config_path} must contain a mapping.") + + expected = _expected_manifest(config) + mismatches = [] + for key, expected_value in expected.items(): + if key not in manifest: + mismatches.append(f"{key}= (expected {expected_value!r})") + continue + actual = _as_tuple(manifest[key]) + if actual != expected_value: + mismatches.append(f"{key}={actual!r} (expected {expected_value!r})") + + manifest_geometry = ( + manifest.get("tokens_per_frame"), + manifest.get("height"), + manifest.get("width"), + ) + expected_geometry = (config.tokens_per_frame, config.height, config.width) + if manifest_geometry != expected_geometry: + mismatches.append( + "checkpoint geometry " + f"{manifest_geometry!r} (expected {expected_geometry!r} for " + f"{config.variant!r})" + ) + conv_kw = _as_tuple(manifest.get("conv_kw")) + expected_conv = {"kernel_size": config.patch, "stride": config.patch} + if conv_kw != expected_conv: + mismatches.append(f"conv_kw={conv_kw!r} (expected {expected_conv!r})") + if mismatches: + raise ValueError( + f"Waypoint checkpoint {config_path} is incompatible with {config.variant!r}: " + + "; ".join(mismatches) + + "." + ) + return directory + + +def resolve_waypoint_checkpoint( + source: str | Path, + config: WaypointConfig, + *, + cache_dir: str | Path | None = None, + revision: str | None = None, +) -> Path: + """Resolve a local directory or HF repo ID and validate it before allocation.""" + local = _local_source(source, "Waypoint checkpoint") + if local is None: + try: + from huggingface_hub import snapshot_download + except ImportError as exc: + raise RuntimeError( + "Resolving a Waypoint Hugging Face ID requires the `waypoint` extra: " + "install with `uv pip install -e '.[waypoint]'`." + ) from exc + try: + local = Path( + snapshot_download( + repo_id=str(source), + cache_dir=None if cache_dir is None else str(cache_dir), + revision=revision, + allow_patterns=list(WAYPOINT_WEIGHT_ALLOW_PATTERNS), + ) + ) + except Exception as exc: + raise RuntimeError( + f"Failed to download required Waypoint files from {source!r}: {exc}" + ) from exc + return validate_waypoint_checkpoint(local, config) + + +def resolve_taehv_checkpoint( + source: str | Path, + *, + cache_dir: str | Path | None = None, + revision: str | None = None, +) -> Path: + """Resolve only ``taehv1_5.pth`` from a local path or HF repository.""" + local = _local_source(source, "TAEHV checkpoint") + if local is None: + try: + from huggingface_hub import hf_hub_download + except ImportError as exc: + raise RuntimeError( + "Resolving TAEHV weights requires the `waypoint` extra: " + "install with `uv pip install -e '.[waypoint]'`." + ) from exc + try: + local = Path( + hf_hub_download( + repo_id=str(source), + filename=TAEHV_CHECKPOINT_FILE, + cache_dir=None if cache_dir is None else str(cache_dir), + revision=revision, + ) + ) + except Exception as exc: + raise RuntimeError( + f"Failed to download required TAEHV file from {source!r}: {exc}" + ) from exc + checkpoint = local if local.is_file() else local / TAEHV_CHECKPOINT_FILE + if not checkpoint.is_file(): + raise FileNotFoundError( + f"No TAEHV checkpoint for source={str(source)!r}; looked for {checkpoint}." + ) + return checkpoint.resolve() diff --git a/mstar/model/waypoint/components/__init__.py b/mstar/model/waypoint/components/__init__.py index e60758b05..bf5cc47c6 100644 --- a/mstar/model/waypoint/components/__init__.py +++ b/mstar/model/waypoint/components/__init__.py @@ -1,29 +1,24 @@ """Waypoint-1.5-1B component modules. -The DiT (``dit.py``, ``attention.py``, ``layers.py``, ``rope.py``) is a native -port of ``world_engine/src/model/world_model.py``. ``apply_inference_patches`` -runs unconditionally in the reference's ``WorldEngine.__init__``, so the -*patched* model is the shipped one -- but the port does not follow it uniformly: -it takes the patched **fused QKV** and keeps the unpatched **packed** -``MLPFusion.fc1``, because that patch *splits* the packed weight rather than -merging it, and packed is the checkpoint's own storage. Both forms are -algebraically identical (measured 0.0 either way). +The DiT is a native port of ``world_engine/src/model/world_model.py``. +``apply_inference_patches`` runs unconditionally in the reference, so the +*patched* model is the shipped one -- but the port does not follow it +uniformly: it takes the patched **fused QKV** and keeps the unpatched +**packed** ``MLPFusion.fc1``, since that patch splits a weight the checkpoint +stores packed. The two forms are algebraically identical. **Nothing here owns the world state.** The ring KV cache and the FlexAttention kernel are engine resources (``engine/resources/kv/ring/``, -``engine/resources/attn/flex.py``), bound onto ``WaypointDiT`` and its 24 +``engine/resources/attn/flex.py``) bound onto ``WaypointDiT`` and its 24 ``WaypointAttention`` layers at load by ``NodeSubmodule.bind_node_resources``. That keeps the rings out of the module tree, where ``to_empty(device)``, -``state_dict()`` and the weight loader would each have a buffer of ours to leave -holding garbage. The superseded model-owned implementation (``kv_backend.py``, -with its ``FlexRingBackend`` and ``WaypointKVBackend`` protocol) was deleted -once the equivalence gate against it passed bit-exactly; ``git show -d31c3e70:mstar/model/waypoint/components/kv_backend.py`` is the last version. +``state_dict()`` and the weight loader would each have a buffer of ours to +leave holding garbage. -``ring_memory_bytes`` / ``describe_ring_memory`` moved up a level to -``waypoint/ring_geometry.py``: they are ``WaypointConfig`` arithmetic for -sizing a deployment and never needed a component to exist. The TAEHV streaming -VAE (``taehv.py``) is phase 7 and does not exist yet. +``taehv.py`` is the exception: a streaming AE session is per-request Python +state, held in ``PerRequestState`` by the two VAE nodes and dropped with the +request. Its ``taehv`` imports are deferred, so this package imports without +the upstream package installed. """ from mstar.model.waypoint.components.attention import WaypointAttention @@ -50,11 +45,13 @@ OrthoRoPEAngles, apply_ortho_rope, ) +from mstar.model.waypoint.components.taehv import ChunkedStreamingTAEHV, load_taehv __all__ = [ "FP32_MODULE_PATHS", "MLP", "AdaLN", + "ChunkedStreamingTAEHV", "CondHead", "ControllerInputEmbedding", "DeviceTableCache", @@ -69,5 +66,6 @@ "ada_gate", "ada_rmsnorm", "apply_ortho_rope", + "load_taehv", "rms_norm", ] diff --git a/mstar/model/waypoint/components/attention.py b/mstar/model/waypoint/components/attention.py index 32ae016b4..d70d175cc 100644 --- a/mstar/model/waypoint/components/attention.py +++ b/mstar/model/waypoint/components/attention.py @@ -1,48 +1,23 @@ """Waypoint self-attention: fused QKV, value residual, OrthoRoPE, ring cache. -Port of ``world_engine/src/model/attn.py::Attn`` **as it is actually served**. -``WorldEngine.__init__`` applies ``patch_model.apply_inference_patches`` -unconditionally, so the shipped module is ``patch_model.MergedQKVAttn``: three -separate ``q_proj``/``k_proj``/``v_proj`` GEMMs fused into one. This port -implements the fused form (CONTRACTS section 5, DECISIONS D5), which makes the -*patched* reference the parity target -- one fused GEMM is not bit-identical to -three separate ones. - -Facts that are load-bearing, in the order they bite: - - * **Fused layout.** ``qkv_proj.weight`` is ``cat([q, k, v], dim=0)`` -- - ``[q_out + 2 * kv_out, d_model] = [4096, 2048]`` here, rows 0:2048 = Q, - 2048:3072 = K, 3072:4096 = V, verified against - ``patch_model.py:110-112`` (the ``cat``) and ``patch_model.py:117`` (the - matching ``split((q_out, kv_out, kv_out), dim=-1)``). GQA makes the three - slabs unequal, so a wrong order is a shape error for Q but *not* between K - and V -- swapping those two loads cleanly and produces wrong video. - * **Order inside forward:** value-residual lerp FIRST, then ``rms_norm(q, k)``, - then RoPE on Q and K, then the cache upsert, then attention. Reordering the - lerp past the norm changes what enters the cache, permanently, for every - future frame that attends to it (CONTRACTS section 4.2). - * **``v1`` is layer 0's V captured PRE-lerp** and threaded unchanged through - all 24 layers. Layer 0 lerps against itself; every later layer lerps against - layer 0. The **lerped** V is what the cache stores. - * **Q and K are RMS-normed and rotated; V is neither.** K enters the ring - already rotated, so replayed history is never re-rotated. - * **The backend's argument order is not the reference's.** The reference calls - ``kv_cache.upsert(k, v, pos_ids, layer_idx)``; the port's protocol is - ``backend.upsert(k, v, layer_idx, frame_pos)``. Both trailing arguments are - positional ints/tensors, so a verbatim transcription passes a layer index - where a frame position belongs, raises nothing, and drifts (CONTRACTS - section 3). - -This module talks to the world state only through ``WaypointKVBackend``. It -never sees the ring, the slot arithmetic or the ``BlockMask``: ``meta`` is -opaque and goes straight back into ``attend``, which is what keeps a future -engine-owned KV implementation a one-class swap (DECISIONS D3). +``WorldEngine.__init__`` applies ``apply_inference_patches`` unconditionally, so +the served reference module is the fused ``patch_model.MergedQKVAttn``. That +fused form, not three separate GEMMs, is the parity target. + +``qkv_proj.weight`` is ``cat([q, k, v], dim=0)`` — rows 0:2048 Q, 2048:3072 K, +3072:4096 V. GQA makes the slabs unequal, so a wrong order is a shape error for +Q but *not* between K and V: swapping those two loads cleanly and serves wrong +video. + +This module reaches the world state only through the two resources bound in +``bind_resources``. It never sees the ring, the slot arithmetic or the +``BlockMask`` — ``visible`` is a ``[capacity]`` bool row it hands straight back +to ``attend``. """ import torch from torch import Tensor, nn -from mstar.model.waypoint.components.kv_backend import WaypointKVBackend from mstar.model.waypoint.components.layers import rms_norm from mstar.model.waypoint.components.rope import OrthoRoPE from mstar.model.waypoint.config import WaypointConfig @@ -53,19 +28,16 @@ class WaypointAttention(nn.Module): """One layer of causal frame attention over the ring KV cache. - Shapes: ``x`` is ``[B, N*T, D]`` (N frames of T tokens, flattened -- N is 1 - for every served call), and the head layout inside is ``[B, H, N*T, d_head]`` - with 32 query heads over 16 KV heads (GQA, 2:1). + ``x`` is ``[B, N*T, D]`` (N frames of T tokens, flattened; N is 1 for every + served call) and the head layout inside is ``[B, H, N*T, d_head]``, 32 query + heads over 16 KV heads. """ def __init__(self, config: WaypointConfig, layer_idx: int): super().__init__() if config.gated_attn: - # The reference supports a per-head sigmoid gate on the attention - # output; this checkpoint does not use it and it carries a - # `gate_proj` the loader could not fill. Same hard-fail stance as - # Wan22DiT's qk_norm check: a drifting checkpoint must not be - # silently mis-served. + # This checkpoint is ungated and carries no `gate_proj` for the + # loader to fill; hard-fail rather than silently mis-serve. raise ValueError( "WaypointAttention implements the ungated attention path only; " "this config declares gated_attn=True." @@ -80,54 +52,65 @@ def __init__(self, config: WaypointConfig, layer_idx: int): self.d_head = config.d_head self.enable_gqa = config.enable_gqa - # Split widths for the fused projection, in the concat order baked into - # the weight: Q (2048) | K (1024) | V (1024). + # Split widths in the concat order baked into the weight: Q | K | V. self.q_out = self.n_heads * self.d_head self.kv_out = self.n_kv_heads * self.d_head if self.value_residual: - # Per-layer scalar; the checkpoint value is what makes layer 0's V - # matter more or less deep in the stack. Initialized to the - # reference's 0.5 so a no-checkpoint structural build is sane. + # Per-layer scalar; 0.5 is the reference's init, so a structural + # build without a checkpoint is still sane. self.v_lamb = nn.Parameter(torch.tensor(0.5)) self.qkv_proj = nn.Linear(config.d_model, self.q_out + 2 * self.kv_out, bias=False) self.out_proj = nn.Linear(config.d_model, config.d_model, bias=False) - # Stateless and parameter-free, but kept as a per-layer submodule - # because that is the reference's shape and the call site reads - # `self.rope(q, rope_angles)`. The angles themselves are built once per - # forward at the DiT root and passed down. + # Per-layer to match the reference's call site; the angles themselves + # are built once per forward at the DiT root. self.rope = OrthoRoPE(config) + # Bound at load by NodeSubmodule.bind_node_resources. + self.kv = None + self.attn = None + + def bind_resources(self, resources: dict) -> None: + """Resolve the two resources this layer calls. + + ``NodeSubmodule.bind_node_resources`` walks ``self.modules()``, so one + bind on the submodule reaches all 24 layers. ``.get``: a layer may be + bound on a node that owns only some of them. + """ + self.kv = resources.get("kv") + self.attn = resources.get("attn") + def forward( self, x: Tensor, frame_pos: Tensor, rope_angles: tuple[Tensor, Tensor], v1: Tensor | None, - backend: WaypointKVBackend, + *, + commit: bool, ) -> tuple[Tensor, Tensor]: """``x`` ``[B, N*T, D]`` -> ``(out [B, N*T, D], v1 [B, H_kv, N*T, d_head])``. - ``frame_pos`` is the ``[]`` int64 ring clock (the reference's - ``pos_ids["f_pos"][0, 0]``); it is the only piece of ``pos_ids`` this - layer needs, since the RoPE angles are precomputed at the root. ``v1`` - is ``None`` at layer 0 and layer 0's pre-lerp V thereafter. + ``frame_pos`` is the ``[]`` int64 ring clock; ``v1`` is ``None`` at + layer 0 and layer 0's pre-lerp V thereafter. Both stay arguments rather + than cursors the KV resource keeps: a cursor that drifts from the + caller does not raise, it rewrites history. """ B, T = x.shape[:2] - # One GEMM, then slice: the split widths and their order are the - # transpose of the qkv_proj.weight row order (Q | K | V). + # One GEMM, then slice: the split widths transpose qkv_proj.weight's + # row order (Q | K | V). q, k, v = self.qkv_proj(x).split((self.q_out, self.kv_out, self.kv_out), dim=-1) q = q.reshape(B, T, self.n_heads, self.d_head).transpose(1, 2) k = k.reshape(B, T, self.n_kv_heads, self.d_head).transpose(1, 2) v = v.reshape(B, T, self.n_kv_heads, self.d_head).transpose(1, 2) if self.value_residual: - # v1 is captured BEFORE the lerp, so layer 0 returns its raw V while - # storing the (self-)lerped one. The lerped V is what the cache - # keeps and what every future frame attends to. + # v1 is captured BEFORE the lerp, so layer 0 returns its raw V and + # caches the self-lerped one. Lerp stays ahead of the norm: what + # this order produces is what the cache keeps, permanently. v1 = v if v1 is None else v1 v = torch.lerp(v, v1.view_as(v), self.v_lamb) @@ -135,11 +118,29 @@ def forward( q, k = rms_norm(q), rms_norm(k) q, k = self.rope(q, rope_angles), self.rope(k, rope_angles) - # NOTE the argument order -- (k, v, layer_idx, frame_pos), NOT the - # reference's (k, v, pos_ids, layer_idx). CONTRACTS section 3. - # `k` goes in post-RoPE; history comes back already rotated. - k, v, meta = backend.upsert(k, v, self.layer_idx, frame_pos) + # Argument order is (k, v, layer_idx, frame_pos), NOT the reference's + # (k, v, pos_ids, layer_idx). `k` goes in post-RoPE. + if getattr(self.attn, "needs_token_visibility", True): + k, v, visible = self.kv.upsert( + k, v, self.layer_idx, frame_pos, commit=commit + ) + else: + k, v, visible = self.kv.upsert( + k, + v, + self.layer_idx, + frame_pos, + commit=commit, + build_visibility=False, + ) - y = backend.attend(q, k, v, meta, enable_gqa=self.enable_gqa) + # No `if self.attn.requires_kv_write` guard: the upsert above IS the + # write, and it has to happen on frozen passes too — the scratch slot + # is what makes this frame visible to itself. + y = self.attn.attend( + q, k, v, visible, + enable_gqa=self.enable_gqa, + layer_idx=self.layer_idx, + ) y = y.transpose(1, 2).reshape(B, T, -1) return self.out_proj(y), v1 diff --git a/mstar/model/waypoint/components/dit.py b/mstar/model/waypoint/components/dit.py index b68fa3558..30300bab4 100644 --- a/mstar/model/waypoint/components/dit.py +++ b/mstar/model/waypoint/components/dit.py @@ -1,47 +1,22 @@ """The Waypoint-1.5 DiT: 24 blocks, the 4+1 pass driver, and the world clock. -Port of ``world_engine/src/model/world_model.py`` (``WorldDiTBlock``, -``WorldDiT``, ``WorldModel``) plus the per-frame driver from -``world_engine/src/world_engine.py`` (``gen_frame`` / ``append_frame`` / -``_denoise_pass`` / ``_cache_pass``). Read ``docs/waypoint/CONTRACTS.md`` -sections 1, 4.4 and 4.6 alongside it. - -Facts that are load-bearing: - - * **Five forwards per generated frame.** Four frozen Euler denoise passes over - ``config.scheduler_sigmas``, then one unfrozen committing pass at sigma=0. - Only the last writes the ring. The loop is plain Python inside this module, - not engine steps (DECISIONS D4): with a model-owned cache the engine has - nothing to do between denoise passes. - * **The ``.clone()`` between denoise and commit is load-bearing**, not - defensive copying. See ``generate_frame``. - * **Two clocks.** ``f_pos`` (ring clock: buckets, slots, visibility) and - ``t_pos = f_pos * config.ts_mult`` (RoPE time coordinate). ``ts_mult == 1`` - for this checkpoint so they are numerically equal, and they are still - threaded separately -- conflating them is silent drift at any other serving - fps (CONTRACTS section 4.4). - * **``cond_proj`` is physically shared by all 24 blocks.** ``__init__`` ties - it, and ``retie_cond_proj()`` exists as a public method because - ``to_empty(device)`` silently un-ties it (``Module._apply`` has no - cross-module memo). The loader MUST call it after ``to_empty`` or it gets - +0.6B resident parameters and 23 blocks of ``cond_proj`` nobody fills, with - no error (CONTRACTS section 6.1). - * **Controller conditioning is fused on 8 of 24 layers** (``i % 3 == 0``), on - ``rms_norm(x)`` and ``rms_norm(ctrl_emb)``. The other 16 blocks have no - ``ctrl_mlpfusion`` submodule at all. - * **No prompt cross-attention.** ``WaypointConfig.__post_init__`` rejects - ``prompt_conditioning``, so there is no dead branch here to mislead a reader - into thinking the checkpoint has one. - -**Module-tree deviation, for the weight loader.** The reference splits this into -``WorldModel`` (embeddings, patchify, head) wrapping ``WorldDiT`` -(``transformer.blocks``); the port collapses them into one ``WaypointDiT``, so -blocks live at ``blocks.{i}`` and not ``transformer.blocks.{i}``. That is one -extra prefix rename on top of CONTRACTS section 6's seven transforms -(``transformer.blocks.`` -> ``blocks.``); everything below that prefix keeps the -reference's spelling exactly. ``layers.FP32_MODULE_PATHS`` already assumes the -collapse (``denoise_step_emb`` is named relative to this root). See -DECISIONS D12. +Five forwards per generated frame: four frozen Euler denoise passes over +``config.scheduler_sigmas``, then one committing pass at sigma=0. Only the last +writes the ring. The loop is plain Python here, not engine steps. + +Two clocks, threaded separately: ``f_pos`` drives buckets, slots and +visibility, ``t_pos = f_pos * config.ts_mult`` is the RoPE time coordinate. +They are numerically equal at this checkpoint's ``ts_mult == 1``; conflating +them is silent drift at any other serving fps. + +``cond_proj`` is physically shared by all 24 blocks, and ``retie_cond_proj()`` +is public because ``to_empty(device)`` silently un-ties it. Controller +conditioning is fused on 8 of 24 layers (``i % 3 == 0``); the other 16 carry no +``ctrl_mlpfusion`` submodule at all. + +The port collapses the reference's ``WorldModel``/``WorldDiT`` pair into one +module, so blocks live at ``blocks.{i}``, not ``transformer.blocks.{i}``. +Everything below that prefix keeps the reference's spelling. """ from typing import NamedTuple @@ -51,7 +26,6 @@ from torch import Tensor, nn from mstar.model.waypoint.components.attention import WaypointAttention -from mstar.model.waypoint.components.kv_backend import WaypointKVBackend from mstar.model.waypoint.components.layers import ( FP32_MODULE_PATHS, MLP, @@ -74,21 +48,10 @@ class WaypointPosIds(NamedTuple): """The four position streams for one frame. - The reference packs these into a ``TensorDict`` keyed ``f_pos``/``t_pos``/ - ``y_pos``/``x_pos``; a ``NamedTuple`` says the same thing without pulling - ``tensordict`` into mstar's dependency set, and it is a pytree so it survives - ``torch.compile`` unchanged. - - * ``f_pos`` -- ``[]`` int64, the ring clock. The reference broadcasts it to - ``[B, T]`` only because ``TensorDict`` demands a uniform batch shape; the - cache reads ``f_pos[0, 0]`` and nothing else ever looks at it. The port - keeps it a scalar, which is exactly what ``WaypointKVBackend.upsert`` - takes. - * ``t_pos`` -- ``[B, T]`` int64, the RoPE time coordinate, - ``f_pos * ts_mult``. Equal to ``f_pos`` for this checkpoint; see the - module docstring. - * ``y_pos`` / ``x_pos`` -- ``[B, T]`` int64 token-grid coordinates, - ``row = i // width``, ``col = i % width``. + ``f_pos`` is the ``[]`` int64 ring clock (the KV resource's ``upsert`` takes + the scalar); ``t_pos`` is the ``[B, T]`` RoPE time coordinate + ``f_pos * ts_mult``; ``y_pos``/``x_pos`` are ``[B, T]`` token-grid + coordinates, ``row = i // width`` and ``col = i % width``. """ f_pos: Tensor @@ -101,9 +64,8 @@ class WaypointDiTBlock(nn.Module): """One DiT block: adaLN-modulated causal frame attention, optional controller fusion, adaLN-modulated MLP. - The six modulation tensors come from this block's ``cond_head``; its - ``bias_in`` is genuinely per-layer while its ``cond_proj`` matrices are - aliases of block 0's (see ``WaypointDiT.retie_cond_proj``). + The six modulation tensors come from this block's ``cond_head``, whose + ``bias_in`` is per-layer and whose ``cond_proj`` matrices alias block 0's. """ def __init__(self, config: WaypointConfig, layer_idx: int): @@ -115,8 +77,8 @@ def __init__(self, config: WaypointConfig, layer_idx: int): self.mlp = MLP(config.d_model, config.d_model * config.mlp_ratio, config.d_model) self.cond_head = CondHead(config) - # 8 of 24 layers. Absent -- not None-gated at the tensor level -- on the - # other 16, so the parameter tree itself records which layers fuse. + # Absent, not None-gated, on the other 16 layers: the parameter tree + # itself records which layers fuse. self.ctrl_mlpfusion = MLPFusion(config) if layer_idx in config.ctrl_layers else None def forward( @@ -127,29 +89,27 @@ def forward( cond: Tensor, ctrl_emb: Tensor, v1: Tensor | None, - backend: WaypointKVBackend, + *, + commit: bool, ) -> tuple[Tensor, Tensor]: """``x`` ``[B, N*T, D]``, ``cond``/``ctrl_emb`` ``[B, N, D]`` (per frame) -> ``(x, v1)``. ``v1`` is layer 0's pre-lerp V, threaded down the stack. - Only ``f_pos`` of the reference's ``pos_ids`` reaches this far -- the - RoPE angles are built once at the root from ``t/y/x_pos`` -- so the - scalar ring clock is passed directly rather than the whole bundle. + Only ``f_pos`` reaches this far -- the RoPE angles are built once at the + root -- so the scalar ring clock is passed rather than the whole bundle. """ s0, b0, g0, s1, b1, g1 = self.cond_head(cond) - # Causal frame attention. residual = x x = ada_rmsnorm(x, s0, b0) - x, v1 = self.attn(x, frame_pos, rope_angles, v1, backend) + x, v1 = self.attn(x, frame_pos, rope_angles, v1, commit=commit) x = ada_gate(x, g0) + residual - # Controller conditioning. Both operands are bare-RMS-normed (no adaLN - # scale here); the fusion output is added ungated. + # Both operands are bare-RMS-normed (no adaLN scale here) and the fusion + # output is added ungated. if self.ctrl_mlpfusion is not None: x = self.ctrl_mlpfusion(rms_norm(x), rms_norm(ctrl_emb)) + x - # MLP. x = ada_gate(self.mlp(ada_rmsnorm(x, s1, b1)), g1) + x return x, v1 @@ -160,7 +120,7 @@ class WaypointDiT(nn.Module): per-frame driver. Built on the meta device and materialized by ``weight_loader`` in a fixed - order (CONTRACTS section 6.1):: + order:: with torch.device("meta"): dit = WaypointDiT(config) @@ -169,9 +129,9 @@ class WaypointDiT(nn.Module): dit.retie_cond_proj() # MUST follow to_empty load_weights_into(dit, ...) - The KV ring is NOT part of this module: it is derived state owned by a - ``WaypointKVBackend`` built after materialization, so no ``to_empty`` or - ``state_dict`` walk can leave it holding garbage (DECISIONS D1/D3). + The KV ring is NOT part of this module: it is derived state owned by an + engine resource bound after materialization, so no ``to_empty`` or + ``state_dict`` walk can leave it holding garbage. """ def __init__(self, config: WaypointConfig): @@ -179,7 +139,11 @@ def __init__(self, config: WaypointConfig): self.config = config self.patch = tuple(config.patch) - self.denoise_step_emb = NoiseConditioner(config.d_model) + self.denoise_step_emb = NoiseConditioner( + config.d_model, + reference_compat=config.reference_compat, + cached_sigmas=config.scheduler_sigmas, + ) self.ctrl_emb = ControllerInputEmbedding(config) self.rope_angles = OrthoRoPEAngles(config) self.blocks = nn.ModuleList( @@ -191,15 +155,12 @@ def __init__(self, config: WaypointConfig): self.patchify = nn.Conv2d(C, D, kernel_size=self.patch, stride=self.patch, bias=False) self.out_norm = AdaLN(D) # The checkpoint stores this as a [D, C, ph, pw] conv kernel; the loader - # permutes it into this Linear's [C*ph*pw, D] and expands the [C] bias - # over the patch (CONTRACTS section 6, transforms 1-2). + # permutes it and expands the [C] bias over the patch (transforms 1-2). self.unpatchify = nn.Linear(D, C * ph * pw, bias=True) - # Token-grid coordinates: derived state, so they live outside the module - # tree (a non-persistent buffer would survive `to_empty` as - # uninitialized garbage that no completeness check covers -- the stance - # layers.DeviceTableCache exists for). device="cpu" is required: this - # runs under `with torch.device("meta")`. + # Token-grid coordinates: derived state, held outside the module tree so + # `to_empty` cannot leave it uninitialized. device="cpu" is required -- + # this runs under `with torch.device("meta")`. idx = torch.arange(config.tokens_per_frame, dtype=torch.long, device="cpu") self._grid = DeviceTableCache( idx.div(config.width, rounding_mode="floor"), idx.remainder(config.width) @@ -212,12 +173,15 @@ def __init__(self, config: WaypointConfig): # to_empty for the meta build path. self.retie_cond_proj() + # No `bind_resources` here: the driver holds no resource handle, it passes + # `commit` down to the layers, which own the only calls into the ring. + # ---- Build-time surface ------------------------------------------------ @property def dtype(self) -> torch.dtype: """Bulk compute dtype (the non-island weights); callers cast latents to - this, mirroring ``Wan22DiT.dtype``.""" + this.""" return self.patchify.weight.dtype def cast_serving_dtypes(self) -> "WaypointDiT": @@ -227,11 +191,6 @@ def cast_serving_dtypes(self) -> "WaypointDiT": allocated directly in the serving dtype. ``.to(dtype)`` on meta preserves the ``cond_proj`` aliasing (``to_empty`` does not), so no retie is needed here. - - The island list is ``layers.FP32_MODULE_PATHS`` rather than a literal: - the modules that must stay fp32 are a fact of the reference's - ``NoCastModule`` set, and it belongs next to the modules themselves - (DECISIONS D7). """ self.to(torch.bfloat16) for path in FP32_MODULE_PATHS: @@ -241,16 +200,13 @@ def cast_serving_dtypes(self) -> "WaypointDiT": def retie_cond_proj(self) -> "WaypointDiT": """Alias blocks 1..23's six ``cond_proj`` matrices onto block 0's. - **Public, and separate from ``__init__``, because ``to_empty(device)`` - destroys the tying.** ``Module._apply`` allocates per parameter object - with no cross-module memo, so the 24 blocks come out of ``to_empty`` - holding 24 independent copies. Nothing raises; the symptoms are +0.6B - resident parameters and 23 unfilled ``cond_proj`` sets. Once re-tied, - ``named_parameters()`` deduplicates and the loader's completeness check - sees block 0's set only (CONTRACTS section 6.1). + Public, and separate from ``__init__``, because ``to_empty(device)`` + destroys the tying: ``Module._apply`` allocates per parameter with no + cross-module memo, so the 24 blocks come out holding 24 independent + copies. Nothing raises; the symptoms are +0.6B resident parameters and + 23 unfilled ``cond_proj`` sets. - ``bias_in`` is deliberately NOT tied -- it is genuinely per-layer, and - the checkpoint has 24 distinct values for it. + ``bias_in`` is deliberately NOT tied -- it is genuinely per-layer. """ ref_proj = self.blocks[0].cond_head.cond_proj for block in self.blocks[1:]: @@ -259,19 +215,12 @@ def retie_cond_proj(self) -> "WaypointDiT": return self def compile_regions(self, **compile_kwargs) -> "WaypointDiT": - """Compile the denoise and cache passes, one region each, matching the - reference's two ``@torch.compile(fullgraph=True, dynamic=False)`` sites. - - Not done in ``__init__``: the serving layer decides, from - ``config.compile_dit``, whether to compile at all (the eager path is the - bit-exact reference and the parity harness wants it). Idempotent. - - Run one eager frame first. The derived tables (this module's token grid, - ``OrthoRoPEAngles``' frequency tables) are copied to the device on first - use by design -- they are deliberately outside the module tree, so - nothing else materializes them -- and a first touch inside a - ``fullgraph=True`` region is at best a specialization and at worst a - graph break. + """Compile the denoise and cache passes, one region each. Idempotent. + + ``materialize_runtime_tables`` must run first: the derived token grid, + RoPE tables and conditioner LUT live outside the module tree, and a first + touch inside a ``fullgraph=True`` region is at best a specialization and + at worst a graph break. """ if not self._regions_compiled: options = {"fullgraph": True, "dynamic": False, **compile_kwargs} @@ -280,6 +229,15 @@ def compile_regions(self, **compile_kwargs) -> "WaypointDiT": self._regions_compiled = True return self + def materialize_runtime_tables(self, device: torch.device | str) -> "WaypointDiT": + """Create every derived table after weight loading and before compile.""" + device = torch.device(device) + self._grid.get(device) + self.rope_angles.materialize(device) + self.denoise_step_emb.materialize(device) + self._sigma_schedule(device, self.dtype) + return self + # ---- Positions --------------------------------------------------------- def _pos_ids(self, frame_pos: Tensor) -> WaypointPosIds: @@ -291,9 +249,8 @@ def _pos_ids(self, frame_pos: Tensor) -> WaypointPosIds: f"{frame_pos.dtype}", ) y_pos, x_pos = self._grid.get(frame_pos.device) - # The multiply is a no-op at ts_mult == 1 and is kept anyway: it is the - # only place the two clocks are related, and deleting it is how a - # different-fps checkpoint would start drifting silently. + # A no-op at ts_mult == 1, kept because it is the only place the two + # clocks are related; deleting it drifts silently at another fps. t_pos = (frame_pos * self.config.ts_mult).reshape(1, 1).expand(1, y_pos.numel()) return WaypointPosIds(f_pos=frame_pos, t_pos=t_pos, y_pos=y_pos[None], x_pos=x_pos[None]) @@ -308,7 +265,7 @@ def forward( mouse: Tensor, button: Tensor, scroll: Tensor, - backend: WaypointKVBackend, + commit: bool, ) -> Tensor: """One pass over one latent frame; returns the rectified-flow velocity. @@ -316,9 +273,9 @@ def forward( ``frame_pos`` ``[]`` int64 ring clock, controller inputs ``[B, N, 2]`` / ``[B, N, n_buttons]`` / ``[B, N, 1]``. Returns ``[B, N, C, H, W]``. - Whether this pass commits to the ring is the *backend's* state - (``set_frozen``), not an argument here -- exactly as in the reference, - where the two compiled driver regions set it and the model is unaware. + ``commit`` says whether this pass keeps its K/V: False for the four + denoise passes, True for the fifth. An argument rather than resource + state -- all five passes sit inside one engine step. """ B, N, C, H, W = x.shape ph, pw = self.patch @@ -330,21 +287,18 @@ def forward( f"{Hp} * {Wp} != {self.config.tokens_per_frame}", ) # One frame per call, batch 1: the ring cache indexes a single frame per - # upsert and the whole driver is built on that (reference asserts the - # same thing). + # upsert and the whole driver is built on that. torch._assert(B == 1 and N == 1, "WaypointDiT.forward supports B == 1, N == 1") pos_ids = self._pos_ids(frame_pos) - # Keyword arguments on purpose: (x, y, t) here vs the reference's - # dict lookup, and a silent x/y swap on a non-square grid is a - # wrong-video-no-error bug. + # Keyword arguments on purpose: a silent x/y swap on a non-square grid + # is a wrong-video-no-error bug. rope_angles = self.rope_angles( x_pos=pos_ids.x_pos, y_pos=pos_ids.y_pos, t_pos=pos_ids.t_pos ) cond = self.denoise_step_emb(sigma) # [B, N, D], fp32 island - # Positional and in this order: (mouse, button, scroll) is a checkpoint - # fact whose widths sum correctly under any permutation. + # Positional, and in this order — see ControllerInputEmbedding. ctrl_emb = self.ctrl_emb(mouse, button, scroll) # [B, N, D] D = self.config.d_model @@ -353,10 +307,12 @@ def forward( v1 = None # layer 0's pre-lerp V, threaded through all 24 blocks for block in self.blocks: - h, v1 = block(h, pos_ids.f_pos, rope_angles, cond, ctrl_emb, v1, backend) + h, v1 = block( + h, pos_ids.f_pos, rope_angles, cond, ctrl_emb, v1, commit=commit + ) - # Output head: silu sits BETWEEN the adaLN norm and the unpatchify - # projection (reference world_model.py:348-352), not after it. + # silu sits BETWEEN the adaLN norm and the unpatchify projection + # (reference world_model.py:348-352), not after it. h = F.silu(self.out_norm(h, cond)) h = self.unpatchify(h) # [B, N*T, C*ph*pw] h = h.view(B, N, Hp, Wp, C, ph, pw).permute(0, 1, 4, 2, 5, 3, 6) @@ -365,18 +321,13 @@ def forward( # ---- The 4+1 driver ---------------------------------------------------- def _sigma_schedule(self, device: torch.device, dtype: torch.dtype) -> Tensor: - """The sigma table, memoized per (device, dtype). Resolved by the caller - *outside* the compiled region -- materializing a tensor from a Python - list inside ``fullgraph=True`` is a graph break, which is also why the - reference keeps this table on the engine and only reads it in - ``_denoise_pass``. - - **The dtype is load-bearing.** The reference builds this table in the - serving dtype and takes ``.diff()`` there, so the Euler step sizes are - bf16 differences of bf16 sigmas: ``bf16(0.9) - 1.0 == -0.1015625``, - whereas an fp32 diff rounded to bf16 is ``-0.10009765625`` (two of the - four steps differ). Building the table in fp32 "for precision" would - change the ODE. + """The sigma table, memoized per (device, dtype) and resolved by the + caller *outside* the compiled region -- materializing a tensor from a + Python list inside ``fullgraph=True`` is a graph break. + + The dtype is load-bearing: the reference builds this table in the serving + dtype and takes ``.diff()`` there, so the Euler step sizes are bf16 + differences of bf16 sigmas. fp32 changes two of the four steps. """ key = (device, dtype) schedule = self._sigma_cache.get(key) @@ -390,7 +341,6 @@ def _denoise_pass( x: Tensor, frame_pos: Tensor, sigmas: Tensor, - backend: WaypointKVBackend, *, mouse: Tensor, button: Tensor, @@ -399,21 +349,17 @@ def _denoise_pass( """Four frozen Euler steps of the rectified-flow ODE. Returns the settled latent; **does not write the ring**. - Frozen matters: each step attends to a different noisy version of the - same frame, so none of them may commit. They still see themselves, - through the unconditional scratch write at the ring tail. - - ``sigmas`` is the ``[5]`` schedule in ``x``'s dtype, passed in rather - than built here -- see ``_sigma_schedule``. + ``commit=False`` matters: each step attends to a different noisy version + of the same frame, so none of them may keep its K/V. They still see + themselves, through the unconditional scratch write at the ring tail. """ - backend.set_frozen(True) - # One reused sigma buffer, filled per step (the reference's shape -- - # a fresh allocation per step would defeat cudagraph capture). + # One reused sigma buffer, filled per step; a fresh allocation per step + # would defeat cudagraph capture. sigma = x.new_empty((x.size(0), x.size(1))) - # strict=False is deliberate: there are 5 sigmas and 4 diffs, the - # trailing 0.0 exists only to produce the last step size, and that - # truncation IS the "4 denoise passes" of the 4+1 structure. - for step_sigma, step_dsigma in zip(sigmas, sigmas.diff(), strict=False): + # 5 sigmas, 4 diffs: the trailing 0.0 exists only to produce the last + # step size. Sliced, not zipped ragged -- dynamo rejects a ragged zip + # under fullgraph. + for step_sigma, step_dsigma in zip(sigmas[:-1], sigmas.diff(), strict=True): v = self( x, sigma.fill_(step_sigma), @@ -421,10 +367,10 @@ def _denoise_pass( mouse=mouse, button=button, scroll=scroll, - backend=backend, + commit=False, ) - # fp32 accumulate, back to the latent dtype -- the reference's exact - # expression; doing the add in bf16 loses the small late steps. + # fp32 accumulate, back to the latent dtype: the add in bf16 loses + # the small late steps. x = (x.float() + step_dsigma.float() * v.float()).type_as(x) return x @@ -432,17 +378,15 @@ def _cache_pass( self, x: Tensor, frame_pos: Tensor, - backend: WaypointKVBackend, *, mouse: Tensor, button: Tensor, scroll: Tensor, ) -> None: - """The committing pass: one unfrozen forward at sigma=0 on the settled - latent. Its only purpose is the side effect -- the K/V it writes into - every layer's ring. The returned velocity is discarded. + """The committing pass: one ``commit=True`` forward at sigma=0 on the + settled latent, for the side effect alone -- the K/V it writes into every + layer's ring. The returned velocity is discarded. """ - backend.set_frozen(False) self( x, x.new_zeros((x.size(0), x.size(1))), @@ -450,14 +394,13 @@ def _cache_pass( mouse=mouse, button=button, scroll=scroll, - backend=backend, + commit=True, ) def generate_frame( self, noise: Tensor, frame_pos: Tensor, - backend: WaypointKVBackend, *, mouse: Tensor, button: Tensor, @@ -465,37 +408,36 @@ def generate_frame( ) -> Tensor: """Denoise one frame from ``noise`` ``[B, N, C, H, W]`` and commit it. - Five forwards: 4 frozen + 1 committing (CONTRACTS section 1). The caller - owns the ring clock and must advance ``frame_pos`` by exactly one per - committed frame -- all five passes of a frame share the same value. + Five forwards: 4 frozen + 1 committing, all sharing one ``frame_pos``. + The caller owns the ring clock and advances it once per committed frame. """ - # The .clone() is load-bearing, not hygiene: _denoise_pass is a compiled - # region and inductor/cudagraphs reuse its output buffer, so the cache - # pass's own allocations would stomp the latent it is supposed to be - # reading. It must stay OUTSIDE the compiled region, on the returned - # tensor, so the copy lands in caller-owned memory. + # The .clone() is load-bearing, and must stay OUTSIDE the compiled + # region. Both passes run inside ONE CUDA-graph capture, so the cache + # pass allocates from the graph's private pool -- where the denoise + # pass's output buffer is a free block. The cache pass's first + # allocation can land on it and stomp the latent it is reading, at an + # address baked into the graph and repeated on every replay. sigmas = self._sigma_schedule(noise.device, noise.dtype) x0 = self._denoise_pass( - noise, frame_pos, sigmas, backend, mouse=mouse, button=button, scroll=scroll + noise, frame_pos, sigmas, mouse=mouse, button=button, scroll=scroll ).clone() - self._cache_pass(x0, frame_pos, backend, mouse=mouse, button=button, scroll=scroll) + self._cache_pass(x0, frame_pos, mouse=mouse, button=button, scroll=scroll) return x0 def append_frame( self, latent: Tensor, frame_pos: Tensor, - backend: WaypointKVBackend, *, mouse: Tensor, button: Tensor, scroll: Tensor, ) -> Tensor: """Prime the world state from a real (VAE-encoded) frame: the committing - pass alone, no denoising, one forward. + pass alone, one forward. ``latent`` is already the settled x0, so there is nothing to solve and nothing to clone. Returned unchanged for the caller to decode. """ - self._cache_pass(latent, frame_pos, backend, mouse=mouse, button=button, scroll=scroll) + self._cache_pass(latent, frame_pos, mouse=mouse, button=button, scroll=scroll) return latent diff --git a/mstar/model/waypoint/components/layers.py b/mstar/model/waypoint/components/layers.py index 4ec296655..5eb1c16c0 100644 --- a/mstar/model/waypoint/components/layers.py +++ b/mstar/model/waypoint/components/layers.py @@ -1,33 +1,20 @@ """Stateless layer primitives for the Waypoint-1.5 DiT. -Faithful port of ``world_engine/src/model/nn.py`` plus the three conditioning -modules from ``world_engine/src/model/world_model.py`` -(``ControllerInputEmbedding``, ``MLPFusion``, ``CondHead``). Attention, the -transformer block and the DiT itself live elsewhere; nothing here holds -sequence state, and nothing here touches the KV ring. - -Two things in this file are load-bearing beyond "it is the same arithmetic": - - * **Parameter names are checkpoint keys.** ``fc1``/``fc2``, ``bias_in``, - ``cond_proj``, ``mlp`` are the reference's attribute names and therefore - the names ``weight_loader`` remaps onto. mstar's shared - ``components.mlp.MLP`` spells its projections ``linear_in``/``linear_out``, - so it is deliberately NOT reused: a local two-line ``MLP`` that keeps the - checkpoint spelling is worth more than the shared class. - * **``NoiseConditioner`` is an fp32 island.** The reference marks it - ``NoCastModule``, i.e. it silently ignores ``.to(dtype)``. That trick is a - bad fit for mstar (it warns, and it fights ``to_empty``), so the port keeps - it an ordinary ``nn.Module`` that runs its own body under - ``autocast(enabled=False)`` on ``.float()`` inputs, and publishes - ``FP32_MODULE_PATHS`` for ``WaypointDiT.cast_serving_dtypes()`` to re-pin - after the global bf16 cast. - -The Fourier frequency table is derived state, not checkpoint state. The -reference registers it as a non-persistent buffer, which under mstar's meta -build would survive ``to_empty(device)`` as uninitialized garbage — a silent -wrong-numbers bug, since no loader completeness check covers buffers. It is -held outside the module tree instead, built on CPU at init and copied per -device on first use (the ``wan22.components.dit.Wan22RoPE3D`` approach). +Port of ``world_engine/src/model/nn.py`` plus the three conditioning modules +from ``world_engine/src/model/world_model.py``. Nothing here holds sequence +state and nothing here touches the KV ring. + +Parameter names are checkpoint keys: ``fc1``/``fc2``, ``bias_in``, ``cond_proj`` +and ``mlp`` are the reference's attribute names and the names ``weight_loader`` +remaps onto. mstar's shared ``components.mlp.MLP`` spells its projections +``linear_in``/``linear_out``, so it is not reused. + +``NoiseConditioner`` is an fp32 island: it runs its body under +``autocast(enabled=False)`` on ``.float()`` inputs and publishes +``FP32_MODULE_PATHS`` for ``WaypointDiT.cast_serving_dtypes()`` to re-pin after +the global bf16 cast. Its Fourier frequency table is derived state, held outside +the module tree so ``to_empty(device)`` cannot leave it uninitialized -- no +loader completeness check covers buffers. """ import torch @@ -36,35 +23,37 @@ from mstar.model.waypoint.config import WaypointConfig -# Submodule paths, relative to the ``WaypointDiT`` root, whose parameters must -# be restored to fp32 after a global ``.to(torch.bfloat16)``. This is the -# contract ``WaypointDiT.cast_serving_dtypes()`` consumes: "everything goes -# bf16, then these paths go back to fp32", mirroring -# ``Wan22DiT.cast_serving_dtypes``. -# -# The reference's ``NoCastModule`` set has three members; only one of them -# appears here, because the other two (``OrthoRoPEAngles``, ``OrthoRoPE`` in -# ``rope.py``) carry no parameters and no buffers at all — their tables live -# outside the module tree and are always fp32 — so there is nothing for a dtype -# cast to corrupt and nothing to pin back. See rope.py's module docstring. +# Submodule paths, relative to the WaypointDiT root, whose parameters must be +# restored to fp32 after a global .to(torch.bfloat16). The reference's +# NoCastModule set has three members; the other two (OrthoRoPEAngles, OrthoRoPE) +# carry no parameters or buffers for a cast to corrupt. FP32_MODULE_PATHS: tuple[str, ...] = ("denoise_step_emb",) +def bf16_roundtrip(table: torch.Tensor) -> torch.Tensor: + """Quantize an fp32 derived table the way ``NoCastModule._apply`` does. + Only reachable under ``WaypointConfig.reference_compat``. + """ + return table.to(torch.bfloat16).to(table.dtype) + + +def _bf16_bits(x: torch.Tensor) -> torch.Tensor: + """bf16 storage reinterpreted as an unsigned ``0..65535`` LUT index.""" + return x.contiguous().view(torch.int16).to(torch.int32) & 0xFFFF + + class DeviceTableCache: """Per-device replicas of small derived fp32 tables (RoPE frequencies, Fourier frequencies). - These tables are a pure function of the config, so they are neither - checkpoint state nor something a dtype cast should reach. Registering them - as non-persistent buffers would put them inside the module tree, where - ``to_empty(device)`` replaces their storage with uninitialized memory and - ``.to(bfloat16)`` would truncate their precision. Holding them here instead - keeps them out of ``state_dict``, out of ``to_empty``'s reach, and fp32 - forever. + A pure function of the config, so neither checkpoint state nor something a + dtype cast should reach. Holding them here rather than as non-persistent + buffers keeps them out of ``state_dict``, out of ``to_empty``'s reach, and + fp32 forever. Callers MUST build the CPU tables with an explicit ``device="cpu"``: module - ``__init__`` runs under ``with torch.device("meta")`` in mstar's build path, - and an ambient-device ``torch.arange`` would produce data-less meta tensors. + ``__init__`` runs under ``with torch.device("meta")``, where an + ambient-device ``torch.arange`` produces a data-less meta tensor. """ _CPU = torch.device("cpu") @@ -88,9 +77,8 @@ def get(self, device: torch.device) -> tuple[torch.Tensor, ...]: def rms_norm(x: torch.Tensor) -> torch.Tensor: """Unweighted RMS norm over the last dim (reference ``nn.rms_norm``). - No learned gain: every use site in Waypoint either has its scale supplied - by adaLN (``ada_rmsnorm``, ``AdaLN``) or wants a bare normalization (Q/K - norm, the two ``MLPFusion`` inputs). + No learned gain: every use site either gets its scale from adaLN or wants a + bare normalization (Q/K norm, the two ``MLPFusion`` inputs). """ return F.rms_norm(x, (x.size(-1),)) @@ -98,10 +86,9 @@ def rms_norm(x: torch.Tensor) -> torch.Tensor: def ada_rmsnorm(x: torch.Tensor, scale: torch.Tensor, bias: torch.Tensor) -> torch.Tensor: """Per-frame adaLN modulation: ``rms_norm(x) * (1 + scale) + bias``. - ``x`` is ``[B, N*T, D]`` (N frames of T tokens, flattened); ``scale`` and - ``bias`` are ``[B, N, D]``, one modulation vector per *frame*. The unflatten - exists purely so the per-frame vectors broadcast over that frame's tokens — - it is the reference's ``eo.rearrange(x, 'b (n m) d -> b n m d')``. + ``x`` is ``[B, N*T, D]``; ``scale`` and ``bias`` are ``[B, N, D]``, one + modulation vector per *frame*. The unflatten is what makes those vectors + broadcast over their own frame's tokens. """ x4 = x.unflatten(1, (scale.size(1), -1)) y4 = rms_norm(x4) * (1 + scale.unsqueeze(2)) + bias.unsqueeze(2) @@ -109,10 +96,9 @@ def ada_rmsnorm(x: torch.Tensor, scale: torch.Tensor, bias: torch.Tensor) -> tor def ada_gate(x: torch.Tensor, gate: torch.Tensor) -> torch.Tensor: - """Per-frame adaLN output gate: ``x * gate``, ``gate`` broadcast over the - tokens of its frame. Same shapes as ``ada_rmsnorm``. Note there is no - ``1 +`` here — the gate multiplies the sublayer output before the residual - add, so a zero gate means "contribute nothing".""" + """Per-frame adaLN output gate: ``x * gate``, same shapes as + ``ada_rmsnorm``. No ``1 +`` here -- the gate multiplies the sublayer output + before the residual add, so a zero gate contributes nothing.""" x4 = x.unflatten(1, (gate.size(1), -1)) return (x4 * gate.unsqueeze(2)).flatten(1, 2) @@ -121,10 +107,8 @@ class AdaLN(nn.Module): """adaLN with the scale/shift projection folded in: one Linear produces ``[scale | shift]`` from ``silu(cond)``. - Used only for the DiT's output head (``out_norm``); the blocks get their - six modulation tensors from ``CondHead`` and apply them with - ``ada_rmsnorm``/``ada_gate`` instead. As there, ``cond`` is per-frame - ``[B, N, D]`` and is expanded over each frame's tokens. + The DiT's output head only; blocks get their six modulation tensors from + ``CondHead``. ``cond`` is per-frame ``[B, N, D]``, expanded over its tokens. """ def __init__(self, dim: int): @@ -145,10 +129,9 @@ def forward(self, x: torch.Tensor, cond: torch.Tensor) -> torch.Tensor: class MLP(nn.Module): """Two-layer SiLU MLP, both projections bias-free. - ``fc1``/``fc2`` and ``bias=False`` are checkpoint facts, not style: the - reference's ``MLPFusion`` calls ``F.linear(h, self.mlp.fc2.weight)`` with no - bias argument at all, which is only correct because there is no bias to - pass. Adding one would load nothing and silently change the output. + ``bias=False`` is a checkpoint fact: the reference's ``MLPFusion`` calls + ``F.linear(h, self.mlp.fc2.weight)`` with no bias argument, which is only + correct because there is no bias to pass. """ def __init__(self, dim_in: int, dim_middle: int, dim_out: int): @@ -163,32 +146,65 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: class NoiseConditioner(nn.Module): """sigma -> Fourier features -> MLP, the DiT's only noise-level input. - fp32 island (reference ``NoCastModule``). ``FP32_MODULE_PATHS`` pins - ``self.mlp`` back to fp32 after the serving bf16 cast, and the body runs - under ``autocast(enabled=False)`` on a ``.float()`` sigma so nothing - downcasts it again. The precision matters more than the 2 MB it costs: the - four denoise sigmas are close together (1.0, 0.9, 0.75, 0.3) and the whole - schedule is only distinguishable to the model through this embedding. - - The ``* 1000`` before the phase computation is the reference's scaling of - the [0, 1] sigma range into a range where the Fourier basis actually - rotates; ``* 2**0.5`` restores unit variance after the sin/cos concat. + fp32 island: ``FP32_MODULE_PATHS`` pins ``self.mlp`` back to fp32 after the + serving bf16 cast, and the body runs under ``autocast(enabled=False)`` on a + ``.float()`` sigma. The four denoise sigmas are close together + (1.0, 0.9, 0.75, 0.3) and this embedding is the only thing that separates + them. ``* 1000`` scales [0, 1] into the Fourier basis's rotating range; + ``* 2**0.5`` restores unit variance after the sin/cos concat. """ - def __init__(self, dim: int, fourier_dim: int = 512, base: float = 10_000.0): + def __init__( + self, + dim: int, + fourier_dim: int = 512, + base: float = 10_000.0, + *, + reference_compat: bool = False, + cached_sigmas: tuple[float, ...] = (), + ): super().__init__() if fourier_dim % 2: raise ValueError(f"NoiseConditioner needs an even fourier_dim; got {fourier_dim}.") self.fourier_dim = fourier_dim - # Derived, not checkpoint state — see the module docstring. device="cpu" - # is required: __init__ runs under the meta-device build context. - self._freq = DeviceTableCache( - torch.logspace(0, -1, steps=fourier_dim // 2, base=base, dtype=torch.float32, device="cpu") + # Derived, not checkpoint state. device="cpu" is required: __init__ runs + # under the meta-device build context. + freq = torch.logspace( + 0, -1, steps=fourier_dim // 2, base=base, dtype=torch.float32, device="cpu" ) + self._freq = DeviceTableCache(bf16_roundtrip(freq) if reference_compat else freq) self.mlp = MLP(fourier_dim, dim * 4, dim) + self.reference_compat = reference_compat + self.cached_sigmas = tuple(float(s) for s in cached_sigmas) + self._lut: dict[torch.device, tuple[torch.Tensor, torch.Tensor, torch.Tensor]] = {} + if reference_compat: + if not self.cached_sigmas: + raise ValueError( + "reference_compat NoiseConditioner needs the sigma schedule to cache." + ) + levels = torch.tensor(self.cached_sigmas, dtype=torch.bfloat16, device="cpu") + if len(set(_bf16_bits(levels).tolist())) != len(self.cached_sigmas): + raise ValueError( + f"scheduler_sigmas {self.cached_sigmas} collide in bf16; the reference's " + "LUT would be ambiguous." + ) + def forward(self, s: torch.Tensor) -> torch.Tensor: - """``s`` is ``[B, N]`` sigma; returns ``[B, N, dim]`` in ``s``'s dtype.""" + """``s`` is ``[B, N]`` sigma; returns ``[B, N, dim]`` in ``s``'s dtype + (bf16 under ``reference_compat``, whose table is stored bf16).""" + if self.reference_compat: + return self._cached_embed(s) + return self._embed(s) + + def materialize(self, device: torch.device | str) -> None: + """Materialize frequency data and the optional post-load sigma LUT.""" + device = torch.device(device) + self._freq.get(device) + if self.reference_compat: + self._reference_lut(device) + + def _embed(self, s: torch.Tensor) -> torch.Tensor: orig_dtype, shape = s.dtype, s.shape (freq,) = self._freq.get(s.device) @@ -200,16 +216,49 @@ def forward(self, s: torch.Tensor) -> torch.Tensor: return emb.to(orig_dtype).view(*shape, -1) + def _cached_embed(self, s: torch.Tensor) -> torch.Tensor: + """The reference's ``CachedDenoiseStepEmb``: read the embedding out of a + bf16 table keyed on sigma's own bf16 bits. A sigma that is not on the + schedule indexes one past the table and raises, rather than returning a + neighbouring row.""" + if s.dtype is not torch.bfloat16: + raise RuntimeError(f"reference_compat NoiseConditioner expects bf16 sigma; got {s.dtype}.") + table, lut, oob = self._reference_lut(s.device) + idx = lut[_bf16_bits(s)] + return table[torch.where(idx >= 0, idx, oob).to(torch.int64)] + + def _reference_lut( + self, device: torch.device + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Build (once per device) the reference's sigma table. + + The batch shape is load-bearing: all ``S`` sigmas in one call is an M=S + GEMM, which under ``float32_matmul_precision('high')`` rounds through + TF32 where the served M=1 GEMV stays exact fp32. Built on first use + because it needs loaded weights, so an eager forward must warm it before + ``compile_regions``. + """ + device = torch.device(device) + if device not in self._lut: + levels = torch.tensor(self.cached_sigmas, dtype=torch.bfloat16, device=device) + with torch.no_grad(): + table = self._embed(levels[:, None]).squeeze(1).to(torch.bfloat16).contiguous() + lut = torch.full((65536,), -1, dtype=torch.int32, device=device) + lut[_bf16_bits(levels)] = torch.arange( + len(self.cached_sigmas), dtype=torch.int32, device=device + ) + oob = torch.tensor(len(self.cached_sigmas), dtype=torch.int32, device=device) + self._lut[device] = (table, lut, oob) + return self._lut[device] + class ControllerInputEmbedding(nn.Module): """Controller state -> one conditioning vector per frame. - The concat order is ``(mouse, button, scroll)``: 2 + n_buttons + 1 = 259 for - this checkpoint. **A permuted order does not raise** — the widths still sum - to 259 and every downstream shape checks out — it just reads the mouse - velocity out of button columns and produces plausible, wrong video. Treat - this ordering as part of the checkpoint, and keep callers passing the three - tensors positionally in this order. + The concat order is ``(mouse, button, scroll)`` -- 2 + n_buttons + 1 = 259 + here -- and it is a checkpoint fact. A permuted order does not raise: the + widths still sum to 259 and every downstream shape checks out, it just reads + mouse velocity out of button columns. Callers pass the three positionally. """ def __init__(self, config: WaypointConfig): @@ -226,18 +275,13 @@ def forward(self, mouse: torch.Tensor, button: torch.Tensor, scroll: torch.Tenso class MLPFusion(nn.Module): """Fuses a per-frame conditioning vector into that frame's tokens. - Nominally ``MLP(2*D, D, D)`` applied to ``cat([x, cond])``, and the - parameter tree says exactly that — ``mlp.fc1`` is one ``[D, 2D]`` matrix, so - ``weight_loader`` has a single key to fill. The *compute* splits it: - ``fc1.weight.chunk(2, dim=1)`` gives the x-half and the cond-half, each - ``[D, D]``, which lets ``cond`` broadcast over the T tokens of its frame - instead of being repeated into a ``[B, N*T, 2D]`` concat. Same arithmetic, - no materialized copy; this is the path the reference actually ships (and - what its ``SplitMLPFusion`` inference patch bakes in). - - The split is at compute time only. Do not turn it into stored ``fc1_x`` / - ``fc1_c`` parameters: the checkpoint stores them split, and the loader's job - is to ``cat(dim=1)`` them back into ``mlp.fc1`` (loader transform 7). + The parameter tree is ``MLP(2*D, D, D)`` over ``cat([x, cond])`` -- one + ``[D, 2D]`` ``mlp.fc1`` for the loader to fill. The compute splits that + matrix instead (``chunk(2, dim=1)``) so ``cond`` broadcasts over the T tokens + of its frame rather than being repeated into a ``[B, N*T, 2D]`` concat. + + That split is compute-time only. Stored ``fc1_x``/``fc1_c`` parameters would + undo transform 7, whose job is to ``cat(dim=1)`` them into ``mlp.fc1``. """ def __init__(self, config: WaypointConfig): @@ -263,29 +307,14 @@ class CondHead(nn.Module): block's six adaLN modulation tensors (scale/shift/gate for attention, then the same three for the MLP). - **The parameters here are split between per-layer and shared, and the split - is the reason the checkpoint is 1.86B on disk but 1.28B resident:** - - * ``bias_in`` — a plain ``[d_model]`` ``nn.Parameter``, genuinely - per-layer, 24 distinct copies. Present only for - ``noise_conditioning == "wan"``. - * ``cond_proj`` — a ``nn.ModuleList`` of 6 bias-free ``[D, D]`` Linears - that is **physically shared across all 24 blocks**. The DiT ties them - after construction by aliasing the ``.weight`` of blocks 1..23 onto - block 0's (reference ``WorldDiT.__init__``), and the loader loads block - 0's copies and drops the other 23 sets. That is why ``cond_proj`` is a - plain ``ModuleList`` of ``nn.Linear`` and nothing cleverer: aliasing - ``blk.cond_head.cond_proj[j].weight = ref.cond_head.cond_proj[j].weight`` - has to remain a one-line assignment. - - **Tie after ``to_empty``, not in ``__init__``.** ``Module._apply`` allocates - per parameter with no cross-module memo, so ``to_empty(device)`` silently - un-aliases tied weights (``.to(dtype)`` on meta does not). Tying only in the - constructor therefore yields 24 independent copies at serve time — no error, - just 0.6B of duplicated resident weights and 23 blocks whose ``cond_proj`` - the loader never fills. Once tied, ``named_parameters()`` deduplicates, so - the loader's completeness check sees block 0's set only, which is what it - assumes. + The per-layer/shared split is why the checkpoint is 1.86B on disk and 1.28B + resident: ``bias_in`` is genuinely per-layer (24 copies, present only for + ``noise_conditioning == "wan"``), while the six ``cond_proj`` Linears are + physically shared across all 24 blocks -- the DiT aliases blocks 1..23's + ``.weight`` onto block 0's and the loader drops the other 23 sets. + + The aliasing is established by ``WaypointDiT.retie_cond_proj``, which must run + after ``to_empty``; see its docstring for what un-ties it. The checkpoint spells this head as two half-heads, ``attn_cond_head`` (indices 0..2) and ``mlp_cond_head`` (indices 3..5), with a ``bias_in`` on @@ -299,8 +328,6 @@ def __init__(self, config: WaypointConfig): if config.noise_conditioning == "wan": self.bias_in = nn.Parameter(torch.zeros(config.d_model)) else: - # register_parameter(None) rather than a bare attribute so the - # `is not None` branch below stays cheap and state_dict stays clean. self.register_parameter("bias_in", None) self.cond_proj = nn.ModuleList( nn.Linear(config.d_model, config.d_model, bias=False) for _ in range(self.n_cond) diff --git a/mstar/model/waypoint/components/rope.py b/mstar/model/waypoint/components/rope.py index c1e731142..54aec931d 100644 --- a/mstar/model/waypoint/components/rope.py +++ b/mstar/model/waypoint/components/rope.py @@ -1,76 +1,27 @@ """OrthoRoPE: Waypoint's orthogonal (x, y, t) rotary position embedding. -Port of ``OrthoRoPEAngles`` and ``OrthoRoPE`` from -``world_engine/src/model/attn.py``. "Ortho" refers to the frequency layout: the -head dim's rotation pairs are partitioned into three disjoint bands, so the x, -y and t coordinates never share a rotation plane and their phases add -independently. - -For ``d_head == 64`` the split is ``d_xy = d_head // 8 = 8`` rotation pairs each -for x and y and ``d_t = d_head // 4 = 16`` pairs for t — **32 pairs, which is -every one of the 64 dims**. Nothing is left unrotated: x owns dims 0..15, y -dims 16..31, t dims 32..63. (Pairs, not dims, is the trap. Reading ``d_xy``/ -``d_t`` as dim counts gives "32 rotated dims, the top 32 untouched", which is -wrong — the reference builds a ``[..., d_head // 2]`` angle table and applies -it to all ``d_head // 2`` pairs. ``CONTRACTS.md`` section 4.3 and ``config.py`` -both stated the wrong reading until this port corrected them.) - -Numerics contract (``docs/waypoint/CONTRACTS.md`` section 4.1): - - * Both classes are fp32 islands — the reference marks them ``NoCastModule``, - i.e. they refuse ``.to(dtype)``. The port does not reproduce that mechanism - (it warns, and it fights ``to_empty``). Instead the arithmetic is - unconditionally fp32: the tables are fp32, the bodies run under - ``autocast(enabled=False)``, and ``OrthoRoPE`` calls ``.float()`` on its - input before rotating and ``.type_as`` on the way out. - * Consequently neither class appears in ``layers.FP32_MODULE_PATHS``: they - hold no parameters and no buffers, so ``.to(torch.bfloat16)`` has nothing to - corrupt and ``cast_serving_dtypes()`` has nothing to pin back. That is not - an accident — the frequency tables are held in a ``DeviceTableCache`` - outside the module tree precisely so that no global cast, and no - ``to_empty(device)``, can reach them. - * The cos/sin tables are DERIVED state, not checkpoint state. As non- - persistent buffers they would survive mstar's meta build as uninitialized - garbage (``to_empty`` allocates, it does not fill, and the loader's - completeness check covers parameters, not buffers). They are built on CPU - at init and copied per device on first use, matching - ``wan22.components.dit.Wan22RoPE3D``. - -The cache stores post-RoPE keys (section 4.2), so replayed history is never -re-rotated; the angles a frame is rotated with are the angles it keeps forever. +``d_xy`` and ``d_t`` count rotation *pairs*, not dims: at ``d_head == 64`` x +owns dims 0..15, y 16..31 and t 32..63, and nothing is left unrotated. The +three bands are disjoint, so the axes' phases add independently. + +Both classes are fp32 islands, made so by running the arithmetic in fp32 rather +than by pinning dtypes: they hold no parameter and no buffer, so neither +appears in ``layers.FP32_MODULE_PATHS``, and their frequency tables live in a +``DeviceTableCache`` outside the module tree. + +The cache stores post-RoPE keys, so replayed history is never re-rotated. """ import torch from torch import nn -from mstar.model.waypoint.components.layers import DeviceTableCache +from mstar.model.waypoint.components.layers import DeviceTableCache, bf16_roundtrip from mstar.model.waypoint.config import WaypointConfig class OrthoRoPEAngles(nn.Module): - """Builds the shared ``(cos, sin)`` angle tables for one forward. - - Lives once under the DiT (not once per block): every block rotates with the - same angles, so the tables are computed once per forward and threaded into - each ``Attn``. - - Frequency layout, verbatim from the reference: - - * spatial — ``(linspace(1.0, max_freq / 2, (d_xy + 1) // 2) * pi)`` - ``.repeat_interleave(2)[:d_xy]``, with - ``max_freq = min(height, width) * rope_nyquist_frac``. The Nyquist cap - is what keeps the highest spatial frequency below one cycle per two - cells on the *shorter* grid axis, so nothing aliases. The - ``repeat_interleave(2)`` makes adjacent rotation pairs share a - frequency; x and y share one table. - * temporal — ``(1 / theta ** (arange(0, d_t, 2) / d_t))`` - ``.repeat_interleave(2)``, the standard NTK-style geometric ladder. - - Positions: x/y are normalized to ``[-1, 1)`` **cell centers** - (``(2 * p + 1) / extent - 1``, so the grid is symmetric about 0 and - resolution-independent), while t stays a raw frame count so that history - beyond the ring is still phase-distinguishable. - """ + """Builds the shared ``(cos, sin)`` angle tables for one forward, once + under the DiT rather than once per block.""" def __init__(self, config: WaypointConfig): super().__init__() @@ -81,9 +32,10 @@ def __init__(self, config: WaypointConfig): raise ValueError(f"OrthoRoPE needs d_head divisible by 8 (x/y/t band split); got {d_head}.") d_xy, d_t = d_head // 8, d_head // 4 - # device="cpu" everywhere below is load-bearing: __init__ runs inside - # `with torch.device("meta")` in mstar's build path, and meta tensors - # carry no data to compute frequencies from. + # device="cpu" is load-bearing: __init__ runs under + # `with torch.device("meta")`, and meta tensors carry no data. + # The Nyquist factor holds the top spatial frequency under one cycle + # per two cells on the *shorter* grid axis. max_freq = min(config.height, config.width) * float(config.rope_nyquist_frac) n = (d_xy + 1) // 2 xy = torch.linspace(1.0, max_freq / 2, n, dtype=torch.float32, device="cpu") * torch.pi @@ -93,24 +45,30 @@ def __init__(self, config: WaypointConfig): inv_t = 1.0 / (theta ** (torch.arange(0, d_t, 2, dtype=torch.float32, device="cpu") / d_t)) inv_t = inv_t.repeat_interleave(2) # [d_t] + if config.reference_compat: + # The reference serves these bf16-quantized; see the config field. + xy, inv_t = bf16_roundtrip(xy), bf16_roundtrip(inv_t) + self._tables = DeviceTableCache(xy, inv_t) + def materialize(self, device: torch.device | str) -> None: + """Copy derived frequency tables before compilation/capture warmup.""" + self._tables.get(torch.device(device)) + def forward( self, x_pos: torch.Tensor, y_pos: torch.Tensor, t_pos: torch.Tensor ) -> tuple[torch.Tensor, torch.Tensor]: - """``x_pos``/``y_pos``/``t_pos`` are ``[B, T]`` integer position grids; - returns ``(cos, sin)``, each ``[B, 1, T, d_head // 2]`` fp32 (the head - axis is a broadcast singleton). + """``[B, T]`` integer position grids -> ``(cos, sin)``, each + ``[B, 1, T, d_head // 2]`` fp32 with a broadcast head axis. - ``t_pos`` is the RoPE clock and is NOT the ring clock ``f_pos``; they - happen to be equal for this checkpoint (``ts_mult == 1``) and diverge - for any other serving fps. Section 4.4. + ``t_pos`` is the RoPE clock, not the ring clock ``f_pos``; they are + equal for this checkpoint (``ts_mult == 1``) and diverge at any other + serving fps. """ xy, inv_t = self._tables.get(x_pos.device) if not torch.compiler.is_compiling(): - # Out-of-range positions produce wrapped phases rather than an - # index error, so this is checked rather than trusted. + # Out-of-range positions wrap the phase instead of raising. torch._assert( (y_pos.max() < self.config.height) & (x_pos.max() < self.config.width), f"pos_ids out of bounds, {self.config.height}, {self.config.width}", @@ -122,8 +80,7 @@ def forward( y = (2.0 * y_pos.float() + 1.0) / self.config.height - 1.0 t = t_pos.float() - # x and y share the `xy` table; the bands are disjoint slices of the - # angle vector, which is what makes the three axes orthogonal. + # x and y share `xy`; the disjoint slices make the axes orthogonal. freqs = torch.cat( (x.unsqueeze(-1) * xy, y.unsqueeze(-1) * xy, t.unsqueeze(-1) * inv_t), dim=-1, # [B, T, d_head // 2] @@ -134,30 +91,10 @@ def forward( def apply_ortho_rope(x: torch.Tensor, rope_angles: tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor: """Rotate ``x`` ``[B, H, T, d_head]`` by the ``(cos, sin)`` tables. - **This is the interleaved-pair form with a concatenated output, and the - asymmetry is deliberate.** Pairs are read interleaved — ``unfold(-1, 2, 2)`` - splits the head into ``(x[..., 0::2], x[..., 1::2])`` — but the two rotated - halves are written back with ``cat``, so result ``i`` of the even stream - lands at output dim ``i`` and result ``i`` of the odd stream at - ``i + d_head // 2``. The output is therefore a *permutation* of the - conventional interleaved layout, not the interleaved layout itself. - - Rewriting this as an in-place interleave (``out[..., 0::2] = ...``) is the - natural "fix". Do not do it — but the reason is narrower than it looks, and - an earlier version of this docstring overstated it as "a scrambled head". - - Measured: the interleave rewrite changes the cached K by **5.6** and the - model output by **7.2e-07**, i.e. the noise floor. It is invisible at the - output because q and k receive the *same* head-dim permutation and V is - never rotated, so the permutation cancels inside ``q @ k.T``. What it is - NOT invisible to is anything that reads K or Q directly: a parity harness - diffing cache contents, a quantizer with per-channel scales, a tensor- - parallel head split, or any future consumer of the stored keys. The port - keeps the reference's layout so that stored state is comparable - bit-for-bit, not because attention would break. See CONTRACTS section 4.3. - - fp32 island: ``x`` is upcast before the rotation and cast back at the end, - so the trig math never runs in bf16 regardless of the ambient autocast. + The output is a permutation of the interleaved layout (``unfold`` in, + ``cat`` out); it cancels inside ``q @ k.T``, and is kept so that stored K/Q + compare bit-for-bit with the reference. fp32 island: ``x`` is upcast for + the rotation and cast back after. """ cos, sin = rope_angles with torch.amp.autocast("cuda", enabled=False): @@ -168,14 +105,9 @@ def apply_ortho_rope(x: torch.Tensor, rope_angles: tuple[torch.Tensor, torch.Ten class OrthoRoPE(nn.Module): - """Module wrapper around :func:`apply_ortho_rope`. - - Stateless — no parameters, no buffers — but kept as an ``nn.Module`` and - constructed per attention layer (``self.rope = OrthoRoPE(config)``) because - that is the reference's shape and the attention port calls it as - ``self.rope(q, rope_angles)``. ``config`` is accepted and stored for that - call-site parity; the reference ignores it here too. - """ + """Stateless module wrapper around :func:`apply_ortho_rope`, constructed + per attention layer to match the reference's call site. ``config`` is + stored for that parity and otherwise unused.""" def __init__(self, config: WaypointConfig | None = None): super().__init__() diff --git a/mstar/model/waypoint/components/taehv.py b/mstar/model/waypoint/components/taehv.py new file mode 100644 index 000000000..6bd89c396 --- /dev/null +++ b/mstar/model/waypoint/components/taehv.py @@ -0,0 +1,402 @@ +import logging +import math +import pathlib +from collections.abc import Sequence + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from mstar.model.waypoint.checkpoint import TAEHV_UPSTREAM_ARCHIVE + +logger = logging.getLogger(__name__) + +# The AE runs on a fixed grid per source resolution: a 720p frame is resized to +# 512x1024 on the way in and the decode is resized back out. +_ENCODE_SIZES = {(720, 1280): (512, 1024), (360, 640): (256, 512)} +_DECODE_SIZES = {v: k for k, v in _ENCODE_SIZES.items()} + +# ``TAEHV.__init__`` reads patch_size and latent_channels off the checkpoint +# *filename*: "taehv1_5" means (2, 32), anything else falls back to (1, 16) and +# the state_dict load then fails on shape. Pinned, not globbed. +_CHECKPOINT_NAME = "taehv1_5.pth" + +DECODER_HISTORY_PREFIX = "decoder_history_" + + +def encoded_size_for_latent(latent_height: int, latent_width: int) -> tuple[int, int]: + encoded = (int(latent_height) * 16, int(latent_width) * 16) + if encoded not in _DECODE_SIZES: + raise ValueError(f"unsupported Waypoint TAEHV latent grid {latent_height}x{latent_width}") + return encoded + + +def pixel_size_for_latent(latent_height: int, latent_width: int) -> tuple[int, int]: + return _DECODE_SIZES[encoded_size_for_latent(latent_height, latent_width)] + + +def load_taehv(ae_uri: str, cache_dir: str | None = None) -> nn.Module: + """Load the shared weights module from a resolved local checkpoint. + + Hub resolution belongs to ``checkpoint.resolve_taehv_checkpoint`` so only + the required file is downloaded and missing artifacts fail before allocation. + ``cache_dir`` remains accepted for compatibility with existing direct + callers, but is intentionally unused here. + """ + del cache_dir + try: + from taehv import TAEHV + except ImportError as exc: + raise RuntimeError( + "Waypoint requires the pinned TAEHV implementation in addition to " + "the index-safe `.[waypoint]` extra. Install it separately with " + "`uv pip install --no-deps " + f"'taehv @ {TAEHV_UPSTREAM_ARCHIVE}'`; use uv>=0.4.0 or pip>=24.3." + ) from exc + + base = pathlib.Path(ae_uri) + checkpoint = base if base.is_file() else base / _CHECKPOINT_NAME + if not checkpoint.is_file(): + raise FileNotFoundError( + f"No TAEHV checkpoint for ae_uri={ae_uri!r}; looked for {checkpoint}." + ) + return TAEHV(str(checkpoint)).eval() + + +def _block_kind(block: nn.Module) -> str: + """Return the three stateful TAEHV block kinds without importing taehv. + + The optional dependency must stay deferred until weights are requested. + These class names are part of the pinned upstream revision and are checked + again by :func:`validate_taehv_architecture` at model startup. + """ + return type(block).__name__ + + +def validate_taehv_architecture(ae_model: nn.Module) -> None: + """Validate the fixed architecture the captured functional path supports.""" + encoder_kinds = [_block_kind(block) for block in ae_model.encoder] + decoder_kinds = [_block_kind(block) for block in ae_model.decoder] + facts = { + "patch_size": getattr(ae_model, "patch_size", None), + "latent_channels": getattr(ae_model, "latent_channels", None), + "t_downscale": getattr(ae_model, "t_downscale", None), + "t_upscale": getattr(ae_model, "t_upscale", None), + "frames_to_trim": getattr(ae_model, "frames_to_trim", None), + "encoder_memblocks": encoder_kinds.count("MemBlock"), + "decoder_memblocks": decoder_kinds.count("MemBlock"), + } + expected = { + "patch_size": 2, + "latent_channels": 32, + "t_downscale": 4, + "t_upscale": 4, + "frames_to_trim": 3, + "encoder_memblocks": 9, + "decoder_memblocks": 9, + } + mismatches = { + key: (facts[key], value) for key, value in expected.items() + if facts[key] != value + } + if mismatches: + detail = ", ".join( + f"{key}={actual!r} (expected {wanted!r})" + for key, (actual, wanted) in mismatches.items() + ) + raise ValueError( + "The installed TAEHV/checkpoint architecture is not Waypoint-1.5 " + f"compatible: {detail}. Install the pinned TAEHV source from " + f"{TAEHV_UPSTREAM_ARCHIVE} separately and use {_CHECKPOINT_NAME}." + ) + + +def _apply_encoder_sequence(model: nn.Sequential, frames: Tensor) -> Tensor: + """Pinned TAEHV's sequential encoder with all state local to this call. + + ``frames`` is NTCHW after pixel unshuffle. The four-frame seed is a fixed + clip, so temporal-pool queues and MemBlock histories can be represented as + local tensor lists and no encoder state survives the prime. + """ + sequence = list(frames.unbind(1)) + for block in model: + kind = _block_kind(block) + if kind == "MemBlock": + past = torch.zeros_like(sequence[0]) + next_sequence = [] + for current in sequence: + next_sequence.append(block(current, past)) + past = current + sequence = next_sequence + elif kind == "TPool": + stride = int(block.stride) + if len(sequence) % stride: + raise ValueError( + f"encoder sequence length {len(sequence)} is not divisible by " + f"TPool stride {stride}" + ) + next_sequence = [] + for start in range(0, len(sequence), stride): + chunk = sequence[start : start + stride] + batch, channels, height, width = chunk[0].shape + joined = torch.cat(chunk, dim=1).view( + batch * stride, channels, height, width + ) + next_sequence.append(block(joined)) + sequence = next_sequence + elif kind == "TGrow": + raise ValueError("TAEHV encoder unexpectedly contains TGrow") + else: + sequence = [block(value) for value in sequence] + return torch.stack(sequence, dim=1) + + +def encode_seed_clip( + ae_model: nn.Module, + frames: Tensor, + *, + output_size: tuple[int, int], +) -> Tensor: + """Encode one fixed four-frame RGB clip into one latent tensor. + + The operation is pure with respect to ``ae_model``: only its parameters are + read, and every temporal history is created and consumed within this call. + """ + expected = int(ae_model.t_downscale) + if frames.ndim != 4 or frames.shape[0] != expected or frames.shape[-1] != 3: + raise ValueError( + f"expected [{expected}, H, W, 3] RGB frames, got {tuple(frames.shape)}" + ) + rgb = frames.unsqueeze(0).permute(0, 1, 4, 2, 3).contiguous() + rgb = F.interpolate( + rgb[0], size=output_size, mode="bilinear", align_corners=False + )[None] + rgb = ae_model.preprocess_input_frames(rgb) + latent = _apply_encoder_sequence(ae_model.encoder, rgb) + if latent.shape[1] != 1: + raise RuntimeError( + f"four-frame TAEHV prime produced {latent.shape[1]} latents, expected one" + ) + return latent.squeeze(1) + + +def _conv_output_size(size: int, block: nn.Conv2d, dim: int) -> int: + kernel = block.kernel_size[dim] + stride = block.stride[dim] + padding = block.padding[dim] + dilation = block.dilation[dim] + return math.floor((size + 2 * padding - dilation * (kernel - 1) - 1) / stride + 1) + + +def decoder_history_shapes( + ae_model: nn.Module, + *, + batch_size: int, + latent_height: int, + latent_width: int, +) -> tuple[tuple[int, int, int, int], ...]: + """The nine fixed MemBlock input shapes for one decoder request.""" + validate_taehv_architecture(ae_model) + height, width = int(latent_height), int(latent_width) + channels = int(ae_model.latent_channels) + shapes: list[tuple[int, int, int, int]] = [] + for block in ae_model.decoder: + kind = _block_kind(block) + if kind == "MemBlock": + block_channels = int(block.conv[0].in_channels // 2) + if channels != block_channels: + raise ValueError( + f"decoder MemBlock expects {block_channels} channels after " + f"a {channels}-channel block" + ) + shapes.append((batch_size, channels, height, width)) + elif kind == "TGrow": + continue + elif isinstance(block, nn.Conv2d): + height = _conv_output_size(height, block, 0) + width = _conv_output_size(width, block, 1) + channels = int(block.out_channels) + elif isinstance(block, nn.Upsample): + scale = block.scale_factor + scale_h, scale_w = ( + (float(scale), float(scale)) + if isinstance(scale, (int, float)) else map(float, scale) + ) + height, width = int(height * scale_h), int(width * scale_w) + if len(shapes) != 9: + raise ValueError(f"Waypoint decoder needs nine histories; derived {len(shapes)}") + return tuple(shapes) + + +def initial_decoder_histories( + ae_model: nn.Module, latent: Tensor, +) -> tuple[Tensor, ...]: + shapes = decoder_history_shapes( + ae_model, + batch_size=int(latent.shape[0]), + latent_height=int(latent.shape[-2]), + latent_width=int(latent.shape[-1]), + ) + return tuple(latent.new_zeros(shape) for shape in shapes) + + +def _apply_decoder_sequence( + model: nn.Sequential, + latent: Tensor, + histories: Sequence[Tensor], +) -> tuple[Tensor, tuple[Tensor, ...]]: + """Decode one latent while explicitly threading every MemBlock history.""" + sequence = [latent] + next_histories: list[Tensor] = [] + history_idx = 0 + for block in model: + kind = _block_kind(block) + if kind == "MemBlock": + past = histories[history_idx] + next_sequence = [] + for current in sequence: + next_sequence.append(block(current, past)) + past = current + next_histories.append(past) + history_idx += 1 + sequence = next_sequence + elif kind == "TGrow": + stride = int(block.stride) + next_sequence = [] + for current in sequence: + batch = current.shape[0] + grown = block(current) + next_sequence.extend(grown.view(batch, stride, *grown.shape[1:]).unbind(1)) + sequence = next_sequence + elif kind == "TPool": + raise ValueError("TAEHV decoder unexpectedly contains TPool") + else: + sequence = [block(value) for value in sequence] + if history_idx != len(histories): + raise ValueError( + f"decoder consumed {history_idx} histories, received {len(histories)}" + ) + return torch.stack(sequence, dim=1), tuple(next_histories) + + +def decode_latent( + ae_model: nn.Module, + latent: Tensor, + histories: Sequence[Tensor], + *, + output_size: tuple[int, int], + initialize: bool, +) -> tuple[Tensor, tuple[Tensor, ...]]: + """Pure decoder initialization or steady-state step. + + Initialization reproduces ``ChunkedStreamingTAEHV.decode`` exactly: the + seed latent is fed ``frames_to_trim`` extra times, those reconstructed clips + are discarded, and a final feed advances the state through the seed frame. + The caller intentionally emits none of those frames. + """ + state = tuple(histories) + feeds = int(ae_model.frames_to_trim) + 1 if initialize else 1 + decoded = None + for _ in range(feeds): + decoded, state = _apply_decoder_sequence(ae_model.decoder, latent, state) + decoded = ae_model.postprocess_output_frames(decoded) + expected_frames = int(ae_model.t_upscale) + if decoded.shape[1] != expected_frames: + raise ValueError( + "TAEHV decoder returned " + f"{decoded.shape[1]} frames for one latent; expected {expected_frames}." + ) + decoded = F.interpolate( + decoded[0], size=output_size, mode="bilinear", align_corners=False + )[None] + frames = (decoded.clamp(0, 1) * 255).round().to(torch.uint8) + frames = frames.squeeze(0).permute(0, 2, 3, 1)[..., :3].contiguous() + return frames, state + + +class ChunkedStreamingTAEHV: + """One streaming session: this request's frames, in order. + + The order-dependent state lives here; ``ae_model`` is shared and never + mutated, so a fresh instance is the reference's ``reset()``. Not an + ``nn.Module``: the module tree would give ``state_dict()``, ``to()`` and the + weight loader a second path to the same parameters. + """ + + def __init__( + self, + ae_model: nn.Module, + auto_aspect_ratio: bool = True, + device: torch.device | None = None, + dtype: torch.dtype = torch.bfloat16, + height: int | None = None, + width: int | None = None, + ): + from taehv import StreamingTAEHV + + self.device = device + self.dtype = dtype + self.auto_aspect_ratio = auto_aspect_ratio + scale = ae_model.patch_size * 2 ** sum( + getattr(m, "stride", None) == (2, 2) for m in ae_model.encoder + ) + # height/width are the LATENT grid; _img_size is the pixel resolution + # encoded from and decoded back to, or None to read it per call. + self._img_size = ( + None if height is None else _DECODE_SIZES[(height * scale, width * scale)] + ) + # The reference places the weights here; they are shared, so the node + # that owns them placed them already. + self.streaming_ae_model = StreamingTAEHV(ae_model) + + def _resize(self, x: Tensor, size: tuple[int, int]) -> Tensor: + return F.interpolate(x[0], size=size, mode="bilinear", align_corners=False)[None] + + @torch.inference_mode() + def encode(self, frames: Tensor) -> Tensor: + """``[t_downscale, H, W, 3]`` in [0, 1] -> one latent ``[B, C, h, w]``. + Exactly that many frames: fewer buffer and return None. The reference + scales from uint8 here; this takes it pre-scaled in the same + cast-then-divide order, so the two agree bit for bit.""" + t = self.streaming_ae_model.taehv.t_downscale + if frames.dim() != 4 or frames.shape[0] != t or frames.shape[-1] != 3: + raise ValueError( + f"expected [{t}, H, W, 3] RGB frames, got {tuple(frames.shape)}." + ) + rgb = frames.unsqueeze(0).to(device=self.device, dtype=self.dtype) + rgb = rgb.permute(0, 1, 4, 2, 3).contiguous() + if self.auto_aspect_ratio: + if frames.shape[1] * 16 != frames.shape[2] * 9: + raise ValueError(f"Expected 16:9 input, got {tuple(frames.shape[1:3])}.") + rgb = self._resize(rgb, _ENCODE_SIZES[self._img_size or tuple(frames.shape[1:3])]) + latent = self.streaming_ae_model.encode(rgb) + assert latent is not None, ( + f"the streaming encoder buffered {t} frames without emitting a latent" + ) + return latent.squeeze(1) + + @torch.inference_mode() + def decode(self, latent: Tensor) -> Tensor: + """One latent ``[B, C, h, w]`` -> ``[t_upscale, H, W, 3]`` uint8. + + Single-use and order-dependent: the temporal memory advances per call, + so a latent decoded twice, out of turn or not at all shifts every frame + after it, silently. The first call primes that memory with + ``frames_to_trim`` extra feeds, which is why the prime walk decodes. + """ + if latent.dim() != 4: + raise ValueError(f"expected a [B, C, h, w] latent, got {tuple(latent.shape)}.") + z = latent.unsqueeze(1).to(device=self.device, dtype=self.dtype) + if self.streaming_ae_model.n_frames_decoded == 0: + for _ in range(self.streaming_ae_model.taehv.frames_to_trim): + self.streaming_ae_model.decode(z) + self.streaming_ae_model.flush_decoder() + first = self.streaming_ae_model.decode(z) + assert first is not None, "the streaming decoder returned no frame for a latent" + decoded = torch.cat([first, *self.streaming_ae_model.flush_decoder()], dim=1) + if self.auto_aspect_ratio: + decoded = self._resize( + decoded, self._img_size or _DECODE_SIZES[tuple(decoded.shape[-2:])] + ) + decoded = (decoded.clamp(0, 1) * 255).round().to(torch.uint8) + return decoded.squeeze(0).permute(0, 2, 3, 1)[..., :3] diff --git a/mstar/model/waypoint/config.py b/mstar/model/waypoint/config.py index fc8f9e066..c052040e1 100644 --- a/mstar/model/waypoint/config.py +++ b/mstar/model/waypoint/config.py @@ -1,40 +1,46 @@ """Configuration for Waypoint-1.5 (autoregressive video world model). -The values here are facts of the ``Overworld/Waypoint-1.5-1B`` checkpoint's -``config.yaml``, hardcoded so that constructing the model never touches the -network. The reference implementation reads that YAML through OmegaConf with -``MODEL_CONFIG_DEFAULTS`` merged underneath; this dataclass is the merged -result, with the defaults that actually matter spelled out. - -Waypoint is not a diffusion pipeline that happens to run several times. It is a -*world model*: one 1.28B DiT denoises exactly one latent frame per step, and the -KV cache IS the world state rather than an optimization over it. Two facts -follow and drive most of this file: - - * Attention geometry is per-layer heterogeneous. 18 layers attend densely over - a 16-frame local window; 6 attend over a 128-frame window subsampled at - stride 8. See ``global_layers`` / ``ring_frames``. - * Every frame costs 5 forwards: 4 non-committing Euler denoise passes over - ``scheduler_sigmas``, then 1 committing pass at sigma=0 that writes the - settled K/V into the ring. +The values here are facts of the published 720P and 360P checkpoint manifests, +hardcoded so that constructing the model never touches the network. The +reference reads those YAML files through OmegaConf with +``MODEL_CONFIG_DEFAULTS`` merged underneath; this dataclass is the merged result. + +One 1.28B DiT denoises exactly one latent frame per step and the KV cache IS the +world state, so attention geometry is per-layer heterogeneous (18 layers over a +16-frame local window, 6 over a 128-frame window at stride 8; see +``global_layers`` / ``ring_frames``) and every frame costs 5 forwards: 4 +non-committing Euler passes over ``scheduler_sigmas``, then 1 committing pass at +sigma=0 that writes the settled K/V into the ring. """ +import math from dataclasses import dataclass, field -# The 720P checkpoint is the one this port implements end to end. The 360P -# sibling differs only in the token grid (see ``waypoint_1_5_1b_360p``). WAYPOINT_VARIANT_720P = "waypoint-1.5-1b-720p" WAYPOINT_VARIANT_360P = "waypoint-1.5-1b-360p" +WAYPOINT_VARIANT_HF_REPOS: dict[str, str] = { + WAYPOINT_VARIANT_720P: "Overworld/Waypoint-1.5-1B", + WAYPOINT_VARIANT_360P: "Overworld/Waypoint-1.5-1B-360P", +} + +WAYPOINT_SCHEDULER_SIGMAS = (1.0, 0.9, 0.75, 0.3, 0.0) + +# Geometry is the only manifest-field difference between the two deployment +# configs; the repositories still hold variant-specific weights. Startup +# validation reads this rather than a downloaded checkpoint. +WAYPOINT_VARIANT_GEOMETRY: dict[str, tuple[int, int, int]] = { + WAYPOINT_VARIANT_720P: (512, 16, 32), + WAYPOINT_VARIANT_360P: (128, 8, 16), +} + @dataclass class WaypointConfig: """Waypoint-1.5-1B model configuration. - Field names track the reference ``config.yaml`` keys rather than mstar's - usual spellings. That is deliberate: the checkpoint's YAML is the ground - truth a reviewer will diff this against, and renaming ``d_model`` to - ``hidden_size`` buys nothing but a translation step during review. + Field names track the reference ``config.yaml`` keys, not mstar's usual + spellings, so this diffs directly against the checkpoint's YAML. """ variant: str = WAYPOINT_VARIANT_720P @@ -57,12 +63,10 @@ class WaypointConfig: patch: tuple[int, int] = (2, 2) # ---- Attention geometry ----------------------------------------------- - # Layer i is "global" iff (i - global_attn_offset % period) % period == 0. - # With offset=-1, period=4 that is {3, 7, 11, 15, 19, 23}; the other 18 are - # local. Local layers see local_window consecutive frames. Global layers see - # global_window frames subsampled at stride global_pinned_dilation, i.e. - # global_window // global_pinned_dilation == 16 retained frames spanning - # 128 frames of history. + # Layer i is "global" iff (i - global_attn_offset % period) % period == 0: + # {3, 7, 11, 15, 19, 23}. Local layers see local_window consecutive frames; + # global layers see global_window frames at stride global_pinned_dilation, + # i.e. 16 retained frames spanning 128 frames of history. local_window: int = 16 global_window: int = 128 global_pinned_dilation: int = 8 @@ -70,11 +74,10 @@ class WaypointConfig: global_attn_offset: int = -1 # ---- RoPE ------------------------------------------------------------- - # OrthoRoPE splits the head into disjoint axis slices. d_head//8 rotation - # PAIRS go to x, d_head//8 to y, d_head//4 to t -- 8+8+16 = 32 pairs for - # d_head=64, i.e. the whole head. x owns dims 0-15, y 16-31, t 32-63; - # nothing is left unrotated. (The counts are pairs, not dims: the angle - # table is d_head//2 wide and unfold(-1, 2, 2) pairs the head up.) + # OrthoRoPE splits the head into disjoint axis slices: d_head//8 rotation + # PAIRS to x, d_head//8 to y, d_head//4 to t -- 8+8+16 = 32 pairs for + # d_head=64, so x owns dims 0-15, y 16-31, t 32-63 and nothing is left + # unrotated. The counts are pairs, not dims. rope_impl: str = "ortho" rope_nyquist_frac: float = 0.8 rope_theta: float = 10000.0 @@ -94,10 +97,9 @@ class WaypointConfig: n_buttons: int = 256 # ---- Sampling --------------------------------------------------------- - # 5 entries -> 4 Euler steps (zip(sigmas, sigmas.diff())) -> then one - # separate committing pass at sigma=0. Not a per-request knob: the cached - # sigma/cond tables in the reference's inference patches are keyed on it. - scheduler_sigmas: tuple[float, ...] = (1.0, 0.9, 0.75, 0.3, 0.0) + # 5 entries -> 4 Euler steps -> one separate committing pass at sigma=0. + # Not a per-request knob: the reference's cached sigma/cond tables key on it. + scheduler_sigmas: tuple[float, ...] = WAYPOINT_SCHEDULER_SIGMAS # ---- Temporal --------------------------------------------------------- base_fps: int = 15 # fps the RoPE time axis was trained against @@ -112,27 +114,34 @@ class WaypointConfig: # ---- Port-local knobs (NOT checkpoint facts) -------------------------- # The reference allocates global-layer ring storage as - # ``global_window * tokens_per_frame`` tokens but can only ever address - # ``global_window // global_pinned_dilation`` frame slots, so 7/8 of that - # buffer is permanently unwritten and permanently masked off. Compacting it - # is bit-exact -- unwritten blocks are absent from the BlockMask, and the - # stable argsort that orders the visited blocks is unaffected by trailing - # False entries -- and saves ~1.35 GiB. Set True to restore the reference's - # allocation for an A/B parity run. + # ``global_window * tokens_per_frame`` tokens but can only address + # ``global_window // global_pinned_dilation`` frame slots, so 7/8 of it is + # permanently unwritten and masked off. Compacting it is bit-exact and saves + # ~1.35 GiB; True restores the reference's allocation for an A/B parity run. full_global_ring: bool = False + # The reference's ``NoCastModule._apply`` casts every tensor it holds to the + # requested dtype and back, so its derived fp32 tables (``rope_angles.xy``/ + # ``inv_t``, ``denoise_step_emb.freq``) are served bf16-quantized — + # parameters recover from ``load_state_dict``, non-persistent buffers never + # do — and its cached sigma LUT rounds the same way through a TF32 batch-5 + # GEMM the served per-sigma GEMV avoids. True reproduces that rounding, which + # is what the live parity gate compares against; False serves exact tables + # and deliberately diverges from the released reference. + reference_compat: bool = True + # torch.compile the two OUTER regions (denoise pass, cache pass), matching # the reference's two @torch.compile(fullgraph=True, dynamic=False) sites. - # - # This is a throughput knob only. It does NOT govern attention correctness: - # the BlockMask carries a no-op mask_mod, so eager flex_attention ignores it and - # attends to unwritten ring slots (measured: 2.7e-01 off a masked-dense - # reference, silently). The FLEX attention resource - # (engine/resources/attn/flex.py) pins its own torch.compile around the - # flex_attention call for that reason. Unlike wan22, this model has no - # eager reference-equivalence mode. + # Independent of CUDA graph capture, and of the masked FlexAttention + # primitive, which stays compiled for correctness (engine/resources/attn/flex.py). compile_dit: bool = True + # Attempt fixed-shape CUDA graph capture for the encoder prime, steady DiT + # rollout, and decoder prime/rollout paths. An optimization: disabled + # declares no buckets, and a failed capture falls back to eager submodule + # forwards. + cuda_graph: bool = True + # Guard rails the ported modules assert against, kept here so a drifting # checkpoint fails loudly at construction rather than silently mis-serving. _supported_rope_impls: tuple[str, ...] = field( @@ -160,6 +169,33 @@ def __post_init__(self) -> None: "WaypointDiT does not implement prompt cross-attention; this " f"checkpoint declares prompt_conditioning={self.prompt_conditioning!r}." ) + positive_ints = { + "n_layers": self.n_layers, + "n_heads": self.n_heads, + "n_kv_heads": self.n_kv_heads, + "d_model": self.d_model, + "mlp_ratio": self.mlp_ratio, + "channels": self.channels, + "tokens_per_frame": self.tokens_per_frame, + "height": self.height, + "width": self.width, + "local_window": self.local_window, + "global_window": self.global_window, + "global_pinned_dilation": self.global_pinned_dilation, + "global_attn_period": self.global_attn_period, + "ctrl_conditioning_period": self.ctrl_conditioning_period, + "n_buttons": self.n_buttons, + "base_fps": self.base_fps, + "inference_fps": self.inference_fps, + "temporal_compression": self.temporal_compression, + "max_frames": self.max_frames, + } + invalid = [name for name, value in positive_ints.items() if type(value) is not int or value <= 0] + if invalid: + values = ", ".join(f"{name}={positive_ints[name]!r}" for name in invalid) + raise ValueError(f"Waypoint positive integer fields are invalid: {values}.") + if len(self.patch) != 2 or any(type(size) is not int or size <= 0 for size in self.patch): + raise ValueError(f"patch must contain two positive integers; got {self.patch!r}.") if self.tokens_per_frame != self.height * self.width: raise ValueError( f"tokens_per_frame ({self.tokens_per_frame}) must equal " @@ -174,11 +210,101 @@ def __post_init__(self) -> None: f"n_heads ({self.n_heads}) must be divisible by n_kv_heads " f"({self.n_kv_heads}) for GQA." ) + if self.d_head % 8: + raise ValueError( + f"OrthoRoPE requires d_head ({self.d_head}) to be divisible by 8." + ) if self.global_window % self.global_pinned_dilation: raise ValueError( f"global_window ({self.global_window}) must be divisible by " f"global_pinned_dilation ({self.global_pinned_dilation})." ) + if self.inference_fps % self.temporal_compression: + raise ValueError( + f"inference_fps ({self.inference_fps}) must be divisible by " + f"temporal_compression ({self.temporal_compression})." + ) + latent_fps = self.inference_fps // self.temporal_compression + if self.base_fps % latent_fps: + raise ValueError( + f"base_fps ({self.base_fps}) must be divisible by latent fps " + f"({self.inference_fps}/{self.temporal_compression}={latent_fps})." + ) + sigmas = tuple(float(value) for value in self.scheduler_sigmas) + if len(sigmas) < 2 or not all(math.isfinite(value) for value in sigmas): + raise ValueError( + f"scheduler_sigmas must contain at least two finite values; got {self.scheduler_sigmas!r}." + ) + pairs = zip(sigmas[:-1], sigmas[1:], strict=True) + if sigmas[-1] != 0.0 or any(left <= right for left, right in pairs): + raise ValueError( + "scheduler_sigmas must be strictly descending and end at 0.0; " + f"got {self.scheduler_sigmas!r}." + ) + + def validate_supported_deployment(self) -> None: + """Reject a config that is internally valid but not a released shape. + + Separate from ``__post_init__`` because unit tests construct smaller, + internally consistent models. Startup calls it before allocating storage. + """ + expected_geometry = WAYPOINT_VARIANT_GEOMETRY.get(self.variant) + if expected_geometry is None: + raise ValueError( + f"Unsupported Waypoint variant {self.variant!r}; expected one of " + f"{sorted(WAYPOINT_VARIANT_GEOMETRY)}." + ) + actual_geometry = (self.tokens_per_frame, self.height, self.width) + if actual_geometry != expected_geometry: + raise ValueError( + f"Waypoint variant {self.variant!r} requires " + f"(tokens_per_frame, height, width)={expected_geometry}; got {actual_geometry}." + ) + checkpoint_facts = { + "n_layers": (self.n_layers, 24), + "n_heads": (self.n_heads, 32), + "n_kv_heads": (self.n_kv_heads, 16), + "d_model": (self.d_model, 2048), + "mlp_ratio": (self.mlp_ratio, 4), + "channels": (self.channels, 32), + "patch": (self.patch, (2, 2)), + "local_window": (self.local_window, 16), + "global_window": (self.global_window, 128), + "global_pinned_dilation": (self.global_pinned_dilation, 8), + "global_attn_period": (self.global_attn_period, 4), + "global_attn_offset": (self.global_attn_offset, -1), + "rope_impl": (self.rope_impl, "ortho"), + "rope_nyquist_frac": (self.rope_nyquist_frac, 0.8), + "rope_theta": (self.rope_theta, 10_000.0), + "noise_conditioning": (self.noise_conditioning, "wan"), + "value_residual": (self.value_residual, True), + "gated_attn": (self.gated_attn, False), + "moe": (self.moe, False), + "prompt_conditioning": (self.prompt_conditioning, None), + "ctrl_conditioning": (self.ctrl_conditioning, True), + "ctrl_cond_dropout": (self.ctrl_cond_dropout, 0.0), + "ctrl_conditioning_period": (self.ctrl_conditioning_period, 3), + "n_buttons": (self.n_buttons, 256), + "scheduler_sigmas": (tuple(self.scheduler_sigmas), WAYPOINT_SCHEDULER_SIGMAS), + "base_fps": (self.base_fps, 15), + "inference_fps": (self.inference_fps, 60), + "temporal_compression": (self.temporal_compression, 4), + "max_frames": (self.max_frames, 512), + "taehv_ae": (self.taehv_ae, True), + "ae_uri": (self.ae_uri, "Overworld-Models/taehv1_5"), + "auto_aspect_ratio": (self.auto_aspect_ratio, True), + } + mismatches = [ + f"{name}={actual!r} (expected {expected!r})" + for name, (actual, expected) in checkpoint_facts.items() + if actual != expected + ] + if mismatches: + raise ValueError( + "Waypoint deployment config disagrees with the released checkpoint: " + + "; ".join(mismatches) + + "." + ) # ---- Derived ---------------------------------------------------------- @@ -193,12 +319,8 @@ def enable_gqa(self) -> bool: @property def d_ctrl_in(self) -> int: - """Controller feature width: mouse(2) + button(n_buttons) + scroll(1). - - The concat order in ``ControllerInputEmbedding.forward`` is - ``(mouse, button, scroll)``. Getting it wrong does not raise -- the - widths still sum to 259 -- it just produces plausible wrong video. - """ + """Controller feature width: mouse(2) + button(n_buttons) + scroll(1), + concatenated in that order by ``ControllerInputEmbedding.forward``.""" return self.n_buttons + 3 @property @@ -220,12 +342,10 @@ def latent_shape(self) -> tuple[int, int, int]: def ts_mult(self) -> int: """RoPE time-axis stride per latent frame. - ``base_fps // (inference_fps // temporal_compression)`` = 15 // 15 = 1 - for this checkpoint, so the RoPE clock ``t_pos`` and the ring-bucketing - clock ``f_pos`` are numerically equal. They are still threaded through - the model as two separate values: a checkpoint served at a different - inference_fps would separate them, and conflating them there is a - silent-drift bug rather than a crash. + ``base_fps // (inference_fps // temporal_compression)`` = 1 here, so the + RoPE clock ``t_pos`` and the ring-bucketing clock ``f_pos`` are equal. + They stay two separate values through the model: another inference_fps + separates them, and conflating them drifts silently rather than crashing. """ return self.base_fps // (self.inference_fps // self.temporal_compression) @@ -238,8 +358,8 @@ def num_denoise_steps(self) -> int: def global_layers(self) -> frozenset[int]: """Layer indices attending over the dilated 128-frame window. - ``{3, 7, 11, 15, 19, 23}`` -- note the offset is applied modulo the - period first, so offset=-1 means "the last layer of each period". + ``{3, 7, 11, 15, 19, 23}``: the offset is taken modulo the period first, + so offset=-1 means "the last layer of each period". """ period = self.global_attn_period off = self.global_attn_offset % period @@ -284,10 +404,9 @@ def ring_buckets(self, layer_idx: int) -> int: def kv_capacity(self, layer_idx: int) -> int: """Total KV slots for this layer, in tokens. - ``ring + one scratch frame``. The scratch frame at the tail is where an - uncommitted (frozen) denoise pass parks its K/V so the current frame can - attend to itself; it is permanently marked visible and is overwritten on - every forward. + ``ring + one scratch frame``. The scratch frame at the tail is where a + frozen denoise pass parks its K/V so the current frame can attend to + itself; permanently visible, overwritten every forward. """ return (self.ring_frames(layer_idx) + 1) * self.tokens_per_frame @@ -298,7 +417,7 @@ def waypoint_1_5_1b_720p() -> WaypointConfig: def waypoint_1_5_1b_360p() -> WaypointConfig: - """The 360P sibling. Identical weights-shape-wise except the token grid.""" + """The 360P checkpoint: 128 tokens/frame over a 16x32 latent grid.""" return WaypointConfig( variant=WAYPOINT_VARIANT_360P, tokens_per_frame=128, height=8, width=16 ) diff --git a/mstar/model/waypoint/submodules.py b/mstar/model/waypoint/submodules.py new file mode 100644 index 000000000..2ce39fd07 --- /dev/null +++ b/mstar/model/waypoint/submodules.py @@ -0,0 +1,725 @@ +import logging + +import torch + +from mstar.communication.tensors import NameToTensorList +from mstar.conductor.request_info import CurrentForwardPassInfo +from mstar.engine.cuda_graph_config import BatchedCudaGraphConfig, CudaGraphConfig +from mstar.engine.resources import AttentionStep, RingKVStep, SubmoduleStep +from mstar.model.submodule_base import ModelInputsFromEngine, NodeInputs, NodeSubmodule +from mstar.model.waypoint.components.dit import WaypointDiT +from mstar.model.waypoint.components.taehv import ( + DECODER_HISTORY_PREFIX, + decode_latent, + encode_seed_clip, + encoded_size_for_latent, + initial_decoder_histories, + pixel_size_for_latent, + validate_taehv_architecture, +) +from mstar.model.waypoint.config import WaypointConfig + +logger = logging.getLogger(__name__) + +PRIME_WALK = "prime" +ROLLOUT_WALK = "rollout" +ROLLOUT_LOOP_NAME = "rollout_loop" + +# Resource labels this node declares; +KV_RESOURCE = "kv" +ATTN_RESOURCE = "attn" + +# splitmix64 constants, used to derive a per-frame seed. See _frame_seed. +_U64 = (1 << 64) - 1 +_SPLITMIX_GAMMA = 0x9E3779B97F4A7C15 +_SPLITMIX_MIX1 = 0xBF58476D1CE4E5B9 +_SPLITMIX_MIX2 = 0x94D049BB133111EB + + +def _frame_seed(request_seed: int, frame_pos: int) -> int: + """A reproducible seed for one ``(request, frame)`` pair. + + Stateless by construction: nothing here reads or advances a generator, so + frame k's noise is a pure function of the request's seed and the ring clock + and a resumed or re-run frame draws the identical tensor. A + ``torch.Generator`` advanced in place would work too, right up + until the state it accumulates — which ``get_state`` does not serialize — + made a resumed rollout diverge from the one it resumed. + + A splitmix64 finalizer rather than ``seed + frame_pos``: the cheap version + makes request seeds 0 and 1 share every frame's noise but the first, which + reads as "the sampler is broken" rather than "the seeds collided". + Re-seeding from ``request_seed`` alone is the other failure — every frame + gets identical noise and the video stops evolving. + """ + z = (request_seed + (frame_pos + 1) * _SPLITMIX_GAMMA) & _U64 + z = ((z ^ (z >> 30)) * _SPLITMIX_MIX1) & _U64 + z = ((z ^ (z >> 27)) * _SPLITMIX_MIX2) & _U64 + z ^= z >> 31 + # manual_seed takes a signed 64-bit; keep it non-negative rather than + # relying on the accepted-range edge. + return z & (_U64 >> 1) + + +class _SingleRequestMixin: + """Serve one request per step, through the engine's batched entry point. + + A copy of the wan22 idiom (``wan22/submodules.py``), deliberately not an + import: the two models share no other code and a cross-model dependency + here would make a wan22 refactor a Waypoint bug. + + The v1 engine always dispatches to ``forward_batched`` — the worker builds + every batch with ``running_batched=True`` — so a submodule that only defines + ``forward`` never runs. + + **The cap is on the step, not on the node.** It used to be both: the ring + held one live world, so a second request in the batch had nowhere to put its + history and neither did a second request anywhere on the node. The ring now + holds ``num_worlds`` of them and they interleave freely across steps; what + is left here is the honest wan22 statement — the *step* is not batched yet. + The DiT's driver still asserts ``B == 1``, ``_pos_ids`` still hardcodes the + leading 1, and ``capture_batch_sizes`` is still ``[1]``, so a batched step + has nowhere to go until those lift together. + + ``max_batch_size`` is what the micro scheduler reads and chunks on; the + assert is the backstop, and ``RingKVManager.admit`` refusing a mixed batch + is the one below that. + """ + + def max_batch_size(self, graph_walk: str): + return 1 + + def forward_batched( + self, + graph_walk: str, + engine_inputs: ModelInputsFromEngine, + **kwargs, + ) -> dict[str, NameToTensorList]: + request_ids = engine_inputs.request_ids + assert len(request_ids) == 1, ( + f"{type(self).__name__} does not batch a step; got " + f"{len(request_ids)} requests in one step (max_batch_size should " + "have capped it at 1)" + ) + return { + request_ids[0]: self.forward( + graph_walk, engine_inputs=engine_inputs, **kwargs + ) + } + + +class WaypointDitSubmodule(_SingleRequestMixin, NodeSubmodule): + """The world DiT: one latent frame per engine step.""" + + # ``WaypointConfig.compile_dit`` exclusively controls the two deliberate + # full-graph regions. Do not let the engine independently compile this + # wrapper and fuse across those boundaries when the flag is disabled. + disable_torch_compile = True + + # Waypoint pins an explicit fp32 island list at build time. The model returns + # BF16 as the resource/allocation dtype, while this flag prevents + # EngineManager from blanket-casting the mixed-dtype module and dragging + # those fp32 islands to bf16. + disable_autocast = True + + def __init__(self, dit: WaypointDiT, config: WaypointConfig): + super().__init__() + self.dit = dit + self.config = config + + def bind_node_resources(self, resources: dict) -> None: + """Require both resources before letting the bind reach the layers.""" + missing = {KV_RESOURCE, ATTN_RESOURCE} - set(resources) + if missing: + raise KeyError( + f"WaypointDitSubmodule was bound without {sorted(missing)}; the " + "dit node declares both in WaypointModel.get_node_resources() " + "and all 24 attention layers call them." + ) + super().bind_node_resources(resources) + + # ------------------------------------------------------------------ + # prepare_inputs / preprocess + # ------------------------------------------------------------------ + + def prepare_inputs( + self, + graph_walk: str, + fwd_info: CurrentForwardPassInfo, + inputs: NameToTensorList, + **kwargs, + ) -> NodeInputs: + """This frame's row: the ring clock, its controller slice, and either + the noise to denoise from (rollout) or the latent to prime with. + + Runs on the host, outside any captured region — which is the whole + reason the noise is drawn here. A captured region + cannot call the RNG, and ``cuda_graph_runner``'s dummy metadata + hardcodes ``random_seed=0``, so a forward that seeded itself would draw + the capture-time dummy's noise forever. + """ + device = self.get_device() + dtype = self.dit.dtype + # The clock is per request and lives on the host; frame 0 is the first + # frame of the session, priming included. + state = self.request_state(fwd_info.request_id) + frame_pos = int(state.get("frame_pos", 0)) + + if graph_walk == PRIME_WALK: + # Prime is an internal cache operation. It has its own idle action + # and must not consume the client's action zero. + mouse, button, scroll = self._idle_controller(device, dtype) + else: + action_index = int(state.get("rollout_step", 0)) + mouse, button, scroll = self._controller_slice( + inputs, action_index, device, dtype + ) + tensor_inputs = { + # [1], never []: see the class docstring. + "frame_pos": torch.full((1,), frame_pos, dtype=torch.int64, device=device), + "mouse": mouse, + "button": button, + "scroll": scroll, + } + if graph_walk == PRIME_WALK: + # Already-settled x0 for a real frame, off the vae_encoder node. + tensor_inputs["latent"] = inputs["latent"][0].to(device=device, dtype=dtype) + elif graph_walk == ROLLOUT_WALK: + tensor_inputs["noise"] = self._frame_noise( + fwd_info.random_seed, frame_pos, device, dtype + ) + else: + raise ValueError(f"Unknown Waypoint graph walk: {graph_walk!r}") + + return NodeInputs( + tensor_inputs=tensor_inputs, + input_seq_len=self.config.tokens_per_frame, + ) + + def preprocess( + self, + graph_walk: str, + engine_inputs: ModelInputsFromEngine, + inputs: list[NodeInputs], + ) -> dict: + assert len(inputs) == 1, ( + f"WaypointDitSubmodule does not batch a step; preprocess got " + f"{len(inputs)} rows (max_batch_size should have capped it at 1)" + ) + return super().preprocess(graph_walk, engine_inputs, inputs) + + def _frame_noise( + self, + request_seed: int, + frame_pos: int, + device: torch.device, + dtype: torch.dtype, + ) -> torch.Tensor: + """``[1, 1, C, H, W]`` of fresh noise for this frame. + + Drawn fp32 on a CPU generator and cast, rather than bf16 straight onto + the device: a CPU draw is reproducible across devices, which is what + makes "same seed, same frame, same tensor" a testable claim. The + reference draws bf16 on device and unseeded, so there is no + bit-exactness here to preserve — only the distribution. + """ + generator = torch.Generator(device="cpu").manual_seed( + _frame_seed(request_seed, frame_pos) + ) + noise = torch.randn( + (1, 1, *self.config.latent_shape), generator=generator, dtype=torch.float32 + ) + return noise.to(device=device, dtype=dtype) + + def _controller_slice( + self, + inputs: NameToTensorList, + action_index: int, + device: torch.device, + dtype: torch.dtype, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """This frame's ``(mouse, button, scroll)``, each ``[1, 1, *]``. + + The request carries the whole scripted action stream as ``[1, F, *]`` + and one frame is sliced out per step (actions are materialized at + request time; interactive conditioning needs a + refillable mid-``Loop`` edge that does not exist yet). The stream is a + loop-external input, so the conductor re-injects the same tensor every + iteration and the request's rollout counter advances through it. Prime + owns a separate idle controller and never calls this method. + + The API boundary already validates exact stream length. Raising here is + a backstop against a scheduler/state bug; repeating the final row would + silently map multiple generated latents to one user action. + """ + widths = {"mouse": 2, "button": self.config.n_buttons, "scroll": 1} + out = [] + for name, width in widths.items(): + supplied = inputs.get(name) if inputs is not None else None + if not supplied: + raise ValueError(f"Waypoint rollout is missing its {name!r} action stream.") + stream = supplied[0] + if stream.ndim != 3 or stream.shape[0] != 1 or stream.shape[2] != width: + raise ValueError( + f"Waypoint {name!r} stream must have shape [1, steps, {width}]; " + f"got {tuple(stream.shape)}." + ) + if not 0 <= action_index < stream.shape[1]: + raise IndexError( + f"Waypoint action index {action_index} is outside the {name!r} " + f"stream of length {stream.shape[1]}." + ) + out.append( + stream[:, action_index : action_index + 1].to(device=device, dtype=dtype) + ) + return tuple(out) + + def _idle_controller( + self, device: torch.device, dtype: torch.dtype, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + return ( + torch.zeros((1, 1, 2), dtype=dtype, device=device), + torch.zeros((1, 1, self.config.n_buttons), dtype=dtype, device=device), + torch.zeros((1, 1, 1), dtype=dtype, device=device), + ) + + # ------------------------------------------------------------------ + # declare_step + # ------------------------------------------------------------------ + + def _declared_frames(self, request_ids: list[str]) -> tuple[tuple[str, int], ...]: + """Every request's ring clock, for ``RingKVStep``. + + Read off the same host ``state["frame_pos"]`` that ``prepare_inputs`` + derives the ``[1]`` device tensor from and that ``postprocess`` + advances — one source, so the number the step declares cannot drift + from the one the forward runs at. Read on the host and never off + ``inputs``: the ``frame_pos`` in there is a device tensor by then, and + an ``.item()`` on it would be a sync per step. + + One pair per rid, and no ``None`` anywhere in the return type. The + singular version this replaces returned ``None`` for any batch it could + not describe with one number, and ``RingKVManager``'s continuity check + — the only thing standing between a stalled clock and a world quietly + rewriting its own history — then did nothing for that step. There must + be no batch shape that switches it off, so there is no shape that + declines to answer: a batch this submodule cannot serve is refused for + being a batch, with every clock in it still named. + """ + return tuple( + (rid, int(self.request_state(rid).get("frame_pos", 0))) for rid in request_ids + ) + + def declare_step( + self, + graph_walk: str, + request_ids: list[str], + inputs: list[NodeInputs], + slot_lease=None, + piecewise_leases=None, + **kwargs, + ) -> SubmoduleStep: + """Name both resources so the runner drives their lifecycle. + + Neither step carries segments and neither resource reserves anything: + a ring overwrites in place, so there is no span to admit and no page + table to plan. Declaring them anyway is not ceremony — ``admit`` is + where a request is handed one of the node's worlds (and refused when + they are all taken), and a node that declares no step is never admitted + at all. + + The one thing the KV step does carry is the ring clock, and it is a + ``RingKVStep`` rather than a ``KVStep`` so that it can. The clock has to + advance by exactly one per committed frame; declaring it here is what + lets ``RingKVManager.admit`` check that against the frame its ``commit`` + last recorded for that rid, at the one point per frame where both + numbers exist. A desynced clock rewrites history without raising. + """ + del graph_walk, inputs, slot_lease, piecewise_leases, kwargs + return SubmoduleStep( + steps={ + KV_RESOURCE: RingKVStep(frames=self._declared_frames(request_ids)), + ATTN_RESOURCE: AttentionStep(), + }, + ) + + # ------------------------------------------------------------------ + # forward + # ------------------------------------------------------------------ + + def forward( + self, + graph_walk: str, + engine_inputs: ModelInputsFromEngine, + frame_pos: torch.Tensor, + mouse: torch.Tensor, + button: torch.Tensor, + scroll: torch.Tensor, + noise: torch.Tensor | None = None, + latent: torch.Tensor | None = None, + **kwargs, + ) -> NameToTensorList: + """One frame. ``rollout`` denoises it from noise, ``prime`` appends a + real one; both commit to the ring and both return ``latent``. + + ``engine_inputs`` is read for nothing at all here, on purpose: under + capture it is the dummy request's forever. The ring and the + attention backend come off ``self.node_resources``, which the DiT and + its 24 attention layers resolved once at ``bind_node_resources`` time. + """ + del engine_inputs, kwargs + # The graph boundary owns [1]; the model owns [] — ``_pos_ids`` + # asserts rank 0 and int64. This reshape is the entire seam. + pos = frame_pos.reshape(()) + + if graph_walk == ROLLOUT_WALK: + out = self.dit.generate_frame( + noise, pos, mouse=mouse, button=button, scroll=scroll + ) + elif graph_walk == PRIME_WALK: + out = self.dit.append_frame( + latent, pos, mouse=mouse, button=button, scroll=scroll + ) + else: + raise ValueError(f"Unknown Waypoint graph walk: {graph_walk!r}") + return {"latent": [out]} + + # ------------------------------------------------------------------ + # capture + # ------------------------------------------------------------------ + + def get_cuda_graph_configs( + self, device: torch.device, tp_world_size: int = 1 + ) -> list[CudaGraphConfig]: + """The optional steady-rollout graph; the one-time prime stays uncaptured. + + The DiT compiles its reference-shaped denoise/cache regions internally. + Compiling this wrapper would fuse across their boundary, while capturing + prime would spend graph memory on one cache-only forward per request. + """ + del tp_world_size # no sharded nodes; the ring and the mask do not shard + if not self.config.cuda_graph: + return [] + dtype = self.dit.dtype + frame = (1, 1, *self.config.latent_shape) + + def template(latent_key: str) -> NodeInputs: + return NodeInputs( + tensor_inputs={ + latent_key: torch.zeros(frame, dtype=dtype, device=device), + "frame_pos": torch.zeros(1, dtype=torch.int64, device=device), + "mouse": torch.zeros((1, 1, 2), dtype=dtype, device=device), + "button": torch.zeros( + (1, 1, self.config.n_buttons), dtype=dtype, device=device + ), + "scroll": torch.zeros((1, 1, 1), dtype=dtype, device=device), + }, + input_seq_len=self.config.tokens_per_frame, + ) + + return [BatchedCudaGraphConfig( + capture_graph_walk=ROLLOUT_WALK, + single_request_inputs=template("noise"), + capture_batch_sizes=[1], + capture_forward_method="forward_batched", + # The DiT compiles its two reference-shaped fullgraph regions + # itself. Compiling this wrapper would fuse across their boundary. + compile=False, + )] + + # ------------------------------------------------------------------ + # step tail + # ------------------------------------------------------------------ + + def postprocess( + self, + request_id: str, + request_info: CurrentForwardPassInfo, + outputs: dict[str, list[torch.Tensor]], + inputs: NodeInputs | None = None, + **kwargs, + ): + """Advance the ring clock by exactly one committed frame. + + Both walks commit — ``append_frame`` runs the cache pass alone and + ``generate_frame`` runs it after the four denoise passes — so both + advance. Metadata only: no ``.item()``, nothing read off ``outputs``. + The clock lives on the host because it has to be readable *before* the + forward that uses it; the device tensor is derived from it in + ``prepare_inputs``, never the other way round. + """ + del outputs, inputs, kwargs + state = self.request_state(request_id) + state.add("frame_pos", int(state.get("frame_pos", 0)) + 1) + if request_info.graph_walk == ROLLOUT_WALK: + state.add("rollout_step", int(state.get("rollout_step", 0)) + 1) + + def check_stop( + self, + request_id: str, + request_info: CurrentForwardPassInfo, + outputs: dict[str, list[torch.Tensor]], + ) -> set[str]: + """Stop the rollout loop after exactly ``num_steps`` iterations. + + While iteration k (0-based) is being postprocessed the loop counter + still reads k, and a stop registered here ends the loop at the end of + that iteration — so N frames means firing at ``k == N - 1``, i.e. + ``k + 1 >= N``. Mirrors ``Wan22DitSubmodule.check_stop``; the ``>=`` + rather than ``==`` keeps it firing if the deferred count ever reads past + N. The rollout node runs with async scheduling OFF (see + ``WaypointModel``), because an overshoot frame here is not a wasted + forward — it commits garbage into the ring, and there is no undo. + """ + del request_id, outputs + if request_info.graph_walk != ROLLOUT_WALK: + return set() + iter_idx = request_info.dynamic_loop_iter_counts.get(ROLLOUT_LOOP_NAME, 0) + requested = int(request_info.step_metadata.get("num_steps", 0) or 0) + if requested > 0 and iter_idx + 1 >= requested: + return {ROLLOUT_LOOP_NAME} + return set() + + def cleanup_request(self, request_id: str): + """Drop the request's clock. The ring is NOT reset here. + + Releasing it is the engine's job — ``remove_request`` sweeps every + resource, and ``RingKVManager.remove_request`` is what drops the + ownership claim and zeroes the buffer. Doing it here as well would + double-free a claim the sweep is about to release, and doing it *only* + here would leave the ring held by a request the engine has already + forgotten. + """ + super().cleanup_request(request_id) + + +class _FunctionalAeMixin: + """Shared fixed-shape facts for the captured functional AE paths.""" + + @property + def ae_dtype(self) -> torch.dtype: + """The dtype the weights are in, read live: an input scaled into a dtype + the convs are not in faults on the first layer.""" + return next(self.taehv.parameters()).dtype + + @property + def encoded_size(self) -> tuple[int, int]: + return encoded_size_for_latent( + self.config.latent_height, self.config.latent_width + ) + + @property + def pixel_size(self) -> tuple[int, int]: + return pixel_size_for_latent( + self.config.latent_height, self.config.latent_width + ) + + +class WaypointVaeEncoderSubmodule(_SingleRequestMixin, _FunctionalAeMixin, NodeSubmodule): + """TAEHV encoder: ``temporal_compression`` raw frames -> one latent frame. + + ``image_inputs`` ``[4, 720, 1280, 3]`` uint8 -> ``latent`` + ``[1, 1, 32, 32, 64]``, the dit's priming input unchanged. Prime walk only. + """ + + disable_torch_compile = True + # The dit node's statement: the dtype layout is settled at load, and an + # engine-level cast would round it underneath the AE. + disable_autocast = True + + def __init__(self, taehv: torch.nn.Module, config: WaypointConfig): + super().__init__() + validate_taehv_architecture(taehv) + self.taehv = taehv + self.config = config + + def prepare_inputs( + self, + graph_walk: str, + fwd_info: CurrentForwardPassInfo, + inputs: NameToTensorList, + **kwargs, + ) -> NodeInputs: + """The seed clip, scaled into the AE's dtype. Cast then divide, the + reference's order: 0-255 is exact in bf16, so the divide rounds once.""" + del graph_walk, fwd_info, kwargs + frames = inputs["image_inputs"][0] + scale = frames.dtype == torch.uint8 + frames = frames.to(device=self.get_device(), dtype=self.ae_dtype) + return NodeInputs(tensor_inputs={"image": frames.div(255) if scale else frames}) + + def forward( + self, + graph_walk: str, + engine_inputs: ModelInputsFromEngine, + image: torch.Tensor, + **kwargs, + ) -> NameToTensorList: + """Encode the seed clip into the latent the dit primes the world on. + Request ids are safe to read here and not on the dit: this node is never + captured, so never handed a capture dummy's ids.""" + del graph_walk, kwargs + del engine_inputs + latent = encode_seed_clip( + self.taehv, image, output_size=self.encoded_size + ) + # [B, C, h, w] -> [B, 1, C, h, w]: the dit's frame axis, added where the + # reference adds it (``WorldEngine.append_frame``). + return {"latent": [latent.unsqueeze(1)]} + + def get_cuda_graph_configs( + self, device: torch.device, tp_world_size: int = 1 + ) -> list[CudaGraphConfig]: + del tp_world_size + if not self.config.cuda_graph: + return [] + height, width = self.pixel_size + image = torch.zeros( + (self.config.temporal_compression, height, width, 3), + dtype=self.ae_dtype, + device=device, + ) + return [BatchedCudaGraphConfig( + capture_graph_walk=PRIME_WALK, + single_request_inputs=NodeInputs( + tensor_inputs={"image": image}, + input_seq_len=self.config.temporal_compression, + ), + capture_batch_sizes=[1], + capture_forward_method="forward_batched", + compile=True, + )] + + +class WaypointVaeDecoderSubmodule(_SingleRequestMixin, _FunctionalAeMixin, NodeSubmodule): + """TAEHV decoder: one latent frame -> ``temporal_compression`` RGB frames. + + ``latent`` ``[1, 1, 32, 32, 64]`` from the dit -> ``video_output`` + ``[4, 720, 1280, 3]`` uint8, one message per engine step. + + Every latent the world commits must reach this node exactly once and in + order, the priming frame included: the temporal memory advances per call, so + a duplicate, gap or reorder shifts the whole stream with nothing raised. + ``enable_async_scheduling=False`` on both rollout nodes is half of what + holds that; the other half is the loop's own iteration boundary. + """ + + disable_torch_compile = True + disable_autocast = True + + def __init__(self, taehv: torch.nn.Module, config: WaypointConfig): + super().__init__() + validate_taehv_architecture(taehv) + self.taehv = taehv + self.config = config + + def _history_state(self, request_id: str, latent: torch.Tensor) -> tuple[torch.Tensor, ...]: + state = self.request_state(request_id) + histories = tuple( + state.get(f"{DECODER_HISTORY_PREFIX}{idx}") for idx in range(9) + ) + if any(value is None for value in histories): + histories = initial_decoder_histories(self.taehv, latent) + for idx, value in enumerate(histories): + state.add(f"{DECODER_HISTORY_PREFIX}{idx}", value) + return histories + + def prepare_inputs( + self, + graph_walk: str, + fwd_info: CurrentForwardPassInfo, + inputs: NameToTensorList, + **kwargs, + ) -> NodeInputs: + del graph_walk, kwargs + latent = inputs["latent"][0] + histories = self._history_state(fwd_info.request_id, latent.squeeze(1)) + tensor_inputs = {"latent": latent} + tensor_inputs.update({ + f"{DECODER_HISTORY_PREFIX}{idx}": value + for idx, value in enumerate(histories) + }) + return NodeInputs(tensor_inputs=tensor_inputs, input_seq_len=1) + + def forward( + self, + graph_walk: str, + engine_inputs: ModelInputsFromEngine, + latent: torch.Tensor, + **kwargs, + ) -> NameToTensorList: + """Decode this step and return its updated fixed-shape histories.""" + del engine_inputs + # [B, 1, C, h, w] -> [B, C, h, w]: the frame axis is the dit's, and the + # AE takes one latent per call. + histories = tuple( + kwargs.pop(f"{DECODER_HISTORY_PREFIX}{idx}") for idx in range(9) + ) + if kwargs: + raise TypeError(f"unexpected decoder inputs: {sorted(kwargs)}") + frames, updated = decode_latent( + self.taehv, + latent.squeeze(1), + histories, + output_size=self.pixel_size, + initialize=graph_walk == PRIME_WALK, + ) + out: NameToTensorList = {"video_output": [frames]} + out.update({ + f"{DECODER_HISTORY_PREFIX}{idx}": [value] + for idx, value in enumerate(updated) + }) + return out + + def _capture_template(self, device: torch.device) -> NodeInputs: + latent = torch.zeros( + (1, 1, *self.config.latent_shape), + dtype=self.ae_dtype, + device=device, + ) + histories = initial_decoder_histories(self.taehv, latent.squeeze(1)) + tensors = {"latent": latent} + tensors.update({ + f"{DECODER_HISTORY_PREFIX}{idx}": value + for idx, value in enumerate(histories) + }) + return NodeInputs(tensor_inputs=tensors, input_seq_len=1) + + def get_cuda_graph_configs( + self, device: torch.device, tp_world_size: int = 1 + ) -> list[CudaGraphConfig]: + del tp_world_size + if not self.config.cuda_graph: + return [] + return [ + BatchedCudaGraphConfig( + capture_graph_walk=walk, + single_request_inputs=self._capture_template(device), + capture_batch_sizes=[1], + capture_forward_method="forward_batched", + compile=True, + ) + for walk in (PRIME_WALK, ROLLOUT_WALK) + ] + + def postprocess( + self, + request_id: str, + request_info: CurrentForwardPassInfo, + outputs: dict[str, list[torch.Tensor]], + inputs: NodeInputs | None = None, + **kwargs, + ) -> None: + """Copy graph outputs into the request's stable history tensors.""" + del request_info, inputs, kwargs + state = self.request_state(request_id) + for idx in range(9): + key = f"{DECODER_HISTORY_PREFIX}{idx}" + values = outputs.get(key) + if not values: + raise RuntimeError(f"captured decoder returned no {key}") + target = state.get(key) + if target is None: + state.add(key, values[0].clone()) + else: + target.copy_(values[0]) diff --git a/mstar/model/waypoint/waypoint_model.py b/mstar/model/waypoint/waypoint_model.py new file mode 100644 index 000000000..d0b2458e9 --- /dev/null +++ b/mstar/model/waypoint/waypoint_model.py @@ -0,0 +1,832 @@ +"""WaypointModel: Waypoint-1.5-1B interactive video world model. + +Architecture (three nodes): + vae_encoder - TAEHV encode. The seed clip (``temporal_compression`` raw + frames) into the one latent frame it stands for. + dit - the 1.28B world DiT. One engine step is one latent frame: + four frozen Euler denoise passes plus one committing cache + pass, all inside a single ``forward``. + vae_decoder - TAEHV decode. One latent frame back into its raw frames. + +Graph walks (2): + prime - vae_encoder -> dit.append_frame -> vae_decoder. Seeds the world + and decoder state from a real frame, advances the ring clock by + one, and emits nothing. + rollout - Loop("rollout_loop") over dit.generate_frame -> vae_decoder -> + client; one latent frame per iteration, emitted as it lands. + +**Prime decodes as well as encodes**, and not for symmetry: the functional +decoder's first call spends ``frames_to_trim`` of temporal memory priming +itself, so a prime that only encoded would leave the first *rollout* frame +paying for it and every frame after that shifted against the world it came +from. Silently — drifting video, no exception. The reconstructed seed frames +are internal initialization output and are never sent to the client. + +The world state is the ring KV cache. It is an engine resource +(``get_node_resources`` below), not a model-owned buffer: a per-request ring in +``PerRequestState`` cannot survive CUDA-graph capture, and the resource +lifecycle is the only place that can hand a request one of the node's worlds — +or refuse it when they are all taken. That refusal is a backstop, though — see +``get_worker_graphs``. + +``num_worlds`` and ``max_batch_size`` are separate numbers and stay separate. +``num_worlds`` is how many sessions are *resident* (one ring span each, folded +into the token dimension by ``LayerRingCache``); ``max_batch_size`` is how many +share one *forward step*, and is still 1, so resident worlds take turns across +steps rather than batching. Raising the second is the next cut and does not +change the layout chosen for the first. +""" + +import logging +import math +from dataclasses import replace + +import torch +import yaml + +from mstar.communication.tensors import NameToTensorList +from mstar.conductor.request_info import ( + CurrentForwardConductorMetadata, + StreamingConnectionState, +) +from mstar.engine.resources import ( + AttentionConfig, + AttentionSpec, + AttnBackend, + KVSpec, + NodeResourceSpec, + RingKVConfig, + RingKVLayerConfig, +) +from mstar.graph.base import ( + GraphEdge, + GraphNode, + GraphSection, + Loop, + Sequential, + TensorPointerInfo, +) +from mstar.graph.special_destinations import EMIT_TO_CLIENT +from mstar.model.base import ForwardPassArgs, Model, TensorAndMetadata, WorkerGraph +from mstar.model.submodule_base import NodeSubmodule +from mstar.model.waypoint.config import ( + WAYPOINT_VARIANT_360P, + WAYPOINT_VARIANT_720P, + WAYPOINT_VARIANT_HF_REPOS, + WaypointConfig, + waypoint_1_5_1b_360p, + waypoint_1_5_1b_720p, +) +from mstar.model.waypoint.ring_geometry import describe_ring_memory +from mstar.model.waypoint.submodules import ( + ATTN_RESOURCE, + KV_RESOURCE, + PRIME_WALK, + ROLLOUT_LOOP_NAME, + ROLLOUT_WALK, + WaypointDitSubmodule, + WaypointVaeDecoderSubmodule, + WaypointVaeEncoderSubmodule, +) + +logger = logging.getLogger(__name__) + +DIT_NODE = "dit" +VAE_ENCODER_NODE = "vae_encoder" +VAE_DECODER_NODE = "vae_decoder" + +# The scripted action stream, one row per frame. Named once because the walk +# declarations, the initial-args validation and the per-walk edge builder all +# have to agree with WaypointDitSubmodule._controller_slice, which reads these +# names off the request's inputs dict. +_CONTROLLER_STREAMS = ("mouse", "button", "scroll") + +_VARIANT_FACTORIES = { + WAYPOINT_VARIANT_720P: waypoint_1_5_1b_720p, + WAYPOINT_VARIANT_360P: waypoint_1_5_1b_360p, +} + + +class WaypointModel(Model): + """Waypoint-1.5-1B (720P by default; the 360P sibling shares the class).""" + + PRIME_WALK = PRIME_WALK + ROLLOUT_WALK = ROLLOUT_WALK + + # Loop name — referenced by ``WaypointDitSubmodule.check_stop`` through + # ``request_info.dynamic_loop_iter_counts[...]``. + ROLLOUT_LOOP_NAME = ROLLOUT_LOOP_NAME + + def __init__( + self, + model_path_hf: str | None = None, + cache_dir: str | None = None, + variant: str = WAYPOINT_VARIANT_720P, + skip_weight_loading: bool = False, + checkpoint_dir: str | None = None, + ae_path: str | None = None, + reference_compat: bool | None = None, + compile_dit: bool | None = None, + cuda_graph: bool | None = None, + full_global_ring: bool | None = None, + checkpoint_revision: str | None = None, + ae_revision: str | None = None, + ): + if variant not in _VARIANT_FACTORIES: + raise NotImplementedError( + f"Waypoint variant {variant!r} is not implemented; known variants " + f"are {sorted(_VARIANT_FACTORIES)}." + ) + # The generic registry passes None so the selected variant chooses its + # published repository. Any explicit source remains authoritative, + # including a local path or a deliberately cross-variant Hub ID; the + # manifest preflight will reject it if its geometry is incompatible. + self.model_path_hf = ( + WAYPOINT_VARIANT_HF_REPOS[variant] + if model_path_hf is None + else model_path_hf + ) + self.cache_dir = cache_dir + config = _VARIANT_FACTORIES[variant]() + overrides = { + key: value for key, value in { + "reference_compat": reference_compat, + "compile_dit": compile_dit, + "cuda_graph": cuda_graph, + "full_global_ring": full_global_ring, + }.items() if value is not None + } + self.config: WaypointConfig = replace(config, **overrides) + # ``build_waypoint_dit`` never downloads: the caller resolves the local + # directory holding model.safetensors. ``cache_dir`` is where a + # snapshot lands if one is fetched out of band; the two are not the same + # thing and conflating them is how a half-downloaded repo gets loaded. + self.checkpoint_dir = checkpoint_dir or self.model_path_hf + # The TAEHV weights ship in their own repo, so ``ae_path`` is a local + # override of ``config.ae_uri`` and not of ``checkpoint_dir``. + self.ae_uri = ae_path or self.config.ae_uri + self.checkpoint_revision = checkpoint_revision + self.ae_revision = ae_revision + # Dummy mode: get_submodule returns None for every node, so engines and + # tests run without weights, GPU or network. + self.skip_weight_loading = skip_weight_loading + + self._submodule_cache: dict[str, NodeSubmodule | None] = {} + self._taehv: torch.nn.Module | None = None + self._checkpoints_resolved = False + + # ------------------------------------------------------------------ + # Model ABC: structure + # ------------------------------------------------------------------ + + def get_node_resources(self) -> list[NodeResourceSpec]: + """The ring KV cache holding the world, and the FlexAttention over it. + + The ring geometry is copied out of ``WaypointConfig`` layer by layer + rather than summarized: Waypoint's layers are not alike (the six global + layers hold 16 frames spaced 8 apart, the other eighteen hold 16 + consecutive frames), and ``ring_frames`` and ``ring_buckets`` are two + separate questions. They happen to agree on every layer of the + *compacted* 720P ring, which is exactly what makes deriving one from the + other look safe — flip ``full_global_ring`` back to the reference's + sizing and a global layer is 128 frames indexed by 16 buckets. + + FlexAttention rather than the paged FlashInfer default: a paged + kernel changes the accumulation order over the KV blocks, and a + mask-or-position bug in this model does not raise, it produces + plausible, smoothly drifting video. Bit-exactness against the reference + is the only check there is, so the kernel has to be the reference's. + + The attention spec names the cache by key, so ``depends_on`` orders the + two: the ring is built first and the attention resource resolves it. + """ + ring_config = RingKVConfig( + num_layers=self.config.n_layers, + num_kv_heads=self.config.n_kv_heads, + num_qo_heads=self.config.n_heads, + head_dim=self.config.d_head, + tokens_per_frame=self.config.tokens_per_frame, + layers=tuple( + RingKVLayerConfig( + ring_frames=self.config.ring_frames(i), + ring_buckets=self.config.ring_buckets(i), + pinned_dilation=self.config.pinned_dilation(i), + ) + for i in range(self.config.n_layers) + ), + # How many sessions this node holds resident. One by default + # because a world is ~816 MiB of ring at 720P and a model has no + # business assuming the box; a deployment raises it under + # ``resources: {kv: {num_worlds: N}}`` and raises + # ``max_concurrent_requests`` with it (see ``get_worker_graphs``). + # Not the step batch — that is ``max_batch_size``, still 1. + num_worlds=1, + ) + # Logged, not merely allocated: this declaration is worth ~816 MiB per + # world and nothing downstream prints it. The report carries the + # counterfactual under the other ``full_global_ring`` setting, which is + # the number you want *before* the engine commits to one of them. + # + # `ring_config.num_worlds` is the DECLARED count, which is what this + # line can honestly report: `EngineManager.build` calls + # `apply_yaml_overrides` on the specs after this hook returns, so a + # deployment's `num_worlds` has not landed yet. The reported total + # scales linearly with it -- the world dim is folded into the token + # axis -- so N worlds is N times the number below. + logger.info( + "%s", describe_ring_memory(self.config, num_worlds=ring_config.num_worlds) + ) + return [ + KVSpec( + resource_key=KV_RESOURCE, nodes={DIT_NODE}, config=ring_config, + ), + AttentionSpec( + resource_key=ATTN_RESOURCE, + nodes={DIT_NODE}, + config=AttentionConfig( + kv_cache=KV_RESOURCE, backend=AttnBackend.FLEX, + ), + ), + ] + + def _emit_frames(self) -> GraphEdge: + return GraphEdge( + next_node=EMIT_TO_CLIENT, + name="video_output", + output_modality="video_frame", + ) + + def get_graph_walk_graphs(self) -> dict[str, GraphSection]: + # -- prime: encode the seed clip, commit it to the world, initialize the + # -- decoder state, and discard the reconstructed seed frames. + # -- + # -- Both `latent` edges carry that name because both endpoints call it + # -- that; a section keys edges on (name, next_node), so they are two + # -- edges and not one. + prime = Sequential([ + GraphNode( + name=VAE_ENCODER_NODE, + input_names={"image_inputs"}, + outputs=[GraphEdge(next_node=DIT_NODE, name="latent")], + ), + GraphNode( + name=DIT_NODE, + input_names={"latent", *_CONTROLLER_STREAMS}, + outputs=[GraphEdge(next_node=VAE_DECODER_NODE, name="latent")], + ), + # Advance the decoder's nine temporal histories, but do not expose + # reconstructed seed frames. Client frame zero is generated. + GraphNode( + name=VAE_DECODER_NODE, + input_names={"latent"}, + outputs=[], + ), + ]) + + # -- rollout: one frame per iteration, emitted as it lands. + # -- + # -- No loop-back edges, and that is not an omission: everything that + # -- crosses a frame boundary is the ring (an engine resource at a fixed + # -- address), the host-side frame_pos, or the decoder's streaming + # -- state. The controller streams are loop-external, re-injected every + # -- iteration, and the submodule slices the current frame's row out. + # -- + # -- EMIT_TO_CLIENT sits on the decoder node, one message per iteration, + # -- rather than on Loop.accumulated_outputs: a client that only sees + # -- frames after the rollout ends has no world to interact with. + # -- + # -- check_stop ends the loop, but the loop's registry calls + # -- complete_iter only once *every* entity in the section is done, so + # -- the final frame is decoded before the loop closes. + rollout = Loop( + name=ROLLOUT_LOOP_NAME, + section=Sequential([ + GraphNode( + name=DIT_NODE, + input_names=set(_CONTROLLER_STREAMS), + outputs=[GraphEdge(next_node=VAE_DECODER_NODE, name="latent")], + # Speculation would dispatch iteration N+1 before + # check_stop's decision on N landed. The overshoot forward + # *commits a frame into the ring* and there is no undo; the + # next real rollout would inherit it. + enable_async_scheduling=False, + ), + GraphNode( + name=VAE_DECODER_NODE, + input_names={"latent"}, + outputs=[self._emit_frames()], + # And here for the decoder's own reason: it is streaming, so + # frames must be decoded exactly once in emission order, and + # a speculative decode of a frame that may not stand is a + # reorder of a stream that cannot be reordered. + enable_async_scheduling=False, + ), + ]), + # Ceiling only; the request's num_steps stops the loop early via + # WaypointDitSubmodule.check_stop. + max_iters=self.config.max_frames, + outputs=[], + accumulated_outputs=[], + ) + + return {PRIME_WALK: prime, ROLLOUT_WALK: rollout} + + def get_worker_graphs(self, config_path: str) -> list[WorkerGraph]: + """Refuse to build unless the deployment caps concurrency at the number + of worlds the ring was sized for. + + This is the **primary** gate on the world pool, not a nicety. A world is + claimed at ``admit``, i.e. once a batch has already been formed — by + then the only thing ``RingKVManager.admit`` can do about a request the + pool cannot hold is fail it terminally. What actually keeps arrivals + inside the pool is the conductor's FIFO admit queue, and that queue only + exists when ``max_concurrent_requests`` is set: the conductor drains + ``waiting_queue`` while ``len(self.requests) < max_concurrent_requests``, + so an unset value admits everything on arrival and every request past + the Nth dies at admit. Unset therefore stays fatal, exactly as before — + what changed is that the accepted value is a range rather than the + single number 1. + + ``max_batch_size = 1`` does NOT cover this, and that is still true with + N worlds. It caps how many requests share one *step*; N admitted + rollouts alternate steps, which is now the intended shape — each holds + its own world and the BlockMask keeps them apart — but it says nothing + about how many may exist, which is the thing the pool bounds. + + A limit *below* ``num_worlds`` is legal and only wasteful: it allocates + rings (~816 MiB each at 720P) for worlds no request can ever reach, so + it is warned about rather than refused. + + Checked here because this hook is the only place a model sees the key: + the Conductor reads it out of the YAML itself and + ``api_server/entrypoint.py`` forwards only ``model_kwargs`` to + ``Model.__init__``. + """ + with open(config_path, "r") as f: + config = yaml.safe_load(f) or {} + # The same block ``EngineManager.build`` feeds to + # ``apply_yaml_overrides``, read here for the same key, so the gate and + # the allocation cannot disagree about how many worlds exist. + overrides = (config.get("resources") or {}).get(KV_RESOURCE) or {} + num_worlds = overrides.get("num_worlds", 1) + if ( + not isinstance(num_worlds, int) + or isinstance(num_worlds, bool) + or num_worlds < 1 + ): + raise ValueError( + f"Waypoint requires `resources.{KV_RESOURCE}.num_worlds` in " + f"{config_path} to be a positive int; got {num_worlds!r}." + ) + limit = config.get("max_concurrent_requests") + if not isinstance(limit, int) or isinstance(limit, bool) or limit < 1: + raise ValueError( + f"Waypoint requires `max_concurrent_requests` in {config_path} to be " + f"a positive int; got {limit!r}. The DiT node holds " + f"{num_worlds} live world(s) in a fixed ring buffer, and the " + "conductor's FIFO admit queue — which exists only when this key " + "is set — is what keeps arrivals inside that pool. max_batch_size " + "alone does not: it caps a step, not the number of requests on " + "the node." + ) + if limit > num_worlds: + raise ValueError( + f"`max_concurrent_requests: {limit}` in {config_path} exceeds the " + f"{num_worlds} world(s) the ring is sized for. Every request past " + "the pool fails terminally at admit — there is nothing to evict. " + f"Set `resources.{KV_RESOURCE}.num_worlds` to {limit} to match, at " + "the cost of ~816 MiB of ring per world at 720P." + ) + if limit < num_worlds: + logger.warning( + "Waypoint ring is sized for %d worlds but max_concurrent_requests " + "is %d: %d world(s) of ring (~816 MiB each at 720P) are allocated " + "and can never be filled.", + num_worlds, limit, num_worlds - limit, + ) + return super().get_worker_graphs(config_path) + + # ------------------------------------------------------------------ + # Model ABC: I/O + # ------------------------------------------------------------------ + + def process_prompt( + self, + prompt: str | None, + input_modalities: list[str], + output_modalities: list[str], + tensors: NameToTensorList | None = None, + **kwargs, + ) -> NameToTensorList: + """Materialize the request's scripted controller stream (and its seed + clip, if any) as the edges the walk's first nodes consume. + + ``prompt`` is ignored: this checkpoint has ``prompt_conditioning=None`` + and carries no cross-attention, so a text prompt would have nowhere to + go. Raising on one would break clients that send an empty default. + + ``actions`` is a list of per-step dicts, exactly one per generated latent: + ``{"mouse": [dx, dy], "buttons": [id, ...], "scroll": s}``. The button + field is a set of pressed ids that gets one-hot scattered into + ``n_buttons`` columns, matching the reference's ``CtrlInput``; an + omitted field is that control's neutral value. Prime uses a separate + internal idle action and never consumes action zero. + """ + del prompt, input_modalities + if output_modalities != ["video_frame"]: + raise ValueError( + "Waypoint requires exactly one output modality, 'video_frame'; " + f"got {output_modalities!r}. Raw RGB frames are served only by " + "the native streaming endpoint, not as encoded video." + ) + num_steps = self._resolve_num_steps(kwargs) + actions = kwargs.get("actions") + if not isinstance(actions, list) or len(actions) != num_steps: + actual = len(actions) if isinstance(actions, list) else 0 + raise ValueError( + "Waypoint requires exactly one action object per generated latent " + f"step; got {actual} actions for " + f"num_steps={num_steps}." + ) + + mouse = torch.zeros((1, num_steps, 2), dtype=torch.float32) + button = torch.zeros((1, num_steps, self.config.n_buttons), dtype=torch.float32) + scroll = torch.zeros((1, num_steps, 1), dtype=torch.float32) + float32_max = torch.finfo(torch.float32).max + for step, action in enumerate(actions): + if not isinstance(action, dict): + raise ValueError(f"action {step} must be an object; got {type(action).__name__}") + unknown = set(action) - {"mouse", "buttons", "scroll"} + if unknown: + raise ValueError(f"action {step} has unknown field(s) {sorted(unknown)}") + motion = action.get("mouse", (0.0, 0.0)) + if not isinstance(motion, (list, tuple)) or len(motion) != 2: + raise ValueError(f"action {step} mouse must contain exactly [dx, dy]") + if any( + isinstance(value, bool) or not isinstance(value, (int, float)) + for value in motion + ): + raise ValueError(f"action {step} mouse values must be numbers") + try: + dx, dy = map(float, motion) + except OverflowError: + raise ValueError( + f"action {step} mouse values must be finite and representable " + "as float32" + ) from None + if ( + not math.isfinite(dx) + or not math.isfinite(dy) + or abs(dx) > float32_max + or abs(dy) > float32_max + ): + raise ValueError( + f"action {step} mouse values must be finite and representable " + "as float32" + ) + mouse[0, step, 0] = dx + mouse[0, step, 1] = dy + pressed_ids = action.get("buttons", ()) + if not isinstance(pressed_ids, (list, tuple)): + raise ValueError(f"action {step} buttons must be a list of ids") + seen: set[int] = set() + for pressed in pressed_ids: + if isinstance(pressed, bool) or not isinstance(pressed, int): + raise ValueError(f"action {step} button ids must be integers") + if not 0 <= pressed < self.config.n_buttons: + raise ValueError( + f"button id {pressed} out of range for n_buttons=" + f"{self.config.n_buttons} (action {step})." + ) + if pressed in seen: + raise ValueError(f"action {step} repeats button id {pressed}") + seen.add(pressed) + button[0, step, pressed] = 1.0 + raw_scroll = action.get("scroll", 0.0) + if isinstance(raw_scroll, bool) or not isinstance(raw_scroll, (int, float)): + raise ValueError(f"action {step} scroll must be a number") + try: + scroll_value = float(raw_scroll) + except OverflowError: + raise ValueError( + f"action {step} scroll must be finite and representable as float32" + ) from None + if not math.isfinite(scroll_value) or abs(scroll_value) > float32_max: + raise ValueError( + f"action {step} scroll must be finite and representable as float32" + ) + scroll[0, step, 0] = scroll_value + + out: NameToTensorList = {"mouse": [mouse], "button": [button], "scroll": [scroll]} + + if not tensors or not tensors.get("image_inputs"): + raise ValueError("Waypoint requires one RGB seed image or four-frame seed clip.") + out["image_inputs"] = [self._seed_clip(tensors["image_inputs"][0])] + return out + + def _seed_clip(self, image: torch.Tensor) -> torch.Tensor: + """The prime walk's ``[temporal_compression, H, W, 3]`` uint8 clip. + + One frame is repeated to fill it, the reference's way of seeding from a + still (``gen_sample.py``'s ``seed_frame_x4``). Fewer or more frames is + refused: the streaming encoder emits one latent per ``t_downscale``, so a + short clip buffers silently and a long one encodes twice. + + Resolution is not checked, only the aspect ratio -- the AE resizes 16:9 + input onto its own grid and decodes back to the variant's resolution. + """ + frames = image if image.dim() == 4 else image.unsqueeze(0) + if frames.dtype != torch.uint8 or frames.shape[-1] != 3: + raise ValueError( + "seed frames must be uint8 RGB shaped [H, W, 3] or [T, H, W, 3]; " + f"got {tuple(image.shape)} of {image.dtype}." + ) + n = self.config.temporal_compression + if frames.shape[0] == 1: + frames = frames.expand(n, -1, -1, -1) + if frames.shape[0] != n: + raise ValueError( + f"the seed clip must be 1 or {n} frames (one latent frame); got " + f"{frames.shape[0]}." + ) + height, width = int(frames.shape[1]), int(frames.shape[2]) + if self.config.auto_aspect_ratio and height * 16 != width * 9: + raise ValueError(f"seed frames must be 16:9; got {height}x{width}.") + return frames.contiguous() + + def load_image(self, filepath: str, device: str) -> TensorAndMetadata: + """The seed frame as uint8 ``[H, W, 3]``, not the base loader's float + ``[C, H, W]``: the AE scales it itself, in its own dtype, and a float + round trip through the request would round twice on the way there.""" + import torchvision + + img = torchvision.io.decode_image(filepath).to(device) # uint8 [C, H, W] + return TensorAndMetadata(img.permute(1, 2, 0).contiguous()) + + def postprocess( + self, output: torch.Tensor, modality: str, request_kwargs: dict | None = None, + ) -> bytes: + """One step's frames as raw uint8 RGB bytes, + ``[temporal_compression, H, W, 3]`` in C order. No container: the emit is + per engine step, and a per-step mp4 is a fragment nothing plays.""" + del request_kwargs + if modality != "video_frame": + raise ValueError(f"Unsupported modality for Waypoint: {modality!r}") + if output.dtype != torch.uint8: + raise ValueError( + f"the vae_decoder emits uint8 RGB frames; got {output.dtype}." + ) + return output.detach().cpu().contiguous().numpy().tobytes() + + def get_output_frame_rate( + self, + modality: str = "video_frame", + request_kwargs: dict | None = None, + ) -> float: + del request_kwargs + if modality != "video_frame": + raise ValueError(f"Unsupported frame modality for Waypoint: {modality!r}") + return float(self.config.inference_fps) + + # ------------------------------------------------------------------ + # Model ABC: forward pass orchestration + # ------------------------------------------------------------------ + + def _resolve_num_steps(self, model_kwargs: dict | None) -> int: + """Validate the generated-latent count against the trained horizon.""" + model_kwargs = model_kwargs or {} + requested = model_kwargs.get("num_steps") + if isinstance(requested, bool) or not isinstance(requested, int) or requested <= 0: + raise ValueError(f"Waypoint requires num_steps > 0; got {requested!r}.") + if requested > self.config.max_frames: + raise ValueError( + f"num_steps={requested} exceeds the checkpoint horizon " + f"({self.config.max_frames})." + ) + return requested + + def _get_step_metadata(self, metadata: CurrentForwardConductorMetadata) -> dict: + """Per-pass metadata the submodule reads off ``request_info``. + + ``num_steps`` is what ``check_stop`` counts the rollout loop against; + nothing else in the shell is per-request. + """ + return { + "is_prefill": metadata.is_prefill, + "num_steps": metadata.kwargs["num_steps"], + } + + def get_initial_forward_pass_args( + self, + partition_name: str, + input_modalities: list[str], + output_modalities: list[str], + input_signals: dict[str, list[TensorPointerInfo]], + model_kwargs: dict | None = None, + ) -> ForwardPassArgs: + del partition_name, input_modalities + if output_modalities != ["video_frame"]: + raise ValueError( + "Waypoint requires exactly one output modality, 'video_frame'; " + f"got {output_modalities!r}." + ) + # A backstop, not the primary guard: process_prompt already rejected a + # malformed request on the data worker, where a ValueError becomes a + # 400. A raise here runs at the conductor, whose main loop swallows it, + # so the client would hang instead. + for name in _CONTROLLER_STREAMS: + if not input_signals.get(name): + raise ValueError( + f"Waypoint needs the {name!r} controller stream; " + "process_prompt emits all three." + ) + + if not input_signals.get("image_inputs"): + raise ValueError("Waypoint cannot start without its required seed clip.") + schedule = [PRIME_WALK, ROLLOUT_WALK] + + kwargs = { + "walk_schedule": schedule, + "walk_step": 0, + "num_steps": self._resolve_num_steps(model_kwargs), + } + full_metadata = CurrentForwardConductorMetadata( + input_modalities=["tensor"], + output_modalities=output_modalities, + graph_walk=schedule[0], + is_prefill=schedule[0] == PRIME_WALK, + kwargs=kwargs, + ) + inputs = self._walk_inputs(schedule[0], input_signals) + return ForwardPassArgs( + full_metadata=full_metadata, + inputs=inputs, + # Nothing is released after the first pass: the controller streams + # are re-read by every frame of the rollout, and the seed clip is + # dropped with the request. Unpersisting either here would strand + # the rollout walk with no inputs and the node would never become + # ready. + unpersist_tensors=[], + step_metadata=self._get_step_metadata(full_metadata), + ) + + def _walk_inputs( + self, walk: str, signals: dict[str, list[TensorPointerInfo]], + ) -> list[GraphEdge]: + """The external edges seeding one walk. Both walks read the controller + streams at the dit; only ``prime`` reads the seed clip, and it reads it + at the vae_encoder.""" + inputs = [ + GraphEdge(next_node=DIT_NODE, name=name, persist=True) + for name in _CONTROLLER_STREAMS + ] + if walk == PRIME_WALK: + inputs.insert( + 0, + GraphEdge( + next_node=VAE_ENCODER_NODE, name="image_inputs", persist=True + ), + ) + for edge in inputs: + edge.tensor_info = signals.get(edge.name, []) + return inputs + + def get_partition_forward_pass_args( + self, + partition_name: str, + partition_metadata: CurrentForwardConductorMetadata, + persist_signals: dict[str, list[TensorPointerInfo]], + incoming_connections: list[StreamingConnectionState] | None = None, + ) -> ForwardPassArgs: + """Step through the request's walk schedule; done after the rollout.""" + del partition_name, incoming_connections + metadata = partition_metadata + schedule = metadata.kwargs["walk_schedule"] + step = metadata.kwargs["walk_step"] + 1 + if step >= len(schedule): + return ForwardPassArgs( + full_metadata=metadata, + inputs=[], + unpersist_tensors=[], + step_metadata=self._get_step_metadata(metadata), + request_done=True, + ) + + metadata.kwargs["walk_step"] = step + walk = schedule[step] + metadata.graph_walk = walk + metadata.is_prefill = walk == PRIME_WALK + inputs = self._walk_inputs(walk, persist_signals) + return ForwardPassArgs( + full_metadata=metadata, + inputs=inputs, + # The rollout is the last walk, so its inputs are consumed for the + # last time here. + unpersist_tensors=sum([inp.tensor_info for inp in inputs], start=[]), + step_metadata=self._get_step_metadata(metadata), + ) + + # ------------------------------------------------------------------ + # Model ABC: submodule loading + # ------------------------------------------------------------------ + + def get_autocast_dtype(self): + """Allocate BF16 resources while every node disables autocast. + + The dtype layout is settled at build time — ``cast_serving_dtypes()`` + takes the meta module to bf16 and pins the fp32 islands back, and the AE + is built bf16 whole. The submodules' ``disable_autocast`` flags preserve + that mixed layout. Returning BF16 here is also the explicit ring-KV + allocation dtype; returning None silently allocated the ring in fp32. + """ + return torch.bfloat16 + + def get_submodule( + self, node_name: str, device: str = "cpu", tp_group=None, + autocast_dtype: torch.dtype | None = None, sp_group=None, + ) -> torch.nn.Module | None: + # ``autocast_dtype``/``tp_group``/``sp_group`` exist for interface + # parity: weights load in the checkpoint's own dtypes and neither the + # ring nor the BlockMask shards yet. + if node_name in self._submodule_cache: + return self._submodule_cache[node_name] + submodule = self._create_submodule(node_name, device) + self._submodule_cache[node_name] = submodule + if submodule is not None: + logger.info("Loaded Waypoint submodule for node %s", node_name) + return submodule + + def _create_submodule( + self, node_name: str, device: str = "cpu", + ) -> NodeSubmodule | None: + """Construct one node's submodule. None in dummy mode and for unknown + nodes, which makes the engine run that node without real computation.""" + if self.skip_weight_loading: + return None + if node_name in {DIT_NODE, VAE_ENCODER_NODE, VAE_DECODER_NODE}: + self._resolve_checkpoints() + if node_name == DIT_NODE: + from mstar.model.waypoint.weight_loader import build_waypoint_dit + + dit = build_waypoint_dit( + self.config, self.checkpoint_dir, device=device, + ) + return WaypointDitSubmodule(dit, self.config) + if node_name == VAE_ENCODER_NODE: + return WaypointVaeEncoderSubmodule(self._taehv_weights(device), self.config) + if node_name == VAE_DECODER_NODE: + return WaypointVaeDecoderSubmodule(self._taehv_weights(device), self.config) + logger.warning("Waypoint has no submodule for node %r; running it dummy.", node_name) + return None + + def _resolve_checkpoints(self) -> None: + """Resolve both artifacts and validate the DiT manifest before allocation. + + The first requested node triggers this once. Resolving both together is + intentional: startup must fail on a missing AE before a multi-gigabyte + DiT has been allocated, even when the engine happens to ask for the DiT + node first. Tensor completeness and the pinned TAEHV runtime architecture + are then validated while loading, before request admission. + """ + if self._checkpoints_resolved: + return + if not self.checkpoint_dir: + raise ValueError( + "Waypoint requires `checkpoint_dir` or `model_path_hf` when " + "weight loading is enabled." + ) + from mstar.model.waypoint.checkpoint import ( + require_taehv_runtime, + resolve_taehv_checkpoint, + resolve_waypoint_checkpoint, + ) + + # Dependency validation is part of the same preflight as both weight + # sources. In particular it must precede the DiT resolver: a missing or + # empty TAEHV install should not trigger a multi-GiB download first. + require_taehv_runtime() + self.checkpoint_dir = str(resolve_waypoint_checkpoint( + self.checkpoint_dir, + self.config, + cache_dir=self.cache_dir, + revision=self.checkpoint_revision, + )) + self.ae_uri = str(resolve_taehv_checkpoint( + self.ae_uri, + cache_dir=self.cache_dir, + revision=self.ae_revision, + )) + self._checkpoints_resolved = True + + def _taehv_weights(self, device: str) -> torch.nn.Module: + """The AE weights, built once and shared by both VAE nodes: they differ + only in streaming state, which is per request and not held here. bf16 at + build is the reference's serving dtype, and with ``disable_autocast`` on + both nodes it is the dtype the convs actually run in.""" + if self._taehv is None: + from mstar.model.waypoint.components.taehv import load_taehv + + self._taehv = load_taehv(self.ae_uri, cache_dir=self.cache_dir).to( + device=device, dtype=torch.bfloat16 + ) + return self._taehv diff --git a/mstar/model/waypoint/weight_loader.py b/mstar/model/waypoint/weight_loader.py index c587878f6..f10ea3a02 100644 --- a/mstar/model/waypoint/weight_loader.py +++ b/mstar/model/waypoint/weight_loader.py @@ -5,13 +5,10 @@ dtypes), moves it to the device, **re-ties ``cond_proj``**, then streams the safetensors shards through ``load_weights_into``. -The order is fixed and ``retie_cond_proj()`` must follow ``to_empty``: -``Module._apply`` has no cross-module memo, so ``to_empty(device)`` silently -un-aliases the six ``cond_proj`` matrices that blocks 1..23 share with block 0. -Nothing raises; the symptoms are +0.6B resident parameters and 23 blocks of -``cond_proj`` the loader never fills. +``retie_cond_proj()`` must follow ``to_empty``; skipping it leaves 23 blocks of +``cond_proj`` this loader never fills. -The key map. Thirteen transforms sit between the checkpoint's 369 keys and this +The key map. Thirteen transforms sit between the checkpoint's 393 keys and this module's 174 parameters: === ============================================================== =========== @@ -30,62 +27,28 @@ T12 any ``.cond_heads.`` key (note the plural) drop === ============================================================== =========== -T0 has no counterpart in the reference, which keeps a two-level -``WorldModel``/``WorldDiT`` split where ``components/dit.py`` collapses them -into one ``WaypointDiT`` whose blocks live at ``blocks.{i}``. Both spellings are -accepted here, as are the canonical post-transform spellings the reference's own -``pop``/``setdefault`` transforms tolerate — which spelling the shipped file -uses could not be established statically, so guessing one was not an option. - -Three things this file does that the mstar machinery does not give you: - - * **The two fusions need a fan-in, and ``name_remapper`` is ``str -> str|None`` - with none.** Both go through ``StackedParamRule``. Neither target module is a - ``FusedColumnLinear`` — ``attention.py`` builds ``qkv_proj`` as a plain - ``nn.Linear`` and ``layers.MLPFusion`` holds a merged ``[D,2D]`` ``mlp.fc1`` - — so neither parameter ships a ``weight_loader`` and ``default_weight_loader`` - would assert on the shard id. ``_attach_shard_loaders`` installs one, **after** - ``to_empty`` (which drops attribute state along with the storage). - * **Per-shard completeness.** ``load_weights_into`` returns *target* names, and - q/k/v all share one target, so ``set(named_parameters()) - loaded`` is - satisfied by any one of the three: a ``k_proj`` missing from every layer - passes the wan22-style check silently. The remapper - therefore tallies ``(target, shard_id)`` pairs and the contract checks those - too. Same hole, same fix, for ``fc1_x``/``fc1_c``. - * **The reshaping transforms T1/T2 have no hook at all**, so they ride a thin - adapter over the shard iterator, which is also where the config facts that - were transcribed rather than read off the checkpoint (``n_kv_heads``, - ``patch``) get validated against the tensor shapes actually on disk. - -Completeness is a hard contract: a checkpoint key that reaches no parameter, a -parameter no key reached, a fused shard that never arrived, or two keys writing -the same slot, all raise. A silently skipped weight is a wrong-output bug, not a -warning. Explicitly dropped keys (T9/T10/T12) are expected and silent. - -Two consequences of "two keys writing the same slot" that a naive -``(target, shard_id)`` tally does not cover, and that both fusions have: - - * A **pre-fused** key (``attn.qkv_proj.weight``, ``ctrl_mlpfusion.mlp.fc1`` - — the canonical spellings the reference tolerates) claims - ``(target, None)``, which does not collide with - ``(target, "q")``. Left alone, a file carrying both spellings assembles one - parameter out of both sources in whatever order the shard iterator happens - to yield — Q and K off the fused blob, V off ``v_proj``, no error. So - ``(target, None)`` is made to conflict with every ``(target, shard)`` of a - target that has a stacked rule. - * ``bias_in`` is the opposite case: three spellings legitimately share one - target (T4), so that collision is *arbitrated* by an explicit precedence - rank rather than refused. See ``_BIAS_IN_SPELLINGS``. - -Order inside the pipeline: the unconditional drops (T10/T12) run **before** the -shape validation in ``_adapt_checkpoint_stream``, because a ``.cond_heads.`` key -that happens to end in ``.k_proj.weight`` is a T12 drop and not a GQA violation. -T9's per-block ``cond_proj`` drop is *not* in that pre-filter: those keys are -exactly what ``_CondProjTieCheck`` exists to compare. - -``load_hf_weights`` is deliberately not used, for the reason wan22 documents: its -``skip_predicate`` runs *before* the remapper and would drop keys outside the -unexpected-key accounting. +T0's source spelling is the reference's two-level ``WorldModel``/``WorldDiT`` +split, which ``components/dit.py`` collapses. Both spellings are accepted, as +are the canonical post-transform spellings the reference's own +``pop``/``setdefault`` transforms tolerate; which the shipped file uses could not +be established statically. + +Three things mstar's machinery does not give you. The fusions need a fan-in and +``name_remapper`` is ``str -> str|None``, so both go through ``StackedParamRule`` +with a ``_SliceShardLoader`` attached after ``to_empty``. ``load_weights_into`` +returns *target* names and q/k/v share one target, so the remapper tallies +``(target, shard_id)`` pairs. T1/T2 have no reshape hook, so they ride an adapter +over the shard iterator, which is also where the transcribed config facts +(``n_kv_heads``, ``patch``) are checked against the shapes on disk. + +Completeness is a hard contract: a key that reaches no parameter, a parameter no +key reached, a fused shard that never arrived, or two keys writing one slot all +raise. Explicitly dropped keys (T9/T10/T12) are expected and silent. + +The unconditional drops (T10/T12) run **before** the shape validation, because a +``.cond_heads.`` key ending in ``.k_proj.weight`` is a T12 drop and not a GQA +violation. T9's per-block ``cond_proj`` drop is not in that pre-filter: those +keys are what ``_CondProjTieCheck`` compares. """ from __future__ import annotations @@ -97,9 +60,8 @@ import torch from torch import nn -# _apply_stacked is imported rather than reimplemented on purpose: the remapper -# has to resolve a key to the same target load_weights_into will, and a local -# copy of a three-line matcher is a thing that drifts. +# _apply_stacked is imported rather than reimplemented: the remapper has to +# resolve a key to the same target load_weights_into will. from mstar.model.loader.base import StackedParamRule, _apply_stacked, load_weights_into from mstar.model.loader.iterators import iter_safetensors_shards from mstar.model.waypoint.components.dit import WaypointDiT @@ -115,46 +77,20 @@ ] -# Which block's cond_proj set is the physical one. The reference ties every -# block's cond_proj to block 0's in __init__ and then loads all 24 stored copies -# into that one tensor, so it effectively keeps block 23's (last write wins); -# the port keeps ONE copy and drops the rest, so it has to name a block. -# -# **Not a knob.** This records a fact of ``components/dit.py``, it does not -# choose one: ``WaypointDiT.retie_cond_proj`` hardcodes -# ``ref_proj = self.blocks[0].cond_head.cond_proj``, so block 0 is the only -# spelling ``named_parameters()`` reports after the retie and any other value -# here would turn all six kept keys into unexpected-key failures and leave the -# six real parameters unloaded. The constant exists to name the fact at its two -# use sites, and ``_assert_cond_proj_tied`` asserts the module tree still agrees -# with it — so if ``retie_cond_proj`` ever moves the owner, that fails with a -# sentence rather than with twelve confusing key errors. Changing this number -# without changing ``retie_cond_proj`` (which this file does not own) is not a -# supported edit. -# -# Block 0 is also what the reference's own __init__ ties to, what -# ``layers.CondHead``'s docstring says, and what PARAM_TREE section 4.9's -# fill-forward loop uses as its reference. -# -# The choice only matters if the 24 stored copies disagree, which is exactly the -# silent divergence PARAM_TREE flags as S9 — so ``verify_cond_proj_tie`` checks -# that they agree instead of relying on the argument. When they agree, block 0 -# and block 23 are the same tensor and the choice is moot; when they do not, the -# load raises rather than quietly disagreeing with the reference. +# Which block's cond_proj set is the physical one. Not a knob: it records that +# WaypointDiT.retie_cond_proj hardcodes self.blocks[0], so any other value turns +# the six kept keys into unexpected-key failures and leaves the six real +# parameters unloaded. _assert_cond_proj_tied checks the tree still agrees. COND_PROJ_SOURCE_BLOCK = 0 # Slot ids for the two port-side fusions. Order defines the layout. QKV_SHARD_IDS: tuple[str, ...] = ("q", "k", "v") CTRL_FC1_SHARD_IDS: tuple[str, ...] = ("x", "c") -# Fused-shard routing. The leading dots matter: without them ``.v_proj`` would -# also match inside ``qkv_proj``. Note this is NOT ``LLAMA_STACKED_PARAMS``, -# whose extra ``gate_proj``/``up_proj`` rules would be live substring matchers -# for parameters Waypoint does not have. -# -# ``_apply_stacked`` rewrites by ``str.replace``, so -# ``blocks.0.attn.q_proj.weight`` -> ``blocks.0.attn.qkv_proj.weight`` -# ``blocks.0.ctrl_mlpfusion.fc1_x.weight`` -> ``blocks.0.ctrl_mlpfusion.mlp.fc1.weight`` +# Fused-shard routing. The leading dots matter: without them ".v_proj" would +# also match inside "qkv_proj". Not LLAMA_STACKED_PARAMS, whose extra +# gate_proj/up_proj rules would be live substring matchers for parameters +# Waypoint does not have. WAYPOINT_STACKED_PARAMS: list[StackedParamRule] = [ StackedParamRule(".qkv_proj", ".q_proj", "q"), StackedParamRule(".qkv_proj", ".k_proj", "k"), @@ -169,16 +105,14 @@ # ``transformer.`` optional: T0. Accepts the reference's two-level spelling and # the collapsed one. _BLOCK_RE = re.compile(r"^(?:transformer\.)?blocks\.(\d+)\.(.+)$") -# Legacy half-heads. j is range(3) on both sides (PARAM_TREE section 4.5/4.6); -# a j >= 3 here is malformed and is left unmapped so it surfaces as unexpected. +# Legacy half-heads. j is range(3) on both sides; a j >= 3 is malformed and is +# left unmapped so it surfaces as unexpected. _LEGACY_COND_PROJ_RE = re.compile(r"^(attn|mlp)_cond_head\.cond_proj\.(\d+)\.weight$") _COND_PROJ_RE = re.compile(r"^cond_head\.cond_proj\.(\d+)\.weight$") -# T3 is an explicit five-name allowlist, not a ``dit_mlp.*`` wildcard: a wildcard -# is over-permissive and would silently absorb a future ``dit_mlp.*`` key instead -# of surfacing it. ``expert_*``/``router`` do not exist under moe=False; they are -# listed because the reference renames them, and the rename lands on a parameter -# this port does not have, which is a loud unexpected-key failure either way. +# T3 is an allowlist, not a dit_mlp.* wildcard, so a future dit_mlp.* key +# surfaces instead of being absorbed. expert_*/router do not exist under +# moe=False; they are listed because the reference renames them. _DIT_MLP_LEAVES: tuple[str, ...] = ( "fc1.weight", "fc2.weight", @@ -187,10 +121,10 @@ "router.weight", ) -# T10. ``CFG.forward`` is a training-time dropout with no call site in the -# reference's ``WorldModel.forward``; the port does not instantiate the tensor. -# Dropped explicitly rather than left unmatched so the unexpected-key accounting -# keeps no hole (PARAM_TREE S10). +# T10. CFG.forward is a training-time dropout with no call site in the +# reference's WorldModel.forward, so the port has no tensor for it. Dropped +# explicitly rather than left unmatched, so unexpected-key accounting keeps +# no hole. _DROPPED_TOP_LEVEL_KEYS = frozenset({"ctrl_cfg.null_emb"}) # T12. Substring filter, unconditional, note the plural. @@ -199,28 +133,15 @@ # Half the CondHead slots come from each legacy half-head. _COND_PROJ_PER_HEAD = CondHead.n_cond // 2 -# T4 precedence over the three spellings that share ``cond_head.bias_in``, -# lowest first, highest wins. This is the reference's pop/setdefault outcome -# (``world_model.py:386-389``) restated as a rank: -# -# if attn_bias is not None or mlp_bias is not None: -# state_dict.setdefault(p + "cond_head.bias_in", -# mlp_bias if mlp_bias is not None else attn_bias) +# T4 precedence over the three spellings that share cond_head.bias_in, lowest +# first, highest wins — the reference's pop/setdefault outcome: mlp beats attn, +# canonical beats both. The attn spelling is a FALLBACK, not a drop; which of the +# two the shipped file carries is unestablished, and dropping it unconditionally +# would leave 24 unloaded bias_in on an attn-only file. # -# so mlp beats attn, and `setdefault` means an already-canonical -# ``cond_head.bias_in`` beats both. Note what this is NOT: the reference does -# not *drop* ``attn_cond_head.bias_in``, it uses it as the fallback when the mlp -# spelling is absent — and PARAM_TREE section 10.2 leaves it unresolved which of -# the two the shipped file actually carries (the 49,152-parameter difference is -# five orders of magnitude below the precision of "1.86B"). Dropping the attn -# copy unconditionally is therefore a plausible day-one failure: 24 unloaded -# ``cond_head.bias_in`` on a file that only has the attn spelling. -# -# A rank is needed rather than "last write wins" because this loader streams: the -# reference arbitrates over a materialized dict, while here the three spellings -# can arrive in any order across shard files, and the resident weight must not -# depend on that order. Arbitration itself lives in ``build_waypoint_dit``, the -# only place that sees every key. +# A rank rather than last-write-wins because this loader streams: the three +# spellings can arrive in any shard order and the resident weight must not depend +# on it. Arbitration lives in build_waypoint_dit, which sees every key. _BIAS_IN_SPELLINGS: tuple[str, ...] = ( "attn_cond_head.bias_in", "mlp_cond_head.bias_in", @@ -242,18 +163,14 @@ def _remap_block_suffix(suffix: str, layer_idx: int) -> str | None: """Map one per-block checkpoint suffix to its parameter suffix, or ``None`` to drop it. ``suffix`` excludes the ``blocks.{i}.`` prefix.""" - # T4: both legacy spellings map to the one target, and the attn copy is a - # FALLBACK, not a drop (see _BIAS_IN_SPELLINGS). Which of the three actually - # gets written is decided by rank in build_waypoint_dit; a pure key->key - # function cannot decide it, because it cannot see whether the winner is - # elsewhere in the file. + # T4: both legacy spellings map to the one target; rank in build_waypoint_dit + # decides which of the three writes. if suffix in ("attn_cond_head.bias_in", "mlp_cond_head.bias_in"): suffix = "cond_head.bias_in" - # T5/T6: identity index map for the attn head (0,1,2 -> 0,1,2), +3 for the - # mlp head (0,1,2 -> 3,4,5). Slots 0-2 drive the attention sublayer and 3-5 - # the MLP sublayer, which is what the source names say; swapping them is - # PARAM_TREE S5, silent and numerically catastrophic. + # T5/T6: identity index map for the attn head, +3 for the mlp head. Slots + # 0-2 drive the attention sublayer and 3-5 the MLP sublayer; swapping them + # is silent and numerically catastrophic. legacy = _LEGACY_COND_PROJ_RE.match(suffix) if legacy is not None: head, j = legacy.group(1), int(legacy.group(2)) @@ -267,8 +184,7 @@ def _remap_block_suffix(suffix: str, layer_idx: int) -> str | None: suffix = "mlp." + leaf break - # T8. Guarded on fc2 alone, separately from T7's both-halves guard; omitting - # it leaves 8 layers' ctrl_mlpfusion.mlp.fc2 unloaded. + # T8. Guarded on fc2 alone, separately from T7's both-halves guard. if suffix == "ctrl_mlpfusion.fc2.weight": suffix = "ctrl_mlpfusion.mlp.fc2.weight" @@ -277,25 +193,18 @@ def _remap_block_suffix(suffix: str, layer_idx: int) -> str | None: if _COND_PROJ_RE.match(suffix) is not None and layer_idx != COND_PROJ_SOURCE_BLOCK: return None - # T7 (fc1_x/fc1_c) and T11 (q/k/v_proj) are deliberately left alone: they are - # fan-ins, which a remapper cannot express, and WAYPOINT_STACKED_PARAMS - # routes them. + # T7 (fc1_x/fc1_c) and T11 (q/k/v_proj) are left alone: fan-ins, which a + # remapper cannot express. WAYPOINT_STACKED_PARAMS routes them. return suffix def _is_unconditionally_dropped(name: str) -> bool: """T12 and T10 — the drops that depend on nothing but the key. - Factored out because the shard adapter has to apply them *before* it - validates tensor shapes: ``…cond_heads.0.k_proj.weight`` is a T12 drop, not - a GQA shape violation, and validating first turns an expected drop into a - hard failure. One predicate, two call sites, so the pre-filter and the - remapper cannot drift apart on what counts as dropped. - - T9 (``cond_proj`` for blocks other than ``COND_PROJ_SOURCE_BLOCK``) is - deliberately NOT here even though it is also a drop: those 138 keys are - exactly what ``_CondProjTieCheck`` has to see, so they must survive the - stream filter and be dropped later, in the remapper. + One predicate, two call sites, so the shard adapter's pre-filter and the + remapper cannot drift apart. T9 is not here even though it is also a drop: + those 138 keys are what ``_CondProjTieCheck`` has to see, so they survive the + stream filter and are dropped later, in the remapper. """ return _COND_HEADS_FRAGMENT in name or name in _DROPPED_TOP_LEVEL_KEYS @@ -316,16 +225,13 @@ def remap_checkpoint_key(name: str) -> str | None: """Map one Waypoint checkpoint key to the native parameter path, or return ``None`` for a key that is intentionally dropped (T9/T10/T12). - Pure function of the key — no model, no config — so it can be exercised - directly. Keys it maps to a name that is not a parameter are the caller's - problem: ``build_waypoint_dit`` treats those as unexpected and raises. + Pure function of the key — no model, no config; a key mapped to a name that + is not a parameter is the caller's problem. - **Not injective, by design, in exactly one place.** All three T4 ``bias_in`` - spellings map to ``blocks.{i}.cond_head.bias_in``; which one is allowed to - write is a precedence question that needs the whole key set, so it is settled - in ``build_waypoint_dit`` and not here (``_BIAS_IN_SPELLINGS``). Everywhere - else a second key resolving to a slot that is already claimed is a hard - error. + Not injective in exactly one place: all three T4 ``bias_in`` spellings map to + ``blocks.{i}.cond_head.bias_in``, and picking a winner needs the whole key + set, so ``build_waypoint_dit`` settles it. Everywhere else a second key + resolving to a claimed slot is a hard error. """ if _is_unconditionally_dropped(name): # T10/T12 return None @@ -357,8 +263,8 @@ def _unpatchify_weight(tensor: torch.Tensor, config: WaypointConfig, key: str) - ordered ``(c, ph, pw)`` with pw fastest, because ``WaypointDiT.forward`` unpacks it as ``view(B, N, Hp, Wp, C, ph, pw)``. Dropping the permute, or transposing ph/pw inside it, keeps the shape and silently reprojects every - output sub-pixel (PARAM_TREE S1/S1b). ``reshape``, not ``view`` — the - permuted tensor is not contiguous. + output sub-pixel. ``reshape``, not ``view`` — the permuted tensor is not + contiguous. """ ph, pw = config.patch if tensor.ndim == 4: @@ -366,8 +272,8 @@ def _unpatchify_weight(tensor: torch.Tensor, config: WaypointConfig, key: str) - if (k_h, k_w) != (ph, pw): raise RuntimeError( f"{key} is a {k_h}x{k_w} patch kernel but WaypointConfig.patch is " - f"{(ph, pw)}. PARAM_TREE section 10.4: patch was transcribed from the " - "checkpoint's config.yaml, not read from it; the checkpoint wins." + f"{(ph, pw)}. patch was transcribed from the checkpoint's config.yaml, " + "not read from it; the checkpoint wins." ) if channels != config.channels or d_model != config.d_model: raise RuntimeError( @@ -394,8 +300,8 @@ def _unpatchify_bias(tensor: torch.Tensor, config: WaypointConfig, key: str) -> ``[C] -> [C,1,1] -> expand(-1, ph, pw) -> reshape(-1)``. The expand target is ``(C, ph, pw)`` so the flatten agrees with T1's row ordering; a ``repeat(ph*pw)`` produces the same ``[128]`` shape with the bias on the - wrong sub-pixel, which integrates into a slow colour drift over an - autoregressive rollout rather than failing (PARAM_TREE S2). + wrong sub-pixel, which integrates into a slow colour drift over a rollout + rather than failing. """ ph, pw = config.patch if tensor.numel() == config.channels: @@ -413,8 +319,7 @@ def _unpatchify_bias(tensor: torch.Tensor, config: WaypointConfig, key: str) -> def _check_patchify(tensor: torch.Tensor, config: WaypointConfig, key: str) -> None: """Validate ``config.patch`` and ``config.channels`` against the conv kernel. - Unlike ``unpatchify``, this one is 4-D in both the file and the module, so it - is validated rather than transformed. + 4-D in both the file and the module, so validated rather than transformed. """ ph, pw = config.patch if tensor.ndim != 4: @@ -423,8 +328,8 @@ def _check_patchify(tensor: torch.Tensor, config: WaypointConfig, key: str) -> N if (k_h, k_w) != (ph, pw): raise RuntimeError( f"{key} is a {k_h}x{k_w} patch kernel but WaypointConfig.patch is " - f"{(ph, pw)}. PARAM_TREE section 10.4: patch was transcribed rather than " - "read; a wrong value silently rescales the token grid." + f"{(ph, pw)}. patch was transcribed rather than read; a wrong value " + "silently rescales the token grid." ) if (d_model, channels) != (config.d_model, config.channels): raise RuntimeError( @@ -448,9 +353,9 @@ def _check_attn_proj( f"{key} has shape {tuple(tensor.shape)}; expected " f"[{what} = {rows}, d_model = {config.d_model}] from n_heads=" f"{config.n_heads}, n_kv_heads={config.n_kv_heads}, d_head={config.d_head}. " - "PARAM_TREE section 10.4: n_kv_heads was transcribed from the checkpoint's " - "config.yaml rather than read from it (the reference default is n_heads), " - "and a wrong value reshapes GQA attention without raising." + "n_kv_heads was transcribed from the checkpoint's config.yaml rather than " + "read from it (the reference default is n_heads), and a wrong value " + "reshapes GQA attention without raising." ) @@ -478,35 +383,16 @@ def _cond_proj_slot(key: str) -> tuple[int, int] | None: class _CondProjTieCheck: """Confirms the checkpoint's 24 stored ``cond_proj`` sets agree before 23 of - them are dropped (PARAM_TREE S9). + them are dropped. The port keeps ``COND_PROJ_SOURCE_BLOCK``'s copy; the reference keeps block 23's, because it loads all 24 into one shared tensor and the last write wins. - The two agree only if the stored copies are identical — which they should be, - since the tie was in place at training time, but nothing in the file or in - either loader enforces it. If a fine-tune ever broke the tie, the port and - the reference would silently produce different video. - - Compares the matrices **in full**. - - An earlier revision compared a fixed ``[:, :64]`` column probe and justified - it as "~3 MB of retained probes instead of 1.16 GB of streamed comparisons". - That trade does not exist: the two figures measure different things. The - ~1.13 GiB of stored ``cond_proj`` (24 blocks x 6 x ``[2048, 2048]`` bf16) - streams past either way — this class sits on a stream the loader is already - consuming and adds no reads at all. The only quantity the probe reduced is - what is **retained**: one reference copy per slot, i.e. 6 x ``[2048, 2048]`` - fp32 = **96 MiB** held for the duration of the load, against ~3 MiB for the - probe. 96 MiB once, at load, next to a 2.56 GB model, is not a cost worth an - unsound check — and the probe was demonstrably unsound: randomizing block 2 - slot 0's columns 64 onward loaded clean with ``verify_cond_proj_tie=True``. - - ``verify_cond_proj_tie=False`` remains the escape hatch, and it is now the - only thing between the check and a machine that cannot spare the 96 MiB. - - Whichever block arrives first for a slot becomes that slot's reference, so - this does not depend on shard ordering, and equality across all 24 makes the - block-0-vs-23 choice moot rather than merely defensible. + Nothing in the file or in either loader enforces that they match, so a + fine-tune that broke the tie would silently make the two serve different + video. Compares the matrices in full; whichever block arrives first for a + slot becomes that slot's reference, so the result does not depend on shard + ordering. Retains 6 x ``[2048, 2048]`` fp32 (96 MiB) for the load; + ``verify_cond_proj_tie=False`` is the escape hatch. """ def __init__(self) -> None: @@ -539,15 +425,12 @@ def _adapt_checkpoint_stream( facts, in one streaming pass over the shards. T1/T2 live here rather than in the remapper because mstar has no reshape - hook: ``name_remapper`` sees names only, and ``weight_loader`` is per-target - (the two ``unpatchify`` parameters have no fused loader to hang it on). - - **Drops run before validation.** These checks fire on key *suffixes*, so a - key that T12 drops unconditionally can still end in ``.k_proj.weight`` — - ``…blocks.0.cond_heads.0.k_proj.weight`` did exactly that and raised a GQA - shape error about a key the loader had already decided to throw away. - Validating only what survives the drop filter is the fix; a dropped key's - shape is not this model's business. + hook: ``name_remapper`` sees names only, and ``weight_loader`` is per-target. + + Drops run before validation: the checks fire on key *suffixes*, so a key + T12 drops unconditionally can still end in ``.k_proj.weight`` + (``…blocks.0.cond_heads.0.k_proj.weight`` does) and a dropped key's shape is + not this model's business. """ q_rows = config.n_heads * config.d_head kv_rows = config.n_kv_heads * config.d_head @@ -582,13 +465,11 @@ def _adapt_checkpoint_stream( class _SliceShardLoader: """``param.weight_loader`` for a port-side fused parameter. - Copies one checkpoint shard into its slice of the fused tensor. Exists - because neither fusion target is a ``FusedColumnLinear``: ``qkv_proj`` is a - plain ``nn.Linear`` in ``components/attention.py`` and ``mlp.fc1`` is a plain - ``nn.Linear`` inside ``layers.MLPFusion``, so neither carries a loader and - ``default_weight_loader`` asserts ``loaded_shard_id is None``. It also - generalizes ``FusedColumnLinear`` in the one way ``ctrl_mlpfusion`` needs: - that fusion concatenates along **dim 1** (the input-feature axis), not dim 0. + Copies one checkpoint shard into its slice of the fused tensor. Neither + fusion target is a ``FusedColumnLinear`` — both are plain ``nn.Linear`` — so + neither carries a loader, and ``default_weight_loader`` asserts + ``loaded_shard_id is None``. ``ctrl_mlpfusion`` also concatenates along dim 1 + (the input-feature axis), which ``FusedColumnLinear`` does not do. """ def __init__(self, param_name: str, dim: int, layout: dict[str, tuple[int, int]]): @@ -604,9 +485,8 @@ def __call__( ) -> None: if loaded_shard_id is None: # A checkpoint already written in the fused spelling. Only reachable - # for ctrl_mlpfusion.mlp.fc1 (PARAM_TREE section 3.5 lists it as a - # canonical spelling the reference accepts); harmless and symmetric - # for qkv_proj. + # for ctrl_mlpfusion.mlp.fc1, whose fused form the reference also + # accepts; harmless and symmetric for qkv_proj. if tuple(param.data.shape) != tuple(loaded_weight.shape): raise RuntimeError( f"{self.param_name}: pre-fused checkpoint tensor has shape " @@ -648,11 +528,9 @@ def _attach_shard_loaders( fused: dict[str, tuple[str, ...]] = {} for name, param in dit.named_parameters(): if name.endswith(".attn.qkv_proj.weight"): - # cat([q, k, v], dim=0) — verified against the reference's own fusion - # (patch_model.MergedQKVAttn's cat) and against the matching - # split((q_out, kv_out, kv_out), dim=-1) in components/attention.py. + # cat([q, k, v], dim=0), matching the split in components/attention.py. # GQA makes the shards unequal, so a q/k swap raises but a k/v swap - # loads cleanly and produces meaningless attention (PARAM_TREE S11). + # loads cleanly and produces meaningless attention. dim, layout = 0, { "q": (0, q_rows), "k": (q_rows, kv_rows), @@ -661,9 +539,9 @@ def _attach_shard_loaders( expected_shape = (q_rows + 2 * kv_rows, d_model) elif name.endswith(".ctrl_mlpfusion.mlp.fc1.weight"): # cat([fc1_x, fc1_c], dim=1) — x first. layers.MLPFusion splits it - # straight back with chunk(2, dim=1), whose low columns are the token - # half; the reverse order keeps the [2048, 4096] shape and applies - # controller conditioning to tokens and vice versa (PARAM_TREE S7). + # back with chunk(2, dim=1), whose low columns are the token half; + # the reverse order keeps the [2048, 4096] shape and applies + # controller conditioning to tokens and vice versa. dim, layout = 1, {"x": (0, d_model), "c": (d_model, d_model)} expected_shape = (d_model, 2 * d_model) else: @@ -691,12 +569,9 @@ def parameter_census(dit: WaypointDiT) -> tuple[int, int, int]: ``named_parameters()`` deduplicates aliased Parameters; ``state_dict()`` does not, so the gap between the two counts is exactly the tied ``cond_proj``. For - the 720P checkpoint this is ``(174, 1_281_958_040, 1_860_771_992)`` — the - "1.28B resident / 1.86B stored" figures. - - Counting straight off the checkpoint gives 1,281,960,088 / 1,860,774,040, - 2,048 more in each: that arithmetic carries ``ctrl_cfg.null_emb`` - ``[1, 1, 2048]`` in both totals, and the port drops it (T10). + the 720P checkpoint this is ``(174, 1_281_958_040, 1_860_771_992)``; counting + off the checkpoint gives 2,048 more in each, because that carries + ``ctrl_cfg.null_emb`` ``[1, 1, 2048]``, which the port drops (T10). """ params = dict(dit.named_parameters()) return ( @@ -711,17 +586,10 @@ def _assert_cond_proj_tied(dit: WaypointDiT, config: WaypointConfig) -> None: ``named_parameters()`` deduplicates aliased Parameters, so a correctly tied model reports 6 ``cond_proj`` tensors and an un-tied one reports - ``6 * n_layers``. This is PARAM_TREE S9b, which is otherwise entirely silent - — the un-tied model is numerically correct and merely 0.6B parameters - heavier, so no output check catches it and the completeness contract would - instead report 138 unloaded parameters with no hint as to why. - - Both the tensor count and the resident/stored numel gap are checked, because - they fail differently: the count catches "never tied", while the numel gap - catches a partial tie (some blocks aliased, some not) that still leaves the - count wrong in a way a reader might not connect to memory. Both bounds are - derived from ``config``, not hardcoded to the 720P variant, so they hold for - the 360P sibling and for the reduced configs used in testing. + ``6 * n_layers``. An un-tied model is otherwise silent: numerically correct, + 0.6B parameters heavier, and visible only as 138 unloaded parameters. The + count catches "never tied"; the resident/stored numel gap catches a partial + tie. Both bounds come from ``config``, not the 720P numbers. """ tied = [name for name, _ in dit.named_parameters() if ".cond_head.cond_proj." in name] if len(tied) != CondHead.n_cond: @@ -731,11 +599,9 @@ def _assert_cond_proj_tied(dit: WaypointDiT, config: WaypointConfig) -> None: f"{config.n_layers} blocks). to_empty(device) un-ties them and " "retie_cond_proj() must be called after it, not before." ) - # COND_PROJ_SOURCE_BLOCK is a record of which block retie_cond_proj aliases - # the others onto, not a choice this file gets to make; assert it rather than - # trust it. If the two ever disagree, T9 drops the six keys the module keeps - # and keeps the six it drops, which surfaces as 6 unexpected keys plus 6 - # unloaded parameters and no explanation. + # COND_PROJ_SOURCE_BLOCK records which block retie_cond_proj aliases the + # others onto; if the two disagree, T9 drops the six keys the module keeps + # and keeps the six it drops. owner_prefix = f"{MODEL_BLOCK_PREFIX}{COND_PROJ_SOURCE_BLOCK}.cond_head.cond_proj." if not all(name.startswith(owner_prefix) for name in tied): raise RuntimeError( @@ -747,8 +613,7 @@ def _assert_cond_proj_tied(dit: WaypointDiT, config: WaypointConfig) -> None: ) _, dedup_numel, raw_numel = parameter_census(dit) # Every block past the first contributes 6 aliased [D, D] matrices that - # state_dict() re-expands and named_parameters() does not. For 720P that is - # 23 * 6 * 2048^2 = 578,813,952, i.e. the 1.86B - 1.28B gap. + # state_dict() re-expands and named_parameters() does not. expected_gap = (config.n_layers - 1) * CondHead.n_cond * config.d_model**2 if raw_numel - dedup_numel != expected_gap: raise RuntimeError( @@ -775,11 +640,9 @@ def _initialize_structurally(dit: WaypointDiT, seed: int = 0) -> None: """Fill a ``to_empty``-materialized model with finite values. ``to_empty`` allocates *uninitialized* storage, which routinely contains NaN - and Inf bit patterns, so a "no checkpoint" model is not usable for a shape or - plumbing smoke test until something writes to every parameter. Not an attempt - to reproduce the reference's init — only the two parameters whose init is a - documented fact are reproduced (``v_lamb`` = 0.5, and the two 1-D tensors - ``cond_head.bias_in``/``unpatchify.bias``, both zeros in the reference). + and Inf bit patterns, so a "no checkpoint" model is unusable even for a shape + smoke test until something writes every parameter. This is not the + reference's init; only ``v_lamb`` = 0.5 and the zeroed 1-D tensors match it. Iterating ``named_parameters()`` writes each tied ``cond_proj`` once, which is what makes the aliasing survive. @@ -816,23 +679,14 @@ def build_waypoint_dit( """Meta-build, materialize on ``device``, and load the checkpoint into a ready-to-serve (eval-mode) native Waypoint DiT. - Args: - config: the checkpoint's config. ``n_kv_heads`` and ``patch`` are - validated against the tensor shapes actually in the file. - checkpoint_dir: local directory holding ``model.safetensors`` (or an - index plus shards). Required unless ``skip_weight_loading``. Never - downloaded — the caller resolves the path. - device: where to materialize. - skip_weight_loading: build the structure only, with no checkpoint access - at all. For shape/plumbing work before the weights exist; the result - is randomly initialized and produces meaningless output. - verify_cond_proj_tie: check that the checkpoint's 24 stored ``cond_proj`` - sets agree before 23 of them are dropped. See ``_CondProjTieCheck``. - - Raises: - RuntimeError: on any completeness failure — an unexpected checkpoint key, - an unloaded parameter, a fused shard that never arrived, two keys - claiming one slot, or divergent ``cond_proj`` copies. + ``config``'s transcribed ``n_kv_heads`` and ``patch`` are validated against + the shapes in the file. ``checkpoint_dir`` is a local directory the caller + has already resolved; nothing here downloads. ``skip_weight_loading`` builds + a randomly initialized structure for shape and plumbing work. + + Raises ``RuntimeError`` on any completeness failure: an unexpected checkpoint + key, an unloaded parameter, a fused shard that never arrived, two keys + claiming one slot, or divergent ``cond_proj`` copies. """ if not skip_weight_loading and checkpoint_dir is None: raise ValueError( @@ -849,7 +703,10 @@ def build_waypoint_dit( if skip_weight_loading: _initialize_structurally(dit) - return dit.eval() + dit.eval().materialize_runtime_tables(device) + if config.compile_dit: + dit.compile_regions() + return dit checkpoint_dir = Path(checkpoint_dir) if not checkpoint_dir.is_dir(): @@ -861,12 +718,10 @@ def build_waypoint_dit( unexpected: list[str] = [] conflicts: list[str] = [] - # (target, shard_id) -> the checkpoint key that claimed it. This is the - # per-shard tally: load_weights_into's returned set holds target names only, - # so q, k and v all collapse to one entry and a k_proj missing from every - # layer would satisfy `set(params) - loaded`. Recorded here - # rather than in _SliceShardLoader because the remapper is the one place that - # sees both the original key (for the error message) and the resolved target. + # (target, shard_id) -> the checkpoint key that claimed it. load_weights_into's + # returned set holds target names only, so q, k and v collapse to one entry + # and a k_proj missing from every layer would still satisfy + # `set(params) - loaded`. arrivals: dict[tuple[str, str | int | None], str] = {} # T4's arbitrated collision: {target: (rank, winning checkpoint key)}. bias_in_claims: dict[str, tuple[int, str]] = {} @@ -880,10 +735,9 @@ def remap(name: str) -> str | None: unexpected.append(name) return None - # T4 is the one collision that is resolved rather than refused: three - # spellings legitimately share ``cond_head.bias_in`` and the reference - # picks between them (mlp > attn, canonical > both). Rank, not arrival - # order — see _BIAS_IN_SPELLINGS. + # T4 is the one collision resolved rather than refused: three spellings + # legitimately share cond_head.bias_in and the reference picks between + # them (mlp > attn, canonical > both). Rank, not arrival order. rank = _bias_in_rank(name) if rank is not None: held = bias_in_claims.get(target) @@ -899,21 +753,17 @@ def remap(name: str) -> str | None: claimed_by = arrivals.get((target, shard_id)) if claimed_by is not None: - # Two distinct checkpoint keys resolving to one slot. The reference - # resolves this with setdefault (first wins); a streaming loader would - # instead let the last one win, non-deterministically across shard - # order, so it is refused rather than arbitrated. + # Two distinct checkpoint keys resolving to one slot. Refused, not + # arbitrated: the winner here would be whichever arrived last, which + # is shard-order dependent. slot = target if shard_id is None else f"{target}[{shard_id}]" conflicts.append(f"{claimed_by} and {name} -> {slot}") # The same "one slot, two writers" failure across the fused/unfused - # spellings, which the (target, shard_id) key above cannot see: a - # pre-fused ``qkv_proj``/``mlp.fc1`` tensor claims (target, None) and a - # split shard claims (target, "q"), and those never collide. Both then - # write, and the surviving parameter is a mix of the two decided by - # shard-iteration order — Q and K off the fused blob, V off ``v_proj``, - # with nothing raised. Either spelling alone is fine; both together are - # a checkpoint whose intent cannot be inferred, so it is refused. + # spellings, which (target, shard_id) cannot see: a pre-fused tensor + # claims (target, None) and a split shard claims (target, "q"), so they + # never collide, both write, and the survivor is a shard-order-dependent + # mix. Either spelling alone is fine; both together are refused. if target in fused_shards: rival_slots = ( [(target, s) for s in fused_shards[target]] @@ -946,10 +796,9 @@ def remap(name: str) -> str | None: missing_shards = sorted( f"{name}[{shard_id}]" for name, shard_ids in fused_shards.items() - # A pre-fused tensor satisfies every shard of its target at once. Safe - # to short-circuit on now: `remap` refuses a file that carries both - # spellings, so reaching here with (name, None) present means the - # pre-fused tensor was the *only* writer. + # A pre-fused tensor satisfies every shard of its target at once, and + # `remap` refuses a file carrying both spellings, so (name, None) here + # means the pre-fused tensor was the only writer. if (name, None) not in arrivals for shard_id in shard_ids if (name, shard_id) not in arrivals @@ -973,4 +822,7 @@ def remap(name: str) -> str | None: "verify_cond_proj_tie=False to load block " f"{COND_PROJ_SOURCE_BLOCK}'s copy anyway." ) - return dit.eval() + dit.eval().materialize_runtime_tables(device) + if config.compile_dit: + dit.compile_regions() + return dit diff --git a/mstar/worker/worker.py b/mstar/worker/worker.py index 8a9ad0523..a156fa7d0 100644 --- a/mstar/worker/worker.py +++ b/mstar/worker/worker.py @@ -530,6 +530,7 @@ def _remove_request(self, body: RemoveRequest) -> None: for node_name in self.engine_manager.evictable_nodes(): self._last_active.pop((body.request_id, node_name), None) + logger.info("Request cleanup complete: %s", body.request_id) def _drain_request(self, body: DrainRequest) -> None: """Phase-1 teardown (abort/fail): stop scheduling and reading this rid, @@ -1350,7 +1351,12 @@ def _execute_on_gpu_thread( from mstar.utils.profiler import range_pop, range_push engine = self.engine_manager.get_engine(batch.node_name) - logger.debug("Executing batch for node %s", node_batch.node_name) + logger.debug( + "Executing: %s graph_walk=%s %s", + node_batch.node_name, + batch.graph_walk, + node_batch.request_ids, + ) if self.enable_nvtx: range_push("worker.gpu_thread_start", synchronize=False) range_pop(synchronize=False) diff --git a/packaging/aliases/mstar-project/pyproject.toml b/packaging/aliases/mstar-project/pyproject.toml index 8aced5f50..fb8647940 100644 --- a/packaging/aliases/mstar-project/pyproject.toml +++ b/packaging/aliases/mstar-project/pyproject.toml @@ -27,6 +27,7 @@ orpheus = ["mstar-ai[orpheus]"] pi05 = ["mstar-ai[pi05]"] vjepa2 = ["mstar-ai[vjepa2]"] wan22 = ["mstar-ai[wan22]"] +waypoint = ["mstar-ai[waypoint]"] vjepa2_ac = ["mstar-ai[vjepa2_ac]"] asr = ["mstar-ai[asr]"] all = ["mstar-ai[all]"] diff --git a/pyproject.toml b/pyproject.toml index 57abee4ef..4052fe251 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -187,6 +187,17 @@ wan22 = [ "av", ] +waypoint = [ + "huggingface-hub", + "safetensors", + # TAEHV is installed separately at a pinned revision. PyPI rejects project + # metadata containing direct-URL dependencies; see docs/installation.rst. + # Only the reference needs this: world_engine passes pos_ids as a TensorDict, + # so the parity tests import it. Serving does not — components/dit.py uses a + # NamedTuple to keep tensordict out of the serving path. + "tensordict==0.10.0", +] + vjepa2_ac = [ "flashinfer-python>=0.6.4", "huggingface-hub", @@ -234,6 +245,9 @@ all = [ "ninja", # speeds up the first-use JIT build of the vendored MoE align kernel # wan22 (see the [wan22] extra for the rationale; av / diffusers already above) "accelerate", + # waypoint. TAEHV is intentionally installed separately because PyPI rejects + # direct-URL dependencies in project metadata; see docs/installation.rst. + "tensordict==0.10.0", # flash-attn is intentionally omitted (Qwen3-Omni needs it) — install it # separately, see docs/installation.rst. "jiwer", diff --git a/test/modular/test_api_completion_guard.py b/test/modular/test_api_completion_guard.py index 7de23313d..8fe7987df 100644 --- a/test/modular/test_api_completion_guard.py +++ b/test/modular/test_api_completion_guard.py @@ -56,7 +56,9 @@ def process_prompt(self, *args, **kwargs): wt.reads_done_queue = queue.Queue() wt.discard_tensor_queue = queue.Queue() wt.stop_event = threading.Event() - wt.communicator = SimpleNamespace(get_all_new_messages=lambda: []) + wt.communicator = SimpleNamespace( + get_all_new_messages=lambda: [], send=lambda *args: None, + ) cleaned = [] wt.tensor_manager = SimpleNamespace( force_cleanup_request=cleaned.append, diff --git a/test/modular/test_client_sdk.py b/test/modular/test_client_sdk.py index 00cb64fc3..f216863cf 100644 --- a/test/modular/test_client_sdk.py +++ b/test/modular/test_client_sdk.py @@ -55,6 +55,9 @@ def test_coerce_and_build_files(): c = MStarClient("http://x") assert c._coerce_file("images", 0, b"\x89PNG") == ("image_0.png", b"\x89PNG") assert c._build_files(None, b"\x00\x01", None) == [("files", ("audio_0.wav", b"\x00\x01"))] + assert c._build_files(("seed.png", b"\x89PNG"), None, None) == [ + ("files", ("seed.png", b"\x89PNG")), + ] def test_stream_without_content_type_charset(): @@ -71,9 +74,56 @@ def test_stream_without_content_type_charset(): c = MStarClient("http://x") ctx = mock.MagicMock() ctx.__enter__.return_value = resp - with mock.patch.object(c._session, "post", return_value=ctx): + with ( + mock.patch.object(c._session, "post", return_value=ctx), + mock.patch.object(resp, "iter_lines", wraps=resp.iter_lines) as iter_lines, + ): events = list(c._stream("http://x/generate", {}, None)) assert [e.text for e in events] == ["hi"] + iter_lines.assert_called_once_with( + chunk_size=1024 * 1024, + decode_unicode=True, + ) + + +@pytest.mark.parametrize( + ("payload", "message"), + [ + ( + { + "modality": "error", + "data": base64.b64encode(b"capture failed").decode(), + "metadata": {"status": 500}, + }, + r"Server stream failed \(status 500\): capture failed", + ), + ({"error": "bridge failed"}, "Server stream failed: bridge failed"), + ], +) +def test_stream_raises_in_band_server_errors(payload, message): + requests = pytest.importorskip("requests") + resp = requests.Response() + resp.status_code = 200 + resp.headers["Content-Type"] = "application/x-ndjson" + resp.raw = io.BytesIO((json.dumps(payload) + "\n").encode()) + + client = MStarClient("http://x") + ctx = mock.MagicMock() + ctx.__enter__.return_value = resp + with mock.patch.object(client._session, "post", return_value=ctx): + with pytest.raises(RuntimeError, match=message): + list(client._stream("http://x/generate", {}, None)) + + +@pytest.mark.parametrize("modality", ["action", "scalar", "tensor", "video"]) +def test_to_event_preserves_text_compatible_modality_fallback(modality): + event = MStarClient._to_event({ + "modality": modality, + "bytes": b"[1, 2, 3]", + "metadata": {"sequence": 4}, + }) + assert event.text == "[1, 2, 3]" + assert event.metadata == {"sequence": 4} def test_audiobuffer_wav_bytes(): diff --git a/test/modular/test_cuda_graph_capture.py b/test/modular/test_cuda_graph_capture.py index 02adcb53b..3c17e6c05 100644 --- a/test/modular/test_cuda_graph_capture.py +++ b/test/modular/test_cuda_graph_capture.py @@ -23,9 +23,15 @@ from mstar.engine.cuda_graph_runner import CudaGraphRunner from mstar.engine.resources import BucketKey, CGSlotSpec -requires_cuda = pytest.mark.skipif( - not torch.cuda.is_available(), reason="capture allocates a graph pool" -) + +@pytest.fixture(autouse=True) +def fake_cuda_runtime(monkeypatch): + """The only real CUDA calls on this path are the graph pool handle and the + memory readings around it; `_FakeRunner` stands in for the capture itself. + Stubbing them keeps these policy tests running where there is no GPU.""" + monkeypatch.setattr(torch.cuda, "is_available", lambda: True) + monkeypatch.setattr(torch.cuda, "memory_allocated", lambda device=None: 0) + monkeypatch.setattr(torch.cuda.graphs, "graph_pool_handle", lambda: object()) class _Group: @@ -57,7 +63,8 @@ def __init__( self, specs, fail: set[tuple[str, int]] = frozenset(), num_slots=2, peer_flags: list[bool] | None = None, ): - self._device = torch.device("cuda") + # only carries the rank-agreement flag vector; nothing is captured here + self._device = torch.device("cpu") self._submodule_name = "node" self._num_slots = num_slots self._specs = specs @@ -106,7 +113,6 @@ def _specs(walks=("decode",), num_slots=2): return out -@requires_cuda def test_a_fully_captured_bucket_registers_every_slot_in_index_order(): runner = _FakeRunner(_specs()) @@ -118,7 +124,6 @@ def test_a_fully_captured_bucket_registers_every_slot_in_index_order(): ) -@requires_cuda def test_a_bucket_missing_a_slot_is_dropped_whole(): """The regression: slot 0 failing used to leave the bucket registered with slot 1's graph sitting at index 0, and only one slot to double-buffer on.""" @@ -129,7 +134,6 @@ def test_a_bucket_missing_a_slot_is_dropped_whole(): assert runner._buckets == {}, "a half-captured bucket must not be usable" -@requires_cuda def test_one_bucket_failing_does_not_take_the_others_with_it(): runner = _FakeRunner( _specs(walks=("decode", "prefill")), fail={("decode", 1)}, @@ -142,7 +146,6 @@ def test_one_bucket_failing_does_not_take_the_others_with_it(): assert bucket.slots == ["prefill:slot0", "prefill:slot1"] -@requires_cuda def test_every_rank_barriers_once_per_spec_whatever_happens(): """Capture can fail on one rank and not another; if the failing rank barriered fewer times the others would hang waiting for it.""" @@ -157,7 +160,6 @@ def test_every_rank_barriers_once_per_spec_whatever_happens(): )) -@requires_cuda def test_a_bucket_another_rank_dropped_is_dropped_here_too(): """Capture failure is per-rank. If this rank kept a bucket the peer dropped, it would lease and replay while the peer ran eager — and a @@ -173,7 +175,6 @@ def test_a_bucket_another_rank_dropped_is_dropped_here_too(): assert [key.graph_walk for key in runner._buckets] == ["decode"] -@requires_cuda def test_a_bucket_this_rank_dropped_stays_dropped_when_the_peer_kept_it(): runner = _FakeRunner( _specs(walks=("decode", "prefill")), @@ -186,7 +187,6 @@ def test_a_bucket_this_rank_dropped_stays_dropped_when_the_peer_kept_it(): assert [key.graph_walk for key in runner._buckets] == ["prefill"] -@requires_cuda def test_ranks_agree_on_the_full_candidate_list_not_just_local_successes(): """The reduced vector is ordered by the configs, which every rank shares, so a rank that captured nothing still lines its flags up with the rest.""" @@ -201,7 +201,6 @@ def test_ranks_agree_on_the_full_candidate_list_not_just_local_successes(): assert runner._buckets == {} -@requires_cuda def test_capture_hands_the_padding_rows_pages_back(): """A capture gives its padding rows real spans; a replay pads with zero-length ones, so that storage is residue the traffic should get.""" @@ -212,7 +211,6 @@ def test_capture_hands_the_padding_rows_pages_back(): assert runner._dummy_rows.released -@requires_cuda def test_single_slot_runners_still_register(): """No pre-planning resource means one slot per bucket, which is complete.""" runner = _FakeRunner(_specs(num_slots=1), num_slots=1) diff --git a/test/modular/test_flex_attention_resource.py b/test/modular/test_flex_attention_resource.py index 259fbd572..c30cb3ddc 100644 --- a/test/modular/test_flex_attention_resource.py +++ b/test/modular/test_flex_attention_resource.py @@ -66,6 +66,8 @@ RingKVConfig, RingKVLayerConfig, ) +from mstar.engine.resources.kv.ring.cache import LayerRingCache +from mstar.engine.resources.kv.ring.manager import RingPlan BLOCK = _DEFAULT_SPARSE_BLOCK_SIZE # 128 TPF = BLOCK # one frame per sparse block keeps the geometry readable @@ -214,6 +216,147 @@ def test_plan_clears_the_inherited_cursors(): assert manager._default_layer_idx is None +def test_plan_stages_one_reused_mask_per_geometry_and_slot(monkeypatch): + config = RingKVConfig( + num_layers=4, + num_kv_heads=N_KV_HEADS, + head_dim=D_HEAD, + num_qo_heads=N_QO_HEADS, + tokens_per_frame=TPF, + num_worlds=2, + layers=( + RingKVLayerConfig(4, 4, 1), + RingKVLayerConfig(4, 4, 1), + RingKVLayerConfig(4, 2, 2), + RingKVLayerConfig(4, 2, 2), + ), + ) + manager = build_attention(AttnBackend.FLEX, config) + ctx = StepContext( + request_ids=("r",), graph_walk="rollout", slot=1, capture=False, + plan_results={"kv": RingPlan("r", 1, 5)}, + ) + + manager.plan(AttentionStep(), ctx) + + assert manager.needs_token_visibility is False + assert len(manager._planned_masks) == 2 + local = manager._mask_for(1, manager._geometry(config.layers[0])) + assert local is manager._mask_for(1, manager._geometry(config.layers[1])) + global_mask = manager._mask_for(1, manager._geometry(config.layers[2])) + assert global_mask is manager._mask_for(1, manager._geometry(config.layers[3])) + assert local is not global_mask + + # One block per frame. World 1 starts after world 0's five-block span. + assert local.full_kv_num_blocks.unique().tolist() == [4] + assert local.full_kv_indices[0, 0, 0, :4].tolist() == [5, 7, 8, 9] + assert global_mask.full_kv_num_blocks.unique().tolist() == [3] + assert global_mask.full_kv_indices[0, 0, 0, :3].tolist() == [5, 6, 9] + + addresses = { + key: (value.full_kv_num_blocks.data_ptr(), value.full_kv_indices.data_ptr()) + for key, value in manager._planned_masks.items() + } + table_addresses = { + key: (value[0].data_ptr(), value[1].data_ptr()) + for key, value in manager._visibility_tables.items() + } + monkeypatch.setattr( + torch, + "tensor", + lambda *args, **kwargs: pytest.fail( + "mask planning must not allocate a new staging tensor" + ), + ) + ctx.plan_results["kv"] = RingPlan("r", 1, 6) + manager.plan(AttentionStep(), ctx) + assert addresses == { + key: (value.full_kv_num_blocks.data_ptr(), value.full_kv_indices.data_ptr()) + for key, value in manager._planned_masks.items() + } + assert table_addresses == { + key: (value[0].data_ptr(), value[1].data_ptr()) + for key, value in manager._visibility_tables.items() + } + + +def test_planned_masks_match_ring_visibility_across_wraps_and_worlds(): + """The host plan must reproduce the ring's pre-commit visibility exactly. + + This crosses two wraps of both geometries while alternating worlds. It + compares against ``LayerRingCache.upsert``'s independently maintained + ``written`` state, so a clock, dilation, overwrite, scratch, or world-offset + error in the planner cannot satisfy the test by sharing its formula. + """ + layers = ( + RingKVLayerConfig(ring_frames=4, ring_buckets=4, pinned_dilation=1), + RingKVLayerConfig(ring_frames=4, ring_buckets=2, pinned_dilation=2), + ) + config = RingKVConfig( + num_layers=len(layers), + num_kv_heads=N_KV_HEADS, + head_dim=D_HEAD, + num_qo_heads=N_QO_HEADS, + tokens_per_frame=TPF, + num_worlds=2, + layers=layers, + ) + manager = build_attention(AttnBackend.FLEX, config) + caches = [ + LayerRingCache( + num_worlds=config.num_worlds, + n_kv_heads=config.num_kv_heads, + ring_frames=layer.ring_frames, + ring_buckets=layer.ring_buckets, + d_head=config.head_dim, + tokens_per_frame=config.tokens_per_frame, + pinned_dilation=layer.pinned_dilation, + dtype=torch.float32, + device="cpu", + ) + for layer in layers + ] + kv = torch.zeros(2, 1, N_KV_HEADS, TPF, D_HEAD) + + for frame in range(10): + for world in (0, 1): + expected = [] + for cache in caches: + *_, visible = cache.upsert( + kv, + torch.tensor(frame, dtype=torch.int64), + True, + torch.tensor([world], dtype=torch.int64), + ) + expected.append( + visible.view(-1, BLOCK).all(-1).nonzero().flatten().tolist() + ) + + ctx = StepContext( + request_ids=(f"r{world}",), + graph_walk="rollout", + slot=0, + capture=False, + plan_results={"kv": RingPlan(f"r{world}", world, frame)}, + ) + manager.plan(AttentionStep(), ctx) + + for layer, expected_blocks in zip(layers, expected, strict=True): + mask = manager._mask_for(0, manager._geometry(layer)) + count = int(mask.full_kv_num_blocks[0, 0, 0]) + assert mask.full_kv_indices[0, 0, 0, :count].tolist() == expected_blocks + + +def test_capture_plan_requires_preallocated_mask_addresses(): + manager = build_attention(AttnBackend.FLEX, ring_config()) + ctx = StepContext( + request_ids=("r",), graph_walk="rollout", slot=0, capture=True, + plan_results={"kv": RingPlan("r", 0, 0)}, + ) + with pytest.raises(RuntimeError, match="not allocated before CUDA graph capture"): + manager.plan(AttentionStep(), ctx) + + # --------------------------------------------------------------------------- # 2. The mask # --------------------------------------------------------------------------- diff --git a/test/modular/test_ring_kv_resource.py b/test/modular/test_ring_kv_resource.py index 1e21a28be..cf55e3603 100644 --- a/test/modular/test_ring_kv_resource.py +++ b/test/modular/test_ring_kv_resource.py @@ -229,8 +229,8 @@ def test_num_worlds_is_the_one_yaml_tunable(): assert kv.total_slots(0) == 4 * kv.capacity(0) -@pytest.mark.parametrize("bad", [0, -1]) -def test_zero_worlds_is_refused_at_both_entry_points(bad): +@pytest.mark.parametrize("bad", [0, -1, True, 1.0, 1.9, "2"]) +def test_invalid_num_worlds_is_refused_at_both_entry_points(bad): """A node sized for zero worlds refuses every request at admit — a deployment that boots, reports healthy, and serves nothing.""" with pytest.raises(ValueError, match="num_worlds"): @@ -426,7 +426,9 @@ def test_concurrent_requests_get_distinct_worlds(num_worlds): worlds = [kv.world_of(rid) for rid in rids] assert None not in worlds - assert len(set(worlds)) == num_worlds, f"worlds collided: {dict(zip(rids, worlds))}" + assert len(set(worlds)) == num_worlds, ( + f"worlds collided: {dict(zip(rids, worlds, strict=True))}" + ) assert set(worlds) == set(range(num_worlds)), "a world was skipped" assert not kv._free_worlds @@ -1139,6 +1141,31 @@ def test_upsert_returns_the_whole_buffer_and_delegates_by_layer(): assert k_all.data_ptr() == kv.layers[layer_idx].kv.data_ptr() +def test_planned_attention_skips_per_upsert_visibility_reconstruction(): + """A planned block mask makes the token-level scratch row dead output.""" + kv = _manager() + _open(kv, "r") + kv.plan(_step("r"), _ctx("r")) + layer = kv.layers[0] + layer._mask_written.copy_( + torch.arange(layer.total_slots).remainder(2).to(torch.bool) + ) + sentinel = layer._mask_written.clone() + k, v = _frame(kv, torch.Generator().manual_seed(91)) + + _, _, returned = kv.upsert( + k, + v, + 0, + torch.tensor(0, dtype=torch.int64), + commit=False, + build_visibility=False, + ) + + assert returned.data_ptr() == layer._mask_written.data_ptr() + assert torch.equal(returned, sentinel) + + def test_frozen_passes_leave_the_ring_byte_identical(): """At the resource seam: `commit` is per call and the manager keeps no frozen state between calls, because all five passes of a frame sit inside diff --git a/test/modular/test_video_frame_protocol.py b/test/modular/test_video_frame_protocol.py new file mode 100644 index 000000000..71c027420 --- /dev/null +++ b/test/modular/test_video_frame_protocol.py @@ -0,0 +1,752 @@ +"""Native raw-video-frame protocol and SDK contract.""" + +from __future__ import annotations + +import asyncio +import queue +import runpy +import signal +import sys +import threading +from pathlib import Path +from types import SimpleNamespace + +import pytest +import torch +import yaml +from fastapi import HTTPException + +REPO_ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(REPO_ROOT)) + +np = pytest.importorskip("numpy") +pytest.importorskip("requests") + +from mstar.api_server import entrypoint # noqa: E402 +from mstar.api_server.data_worker import ( # noqa: E402 + PreprocessWorkerThread, + _video_frame_metadata, +) +from mstar.api_server.entrypoint import SUPPORTED_MODALITIES, APIServer # noqa: E402 +from mstar.api_server.request_types import PreprocessInput, ResultTensors # noqa: E402 +from mstar.client import MStarClient, VideoFrameChunk # noqa: E402 +from mstar.graph.base import GraphEdge # noqa: E402 +from mstar.graph.loop_indices import NestedLoopIndices # noqa: E402 +from mstar.model.waypoint.submodules import PRIME_WALK, ROLLOUT_WALK # noqa: E402 +from mstar.model.waypoint.waypoint_model import WaypointModel # noqa: E402 + + +def _metadata(**overrides): + values = { + "width": 3, + "height": 2, + "fps": 60, + "pixel_format": "rgb24", + "frame_index": 0, + "frame_count": 4, + } + values.update(overrides) + return values + + +def test_sdk_video_frame_chunk_is_a_zero_copy_shaped_view(): + raw = bytes(range(4 * 2 * 3 * 3)) + + event = MStarClient._to_event( + { + "modality": "video_frame", + "bytes": raw, + "metadata": _metadata(frame_index=8), + } + ) + + assert isinstance(event, VideoFrameChunk) + assert (event.frame_index, event.frame_count, event.fps) == (8, 4, 60.0) + frames = event.to_numpy() + assert frames.shape == (4, 2, 3, 3) + assert frames.dtype == np.uint8 + assert not frames.flags.owndata + assert not frames.flags.writeable + assert np.shares_memory(frames, np.frombuffer(raw, dtype=np.uint8)) + assert frames[0, 0, 0].tolist() == [0, 1, 2] + + +@pytest.mark.parametrize( + ("metadata", "data", "message"), + [ + ({}, b"", "missing required"), + (_metadata(), bytearray(72), "immutable bytes"), + (_metadata(pixel_format="bgr24"), bytes(72), "pixel_format"), + (_metadata(fps=float("nan")), bytes(72), "finite positive"), + (_metadata(frame_index=-1), bytes(72), "frame_index"), + (_metadata(), bytes(71), "payload length"), + ], +) +def test_sdk_video_frame_chunk_validates_metadata_and_payload(metadata, data, message): + with pytest.raises(ValueError, match=message): + VideoFrameChunk(data, metadata) + + +def test_sdk_rejects_nonstreaming_raw_frames_before_http(): + client = MStarClient("http://unused") + with pytest.raises(ValueError, match="requires stream=True"): + client.generate(output_modalities=("video_frame",), stream=False) + + +def test_api_core_rejects_nonstreaming_raw_frames_before_preprocessing(): + server = APIServer.__new__(APIServer) + assert "video_frame" in SUPPORTED_MODALITIES + + with pytest.raises(ValueError, match="requires streaming=True"): + server.submit_request( + input_modalities=["image"], + output_modalities=["video_frame"], + streaming=False, + ) + + with pytest.raises(ValueError, match="output-only"): + server.submit_request( + input_modalities=["video_frame"], + output_modalities=["text"], + streaming=True, + ) + + +def test_native_endpoint_reports_nonstreaming_raw_frames_as_bad_request(monkeypatch): + monkeypatch.setattr(entrypoint, "api_server", object()) + + with pytest.raises(HTTPException) as raised: + asyncio.run( + entrypoint.generate( + request=SimpleNamespace(), + text=None, + files=None, + input_modalities=None, + output_modalities="video_frame", + streaming=False, + model_kwargs=None, + request_id=None, + ) + ) + + assert raised.value.status_code == 400 + assert "requires streaming=true" in str(raised.value.detail) + + +def test_native_endpoint_rejects_raw_frames_as_input(monkeypatch): + monkeypatch.setattr(entrypoint, "api_server", object()) + + with pytest.raises(HTTPException) as raised: + asyncio.run( + entrypoint.generate( + request=SimpleNamespace(), + text=None, + files=None, + input_modalities="video_frame", + output_modalities="text", + streaming=True, + model_kwargs=None, + request_id=None, + ) + ) + + assert raised.value.status_code == 400 + assert raised.value.detail == "'video_frame' is an output-only modality" + + +def test_server_metadata_is_canonical_and_counts_raw_frames(): + tensor = torch.zeros((4, 2, 3, 3), dtype=torch.uint8) + metadata = _video_frame_metadata( + tensor, + fps=60, + frame_index=12, + metadata={"width": 999, "producer": "decoder"}, + ) + assert metadata == { + "producer": "decoder", + "width": 3, + "height": 2, + "fps": 60, + "pixel_format": "rgb24", + "frame_index": 12, + "frame_count": 4, + } + + +class _FrameModel: + def postprocess(self, tensor, modality, request_kwargs=None): + assert modality == "video_frame" + return tensor.cpu().contiguous().numpy().tobytes() + + def get_output_frame_rate(self, modality, request_kwargs=None): + assert modality == "video_frame" + assert request_kwargs == {"world": "test"} + return 60 + + +class _ReadyTensorManager: + def __init__(self, tensors_by_request): + self.tensors_by_request = tensors_by_request + self.ready = tensors_by_request + self.dereferenced = [] + self.cleaned = [] + + def get_ready_tensors(self): + ready, self.ready = self.ready, {} + return { + request_id: [ + GraphEdge( + next_node="api", + name="video_frame_output", + tensor_info=[SimpleNamespace(uuid=name) for name in tensors], + ) + ] + for request_id, tensors in ready.items() + } + + def start_read_tensors(self, request_id, graph_edges): + pass + + def get_tensor(self, request_id, uuid): + return self.tensors_by_request[request_id][uuid] + + def dereference(self, request_id, uuid): + self.dereferenced.append((request_id, uuid)) + + def cleanup_request(self, request_id): + self.cleaned.append(request_id) + + def store_and_return_tensor_info(self, request_id, tensors): + return {} + + def register_for_send(self, request_id, tensor_infos): + pass + + +def _set_output_order_state(worker, tensors_by_request): + loop_indices = NestedLoopIndices( + loop_name_order=["rollout_loop"], + loop_indices={"rollout_loop": 0}, + wg_fwd_pass_idx=0, + ) + worker.tensor_uuid_to_output_order_per_request = { + request_id: { + name: (sequence, loop_indices) + for sequence, name in enumerate(tensors) + } + for request_id, tensors in tensors_by_request.items() + } + worker.request_next_output_sequence = { + request_id: len(tensors) + for request_id, tensors in tensors_by_request.items() + } + worker.request_next_emit_sequence = { + request_id: 0 for request_id in tensors_by_request + } + worker.request_pending_output_chunks = { + request_id: {} for request_id in tensors_by_request + } + + +def test_data_worker_emits_complete_metadata_and_monotonic_frame_indices(): + tensors = { + "first": torch.arange(72, dtype=torch.uint8).reshape(4, 2, 3, 3), + "second": torch.arange(72, dtype=torch.uint8).reshape(4, 2, 3, 3), + } + worker = PreprocessWorkerThread.__new__(PreprocessWorkerThread) + worker.tensor_manager = _ReadyTensorManager({"request": tensors}) + worker.model = _FrameModel() + worker.out_queue = queue.Queue() + worker.request_model_kwargs = {"request": {"world": "test"}} + worker.request_output_frame_indices = {"request": 0} + worker.tensor_uuid_to_metadata_per_request = {"request": {name: {"producer": "decoder"} for name in tensors}} + _set_output_order_state(worker, {"request": tensors}) + + assert worker._process_read_tensors() is True + chunks = [worker.out_queue.get_nowait(), worker.out_queue.get_nowait()] + + assert [chunk.modality for chunk in chunks] == ["video_frame", "video_frame"] + assert [chunk.metadata["frame_index"] for chunk in chunks] == [0, 4] + assert all(chunk.metadata["frame_count"] == 4 for chunk in chunks) + assert all(chunk.metadata["pixel_format"] == "rgb24" for chunk in chunks) + assert all(chunk.metadata["producer"] == "decoder" for chunk in chunks) + assert worker.request_output_frame_indices["request"] == 8 + assert worker.tensor_manager.dereferenced == [ + ("request", "first"), + ("request", "second"), + ] + + +def test_data_worker_tracks_interleaved_frame_indices_per_request(): + frame = torch.zeros((4, 2, 3, 3), dtype=torch.uint8) + tensors = { + "request-a": {"a-first": frame, "a-second": frame}, + "request-b": {"b-first": frame}, + } + worker = PreprocessWorkerThread.__new__(PreprocessWorkerThread) + worker.tensor_manager = _ReadyTensorManager(tensors) + worker.tensor_manager.ready = { + "request-a": {"a-first": frame}, + "request-b": {"b-first": frame}, + } + worker.model = _FrameModel() + worker.out_queue = queue.Queue() + worker.request_model_kwargs = { + "request-a": {"world": "test"}, + "request-b": {"world": "test"}, + } + worker.request_output_frame_indices = {"request-a": 0, "request-b": 0} + worker.tensor_uuid_to_metadata_per_request = { + request_id: {name: {} for name in request_tensors} for request_id, request_tensors in tensors.items() + } + _set_output_order_state(worker, tensors) + + assert worker._process_read_tensors() is True + worker.tensor_manager.ready = {"request-a": {"a-second": frame}} + assert worker._process_read_tensors() is True + + chunks = [worker.out_queue.get_nowait() for _ in range(3)] + assert [chunk.request_id for chunk in chunks] == [ + "request-a", + "request-b", + "request-a", + ] + assert [chunk.metadata["frame_index"] for chunk in chunks] == [0, 0, 4] + assert worker.request_output_frame_indices == { + "request-a": 8, + "request-b": 4, + } + + +def test_data_worker_reorders_async_completions_before_frame_emission(): + first = torch.zeros((4, 2, 3, 3), dtype=torch.uint8) + second = torch.ones((4, 2, 3, 3), dtype=torch.uint8) + tensors = {"request": {"first": first, "second": second}} + worker = PreprocessWorkerThread.__new__(PreprocessWorkerThread) + worker.tensor_manager = _ReadyTensorManager(tensors) + worker.tensor_manager.ready = {} + worker.model = _FrameModel() + worker.out_queue = queue.Queue() + worker.request_model_kwargs = {"request": {"world": "test"}} + worker.request_output_frame_indices = {"request": 0} + worker.tensor_uuid_to_metadata_per_request = {} + worker.tensor_uuid_to_output_order_per_request = {"request": {}} + worker.request_next_output_sequence = {"request": 0} + worker.request_next_emit_sequence = {"request": 0} + worker.request_pending_output_chunks = {"request": {}} + + for iteration, name in enumerate(("first", "second")): + worker._read_result_tensor(ResultTensors( + request_id="request", + modality="video_frame", + graph_edge=GraphEdge( + next_node="api", + name="video_frame", + tensor_info=[SimpleNamespace(uuid=name)], + ), + loop_indices=NestedLoopIndices( + loop_name_order=["rollout_loop"], + loop_indices={"rollout_loop": iteration}, + wg_fwd_pass_idx=1, + ), + )) + + worker.tensor_manager.ready = {"request": {"second": second}} + assert worker._process_read_tensors() is True + assert worker.out_queue.empty() + + worker.tensor_manager.ready = {"request": {"first": first}} + assert worker._process_read_tensors() is True + chunks = [worker.out_queue.get_nowait(), worker.out_queue.get_nowait()] + assert [chunk.metadata["frame_index"] for chunk in chunks] == [0, 4] + assert chunks[0].data == first.numpy().tobytes() + assert chunks[1].data == second.numpy().tobytes() + + +def test_data_worker_cleanup_drops_all_frame_protocol_state(): + worker = PreprocessWorkerThread.__new__(PreprocessWorkerThread) + worker.tensor_manager = _ReadyTensorManager({}) + worker.tensor_uuid_to_metadata_per_request = { + "reused": {"old": {"producer": "decoder"}}, + "other": {"keep": {}}, + } + worker.request_model_kwargs = { + "reused": {"world": "old"}, + "other": {"world": "keep"}, + } + worker.request_output_frame_indices = {"reused": 24, "other": 8} + _set_output_order_state(worker, { + "reused": {"old": object()}, + "other": {"keep": object()}, + }) + + worker._cleanup_request_state("reused") + + assert worker.tensor_manager.cleaned == ["reused"] + assert "reused" not in worker.tensor_uuid_to_metadata_per_request + assert "reused" not in worker.request_model_kwargs + assert "reused" not in worker.request_output_frame_indices + assert "reused" not in worker.tensor_uuid_to_output_order_per_request + assert "reused" not in worker.request_next_output_sequence + assert "reused" not in worker.request_next_emit_sequence + assert "reused" not in worker.request_pending_output_chunks + assert worker.request_output_frame_indices == {"other": 8} + + worker.model = SimpleNamespace(process_prompt=lambda *args, **kwargs: {}) + worker.device = "cpu" + worker.enable_prof = False + worker.communicator = SimpleNamespace(send=lambda *args: None) + worker._process_input( + PreprocessInput( + request_id="reused", + text=None, + file_paths=None, + input_modalities=["image"], + output_modalities=["video_frame"], + model_kwargs={"world": "new"}, + ) + ) + + assert worker.request_output_frame_indices["reused"] == 0 + assert worker.request_model_kwargs["reused"] == {"world": "new"} + + +def test_rollout_harness_requires_exactly_four_frames_per_step_from_index_zero(): + harness = runpy.run_path(str(Path(__file__).parents[1] / "waypoint" / "serve_rollout.py")) + check_rollout = harness["_check"] + + def chunk(frame_index): + return VideoFrameChunk(bytes(4 * 2 * 3 * 3), _metadata(frame_index=frame_index)) + + valid = [chunk(0), chunk(4)] + assert check_rollout(valid, num_steps=2, height=2, width=3) == [] + + missing = check_rollout(valid[:1], num_steps=2, height=2, width=3) + assert "expected 2 video chunks, got 1" in missing + assert "expected exactly 8 generated frames" in missing + + bad_index = [chunk(0), chunk(5)] + assert any( + "frame_index" in failure + for failure in check_rollout( + bad_index, + num_steps=2, + height=2, + width=3, + ) + ) + + +@pytest.mark.parametrize( + ("name", "model_variant", "height", "width", "tokens", "checkpoint_name"), + [ + ("360p", "waypoint-1.5-1b-360p", 360, 640, 128, "Waypoint-1.5-1B-360P"), + ("720p", "waypoint-1.5-1b-720p", 720, 1280, 512, "Waypoint-1.5-1B"), + ], +) +def test_rollout_harness_variant_controls_config_and_checkpoint_default( + tmp_path, + name, + model_variant, + height, + width, + tokens, + checkpoint_name, +): + harness = runpy.run_path(str(Path(__file__).parents[1] / "waypoint" / "serve_rollout.py")) + variant = harness["VARIANTS"][name] + base = tmp_path / "base.yaml" + base.write_text( + yaml.safe_dump( + { + "model": "waypoint", + "model_kwargs": {"variant": "stale", "compile_dit": True}, + "max_seq_len": 999, + } + ) + ) + output = tmp_path / "run.yaml" + + harness["_run_config"]( + base, + variant, + Path("custom/checkpoint"), + Path("custom/ae"), + output, + worlds=2, + ) + generated = yaml.safe_load(output.read_text()) + + assert (variant.model_variant, variant.height, variant.width) == ( + model_variant, + height, + width, + ) + assert variant.tokens_per_frame == tokens + assert variant.checkpoint_dir.name == checkpoint_name + assert generated["model_kwargs"] == { + "variant": model_variant, + "compile_dit": True, + "checkpoint_dir": "custom/checkpoint", + "ae_path": "custom/ae", + } + assert generated["max_seq_len"] == tokens + assert generated["max_concurrent_requests"] == 2 + assert generated["resources"]["kv"]["num_worlds"] == 2 + + +def test_rollout_harness_hub_config_omits_local_overrides_and_forwards_cache(tmp_path): + harness = runpy.run_path(str(Path(__file__).parents[1] / "waypoint" / "serve_rollout.py")) + variant = harness["VARIANTS"]["360p"] + base = tmp_path / "base.yaml" + base.write_text( + yaml.safe_dump( + { + "model": "waypoint", + "model_kwargs": { + "checkpoint_dir": "stale-checkpoint", + "ae_path": "stale-ae", + "compile_dit": True, + }, + } + ) + ) + output = tmp_path / "run.yaml" + + harness["_run_config"](base, variant, None, None, output, worlds=2) + generated = yaml.safe_load(output.read_text()) + command = harness["_server_command"]( + output, + 8123, + tmp_path, + "DEBUG", + 90.0, + tmp_path / "hub-cache", + True, + ) + + assert generated["model_kwargs"] == { + "compile_dit": True, + "variant": "waypoint-1.5-1b-360p", + } + assert generated["max_concurrent_requests"] == 2 + assert generated["resources"]["kv"]["num_worlds"] == 2 + assert command[command.index("--cache-dir") + 1] == str(tmp_path / "hub-cache") + assert "--enable-nvtx" in command + + +@pytest.mark.parametrize("name", ["360p", "720p"]) +def test_rollout_harness_resizes_seed_to_variant_with_pillow(tmp_path, name): + image_module = pytest.importorskip("PIL.Image") + harness = runpy.run_path(str(Path(__file__).parents[1] / "waypoint" / "serve_rollout.py")) + variant = harness["VARIANTS"][name] + source = tmp_path / "source.jpg" + output = tmp_path / "seed.png" + image_module.new("RGB", (19, 11), color=(1, 2, 3)).save(source) + + harness["_seed_png"](source, variant, output) + + with image_module.open(output) as seed: + assert seed.format == "PNG" + assert seed.mode == "RGB" + assert seed.size == (variant.width, variant.height) + + +def test_rollout_harness_submits_and_consumes_typed_sdk_stream(tmp_path): + harness = runpy.run_path(str(Path(__file__).parents[1] / "waypoint" / "serve_rollout.py")) + frame = VideoFrameChunk(bytes(4 * 2 * 3 * 3), _metadata()) + + class Client: + kwargs = None + + def stream(self, **kwargs): + self.kwargs = kwargs + return iter([frame]) + + client = Client() + seed = tmp_path / "seed.png" + seed.write_bytes(b"PNG") + + chunks = harness["_rollout"](client, seed, num_steps=1, request_id="rid", rng_seed=17) + + assert chunks == [frame] + assert client.kwargs == { + "images": seed, + "input_modalities": ("image",), + "output_modalities": ("video_frame",), + "request_id": "rid", + "num_steps": 1, + "actions": [{"mouse": [-12.0, 0.0], "buttons": [0], "scroll": 0.0}], + "seed": 17, + } + + +def test_rollout_harness_concurrent_pair_uses_separate_clients_and_starts_together(tmp_path): + harness = runpy.run_path(str(Path(__file__).parents[1] / "waypoint" / "serve_rollout.py")) + rollout_spec = harness["RolloutSpec"] + clients = [] + active = 0 + lock = threading.Lock() + both_active = threading.Event() + + class Client: + def __init__(self): + clients.append(self) + + def stream(self, **kwargs): + def events(): + nonlocal active + with lock: + active += 1 + if active == 2: + both_active.set() + assert both_active.wait(timeout=2), "the peer stream was not active" + yield VideoFrameChunk(bytes([kwargs["seed"]]) * 72, _metadata()) + + return events() + + seed = tmp_path / "seed.png" + seed.write_bytes(b"PNG") + results = harness["_concurrent_rollouts"]( + Client, + seed, + 1, + (rollout_spec("A", "rid-a", 1), rollout_spec("B", "rid-b", 2)), + ) + + assert len(clients) == 2 + assert results["A"][0].data == bytes([1]) * 72 + assert results["B"][0].data == bytes([2]) * 72 + + +def test_rollout_harness_requires_worker_schedule_interleaving_and_cleanup_markers(): + harness = runpy.run_path(str(Path(__file__).parents[1] / "waypoint" / "serve_rollout.py")) + interleaving_failure = harness["_interleaving_failure"] + log = "\n".join( + [ + "DEBUG Executing: dit graph_walk=prime ('rid-a',)", + "DEBUG Executing: dit graph_walk=rollout ('rid-a',)", + "DEBUG Executing: dit graph_walk=rollout ('rid-b',)", + "DEBUG Executing: dit graph_walk=rollout ('rid-a',)", + "DEBUG Executing: dit graph_walk=rollout ('rid-b',)", + "INFO Request cleanup complete: rid-a", + "INFO Request cleanup complete: rid-b", + ] + ) + + assert interleaving_failure(log, ("rid-a", "rid-b")) is None + assert harness["_execution_count_failure"](log, ("rid-a", "rid-b"), 2) is None + assert harness["_cleaned_request_ids"](log) == {"rid-a", "rid-b"} + serial = "\n".join( + [ + "DEBUG Executing: dit graph_walk=rollout ('rid-a',)", + "DEBUG Executing: dit graph_walk=rollout ('rid-a',)", + "DEBUG Executing: dit graph_walk=rollout ('rid-b',)", + "DEBUG Executing: dit graph_walk=rollout ('rid-b',)", + ] + ) + assert "did not contain A/B/A" in interleaving_failure(serial, ("rid-a", "rid-b")) + assert "expected 3" in harness["_execution_count_failure"]( + serial, ("rid-a", "rid-b"), 3 + ) + + +def test_rollout_harness_parses_and_filters_memory_telemetry(monkeypatch): + harness = runpy.run_path(str(Path(__file__).parents[1] / "waypoint" / "serve_rollout.py")) + assert harness["_parse_pss_kib"]("Rss: 12 kB\nPss: 7 kB\n") == 7 + assert harness["_parse_nvidia_smi_processes"]("101, 512\n202, 128.5\n") == [ + (101, 512.0), + (202, 128.5), + ] + + completed = SimpleNamespace(returncode=0, stdout="101, 512\n202, 128\n", stderr="") + monkeypatch.setattr(harness["subprocess"], "run", lambda *args, **kwargs: completed) + monkeypatch.setattr(harness["os"], "getpgid", lambda pid: 77 if pid == 101 else 88) + + assert harness["_process_group_gpu_mib"](77, 2) == 512.0 + with pytest.raises(RuntimeError, match="no compute process"): + harness["_process_group_gpu_mib"](99, 2) + with pytest.raises(ValueError, match="unusable"): + harness["_parse_nvidia_smi_processes"]("101, N/A\n") + + +def test_rollout_harness_memory_plateau_excludes_warmup_and_rejects_growth(): + harness = runpy.run_path(str(Path(__file__).parents[1] / "waypoint" / "serve_rollout.py")) + memory_sample = harness["MemorySample"] + wave_memory = harness["WaveMemory"] + stable = [ + memory_sample(1.0, "quiet", 100.0, 1000.0), + memory_sample(2.0, "quiet", 101.0, 1000.0), + memory_sample(3.0, "quiet", 100.5, 1000.0), + ] + unstable = stable[:-1] + [memory_sample(3.0, "quiet", 104.0, 1000.0)] + + assert harness["_stable_memory_plateau"](stable) == (101.0, 1000.0) + assert harness["_stable_memory_plateau"](unstable) is None + + measured = [ + wave_memory("wave-2", 400.0, 2000.0, 200.0, 1500.0), + wave_memory("wave-3", 410.0, 2010.0, 220.0, 1510.0), + ] + assert harness["_bounded_memory_failures"](measured, host_growth_mib=128.0, gpu_growth_mib=64.0) == [] + failures = harness["_bounded_memory_failures"]( + measured[:-1] + [wave_memory("wave-3", 410.0, 2010.0, 329.0, 1565.0)], + host_growth_mib=128.0, + gpu_growth_mib=64.0, + ) + assert len(failures) == 2 + + +def test_rollout_harness_shutdown_signals_only_the_api_parent(monkeypatch): + harness = runpy.run_path(str(Path(__file__).parents[1] / "waypoint" / "serve_rollout.py")) + + class Process: + pid = 101 + signals = [] + + def poll(self): + return None + + def send_signal(self, sig): + self.signals.append(sig) + + def wait(self, timeout): + assert timeout == 60 + return 0 + + proc = Process() + monkeypatch.setattr("os.killpg", lambda *_: pytest.fail("normal shutdown killed the group")) + + harness["_shutdown"](proc) + + assert proc.signals == [signal.SIGINT] + + +def test_waypoint_has_no_encoded_video_adapter(monkeypatch): + from mstar.api_server.openai import router + from mstar.api_server.openai.adapters import get_adapter + + assert get_adapter("waypoint") is None + monkeypatch.setattr(entrypoint, "api_server", SimpleNamespace(model_name="waypoint")) + _api, _model_name, _adapter, error = router._resolve("supports_videos") + assert error.status_code == 404 + + +def test_waypoint_emits_only_generated_raw_frame_chunks(): + model = WaypointModel(skip_weight_loading=True) + walks = model.get_graph_walk_graphs() + + assert walks[PRIME_WALK].sections[-1].outputs == [] + edge = walks[ROLLOUT_WALK].section.sections[-1].outputs[0] + assert edge.output_modality == "video_frame" + assert model.get_output_frame_rate() == 60.0 + + with pytest.raises(ValueError, match="video_frame"): + model.process_prompt( + None, + ["image"], + ["video"], + tensors=None, + num_steps=1, + actions=[{}], + ) diff --git a/test/modular/test_waypoint_360p_reference_equivalence.py b/test/modular/test_waypoint_360p_reference_equivalence.py new file mode 100644 index 000000000..d8715e577 --- /dev/null +++ b/test/modular/test_waypoint_360p_reference_equivalence.py @@ -0,0 +1,346 @@ +"""Live, zero-tolerance 360p Waypoint parity against ``world_engine``. + +The existing stored oracle was recorded from the distinct 720p checkpoint and +cannot be resized into a numerical target for the 360p model. This gate instead +drives both implementations in one process with the canonical seed, controller +script, and seeded CPU noise recipe used by the oracle recorder. Both DiTs run +eager and the reference's eager Flex call is rebound to the port's masked Flex +kernel, exactly as in the 720p localization harness. Consequently this checks +model arithmetic, state progression, and the functional AE boundary; it is not +a comparison between separately compiled serving processes. +""" + +from __future__ import annotations + +import hashlib +import importlib.util +import json +import os +import platform +from dataclasses import replace +from pathlib import Path + +import pytest +import torch +from test_waypoint_reference_equivalence import ( + DEVICE, + DTYPE, + REFERENCE_SRC, + _admit, + _assert_ring, + _build_port, + _commit, + _deviation, + _divergent_stages, + _import_reference, + _island_tables, + _new_request, + _port_forward, + _port_frame, + _reference_forward, + _reference_frame, + _reference_importable, + _reset, + _ring_deviation, + _stage_capture, + _stage_modules, +) + +from mstar.model.waypoint.components.taehv import ( + decode_latent, + encode_seed_clip, + initial_decoder_histories, + load_taehv, +) +from mstar.model.waypoint.config import waypoint_1_5_1b_360p + +_ROOT = Path("/mnt/storage/garv901/waypoint-1.5-1B") +CHECKPOINT = Path( + os.environ.get( + "WAYPOINT_360P_CHECKPOINT", + "/tmp/waypoint-hf-cache/models--Overworld--Waypoint-1.5-1B-360P/" + "snapshots/35acd20e649fe79c1c1002df456696408547202d", + ) +) +AE_CHECKPOINT = Path(os.environ.get("WAYPOINT_AE_CHECKPOINT", _ROOT / "checkpoints/taehv1_5")) +SEED_IMAGE = Path(os.environ.get("WAYPOINT_SEED_IMAGE", _ROOT / "checkpoints/seed/default.jpg")) +REPORT_PATH = Path(os.environ.get("WAYPOINT_360P_PARITY_REPORT", "/tmp/waypoint-360p-reference-parity.json")) + +CHECKPOINT_REVISION = "35acd20e649fe79c1c1002df456696408547202d" +CHECKPOINT_SHA256 = "a2cdccb5eb074afc48a1c99b0868cebef38cf944d4b366aed2a866f50c8101d9" +SEED_SHA256 = "c61c9393311d7281f793d86329dca343e12c93bf0409980a186eb39269cf6862" +NOISE_SEED = 42 +ROLLOUT_FRAMES = int(os.environ.get("WAYPOINT_360P_PARITY_FRAMES", "41")) + +# Same 40 post-prime actions as ``test/waypoint/record_oracle.py``. Inputs are +# resolution-independent; only the seeded noise tensor takes the 360p shape. +CONTROL_SEQUENCE: list[tuple[set[int], tuple[float, float], int]] = ( + [({87}, (0.0, 0.0), 0)] * 8 + + [({87}, (0.2, 0.0), 0)] * 4 + + [({65}, (0.0, 0.0), 0)] * 4 + + [({68}, (0.0, 0.0), 0)] * 4 + + [({83}, (0.0, 0.0), 0)] * 4 + + [({87, 32}, (0.0, 0.0), 0)] * 4 + + [(set(), (0.0, 0.0), 0)] * 4 + + [(set(), (0.0, -0.2), 0)] * 4 + + [({87, 1}, (0.0, 0.0), 0)] * 4 +) + + +pytestmark = [ + pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA"), + pytest.mark.skipif(not _reference_importable(), reason=f"reference not at {REFERENCE_SRC}"), + pytest.mark.skipif( + not (CHECKPOINT / "model.safetensors").is_file(), + reason=f"360p checkpoint missing at {CHECKPOINT}", + ), + pytest.mark.skipif(not AE_CHECKPOINT.exists(), reason=f"TAEHV missing at {AE_CHECKPOINT}"), + pytest.mark.skipif(not SEED_IMAGE.is_file(), reason=f"seed missing at {SEED_IMAGE}"), + pytest.mark.skipif(importlib.util.find_spec("taehv") is None, reason="taehv is not installed"), +] + + +def _context( + config, + frame: int, + buttons: set[int] | None = None, + mouse: tuple[float, float] = (0.0, 0.0), + scroll: int = 0, +) -> dict[str, torch.Tensor]: + button = torch.zeros((1, 1, config.n_buttons), dtype=DTYPE, device=DEVICE) + if buttons: + button[..., sorted(buttons)] = 1 + return { + "button": button, + "mouse": torch.tensor([[mouse]], dtype=DTYPE, device=DEVICE), + "scroll": torch.tensor([[[scroll]]], dtype=DTYPE, device=DEVICE), + "frame_timestamp": torch.tensor([[frame]], dtype=torch.int64, device=DEVICE), + "frame_idx": torch.tensor([[frame]], dtype=torch.int64, device=DEVICE), + } + + +def _seed_clip() -> tuple[torch.Tensor, str]: + import cv2 + import numpy as np + + raw = SEED_IMAGE.read_bytes() + digest = hashlib.sha256(raw).hexdigest() + image = cv2.imdecode(np.frombuffer(raw, np.uint8), cv2.IMREAD_COLOR) + image = cv2.cvtColor(cv2.resize(image, (640, 360)), cv2.COLOR_BGR2RGB) + return torch.from_numpy(np.repeat(image[None], 4, axis=0)), digest + + +def _load_reference() -> dict: + WorldModel, StaticKVCache, patch_model = _import_reference() + torch.set_float32_matmul_precision("high") + cfg = WorldModel.load_config(str(CHECKPOINT)) + assert (cfg.tokens_per_frame, cfg.height, cfg.width) == (128, 8, 16) + model = WorldModel.from_pretrained(str(CHECKPOINT), cfg=cfg, device=DEVICE, dtype=DTYPE).eval() + islands = { + "freq": model.denoise_step_emb.freq.clone(), + "xy": model.transformer.rope_angles.xy.clone(), + "inv_t": model.transformer.rope_angles.inv_t.clone(), + } + bare_conditioner = model.denoise_step_emb + patch_model.apply_inference_patches(model) + patch_model.flex_attention = __import__( + "mstar.engine.resources.attn.flex", fromlist=["flex_attention_masked"] + ).flex_attention_masked + cache = StaticKVCache(cfg, batch_size=1, dtype=DTYPE).to(device=DEVICE) + return { + "cfg": cfg, + "model": model, + "kv": cache, + "islands": islands, + "bare_conditioner": bare_conditioner, + } + + +def _reference_decoder_histories(session) -> tuple[torch.Tensor, ...]: + return tuple(value for value in session.streaming_ae_model.decoder_memory if torch.is_tensor(value)) + + +def _assert_histories_equal(actual, expected, frame: int) -> None: + assert len(actual) == len(expected) == 9 + for index, (left, right) in enumerate(zip(actual, expected, strict=True)): + assert torch.equal(left, right), ( + f"frame {frame} decoder history {index} differs: maxabs={_deviation(left, right)[0]:.4e}" + ) + + +@torch.inference_mode() +def test_360p_reference_compat_is_bit_exact_end_to_end(): + """41 live frames: tables through pixels, including two local-ring wraps.""" + assert ROLLOUT_FRAMES > 32, "the live gate must cross two 16-frame local windows" + assert ROLLOUT_FRAMES <= len(CONTROL_SEQUENCE) + 1 + + reference = _load_reference() + config = replace(waypoint_1_5_1b_360p(), reference_compat=True, compile_dit=False) + port = _build_port(config, CHECKPOINT) + + table_names = [] + for name, (expected, actual) in _island_tables(port, reference).items(): + gap, relative = _deviation(expected, actual) + print(f"table {name:>24} maxabs={gap:.4e} rel={relative:.4e}") + assert gap == 0.0, f"360p {name} maxabs={gap:.4e} rel={relative:.4e}" + table_names.append(name) + + for value in reference["cfg"].scheduler_sigmas: + sigma = torch.tensor([[value]], dtype=DTYPE, device=DEVICE) + expected = reference["model"].denoise_step_emb(sigma) + actual = port["dit"].denoise_step_emb(sigma) + gap, relative = _deviation(expected, actual) + print(f"conditioner sigma={value:<7.4f} maxabs={gap:.4e} rel={relative:.4e}") + assert gap == 0.0, f"360p conditioner sigma={value} maxabs={gap:.4e} rel={relative:.4e}" + + clip, seed_digest = _seed_clip() + assert seed_digest == SEED_SHA256 + ae = load_taehv(str(AE_CHECKPOINT)).to(device=DEVICE, dtype=DTYPE) + from src.ae import ChunkedStreamingTAEHV as ReferenceTAEHV + + reference_ae = ReferenceTAEHV( + ae, + auto_aspect_ratio=True, + device=DEVICE, + dtype=DTYPE, + height=config.latent_height, + width=config.latent_width, + ) + reference_seed = reference_ae.encode(clip) + functional_seed = encode_seed_clip( + ae, + clip.to(device=DEVICE, dtype=DTYPE).div(255), + output_size=(256, 512), + ) + assert torch.equal(reference_seed, functional_seed), ( + f"360p functional encoder differs from upstream: maxabs={_deviation(reference_seed, functional_seed)[0]:.4e}" + ) + + idle = _context(config, 0) + stage_names = [name for name, _ in _stage_modules(port["dit"], is_port=True)] + assert len(stage_names) == 30 + for sigma_value in (1.0, 0.0): + commit = sigma_value == 0.0 + _new_request(port) + _reset(port, reference) + reference_stages: dict[str, torch.Tensor] = {} + with _stage_capture(_stage_modules(reference["model"], is_port=False), reference_stages): + expected = _reference_forward(reference, reference_seed.unsqueeze(1), sigma_value, idle, commit=commit) + port_stages: dict[str, torch.Tensor] = {} + _admit(port, 0) + with _stage_capture(_stage_modules(port["dit"], is_port=True), port_stages): + actual = _port_forward(port, functional_seed.unsqueeze(1), sigma_value, idle, 0, commit=commit) + _commit(port, 0) + divergent = _divergent_stages(reference_stages, port_stages, stage_names) + gap, relative = _deviation(expected, actual) + print(f"stages sigma={sigma_value}: divergent={len(divergent)}/30 output maxabs={gap:.4e} rel={relative:.4e}") + assert not divergent, f"360p sigma={sigma_value} first divergence: {divergent[0]}" + assert gap == 0.0 + _assert_ring(_ring_deviation(reference, port), exact=True) + + _new_request(port) + _reset(port, reference) + histories = initial_decoder_histories(ae, functional_seed) + sigmas = torch.tensor(list(reference["cfg"].scheduler_sigmas), dtype=DTYPE, device=DEVICE) + noise_generator = torch.Generator(device="cpu").manual_seed(NOISE_SEED) + frame_shape = ( + 1, + 1, + config.channels, + config.latent_height, + config.latent_width, + ) + + max_gaps = { + "tables": 0.0, + "conditioner": 0.0, + "stages": 0.0, + "passes": 0.0, + "latents": 0.0, + "ring_kv": 0.0, + "encoder": 0.0, + "decoder_histories": 0.0, + "pixels": 0, + } + for frame in range(ROLLOUT_FRAMES): + if frame == 0: + ctx = idle + expected = reference_seed.unsqueeze(1) + actual = functional_seed.unsqueeze(1) + expected_velocity = _reference_forward(reference, expected, 0.0, ctx, commit=True) + _admit(port, 0) + actual_velocity = _port_forward(port, actual, 0.0, ctx, 0, commit=True) + _commit(port, 0) + pass_outputs = [(expected_velocity, actual_velocity)] + else: + buttons, mouse, scroll = CONTROL_SEQUENCE[frame - 1] + ctx = _context(config, frame, buttons, mouse, scroll) + noise = torch.randn(frame_shape, generator=noise_generator, dtype=torch.float32) + noise = noise.to(device=DEVICE, dtype=DTYPE) + expected, reference_passes = _reference_frame(reference, noise, ctx, sigmas) + actual, port_passes = _port_frame(port, noise, ctx, frame) + assert len(reference_passes) == len(port_passes) == 5 + pass_outputs = list(zip(reference_passes, port_passes, strict=True)) + + for pass_index, (left, right) in enumerate(pass_outputs): + pass_gap, pass_relative = _deviation(left, right) + assert pass_gap == 0.0, ( + f"360p frame {frame} pass {pass_index}: maxabs={pass_gap:.4e} rel={pass_relative:.4e}" + ) + latent_gap, latent_relative = _deviation(expected, actual) + assert latent_gap == 0.0, f"360p frame {frame} latent maxabs={latent_gap:.4e} rel={latent_relative:.4e}" + ring = _ring_deviation(reference, port) + ring_worst = _assert_ring(ring, exact=True) + + reference_pixels = reference_ae.decode(expected.squeeze(1)) + functional_pixels, histories = decode_latent( + ae, + actual.squeeze(1), + histories, + output_size=(360, 640), + initialize=frame == 0, + ) + pixel_gap = int((reference_pixels.to(torch.int16) - functional_pixels.to(torch.int16)).abs().max().item()) + assert pixel_gap == 0, f"360p frame {frame} pixels maxabs={pixel_gap}/255" + _assert_histories_equal(histories, _reference_decoder_histories(reference_ae), frame) + print( + f"frame {frame:3d} passes={len(pass_outputs)} latent maxabs={latent_gap:.4e} " + f"ring maxabs={ring_worst[1]:.4e} pixels maxabs={pixel_gap}" + ) + + report = { + "status": "passed", + "variant": "waypoint-1.5-1b-360p", + "checkpoint": str(CHECKPOINT.resolve()), + "checkpoint_revision": CHECKPOINT_REVISION, + "checkpoint_sha256": CHECKPOINT_SHA256, + "reference_source": str(REFERENCE_SRC.resolve()), + "device": torch.cuda.get_device_name(DEVICE), + "device_index_visible": DEVICE.index, + "dtype": str(DTYPE), + "torch": torch.__version__, + "python": platform.python_version(), + "reference_compat": True, + "rollout_latent_frames": ROLLOUT_FRAMES, + "generated_latent_frames": ROLLOUT_FRAMES - 1, + "denoise_and_commit_passes": 1 + 5 * (ROLLOUT_FRAMES - 1), + "decoded_rgb_frames_including_internal_prime": 4 * ROLLOUT_FRAMES, + "stage_names": stage_names, + "stage_probes": {"sigmas": [1.0, 0.0], "stages_per_probe": len(stage_names)}, + "derived_tables": table_names, + "scheduler_sigmas": list(config.scheduler_sigmas), + "noise": {"source": "seeded CPU fp32 then cast to CUDA BF16", "seed": NOISE_SEED}, + "controls": "canonical 40-action record_oracle.py sequence", + "seed_sha256": seed_digest, + "maximum_absolute_differences": max_gaps, + "scope": { + "reference": "live same-process world_engine", + "dit_execution": "eager on both sides", + "attention": "shared mstar flex_attention_masked kernel", + "stored_oracle": "not used; existing artifacts belong to the 720p checkpoint", + "taehv": "upstream streaming state versus functional explicit nine-history path", + }, + } + REPORT_PATH.parent.mkdir(parents=True, exist_ok=True) + REPORT_PATH.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n") + print(f"360p parity report: {REPORT_PATH}") diff --git a/test/modular/test_waypoint_checkpoint.py b/test/modular/test_waypoint_checkpoint.py new file mode 100644 index 000000000..4c3476a32 --- /dev/null +++ b/test/modular/test_waypoint_checkpoint.py @@ -0,0 +1,418 @@ +from __future__ import annotations + +import sys +import types +from dataclasses import replace +from pathlib import Path + +import pytest +import torch +import yaml + +sys.path.insert(0, ".") + +from mstar.communication.tensors import LocalTransferEngine +from mstar.distributed.communication import WorkerParallelGroups +from mstar.engine.resources.kv.transfer import TransferEngineInfo +from mstar.model.registry import HF_MODELS, get_model_class +from mstar.model.waypoint.checkpoint import ( + TAEHV_CHECKPOINT_FILE, + WAYPOINT_WEIGHT_ALLOW_PATTERNS, + require_taehv_runtime, + resolve_taehv_checkpoint, + resolve_waypoint_checkpoint, +) +from mstar.model.waypoint.config import ( + WAYPOINT_VARIANT_360P, + WAYPOINT_VARIANT_720P, + WAYPOINT_VARIANT_HF_REPOS, + WaypointConfig, + waypoint_1_5_1b_360p, + waypoint_1_5_1b_720p, +) +from mstar.model.waypoint.waypoint_model import ( + DIT_NODE, + VAE_DECODER_NODE, + VAE_ENCODER_NODE, + WaypointModel, +) +from mstar.worker.engine_manager import EngineManager + + +def _manifest(geometry: tuple[int, int, int] = (512, 16, 32)) -> dict: + tokens_per_frame, height, width = geometry + return { + "model_type": "waypoint-1.5", + "inference_fps": 60, + "temporal_compression": 4, + "taehv_ae": True, + "ae_uri": "Overworld-Models/taehv1_5", + "prompt_conditioning": None, + "channels": 32, + "n_layers": 24, + "n_heads": 32, + "n_kv_heads": 16, + "d_model": 2048, + "mlp_ratio": 4, + "causal": True, + "moe": False, + "n_buttons": 256, + "tokens_per_frame": tokens_per_frame, + "height": height, + "width": width, + "conv_kw": {"kernel_size": [2, 2], "stride": [2, 2]}, + "patch": [2, 2], + "base_fps": 15, + "local_window": 16, + "global_window": 128, + "global_pinned_dilation": 8, + "global_attn_period": 4, + "global_attn_offset": -1, + "n_frames": 512, + "rope_impl": "ortho", + "value_residual": True, + "gated_attn": False, + "noise_conditioning": "wan", + "ctrl_conditioning": True, + "ctrl_cond_dropout": 0.0, + "ctrl_conditioning_period": 3, + "scheduler_sigmas": [1.0, 0.9, 0.75, 0.3, 0.0], + } + + +def _checkpoint(tmp_path: Path, manifest: dict | None = None) -> Path: + directory = tmp_path / "waypoint" + directory.mkdir() + (directory / "config.yaml").write_text(yaml.safe_dump(manifest or _manifest())) + (directory / "model.safetensors").touch() + return directory + + +def test_serving_defaults_to_reference_compatible_optimized_execution(): + for config in (waypoint_1_5_1b_720p(), waypoint_1_5_1b_360p()): + assert config.reference_compat is True + assert config.compile_dit is True + assert config.cuda_graph is True + + +@pytest.mark.parametrize("compile_dit", [False, True]) +@pytest.mark.parametrize("cuda_graph", [False, True]) +@pytest.mark.parametrize("reference_compat", [False, True]) +def test_execution_and_numerical_modes_are_independent( + compile_dit, cuda_graph, reference_compat, +): + config = replace( + waypoint_1_5_1b_720p(), + compile_dit=compile_dit, + cuda_graph=cuda_graph, + reference_compat=reference_compat, + ) + config.validate_supported_deployment() + assert config.compile_dit is compile_dit + assert config.cuda_graph is cuda_graph + assert config.reference_compat is reference_compat + + +def test_model_constructor_threads_all_execution_modes_independently(): + model = WaypointModel( + skip_weight_loading=True, + compile_dit=False, + cuda_graph=False, + reference_compat=False, + ) + assert model.config.compile_dit is False + assert model.config.cuda_graph is False + assert model.config.reference_compat is False + + +@pytest.mark.parametrize( + "kwargs,fragment", + [ + ({"scheduler_sigmas": (1.0, 0.3, 0.4, 0.0)}, "strictly descending"), + ({"scheduler_sigmas": (1.0, 0.3)}, "end at 0.0"), + ({"inference_fps": 59}, "temporal_compression"), + ({"base_fps": 16}, "latent fps"), + ({"patch": (2, 0)}, "two positive integers"), + ({"d_model": 2016}, "OrthoRoPE"), + ], +) +def test_config_rejects_invalid_scheduler_geometry_and_fps(kwargs, fragment): + with pytest.raises(ValueError, match=fragment): + WaypointConfig(**kwargs) + + +@pytest.mark.parametrize( + "changes,fragment", + [ + ({"tokens_per_frame": 128, "height": 8, "width": 16}, "requires.*tokens_per_frame"), + ({"d_model": 4096}, "d_model"), + ({"global_window": 256}, "global_window"), + ({"scheduler_sigmas": (1.0, 0.5, 0.0)}, "scheduler_sigmas"), + ({"base_fps": 30}, "base_fps"), + ], +) +def test_deployment_validation_rejects_checkpoint_fact_drift(changes, fragment): + config = replace(waypoint_1_5_1b_720p(), **changes) + with pytest.raises(ValueError, match=fragment): + config.validate_supported_deployment() + + +def test_local_checkpoint_is_validated_without_importing_huggingface(tmp_path, monkeypatch): + directory = _checkpoint(tmp_path) + monkeypatch.setitem(sys.modules, "huggingface_hub", None) + assert resolve_waypoint_checkpoint(directory, waypoint_1_5_1b_720p()) == directory.resolve() + + +@pytest.mark.parametrize( + ("config", "geometry"), + [ + (waypoint_1_5_1b_720p(), (512, 16, 32)), + (waypoint_1_5_1b_360p(), (128, 8, 16)), + ], +) +def test_selected_variant_accepts_only_its_manifest_geometry(tmp_path, config, geometry): + directory = _checkpoint(tmp_path, _manifest(geometry)) + assert resolve_waypoint_checkpoint(directory, config) == directory.resolve() + + +@pytest.mark.parametrize( + ("config", "wrong_geometry"), + [ + (waypoint_1_5_1b_720p(), (128, 8, 16)), + (waypoint_1_5_1b_360p(), (512, 16, 32)), + ], +) +def test_selected_variant_rejects_the_other_manifest_geometry( + tmp_path, config, wrong_geometry +): + directory = _checkpoint(tmp_path, _manifest(wrong_geometry)) + with pytest.raises(ValueError, match=rf"checkpoint geometry.*{config.variant}"): + resolve_waypoint_checkpoint(directory, config) + + +@pytest.mark.parametrize( + "key,value,fragment", + [ + ("n_layers", 23, "n_layers"), + ("tokens_per_frame", 129, "checkpoint geometry"), + ("scheduler_sigmas", [1.0, 0.5, 0.0], "scheduler_sigmas"), + ("inference_fps", 30, "inference_fps"), + ], +) +def test_manifest_mismatch_fails_before_loading_weights(tmp_path, key, value, fragment): + manifest = _manifest() + manifest[key] = value + directory = _checkpoint(tmp_path, manifest) + with pytest.raises(ValueError, match=fragment): + resolve_waypoint_checkpoint(directory, waypoint_1_5_1b_720p()) + + +def test_missing_or_partial_local_checkpoint_fails_clearly(tmp_path): + directory = tmp_path / "partial" + directory.mkdir() + (directory / "config.yaml").write_text(yaml.safe_dump(_manifest())) + with pytest.raises(FileNotFoundError, match="no model.safetensors"): + resolve_waypoint_checkpoint(directory, waypoint_1_5_1b_720p()) + with pytest.raises(FileNotFoundError, match="local path does not exist"): + resolve_waypoint_checkpoint(tmp_path / "typo", waypoint_1_5_1b_720p()) + + +def test_hf_resolution_downloads_only_native_checkpoint_files(tmp_path, monkeypatch): + directory = _checkpoint(tmp_path) + calls = [] + + def snapshot_download(**kwargs): + calls.append(kwargs) + return str(directory) + + monkeypatch.setitem( + sys.modules, "huggingface_hub", types.SimpleNamespace(snapshot_download=snapshot_download) + ) + resolved = resolve_waypoint_checkpoint( + "Overworld/Waypoint-1.5-1B", + waypoint_1_5_1b_720p(), + cache_dir=tmp_path / "cache", + revision="weights-revision", + ) + assert resolved == directory.resolve() + assert calls == [ + { + "repo_id": "Overworld/Waypoint-1.5-1B", + "cache_dir": str(tmp_path / "cache"), + "revision": "weights-revision", + "allow_patterns": list(WAYPOINT_WEIGHT_ALLOW_PATTERNS), + } + ] + + +def test_taehv_local_and_hf_resolution_fetch_exactly_one_file(tmp_path, monkeypatch): + ae_dir = tmp_path / "ae" + ae_dir.mkdir() + checkpoint = ae_dir / TAEHV_CHECKPOINT_FILE + checkpoint.touch() + assert resolve_taehv_checkpoint(ae_dir) == checkpoint.resolve() + + calls = [] + + def hf_hub_download(**kwargs): + calls.append(kwargs) + return str(checkpoint) + + monkeypatch.setitem( + sys.modules, "huggingface_hub", types.SimpleNamespace(hf_hub_download=hf_hub_download) + ) + assert resolve_taehv_checkpoint( + "Overworld-Models/taehv1_5", revision="ae-revision" + ) == checkpoint.resolve() + assert calls == [ + { + "repo_id": "Overworld-Models/taehv1_5", + "filename": TAEHV_CHECKPOINT_FILE, + "cache_dir": None, + "revision": "ae-revision", + } + ] + + +@pytest.mark.parametrize("installed", [None, types.SimpleNamespace()]) +def test_taehv_runtime_preflight_fails_clearly(installed, monkeypatch): + monkeypatch.setitem(sys.modules, "taehv", installed) + with pytest.raises(RuntimeError, match=r"TAEHV.*\.\[waypoint\].*(uv|pip)"): + require_taehv_runtime() + + +@pytest.mark.parametrize( + ("variant", "expected_source"), + list(WAYPOINT_VARIANT_HF_REPOS.items()), +) +def test_model_resolves_variant_artifacts_before_first_module_allocation( + monkeypatch, variant, expected_source +): + calls = [] + + def resolve_waypoint(source, config, **kwargs): + calls.append(("waypoint", source, kwargs)) + return Path("/resolved/waypoint") + + def resolve_taehv(source, **kwargs): + calls.append(("taehv", source, kwargs)) + return Path("/resolved/taehv1_5.pth") + + def build_dit(config, checkpoint_dir, device): + calls.append(("allocate", checkpoint_dir, device)) + return torch.nn.Linear(1, 1) + + monkeypatch.setattr( + "mstar.model.waypoint.checkpoint.require_taehv_runtime", + lambda: calls.append(("runtime",)), + ) + monkeypatch.setattr( + "mstar.model.waypoint.checkpoint.resolve_waypoint_checkpoint", + resolve_waypoint, + ) + monkeypatch.setattr( + "mstar.model.waypoint.checkpoint.resolve_taehv_checkpoint", resolve_taehv, + ) + monkeypatch.setattr( + "mstar.model.waypoint.weight_loader.build_waypoint_dit", build_dit, + ) + model = WaypointModel( + **HF_MODELS["waypoint"], + variant=variant, + ae_path="Overworld-Models/taehv1_5", + cache_dir="/cache", + checkpoint_revision="dit-rev", + ae_revision="ae-rev", + ) + + model.get_submodule(DIT_NODE) + + assert [entry[0] for entry in calls] == ["runtime", "waypoint", "taehv", "allocate"] + assert calls[1][1:] == ( + expected_source, + {"cache_dir": "/cache", "revision": "dit-rev"}, + ) + assert calls[2][1:] == ( + "Overworld-Models/taehv1_5", + {"cache_dir": "/cache", "revision": "ae-rev"}, + ) + assert calls[3][1] == "/resolved/waypoint" + + +def test_registry_hub_id_uses_the_waypoint_startup_contract(): + waypoint = get_model_class("waypoint") + assert waypoint is WaypointModel + assert HF_MODELS["waypoint"] == {"model_path_hf": None} + assert WAYPOINT_VARIANT_HF_REPOS == { + WAYPOINT_VARIANT_720P: "Overworld/Waypoint-1.5-1B", + WAYPOINT_VARIANT_360P: "Overworld/Waypoint-1.5-1B-360P", + } + + +@pytest.mark.parametrize("variant", [WAYPOINT_VARIANT_720P, WAYPOINT_VARIANT_360P]) +def test_registry_default_selects_the_repository_for_the_variant(variant): + model = get_model_class("waypoint")( + **HF_MODELS["waypoint"], variant=variant, skip_weight_loading=True + ) + assert model.model_path_hf == WAYPOINT_VARIANT_HF_REPOS[variant] + assert model.checkpoint_dir == WAYPOINT_VARIANT_HF_REPOS[variant] + + +@pytest.mark.parametrize( + "explicit_source", + ["Example/custom-waypoint", "/models/local-waypoint"], +) +def test_explicit_model_source_is_not_rewritten_for_the_selected_variant(explicit_source): + model = WaypointModel( + model_path_hf=explicit_source, + variant=WAYPOINT_VARIANT_360P, + skip_weight_loading=True, + ) + assert model.model_path_hf == explicit_source + assert model.checkpoint_dir == explicit_source + + +def test_explicit_checkpoint_directory_overrides_the_variant_repository(): + model = WaypointModel( + model_path_hf=None, + checkpoint_dir="/models/local-waypoint", + variant=WAYPOINT_VARIANT_360P, + skip_weight_loading=True, + ) + assert model.model_path_hf == WAYPOINT_VARIANT_HF_REPOS[WAYPOINT_VARIANT_360P] + assert model.checkpoint_dir == "/models/local-waypoint" + + +def test_shipped_config_builds_through_registry_and_engine_manager_without_network(): + config_path = Path(__file__).resolve().parents[2] / "configs" / "waypoint.yaml" + model_config = yaml.safe_load(config_path.read_text()) + model_class = get_model_class(model_config["model"]) + model = model_class( + **HF_MODELS[model_config["model"]], + **model_config["model_kwargs"], + skip_weight_loading=True, + ) + + model.get_worker_graphs(str(config_path)) + manager = EngineManager.build( + node_names={DIT_NODE, VAE_ENCODER_NODE, VAE_DECODER_NODE}, + device=torch.device("cpu"), + model_config=model_config, + parallel_groups=WorkerParallelGroups(num_workers=1, global_rank=0), + transfer_engine_info=TransferEngineInfo( + my_entity_id="test", + my_session_id="test", + transfer_engine=LocalTransferEngine("test"), + ), + model=model, + ) + try: + assert manager.node_names == {DIT_NODE, VAE_ENCODER_NODE, VAE_DECODER_NODE} + assert manager.engine._autocast_dtype is torch.bfloat16 + assert model.config.reference_compat is True + assert model.config.compile_dit is True + assert model.config.cuda_graph is True + assert model.checkpoint_dir == "Overworld/Waypoint-1.5-1B" + assert model._checkpoints_resolved is False + finally: + manager.shutdown() diff --git a/test/modular/test_waypoint_components.py b/test/modular/test_waypoint_components.py index 123737185..c2453c2c1 100644 --- a/test/modular/test_waypoint_components.py +++ b/test/modular/test_waypoint_components.py @@ -95,6 +95,9 @@ def reduced_config(**overrides) -> WaypointConfig: "global_window": 32, "global_pinned_dilation": 8, "n_buttons": 8, + # Structural CPU tests use fp32 inputs. Reference-compatible serving is + # exercised separately with bf16 tables and scheduler values. + "reference_compat": False, } return WaypointConfig(**{**base, **overrides}) @@ -656,7 +659,7 @@ def grid_positions(config: WaypointConfig, frame: int): def test_ortho_rope_angles_match_the_reference_construction_bitwise(): - config = waypoint_1_5_1b_720p() + config = dataclasses.replace(waypoint_1_5_1b_720p(), reference_compat=False) module = OrthoRoPEAngles(config) x_pos, y_pos, t_pos = grid_positions(config, frame=5) diff --git a/test/modular/test_waypoint_dit.py b/test/modular/test_waypoint_dit.py index b101793e6..9f3903419 100644 --- a/test/modular/test_waypoint_dit.py +++ b/test/modular/test_waypoint_dit.py @@ -64,6 +64,9 @@ def reduced_config(**overrides) -> WaypointConfig: "global_window": 32, "global_pinned_dilation": 8, "n_buttons": 8, + # Most CPU unit inputs are fp32. Reference-compatible serving is bf16 + # and has its own numerical tests; keep these structural tests exact. + "reference_compat": False, } return WaypointConfig(**{**base, **overrides}) @@ -170,7 +173,10 @@ def __init__(self, config: WaypointConfig, dtype: torch.dtype = torch.float32): # -- the model-facing surface, delegated --------------------------------- - def upsert(self, k, v, layer_idx, frame_pos, *, commit): + def upsert( + self, k, v, layer_idx, frame_pos, *, commit, build_visibility=True, + ): + del build_visibility self.upserts.append( { "commit": commit, @@ -571,7 +577,10 @@ class CaptureKV: def __init__(self): self.calls: list[tuple[torch.Tensor, torch.Tensor]] = [] - def upsert(self, k, v, layer_idx, frame_pos, *, commit): + def upsert( + self, k, v, layer_idx, frame_pos, *, commit, build_visibility=True, + ): + del build_visibility self.calls.append((k.detach().clone(), v.detach().clone())) return k, v, None @@ -584,7 +593,8 @@ class DenseAttn: requires_kv_write = False - def attend(self, q, k, v, visible, *, enable_gqa): + def attend(self, q, k, v, visible, *, enable_gqa, layer_idx=None): + del layer_idx assert visible is None, "CaptureKV returns no visibility row" return torch.nn.functional.scaled_dot_product_attention(q, k, v, enable_gqa=enable_gqa) diff --git a/test/modular/test_waypoint_gpu.py b/test/modular/test_waypoint_gpu.py new file mode 100644 index 000000000..590c7bce0 --- /dev/null +++ b/test/modular/test_waypoint_gpu.py @@ -0,0 +1,774 @@ +"""GPU gates for the Waypoint port: fullgraph capture, compiled/eager parity, +CUDA-graph replay, rollout isolation, and the BlockMask rebuild cost. + +Checkpoint-free -- the weights are random and every claim here is a +self-consistency one. The geometry is reduced (4 layers, 128 tokens per frame) +but structurally identical to 720P: one global layer at a dilated stride, +controller fusion on ``i % 3 == 0``, GQA live, and local/global rings of +*different* capacity so a layer-indexing mistake cannot hide behind a uniform +buffer. + +Two facts drive the shape of this file: + + * ``compile_regions()`` is an optional execution mode, with all runtime tables + materialized first when selected. Planned masks are staged at fixed addresses + outside the compiled region and outside CUDA-graph replay. + * There is no eager reference mode. Both sides of every comparison run with + the ``flex_attention_masked`` compile on, because eager ``flex_attention`` + ignores the no-op ``mask_mod`` and reads unwritten ring slots. + +Set ``WAYPOINT_GPU_TESTS=0`` to skip; a full run is a few minutes, most of it +``torch.compile``. +""" + +import os +import sys +import time +import zlib + +import pytest +import torch + +sys.path.insert(0, ".") + +from mstar.engine.resources.attn.base import AttentionManager +from mstar.engine.resources.attn.config import ( + AttentionConfig, + AttentionSpec, + AttentionStep, + AttnBackend, +) +from mstar.engine.resources.attn.flex import flex_attention_masked, make_block_mask +from mstar.engine.resources.base import EngineResourceInfo +from mstar.engine.resources.kv.config import ( + KVSpec, + RingKVConfig, + RingKVLayerConfig, + RingKVStep, +) +from mstar.engine.resources.kv.ring import RingKVManager +from mstar.engine.resources.step import StepContext +from mstar.model.submodule_base import NodeSubmodule +from mstar.model.waypoint.components.dit import WaypointDiT +from mstar.model.waypoint.components.layers import NoiseConditioner +from mstar.model.waypoint.config import WaypointConfig, waypoint_1_5_1b_720p + +pytestmark = [ + pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA"), + pytest.mark.skipif( + os.environ.get("WAYPOINT_GPU_TESTS", "1") == "0", + reason="WAYPOINT_GPU_TESTS=0", + ), +] + +DEVICE = torch.device("cuda") +DTYPE = torch.bfloat16 + +# bf16 carries 8 mantissa bits, so one ulp at magnitude m is 2**-8 * 2**floor(log2 m). +BF16_EPS = 2.0**-8 + +# Compiled-vs-eager budget, in ulp of a frame's peak magnitude. Measured worst +# case is 2.35; see the parity test for what it is a budget for. +COMPILE_TOL_ULP = 4 + +# Frames per rollout in the capture gates. Wraps both rings at least twice: +# the local ring holds 8 frames, and the dilated ring's 4 buckets span +# 4 * 2 = 8 frames. +ROLLOUT_FRAMES = 20 + + +def gpu_config(**overrides) -> WaypointConfig: + """Reduced geometry with 720P's structure. Layer 3 is the global one + (period 4, offset -1); its ring holds 4 buckets at stride 2 against the + local layers' 8 frames, so the two capacities differ.""" + base = { + "n_layers": 4, + "n_heads": 2, + "n_kv_heads": 1, + "d_model": 64, + "mlp_ratio": 2, + "channels": 4, + "tokens_per_frame": 128, + "height": 8, + "width": 16, + "local_window": 8, + "global_window": 8, + "global_pinned_dilation": 2, + "n_buttons": 8, + } + return WaypointConfig(**{**base, **overrides}) + + +def _kv_spec(config: WaypointConfig, num_worlds: int) -> KVSpec: + return KVSpec( + resource_key="kv", + nodes={"dit"}, + config=RingKVConfig( + num_layers=config.n_layers, + num_kv_heads=config.n_kv_heads, + head_dim=config.d_head, + num_qo_heads=config.n_heads, + tokens_per_frame=config.tokens_per_frame, + num_worlds=num_worlds, + layers=tuple( + RingKVLayerConfig( + ring_frames=config.ring_frames(i), + ring_buckets=config.ring_buckets(i), + pinned_dilation=config.pinned_dilation(i), + ) + for i in range(config.n_layers) + ), + ), + ) + + +class _DitNode(NodeSubmodule): + """Binds through the engine's own walk: one call on the node has to reach + all of the attention layers.""" + + def __init__(self, dit: WaypointDiT): + super().__init__() + self.dit = dit + + def prepare_inputs(self, *args, **kwargs): + raise NotImplementedError("binding stand-in; nothing here runs a step") + + def forward(self, *args, **kwargs): + raise NotImplementedError("binding stand-in; nothing here runs a step") + + +def build(config: WaypointConfig, *, seed: int = 0, num_worlds: int = 1): + """The serving build order -- meta, cast, ``to_empty``, retie -- with random + weights, wired to a real ring and a real flex backend on the GPU. + + Parameters are filled from a CPU generator so two builds at the same seed + are bit-identical, which is what lets a test compare two independent + instances. + """ + with torch.device("meta"): + dit = WaypointDiT(config) + dit.cast_serving_dtypes() + dit.to_empty(device=DEVICE) + dit.retie_cond_proj() + + gen = torch.Generator(device="cpu").manual_seed(seed) + with torch.no_grad(): + for param in dit.parameters(): + param.copy_(torch.randn(param.shape, generator=gen, dtype=torch.float32) * 0.05) + for block in dit.blocks: + block.attn.v_lamb.fill_(0.25) + dit.eval() + + spec = _kv_spec(config, num_worlds) + info = EngineResourceInfo(device=DEVICE, kv_dtype=DTYPE) + kv = RingKVManager.build(spec, info) + attn = AttentionManager.build( + AttentionSpec( + resource_key="attn", + nodes={"dit"}, + config=AttentionConfig(kv_cache="kv", backend=AttnBackend.FLEX), + ), + EngineResourceInfo(device=DEVICE, kv_dtype=DTYPE, dependencies={"kv": spec}), + ) + _DitNode(dit).bind_node_resources({"kv": kv, "attn": attn}) + return dit, kv, attn + + +def controls(config: WaypointConfig): + mouse = torch.tensor([[[0.25, -0.5]]], dtype=DTYPE, device=DEVICE) + button = torch.zeros(1, 1, config.n_buttons, dtype=DTYPE, device=DEVICE) + button[..., 2] = 1.0 + scroll = torch.tensor([[[1.0]]], dtype=DTYPE, device=DEVICE) + return mouse, button, scroll + + +def noise_for(config: WaypointConfig, stream: str, frame: int) -> torch.Tensor: + """This frame's noise, a pure function of ``(stream, frame)`` -- so an + interleave order cannot change what a rollout is fed, only where it lands. + + ``crc32`` and not ``hash``: str hashing is salted per process, which would + make a failure here unreproducible from its own seed.""" + gen = torch.Generator(device="cpu").manual_seed( + zlib.crc32(f"{stream}:{frame}".encode()) + ) + return torch.randn( + (1, 1, *config.latent_shape), generator=gen, dtype=torch.float32 + ).to(DEVICE, DTYPE) + + +# ---- resource lifecycle, driven by hand ------------------------------------ + + +def _ctx(rid: str) -> StepContext: + return StepContext(request_ids=(rid,), graph_walk="rollout", slot=0, capture=False) + + +def _step(rid: str, frame: int) -> RingKVStep: + return RingKVStep(frames=((rid, frame),)) + + +def admit_frame( + kv: RingKVManager, + rid: str, + frame: int, + attn: AttentionManager | None = None, +) -> None: + """Admit and stage one frame exactly as the engine's resource runner does. + + KV planning writes the world index read by replay and returns the host ring + facts the dependent attention plan uses to stage its fixed-address mask. + ``attn=None`` is reserved for the standalone fallback diagnostic. + """ + ctx = _ctx(rid) + outcome = kv.admit(_step(rid, frame), ctx) + assert outcome.ok, f"{rid} refused at frame {frame}: {outcome.reason}" + ring_plan = kv.plan(_step(rid, frame), ctx) + if attn is not None: + ctx.plan_results["kv"] = ring_plan + attn.plan(AttentionStep(), ctx) + + +def scrub(kv: RingKVManager, *rids: str) -> None: + """Return every world to the pool and zero every ring. The tests share one + captured graph, so each one starts from a ring that holds nothing.""" + for rid in rids: + kv.reset_request(rid, free=True) + kv.remove_request(rid) + for layer in kv.layers: + for world in range(layer.num_worlds): + layer.reset(world) + + +def world_snapshot(kv: RingKVManager, world_idx: int): + return [ + ( + layer.kv[:, :, :, slice(*layer.world_span(world_idx))].clone(), + layer.written[slice(*layer.world_span(world_idx))].clone(), + ) + for layer in kv.layers + ] + + +def assert_worlds_equal(left, right, what: str) -> None: + for i, ((kv_a, written_a), (kv_b, written_b)) in enumerate(zip(left, right, strict=True)): + assert torch.equal(written_a, written_b), f"{what}: layer {i} visibility differs" + assert torch.equal(kv_a, kv_b), f"{what}: layer {i} ring bytes differ" + + +# --------------------------------------------------------------------------- +# A.1 -- the fullgraph regions +# --------------------------------------------------------------------------- + + +def test_compile_regions_holds_under_fullgraph(): + """``fullgraph=True`` on both regions, which is what makes capture possible + at all -- a break here would leave the driver un-capturable. + + Derived tables are explicitly materialized after weight loading and before + compilation, matching the serving lifecycle. No eager model frame mutates + initialization state before the compiled call. + + ``torch._dynamo.config.capture_scalar_outputs`` is deliberately NOT set. + The reference sets it as a documented graph-break fix; this port compiles + clean without it, and setting it here would hide a future break behind an + unbacked symint. + """ + config = gpu_config() + dit, kv, attn = build(config) + mouse, button, scroll = controls(config) + kv.ingest_request("r") + admit_frame(kv, "r", 0, attn) + + assert dit.materialize_runtime_tables(DEVICE) is dit + assert dit.compile_regions() is dit + assert dit._regions_compiled + with torch.no_grad(): + out = dit.generate_frame( + noise_for(config, "warm", 0), + torch.tensor(0, dtype=torch.int64, device=DEVICE), + mouse=mouse, button=button, scroll=scroll, + ) + torch.cuda.synchronize() + + assert out.shape == (1, 1, *config.latent_shape) and out.dtype == DTYPE + assert bool(torch.isfinite(out.float()).all()) + assert not torch._dynamo.config.capture_scalar_outputs + + +# --------------------------------------------------------------------------- +# A.2 -- compiled vs eager +# --------------------------------------------------------------------------- + + +def test_flex_attention_is_bit_exact_in_and_out_of_a_compiled_region(): + """The attention kernel does not change when the caller is traced into the + same graph. This is the floor of the A.2 ladder: it makes any compiled/eager + difference in a frame attributable to the pointwise chains around + attention rather than to the kernel the ring is read through. + """ + gen = torch.Generator(device="cpu").manual_seed(7) + q = torch.randn(1, 2, 128, 32, generator=gen).to(DEVICE, DTYPE) + k = torch.randn(1, 1, 640, 32, generator=gen).to(DEVICE, DTYPE) + v = torch.randn(1, 1, 640, 32, generator=gen).to(DEVICE, DTYPE) + written = torch.zeros(640, dtype=torch.bool, device=DEVICE) + written[:384] = True + + def attend(q, k, v, written): + mask = make_block_mask(q.size(-2), k.size(-2), written) + return flex_attention_masked(q, k, v, block_mask=mask, enable_gqa=True) + + with torch.no_grad(): + outer_eager = attend(q, k, v, written) + outer_compiled = torch.compile(attend, fullgraph=True, dynamic=False)(q, k, v, written) + torch.cuda.synchronize() + + assert torch.equal(outer_eager, outer_compiled) + + +def test_the_gemms_are_bit_exact_compiled_vs_eager(): + """Second rung: ``nn.Linear`` lowers to the same cuBLAS call either way, so + the projections are not the source of the drift either. What is left is the + fused pointwise chains -- ``rms_norm`` into RoPE, adaLN into the residual -- + where inductor keeps the intermediate in fp32 across a fusion while eager + round-trips it through bf16. + """ + gen = torch.Generator(device="cpu").manual_seed(11) + x = torch.randn(1, 128, 64, generator=gen).to(DEVICE, DTYPE) + weight = torch.randn(64, 64, generator=gen).to(DEVICE, DTYPE) + + def gemm(x, weight): + return torch.nn.functional.linear(x, weight) + + def normed(x): + return torch.nn.functional.rms_norm(x, (x.size(-1),)) * 1.25 + + with torch.no_grad(): + assert torch.equal( + gemm(x, weight), + torch.compile(gemm, fullgraph=True, dynamic=False)(x, weight), + ) + norm_eager = normed(x) + norm_compiled = torch.compile(normed, fullgraph=True, dynamic=False)(x) + # ...and the fused chain is the one that moves, toward fp32 rather than + # away from it. + reference = torch.nn.functional.rms_norm(x.float(), (x.size(-1),)) * 1.25 + torch.cuda.synchronize() + + assert not torch.equal(norm_eager, norm_compiled) + assert (norm_compiled.float() - reference).abs().max() <= ( + norm_eager.float() - reference + ).abs().max() + + +def test_compiled_matches_eager_to_four_bf16_ulp_of_peak(): + """NOT bit-exact, and the two rungs above say why: inductor's fp32-carrying + pointwise fusions. The gap is a fixed handful of bf16 quanta, and the bound + below is measured, not chosen -- worst 2.35 ulp of the frame peak over 120 + frame comparisons spanning six weight/noise seeds, so ``COMPILE_TOL_ULP`` + sits at 4 with ~1.7x headroom. + + Run over the full rollout because the interesting claim is that the gap does + not compound even though the ring feeds itself: the deviation at frame 19 is + the same size as at frame 0 (measured 3.4e-3 to 6.4e-3 of peak either way), + which a per-frame bound over 20 self-feeding frames is what catches. + + Both sides run the pinned ``flex_attention_masked`` compile; this model has + no eager reference mode. What stays exact is the structure: the two runs + write the same ring slots and hide the same ones, and a visibility row that + differed would be a slot bug rather than a rounding one. + """ + config = gpu_config() + frames = ROLLOUT_FRAMES + mouse, button, scroll = controls(config) + + def rollout(compiled: bool): + dit, kv, attn = build(config, seed=0) + dit.materialize_runtime_tables(DEVICE) + if compiled: + dit.compile_regions() + kv.ingest_request("r") + latents = [] + with torch.no_grad(): + for frame in range(frames): + admit_frame(kv, "r", frame, attn) + latents.append( + dit.generate_frame( + noise_for(config, "a2", frame), + torch.tensor(frame, dtype=torch.int64, device=DEVICE), + mouse=mouse, button=button, scroll=scroll, + ).clone() + ) + kv.commit(_step("r", frame), _ctx("r")) + torch.cuda.synchronize() + return latents, world_snapshot(kv, kv.world_of("r")) + + eager_latents, eager_ring = rollout(False) + compiled_latents, compiled_ring = rollout(True) + + for frame, (want, got) in enumerate(zip(eager_latents, compiled_latents, strict=True)): + peak = want.float().abs().max().item() + deviation = (want.float() - got.float()).abs().max().item() + assert deviation <= COMPILE_TOL_ULP * BF16_EPS * peak, ( + f"frame {frame}: compiled and eager diverge by {deviation:.3e} on a " + f"latent peaking at {peak:.3e} -- {deviation / peak / BF16_EPS:.2f} ulp " + f"of peak, past the {COMPILE_TOL_ULP} the pointwise fusions account for" + ) + + for i, ((_, eager_written), (_, compiled_written)) in enumerate( + zip(eager_ring, compiled_ring, strict=True) + ): + assert torch.equal(eager_written, compiled_written), ( + f"layer {i}: compiling the regions changed which ring slots are visible" + ) + + +# --------------------------------------------------------------------------- +# A.3 / A.4 / A.5 -- one captured graph, three claims +# --------------------------------------------------------------------------- + + +@pytest.fixture(scope="module") +def captured(): + """A compiled DiT and one CUDA graph over ``generate_frame``, plus the + static buffers a replay reads. + + Two worlds, so the interleave gate can use the same graph the single-world + gates do -- ``world_idx`` is staged by ``plan`` and read at replay, never + baked. Capture holds a dummy rid and its warmup frames land in the ring; + every test scrubs on entry. + """ + config = gpu_config() + dit, kv, attn = build(config, seed=0, num_worlds=2) + dit.materialize_runtime_tables(DEVICE) + dit.compile_regions() + mouse, button, scroll = controls(config) + + kv.ingest_request("capture") + admit_frame(kv, "capture", 0, attn) + static_noise = torch.zeros(1, 1, *config.latent_shape, dtype=DTYPE, device=DEVICE) + static_frame = torch.zeros((), dtype=torch.int64, device=DEVICE) + + stream = torch.cuda.Stream() + stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(stream), torch.no_grad(): + for _ in range(3): + dit.generate_frame( + static_noise, static_frame, mouse=mouse, button=button, scroll=scroll + ) + torch.cuda.current_stream().wait_stream(stream) + + graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(graph), torch.no_grad(): + static_out = dit.generate_frame( + static_noise, static_frame, mouse=mouse, button=button, scroll=scroll + ) + torch.cuda.synchronize() + + # Warmup and capture wrote real frames into the ring and left the dummy rid + # holding a world. Asserted here rather than in a test because this is the + # one place the contamination is unambiguous -- every test below scrubs on + # entry, so by then it is gone. + with pytest.raises(RuntimeError, match="capture"): + kv.post_warmup_validate() + + return { + "config": config, "dit": dit, "kv": kv, "attn": attn, "graph": graph, + "noise": static_noise, "frame": static_frame, "out": static_out, + "controls": (mouse, button, scroll), + } + + +def replay_frame(captured, rid: str, frame: int, stream: str) -> torch.Tensor: + """One frame through the captured graph: stage the world, refill the + inputs, replay, read the output back before the next replay overwrites it.""" + admit_frame(captured["kv"], rid, frame, captured["attn"]) + captured["noise"].copy_(noise_for(captured["config"], stream, frame)) + captured["frame"].fill_(frame) + captured["graph"].replay() + torch.cuda.synchronize() + latent = captured["out"].clone() + captured["kv"].commit(_step(rid, frame), _ctx(rid)) + return latent + + +def eager_frame(captured, rid: str, frame: int, stream: str) -> torch.Tensor: + """The same frame through the same compiled regions, uncaptured. This is the + control A.3 needs: compiled/eager parity is A.2's subject, so replaying must + be compared against the code the graph was captured from, not against a + differently-fused build of it.""" + admit_frame(captured["kv"], rid, frame, captured["attn"]) + mouse, button, scroll = captured["controls"] + with torch.no_grad(): + latent = captured["dit"].generate_frame( + noise_for(captured["config"], stream, frame), + torch.tensor(frame, dtype=torch.int64, device=DEVICE), + mouse=mouse, button=button, scroll=scroll, + ).clone() + torch.cuda.synchronize() + captured["kv"].commit(_step(rid, frame), _ctx(rid)) + return latent + + +def test_capture_replays_fixed_address_planned_masks(captured): + """The manual CUDA gate uses the served preplanned-mask path.""" + attn = captured["attn"] + assert attn.needs_token_visibility is False + assert len(attn._planned_masks) == 2 + addresses = { + key: ( + mask.full_kv_num_blocks.data_ptr(), + mask.full_kv_indices.data_ptr(), + ) + for key, mask in attn._planned_masks.items() + } + + scrub(captured["kv"], "capture") + captured["kv"].ingest_request("mask-address") + replay_frame(captured, "mask-address", 0, "mask-address") + assert { + key: ( + mask.full_kv_num_blocks.data_ptr(), + mask.full_kv_indices.data_ptr(), + ) + for key, mask in attn._planned_masks.items() + } == addresses + scrub(captured["kv"], "mask-address") + + +def test_replay_matches_the_uncaptured_regions_over_two_ring_wraps(captured): + """The gate capture exists for: 20 frames of replay against 20 frames of + the same compiled code, bit-exact on the emitted latents AND on the ring -- + the ring is the world state, and a latent check alone would pass on a + rollout whose history had quietly gone somewhere else. + + Long enough to wrap both rings twice, so a slot that was only ever appended + to has to be overwritten and read back. + """ + config = captured["config"] + kv = captured["kv"] + assert ROLLOUT_FRAMES >= 2 * config.local_window + assert ROLLOUT_FRAMES >= 2 * config.ring_buckets(3) * config.pinned_dilation(3) + assert config.ring_frames(0) != config.ring_frames(3), "the two rings are the same size" + + scrub(kv, "capture") + kv.post_warmup_validate() + + kv.ingest_request("uncaptured") + uncaptured = [ + eager_frame(captured, "uncaptured", f, "a3") for f in range(ROLLOUT_FRAMES) + ] + uncaptured_ring = world_snapshot(kv, kv.world_of("uncaptured")) + scrub(kv, "uncaptured") + + kv.ingest_request("replayed") + replayed = [ + replay_frame(captured, "replayed", f, "a3") for f in range(ROLLOUT_FRAMES) + ] + replayed_ring = world_snapshot(kv, kv.world_of("replayed")) + scrub(kv, "replayed") + + for frame, (want, got) in enumerate(zip(uncaptured, replayed, strict=True)): + assert torch.equal(want, got), ( + f"frame {frame}: replay diverged from the code it was captured from by " + f"{(want.float() - got.float()).abs().max().item():.3e}" + ) + assert_worlds_equal(uncaptured_ring, replayed_ring, "replay vs uncaptured") + assert not torch.equal(uncaptured[0], uncaptured[ROLLOUT_FRAMES - 1]), ( + "the rollout is static; a graph that replayed frame 0 forever would pass" + ) + + +def test_a_second_rollout_starts_from_nothing(captured): + """Two rollouts in one process on the same fixed ring. The second is fed the + identical noise and must produce the identical frames -- so it saw neither + the first rollout's history nor the capture warmup's, both of which are + still physically in the buffer until something zeroes them. + + ``post_warmup_validate`` is asserted to raise on the dirty ring the first + rollout leaves behind: a scrub that silently did nothing would make the + comparison vacuous, and this is what distinguishes the two. + """ + kv = captured["kv"] + frames = 8 + + scrub(kv, "capture") + kv.post_warmup_validate() + + kv.ingest_request("first") + first = [replay_frame(captured, "first", f, "a4") for f in range(frames)] + first_ring = world_snapshot(kv, kv.world_of("first")) + with pytest.raises(RuntimeError): + kv.post_warmup_validate() + scrub(kv, "first") + kv.post_warmup_validate() + + kv.ingest_request("second") + second = [replay_frame(captured, "second", f, "a4") for f in range(frames)] + second_ring = world_snapshot(kv, kv.world_of("second")) + + for frame, (want, got) in enumerate(zip(first, second, strict=True)): + assert torch.equal(want, got), ( + f"frame {frame}: the second rollout differs from the first by " + f"{(want.float() - got.float()).abs().max().item():.3e}; it inherited " + "state from the first rollout or from the capture warmup" + ) + assert_worlds_equal(first_ring, second_ring, "second rollout vs first") + scrub(kv, "second") + + +def test_two_worlds_interleaved_match_the_same_rollouts_run_alone(captured): + """Two rollouts admitted at once and advanced frame by frame in turn, each + bit-identical to itself run alone. + + Nothing physical separates the worlds -- they share one buffer per layer, + folded into the token dimension -- so the whole isolation mechanism is the + visibility row, and a leak is silent. Both rollouts go through the *same* + captured graph, which is also the claim that ``world_idx`` is read at replay + rather than baked at capture. + """ + kv = captured["kv"] + frames = 10 + assert kv.num_worlds == 2 + + scrub(kv, "capture") + alone = {} + for stream in ("A", "B"): + rid = f"alone{stream}" + kv.ingest_request(rid) + latents = [replay_frame(captured, rid, f, stream) for f in range(frames)] + alone[stream] = (latents, world_snapshot(kv, kv.world_of(rid))) + scrub(kv, rid) + + kv.ingest_request("both_a") + kv.ingest_request("both_b") + admit_frame(kv, "both_a", 0, captured["attn"]) + admit_frame(kv, "both_b", 0, captured["attn"]) + assert {kv.world_of("both_a"), kv.world_of("both_b")} == {0, 1} + + interleaved = {"A": [], "B": []} + for frame in range(frames): + interleaved["A"].append(replay_frame(captured, "both_a", frame, "A")) + interleaved["B"].append(replay_frame(captured, "both_b", frame, "B")) + + for stream, rid in (("A", "both_a"), ("B", "both_b")): + want_latents, want_ring = alone[stream] + for frame, (want, got) in enumerate( + zip(want_latents, interleaved[stream], strict=True) + ): + assert torch.equal(want, got), ( + f"world {stream} frame {frame}: sharing the node changed the rollout by " + f"{(want.float() - got.float()).abs().max().item():.3e}" + ) + assert_worlds_equal(want_ring, world_snapshot(kv, kv.world_of(rid)), f"world {stream}") + + assert not torch.equal(interleaved["A"][0], interleaved["B"][0]), ( + "the two rollouts are identical; an isolation leak would be invisible" + ) + scrub(kv, "both_a", "both_b") + + +# --------------------------------------------------------------------------- +# B -- what the BlockMask rebuild costs +# --------------------------------------------------------------------------- + + +def test_block_mask_rebuild_cost_at_720p(record_property): + """A measurement, not a bound. ``FlexAttentionManager.attend`` rebuilds the + mask on every call -- 24 layers x 5 passes = 120 times per frame -- and the + note there asks for a number before anyone caches it. + + Reported for the eager path, where the rebuild is host work. Under the + compiled regions it is traced into the graph instead, so a captured replay + pays device time and no host time at all. The block-alignment ``torch.equal`` + is timed separately because it is a full sync and it is also what blocks + capture; ``torch.compiler.is_compiling()`` is what switches it off. + """ + config = waypoint_1_5_1b_720p() + kv_len = config.kv_capacity(0) + rebuilds = config.n_layers * len(config.scheduler_sigmas) + assert (rebuilds, kv_len) == (120, 8704) + + written = torch.zeros(kv_len, dtype=torch.bool, device=DEVICE) + frames = written.view(-1, config.tokens_per_frame) + frames[:9] = True # nine committed frames of history + frames[-1] = True # the scratch tail is always visible + + def measure(fn, repeats: int) -> float: + for _ in range(3): + fn() + torch.cuda.synchronize() + start = time.perf_counter() + for _ in range(repeats): + fn() + torch.cuda.synchronize() + return (time.perf_counter() - start) / repeats * 1e3 + + blocks = written.view(-1, 128) + per_frame = measure( + lambda: [make_block_mask(config.tokens_per_frame, kv_len, written) + for _ in range(rebuilds)], + repeats=20, + ) + # The block-alignment assert, alone. It is a device-to-host sync, so it is + # both the largest single line in the rebuild and the reason a capture of + # the eager path is impossible. + sync = measure(lambda: torch.equal(blocks.any(-1), blocks.all(-1)), repeats=200) + cached = per_frame / rebuilds * config.n_layers + + record_property("block_mask_ms_per_frame", per_frame) + print(f"\n[Gate B] {rebuilds} eager BlockMask rebuilds per frame: {per_frame:.2f} ms " + f"({per_frame / rebuilds * 1e3:.0f} us each), q={config.tokens_per_frame} kv={kv_len}" + f"\n[Gate B] of which the block-alignment sync: {sync * rebuilds:.2f} ms" + f"\n[Gate B] a (layer, frame) cache would leave {config.n_layers} rebuilds: " + f"{cached:.2f} ms, saving {per_frame - cached:.2f} ms/frame") + + # The mask itself is what the timing is about, so assert it is the right one: + # exactly the written blocks, and nothing partial. + mask = make_block_mask(config.tokens_per_frame, kv_len, written) + visible = int(written.view(-1, 128).any(-1).sum()) + assert int(mask.full_kv_num_blocks[0, 0, 0]) == visible == 10 * 4 + assert int(mask.kv_num_blocks.sum()) == 0 + + +# --------------------------------------------------------------------------- +# The fp32 island vs a process-wide matmul precision +# --------------------------------------------------------------------------- + + +def test_the_fp32_island_is_pinned_against_the_engine_matmul_precision(): + """``mstar/engine/__init__.py`` sets ``float32_matmul_precision`` process-wide + (the reference sets ``medium``), and ``NoiseConditioner`` is a deliberate + fp32 island -- so the setting decides what "fp32" means there. + + Pinned here so a change to the engine default fails loudly. The second half + is the reason it does not bite *today*: the model serves B == N == 1, so the + island's matmuls are ``[1, 512] @ [512, 8192]`` -- a GEMV, which uses no + tensor cores and is bit-identical under every setting. From N >= 2 the + setting reaches it, which is what a future batched step would walk into. + """ + assert torch.get_float32_matmul_precision() == "high" + + config = waypoint_1_5_1b_720p() + torch.manual_seed(0) + island = NoiseConditioner(config.d_model).to(DEVICE, torch.float32).eval() + previous = torch.get_float32_matmul_precision() + try: + outputs = {} + for served in (1, 8): + sigma = torch.full((1, served), 0.9, dtype=DTYPE, device=DEVICE) + for precision in ("highest", "high", "medium"): + torch.set_float32_matmul_precision(precision) + with torch.no_grad(): + outputs[served, precision] = island(sigma).clone() + torch.cuda.synchronize() + finally: + torch.set_float32_matmul_precision(previous) + + for precision in ("high", "medium"): + assert torch.equal(outputs[1, "highest"], outputs[1, precision]), ( + f"the served shape became sensitive to float32_matmul_precision={precision!r}; " + "the island is no longer a GEMV and the engine default now changes its numbers" + ) + assert not torch.equal(outputs[8, "highest"], outputs[8, "medium"]), ( + "precondition: float32_matmul_precision is live on this device at N >= 2" + ) diff --git a/test/modular/test_waypoint_packaging.py b/test/modular/test_waypoint_packaging.py new file mode 100644 index 000000000..8b34e9850 --- /dev/null +++ b/test/modular/test_waypoint_packaging.py @@ -0,0 +1,96 @@ +"""Packaging contracts that keep Waypoint usable from every install name.""" + +from __future__ import annotations + +import sys +from pathlib import Path + +try: + import tomllib +except ModuleNotFoundError: # pragma: no cover - the project requires Python 3.12 + import tomli as tomllib + +import pytest +from packaging.requirements import Requirement + +from mstar.cli.main import DEFAULT_CONFIGS, _resolve_config +from mstar.model.waypoint.checkpoint import ( + TAEHV_UPSTREAM_ARCHIVE, + TAEHV_UPSTREAM_REVISION, + require_taehv_runtime, +) +from mstar.model.waypoint.components.taehv import load_taehv + +ROOT = Path(__file__).resolve().parents[2] +ROOT_PYPROJECT = ROOT / "pyproject.toml" +ALIAS_PYPROJECTS = ( + ROOT / "packaging" / "aliases" / "mstar-ai" / "pyproject.toml", + ROOT / "packaging" / "aliases" / "mstar-project" / "pyproject.toml", +) + + +def _project(path: Path) -> dict: + with path.open("rb") as file: + return tomllib.load(file)["project"] + + +def test_published_metadata_contains_no_direct_url_dependencies(): + project = _project(ROOT_PYPROJECT) + grouped = {"base": project["dependencies"], **project["optional-dependencies"]} + direct = [ + (group, raw) + for group, requirements in grouped.items() + for raw in requirements + if Requirement(raw).url is not None + ] + assert direct == [], "PyPI rejects distributions that declare direct-URL dependencies" + + +def test_alias_packages_forward_every_root_extra(): + root_extras = _project(ROOT_PYPROJECT)["optional-dependencies"] + for path in ALIAS_PYPROJECTS: + alias_extras = _project(path)["optional-dependencies"] + assert set(alias_extras) == set(root_extras) + for extra in root_extras: + assert alias_extras[extra] == [f"m-star[{extra}]"] + + +def test_waypoint_extra_is_index_safe_and_taehv_pin_is_runtime_contract(): + requirements = { + Requirement(raw).name + for raw in _project(ROOT_PYPROJECT)["optional-dependencies"]["waypoint"] + } + assert {"huggingface-hub", "safetensors", "tensordict"} <= requirements + assert "taehv" not in requirements + assert TAEHV_UPSTREAM_REVISION in TAEHV_UPSTREAM_ARCHIVE + + +def test_waypoint_has_a_resolvable_packaged_default_config(): + assert DEFAULT_CONFIGS["waypoint"] == "waypoint.yaml" + assert (ROOT / "configs" / DEFAULT_CONFIGS["waypoint"]).is_file() + assert Path(_resolve_config("waypoint", None)).resolve() == ( + ROOT / "configs" / "waypoint.yaml" + ).resolve() + + +@pytest.mark.parametrize("installed", [None, object()]) +def test_missing_or_invalid_taehv_names_the_exact_separate_install(installed, monkeypatch): + monkeypatch.setitem(sys.modules, "taehv", installed) + with pytest.raises(RuntimeError) as error: + require_taehv_runtime() + message = str(error.value) + assert ".[waypoint]" in message + assert "--no-deps" in message + assert f"taehv @ {TAEHV_UPSTREAM_ARCHIVE}" in message + assert "uv>=0.4.0 or pip>=24.3" in message + + +def test_low_level_taehv_loader_names_the_exact_separate_install(monkeypatch): + monkeypatch.setitem(sys.modules, "taehv", None) + with pytest.raises(RuntimeError) as error: + load_taehv("unused") + message = str(error.value) + assert ".[waypoint]" in message + assert "--no-deps" in message + assert f"taehv @ {TAEHV_UPSTREAM_ARCHIVE}" in message + assert "uv>=0.4.0 or pip>=24.3" in message diff --git a/test/modular/test_waypoint_pixel_equivalence.py b/test/modular/test_waypoint_pixel_equivalence.py new file mode 100644 index 000000000..741bcef9f --- /dev/null +++ b/test/modular/test_waypoint_pixel_equivalence.py @@ -0,0 +1,396 @@ +"""L4: the whole pipeline, pixels out, against the reference. + +Seed clip -> TAEHV encode -> ``append_frame`` -> decode, then N x (noise -> the +4+1 driver -> decode). The reference is driven live in-process under +``reference_compat=True`` and the bar is bit-exact pixels on every emitted +frame. ``test_waypoint_reference_equivalence.py`` builds both sides and this +file imports its fixtures' internals rather than restating them, so a failure +here cannot be a harness difference between L3 and L4. + +The stored oracle's ``pixels`` are not a bit-exact target -- two reference +processes disagree by up to 106/255 by frame 6, measured from ``repro/``. Two +things it is still authoritative for, both gated below at zero tolerance: + + * **Frame 0's pixels**, which the two recordings agree on exactly. Priming is + a pure AE round trip -- ``append_frame`` decodes ``vae.encode(img)`` and the + DiT only writes the ring -- so no compiled DiT kernel reaches those bytes. + * **Frame ordering.** Across the 28 raw frames of the ``repro/`` pair, every + frame's nearest neighbour in the other recording is itself, by a factor of + at least 2.06 in L2. A streaming-decoder shift is therefore separable from + the reference's own run-to-run noise without picking a tolerance. +""" + +from __future__ import annotations + +import hashlib +import os +import sys +from dataclasses import replace +from pathlib import Path + +import pytest +import torch + +sys.path.insert(0, ".") + +from test_waypoint_reference_equivalence import ( + CHECKPOINT, + DEVICE, + DTYPE, + ORACLE, + _admit, + _build_port, + _commit, + _ctx, + _deviation, + _frame, + _import_reference, + _new_request, + _port_frame, + _reference_frame, + _reference_importable, + _reset, +) + +from mstar.model.waypoint.components.taehv import ChunkedStreamingTAEHV, load_taehv +from mstar.model.waypoint.config import waypoint_1_5_1b_720p + +AE_CHECKPOINT = Path(os.environ.get("WAYPOINT_AE_CHECKPOINT", CHECKPOINT.parent / "taehv1_5")) +# The oracle's seed image, cached by test/waypoint/record_oracle.py. The digest +# is what makes the oracle comparisons below mean anything: a different image is +# a different world, and the port would then be compared against a recording of +# something else. +SEED_IMAGE = Path(os.environ.get("WAYPOINT_SEED_IMAGE", CHECKPOINT.parent / "seed/default.jpg")) +SEED_SHA256 = "c61c9393311d7281f793d86329dca343e12c93bf0409980a186eb39269cf6862" + +REPRO = ORACLE / "repro" + +# Frames in the pixel rollout, priming included. 21 wraps the 16-frame local +# ring once, which is where a ring-addressing fault would first reach pixels. +L4_FRAMES = int(os.environ.get("WAYPOINT_L4_FRAMES", "21")) + + +def _repro_frames() -> int: + return len(sorted((REPRO / "frames").glob("frame_*.pt"))) + + +pytestmark = [ + pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA"), + pytest.mark.skipif(not _reference_importable(), reason="reference source missing"), + pytest.mark.skipif(not (CHECKPOINT / "model.safetensors").exists(), reason="checkpoint missing"), + pytest.mark.skipif(not (ORACLE / "frames").is_dir(), reason=f"oracle not at {ORACLE}"), + pytest.mark.skipif(not AE_CHECKPOINT.exists(), reason=f"TAEHV not at {AE_CHECKPOINT}"), + pytest.mark.skipif(not SEED_IMAGE.exists(), reason=f"seed image not at {SEED_IMAGE}"), +] + + +# --------------------------------------------------------------------------- +# Builds +# --------------------------------------------------------------------------- + + +@pytest.fixture(scope="module") +def reference(): + """The served reference, minus the fp32-island capture L0 needs. + ``float32_matmul_precision`` is what the oracle recorded under and what the + compat port's sigma LUT depends on; see the L1-L3 file's fixture.""" + from mstar.engine.resources.attn.flex import flex_attention_masked + + WorldModel, StaticKVCache, patch_model = _import_reference() + torch.set_float32_matmul_precision("high") + + cfg = WorldModel.load_config(str(CHECKPOINT)) + model = WorldModel.from_pretrained( + str(CHECKPOINT), cfg=cfg, device=DEVICE, dtype=DTYPE + ).eval() + patch_model.apply_inference_patches(model) + patch_model.flex_attention = flex_attention_masked + kv = StaticKVCache(cfg, batch_size=1, dtype=DTYPE).to(device=DEVICE) + return {"cfg": cfg, "model": model, "kv": kv} + + +@pytest.fixture(scope="module") +def port(reference): + """The compat port. Depends on ``reference`` for the ordering, not the + object: the sigma LUT is a fp32 GEMM and that fixture sets the precision.""" + return _build_port( + replace( + waypoint_1_5_1b_720p(), + reference_compat=True, + compile_dit=False, + ) + ) + + +@pytest.fixture(scope="module") +def taehv_weights(): + """One TAEHV, shared by both sides' sessions. ``StreamingTAEHV`` keeps every + piece of stream state on itself and never writes back to the weights, so + sharing is what isolates the two wrappers as the thing under test.""" + return load_taehv(str(AE_CHECKPOINT)).to(device=DEVICE, dtype=DTYPE) + + +@pytest.fixture(scope="module") +def oracle(): + return {"frames": sorted((ORACLE / "frames").glob("frame_*.pt"))} + + +@pytest.fixture(scope="module") +def seed_clip(): + """The oracle's seed frame as ``[4, 720, 1280, 3]`` uint8, decoded and + resized in ``record_oracle.load_seed_frame``'s order.""" + import cv2 + import numpy as np + + raw = SEED_IMAGE.read_bytes() + digest = hashlib.sha256(raw).hexdigest() + img = cv2.imdecode(np.frombuffer(raw, np.uint8), cv2.IMREAD_COLOR) + img = cv2.cvtColor(cv2.resize(img, (1280, 720)), cv2.COLOR_BGR2RGB) + return torch.from_numpy(np.repeat(img[None], 4, axis=0)), digest + + +# --------------------------------------------------------------------------- +# Driving both sides +# --------------------------------------------------------------------------- + + +def _reference_session(weights, cfg): + from src.ae import ChunkedStreamingTAEHV as ReferenceSession + + ph, pw = cfg.patch + return ReferenceSession( + weights, + auto_aspect_ratio=cfg.auto_aspect_ratio, + device=DEVICE, + dtype=DTYPE, + height=cfg.height * ph, + width=cfg.width * pw, + ) + + +def _port_session(weights, config): + return ChunkedStreamingTAEHV( + weights, + auto_aspect_ratio=config.auto_aspect_ratio, + device=DEVICE, + dtype=DTYPE, + height=config.latent_height, + width=config.latent_width, + ) + + +def _reference_prime(reference, session, clip, ctx): + """``WorldEngine.append_frame`` unrolled: encode, one committing pass at + sigma 0, decode.""" + with torch.inference_mode(): + x0 = session.encode(clip).unsqueeze(1) + reference["kv"].set_frozen(False) + reference["model"](x0, x0.new_zeros((1, 1)), **ctx, kv_cache=reference["kv"]) + return x0, session.decode(x0.squeeze(1)) + + +def _port_prime(port, session, clip, ctx): + """The prime walk's three nodes, in the order the graph runs them.""" + with torch.inference_mode(): + # WaypointVaeEncoderSubmodule.prepare_inputs: cast, then divide. + latent = session.encode(clip.to(device=DEVICE, dtype=DTYPE).div(255)).unsqueeze(1) + _admit(port, 0) + x0 = port["dit"].append_frame( + latent, + torch.tensor(0, dtype=torch.int64, device=DEVICE), + mouse=ctx["mouse"], + button=ctx["button"], + scroll=ctx["scroll"], + ) + _commit(port, 0) + return x0, session.decode(x0.squeeze(1)) + + +def _pixel_gap(left: torch.Tensor, right: torch.Tensor) -> int: + return int((left.to(torch.int16) - right.to(torch.int16)).abs().max().item()) + + +@pytest.fixture(scope="module") +def rollout(port, reference, taehv_weights, oracle, seed_clip): + """One shared pixel rollout: both sides stepped in lockstep on the oracle's + noise and controls, from an empty ring and a fresh decoder on each side. + Pixels are kept only as far as ``repro/`` reaches; a raw frame is 2.8 MB.""" + clip, digest = seed_clip + sessions = ( + _reference_session(taehv_weights, reference["cfg"]), + _port_session(taehv_weights, port["config"]), + ) + sigmas = torch.tensor( + list(reference["cfg"].scheduler_sigmas), dtype=DTYPE, device=DEVICE + ) + keep = min(_repro_frames(), L4_FRAMES) + + _new_request(port) + _reset(port, reference) + out = {"seed_sha256": digest, "latent_gap": [], "pixel_gap": [], "port_pixels": []} + with torch.inference_mode(): + for index in range(L4_FRAMES): + record = _frame(oracle, index) + ctx = _ctx(record) + if record["kind"] == "seed": + left, left_pixels = _reference_prime(reference, sessions[0], clip, ctx) + right, right_pixels = _port_prime(port, sessions[1], clip, ctx) + else: + noise = record["noise_bf16"].to(DEVICE) + left, _ = _reference_frame(reference, noise, ctx, sigmas) + left_pixels = sessions[0].decode(left.squeeze(1)) + right, _ = _port_frame(port, noise, ctx, index) + right_pixels = sessions[1].decode(right.squeeze(1)) + + out["latent_gap"].append(_deviation(left, right)[0]) + out["pixel_gap"].append(_pixel_gap(left_pixels, right_pixels)) + if index < keep: + out["port_pixels"].append(right_pixels.cpu().clone()) + out.setdefault("emitted", []).append( + (tuple(right_pixels.shape), right_pixels.dtype) + ) + return out + + +# --------------------------------------------------------------------------- +# L4 -- pixels against the live reference +# --------------------------------------------------------------------------- + + +def test_the_decoded_pixels_are_bit_exact_under_reference_compat(rollout): + """The headline claim, on every emitted frame rather than the last: the + streaming decoder's memory advances per call, so a duplicate, gap or reorder + shifts the stream from that point on and the final frame cannot see it. + + A divergent latent means the DiT; an equal latent with divergent pixels + means this file's own seam, the AE. + """ + assert len(rollout["pixel_gap"]) == L4_FRAMES, "the rollout stopped early" + for index, (latent_gap, pixel_gap) in enumerate( + zip(rollout["latent_gap"], rollout["pixel_gap"], strict=True) + ): + print(f"frame {index:3d} latent maxabs={latent_gap:.4e} pixels maxabs={pixel_gap}") + + divergent = [ + (index, latent_gap, pixel_gap) + for index, (latent_gap, pixel_gap) in enumerate( + zip(rollout["latent_gap"], rollout["pixel_gap"], strict=True) + ) + if pixel_gap != 0 + ] + assert not divergent, ( + f"pixels differ from the reference at {len(divergent)} frame(s); first is " + f"frame={divergent[0][0]} latent maxabs={divergent[0][1]:.4e} " + f"pixels maxabs={divergent[0][2]}/255" + ) + + +def test_the_seed_clip_encodes_identically_on_both_sides(rollout): + """``append_frame`` returns its input unchanged, so frame 0's latent gap is + the encoder's alone -- the one place in the pipeline the DiT cannot reach.""" + assert rollout["latent_gap"][0] == 0.0, ( + f"the two TAEHV sessions encoded the seed clip differently: maxabs=" + f"{rollout['latent_gap'][0]:.4e}" + ) + + +def test_every_emit_carries_one_latent_frame_of_raw_rgb(rollout, port): + """The payload the client is handed: ``[temporal_compression, H, W, 3]`` + uint8 per engine step, priming included.""" + config = port["config"] + expected = (config.temporal_compression, 720, 1280, 3) + assert rollout["emitted"] == [(expected, torch.uint8)] * L4_FRAMES + + +# --------------------------------------------------------------------------- +# L4 -- the stored oracle, where it is authoritative +# --------------------------------------------------------------------------- + + +def _oracle_pixels(root: Path, count: int) -> torch.Tensor: + """``[4 * count, H, W, 3]`` -- one recording's raw frames, in emission + order.""" + return torch.cat( + [ + torch.load(root / f"frame_{i:03d}.pt", map_location="cpu", weights_only=False)[ + "pixels" + ] + for i in range(count) + ] + ) + + +def _distances(left: torch.Tensor, right: torch.Tensor) -> torch.Tensor: + """``[i, j] = ||left[i] - right[j]||_2``, by explicit difference. + + Not ``torch.cdist``: it expands to ``|a|^2 + |b|^2 - 2a.b``, and under the + ``float32_matmul_precision("high")`` this file runs at, the TF32 dot product + cancels away every digit separating two near-identical 720p frames. + """ + return torch.stack([(right - row).flatten(1).norm(dim=1) for row in left]) + + +def test_the_primed_frame_matches_the_oracle_exactly(rollout, oracle): + """The one frame the oracle *is* a bit-exact target for. + + Priming decodes ``vae.encode(seed)`` and the DiT's committing pass only + writes the ring, so no compiled DiT kernel reaches these bytes and the two + recordings agree on them exactly. A zero-tolerance gate on the seed clip, + the encoder, ``frames_to_trim`` priming, both resizes and the uint8 + quantization at once. + """ + assert rollout["seed_sha256"] == SEED_SHA256, ( + f"{SEED_IMAGE} is not the image the oracle was recorded from " + f"({rollout['seed_sha256']} != {SEED_SHA256})" + ) + recorded = _frame(oracle, 0)["pixels"] + floor = _pixel_gap(recorded, _oracle_pixels(REPRO / "frames", 1)) + gap = _pixel_gap(rollout["port_pixels"][0], recorded) + print(f"prime pixels: port vs oracle maxabs={gap}, oracle vs repro maxabs={floor}") + assert floor == 0, ( + "the two reference recordings disagree on the primed frame, so it is no " + f"longer a bit-exact target: maxabs={floor}/255" + ) + assert gap == 0, f"primed pixels differ from the oracle by maxabs={gap}/255" + + +def test_the_emitted_stream_stays_aligned_with_the_oracle(rollout, oracle): + """A decoder desync is a *shift*, and a shift is visible without a tolerance: + every raw frame's nearest neighbour in the recording must be itself. Over + the 28 raw frames of the ``repro/`` pair that holds with the closest wrong + frame at least 2.06x further away. + + The magnitudes printed here are reported, not gated: the port runs eager and + the oracle ran compiled under ``max_autotune``, and nothing in the artifacts + bounds that. + """ + count = min(_repro_frames(), L4_FRAMES) + recorded = _oracle_pixels(ORACLE / "frames", count).to(DEVICE, torch.float32) + port_stream = torch.cat(rollout["port_pixels"]).to(DEVICE, torch.float32) + repro = _oracle_pixels(REPRO / "frames", count).to(DEVICE, torch.float32) + + n = recorded.shape[0] + assert port_stream.shape[0] == n, f"{port_stream.shape[0]} raw frames against {n}" + distance = _distances(port_stream, recorded) + floor = _distances(repro, recorded).diagonal() + del port_stream, repro + + diagonal = distance.diagonal() + # inf on the diagonal, not `eye * inf`: that leaves 0 * inf = nan everywhere + # else, and every `<=` against a nan is False -- the assertion below would + # hold whatever the pixels did. + mask = torch.eye(n, dtype=torch.bool, device=DEVICE) + off = distance.masked_fill(mask, torch.inf).min(dim=1) + assert torch.isfinite(off.values).all(), "the off-diagonal search produced non-finite distances" + for i in range(n): + print( + f"raw frame {i:3d} L2 to oracle={diagonal[i]:9.1f} " + f"(repro floor {floor[i]:9.1f}) nearest other=" + f"{off.values[i]:9.1f} at {off.indices[i].item()}" + ) + shifted = [i for i in range(n) if off.values[i] <= diagonal[i]] + assert not shifted, ( + f"raw frames closer to a different oracle frame than to their own: " + f"{[(i, off.indices[i].item()) for i in shifted]}; the streaming decoder " + "is out of step with the world" + ) diff --git a/test/modular/test_waypoint_profiler.py b/test/modular/test_waypoint_profiler.py new file mode 100644 index 000000000..3d6fe6db2 --- /dev/null +++ b/test/modular/test_waypoint_profiler.py @@ -0,0 +1,117 @@ +from __future__ import annotations + +import importlib.util +import sqlite3 +import sys +from pathlib import Path + +_CHECK_PATH = Path(__file__).parents[1] / "waypoint/check_nsys_replay.py" +_SPEC = importlib.util.spec_from_file_location("waypoint_nsys_check", _CHECK_PATH) +assert _SPEC is not None and _SPEC.loader is not None +_CHECK = importlib.util.module_from_spec(_SPEC) +sys.modules[_SPEC.name] = _CHECK +_SPEC.loader.exec_module(_CHECK) +DEFAULT_ROLLOUT_RANGE = _CHECK.DEFAULT_ROLLOUT_RANGE +inspect_replay = _CHECK.inspect_replay + + +def _profile_database(path: Path, apis: list[list[str]]) -> Path: + with sqlite3.connect(path) as connection: + connection.executescript( + """ + CREATE TABLE StringIds (id INTEGER PRIMARY KEY, value TEXT); + CREATE TABLE NVTX_EVENTS ( + start INTEGER, end INTEGER, globalTid INTEGER, + textId INTEGER, text TEXT + ); + CREATE TABLE CUPTI_ACTIVITY_KIND_RUNTIME ( + start INTEGER, end INTEGER, globalTid INTEGER, nameId INTEGER + ); + """ + ) + strings = {DEFAULT_ROLLOUT_RANGE, "engine.forward", *(api for row in apis for api in row)} + ids = {value: index for index, value in enumerate(sorted(strings), start=1)} + connection.executemany("INSERT INTO StringIds VALUES (?, ?)", ((ids[v], v) for v in ids)) + for index, forward_apis in enumerate(apis): + base = index * 100 + connection.execute( + "INSERT INTO NVTX_EVENTS VALUES (?, ?, ?, ?, NULL)", + (base, base + 90, 7, ids[DEFAULT_ROLLOUT_RANGE]), + ) + connection.execute( + "INSERT INTO NVTX_EVENTS VALUES (?, ?, ?, ?, NULL)", + (base + 10, base + 80, 7, ids["engine.forward"]), + ) + connection.executemany( + "INSERT INTO CUPTI_ACTIVITY_KIND_RUNTIME VALUES (?, ?, ?, ?)", + ( + (base + 20 + offset, base + 21 + offset, 7, ids[api]) + for offset, api in enumerate(forward_apis) + ), + ) + return path + + +def test_inspect_replay_accepts_graph_only_forwards(tmp_path: Path): + database = _profile_database( + tmp_path / "clean.sqlite", + [["cudaMemcpyAsync", "cudaGraphLaunch_v10000"] for _ in range(3)], + ) + + result = inspect_replay(database) + + assert result.forwards == 3 + assert result.graph_replays == 3 + assert result.sync_or_blocking_calls == 0 + assert result.offending_apis == () + + +def test_inspect_replay_reports_each_blocking_api(tmp_path: Path): + database = _profile_database( + tmp_path / "blocking.sqlite", + [ + ["cudaGraphLaunch_v10000", "cudaDeviceSynchronize"], + ["cudaGraphLaunch_v10000", "cudaMemcpy"], + ["cudaGraphLaunch_v10000", "cudaMalloc"], + ["cudaGraphLaunch_v10000", "cudaFree"], + ], + ) + + result = inspect_replay(database) + + assert result.forwards == 4 + assert result.graph_replays == 4 + assert result.sync_or_blocking_calls == 4 + assert result.offending_apis == ( + ("cudaDeviceSynchronize", 1), + ("cudaFree", 1), + ("cudaMalloc", 1), + ("cudaMemcpy", 1), + ) + + +def test_inspect_replay_reports_missing_graph_launch(tmp_path: Path): + database = _profile_database( + tmp_path / "missing-graph.sqlite", + [["cudaGraphLaunch_v10000"], ["cudaMemcpyAsync"]], + ) + + result = inspect_replay(database) + + assert result.forwards == 2 + assert result.graph_replays == 1 + + +def test_inspect_replay_rejects_multiple_graph_launches_in_one_forward(tmp_path: Path): + database = _profile_database( + tmp_path / "multiple-graphs.sqlite", + [ + ["cudaGraphLaunch_v10000"], + ["cudaGraphLaunch_v10000", "cudaGraphLaunch_v10000"], + ], + ) + + result = inspect_replay(database) + + assert result.forwards == 2 + assert result.graph_replays == 1 diff --git a/test/modular/test_waypoint_reference_compat.py b/test/modular/test_waypoint_reference_compat.py new file mode 100644 index 000000000..308a558c6 --- /dev/null +++ b/test/modular/test_waypoint_reference_compat.py @@ -0,0 +1,187 @@ +"""``WaypointConfig.reference_compat``: what it changes, and what it must not. + +The flag reproduces the reference's lossy derived state so the parity gate can be +bit-exact (``test_waypoint_reference_equivalence.py`` is where that is measured +against the reference itself). Here the two positions are pinned against each other +and against the exact arithmetic, on CPU, with no checkpoint: + + * flag off -- the tables are the mathematically exact fp32 expressions; + * flag on -- they are those same tables round-tripped through bf16, which is what + ``NoCastModule._apply`` leaves behind; + * the two differ, in every table and through a built DiT. + +A flag that silently does nothing in either position is the failure this file exists +to catch. +""" + +from dataclasses import replace + +import pytest +import torch + +from mstar.model.waypoint.components.layers import NoiseConditioner, bf16_roundtrip +from mstar.model.waypoint.components.rope import OrthoRoPEAngles +from mstar.model.waypoint.config import WaypointConfig, waypoint_1_5_1b_720p +from mstar.model.waypoint.weight_loader import build_waypoint_dit + +CPU = torch.device("cpu") +SIGMAS = waypoint_1_5_1b_720p().scheduler_sigmas + + +def _small(**overrides) -> WaypointConfig: + """A DiT small enough to materialize in a test. d_head stays 16, so OrthoRoPE's + x/y/t band split is still exercised.""" + return WaypointConfig( + n_layers=2, n_heads=4, n_kv_heads=2, d_model=64, + tokens_per_frame=8, height=2, width=4, **overrides, + ) + + +def _exact_rope_tables(config: WaypointConfig): + """``world_engine/src/model/attn.py::OrthoRoPEAngles.__init__``, transcribed, so + the exact path is checked against the reference's expression rather than against + the port's own code.""" + d_head = config.d_model // config.n_heads + d_xy, d_t = d_head // 8, d_head // 4 + max_freq = min(config.height, config.width) * float(config.rope_nyquist_frac) + xy = ( + torch.linspace(1.0, max_freq / 2, (d_xy + 1) // 2, dtype=torch.float32) * torch.pi + ).repeat_interleave(2)[:d_xy] + theta = float(config.rope_theta) + inv_t = (1.0 / (theta ** (torch.arange(0, d_t, 2, dtype=torch.float32) / d_t))).repeat_interleave(2) + return xy, inv_t + + +def _exact_fourier_freq(fourier_dim: int = 512, base: float = 10_000.0): + """``world_engine/src/model/nn.py::NoiseConditioner.__init__``, transcribed.""" + return torch.logspace(0, -1, steps=fourier_dim // 2, base=base, dtype=torch.float32) + + +# --------------------------------------------------------------------------- +# The three derived tables +# --------------------------------------------------------------------------- + + +def test_the_experimental_exact_rope_tables_are_the_fp32_expression(): + config = replace(waypoint_1_5_1b_720p(), reference_compat=False) + xy, inv_t = OrthoRoPEAngles(config)._tables.get(CPU) + exact_xy, exact_inv_t = _exact_rope_tables(config) + assert torch.equal(xy, exact_xy) + assert torch.equal(inv_t, exact_inv_t) + assert xy.dtype is torch.float32 and inv_t.dtype is torch.float32 + + +def test_the_default_fourier_freq_is_the_exact_fp32_expression(): + (freq,) = NoiseConditioner(64)._freq.get(CPU) + assert torch.equal(freq, _exact_fourier_freq()) + assert freq.dtype is torch.float32 + + +def test_reference_compat_bf16_roundtrips_all_three_tables(): + """Off vs on must differ in every table, and on must be exactly the round-trip. + Both halves matter: the first catches a flag that never reaches the builder, the + second catches one that quantizes differently from ``NoCastModule._apply``.""" + config = replace(waypoint_1_5_1b_720p(), reference_compat=False) + compat = replace(config, reference_compat=True) + tables = { + "rope_angles.xy": (OrthoRoPEAngles(config)._tables.get(CPU)[0], + OrthoRoPEAngles(compat)._tables.get(CPU)[0]), + "rope_angles.inv_t": (OrthoRoPEAngles(config)._tables.get(CPU)[1], + OrthoRoPEAngles(compat)._tables.get(CPU)[1]), + "denoise_step_emb.freq": ( + NoiseConditioner(64)._freq.get(CPU)[0], + NoiseConditioner(64, reference_compat=True, cached_sigmas=SIGMAS)._freq.get(CPU)[0], + ), + } + for name, (exact, quantized) in tables.items(): + assert not torch.equal(exact, quantized), f"{name}: reference_compat left the table alone" + assert torch.equal(quantized, bf16_roundtrip(exact)), f"{name}: not a bf16 round-trip" + assert quantized.dtype is torch.float32, f"{name}: the fp32 island lost its dtype" + + +def test_the_flag_reaches_a_built_dit(): + """Through ``build_waypoint_dit``'s meta build, ``cast_serving_dtypes`` and + ``to_empty`` -- the path the derived tables have to survive outside the module + tree, and the one the compat conditioner's constructor runs under.""" + exact = build_waypoint_dit( + _small(reference_compat=False), skip_weight_loading=True, device=CPU + ) + compat = build_waypoint_dit( + _small(reference_compat=True), skip_weight_loading=True, device=CPU + ) + for left, right in zip(exact.rope_angles._tables.get(CPU), + compat.rope_angles._tables.get(CPU), strict=True): + assert not torch.equal(left, right) + assert torch.equal(bf16_roundtrip(left), right) + (exact_freq,) = exact.denoise_step_emb._freq.get(CPU) + (compat_freq,) = compat.denoise_step_emb._freq.get(CPU) + assert not torch.equal(exact_freq, compat_freq) + assert torch.equal(bf16_roundtrip(exact_freq), compat_freq) + assert exact.denoise_step_emb.reference_compat is False + assert compat.denoise_step_emb.reference_compat is True + + +# --------------------------------------------------------------------------- +# The cached sigma LUT +# --------------------------------------------------------------------------- + + +def _compat_conditioner(dim: int = 64, sigmas=SIGMAS) -> NoiseConditioner: + torch.manual_seed(0) + return NoiseConditioner(dim, reference_compat=True, cached_sigmas=sigmas).eval() + + +def test_the_cached_lut_serves_the_batch_evaluated_row(): + """Each scheduler sigma reads back its own row of the table the reference builds + with one batched fp32 call, in bf16. Batch shape is the whole point of the patch, + so the table is built batched and read one sigma at a time.""" + conditioner = _compat_conditioner() + table, _, oob = conditioner._reference_lut(CPU) + assert table.shape == (len(SIGMAS), 64) and table.dtype is torch.bfloat16 + assert int(oob) == len(SIGMAS) + + with torch.no_grad(): + for row, value in enumerate(SIGMAS): + out = conditioner(torch.tensor([[value]], dtype=torch.bfloat16)) + assert out.shape == (1, 1, 64) and out.dtype is torch.bfloat16 + assert torch.equal(out[0, 0], table[row]) + batched = conditioner(torch.tensor([list(SIGMAS)], dtype=torch.bfloat16)) + assert torch.equal(batched[0], table) + + +def test_an_off_schedule_sigma_raises_rather_than_reading_a_neighbour(): + """The reference's "no silent wrong": an unknown sigma indexes one past the table. + Checked on CPU, where the out-of-bounds index is an exception rather than a + device-side assert that would poison the CUDA context.""" + conditioner = _compat_conditioner() + with pytest.raises(IndexError): + conditioner(torch.tensor([[0.5]], dtype=torch.bfloat16)) + + +def test_the_cached_lut_refuses_a_non_bf16_sigma(): + """The key is sigma's bf16 bit pattern, so an fp32 sigma has no valid index.""" + conditioner = _compat_conditioner() + with pytest.raises(RuntimeError, match="bf16 sigma"): + conditioner(torch.tensor([[1.0]], dtype=torch.float32)) + + +@pytest.mark.parametrize( + "sigmas, message", + [((1.0, 1.0), "collide in bf16"), ((1.0, 1.0 + 2**-9), "collide in bf16"), ((), "sigma schedule")], +) +def test_an_uncacheable_schedule_is_refused_at_construction(sigmas, message): + """bf16 has 8 mantissa bits, so two schedule entries can round together; the LUT + would then serve one row for both. Refused where it is cheap to see.""" + with pytest.raises(ValueError, match=message): + NoiseConditioner(64, reference_compat=True, cached_sigmas=sigmas) + + +def test_the_default_conditioner_keeps_the_live_per_sigma_path(): + """Flag off: no table, and the fp32 body runs per call in the input's dtype.""" + torch.manual_seed(0) + conditioner = NoiseConditioner(64).eval() + assert conditioner.reference_compat is False + with torch.no_grad(): + out = conditioner(torch.tensor([[1.0, 0.3]], dtype=torch.float32)) + assert out.shape == (1, 2, 64) and out.dtype is torch.float32 + assert not conditioner._lut diff --git a/test/modular/test_waypoint_reference_equivalence.py b/test/modular/test_waypoint_reference_equivalence.py new file mode 100644 index 000000000..2afa719c7 --- /dev/null +++ b/test/modular/test_waypoint_reference_equivalence.py @@ -0,0 +1,845 @@ +"""Port vs the Waypoint reference implementation: one forward, one frame, one rollout. + +The reference is driven live in-process rather than read out of the oracle's +recorded activations. ``test/waypoint/record_oracle.py`` calls ``engine.model`` +directly and so bypasses the engine's two ``torch.compile`` regions, which means its +``dit_out`` and ``committed_kv`` were produced by *eager* ``flex_attention`` -- and +eager flex ignores a ``BlockMask``'s block index lists, attending over the whole ring +including unwritten slots (``test_flex_attention_resource.py`` pins both halves of +that). The oracle stays authoritative for inputs (noise, controls, the seed latent) +and for kernel-independent ring bookkeeping (``written``, which buckets are live); its +activations are not the reference as served. + +Both sides here run everything eager except one shared attention kernel: +``src.patch_model.flex_attention`` is rebound to the port's own +``flex_attention_masked``, so attention cannot be the variable under test. + +**Every test is run on two ports**, built from the same checkpoint and differing only +in ``WaypointConfig.reference_compat``: + + * ``reference_compat=True`` reproduces the reference's lossy derived state -- the + bf16 round-trip its ``NoCastModule._apply`` leaves in three non-persistent fp32 + tables, and the batch-5 sigma LUT of ``patch_cached_noise_conditioning``. The bar + there is bit-exact, and no tolerance below is looser than zero. + * The experimental exact-table port is deliberately more mathematically direct than + the reference and so + diverges from it. What the exact tests assert is the shape of that divergence: + that it is present, that it is confined downstream of the three tables, and that + nothing kernel-independent (the ring's ``written`` masks) moves with it. + +``test_every_stage_is_bit_exact_under_reference_compat`` is what earns the zero +tolerances: with the flag on, all 30 stages plus the output agree exactly, at a frozen +and at a committing sigma. So a nonzero under the flag is a port bug, not accumulated +rounding. +""" + +from __future__ import annotations + +import contextlib +import os +import sys +from dataclasses import replace +from pathlib import Path + +import pytest +import torch + +sys.path.insert(0, ".") + +from mstar.engine.resources.attn.base import AttentionManager +from mstar.engine.resources.attn.config import ( + AttentionConfig, + AttentionSpec, + AttentionStep, + AttnBackend, +) +from mstar.engine.resources.attn.flex import flex_attention_masked +from mstar.engine.resources.base import EngineResourceInfo +from mstar.engine.resources.kv.config import KVSpec, RingKVConfig, RingKVLayerConfig, RingKVStep +from mstar.engine.resources.kv.ring import RingKVManager +from mstar.engine.resources.step import StepContext +from mstar.model.submodule_base import NodeSubmodule +from mstar.model.waypoint.components.layers import bf16_roundtrip +from mstar.model.waypoint.config import waypoint_1_5_1b_720p +from mstar.model.waypoint.weight_loader import build_waypoint_dit + +_ROOT = Path("/mnt/storage/garv901/waypoint-1.5-1B") +REFERENCE_SRC = Path(os.environ.get("WAYPOINT_REFERENCE_SRC", _ROOT / "world_engine")) +CHECKPOINT = Path(os.environ.get("WAYPOINT_CHECKPOINT", _ROOT / "checkpoints/Waypoint-1.5-1B")) +ORACLE = Path(os.environ.get("WAYPOINT_ORACLE_DIR", _ROOT / "oracle")) + +# Frames in the rollout layer. The local ring holds 16 frames, so this wraps it +# twice; the oracle recorded 41 (a seed plus 40 generated) and holds full ring +# snapshots at 0, 20 and 40. +ROLLOUT_FRAMES = int(os.environ.get("WAYPOINT_PARITY_FRAMES", "41")) +# The exact port diverges at frame 0 and compounds; a short rollout is enough to +# measure it, and the reference_compat run is the full-length bit-exact one. +DIVERGENCE_FRAMES = 6 + +# Explicit index, not bare "cuda": the reference's ``BaseModel.from_pretrained`` +# asserts one so it can hand safetensors an ordinal to load onto. +DEVICE = torch.device("cuda", 0) +DTYPE = torch.bfloat16 + +# The two ports every test below is run against. ``ids`` are what a failure is +# reported under, so they say which numerics were expected, not which flag was set. +COMPAT_MODES = pytest.mark.parametrize("compat", [False, True], ids=["exact", "reference_compat"]) + +# Stages that read none of the three tables and so must agree with the reference in +# both modes. Their divergence would mean the port drifted somewhere new. +TABLE_INDEPENDENT_STAGES = ("patchify", "ctrl_emb") +# ``cond`` reads ``denoise_step_emb.freq``, ``rope`` reads ``rope_angles.xy``/ +# ``inv_t``; with the exact tables both must differ from the reference. +TABLE_DEPENDENT_STAGES = ("cond", "rope") + + +def _reference_importable() -> bool: + return (REFERENCE_SRC / "src" / "model" / "world_model.py").exists() + + +pytestmark = [ + pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA"), + pytest.mark.skipif(not _reference_importable(), reason=f"reference not at {REFERENCE_SRC}"), + pytest.mark.skipif(not (CHECKPOINT / "model.safetensors").exists(), reason="checkpoint missing"), + pytest.mark.skipif(not (ORACLE / "frames").is_dir(), reason=f"oracle not at {ORACLE}"), +] + + +def _import_reference(): + sys.path.insert(0, str(REFERENCE_SRC)) + import src as reference_pkg + + sys.modules.setdefault("world_engine", reference_pkg) + from src import patch_model + from src.model import StaticKVCache, WorldModel + + return WorldModel, StaticKVCache, patch_model + + +# --------------------------------------------------------------------------- +# Builds +# --------------------------------------------------------------------------- + + +class _DitNode(NodeSubmodule): + def __init__(self, dit): + super().__init__() + self.dit = dit + + def prepare_inputs(self, *args, **kwargs): + raise NotImplementedError("binding stand-in") + + def forward(self, *args, **kwargs): + raise NotImplementedError("binding stand-in") + + +@pytest.fixture(scope="module") +def reference(): + """The served reference: inference patches applied, flex pinned to the port's + compiled kernel. The three fp32 island tables are captured *before* patching + -- ``CachedDenoiseStepEmb`` keeps no handle back to the module it replaces. + """ + WorldModel, StaticKVCache, patch_model = _import_reference() + # The oracle recorded at 'high'; its own calibration measured high and medium + # bit-identical for this model, and high vs highest differing (metadata.json). + # It also decides the reference's sigma LUT: that table is a batch-5 fp32 GEMM, + # which rounds through TF32 here and not at 'highest'. + torch.set_float32_matmul_precision("high") + + cfg = WorldModel.load_config(str(CHECKPOINT)) + model = WorldModel.from_pretrained(str(CHECKPOINT), cfg=cfg, device=DEVICE, dtype=DTYPE).eval() + islands = { + "freq": model.denoise_step_emb.freq.clone(), + "xy": model.transformer.rope_angles.xy.clone(), + "inv_t": model.transformer.rope_angles.inv_t.clone(), + } + bare_conditioner = model.denoise_step_emb + bare_cond_head = model.transformer.blocks[0].cond_head + + patch_model.apply_inference_patches(model) + patch_model.flex_attention = flex_attention_masked + cache = StaticKVCache(cfg, batch_size=1, dtype=DTYPE).to(device=DEVICE) + yield { + "cfg": cfg, + "model": model, + "kv": cache, + "islands": islands, + "bare_conditioner": bare_conditioner, + "bare_cond_head": bare_cond_head, + } + + +def _build_port(config, checkpoint: Path = CHECKPOINT): + """Build a port from the checkpoint belonging to ``config``. + + The default preserves the original 720p harness. The explicit path is used + by the 360p live-reference gate, whose weights are a distinct publication. + """ + dit = build_waypoint_dit(config, str(checkpoint), device=DEVICE) + spec = KVSpec( + resource_key="kv", + nodes={"dit"}, + config=RingKVConfig( + num_layers=config.n_layers, + num_kv_heads=config.n_kv_heads, + head_dim=config.d_head, + num_qo_heads=config.n_heads, + tokens_per_frame=config.tokens_per_frame, + num_worlds=1, + layers=tuple( + RingKVLayerConfig( + ring_frames=config.ring_frames(i), + ring_buckets=config.ring_buckets(i), + pinned_dilation=config.pinned_dilation(i), + ) + for i in range(config.n_layers) + ), + ), + ) + info = EngineResourceInfo(device=DEVICE, kv_dtype=DTYPE) + kv = RingKVManager.build(spec, info) + attn = AttentionManager.build( + AttentionSpec( + resource_key="attn", + nodes={"dit"}, + config=AttentionConfig(kv_cache="kv", backend=AttnBackend.FLEX), + ), + EngineResourceInfo(device=DEVICE, kv_dtype=DTYPE, dependencies={"kv": spec}), + ) + _DitNode(dit).bind_node_resources({"kv": kv, "attn": attn}) + assert not dit._regions_compiled + return { + "config": config, + "dit": dit, + "kv": kv, + "attn": attn, + "rid": None, + "seq": 0, + } + + +@pytest.fixture(scope="module") +def ports(reference): + """Both ports, keyed by ``reference_compat``. Two full checkpoint loads, and + two rings, so a test can hold one side's state while comparing the other. + + Depends on ``reference`` for the ordering, not the object: the compat port's + sigma LUT is a fp32 GEMM whose result depends on + ``float32_matmul_precision``, and the reference fixture is what sets it. + """ + # This harness decomposes the reference's five passes in Python so it can + # compare every intermediate. Keep the port's outer driver eager too; + # Keep outer compilation off so this test isolates numerical compatibility; + # compilation and CUDA graph selection have separate execution-mode gates. + default = replace(waypoint_1_5_1b_720p(), compile_dit=False) + exact = replace(default, reference_compat=False) + return {False: _build_port(exact), True: _build_port(default)} + + +@pytest.fixture(scope="module") +def oracle(): + frames = sorted((ORACLE / "frames").glob("frame_*.pt")) + return {"frames": frames, "rings": ORACLE / "ring"} + + +def _frame(oracle, index: int) -> dict: + return torch.load(oracle["frames"][index], map_location="cpu", weights_only=False) + + +def _ctx(frame: dict) -> dict: + return {k: v.to(DEVICE) for k, v in frame["ctx"].items()} + + +# --------------------------------------------------------------------------- +# Driving both sides +# --------------------------------------------------------------------------- + + +def _reset(port, reference): + for layer in reference["kv"].layers: + layer.reset() + for layer in port["kv"].layers: + layer.reset(0) + + +def _reference_forward(reference, x, sigma_value: float, ctx, *, commit: bool): + reference["kv"].set_frozen(not commit) + with torch.inference_mode(): + sigma = x.new_full((x.size(0), x.size(1)), sigma_value) + return reference["model"](x, sigma, **ctx, kv_cache=reference["kv"]).clone() + + +def _port_forward(port, x, sigma_value: float, ctx, frame_pos: int, *, commit: bool): + with torch.inference_mode(): + sigma = x.new_full((x.size(0), x.size(1)), sigma_value) + return port["dit"]( + x, + sigma, + torch.tensor(frame_pos, dtype=torch.int64, device=DEVICE), + mouse=ctx["mouse"], + button=ctx["button"], + scroll=ctx["scroll"], + commit=commit, + ).clone() + + +def _reference_frame(reference, noise, ctx, sigmas): + """The reference's ``_denoise_pass`` + ``_cache_pass``, unrolled so every pass + output is observable. ``zip(sigmas, sigmas.diff())`` is 4 steps over a 5-entry + schedule; the fifth pass is the committing one at sigma 0. + """ + cache = reference["kv"] + outputs = [] + cache.set_frozen(True) + x = noise + sigma = x.new_empty((x.size(0), x.size(1))) + with torch.inference_mode(): + for step_sigma, step_dsigma in zip(sigmas[:-1], sigmas.diff(), strict=True): + v = reference["model"](x, sigma.fill_(step_sigma), **ctx, kv_cache=cache) + outputs.append(v.clone()) + x = (x.float() + step_dsigma.float() * v.float()).type_as(x) + x0 = x.clone() + cache.set_frozen(False) + outputs.append(reference["model"](x0, x0.new_zeros((1, 1)), **ctx, kv_cache=cache).clone()) + return x0, outputs + + +def _port_frame(port, noise, ctx, frame_pos: int): + """``generate_frame`` with every pass output captured. Hooked rather than read + from the return value: ``generate_frame`` hands back only the settled latent, and + the committing pass's velocity -- the one that proves the fifth pass ran on the + right input -- is discarded inside it.""" + outputs = [] + dit = port["dit"] + handle = dit.register_forward_hook(lambda mod, inputs, output: outputs.append(output.clone())) + _admit(port, frame_pos) + try: + with torch.inference_mode(): + x0 = dit.generate_frame( + noise, + torch.tensor(frame_pos, dtype=torch.int64, device=DEVICE), + mouse=ctx["mouse"], + button=ctx["button"], + scroll=ctx["scroll"], + ) + finally: + handle.remove() + _commit(port, frame_pos) + return x0, outputs + + +def _new_request(port) -> None: + """A fresh request id per test. The manager enforces ``frame == last + 1``, so a + test that replays frame 0 or starts at frame 21 needs its own clock; the old + request has to hand its world back first.""" + kv = port["kv"] + if port["rid"] is not None: + kv.reset_request(port["rid"], free=True) + kv.remove_request(port["rid"]) + port["rid"] = f"parity{port['seq']}" + port["seq"] += 1 + kv.ingest_request(port["rid"]) + + +def _step_and_context(port, frame: int): + rid = port["rid"] + return ( + RingKVStep(frames=((rid, frame),)), + StepContext(request_ids=(rid,), graph_walk="rollout", slot=0, capture=False), + ) + + +def _admit(port, frame: int) -> None: + """Stage the fixed-address world index and local/global attention masks.""" + step, context = _step_and_context(port, frame) + outcome = port["kv"].admit(step, context) + assert outcome.ok, f"refused at frame {frame}: {outcome.reason}" + context.plan_results["kv"] = port["kv"].plan(step, context) + port["attn"].plan(AttentionStep(), context) + assert not port["attn"].needs_token_visibility + + +def _commit(port, frame: int) -> None: + port["kv"].commit(*_step_and_context(port, frame)) + + +# --------------------------------------------------------------------------- +# Measuring +# --------------------------------------------------------------------------- + + +def _deviation(a: torch.Tensor, b: torch.Tensor) -> tuple[float, float]: + """``(max abs, max abs / peak of the reference)``.""" + left, right = a.float(), b.float() + peak = left.abs().max().item() + gap = (left - right).abs().max().item() + return gap, gap / max(peak, 1e-30) + + +def _stage_modules(model, *, is_port: bool): + blocks = model.blocks if is_port else model.transformer.blocks + rope = model.rope_angles if is_port else model.transformer.rope_angles + return ( + [("patchify", model.patchify), ("ctrl_emb", model.ctrl_emb), ("cond", model.denoise_step_emb), ("rope", rope)] + + [(f"block{i}", block) for i, block in enumerate(blocks)] + + [("out_norm", model.out_norm), ("unpatchify", model.unpatchify)] + ) + + +@contextlib.contextmanager +def _stage_capture(modules, store): + """Keyed by name, not appended: the two sides run these modules in different + orders (the port builds RoPE before the conditioner), and positional pairing + would silently compare the wrong rows.""" + handles = [] + for name, module in modules: + + def hook(mod, inputs, output, name=name): + tensor = output[0] if isinstance(output, tuple) else output + store[name] = (tensor[0] if isinstance(tensor, tuple) else tensor).detach().float().cpu() + + handles.append(module.register_forward_hook(hook)) + try: + yield + finally: + for handle in handles: + handle.remove() + + +def _divergent_stages(reference_stages, port_stages, names): + return [ + (name, *_deviation(reference_stages[name], port_stages[name])) + for name in names + if not torch.equal(reference_stages[name], port_stages[name]) + ] + + +def _comparable_ring(reference_layer, port_layer): + """One world of ring state, in a shape the two sides share. + + The reference allocates 128 frame slots for a global layer but addresses only + its 16 buckets, so its live region is ``[0, port ring_len)`` and its scratch is + the tail ``[L, capacity)``. The port compacts the gap away. Callers assert the + gap stays clear rather than trusting it. + """ + ring_len = port_layer.ring_len + reference_kv = torch.cat( + (reference_layer.kv[:, :, :, :ring_len], reference_layer.kv[:, :, :, reference_layer.L :]), + dim=3, + ) + reference_written = torch.cat((reference_layer.written[:ring_len], reference_layer.written[reference_layer.L :])) + return (reference_kv, reference_written), (port_layer.kv, port_layer.written) + + +def _ring_deviation(reference, port) -> list[tuple[int, float, float, bool]]: + """Per layer: ``(index, max abs, relative, written masks equal)``.""" + rows = [] + for i, (reference_layer, port_layer) in enumerate(zip(reference["kv"].layers, port["kv"].layers, strict=True)): + (ref_kv, ref_written), (port_kv, port_written) = _comparable_ring(reference_layer, port_layer) + gap, relative = _deviation(ref_kv, port_kv) + rows.append((i, gap, relative, torch.equal(ref_written, port_written))) + return rows + + +def _assert_ring(ring, *, exact: bool): + """``written`` is kernel- and table-independent, so it must match in both modes. + The KV bytes must match only under ``reference_compat``.""" + assert all(row[3] for row in ring), "ring visibility masks differ" + worst = max(ring, key=lambda row: row[1]) + if exact: + assert worst[1] == 0.0, f"ring differs at layer {worst[0]}: maxabs={worst[1]:.4e}" + return worst + + +# --------------------------------------------------------------------------- +# L0 -- the derived fp32 tables +# --------------------------------------------------------------------------- + + +def _island_tables(port, reference): + dit = port["dit"] + (port_freq,) = dit.denoise_step_emb._freq.get(DEVICE) + port_xy, port_inv_t = dit.rope_angles._tables.get(DEVICE) + return { + "denoise_step_emb.freq": (reference["islands"]["freq"], port_freq), + "rope_angles.xy": (reference["islands"]["xy"], port_xy), + "rope_angles.inv_t": (reference["islands"]["inv_t"], port_inv_t), + } + + +def test_the_fp32_island_tables_match_the_reference_under_reference_compat(ports, reference): + """``reference_compat`` rebuilds the reference's lossy derived state, and these + three tables are where it starts.""" + failures = [] + for name, (reference_table, port_table) in _island_tables(ports[True], reference).items(): + gap, relative = _deviation(reference_table, port_table) + print(f"{name:>24} maxabs={gap:.4e} rel={relative:.4e}") + if gap != 0.0: + failures.append(f"{name}: maxabs={gap:.4e} rel={relative:.4e}") + assert not failures, "compat fp32 tables differ from the reference's: " + "; ".join(failures) + + +def test_the_exact_tables_differ_from_the_reference_by_exactly_a_bf16_roundtrip(ports, reference): + """The characterisation of the served divergence, at zero tolerance. + + The reference's ``NoCastModule._apply`` round-trips every tensor it holds through + the requested dtype -- ``fn(t)`` casts fp32 to bf16, then the guard casts the + *result* back. Parameters recover, because ``load_state_dict`` runs after the cast + and refills them from the checkpoint. These three derived non-persistent buffers + never get refilled, so the served reference runs on bf16-quantized frequencies and + the exact port does not. + + Not a rounding detail. ``freq`` is multiplied by ``sigma * 1000`` and ``xy`` by a + normalized coordinate, so a 1.8e-3 relative table error is a phase error of up to + ~1.8 rad, and it is the dominant term in every layer below. + """ + for name, (reference_table, port_table) in _island_tables(ports[False], reference).items(): + gap, relative = _deviation(reference_table, port_table) + print(f"{name:>24} maxabs={gap:.4e} rel={relative:.4e}") + assert gap != 0.0, f"{name}: the exact port already matches the reference's lossy table" + assert torch.equal(bf16_roundtrip(port_table), reference_table), ( + f"{name}: the reference's table is not a bf16 round-trip of the exact one, so " + "the divergence is no longer only NoCastModule's cast" + ) + + +# --------------------------------------------------------------------------- +# L1 -- one forward +# --------------------------------------------------------------------------- + + +def test_cached_noise_conditioning_is_not_a_numerical_no_op(reference): + """``patch_cached_noise_conditioning`` is applied unconditionally by the reference, + and the claim was that it is a numerical no-op. It is not, and the mechanism is the + batch shape, not the LUT. + + Both halves are measured on the reference's own conditioner so the table difference + above cannot leak in. ``CachedCondHead`` is exact, which is why ``reference_compat`` + covers only the embedding half. ``CachedDenoiseStepEmb`` is not: it builds its table + by evaluating the fp32 MLP on all five sigmas at once, and at M=5 that dispatches to + a TF32 tensor-core GEMM under ``float32_matmul_precision='high'``, while serving one + sigma at a time is an M=1 GEMV that stays exact fp32. + """ + from src.patch_model import CachedDenoiseStepEmb + + sigmas = list(reference["cfg"].scheduler_sigmas) + conditioner = reference["bare_conditioner"] + lut = CachedDenoiseStepEmb(conditioner, sigmas) + + embedding_gaps, head_gaps = [], [] + with torch.inference_mode(): + for value in sigmas: + sigma = torch.tensor([[value]], device=DEVICE, dtype=DTYPE) + cached, live = lut(sigma), conditioner(sigma) + embedding_gaps.append(_deviation(cached, live)) + head_gaps.append( + max( + _deviation(a, b)[0] + for a, b in zip( + reference["model"].transformer.blocks[0].cond_head(cached), + reference["bare_cond_head"](cached), + strict=True, + ) + ) + ) + print( + f"sigma={value:<7.4f} embedding maxabs={embedding_gaps[-1][0]:.4e} " + f"rel={embedding_gaps[-1][1]:.4e} cond_head maxabs={head_gaps[-1]:.4e}" + ) + + assert max(head_gaps) == 0.0, "CachedCondHead was expected to be exact" + assert max(gap for gap, _ in embedding_gaps) > 0.0, ( + "CachedDenoiseStepEmb agreed with the live conditioner; if the batch-5 LUT " + "build has stopped reaching TF32, the port may drop this patch as a no-op" + ) + + +def test_the_compat_conditioner_reproduces_the_reference_sigma_lut(ports, reference): + """The second half of ``reference_compat``: the port builds its own batch-5 table + rather than borrowing the reference's, so this pins the two tables against each + other row by row, and pins the exact port as the per-sigma path that differs.""" + compat, exact = ports[True]["dit"].denoise_step_emb, ports[False]["dit"].denoise_step_emb + reference_lut = reference["model"].denoise_step_emb + + live_gaps = [] + with torch.inference_mode(): + for value in reference["cfg"].scheduler_sigmas: + sigma = torch.tensor([[value]], device=DEVICE, dtype=DTYPE) + gap, relative = _deviation(reference_lut(sigma), compat(sigma)) + live_gaps.append(_deviation(reference_lut(sigma), exact(sigma))[0]) + print(f"sigma={value:<7.4f} compat maxabs={gap:.4e} rel={relative:.4e} exact maxabs={live_gaps[-1]:.4e}") + assert gap == 0.0, f"sigma={value}: compat LUT differs from the reference's" + + assert max(live_gaps) > 0.0, ( + "the exact conditioner already agrees with the reference's LUT, so the flag's conditioner half is a no-op" + ) + + +@COMPAT_MODES +def test_one_forward_matches_the_reference_on_an_empty_ring(ports, reference, oracle, compat): + """The committing pass of the oracle's seed frame: weight loading, RoPE, adaLN + and the conditioning head, with nothing in the ring behind it.""" + port = ports[compat] + frame = _frame(oracle, 0) + ctx = _ctx(frame) + x = frame["latent"].to(DEVICE) + + _new_request(port) + _reset(port, reference) + reference_stages: dict[str, torch.Tensor] = {} + with _stage_capture(_stage_modules(reference["model"], is_port=False), reference_stages): + reference_out = _reference_forward(reference, x, 0.0, ctx, commit=True) + port_stages: dict[str, torch.Tensor] = {} + _admit(port, 0) + with _stage_capture(_stage_modules(port["dit"], is_port=True), port_stages): + port_out = _port_forward(port, x, 0.0, ctx, 0, commit=True) + _commit(port, 0) + + names = [name for name, _ in _stage_modules(port["dit"], is_port=True)] + divergent = _divergent_stages(reference_stages, port_stages, names) + gap, relative = _deviation(reference_out, port_out) + for name, stage_gap, stage_relative in divergent[:4]: + print(f"stage {name:>10} maxabs={stage_gap:.4e} rel={stage_relative:.4e}") + print(f"output maxabs={gap:.4e} rel={relative:.4e}") + worst = _assert_ring(_ring_deviation(reference, port), exact=compat) + print(f"ring worst layer={worst[0]} maxabs={worst[1]:.4e} rel={worst[2]:.4e}") + + if compat: + assert gap == 0.0, ( + f"velocity differs: maxabs={gap:.4e} rel={relative:.4e}; " + f"first divergent stage {divergent[0] if divergent else None}" + ) + return + + # Exact port: the divergence has to reach the output, and it has to start at the + # table consumers -- anything earlier is a new bug wearing this one's clothes. + assert gap != 0.0, "the exact port matched the reference; reference_compat is a no-op" + diverged = {name for name, _, _ in divergent} + assert not diverged.intersection(TABLE_INDEPENDENT_STAGES), ( + f"stages that read no derived table diverged: {sorted(diverged.intersection(TABLE_INDEPENDENT_STAGES))}" + ) + assert diverged.issuperset(TABLE_DEPENDENT_STAGES), ( + f"a table consumer agreed with the reference anyway: {sorted(set(TABLE_DEPENDENT_STAGES) - diverged)}" + ) + + +@COMPAT_MODES +def test_one_forward_matches_the_reference_on_a_wrapped_ring(ports, reference, oracle, compat): + """The same forward with 20 frames of history behind it, so the ring has wrapped + once and the dilated global layers hold more than one bucket.""" + port = ports[compat] + frame = _frame(oracle, 21) + ctx = _ctx(frame) + x = frame["noise_bf16"].to(DEVICE) + + _load_snapshot(port, reference, oracle, 20) + reference_out = _reference_forward(reference, x, 1.0, ctx, commit=False) + _admit(port, 21) + port_out = _port_forward(port, x, 1.0, ctx, 21, commit=False) + + gap, relative = _deviation(reference_out, port_out) + print(f"output maxabs={gap:.4e} rel={relative:.4e}") + if compat: + assert gap == 0.0, f"velocity differs at frame 21: maxabs={gap:.4e} rel={relative:.4e}" + else: + assert gap != 0.0, "the exact port matched the reference; reference_compat is a no-op" + + +def test_every_stage_is_bit_exact_under_reference_compat(ports, reference, oracle): + """The localization: with the flag on, nothing else differs. Both a frozen sigma + and the committing one, so adaLN's two regimes and the ring write are both covered. + + This is what makes a failure elsewhere attributable. If this test goes red, the + divergence is no longer only the tables and the LUT, and the layers below are + measuring something new. + """ + port = ports[True] + frame = _frame(oracle, 0) + ctx = _ctx(frame) + x = frame["latent"].to(DEVICE) + + for sigma_value in (1.0, 0.0): + commit = sigma_value == 0.0 + _new_request(port) + _reset(port, reference) + reference_stages: dict[str, torch.Tensor] = {} + with _stage_capture(_stage_modules(reference["model"], is_port=False), reference_stages): + reference_out = _reference_forward(reference, x, sigma_value, ctx, commit=commit) + + port_stages: dict[str, torch.Tensor] = {} + _admit(port, 0) + with _stage_capture(_stage_modules(port["dit"], is_port=True), port_stages): + port_out = _port_forward(port, x, sigma_value, ctx, 0, commit=commit) + + names = [name for name, _ in _stage_modules(port["dit"], is_port=True)] + divergent = _divergent_stages(reference_stages, port_stages, names) + gap, relative = _deviation(reference_out, port_out) + print(f"sigma={sigma_value}: {len(divergent)} divergent stages, output maxabs={gap:.4e}") + assert not divergent, f"sigma={sigma_value} first divergence: {divergent[0]}" + assert gap == 0.0, f"sigma={sigma_value} output maxabs={gap:.4e} rel={relative:.4e}" + + +# --------------------------------------------------------------------------- +# L2 -- one frame, all five passes +# --------------------------------------------------------------------------- + + +def _five_pass_deviations(port, reference, oracle): + frame = _frame(oracle, 1) + ctx = _ctx(frame) + noise = frame["noise_bf16"].to(DEVICE) + sigmas = torch.tensor(list(reference["cfg"].scheduler_sigmas), dtype=DTYPE, device=DEVICE) + + _load_snapshot(port, reference, oracle, 0) + reference_x0, reference_outs = _reference_frame(reference, noise, ctx, sigmas) + port_x0, port_outs = _port_frame(port, noise, ctx, 1) + + assert len(reference_outs) == len(port_outs) == 5 + rows = [(i, *_deviation(r, p)) for i, (r, p) in enumerate(zip(reference_outs, port_outs, strict=True))] + return rows, _deviation(reference_x0, port_x0), _ring_deviation(reference, port) + + +@COMPAT_MODES +def test_all_five_pass_outputs_of_one_frame_match(ports, reference, oracle, compat): + """The 4+1 driver: four frozen Euler steps then the committing pass. Every pass + output is compared, not just the settled latent -- a wrong sigma or a wrong + ``commit`` on an inner step is invisible in the last one alone.""" + port = ports[compat] + passes, (x0_gap, x0_relative), ring = _five_pass_deviations(port, reference, oracle) + for index, gap, relative in passes: + print(f"pass {index} maxabs={gap:.4e} rel={relative:.4e}") + print(f"latent maxabs={x0_gap:.4e} rel={x0_relative:.4e}") + _assert_ring(ring, exact=compat) + + first = next((row for row in passes if row[1] != 0.0), None) + if compat: + assert first is None, f"first divergent pass: index={first[0]} maxabs={first[1]:.4e} rel={first[2]:.4e}" + assert x0_gap == 0.0, f"settled latent maxabs={x0_gap:.4e} rel={x0_relative:.4e}" + return + + # Exact port: pass 0 runs on an unwrapped, shared ring, so a divergence there is + # the tables and nothing accumulated. All five passes and the latent carry it. + assert first is not None and first[0] == 0, f"first divergent pass: {first}" + assert all(gap != 0.0 for _, gap, _ in passes), f"a pass matched the reference: {passes}" + assert x0_gap != 0.0, "the settled latent matched the reference" + + +# --------------------------------------------------------------------------- +# L3 -- rollout +# --------------------------------------------------------------------------- + + +def _load_snapshot(port, reference, oracle, frame_index: int) -> None: + """Put both rings into the state the oracle recorded after ``frame_index``. + + The oracle's ring bytes came from the eager-flex recording, so they are not the + reference as served -- but as a *shared* starting state for both sides they are + exactly as good as any other, and they cost nothing to produce. + """ + _new_request(port) + _reset(port, reference) + snapshot = torch.load(oracle["rings"] / f"ring_{frame_index:03d}.pt", map_location="cpu", weights_only=False) + for layer, saved in zip(reference["kv"].layers, snapshot, strict=True): + layer.kv.copy_(saved["kv"].to(DEVICE)) + layer.written.copy_(saved["written"].to(DEVICE)) + for reference_layer, port_layer in zip(reference["kv"].layers, port["kv"].layers, strict=True): + (ref_kv, ref_written), _ = _comparable_ring(reference_layer, port_layer) + port_layer.kv.copy_(ref_kv) + port_layer.written.copy_(ref_written) + + +def _rollout(port, reference, oracle, frames: int): + """Step both sides in lockstep from an empty ring, on the oracle's noise and + controls. Yields ``(frame, latent deviation, ring rows)`` per frame.""" + _new_request(port) + _reset(port, reference) + sigmas = torch.tensor(list(reference["cfg"].scheduler_sigmas), dtype=DTYPE, device=DEVICE) + for index in range(frames): + frame = _frame(oracle, index) + ctx = _ctx(frame) + if frame["kind"] == "seed": + # A real VAE-encoded frame: one committing pass, no ODE. Both sides + # are handed the same latent, so the velocity is what is compared. + latent = frame["latent"].to(DEVICE) + left = _reference_forward(reference, latent, 0.0, ctx, commit=True) + _admit(port, index) + right = _port_forward(port, latent, 0.0, ctx, index, commit=True) + _commit(port, index) + else: + noise = frame["noise_bf16"].to(DEVICE) + left, _ = _reference_frame(reference, noise, ctx, sigmas) + right, _ = _port_frame(port, noise, ctx, index) + yield index, _deviation(left, right), _ring_deviation(reference, port) + + +def test_the_served_rollout_diverges_from_the_reference_at_every_frame(ports, reference, oracle): + """The exact port as served, over enough frames for the ring to start carrying the + divergence. Reported rather than gated on a magnitude: what is asserted is that it + is there from frame 0 (so it is the tables, not accumulation), that it never + accidentally comes back to zero, and that nothing kernel-independent moved. + """ + port = ports[False] + visited, clean = 0, [] + for index, (gap, relative), ring in _rollout(port, reference, oracle, DIVERGENCE_FRAMES): + worst = _assert_ring(ring, exact=False) + print( + f"frame {index:3d} latent maxabs={gap:.4e} rel={relative:.4e} " + f"ring worst layer={worst[0]} maxabs={worst[1]:.4e}" + ) + if gap == 0.0 or worst[1] == 0.0: + clean.append((index, gap, worst[1])) + visited += 1 + assert visited == DIVERGENCE_FRAMES, f"rollout stopped after {visited} frames" + assert not clean, ( + f"frames where the exact port matched the reference: {clean}; reference_compat " + "is not the only thing separating the two sides" + ) + + +def test_rollout_is_bit_exact_under_reference_compat(ports, reference, oracle): + """The ring over a full rollout: slot addressing, the dilated write step, and the + port's compaction of the global layers, all under the one condition that makes a + nonzero attributable to the ring rather than to arithmetic. + + ``ROLLOUT_FRAMES`` wraps the 16-frame local ring twice. + """ + port = ports[True] + frames = min(ROLLOUT_FRAMES, len(oracle["frames"])) + assert frames > 32, f"need more than two local ring wraps; oracle has {frames} frames" + visited = 0 + for index, (gap, relative), ring in _rollout(port, reference, oracle, frames): + assert gap == 0.0, f"frame {index}: latent maxabs={gap:.4e} rel={relative:.4e}" + _assert_ring(ring, exact=True) + visited += 1 + # A generator that stopped early would leave every assertion above unrun. + assert visited == frames, f"rollout stopped after {visited} of {frames} frames" + + +def test_the_port_compacts_only_slots_the_reference_never_addresses(ports, oracle): + """The compaction claim, against the oracle rather than against the port's own + arithmetic: the reference allocates 128 frame slots for a global layer and writes + 16 of them, so the region the port drops is provably dead. + + ``written`` and which buckets hold energy are properties of the ring's addressing, + not of the attention kernel, so the oracle is authoritative for them even though + its activations are not. + """ + port = ports[False] + config = port["config"] + checked = 0 + for index in sorted(int(p.stem.split("_")[1]) for p in oracle["rings"].glob("ring_*.pt")): + snapshot = torch.load(oracle["rings"] / f"ring_{index:03d}.pt", map_location="cpu", weights_only=False) + for layer_index, (saved, port_layer) in enumerate(zip(snapshot, port["kv"].layers, strict=True)): + written, kv = saved["written"], saved["kv"] + if kv.size(3) == port_layer.capacity: + continue # local layer: no compaction to check + dead = slice(port_layer.ring_len, kv.size(3) - config.tokens_per_frame) + assert not written[dead].any(), ( + f"frame {index} layer {layer_index}: the reference marked " + f"{int(written[dead].sum())} slots written inside the region the port drops" + ) + assert kv[:, :, :, dead].eq(0).all(), ( + f"frame {index} layer {layer_index}: nonzero KV inside the dropped region" + ) + checked += 1 + assert checked, "no global-layer ring snapshots found; the compaction claim is untested" diff --git a/test/modular/test_waypoint_shell.py b/test/modular/test_waypoint_shell.py index 15e5d10b2..f800afed8 100644 --- a/test/modular/test_waypoint_shell.py +++ b/test/modular/test_waypoint_shell.py @@ -19,7 +19,10 @@ * a deployment whose ``max_concurrent_requests`` is unset or larger than the ring's world pool, which is the *only* thing keeping arrivals inside a pool that fails terminally when it is overrun, - * a resource that never reaches the 24 attention layers. + * a resource that never reaches the 24 attention layers, + * a latent that reaches the streaming decoder twice, out of order, or not at + all -- the prime walk skipping its decode is the version of this the port + nearly shipped. CPU-only, checkpoint-free, and no engine. The real 720P config is used throughout, because the numbers that collide are that config's numbers; the DiT @@ -28,11 +31,15 @@ assertions touch a weight. The two places that need a value read back go around it: the noise draw takes an explicit device (which is why that helper takes one), and the frame-clock test runs over ``_HostOnlyDit``, since what it -asserts is host bookkeeping the DiT is not part of. +asserts is host bookkeeping the DiT is not part of. The VAE section is the +third: it runs on the 360P config and a fake ``taehv`` package, for the reasons +given there. """ import dataclasses +import importlib import logging +import pathlib import sys import pytest @@ -50,11 +57,13 @@ RingKVStep, ) from mstar.engine.resources.runner import topo_sort -from mstar.graph.base import GraphNode, Loop +from mstar.graph.base import GraphEdge, Loop, Sequential +from mstar.graph.graph_io import WorkerGraphIO from mstar.graph.special_destinations import EMIT_TO_CLIENT +from mstar.model.submodule_base import ModelInputsFromEngine from mstar.model.waypoint.components.attention import WaypointAttention from mstar.model.waypoint.components.dit import WaypointDiT -from mstar.model.waypoint.config import waypoint_1_5_1b_720p +from mstar.model.waypoint.config import waypoint_1_5_1b_360p, waypoint_1_5_1b_720p from mstar.model.waypoint.submodules import ( ATTN_RESOURCE, KV_RESOURCE, @@ -62,8 +71,15 @@ ROLLOUT_LOOP_NAME, ROLLOUT_WALK, WaypointDitSubmodule, + WaypointVaeDecoderSubmodule, + WaypointVaeEncoderSubmodule, +) +from mstar.model.waypoint.waypoint_model import ( + DIT_NODE, + VAE_DECODER_NODE, + VAE_ENCODER_NODE, + WaypointModel, ) -from mstar.model.waypoint.waypoint_model import DIT_NODE, WaypointModel @pytest.fixture(scope="module") @@ -121,7 +137,7 @@ def _fwd_info( request_id: str = "r0", graph_walk: str = ROLLOUT_WALK, random_seed: int = 1234, - num_frames: int = 8, + num_steps: int = 8, loop_iter: int | None = None, ) -> CurrentForwardPassInfo: return CurrentForwardPassInfo( @@ -131,7 +147,7 @@ def _fwd_info( random_seed=random_seed, max_tokens=0, resource_configs={}, - step_metadata={"is_prefill": graph_walk == PRIME_WALK, "num_frames": num_frames}, + step_metadata={"is_prefill": graph_walk == PRIME_WALK, "num_steps": num_steps}, resource_publish_info={}, loop_stop_times={}, dynamic_loop_iter_counts=( @@ -229,32 +245,128 @@ def test_attention_resolves_after_the_cache_it_names(model): # --------------------------------------------------------------------------- -def test_walks_are_dit_only_and_the_rollout_emits_per_iteration(model, config): +def test_prime_encodes_commits_and_initializes_decoder_without_emitting(model): + """The seed frame advances decoder state. Encoding it and dropping the latent would + prime the world correctly and still corrupt every emitted frame: the + streaming decoder spends its first call on ``frames_to_trim`` of temporal + memory, so the first *rollout* frame would pay for it and the whole stream + would sit one priming short of the world it came from. Silently.""" walks = model.get_graph_walk_graphs() assert set(walks) == {PRIME_WALK, ROLLOUT_WALK} - # The VAE nodes are a later phase. Model.nodes is derived from - # the walks, so inventing one here would make the engine wait on a node - # nothing builds. - assert model.nodes == [DIT_NODE] + assert model.nodes == [DIT_NODE, VAE_DECODER_NODE, VAE_ENCODER_NODE] prime = walks[PRIME_WALK] - assert isinstance(prime, GraphNode) and prime.name == DIT_NODE - assert "latent" in prime.input_names + assert isinstance(prime, Sequential) + assert [s.name for s in prime.sections] == [ + VAE_ENCODER_NODE, DIT_NODE, VAE_DECODER_NODE, + ] + encoder, dit, decoder = prime.sections + assert encoder.input_names == {"image_inputs"} + assert [(e.name, e.next_node) for e in encoder.outputs] == [("latent", DIT_NODE)] + # The dit node's contract is unchanged by the nodes bracketing it. + assert dit.input_names == {"latent", "mouse", "button", "scroll"} + assert [(e.name, e.next_node) for e in dit.outputs] == [("latent", VAE_DECODER_NODE)] + assert decoder.input_names == {"latent"} + assert decoder.outputs == [] + + # The two `latent` edges are distinct because a section keys on + # (name, next_node); collapsing them would route the seed latent past the dit. + io = prime.get_inputs_outputs() + assert io.ext_inputs == { + ("image_inputs", VAE_ENCODER_NODE), + ("mouse", DIT_NODE), ("button", DIT_NODE), ("scroll", DIT_NODE), + } + assert io.ext_outputs == [] - rollout = walks[ROLLOUT_WALK] + +def test_the_rollout_loop_decodes_and_emits_every_iteration(model, config): + rollout = model.get_graph_walk_graphs()[ROLLOUT_WALK] assert isinstance(rollout, Loop) assert rollout.name == ROLLOUT_LOOP_NAME # what check_stop's signal is keyed by assert rollout.max_iters == config.max_frames + section = rollout.section - assert isinstance(section, GraphNode) and section.name == DIT_NODE + assert isinstance(section, Sequential) + dit, decoder = section.sections + assert (dit.name, decoder.name) == (DIT_NODE, VAE_DECODER_NODE) + assert [(e.name, e.next_node) for e in dit.outputs] == [("latent", VAE_DECODER_NODE)] # Emitted from inside the loop, one frame per iteration -- an interactive # world model whose frames only arrive after the rollout ends has no world # to interact with. - assert [e.next_node for e in section.outputs] == [EMIT_TO_CLIENT] + assert [e.next_node for e in decoder.outputs] == [EMIT_TO_CLIENT] assert rollout.accumulated_outputs == [] - # An overshoot iteration here is not a wasted forward: it commits a frame - # into the ring, and there is no undo. - assert section.enable_async_scheduling is False + # An overshoot iteration is not a wasted forward: the dit commits a frame + # into the ring, and a speculative decode is a reorder of a stream that + # cannot be reordered. + assert dit.enable_async_scheduling is False + assert decoder.enable_async_scheduling is False + # The controller streams stay loop-external; the dit->decoder latent does + # not become one, or the conductor would re-inject a stale frame. + assert rollout._external_inputs == { + ("mouse", DIT_NODE), ("button", DIT_NODE), ("scroll", DIT_NODE), + } + + +def test_every_committed_frame_is_decoded_once_including_the_last(model): + """Driven through ``WorkerGraphIO``, because what is under test is the order + the worker runs these in, not the order they are declared in. + + The decoder is order-dependent and its memory advances per call, so a latent + decoded twice, skipped, or taken out of turn shifts every frame after it + with nothing raised. Two things have to hold. The decode runs between its + own dit pass and the next one -- which it does because scheduling *pops* a + node off the ready set, and the dit's controller streams are only + re-injected at the iteration boundary. And the stop signal, which fires + during the dit's postprocess, closes the loop only after that iteration's + decode: ``LoopStateRegistry`` calls ``complete_iter`` once every entity is + finished, so the finish cannot short-circuit the decoder. + """ + rollout = model.get_graph_walk_graphs()[ROLLOUT_WALK] + wgio = WorkerGraphIO(rollout) + decoder = wgio.get_node(VAE_DECODER_NODE) + for name in ("mouse", "button", "scroll"): + wgio.ingest_input(GraphEdge(next_node=DIT_NODE, name=name, persist=True)) + + emitted, decoded = [], [] + + def run(node_name: str, latent_id: int | None = None) -> None: + """One scheduling round: pop, execute, route. The pop is what + ``NodeManager.pop_ready_nodes`` does, and it is the whole reason the dit + cannot be picked twice in an iteration.""" + assert node_name in wgio.ready_node_names + wgio.ready_node_names.discard(node_name) + queued = decoder.ready_signals.ready_inputs.get("latent") + if queued is not None: + decoded.append(queued.latent_id) + for edge in wgio.mark_node_complete(node_name).output_edges: + if edge.next_node == EMIT_TO_CLIENT: + emitted.append(edge.name) + continue + if edge.next_node == VAE_DECODER_NODE: + # A fresh edge per iteration: the node's declared outputs are + # one reused object, so identity is the only way to tell which + # frame's latent the decoder actually consumed. + edge = GraphEdge(next_node=edge.next_node, name=edge.name) + edge.latent_id = latent_id + wgio.ingest_input(edge) + + for frame in range(3): + assert wgio.ready_node_names == {DIT_NODE} + run(DIT_NODE, latent_id=frame) + assert wgio.ready_node_names == {VAE_DECODER_NODE} + run(VAE_DECODER_NODE) + assert rollout.is_done is False + assert rollout.curr_iter == frame + 1 + + wgio.register_loop_finish_signal(ROLLOUT_LOOP_NAME) # what check_stop does + run(DIT_NODE, latent_id=3) + assert rollout.is_done is False, "the loop closed before the frame was decoded" + assert wgio.ready_node_names == {VAE_DECODER_NODE} + run(VAE_DECODER_NODE) + + assert rollout.is_done is True + assert decoded == [0, 1, 2, 3], "a latent was skipped, repeated or reordered" + assert emitted == ["video_output"] * 4, "one emit per committed frame" # --------------------------------------------------------------------------- @@ -375,36 +487,46 @@ def test_noise_differs_across_frames_and_across_seeds(submodule, config): ) -def test_the_frame_clock_advances_on_the_host_and_drives_the_controller_slice( +def test_prime_is_idle_and_rollout_zero_receives_action_zero( host_submodule, config ): - """The clock is a host int in PerRequestState, and the device tensor is - derived from it -- never the other way round. The scripted stream is - loop-external, so this is the only thing that moves through it.""" + """Prime advances the ring clock but not the user-action cursor.""" rid = "clock" host_submodule.request_states.pop(rid, None) - inputs = _controller_stream(config, frames=3) + inputs = _controller_stream(config, frames=2) # Distinguishable rows, so a slice off by one is visible. - for row in range(3): + for row in range(2): inputs["scroll"][0][0, row, 0] = float(row + 1) + prime_inputs = { + **inputs, + "latent": [torch.zeros((1, 1, *config.latent_shape), dtype=torch.float32)], + } + prime = host_submodule.prepare_inputs( + PRIME_WALK, _fwd_info(request_id=rid, graph_walk=PRIME_WALK), prime_inputs + ) + assert int(prime.tensor_inputs["frame_pos"][0]) == 0 + assert float(prime.tensor_inputs["scroll"][0, 0, 0]) == 0.0 + host_submodule.postprocess( + rid, _fwd_info(request_id=rid, graph_walk=PRIME_WALK), {} + ) + seen = [] - for _ in range(5): + for _ in range(2): node_inputs = host_submodule.prepare_inputs( ROLLOUT_WALK, _fwd_info(request_id=rid), inputs ) - seen.append( - ( - int(node_inputs.tensor_inputs["frame_pos"][0]), - float(node_inputs.tensor_inputs["scroll"][0, 0, 0]), - ) - ) + seen.append(( + int(node_inputs.tensor_inputs["frame_pos"][0]), + float(node_inputs.tensor_inputs["scroll"][0, 0, 0]), + )) host_submodule.postprocess(rid, _fwd_info(request_id=rid), {}) - # The clock advances by one per committed frame; the stream is shorter than - # the rollout, so its last row holds rather than raising mid-flight. - assert [pos for pos, _ in seen] == [0, 1, 2, 3, 4] - assert [scroll for _, scroll in seen] == [1.0, 2.0, 3.0, 3.0, 3.0] + assert seen == [(1, 1.0), (2, 2.0)] + with pytest.raises(IndexError, match="action index 2"): + host_submodule.prepare_inputs( + ROLLOUT_WALK, _fwd_info(request_id=rid), inputs + ) host_submodule.cleanup_request(rid) assert rid not in host_submodule.request_states @@ -484,25 +606,37 @@ def test_declare_step_names_a_clock_for_every_request_in_the_batch(host_submodul # --------------------------------------------------------------------------- -def test_check_stop_fires_at_exactly_num_frames(submodule): +def test_check_stop_fires_at_exactly_num_steps(submodule): """N frames means firing while iteration N-1 is postprocessed: the loop counter still reads N-1 there and the stop ends that iteration. One early truncates the video; one late commits an extra frame into the ring.""" - num_frames = 6 + num_steps = 6 fired = [ bool( submodule.check_stop( - "r0", _fwd_info(num_frames=num_frames, loop_iter=k), {} + "r0", _fwd_info(num_steps=num_steps, loop_iter=k), {} ) ) - for k in range(num_frames + 2) + for k in range(num_steps + 2) ] - assert fired == [False] * (num_frames - 1) + [True, True, True] + assert fired == [False] * (num_steps - 1) + [True, True, True] assert submodule.check_stop( - "r0", _fwd_info(num_frames=num_frames, loop_iter=num_frames - 1), {} + "r0", _fwd_info(num_steps=num_steps, loop_iter=num_steps - 1), {} ) == {ROLLOUT_LOOP_NAME} +def test_check_stop_never_signals_rollout_loop_during_prime(submodule): + assert submodule.check_stop( + "r0", + _fwd_info( + graph_walk=PRIME_WALK, + num_steps=1, + loop_iter=0, + ), + {}, + ) == set() + + # --------------------------------------------------------------------------- # Serialization gate # --------------------------------------------------------------------------- @@ -512,7 +646,9 @@ def _write_config(tmp_path, name: str, **extra) -> str: body = { "model": "waypoint", "max_seq_len": 512, - "node_groups": [{"node_names": [DIT_NODE], "ranks": [0]}], + "node_groups": [ + {"node_names": [VAE_ENCODER_NODE, DIT_NODE, VAE_DECODER_NODE], "ranks": [0]} + ], **extra, } path = tmp_path / name @@ -554,6 +690,20 @@ def test_get_worker_graphs_refuses_a_deployment_with_no_admit_queue( model.get_worker_graphs(path) +@pytest.mark.parametrize("worlds", [0, -1, True, 1.0, 1.9, "2"]) +def test_get_worker_graphs_refuses_invalid_world_pool_size( + model, tmp_path, worlds +): + path = _write_config( + tmp_path, + f"invalid_worlds_{worlds}.yaml", + max_concurrent_requests=1, + **_worlds(worlds), + ) + with pytest.raises(ValueError, match=r"resources\.kv\.num_worlds"): + model.get_worker_graphs(path) + + @pytest.mark.parametrize(("limit", "worlds"), [(2, 1), (8, 4), (2, None)]) def test_get_worker_graphs_refuses_more_arrivals_than_worlds( model, tmp_path, limit, worlds @@ -597,6 +747,27 @@ def test_get_worker_graphs_accepts_a_deployment_inside_its_pool( } +def test_the_shipped_config_serializes_all_three_nodes_onto_one_rank(model): + """``configs/waypoint.yaml`` is the deployment and has to pass its own gate. + + All three nodes in one group, on rank 0: a node missing from + ``node_groups`` has no rank to run on and the split fails there, and a + worker boundary inside the rollout loop would put a process hop between the + dit and a decoder whose frames must arrive in order. + """ + path = pathlib.Path(__file__).resolve().parents[2] / "configs" / "waypoint.yaml" + + graphs = model.get_worker_graphs(str(path)) + + assert {walk for g in graphs for walk in g.graph_walks} == {PRIME_WALK, ROLLOUT_WALK} + assert {tuple(g.ranks) for g in graphs} == {(0,)} + by_walk = {walk: g for g in graphs for walk in g.graph_walks} + assert set(by_walk[PRIME_WALK].section.get_nodes()) == { + VAE_ENCODER_NODE, DIT_NODE, VAE_DECODER_NODE, + } + assert set(by_walk[ROLLOUT_WALK].section.get_nodes()) == {DIT_NODE, VAE_DECODER_NODE} + + def test_get_worker_graphs_warns_about_worlds_no_request_can_reach( model, tmp_path, caplog ): @@ -675,20 +846,11 @@ def test_binding_without_a_declared_resource_fails_at_bind(submodule): # --------------------------------------------------------------------------- -def test_one_capture_config_per_walk_both_uncompiled(submodule): - """Two configs because the two walks take different input keys, and a walk - with no bucket would run eager against a ring the other walk's captured - graph holds baked addresses into. - - ``compile=False`` on both: ``_forward_for`` would otherwise run a - max-autotune compile of the whole 4+1 driver once per config at warmup, for - a model whose correctness-critical compile is the ``flex_attention_masked`` - pin inside the attention resource -- which runs regardless. The outer - compile is an unmeasured throughput bet. - """ +def test_only_the_steady_dit_rollout_is_an_optional_capture(submodule): + """The one-time prime/cache pass is compiled internally but uncaptured.""" configs = submodule.get_cuda_graph_configs(torch.device("meta")) - assert len(configs) == 2 - assert {c.capture_graph_walk for c in configs} == {PRIME_WALK, ROLLOUT_WALK} + assert len(configs) == 1 + assert configs[0].capture_graph_walk == ROLLOUT_WALK for cfg in configs: assert cfg.compile is False # One live world again: the bucket cannot be wider than it. @@ -697,11 +859,281 @@ def test_one_capture_config_per_walk_both_uncompiled(submodule): # `forward` is captured on a method that never runs. assert cfg.capture_forward_method == "forward_batched" - by_walk = {c.capture_graph_walk: c for c in configs} - assert "latent" in by_walk[PRIME_WALK].single_request_inputs.tensor_inputs - assert "noise" in by_walk[ROLLOUT_WALK].single_request_inputs.tensor_inputs - assert "noise" not in by_walk[PRIME_WALK].single_request_inputs.tensor_inputs - assert "latent" not in by_walk[ROLLOUT_WALK].single_request_inputs.tensor_inputs + assert "noise" in configs[0].single_request_inputs.tensor_inputs + assert "latent" not in configs[0].single_request_inputs.tensor_inputs + assert submodule.disable_torch_compile is True + + +def test_dit_declares_no_capture_when_cuda_graph_is_disabled(config): + eager_config = dataclasses.replace(config, cuda_graph=False) + with torch.device("meta"): + dit = WaypointDiT(eager_config) + dit.cast_serving_dtypes() + eager = WaypointDitSubmodule(dit, eager_config) + + assert eager.get_cuda_graph_configs(torch.device("meta")) == [] + + +# --------------------------------------------------------------------------- +# VAE nodes +# --------------------------------------------------------------------------- + + +class MemBlock(torch.nn.Module): + """Small block with the pinned upstream class and shape contract.""" + + def __init__(self, channels: int): + super().__init__() + # History-shape derivation reads this exact upstream attribute. + self.conv = torch.nn.ModuleList([ + torch.nn.Conv2d(channels * 2, channels, 1, bias=False) + ]) + + def forward(self, current, past): + return current + past * 0.25 + + +class TPool(torch.nn.Module): + def __init__(self, channels: int, stride: int): + super().__init__() + self.stride = stride + self.conv = torch.nn.Conv2d(channels * stride, channels, 1, bias=False) + + def forward(self, value): + batch_time, channels, height, width = value.shape + return self.conv(value.reshape( + batch_time // self.stride, channels * self.stride, height, width + )) + + +class TGrow(torch.nn.Module): + def __init__(self, stride: int): + super().__init__() + self.stride = stride + + def forward(self, value): + return value.repeat_interleave(self.stride, dim=0) + + +class _FakeTaehv(torch.nn.Module): + """Cheap tensor-only TAEHV with the released architecture facts.""" + + patch_size = 2 + latent_channels = 32 + image_channels = 3 + t_downscale = 4 + t_upscale = 4 + frames_to_trim = 3 + is_cogvideox = False + + def __init__(self): + super().__init__() + self.encoder = torch.nn.ModuleList([ + torch.nn.Conv2d(12, 32, 1, stride=8, bias=False), + TPool(32, self.t_downscale), + *(MemBlock(32) for _ in range(9)), + ]) + self.decoder = torch.nn.ModuleList([ + *(MemBlock(32) for _ in range(9)), + TGrow(self.t_upscale), + ]) + self.to(torch.bfloat16) + + def preprocess_input_frames(self, frames): + return torch.nn.functional.pixel_unshuffle(frames, self.patch_size) + + def postprocess_output_frames(self, frames): + return torch.nn.functional.pixel_shuffle(frames[:, :, :12], self.patch_size).clamp(0, 1) + + +@pytest.fixture +def taehv_weights(): + return _FakeTaehv() + + +@pytest.fixture +def ae_config(): + """360P, not the 720P default: the priming path decodes 16 frames per + session and 720P would allocate tens of MB of host tensors to say the same + thing. Latent 16x32 -> 256x512 encode grid -> 360x640 out.""" + return waypoint_1_5_1b_360p() + + +@pytest.fixture +def encoder(taehv_weights, ae_config): + return WaypointVaeEncoderSubmodule(taehv_weights, ae_config) + + +@pytest.fixture +def decoder(taehv_weights, ae_config): + return WaypointVaeDecoderSubmodule(taehv_weights, ae_config) + + +def _seed_clip(ae_config, value: int = 200) -> torch.Tensor: + return torch.full((ae_config.temporal_compression, 360, 640, 3), value, dtype=torch.uint8) + + +def _engine_inputs( + request_id: str = "r0", graph_walk: str = PRIME_WALK, +) -> ModelInputsFromEngine: + return ModelInputsFromEngine( + request_ids=[request_id], + per_request_info={request_id: _fwd_info(request_id, graph_walk=graph_walk)}, + ) + + +def _decode(decoder, latent, *, request_id="r0", graph_walk=ROLLOUT_WALK): + info = _fwd_info(request_id, graph_walk=graph_walk) + prepared = decoder.prepare_inputs( + graph_walk, info, {"latent": [latent]} + ) + outputs = decoder.forward( + graph_walk, + _engine_inputs(request_id, graph_walk), + **prepared.tensor_inputs, + ) + decoder.postprocess(request_id, info, outputs, prepared) + return outputs + + +def test_the_encoder_scales_the_clip_once_in_the_ae_dtype(encoder, ae_config): + """Cast then divide, which is the reference's order. 0-255 is exact in + bf16, so that divide rounds once; dividing in fp32 and casting after rounds + twice and lands on a different latent.""" + clip = _seed_clip(ae_config, value=200) + prepared = encoder.prepare_inputs(PRIME_WALK, _fwd_info(), {"image_inputs": [clip]}) + + image = prepared.tensor_inputs["image"] + assert image.dtype == torch.bfloat16 + assert torch.equal(image, clip.to(torch.bfloat16).div(255)) + assert image.shape == (ae_config.temporal_compression, 360, 640, 3) + + +def test_the_encoder_emits_the_dit_s_priming_latent(encoder, ae_config): + image = encoder.prepare_inputs( + PRIME_WALK, _fwd_info(), {"image_inputs": [_seed_clip(ae_config)]} + ).tensor_inputs["image"] + + out = encoder.forward(PRIME_WALK, _engine_inputs(), image) + + latent = out["latent"][0] + # [B, frame, C, h, w] -- the frame axis the dit indexes the ring by, added + # here rather than left for the dit to guess at. + assert latent.shape == (1, 1, ae_config.channels, *ae_config.latent_shape[1:]) + assert latent.dtype == torch.bfloat16 + + +def test_the_decoder_turns_one_latent_into_one_raw_clip(decoder, ae_config): + latent = torch.zeros( + (1, 1, ae_config.channels, *ae_config.latent_shape[1:]), dtype=torch.bfloat16 + ) + + out = _decode(decoder, latent) + + frames = out["video_output"][0] + assert frames.shape == (ae_config.temporal_compression, 360, 640, 3) + assert frames.dtype == torch.uint8 + + +def test_decoder_prime_and_steady_state_use_fixed_tensor_histories(decoder, ae_config): + latent = torch.full( + (1, 1, ae_config.channels, *ae_config.latent_shape[1:]), dtype=torch.bfloat16 + , fill_value=0.125 + ) + _decode(decoder, latent, graph_walk=PRIME_WALK) + state = decoder.request_state("r0") + keys = [f"decoder_history_{idx}" for idx in range(9)] + assert set(state.tensors) == set(keys) + addresses = [state[key].data_ptr() for key in keys] + + _decode(decoder, latent * 2, graph_walk=ROLLOUT_WALK) + assert [state[key].data_ptr() for key in keys] == addresses + assert all(state[key].dtype == torch.bfloat16 for key in keys) + + +def test_decoder_histories_are_isolated_interleaved_and_cleaned_up(decoder, ae_config): + latent = torch.full( + (1, 1, ae_config.channels, *ae_config.latent_shape[1:]), dtype=torch.bfloat16 + , fill_value=0.125 + ) + first = _decode(decoder, latent, request_id="a", graph_walk=PRIME_WALK) + second = _decode(decoder, latent, request_id="b", graph_walk=PRIME_WALK) + assert torch.equal(first["video_output"][0], second["video_output"][0]) + before_b = { + key: value.clone() for key, value in decoder.request_state("b").tensors.items() + } + for _ in range(20): + _decode(decoder, latent * 2, request_id="a") + _decode(decoder, latent * 3, request_id="b") + assert all( + not torch.equal(before_b[key], value) + for key, value in decoder.request_state("b").tensors.items() + ) + assert all( + decoder.request_state("a")[key].data_ptr() + != decoder.request_state("b")[key].data_ptr() + for key in before_b + ) + + decoder.cleanup_request("a") + assert "a" not in decoder.request_states + restarted = _decode(decoder, latent, request_id="a", graph_walk=PRIME_WALK) + assert torch.equal(restarted["video_output"][0], first["video_output"][0]) + + +def test_the_encoder_and_decoder_share_only_weights( + encoder, decoder, taehv_weights, ae_config +): + """Encoder prime is stateless; only decoder histories survive a request.""" + image = encoder.prepare_inputs( + PRIME_WALK, _fwd_info(), {"image_inputs": [_seed_clip(ae_config)]} + ).tensor_inputs["image"] + latent = encoder.forward(PRIME_WALK, _engine_inputs(), image)["latent"][0] + _decode(decoder, latent, graph_walk=PRIME_WALK) + assert encoder.taehv is decoder.taehv is taehv_weights + assert encoder.request_states == {} + assert len(decoder.request_state("r0").tensors) == 9 + + +def test_ae_graphs_are_compiled_for_capture_but_remain_optional(encoder, decoder): + encoder_configs = encoder.get_cuda_graph_configs(torch.device("cpu")) + decoder_configs = decoder.get_cuda_graph_configs(torch.device("cpu")) + assert [cfg.capture_graph_walk for cfg in encoder_configs] == [PRIME_WALK] + assert {cfg.capture_graph_walk for cfg in decoder_configs} == { + PRIME_WALK, ROLLOUT_WALK, + } + for node, configs in ((encoder, encoder_configs), (decoder, decoder_configs)): + assert configs + assert all(cfg.compile for cfg in configs) + assert node.disable_torch_compile is True + assert node.disable_autocast is True + + +def test_ae_nodes_declare_no_capture_when_cuda_graph_is_disabled( + taehv_weights, ae_config, +): + eager_config = dataclasses.replace(ae_config, cuda_graph=False) + encoder = WaypointVaeEncoderSubmodule(taehv_weights, eager_config) + decoder = WaypointVaeDecoderSubmodule(taehv_weights, eager_config) + + assert encoder.get_cuda_graph_configs(torch.device("cpu")) == [] + assert decoder.get_cuda_graph_configs(torch.device("cpu")) == [] + + +def test_the_shell_builds_without_the_taehv_package(monkeypatch): + """``taehv`` is a separate install with its own checkpoint. Every import of + it is deferred to the call that needs weights, so the graph, the resources + and the serialization gate all work on a box that has neither.""" + monkeypatch.setitem(sys.modules, "taehv", None) + with pytest.raises(ImportError): + importlib.import_module("taehv") + + unweighted = WaypointModel(skip_weight_loading=True) + assert set(unweighted.get_graph_walk_graphs()) == {PRIME_WALK, ROLLOUT_WALK} + assert unweighted.nodes == [DIT_NODE, VAE_DECODER_NODE, VAE_ENCODER_NODE] + assert unweighted.get_node_resources() + for node in (VAE_ENCODER_NODE, VAE_DECODER_NODE): + assert unweighted.get_submodule(node) is None # --------------------------------------------------------------------------- @@ -718,25 +1150,172 @@ def test_process_prompt_materializes_the_whole_action_stream(model, config): {"mouse": (1.0, -2.0), "buttons": [3, 5], "scroll": 0.5}, {"buttons": [3]}, ] + seed = torch.zeros((720, 1280, 3), dtype=torch.uint8) out = model.process_prompt( - None, ["tensor"], ["video"], tensors=None, num_frames=4, actions=actions + None, ["image"], ["video_frame"], + tensors={"image_inputs": [seed]}, num_steps=2, actions=actions, ) - assert set(out) == {"mouse", "button", "scroll"} + assert set(out) == {"image_inputs", "mouse", "button", "scroll"} mouse, button, scroll = out["mouse"][0], out["button"][0], out["scroll"][0] - assert mouse.shape == (1, 4, 2) - assert button.shape == (1, 4, config.n_buttons) - assert scroll.shape == (1, 4, 1) + assert mouse.shape == (1, 2, 2) + assert button.shape == (1, 2, config.n_buttons) + assert scroll.shape == (1, 2, 1) assert mouse[0, 0].tolist() == [1.0, -2.0] assert button[0, 0].nonzero().flatten().tolist() == [3, 5] assert button[0, 1].nonzero().flatten().tolist() == [3] - # Unscripted frames are the idle controller, which is what the reference's - # default CtrlInput() produces. - assert button[0, 2].sum() == 0 and scroll[0, 2].sum() == 0 - with pytest.raises(ValueError, match="out of range"): model.process_prompt( - None, ["tensor"], ["video"], num_frames=1, + None, ["image"], ["video_frame"], + tensors={"image_inputs": [seed]}, num_steps=1, actions=[{"buttons": [config.n_buttons]}], ) - with pytest.raises(ValueError, match="never be read"): - model.process_prompt(None, ["tensor"], ["video"], num_frames=1, actions=actions) + with pytest.raises(ValueError, match="exactly one action"): + model.process_prompt( + None, ["image"], ["video_frame"], + tensors={"image_inputs": [seed]}, num_steps=1, actions=actions, + ) + + +@pytest.mark.parametrize("num_steps", [None, 0, -1, True, 1.5, "1"]) +def test_process_prompt_rejects_non_positive_integer_steps(model, num_steps): + seed = torch.zeros((720, 1280, 3), dtype=torch.uint8) + with pytest.raises(ValueError, match="num_steps > 0"): + model.process_prompt( + None, + ["image"], + ["video_frame"], + tensors={"image_inputs": [seed]}, + num_steps=num_steps, + actions=[], + ) + + +def test_process_prompt_rejects_steps_past_the_checkpoint_horizon(model, config): + seed = torch.zeros((720, 1280, 3), dtype=torch.uint8) + with pytest.raises(ValueError, match="exceeds the checkpoint horizon"): + model.process_prompt( + None, + ["image"], + ["video_frame"], + tensors={"image_inputs": [seed]}, + num_steps=config.max_frames + 1, + actions=[], + ) + + +@pytest.mark.parametrize( + ("action", "message"), + [ + (None, "must be an object"), + ({"unknown": 1}, "unknown field"), + ({"mouse": [float("nan"), 0]}, "mouse values must be finite"), + ({"mouse": [True, 0]}, "mouse values must be numbers"), + ({"mouse": ["1", 0]}, "mouse values must be numbers"), + ({"mouse": [1e39, 0]}, "mouse values must be finite"), + ({"buttons": [1, 1]}, "repeats button id"), + ({"buttons": [True]}, "button ids must be integers"), + ({"scroll": float("inf")}, "scroll must be finite"), + ({"scroll": True}, "scroll must be a number"), + ({"scroll": "1"}, "scroll must be a number"), + ({"scroll": 1e39}, "scroll must be finite"), + ], +) +def test_process_prompt_rejects_invalid_action_values(model, action, message): + seed = torch.zeros((720, 1280, 3), dtype=torch.uint8) + with pytest.raises(ValueError, match=message): + model.process_prompt( + None, + ["image"], + ["video_frame"], + tensors={"image_inputs": [seed]}, + num_steps=1, + actions=[action], + ) + + +def test_the_seed_clip_is_one_latent_frame_of_uint8_rgb(model, config): + """``image_inputs`` is what the vae_encoder node consumes, and the streaming + encoder emits one latent per ``temporal_compression`` frames: a short clip + would buffer and return nothing, a long one would encode twice and leave the + second latent unclaimed. Checked here, at the API boundary, so a malformed + request is a 400 rather than a rollout that dies on a worker.""" + n = config.temporal_compression + frame = torch.zeros((720, 1280, 3), dtype=torch.uint8) + + def prompt(image): + return model.process_prompt( + None, + ["image"], + ["video_frame"], + tensors={"image_inputs": [image]}, + num_steps=2, + actions=[{}, {}], + ) + + # A still seeds the world by being repeated, which is gen_sample.py's + # seed_frame_x4. + clip = prompt(frame)["image_inputs"][0] + assert clip.shape == (n, 720, 1280, 3) and clip.dtype == torch.uint8 + # A real clip of exactly one latent frame passes through. + assert prompt(torch.zeros((n, 720, 1280, 3), dtype=torch.uint8))["image_inputs"][ + 0 + ].shape == (n, 720, 1280, 3) + + with pytest.raises(ValueError, match="one latent frame"): + prompt(torch.zeros((n + 1, 720, 1280, 3), dtype=torch.uint8)) + with pytest.raises(ValueError, match="uint8"): + prompt(torch.zeros((720, 1280, 3), dtype=torch.float32)) + with pytest.raises(ValueError, match="16:9"): + prompt(torch.zeros((720, 720, 3), dtype=torch.uint8)) + + with pytest.raises(ValueError, match="requires one RGB seed"): + model.process_prompt( + None, ["tensor"], ["video_frame"], tensors=None, + num_steps=2, actions=[{}, {}], + ) + + +def test_the_required_prime_walk_addresses_the_seed_to_the_encoder(model): + """The seed clip is addressed to the vae_encoder. The + controller streams stay addressed to the dit on both walks: they are read + once per frame for the whole rollout, and the encoder never sees them.""" + signals = {name: [object()] for name in ("mouse", "button", "scroll")} + + seeded = model.get_initial_forward_pass_args( + "p", ["image"], ["video_frame"], + {**signals, "image_inputs": [object()]}, {"num_steps": 2}, + ) + + assert seeded.full_metadata.kwargs["walk_schedule"] == [PRIME_WALK, ROLLOUT_WALK] + assert seeded.full_metadata.graph_walk == PRIME_WALK + assert seeded.full_metadata.is_prefill is True + assert [(e.name, e.next_node) for e in seeded.inputs] == [ + ("image_inputs", VAE_ENCODER_NODE), + ("mouse", DIT_NODE), ("button", DIT_NODE), ("scroll", DIT_NODE), + ] + + with pytest.raises(ValueError, match="required seed clip"): + model.get_initial_forward_pass_args( + "p", ["tensor"], ["video_frame"], signals, {"num_steps": 2}, + ) + # Nothing is unpersisted: the streams are re-read every frame, and the seed + # clip goes with the request. + assert seeded.unpersist_tensors == [] + + +def test_postprocess_emits_the_step_s_frames_as_raw_rgb_bytes(model, config): + """No container. The emit is per engine step so a client can act on the + world while it runs, and a per-step mp4 would be a fragment nothing plays.""" + frames = torch.arange( + config.temporal_compression * 2 * 4 * 3, dtype=torch.uint8 + ).reshape(config.temporal_compression, 2, 4, 3) + + payload = model.postprocess(frames, "video_frame") + + assert payload == frames.numpy().tobytes() + assert len(payload) == frames.numel() + + with pytest.raises(ValueError, match="uint8"): + model.postprocess(frames.float(), "video_frame") + with pytest.raises(ValueError, match="modality"): + model.postprocess(frames, "image") diff --git a/test/modular/test_waypoint_streaming_benchmark.py b/test/modular/test_waypoint_streaming_benchmark.py new file mode 100644 index 000000000..bb7e15a7b --- /dev/null +++ b/test/modular/test_waypoint_streaming_benchmark.py @@ -0,0 +1,205 @@ +from __future__ import annotations + +import hashlib +import runpy +from pathlib import Path + +import pytest + +from mstar.client import VideoFrameChunk + + +@pytest.fixture(scope="module") +def benchmark(): + return runpy.run_path( + str(Path(__file__).parents[1] / "waypoint" / "benchmark_streaming.py") + ) + + +def test_streaming_metric_math_includes_pacing_jitter_and_stalls(benchmark): + observation = benchmark["ChunkObservation"] + metrics = benchmark["_stream_metrics"]( + [ + observation(1.0, 100, 0, 4, 4.0), + observation(2.0, 100, 4, 4, 4.0), + observation(4.0, 100, 8, 4, 4.0), + ], + request_wall_seconds=4.2, + stall_threshold_seconds=1.5, + consumer_pause_seconds=0.25, + consumer_pause_count=2, + payload_sha256="abc", + ) + + assert metrics["time_to_first_frame_seconds"] == 1.0 + assert metrics["generated_media_seconds"] == 3.0 + assert metrics["sustained_media_to_wall_ratio"] == pytest.approx(2 / 3) + assert metrics["overall_media_to_wall_ratio"] == pytest.approx(3 / 4.2) + assert metrics["inter_chunk_gap_seconds"] == { + "sample_count": 2, + "p50": 1.5, + "p95": 1.95, + "mean": 1.5, + "jitter_population_stddev": 0.5, + "maximum": 2.0, + } + assert metrics["stalls"] == { + "threshold_seconds": 1.5, + "count": 1, + "longest_seconds": 2.0, + "total_excess_seconds": 0.5, + } + assert metrics["consumer"]["injected_pause_seconds"] == 0.5 + + +def test_streaming_metric_math_handles_one_chunk_without_fake_gap(benchmark): + observation = benchmark["ChunkObservation"] + metrics = benchmark["_stream_metrics"]( + [observation(0.5, 72, 0, 4, 60.0)], + request_wall_seconds=0.6, + stall_threshold_seconds=0.25, + consumer_pause_seconds=0.0, + consumer_pause_count=0, + payload_sha256="def", + ) + + assert metrics["sustained_media_to_wall_ratio"] is None + assert metrics["inter_chunk_gap_seconds"]["sample_count"] == 0 + assert metrics["inter_chunk_gap_seconds"]["p50"] is None + assert metrics["inter_chunk_gap_seconds"]["jitter_population_stddev"] is None + assert metrics["stalls"]["count"] == 0 + + +def test_backpressure_summary_reports_deltas_without_a_threshold(benchmark): + baseline = { + "request_wall_seconds": 2.0, + "time_to_first_frame_seconds": 0.5, + "sustained_media_to_wall_ratio": 0.8, + "payload_sha256": "same", + "consumer": {"pause_seconds": 0.0, "injected_pause_seconds": 0.0}, + } + slow = { + "request_wall_seconds": 3.2, + "time_to_first_frame_seconds": 0.6, + "sustained_media_to_wall_ratio": 0.4, + "payload_sha256": "same", + "consumer": {"pause_seconds": 0.25, "injected_pause_seconds": 1.0}, + } + baseline_memory = {"peak_host_pss_mib": 100.0, "peak_gpu_mib": 1000.0} + slow_memory = {"peak_host_pss_mib": 112.0, "peak_gpu_mib": 1004.0} + + result = benchmark["_backpressure_metrics"]( + baseline, slow, baseline_memory, slow_memory + ) + + assert result["observed_request_wall_increase_seconds"] == pytest.approx(1.2) + assert result["wall_increase_beyond_injected_pause_seconds"] == pytest.approx(0.2) + assert result["peak_host_pss_change_mib"] == 12.0 + assert result["peak_gpu_memory_change_mib"] == 4.0 + assert result["payloads_match"] is True + assert "threshold" not in result + + +def test_measurement_loop_consumes_typed_chunks_and_pauses_only_between_them( + benchmark, tmp_path +): + variant = benchmark["rollout"].Variant("test", 2, 3, 1, "unused") + + def metadata(frame_index): + return { + "width": 3, + "height": 2, + "fps": 60.0, + "pixel_format": "rgb24", + "frame_index": frame_index, + "frame_count": 4, + } + + payloads = [bytes([1]) * 72, bytes([2]) * 72] + + class Client: + kwargs = None + + def stream(self, **kwargs): + self.kwargs = kwargs + return iter( + [ + VideoFrameChunk(payloads[0], metadata(0)), + VideoFrameChunk(payloads[1], metadata(4)), + ] + ) + + client = Client() + clock_values = iter([10.0, 10.5, 11.0, 11.1]) + pauses = [] + metrics, failures = benchmark["_measure_stream"]( + client, + tmp_path / "seed.png", + variant, + num_steps=2, + request_id="rid", + rng_seed=7, + consumer_pause_seconds=0.25, + stall_threshold_seconds=0.4, + clock=lambda: next(clock_values), + sleep=pauses.append, + ) + + assert failures == [] + assert pauses == [0.25] + assert metrics["time_to_first_frame_seconds"] == 0.5 + assert metrics["inter_chunk_gap_seconds"]["p95"] == 0.5 + assert metrics["consumer"]["pause_count"] == 1 + assert metrics["payload_sha256"] == hashlib.sha256(b"".join(payloads)).hexdigest() + assert client.kwargs["output_modalities"] == ("video_frame",) + assert len(client.kwargs["actions"]) == 2 + + +@pytest.mark.parametrize( + "extra, message", + [ + (["--steps", "0"], "--steps must be positive"), + (["--warmup-steps", "-1"], "--warmup-steps cannot be negative"), + (["--slow-consumer-delay", "-0.1"], "--slow-consumer-delay cannot be negative"), + (["--stall-threshold", "0"], "--stall-threshold must be positive"), + (["--memory-sample-interval", "0"], "--memory-sample-interval must be positive"), + ], +) +def test_benchmark_cli_rejects_invalid_measurement_configuration( + benchmark, capsys, extra, message +): + with pytest.raises(SystemExit, match="2"): + benchmark["_parse_args"]( + ["--variant", "360p", "--physical-gpu", "2", *extra] + ) + assert message in capsys.readouterr().err + + +def test_benchmark_cli_rejects_hub_with_local_overrides(benchmark, capsys): + with pytest.raises(SystemExit, match="2"): + benchmark["_parse_args"]( + [ + "--variant", + "720p", + "--physical-gpu", + "2", + "--source", + "hub", + "--checkpoint-dir", + "/tmp/checkpoint", + ] + ) + assert "--source hub cannot be combined" in capsys.readouterr().err + + +def test_benchmark_cli_derives_stall_threshold_and_has_no_release_gate(benchmark): + args = benchmark["_parse_args"]( + ["--variant", "360p", "--physical-gpu", "2"] + ) + + assert args.stall_threshold is None + assert benchmark["_resolve_stall_threshold"](args) == pytest.approx(4.0 / 15.0) + assert not any( + action.dest.startswith("release") + for action in benchmark["_build_parser"]()._actions + ) diff --git a/test/modular/test_waypoint_taehv_equivalence.py b/test/modular/test_waypoint_taehv_equivalence.py new file mode 100644 index 000000000..d34ff970b --- /dev/null +++ b/test/modular/test_waypoint_taehv_equivalence.py @@ -0,0 +1,108 @@ +"""Real-weight CPU parity for Waypoint's functional TAEHV graph boundary.""" + +from __future__ import annotations + +import os +from pathlib import Path + +import pytest +import torch + +from mstar.model.waypoint.components.taehv import ( + decode_latent, + encode_seed_clip, + initial_decoder_histories, +) + +taehv = pytest.importorskip("taehv") + + +_ROOT = Path(__file__).resolve().parents[4] +_AE_SOURCE = Path( + os.environ.get( + "WAYPOINT_AE_CHECKPOINT", + _ROOT / "checkpoints" / "taehv1_5" / "taehv1_5.pth", + ) +) +_AE_CHECKPOINT = ( + _AE_SOURCE / "taehv1_5.pth" if _AE_SOURCE.is_dir() else _AE_SOURCE +) + +pytestmark = pytest.mark.skipif( + not _AE_CHECKPOINT.is_file(), + reason=f"real TAEHV checkpoint not found at {_AE_CHECKPOINT}", +) + + +def _reference_decode(stream, latent: torch.Tensor) -> tuple[torch.Tensor, ...]: + first = stream.decode(latent[:, None]) + assert first is not None + return (first, *stream.flush_decoder()) + + +def _rgb24(frames: tuple[torch.Tensor, ...]) -> torch.Tensor: + decoded = torch.cat(frames, dim=1) + return ( + (decoded.clamp(0, 1) * 255) + .round() + .to(torch.uint8) + .squeeze(0) + .permute(0, 2, 3, 1)[..., :3] + .contiguous() + ) + + +@torch.inference_mode() +def test_functional_taehv_matches_upstream_init_and_steady_state(): + """Exercise actual block types while keeping the spatial grid inexpensive.""" + ae = taehv.TAEHV(str(_AE_CHECKPOINT)).eval().float() + generator = torch.Generator(device="cpu").manual_seed(123) + seed = torch.rand((4, 32, 32, 3), generator=generator) + + encoded = encode_seed_clip(ae, seed, output_size=(32, 32)) + encoder_reference = taehv.StreamingTAEHV(ae) + expected_encoded = encoder_reference.encode( + seed[None].permute(0, 1, 4, 2, 3).contiguous() + ) + assert expected_encoded is not None + assert torch.equal(encoded, expected_encoded.squeeze(1)) + + histories = initial_decoder_histories(ae, encoded) + initialized_frames, initialized_histories = decode_latent( + ae, encoded, histories, output_size=(32, 32), initialize=True + ) + + decoder_reference = taehv.StreamingTAEHV(ae) + for _ in range(ae.frames_to_trim): + decoder_reference.decode(encoded[:, None]) + decoder_reference.flush_decoder() + expected_initialized = _rgb24(_reference_decode(decoder_reference, encoded)) + reference_histories = tuple( + value for value in decoder_reference.decoder_memory if torch.is_tensor(value) + ) + assert torch.equal(initialized_frames, expected_initialized) + assert len(reference_histories) == len(initialized_histories) == 9 + assert all( + torch.equal(actual, expected) + for actual, expected in zip( + initialized_histories, reference_histories, strict=True + ) + ) + + next_latent = torch.randn(encoded.shape, generator=generator) + steady_frames, steady_histories = decode_latent( + ae, + next_latent, + initialized_histories, + output_size=(32, 32), + initialize=False, + ) + expected_steady = _rgb24(_reference_decode(decoder_reference, next_latent)) + reference_histories = tuple( + value for value in decoder_reference.decoder_memory if torch.is_tensor(value) + ) + assert torch.equal(steady_frames, expected_steady) + assert all( + torch.equal(actual, expected) + for actual, expected in zip(steady_histories, reference_histories, strict=True) + ) diff --git a/test/waypoint/benchmark_streaming.py b/test/waypoint/benchmark_streaming.py new file mode 100644 index 000000000..868cf600b --- /dev/null +++ b/test/waypoint/benchmark_streaming.py @@ -0,0 +1,644 @@ +#!/usr/bin/env python3 +"""Measure Waypoint's typed-frame streaming behavior without setting a gate. + +This starts the normal mstar server, performs a short warmup, then records a +baseline SDK stream and an otherwise identical stream whose consumer pauses +between chunks. The JSON artifact contains latency, pacing, stalls, +backpressure observations, and process-group memory for both runs. + +The benchmark deliberately has no release threshold. A nonzero exit means the +server, typed frame protocol, telemetry, or deterministic replay failed, not +that a performance number was judged too slow. + +Example: + + CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. python3 test/waypoint/benchmark_streaming.py \ + --variant 360p --physical-gpu 2 --steps 16 \ + --artifact /tmp/waypoint-streaming-360p.json +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import os +import statistics +import subprocess +import sys +import tempfile +import time +from dataclasses import asdict, dataclass +from datetime import datetime, timezone +from pathlib import Path +from typing import Callable, Sequence + +SCRIPT_DIR = Path(__file__).resolve().parent +REPO = SCRIPT_DIR.parents[1] +if str(SCRIPT_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPT_DIR)) +if str(REPO) not in sys.path: + sys.path.insert(0, str(REPO)) + +import serve_rollout as rollout # noqa: E402 + +from mstar.client import MStarClient, VideoFrameChunk # noqa: E402 + + +@dataclass(frozen=True) +class ChunkObservation: + arrival_seconds: float + byte_count: int + frame_index: int + frame_count: int + fps: float + + +def _percentile(values: Sequence[float], quantile: float) -> float | None: + """Linearly interpolated percentile, or None when there are no samples.""" + if not 0.0 <= quantile <= 1.0: + raise ValueError(f"quantile must be in [0, 1]; got {quantile}") + if not values: + return None + ordered = sorted(values) + position = quantile * (len(ordered) - 1) + lower = int(position) + upper = min(lower + 1, len(ordered) - 1) + fraction = position - lower + return ordered[lower] + (ordered[upper] - ordered[lower]) * fraction + + +def _stream_metrics( + observations: Sequence[ChunkObservation], + *, + request_wall_seconds: float, + stall_threshold_seconds: float, + consumer_pause_seconds: float, + consumer_pause_count: int, + payload_sha256: str, +) -> dict: + """Summarize SDK-observed arrivals using media time from frame metadata.""" + arrivals = [observation.arrival_seconds for observation in observations] + gaps = [later - earlier for earlier, later in zip(arrivals, arrivals[1:], strict=False)] + total_frames = sum(observation.frame_count for observation in observations) + total_bytes = sum(observation.byte_count for observation in observations) + media_seconds = sum( + observation.frame_count / observation.fps for observation in observations + ) + + # Startup is excluded from the sustained ratio. The numerator likewise + # excludes the first chunk, which was already available at the start of the + # measured delivery interval. + if len(observations) > 1 and arrivals[-1] > arrivals[0]: + delivered_after_first = sum( + observation.frame_count / observation.fps + for observation in observations[1:] + ) + sustained_ratio = delivered_after_first / (arrivals[-1] - arrivals[0]) + else: + sustained_ratio = None + + stalls = [gap for gap in gaps if gap > stall_threshold_seconds] + return { + "chunk_count": len(observations), + "frame_count": total_frames, + "payload_bytes": total_bytes, + "payload_sha256": payload_sha256, + "time_to_first_frame_seconds": arrivals[0] if arrivals else None, + "request_wall_seconds": request_wall_seconds, + "generated_media_seconds": media_seconds, + "overall_media_to_wall_ratio": ( + media_seconds / request_wall_seconds if request_wall_seconds > 0 else None + ), + "sustained_media_to_wall_ratio": sustained_ratio, + "inter_chunk_gap_seconds": { + "sample_count": len(gaps), + "p50": _percentile(gaps, 0.50), + "p95": _percentile(gaps, 0.95), + "mean": statistics.fmean(gaps) if gaps else None, + "jitter_population_stddev": statistics.pstdev(gaps) if gaps else None, + "maximum": max(gaps) if gaps else None, + }, + "stalls": { + "threshold_seconds": stall_threshold_seconds, + "count": len(stalls), + "longest_seconds": max(stalls) if stalls else None, + "total_excess_seconds": sum( + gap - stall_threshold_seconds for gap in stalls + ), + }, + "consumer": { + "pause_seconds": consumer_pause_seconds, + "pause_count": consumer_pause_count, + "injected_pause_seconds": consumer_pause_seconds * consumer_pause_count, + }, + } + + +def _validate_chunk( + chunk: VideoFrameChunk, + chunk_index: int, + variant: rollout.Variant, +) -> list[str]: + expected_bytes = 4 * variant.height * variant.width * 3 + expected = { + "width": variant.width, + "height": variant.height, + "fps": 60.0, + "pixel_format": "rgb24", + "frame_index": chunk_index * 4, + "frame_count": 4, + } + failures = [] + if len(chunk.data) != expected_bytes: + failures.append( + f"chunk {chunk_index} has {len(chunk.data)} bytes; expected {expected_bytes}" + ) + mismatches = { + key: (chunk.metadata.get(key), wanted) + for key, wanted in expected.items() + if chunk.metadata.get(key) != wanted + } + if mismatches: + failures.append(f"chunk {chunk_index} metadata mismatches: {mismatches}") + return failures + + +def _measure_stream( + client: MStarClient, + seed_image: Path, + variant: rollout.Variant, + *, + num_steps: int, + request_id: str, + rng_seed: int, + consumer_pause_seconds: float, + stall_threshold_seconds: float, + clock: Callable[[], float] = time.perf_counter, + sleep: Callable[[float], None] = time.sleep, +) -> tuple[dict, list[str]]: + """Consume one stream while retaining only timings and an incremental hash.""" + stream = client.stream( + images=seed_image, + input_modalities=("image",), + output_modalities=("video_frame",), + request_id=request_id, + num_steps=num_steps, + actions=rollout._actions(num_steps), + seed=rng_seed, + ) + iterator = iter(stream) + started = clock() + observations: list[ChunkObservation] = [] + failures: list[str] = [] + digest = hashlib.sha256() + pause_count = 0 + + while True: + try: + event = next(iterator) + except StopIteration: + completed = clock() + break + arrived = clock() + if not isinstance(event, VideoFrameChunk): + failures.append( + f"stream event {len(observations)} was {type(event).__name__}, not VideoFrameChunk" + ) + continue + + chunk_index = len(observations) + failures.extend(_validate_chunk(event, chunk_index, variant)) + digest.update(event.data) + observations.append( + ChunkObservation( + arrival_seconds=arrived - started, + byte_count=len(event.data), + frame_index=event.frame_index, + frame_count=event.frame_count, + fps=event.fps, + ) + ) + # Pause only between expected chunks. Sleeping after the final chunk + # would measure delayed EOF discovery rather than stream backpressure. + if consumer_pause_seconds and len(observations) < num_steps: + sleep(consumer_pause_seconds) + pause_count += 1 + + if len(observations) != num_steps: + failures.append(f"expected {num_steps} chunks, got {len(observations)}") + expected_frames = 4 * num_steps + actual_frames = sum(observation.frame_count for observation in observations) + if actual_frames != expected_frames: + failures.append(f"expected {expected_frames} generated frames, got {actual_frames}") + + metrics = _stream_metrics( + observations, + request_wall_seconds=completed - started, + stall_threshold_seconds=stall_threshold_seconds, + consumer_pause_seconds=consumer_pause_seconds, + consumer_pause_count=pause_count, + payload_sha256=digest.hexdigest(), + ) + metrics["request_id"] = request_id + return metrics, failures + + +def _wait_for_phase_sample( + sampler: rollout.MemorySampler, + proc: subprocess.Popen, + phase: str, + timeout: float = 30.0, +) -> None: + before = sum(sample.phase == phase for sample in sampler.snapshot()[0]) + sampler.set_phase(phase) + deadline = time.monotonic() + timeout + while time.monotonic() < deadline: + samples, error = sampler.snapshot() + if sum(sample.phase == phase for sample in samples) > before: + return + if proc.poll() is not None: + raise RuntimeError( + f"server exited with code {proc.returncode} before {phase} telemetry" + ) + if error: + last_error = error + time.sleep(0.05) + detail = f": {last_error}" if "last_error" in locals() else "" + raise RuntimeError(f"memory telemetry captured no {phase!r} sample{detail}") + + +def _memory_metrics( + sampler: rollout.MemorySampler, + phase: str, + quiet: tuple[float, float], +) -> dict: + samples, error = sampler.snapshot() + if error: + raise RuntimeError(f"memory telemetry failed: {error}") + summary = rollout._summarize_wave_memory(samples, phase, quiet) + result = asdict(summary) + result["sample_count"] = sum(sample.phase == phase for sample in samples) + return result + + +def _backpressure_metrics( + baseline: dict, + slow: dict, + baseline_memory: dict, + slow_memory: dict, +) -> dict: + injected = slow["consumer"]["injected_pause_seconds"] + wall_increase = slow["request_wall_seconds"] - baseline["request_wall_seconds"] + return { + "configured_consumer_pause_seconds": slow["consumer"]["pause_seconds"], + "injected_pause_seconds": injected, + "observed_request_wall_increase_seconds": wall_increase, + "wall_increase_beyond_injected_pause_seconds": wall_increase - injected, + "time_to_first_frame_change_seconds": ( + slow["time_to_first_frame_seconds"] - baseline["time_to_first_frame_seconds"] + ), + "sustained_media_to_wall_ratio_change": ( + slow["sustained_media_to_wall_ratio"] + - baseline["sustained_media_to_wall_ratio"] + if slow["sustained_media_to_wall_ratio"] is not None + and baseline["sustained_media_to_wall_ratio"] is not None + else None + ), + "peak_host_pss_change_mib": ( + slow_memory["peak_host_pss_mib"] - baseline_memory["peak_host_pss_mib"] + ), + "peak_gpu_memory_change_mib": ( + slow_memory["peak_gpu_mib"] - baseline_memory["peak_gpu_mib"] + ), + "payloads_match": slow["payload_sha256"] == baseline["payload_sha256"], + } + + +def _format_number(value: float | None, digits: int = 3) -> str: + return "n/a" if value is None else f"{value:.{digits}f}" + + +def _human_summary(result: dict, artifact: Path) -> str: + lines = [ + ( + f"Waypoint streaming viability: {result['variant']} " + f"({result['geometry']['width']}x{result['geometry']['height']})" + ) + ] + for name in ("baseline", "slow_consumer"): + run = result["runs"][name] + gaps = run["inter_chunk_gap_seconds"] + memory = run["memory"] + lines.append( + f" {name}: TTFF={_format_number(run['time_to_first_frame_seconds'])}s " + f"sustained={_format_number(run['sustained_media_to_wall_ratio'])}x " + f"gap p50/p95={_format_number(gaps['p50'])}/{_format_number(gaps['p95'])}s " + f"jitter={_format_number(gaps['jitter_population_stddev'])}s " + f"stalls={run['stalls']['count']}" + ) + lines.append( + f" memory peak/quiet: host={memory['peak_host_pss_mib']:.1f}/" + f"{memory['quiet_host_pss_mib']:.1f} MiB, GPU={memory['peak_gpu_mib']:.1f}/" + f"{memory['quiet_gpu_mib']:.1f} MiB" + ) + backpressure = result["backpressure"] + lines.extend( + [ + ( + " slow-consumer effect: " + f"wall +{backpressure['observed_request_wall_increase_seconds']:.3f}s " + f"for {backpressure['injected_pause_seconds']:.3f}s injected; " + f"host peak delta={backpressure['peak_host_pss_change_mib']:.1f} MiB, " + f"GPU peak delta={backpressure['peak_gpu_memory_change_mib']:.1f} MiB" + ), + " release threshold: not defined (measurement baseline only)", + f" correctness: {'PASS' if result['correctness']['passed'] else 'FAIL'}", + f" artifact: {artifact}", + ] + ) + return "\n".join(lines) + + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--config", type=Path, default=rollout.DEFAULT_CONFIG) + parser.add_argument("--variant", choices=sorted(rollout.VARIANTS), required=True) + parser.add_argument("--source", choices=("local", "hub"), default="local") + parser.add_argument("--checkpoint-dir", type=Path) + parser.add_argument("--ae-path", type=Path) + parser.add_argument("--cache-dir", type=Path) + parser.add_argument( + "--seed-image", type=Path, default=rollout.DEFAULT_ROOT / "seed/default.jpg" + ) + parser.add_argument("--steps", type=int, default=16) + parser.add_argument("--warmup-steps", type=int, default=1) + parser.add_argument("--slow-consumer-delay", type=float, default=0.25) + parser.add_argument( + "--stall-threshold", + type=float, + help="gap classified as a stall; default=max(stall-floor, stall-multiplier*4/60)", + ) + parser.add_argument("--stall-multiplier", type=float, default=4.0) + parser.add_argument("--stall-floor", type=float, default=0.25) + parser.add_argument("--physical-gpu", type=int, required=True) + parser.add_argument("--memory-sample-interval", type=float, default=0.10) + parser.add_argument("--startup-timeout", type=float, default=900.0) + parser.add_argument("--request-timeout", type=float, default=900.0) + parser.add_argument("--port", type=int, default=0) + parser.add_argument("--request-id", default="waypoint-streaming-benchmark") + parser.add_argument("--seed", type=int, default=112464007) + parser.add_argument( + "--artifact", type=Path, default=Path("/tmp/waypoint_streaming_benchmark.json") + ) + parser.add_argument( + "--log", type=Path, default=Path("/tmp/waypoint_streaming_benchmark_server.log") + ) + parser.add_argument("--log-level", default="INFO") + parser.add_argument("--enable-nvtx", action="store_true") + return parser + + +def _parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: + parser = _build_parser() + args = parser.parse_args(argv) + if args.steps <= 0: + parser.error("--steps must be positive") + if args.warmup_steps < 0: + parser.error("--warmup-steps cannot be negative") + if args.slow_consumer_delay < 0: + parser.error("--slow-consumer-delay cannot be negative") + if args.stall_threshold is not None and args.stall_threshold <= 0: + parser.error("--stall-threshold must be positive") + if args.stall_multiplier <= 0: + parser.error("--stall-multiplier must be positive") + if args.stall_floor < 0: + parser.error("--stall-floor cannot be negative") + if args.physical_gpu < 0: + parser.error("--physical-gpu cannot be negative") + if args.memory_sample_interval <= 0: + parser.error("--memory-sample-interval must be positive") + if args.startup_timeout <= 0 or args.request_timeout <= 0: + parser.error("timeouts must be positive") + if args.port < 0 or args.port > 65535: + parser.error("--port must be between 0 and 65535") + if args.source == "hub" and (args.checkpoint_dir is not None or args.ae_path is not None): + parser.error("--source hub cannot be combined with --checkpoint-dir or --ae-path") + return args + + +def _resolve_stall_threshold(args: argparse.Namespace) -> float: + if args.stall_threshold is not None: + return args.stall_threshold + return max(args.stall_floor, args.stall_multiplier * 4.0 / 60.0) + + +def _run_benchmark(args: argparse.Namespace) -> dict: + variant = rollout.VARIANTS[args.variant] + if args.source == "local": + checkpoint_dir = args.checkpoint_dir or variant.checkpoint_dir + ae_path = args.ae_path or rollout.DEFAULT_ROOT / "taehv1_5" + weight_source = str(checkpoint_dir) + else: + checkpoint_dir = None + ae_path = None + weight_source = f"registry Hub mapping for {variant.model_variant}" + + stall_threshold = _resolve_stall_threshold(args) + + port = args.port or rollout._free_port() + url = f"http://127.0.0.1:{port}" + workdir = Path(tempfile.mkdtemp(prefix="waypoint-stream-benchmark-")) + config = rollout._run_config( + args.config, + variant, + checkpoint_dir, + ae_path, + workdir / "run.yaml", + worlds=1, + ) + seed_image = rollout._seed_png(args.seed_image, variant, workdir / "seed.png") + server_command = rollout._server_command( + config, + port, + workdir, + args.log_level, + args.request_timeout, + args.cache_dir, + args.enable_nvtx, + ) + print( + f"starting {args.variant} Waypoint server on {url}\n" + f"weights: {weight_source}\nserver log: {args.log}" + ) + args.log.parent.mkdir(parents=True, exist_ok=True) + with args.log.open("wb") as log: + proc = subprocess.Popen( + server_command, + cwd=str(REPO), + stdout=log, + stderr=subprocess.STDOUT, + start_new_session=True, + env={**os.environ, "PYTHONUNBUFFERED": "1", "PYTHONPATH": str(REPO)}, + ) + + sampler: rollout.MemorySampler | None = None + failures: list[str] = [] + runs: dict[str, dict] = {} + startup_started = time.perf_counter() + try: + client = MStarClient(url, timeout=args.request_timeout) + rollout._wait_for_health(client, proc, args.startup_timeout) + startup_seconds = time.perf_counter() - startup_started + print(f"server ready after {startup_seconds:.1f}s") + sampler = rollout.MemorySampler( + proc.pid, + args.physical_gpu, + interval=args.memory_sample_interval, + ) + sampler.start() + rollout._wait_for_first_memory_sample(sampler, proc) + + if args.warmup_steps: + phase = "warmup" + print(f"warmup: {args.warmup_steps} step(s)") + _wait_for_phase_sample(sampler, proc, phase) + warmup_id = f"{args.request_id}-warmup" + _, warmup_failures = _measure_stream( + client, + seed_image, + variant, + num_steps=args.warmup_steps, + request_id=warmup_id, + rng_seed=args.seed, + consumer_pause_seconds=0.0, + stall_threshold_seconds=stall_threshold, + ) + failures.extend(f"warmup: {failure}" for failure in warmup_failures) + rollout._wait_for_cleanup( + args.log, (warmup_id,), proc, args.request_timeout + ) + rollout._wait_for_quiescent_memory(sampler, "warmup-quiet") + + for name, pause in ( + ("baseline", 0.0), + ("slow_consumer", args.slow_consumer_delay), + ): + phase = name.replace("_", "-") + print( + f"{phase}: {args.steps} steps, " + f"consumer pause {pause:.3f}s between chunks" + ) + _wait_for_phase_sample(sampler, proc, phase) + request_id = f"{args.request_id}-{phase}" + metrics, stream_failures = _measure_stream( + client, + seed_image, + variant, + num_steps=args.steps, + request_id=request_id, + rng_seed=args.seed, + consumer_pause_seconds=pause, + stall_threshold_seconds=stall_threshold, + ) + failures.extend(f"{name}: {failure}" for failure in stream_failures) + rollout._wait_for_cleanup( + args.log, (request_id,), proc, args.request_timeout + ) + quiet = rollout._wait_for_quiescent_memory(sampler, f"{phase}-quiet") + metrics["memory"] = _memory_metrics(sampler, phase, quiet) + runs[name] = metrics + print( + f" received {metrics['chunk_count']} chunks in " + f"{metrics['request_wall_seconds']:.3f}s" + ) + + if runs["baseline"]["payload_sha256"] != runs["slow_consumer"]["payload_sha256"]: + failures.append( + "slow-consumer payload differs from the identical-seed baseline" + ) + finally: + try: + if sampler is not None: + sampler.stop() + finally: + rollout._shutdown(proc) + + return { + "schema_version": 1, + "benchmark": "waypoint_streaming_viability", + "created_at_utc": datetime.now(timezone.utc).isoformat(), + "status": "completed", + "release_threshold": None, + "variant": args.variant, + "model_variant": variant.model_variant, + "geometry": {"width": variant.width, "height": variant.height, "fps": 60.0}, + "configuration": { + "weight_source": weight_source, + "steps": args.steps, + "warmup_steps": args.warmup_steps, + "rng_seed": args.seed, + "physical_gpu": args.physical_gpu, + "stall_threshold_seconds": stall_threshold, + "slow_consumer_delay_seconds": args.slow_consumer_delay, + "memory_sample_interval_seconds": args.memory_sample_interval, + "server_command": server_command, + "server_log": str(args.log), + }, + "server": {"startup_seconds": startup_seconds}, + "runs": runs, + "backpressure": _backpressure_metrics( + runs["baseline"], + runs["slow_consumer"], + runs["baseline"]["memory"], + runs["slow_consumer"]["memory"], + ), + "correctness": {"passed": not failures, "failures": failures}, + "metric_definitions": { + "time_to_first_frame_seconds": ( + "request iterator start to first fully decoded SDK VideoFrameChunk" + ), + "sustained_media_to_wall_ratio": ( + "media seconds in chunks after the first divided by first-to-last chunk arrival time" + ), + "jitter_population_stddev": "population standard deviation of inter-chunk gaps", + "stall": "inter-chunk gap strictly greater than stall_threshold_seconds", + "memory": "PSS and nvidia-smi GPU process memory summed over the server process group only", + "backpressure": ( + "delta between an unpaused stream and an identical stream paused between SDK reads" + ), + }, + } + + +def _write_artifact(path: Path, result: dict) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n") + + +def main(argv: Sequence[str] | None = None) -> int: + args = _parse_args(argv) + try: + result = _run_benchmark(args) + except (Exception, SystemExit) as exc: # lifecycle helpers use SystemExit + result = { + "schema_version": 1, + "benchmark": "waypoint_streaming_viability", + "created_at_utc": datetime.now(timezone.utc).isoformat(), + "status": "error", + "release_threshold": None, + "variant": args.variant, + "error": f"{type(exc).__name__}: {exc}", + } + _write_artifact(args.artifact, result) + print(f"ERROR {result['error']}", file=sys.stderr) + print(f"artifact: {args.artifact}", file=sys.stderr) + return 2 + + _write_artifact(args.artifact, result) + print(_human_summary(result, args.artifact)) + return 0 if result["correctness"]["passed"] else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/test/waypoint/check_nsys_replay.py b/test/waypoint/check_nsys_replay.py new file mode 100644 index 000000000..3fb3f64dc --- /dev/null +++ b/test/waypoint/check_nsys_replay.py @@ -0,0 +1,175 @@ +#!/usr/bin/env python3 +"""Validate steady Waypoint DiT CUDA replay in an Nsight SQLite export. + +Export a report before running this check:: + + nsys export -t sqlite -f true -o trace.sqlite trace.nsys-rep + python3 test/waypoint/check_nsys_replay.py trace.sqlite \ + --expected-forwards 16 + +Only CUDA runtime calls nested inside the rollout ``engine.forward`` ranges are +examined. Synchronization in startup, output transfer, and postprocessing is +outside the steady DiT replay contract. +""" + +from __future__ import annotations + +import argparse +import sqlite3 +from dataclasses import dataclass +from pathlib import Path + +DEFAULT_ROLLOUT_RANGE = "worker[worker_0].node[dit].graph_walk[rollout]" + + +@dataclass(frozen=True) +class ReplayInspection: + forwards: int + graph_replays: int + sync_or_blocking_calls: int + offending_apis: tuple[tuple[str, int], ...] + + +_RANGE_CTE = """ +WITH nvtx AS ( + SELECT n.rowid AS id, n.start, n.end, n.globalTid, + coalesce(s.value, n.text) AS name + FROM NVTX_EVENTS AS n + LEFT JOIN StringIds AS s ON s.id = n.textId + WHERE n.end IS NOT NULL +), rollout AS ( + SELECT * FROM nvtx WHERE name = :rollout_range +), forwards AS ( + SELECT DISTINCT f.* + FROM nvtx AS f + JOIN rollout AS r + ON f.globalTid = r.globalTid + AND f.start >= r.start + AND f.end <= r.end + WHERE f.name = 'engine.forward' +), calls AS ( + SELECT f.id AS forward_id, s.value AS api + FROM forwards AS f + JOIN CUPTI_ACTIVITY_KIND_RUNTIME AS c + ON c.globalTid = f.globalTid + AND c.start >= f.start + AND c.end <= f.end + JOIN StringIds AS s ON s.id = c.nameId +), graph_launch_counts AS ( + SELECT forward_id, count(*) AS launches + FROM calls + WHERE api LIKE 'cudaGraphLaunch%' + GROUP BY forward_id +) +""" + +_BLOCKING_PREDICATE = """ +api LIKE '%Synchronize%' +OR (api LIKE 'cudaMemcpy%' AND api NOT LIKE '%Async%') +OR ( + (api LIKE 'cudaMalloc%' OR api LIKE 'cudaFree%') + AND api NOT LIKE '%Async%' +) +""" + + +def inspect_replay( + database: Path, + rollout_range: str = DEFAULT_ROLLOUT_RANGE, +) -> ReplayInspection: + if not database.is_file(): + raise ValueError(f"Nsight SQLite export does not exist: {database}") + + summary_query = ( + _RANGE_CTE + + """ +SELECT count(*) AS forwards, + sum(CASE WHEN coalesce(g.launches, 0) = 1 THEN 1 ELSE 0 END) AS graph_replays, + sum(EXISTS( + SELECT 1 FROM calls AS c + WHERE c.forward_id = f.id AND ( +""" + + _BLOCKING_PREDICATE + + """ + ) + )) AS sync_or_blocking_calls +FROM forwards AS f +LEFT JOIN graph_launch_counts AS g ON g.forward_id = f.id +""" + ) + offenders_query = ( + _RANGE_CTE + + """ +SELECT api, count(*) AS occurrences +FROM calls +WHERE +""" + + _BLOCKING_PREDICATE + + """ +GROUP BY api +ORDER BY api +""" + ) + + try: + with sqlite3.connect(database) as connection: + row = connection.execute(summary_query, {"rollout_range": rollout_range}).fetchone() + offenders = tuple( + (str(api), int(count)) + for api, count in connection.execute( + offenders_query, + {"rollout_range": rollout_range}, + ) + ) + except sqlite3.DatabaseError as exc: + raise ValueError(f"could not inspect Nsight SQLite export {database}: {exc}") from exc + + assert row is not None + return ReplayInspection( + forwards=int(row[0]), + graph_replays=int(row[1] or 0), + sync_or_blocking_calls=int(row[2] or 0), + offending_apis=offenders, + ) + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("database", type=Path, help="SQLite file produced by `nsys export -t sqlite`") + parser.add_argument("--expected-forwards", type=int, required=True) + parser.add_argument("--rollout-range", default=DEFAULT_ROLLOUT_RANGE) + args = parser.parse_args() + + if args.expected_forwards < 1: + parser.error("--expected-forwards must be positive") + try: + result = inspect_replay(args.database, args.rollout_range) + except ValueError as exc: + parser.error(str(exc)) + + print( + f"forwards={result.forwards} graph_replays={result.graph_replays} " + f"sync_or_blocking_calls={result.sync_or_blocking_calls}" + ) + failures = [] + if result.forwards != args.expected_forwards: + failures.append(f"expected {args.expected_forwards} forwards, found {result.forwards}") + if result.graph_replays != result.forwards: + failures.append( + f"only {result.graph_replays}/{result.forwards} forwards launched exactly one CUDA graph" + ) + if result.sync_or_blocking_calls: + detail = ", ".join(f"{api}={count}" for api, count in result.offending_apis) + failures.append( + f"{result.sync_or_blocking_calls} forwards contain blocking CUDA calls ({detail})" + ) + if failures: + for failure in failures: + print(f"FAIL: {failure}") + return 1 + print("PASS") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/test/waypoint/record_oracle.py b/test/waypoint/record_oracle.py new file mode 100644 index 000000000..99b166cc0 --- /dev/null +++ b/test/waypoint/record_oracle.py @@ -0,0 +1,678 @@ +#!/usr/bin/env python3 +"""Record the world_engine golden-reference oracle for Waypoint-1.5-1B. + +Runs **only** ``world_engine`` — importing any mstar module is a hard error — so +the artifact owes nothing to the code it will be used to judge. + + /frames/frame_000.pt ... frame_NNN.pt per-frame tensors (below) + /ring/ring_000.pt ... full KV ring at selected frames + /metadata.json resolved numerics + params + +Each ``frame_*.pt`` holds one engine step = one latent frame: + + dit_out the DiT output of every pass: 4 non-committing denoise passes + at sigma 1.0/0.9/0.75/0.3, then the committing pass at sigma 0 + (frame 0 is the seed and has only the committing pass) + latent x0, the emitted latent after the Euler steps + pixels the 4 raw frames the streaming VAE decoded from it + noise_f32 the CPU fp32 draw, and noise_bf16, the device tensor actually fed + committed_kv per layer, the tail KV slice this frame committed + ring per layer, the `written` mask and per-bucket sum-of-squares + +``committed_kv`` plus the ring digest localize a mismatch to a layer, and the +digest's per-bucket resolution localizes it to a ring slot, without writing the +2.2 GB full ring every frame. Full rings are written for ``--ring-snapshot-frames``. + +``latent``, ``pixels``, ``committed_kv`` and ``ring`` come from the reference's +own compiled regions, called unmodified, and are bit-exact targets. ``dit_out`` +is not: see Execution. + +Execution +--------- +The reference's driver is two ``@torch.compile(fullgraph=True, dynamic=False)`` +regions, and the compile is correctness, not throughput — eager ``flex_attention`` +ignores a ``BlockMask``'s block index lists, and the mask carries a no-op +``mask_mod``, so an eager pass attends over unwritten ring slots. Nothing here +may call ``engine.model(...)`` directly. + +That makes the per-pass DiT output unobservable where it is produced: adding it as +an output of ``_denoise_pass``, or splitting that region into five, changes +inductor's fusion and moves the result by about one bf16 ULP, which then compounds +through the ring. So state comes from the reference driver untouched, and +``dit_out`` comes from separate frozen passes run first — ``upsert`` only writes +the ring when unfrozen, which ``--verify-shadow`` checks on every run. Treat +``dit_out`` as a per-pass diagnostic recorded under a stated decomposition. + +Nothing recorded here is a bit-exact target. The reference driver is deterministic +within a process and not across them: two processes running it alone disagree by +one bf16 ULP at layer 0, which 24 layers compound. ``repro/`` is a second +independent recording of the opening frames so that floor can be measured rather +than assumed. See ``reproducibility`` in the metadata. + +Numerics +-------- +An oracle is only a reference if it is recorded under the same numerics as the +serving process, and only comparable against the torch build it was recorded on. + +Two settings disagree here: mstar sets ``float32_matmul_precision('high')`` +process-wide (``mstar/engine/__init__.py``), ``world_engine`` sets ``'medium'`` at +import. This records under **'high'**, the serving value, for the reason above — +and, because that is a deviation from the reference as shipped, it also measures +what the deviation costs. The only fp32 matmul in the patched inference path is +``NoiseConditioner.mlp`` (a ``NoCastModule``, so it survives the bf16 cast), and +after ``patch_cached_noise_conditioning`` it runs once per sigma level to build a +LUT that is then rounded to bf16. ``matmul_precision_calibration`` in the metadata +is that LUT evaluated both ways, so Phase 9 has the number instead of an argument. + +Noise is an input, not model behaviour, and the reference draws it unseeded +(``torch.randn(..., device=cuda, dtype=bf16)``), which no oracle can reproduce. +This draws fp32 from a seeded CPU generator and casts, matching how the port +draws it (``mstar/model/waypoint/submodules.py::_frame_noise``), saves both +tensors, and records the substitution. Seeding also makes the run re-recordable, +which is what lets ``--ring-snapshot-frames`` be narrowed by default. + +Usage: + + CUDA_VISIBLE_DEVICES=2 python3 test/waypoint/record_oracle.py \ + --out-dir /path/to/oracle \ + --model-dir /path/to/Waypoint-1.5-1B --ae-dir /path/to/taehv1_5 +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import platform +import sys +import time +from pathlib import Path + +import torch + +WORLD_ENGINE_DEFAULT = "/mnt/storage/garv901/waypoint-1.5-1B/world_engine" +CKPT_DEFAULT = "/mnt/storage/garv901/waypoint-1.5-1B/checkpoints/Waypoint-1.5-1B" +AE_DEFAULT = "/mnt/storage/garv901/waypoint-1.5-1B/checkpoints/taehv1_5" +SEED_DEFAULT = "/mnt/storage/garv901/waypoint-1.5-1B/checkpoints/seed/default.jpg" + +# Pinned so the seed frame is a fact of the run, not of whatever the repo holds +# today. Cached at SEED_DEFAULT; --seed-image overrides. +SEED_URL = "https://raw.githubusercontent.com/Overworldai/Biome/14343a6/seeds/default.jpg" +SEED_SHA256 = "c61c9393311d7281f793d86329dca343e12c93bf0409980a186eb39269cf6862" + +NOISE_SEED = 42 +NUM_FRAMES = 40 # local window is 16 frames, so 41 total frames wrap the ring twice + +# Scripted controller inputs: (button ids, (mouse dx, dy), scroll). Buttons are +# WASD 87/65/83/68, space 32, mouse trigger 1 — the ids gen_sample.py drives. +CONTROL_SEQUENCE: list[tuple[set[int], tuple[float, float], int]] = ( + [({87}, (0.0, 0.0), 0)] * 8 # forward + + [({87}, (0.2, 0.0), 0)] * 4 # forward, panning right + + [({65}, (0.0, 0.0), 0)] * 4 # strafe left + + [({68}, (0.0, 0.0), 0)] * 4 # strafe right + + [({83}, (0.0, 0.0), 0)] * 4 # back + + [({87, 32}, (0.0, 0.0), 0)] * 4 # forward + jump + + [(set(), (0.0, 0.0), 0)] * 4 # idle + + [(set(), (0.0, -0.2), 0)] * 4 # look up + + [({87, 1}, (0.0, 0.0), 0)] * 4 # forward + trigger +) +assert len(CONTROL_SEQUENCE) == NUM_FRAMES + + +def load_world_engine(root: str): + """Import world_engine from a source checkout and pin the serving numerics. + + The flag is set after the import on purpose: world_engine sets 'medium' at + import time, so setting it earlier would be silently undone. + """ + sys.path.insert(0, root) + import src as world_engine + + sys.modules.setdefault("world_engine", world_engine) + torch.set_float32_matmul_precision("high") + if "mstar" in sys.modules: + raise SystemExit("mstar was imported; the oracle must run world_engine only.") + return world_engine + + +def load_seed_frame(path: str, url: str) -> "torch.Tensor": + """The seed image as [4, 720, 1280, 3] uint8, the x4 repeat append_frame wants.""" + import cv2 + import numpy as np + + p = Path(path) + if p.exists(): + raw = p.read_bytes() + else: + import urllib.request + + raw = urllib.request.urlopen(url).read() + p.parent.mkdir(parents=True, exist_ok=True) + p.write_bytes(raw) + + digest = hashlib.sha256(raw).hexdigest() + img = cv2.imdecode(np.frombuffer(raw, np.uint8), cv2.IMREAD_COLOR) + img = cv2.cvtColor(cv2.resize(img, (1280, 720)), cv2.COLOR_BGR2RGB) + return torch.from_numpy(np.repeat(img[None], 4, axis=0)), digest + + +def describe_patches(model) -> dict: + """Count the modules apply_inference_patches installed, from the live model.""" + from src.patch_model import CachedCondHead, CachedDenoiseStepEmb, MergedQKVAttn, SplitMLPFusion + + counts = {"CachedDenoiseStepEmb": 0, "CachedCondHead": 0, "MergedQKVAttn": 0, "SplitMLPFusion": 0} + for mod in model.modules(): + for cls in (CachedDenoiseStepEmb, CachedCondHead, MergedQKVAttn, SplitMLPFusion): + if isinstance(mod, cls): + counts[cls.__name__] += 1 + return counts + + +def calibrate_matmul_precision(ckpt_dir: str, d_model: int, sigmas, device) -> dict: + """Measure what 'high' (mstar) vs 'medium' (world_engine) costs on this build. + + Two probes. The model probe is the only fp32 matmul in the patched inference + path: NoiseConditioner.mlp, which is a NoCastModule and so stays fp32 through + the bf16 cast, and which patch_cached_noise_conditioning evaluates once per + sigma level to build a LUT it then rounds to bf16. The control probe is a + plain fp32 GEMM, and it is what makes a zero in the model probe readable — a + 'high' vs 'highest' difference proves the flag is live and the instrument + works, so a 'high' vs 'medium' zero is a fact about the build, not a broken + measurement. + """ + from safetensors.torch import load_file + from src.model.nn import NoiseConditioner + + sd = load_file(str(Path(ckpt_dir) / "model.safetensors"), device="cpu") + prefix = "denoise_step_emb." + weights = {k[len(prefix):]: v for k, v in sd.items() if k.startswith(prefix)} + + nc = NoiseConditioner(d_model).to(device=device) + missing, unexpected = nc.load_state_dict(weights, strict=False) + if missing or unexpected: + raise SystemExit(f"NoiseConditioner weights did not load: {missing=} {unexpected=}") + + levels = torch.tensor(sigmas, device=device, dtype=torch.bfloat16)[:, None] + a = torch.randn(4096, 4096, device=device, dtype=torch.float32) + b = torch.randn(4096, 4096, device=device, dtype=torch.float32) + + model_out, control_out, shape_gap = {}, {}, {} + for precision in ("high", "medium", "highest"): + torch.set_float32_matmul_precision(precision) + with torch.inference_mode(): + # CachedDenoiseStepEmb builds its table with base(levels[:, None]), so the + # reference's LUT is one M=5 GEMM, not five GEMVs. + model_out[precision] = nc(levels).squeeze(1).float().clone() + control_out[precision] = (a @ b).clone() + per_sigma = torch.cat([nc(levels[i:i + 1]) for i in range(levels.size(0))]) + shape_gap[precision] = (model_out[precision] - per_sigma.squeeze(1).float()).abs().max().item() + torch.set_float32_matmul_precision("high") + + def gap(d, x, y): + return (d[x] - d[y]).abs().max().item() + + model_gap = gap(model_out, "high", "medium") + rounded = (model_out["high"].bfloat16().float() - model_out["medium"].bfloat16().float()).abs().max().item() + return { + "model_probe": { + "what": "NoiseConditioner.mlp on the scheduler sigmas — the only fp32 matmul in the path", + "high_vs_medium": model_gap, + "high_vs_medium_after_bf16_round": rounded, + "high_vs_highest": gap(model_out, "high", "highest"), + }, + "control_probe": { + "what": "fp32 4096x4096 GEMM, to show the flag is live on this build", + "high_vs_medium": gap(control_out, "high", "medium"), + "high_vs_highest": gap(control_out, "high", "highest"), + }, + "lut_build_shape": { + "what": "the reference's batched LUT build (M=5) against embedding one sigma at a time", + "batched_vs_per_sigma": shape_gap, + "note": ( + "under 'high' the M=5 GEMM reaches TF32 tensor cores and the M=1 GEMV does not, " + "so an implementation that embeds one sigma at a time computes a more accurate " + "LUT than the reference does. It is exact under 'highest'. That difference " + "reaches every block's cond_head, so it is a divergence source in its own right " + "and not a defect in this measurement." + ), + }, + "conclusion": ( + "mstar's 'high' and world_engine's 'medium' are bit-identical here" + if model_gap == 0.0 and gap(control_out, "high", "medium") == 0.0 + else "'high' and 'medium' differ; the recorded value is load-bearing" + ), + } + + +def ring_digest(kv_cache) -> list[dict]: + """Per layer: which slots are live, and the energy in each ring bucket. + + Bucket resolution is what localizes a wrong ring slot; it is derived from the + cache's own tpf/capacity rather than re-deriving the bucket count. + """ + digest = [] + for i, layer in enumerate(kv_cache.layers): + kv = layer.kv + buckets = kv.size(3) // layer.tpf + sumsq = kv.float().pow(2).sum(dim=(0, 1, 2, 4)).view(buckets, layer.tpf).sum(-1) + digest.append({ + "layer": i, + "L": layer.L, + "capacity": layer.capacity, + "tokens_per_frame": layer.tpf, + "pinned_dilation": layer.pinned_dilation, + "num_buckets": layer.num_buckets, + "written": layer.written.detach().cpu().clone(), + "bucket_sumsq": sumsq.double().cpu(), + "sum": kv.double().sum().cpu(), + "absmax": kv.abs().float().max().cpu(), + }) + return digest + + +def committed_kv(kv_cache) -> list[torch.Tensor]: + """Per layer, the tail slice [L, L+tpf) holding the frame just committed.""" + return [layer.kv[:, :, :, layer.L:].detach().cpu().clone() for layer in kv_cache.layers] + + +def full_ring(kv_cache) -> list[dict]: + return [ + {"kv": layer.kv.detach().cpu().clone(), "written": layer.written.detach().cpu().clone()} + for layer in kv_cache.layers + ] + + +def snapshot_ctx(ctx: dict) -> dict: + """engine._ctx is reused every frame, so anything kept must be cloned.""" + return {k: (v.detach().cpu().clone() if torch.is_tensor(v) else v) for k, v in ctx.items()} + + +_SHADOW_PASS = None + + +def shadow_pass(): + """One frozen DiT pass, compiled with the reference's own decorator settings.""" + global _SHADOW_PASS + if _SHADOW_PASS is None: + from src.world_engine import COMPILE_OPTIONS + + def _pass(model, kv_cache, x, step_sig, ctx): + kv_cache.set_frozen(True) + sigma = x.new_empty((x.size(0), x.size(1))) + return model(x, sigma.fill_(step_sig), **ctx, kv_cache=kv_cache) + + _SHADOW_PASS = torch.compile(_pass, fullgraph=True, dynamic=False, options=COMPILE_OPTIONS) + return _SHADOW_PASS + + +def record_dit_outputs(engine, x, ctx, sigmas, dsigmas): + """The 5 per-pass DiT outputs, from frozen passes that leave the ring alone.""" + shadow = shadow_pass() + outs = [] + # strict=False is the reference's behaviour and load-bearing: 5 sigmas zipped + # against their 4 diffs is what makes this 4 denoise passes, not 5. + for step_sig, step_dsig in zip(sigmas, dsigmas, strict=False): + v = shadow(engine.model, engine.kv_cache, x, step_sig, ctx) + outs.append(v.detach().cpu().clone()) + x = (x.float() + step_dsig.float() * v.float()).type_as(x) + del v # a cudagraph output is invalid once the next replay overwrites it + v = shadow(engine.model, engine.kv_cache, x, sigmas[-1], ctx) + outs.append(v.detach().cpu().clone()) + return outs + + +def run_frame(engine, x, ctx, sigmas, dsigmas): + """One engine step: capture the per-pass outputs, then advance the real state. + + The capture runs first because it must not be able to influence what is + recorded; the state comes from `_denoise_pass`/`_cache_pass` verbatim. + """ + outs = record_dit_outputs(engine, x, ctx, sigmas, dsigmas) + x0 = engine._denoise_pass(x, ctx, engine.kv_cache).clone() + engine._cache_pass(x0, ctx, engine.kv_cache) + return x0, outs + + +def verify_shadow(engine, seed_frame, CtrlInput, sigmas, dsigmas, frames: int) -> dict: + """Roll out with and without the capture passes; the state must be identical. + + Leaves the engine reset. Raises rather than record an oracle whose state the + instrumentation reached. + """ + def rollout(with_capture): + engine.reset() + gen = torch.Generator(device="cpu").manual_seed(0) + latents = [] + with torch.inference_mode(): + x0 = engine.vae.encode(seed_frame).unsqueeze(1) + engine._cache_pass(x0, engine.prep_inputs(x=x0, ctrl=CtrlInput()), engine.kv_cache) + for _ in range(frames): + x = torch.randn(engine.frm_shape, generator=gen, dtype=torch.float32).to( + device=engine.device, dtype=engine.dtype) + ctx = engine.prep_inputs(x=x, ctrl=CtrlInput(button={87})) + if with_capture: + x0, _ = run_frame(engine, x, ctx, sigmas, dsigmas) + else: + x0 = engine._denoise_pass(x, ctx, engine.kv_cache).clone() + engine._cache_pass(x0, ctx, engine.kv_cache) + latents.append(x0.detach().cpu().clone()) + ring = [(la.kv.double().sum().item(), int(la.written.sum().item())) for la in engine.kv_cache.layers] + return latents, ring + + plain_lat, plain_ring = rollout(False) + cap_lat, cap_ring = rollout(True) + engine.reset() + + latent_gap = max((a.float() - b.float()).abs().max().item() + for a, b in zip(plain_lat, cap_lat, strict=True)) + ring_gap = sum(1 for a, b in zip(plain_ring, cap_ring, strict=True) if a != b) + if latent_gap != 0.0 or ring_gap: + raise SystemExit(f"the capture passes perturbed the reference state: {latent_gap=} {ring_gap=}") + return { + "what": "reference driver rolled out with and without the dit_out capture passes", + "frames": frames, + "latent_maxabs": latent_gap, + "ring_layers_differing": ring_gap, + } + + +def parse_args() -> argparse.Namespace: + ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--out-dir", required=True) + ap.add_argument("--world-engine", default=WORLD_ENGINE_DEFAULT, help="dir containing world_engine's src/") + ap.add_argument("--model-dir", default=CKPT_DEFAULT) + ap.add_argument("--ae-dir", default=AE_DEFAULT) + ap.add_argument("--seed-image", default=SEED_DEFAULT) + ap.add_argument("--num-frames", type=int, default=NUM_FRAMES, help="gen_frame steps after the seed") + ap.add_argument("--noise-seed", type=int, default=NOISE_SEED) + ap.add_argument("--expect-gpu", default="H100", help="substring the GPU name must contain") + ap.add_argument( + "--ring-snapshot-frames", default="", + help="comma-separated frame indices to dump the full KV ring for " + "(~2.2 GiB each), or 'none'. Default: first, middle and last.", + ) + ap.add_argument("--no-checksums", action="store_true", help="skip sha256 of the checkpoint files") + ap.add_argument( + "--verify-shadow", type=int, default=2, metavar="N", + help="frames to roll out twice, checking the dit_out capture leaves the state alone (0 to skip)", + ) + return ap.parse_args() + + +def sha256_file(path: Path) -> str: + h = hashlib.sha256() + with open(path, "rb") as f: + for chunk in iter(lambda: f.read(1 << 24), b""): + h.update(chunk) + return h.hexdigest() + + +def main() -> None: + args = parse_args() + world_engine = load_world_engine(args.world_engine) + CtrlInput, WorldEngine = world_engine.CtrlInput, world_engine.WorldEngine + + if not torch.cuda.is_available(): + raise SystemExit("CUDA is required to record the oracle.") + gpu_name = torch.cuda.get_device_name(0) + if args.expect_gpu and args.expect_gpu not in gpu_name: + raise SystemExit(f"expected a GPU matching {args.expect_gpu!r}, got {gpu_name!r}") + + out_dir = Path(args.out_dir) + frames_dir, ring_dir = out_dir / "frames", out_dir / "ring" + frames_dir.mkdir(parents=True, exist_ok=True) + ring_dir.mkdir(parents=True, exist_ok=True) + + control = CONTROL_SEQUENCE[: args.num_frames] + if len(control) < args.num_frames: + raise SystemExit(f"CONTROL_SEQUENCE has {len(CONTROL_SEQUENCE)} entries, need {args.num_frames}") + + if args.ring_snapshot_frames.strip().lower() == "none": + snapshot_frames: set[int] = set() + elif args.ring_snapshot_frames.strip(): + snapshot_frames = {int(s) for s in args.ring_snapshot_frames.split(",") if s.strip()} + else: + snapshot_frames = {0, args.num_frames // 2, args.num_frames} + + seed_frame, seed_sha = load_seed_frame(args.seed_image, SEED_URL) + + t_start = time.perf_counter() + torch.cuda.reset_peak_memory_stats() + + # ae_uri is overridden to the local taehv snapshot so recording never depends + # on the network resolving a repo id to the same files. + engine = WorldEngine( + args.model_dir, quant=None, device="cuda", dtype=torch.bfloat16, + model_config_overrides={"ae_uri": args.ae_dir}, + ) + cfg = engine.model_cfg + patches = describe_patches(engine.model) + calibration = calibrate_matmul_precision(args.model_dir, cfg.d_model, list(cfg.scheduler_sigmas), engine.device) + + sigmas = engine.scheduler_sigmas + dsigmas = sigmas.diff() + noise_gen = torch.Generator(device="cpu").manual_seed(args.noise_seed) + per_frame: list[dict] = [] + + shadow_check = ( + verify_shadow(engine, seed_frame, CtrlInput, sigmas, dsigmas, args.verify_shadow) + if args.verify_shadow else None + ) + + # Frame 0: the seed. append_frame unrolled — encode, one committing pass, decode. + with torch.inference_mode(): + x0 = engine.vae.encode(seed_frame).unsqueeze(1) + ctx = engine.prep_inputs(x=x0, ctrl=CtrlInput()) + v = shadow_pass()(engine.model, engine.kv_cache, x0, sigmas[-1], ctx) + v = v.detach().cpu().clone() + engine._cache_pass(x0, ctx, engine.kv_cache) + pixels = engine.vae.decode(x0.squeeze(1)) + + record = { + "frame": 0, "kind": "seed", + "dit_out": [v], + "pass_sigmas": [0.0], + "latent": x0.detach().cpu().clone(), + "pixels": pixels.detach().cpu().clone(), + "noise_f32": None, "noise_bf16": None, + "ctx": snapshot_ctx(ctx), + "committed_kv": committed_kv(engine.kv_cache), + "ring": ring_digest(engine.kv_cache), + } + torch.save(record, frames_dir / "frame_000.pt") + if 0 in snapshot_frames: + torch.save(full_ring(engine.kv_cache), ring_dir / "ring_000.pt") + per_frame.append({"frame": 0, "kind": "seed", "control": None, + "latent_absmax": x0.abs().float().max().item()}) + print(f"frame 000 seed latent|max|={x0.abs().float().max().item():.4f}", flush=True) + + for i, (buttons, mouse, scroll) in enumerate(control, start=1): + # A fresh CtrlInput per frame: prep_inputs replaces its fields with + # tensors in place, so a reused instance is not the same input twice. + ctrl = CtrlInput(button=set(buttons), mouse=tuple(mouse), scroll_wheel=scroll) + + noise_f32 = torch.randn(engine.frm_shape, generator=noise_gen, dtype=torch.float32) + x = noise_f32.to(device=engine.device, dtype=engine.dtype) + + ctx = engine.prep_inputs(x=x, ctrl=ctrl) + x0, outs = run_frame(engine, x, ctx, sigmas, dsigmas) + pixels = engine.vae.decode(x0.squeeze(1)) + + record = { + "frame": i, "kind": "gen", + "dit_out": outs, + "pass_sigmas": [float(s) for s in sigmas[:-1]] + [0.0], + "latent": x0.detach().cpu().clone(), + "pixels": pixels.detach().cpu().clone(), + "noise_f32": noise_f32.clone(), + "noise_bf16": x.detach().cpu().clone(), + "ctx": snapshot_ctx(ctx), + "committed_kv": committed_kv(engine.kv_cache), + "ring": ring_digest(engine.kv_cache), + } + torch.save(record, frames_dir / f"frame_{i:03d}.pt") + if i in snapshot_frames: + torch.save(full_ring(engine.kv_cache), ring_dir / f"ring_{i:03d}.pt") + + per_frame.append({ + "frame": i, "kind": "gen", + "control": {"button": sorted(buttons), "mouse": list(mouse), "scroll_wheel": scroll}, + "latent_absmax": x0.abs().float().max().item(), + "pixels_mean": pixels.float().mean().item(), + }) + print(f"frame {i:03d} gen latent|max|={x0.abs().float().max().item():.4f} " + f"pixels_mean={pixels.float().mean().item():.2f}", flush=True) + + wall = time.perf_counter() - t_start + peak = torch.cuda.max_memory_allocated() + + checksums = {} + if not args.no_checksums: + for label, path in (("model.safetensors", Path(args.model_dir) / "model.safetensors"), + ("config.yaml", Path(args.model_dir) / "config.yaml"), + ("taehv1_5.pth", Path(args.ae_dir) / "taehv1_5.pth")): + if path.exists(): + checksums[label] = sha256_file(path) + + metadata = { + "model_uri": "Overworld/Waypoint-1.5-1B", + "ae_uri": "Overworld-Models/taehv1_5", + "model_dir": str(args.model_dir), + "ae_dir": str(args.ae_dir), + "checkpoint_sha256": checksums, + "seed_image": {"url": SEED_URL, "sha256": seed_sha, "expected_sha256": SEED_SHA256, + "resized_to": [1280, 720], "repeated": 4}, + + # What makes this artifact comparable to a server, and to nothing else. + "float32_matmul_precision": torch.get_float32_matmul_precision(), + "float32_matmul_precision_note": ( + "mstar/engine/__init__.py sets 'high' process-wide; world_engine sets 'medium' at " + "import. Recorded under 'high', the serving value, and set after the world_engine " + "import so it is not undone. See matmul_precision_calibration for the cost." + ), + "matmul_precision_calibration": calibration, + + "torch": { + "version": torch.__version__, + "cuda": torch.version.cuda, + "cudnn": torch.backends.cudnn.version(), + "reference_pins": "torch==2.11.0", + "deviation": ( + "recorded on the torch in this environment, not the reference's pin, so both " + "sides of the Phase 9 comparison run the same build. Do not compare across builds." + ), + }, + "device": {"gpu": gpu_name, "count": torch.cuda.device_count()}, + "python": platform.python_version(), + + # apply_inference_patches is unconditional (world_engine.py:84), so the + # patched model is the reference. These counts are read off the live model. + "inference_patches": { + "applied": "apply_inference_patches (unconditional in WorldEngine.__init__)", + "module_counts": patches, + "functions": ["patch_cached_noise_conditioning", "patch_Attn_merge_qkv", "patch_MLPFusion_split"], + }, + "quantization": None, + + "noise": { + "seed": args.noise_seed, + "generator": f"torch.Generator(device='cpu').manual_seed({args.noise_seed})", + "drawn": "fp32 on CPU, cast to bfloat16 on device", + "reference_draws": "torch.randn(frm_shape, device=cuda, dtype=bfloat16), unseeded", + "deviation": ( + "noise is an input, not model behaviour; the reference's unseeded device draw is " + "not reproducible and cannot be handed to the port. Both tensors are saved per " + "frame as noise_f32 (feed this to the port) and noise_bf16 (what the reference ran)." + ), + }, + + "execution": { + "mode": "compiled", + "state_from": ( + "engine._denoise_pass and engine._cache_pass called unmodified — " + "@torch.compile(fullgraph=True, dynamic=False, options=COMPILE_OPTIONS) with " + "max_autotune, coordinate_descent_tuning and triton.cudagraphs. latent, pixels, " + "committed_kv and ring are the reference's own values for this run. They are " + "not bit-exact targets: see reproducibility." + ), + "dit_out_from": ( + "separate frozen passes through one compiled region carrying the same decorator " + "settings, run before the driver. A per-pass diagnostic, not a bit-exact target: " + "the value depends on which compiled region the pass sits in." + ), + "do_not_call_model_directly": ( + "eager flex_attention ignores a BlockMask's block index lists, and the mask " + "carries a no-op mask_mod, so an eager pass attends over unwritten ring slots. " + "An earlier eager recording of this oracle was wrong by maxabs 5.64 on peak 8.25 " + "(rel 0.68). Every DiT pass here must go through a compiled region." + ), + "decomposition_note": ( + "measured on this build: making the per-pass output an extra output of " + "_denoise_pass, or splitting it into five single-pass regions, moves the emitted " + "latent by 0.031 at the first generated frame and 0.09-0.15 by the third, and " + "changes every layer's ring. Pass 0 shifts by 0.031 on peak 7.44 with identical " + "inputs, so this is inductor's fusion choice, not the arithmetic. A port that " + "runs its denoise passes as separate compiled regions cannot be bit-exact " + "against latent; ~1 bf16 ULP at frame 1 is its floor." + ), + "shadow_isolation_check": shadow_check, + }, + + "reproducibility": { + "within_a_process": "deterministic — the same driver rolled out twice matches bit for bit", + "across_processes": ( + "NOT deterministic. Two processes running the reference driver alone — no " + "recorder, no capture passes — disagree. Layer 0 of the seed frame's committed KV " + "differs by one bf16 ULP on 0.02% of its elements, and 24 layers of bf16 compound " + "that: by layer 23 it is 11.6 on a peak of 20.4." + ), + "measured_gap": { + "what": "two independent processes, reference driver only, seed + 3 generated frames", + "latent_maxabs": [None, 0.0546875, 0.08984375, 0.1328125], + "latent_peak": 4.53, + "committed_kv_maxabs": [11.921875, 9.09375, 16.28125, 13.6875], + "committed_kv_peak": 20.4, + }, + "cause": ( + "compile-time kernel selection, not a runtime race: within one process every " + "call reproduces. Disabling triton.cudagraphs does not help. Disabling " + "max_autotune makes _cache_pass reproducible but not _denoise_pass, so autotune " + "is one source and not the only one. The inductor and triton caches are warm and " + "shared across these runs." + ), + "consequence": ( + "no comparison against this oracle can assert bit-exactness, because the " + "reference is not bit-exact against itself. The numbers above are the floor any " + "tolerance has to clear. companion_run is a second independent recording of the " + "opening frames, so that floor can be re-measured from artifacts rather than " + "taken on trust." + ), + "companion_run": "repro/ — same seed and controls, recorded by a separate process", + }, + + "num_frames": args.num_frames, + "total_frames_recorded": args.num_frames + 1, + "scheduler_sigmas": [float(s) for s in cfg.scheduler_sigmas], + "passes_per_frame": "4 non-committing denoise + 1 committing", + "control_sequence": [ + {"frame": i, "button": sorted(b), "mouse": list(m), "scroll_wheel": s} + for i, (b, m, s) in enumerate(control, start=1) + ], + "ring_snapshot_frames": sorted(snapshot_frames), + "model_config": {k: v for k, v in dict(cfg).items() if not isinstance(v, dict)}, + "frame_shape": list(engine.frm_shape), + "ts_mult": engine.ts_mult, + "per_frame": per_frame, + "wall_time_s": wall, + "peak_vram_bytes": int(peak), + "peak_vram_gib": round(peak / 2**30, 2), + } + with open(out_dir / "metadata.json", "w") as f: + json.dump(metadata, f, indent=2, default=str) + + print(f"DONE frames={args.num_frames + 1} wall={wall:.1f}s peak_vram={peak / 2**30:.2f}GiB") + print(f"matmul_precision={torch.get_float32_matmul_precision()} oracle -> {out_dir}") + + +if __name__ == "__main__": + main() diff --git a/test/waypoint/serve_rollout.py b/test/waypoint/serve_rollout.py new file mode 100644 index 000000000..975d53ba0 --- /dev/null +++ b/test/waypoint/serve_rollout.py @@ -0,0 +1,959 @@ +#!/usr/bin/env python3 +"""Drive Waypoint rollouts through the mstar server and Python SDK, end to end. + +Launches ``mstar/api_server/entrypoint.py`` on ``configs/waypoint.yaml`` -- the +real API server, conductor process and GPU worker -- then sends a seed frame and +an action script through ``MStarClient`` and consumes typed ``VideoFrameChunk`` +objects from the stream. + +By default the request is sent twice, under *different* ids and one explicit +``model_kwargs.seed``, so the two rollouts draw the same noise. Identical bytes +the second time are what shows the first request left nothing behind: with +``num_worlds: 1`` a leaked world fails the second admission outright, and a +leaked ``ChunkedStreamingTAEHV`` in ``PerRequestState.kwargs`` would resume the +first rollout's stream and change the pixels. + +The ids have to differ. A worker defers ``REMOVE_REQUEST`` while a step is in +flight and keys the deferral on the rid alone, so reusing an id lets the first +request's teardown land on the second and drop its in-flight reads. + +``--concurrent-waves`` switches to the two-world isolation gate: two distinct +solo baselines are replayed concurrently through separate SDK clients, then +checked byte-for-byte across repeated world reuse. Optional memory sampling +tracks only the server process group and excludes the first concurrent wave as +allocator warmup. + +Deployment details that the checked-in config cannot carry are supplied here +rather than edited into it: + + * Local mode adds ``model_kwargs.checkpoint_dir`` / ``ae_path``. Hub mode + deliberately omits both, exercising the registry's variant-to-repository + mapping, and can forward ``--cache-dir`` to Hugging Face. + * a 16:9 seed. ``WaypointModel.load_image`` decodes without resizing and + ``_seed_clip`` refuses any other ratio, so the shipped 1927x1080 asset is + resized to the selected variant's output geometry first. + + CUDA_VISIBLE_DEVICES=2 python3 test/waypoint/serve_rollout.py \ + --variant 720p --steps 8 --worlds 2 --concurrent-waves 4 \ + --measure-memory --physical-gpu 2 + + CUDA_VISIBLE_DEVICES=2 python3 test/waypoint/serve_rollout.py \ + --variant 360p --steps 8 +""" + +from __future__ import annotations + +import argparse +import ast +import concurrent.futures +import os +import signal +import subprocess +import sys +import tempfile +import threading +import time +from dataclasses import dataclass +from pathlib import Path +from typing import Callable + +import yaml + +REPO = Path(__file__).resolve().parents[2] +DEFAULT_CONFIG = REPO / "configs/waypoint.yaml" +DEFAULT_ROOT = Path("/mnt/storage/garv901/waypoint-1.5-1B/checkpoints") +if str(REPO) not in sys.path: + sys.path.insert(0, str(REPO)) + +from mstar.client import MStarClient, VideoFrameChunk # noqa: E402 + + +@dataclass(frozen=True) +class Variant: + model_variant: str + height: int + width: int + tokens_per_frame: int + checkpoint_name: str + + @property + def checkpoint_dir(self) -> Path: + return DEFAULT_ROOT / self.checkpoint_name + + +@dataclass(frozen=True) +class RolloutSpec: + label: str + request_id: str + rng_seed: int + + +@dataclass(frozen=True) +class MemorySample: + timestamp: float + phase: str + host_pss_mib: float + gpu_mib: float + + +@dataclass(frozen=True) +class WaveMemory: + phase: str + peak_host_pss_mib: float + peak_gpu_mib: float + quiet_host_pss_mib: float + quiet_gpu_mib: float + + +VARIANTS = { + "360p": Variant( + model_variant="waypoint-1.5-1b-360p", + height=360, + width=640, + tokens_per_frame=128, + checkpoint_name="Waypoint-1.5-1B-360P", + ), + "720p": Variant( + model_variant="waypoint-1.5-1b-720p", + height=720, + width=1280, + tokens_per_frame=512, + checkpoint_name="Waypoint-1.5-1B", + ), +} + + +def _run_config( + base: Path, + variant: Variant, + checkpoint_dir: Path | None, + ae_path: Path | None, + out: Path, + worlds: int = 1, +) -> Path: + """Build one deployment config without modifying the checked-in YAML.""" + if worlds < 1: + raise ValueError(f"worlds must be positive; got {worlds}") + config = yaml.safe_load(base.read_text()) + model_kwargs = { + **(config.get("model_kwargs") or {}), + "variant": variant.model_variant, + } + # Hub mode passes None for these and must not inherit a local override from + # the base config: omitting checkpoint_dir is what exercises the registry's + # variant -> repository selection. + model_kwargs.pop("checkpoint_dir", None) + model_kwargs.pop("ae_path", None) + if checkpoint_dir is not None: + model_kwargs["checkpoint_dir"] = str(checkpoint_dir) + if ae_path is not None: + model_kwargs["ae_path"] = str(ae_path) + config["model_kwargs"] = model_kwargs + config["max_seq_len"] = variant.tokens_per_frame + config["max_concurrent_requests"] = worlds + resources = config["resources"] = config.get("resources") or {} + kv = resources["kv"] = resources.get("kv") or {} + kv["num_worlds"] = worlds + out.write_text(yaml.safe_dump(config, sort_keys=False)) + return out + + +def _seed_png(source: Path, variant: Variant, out: Path) -> Path: + """Resize the seed to the variant's 16:9 output geometry and write PNG.""" + from PIL import Image + + try: + with Image.open(source) as image: + image.convert("RGB").resize((variant.width, variant.height), Image.Resampling.BILINEAR).save( + out, format="PNG" + ) + except OSError as exc: + raise SystemExit(f"could not decode seed image {source}: {exc}") from exc + return out + + +def _actions(num_steps: int) -> list[dict]: + """A scripted pan with a button held, so the run is not the idle world. + + There is exactly one action row per generated latent step. Prime uses its + own internal idle action, so it does not consume action row zero. + """ + return [ + {"mouse": [12.0 if i % 2 else -12.0, 0.0], "buttons": [0] if i % 4 == 0 else [], "scroll": 0.0} + for i in range(num_steps) + ] + + +def _wait_for_health(client: MStarClient, proc: subprocess.Popen, timeout: float) -> None: + deadline = time.time() + timeout + while time.time() < deadline: + if proc.poll() is not None: + raise SystemExit(f"server exited with code {proc.returncode} before serving") + if client.health(): + return + time.sleep(1.0) + raise SystemExit(f"server did not answer /health within {timeout:.0f}s") + + +def _rollout( + client: MStarClient, + seed: Path, + num_steps: int, + request_id: str, + rng_seed: int, + start_barrier: threading.Barrier | None = None, +) -> list[VideoFrameChunk]: + """One SDK-streamed request, typed frame chunks in arrival order.""" + chunks: list[VideoFrameChunk] = [] + stream = client.stream( + images=seed, + input_modalities=("image",), + output_modalities=("video_frame",), + request_id=request_id, + num_steps=num_steps, + actions=_actions(num_steps), + seed=rng_seed, + ) + # MStarClient._stream is lazy: crossing here means both tasks have built + # their multipart bodies before either opens its HTTP request. + if start_barrier is not None: + start_barrier.wait(timeout=30) + for event in stream: + if not isinstance(event, VideoFrameChunk): + raise RuntimeError(f"Waypoint returned an unexpected SDK stream event: {type(event).__name__}") + chunks.append(event) + print(f" {request_id} chunk {len(chunks) - 1:3d} bytes={len(event.data):9d} metadata={event.metadata}") + return chunks + + +def _concurrent_rollouts( + client_factory: Callable[[], MStarClient], + seed: Path, + num_steps: int, + specs: tuple[RolloutSpec, RolloutSpec], +) -> dict[str, list[VideoFrameChunk]]: + """Start exactly two lazy SDK streams together, each on its own Session.""" + barrier = threading.Barrier(3) + with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor: + futures = { + spec.label: executor.submit( + _rollout, + client_factory(), + seed, + num_steps, + spec.request_id, + spec.rng_seed, + barrier, + ) + for spec in specs + } + barrier.wait(timeout=30) + return {label: future.result() for label, future in futures.items()} + + +def _video_bytes(chunks: list[VideoFrameChunk]) -> bytes: + return b"".join(chunk.data for chunk in chunks) + + +def _dit_schedule(log_text: str, request_ids: set[str]) -> list[str]: + """Extract single-request DiT rollout executions from worker DEBUG logs.""" + marker = "Executing: dit graph_walk=rollout " + scheduled: list[str] = [] + for line in log_text.splitlines(): + if marker not in line: + continue + try: + batch = ast.literal_eval(line.split(marker, 1)[1].strip()) + except (SyntaxError, ValueError): + continue + if ( + isinstance(batch, (list, tuple)) + and len(batch) == 1 + and batch[0] in request_ids + ): + scheduled.append(batch[0]) + return scheduled + + +def _interleaving_failure(log_text: str, request_ids: tuple[str, str]) -> str | None: + """Require an A/B/A or B/A/B DiT schedule, not just overlapping clients.""" + scheduled = _dit_schedule(log_text, set(request_ids)) + compressed = [rid for i, rid in enumerate(scheduled) if i == 0 or rid != scheduled[i - 1]] + interleaved = any( + first == third and first != second + for first, second, third in zip(compressed, compressed[1:], compressed[2:], strict=False) + ) + if interleaved: + return None + counts = {rid: scheduled.count(rid) for rid in request_ids} + return ( + f"worker DEBUG schedule did not contain A/B/A interleaving for {request_ids}; DiT schedule counts were {counts}" + ) + + +def _execution_count_failure( + log_text: str, + request_ids: tuple[str, str], + num_steps: int, +) -> str | None: + scheduled = _dit_schedule(log_text, set(request_ids)) + counts = {rid: scheduled.count(rid) for rid in request_ids} + if all(count == num_steps for count in counts.values()): + return None + return f"expected {num_steps} DiT rollout executions per request; got {counts}" + + +_CLEANUP_MARKER = "Request cleanup complete:" + + +def _cleaned_request_ids(log_text: str) -> set[str]: + return {line.split(_CLEANUP_MARKER, 1)[1].strip() for line in log_text.splitlines() if _CLEANUP_MARKER in line} + + +def _read_log_since(log_path: Path, offset: int) -> str: + with log_path.open("rb") as log: + log.seek(offset) + return log.read().decode("utf-8", "replace") + + +def _wait_for_cleanup( + log_path: Path, + request_ids: tuple[str, ...], + proc: subprocess.Popen, + timeout: float, + offset: int = 0, +) -> None: + """Wait for actual worker cleanup, including any deferred remove.""" + deadline = time.monotonic() + timeout + wanted = set(request_ids) + while time.monotonic() < deadline: + if proc.poll() is not None: + raise RuntimeError(f"server exited with code {proc.returncode} before request cleanup") + try: + cleaned = _cleaned_request_ids(_read_log_since(log_path, offset)) + except FileNotFoundError: + cleaned = set() + if wanted <= cleaned: + return + time.sleep(0.1) + missing = sorted(wanted - cleaned) + raise RuntimeError(f"worker did not confirm cleanup for {missing} within {timeout:.0f}s") + + +def _check( + chunks: list[VideoFrameChunk], + num_steps: int, + height: int, + width: int, +) -> list[str]: + """Every failed expectation, rather than the first: a run costs minutes and + a short stream and a wrong payload size have different causes.""" + failures = [] + + # Prime advances internal encoder/DiT/decoder state but emits no seed + # reconstruction. Every generated latent produces one chunk of four frames. + expected = num_steps + if len(chunks) != expected: + failures.append(f"expected {expected} video chunks, got {len(chunks)}") + + # The SDK validates each payload against its own metadata. Keep the expected + # variant geometry and frame sequence as independent end-to-end assertions. + size = 4 * height * width * 3 + wrong = [i for i, chunk in enumerate(chunks) if len(chunk.data) != size] + if wrong: + failures.append( + f"chunks {wrong[:5]} are not {size} bytes " + f"(4 x {height} x {width} x 3 uint8); first is " + f"{len(chunks[wrong[0]].data)}" + ) + for chunk_idx, chunk in enumerate(chunks): + wanted = { + "width": width, + "height": height, + "fps": 60.0, + "pixel_format": "rgb24", + "frame_index": chunk_idx * 4, + "frame_count": 4, + } + mismatches = { + key: (chunk.metadata.get(key), value) for key, value in wanted.items() if chunk.metadata.get(key) != value + } + if mismatches: + failures.append(f"chunk {chunk_idx} has invalid video_frame metadata: {mismatches}") + if sum(chunk.frame_count for chunk in chunks) != 4 * num_steps: + failures.append(f"expected exactly {4 * num_steps} generated frames") + return failures + + +def _parse_pss_kib(smaps_rollup: str) -> int: + for line in smaps_rollup.splitlines(): + if line.startswith("Pss:"): + return int(line.split()[1]) + raise ValueError("smaps_rollup contained no Pss total") + + +def _process_group_pids(process_group: int) -> list[int]: + pids = [] + for entry in Path("/proc").iterdir(): + if not entry.name.isdigit(): + continue + pid = int(entry.name) + try: + if os.getpgid(pid) == process_group: + pids.append(pid) + except ProcessLookupError: + continue + return sorted(pids) + + +def _process_group_pss_mib(process_group: int) -> float: + total_kib = 0 + read_count = 0 + for pid in _process_group_pids(process_group): + try: + text = Path(f"/proc/{pid}/smaps_rollup").read_text() + except FileNotFoundError: + continue + total_kib += _parse_pss_kib(text) + read_count += 1 + if read_count == 0: + raise RuntimeError(f"no readable PSS telemetry for process group {process_group}") + return total_kib / 1024 + + +def _parse_nvidia_smi_processes(output: str) -> list[tuple[int, float]]: + rows = [] + for line in output.splitlines(): + if not line.strip(): + continue + fields = [field.strip() for field in line.split(",")] + if len(fields) != 2: + raise ValueError(f"unexpected nvidia-smi row: {line!r}") + try: + rows.append((int(fields[0]), float(fields[1]))) + except ValueError as exc: + raise ValueError(f"unusable nvidia-smi row: {line!r}") from exc + return rows + + +def _process_group_gpu_mib(process_group: int, physical_gpu: int) -> float: + try: + result = subprocess.run( + [ + "nvidia-smi", + f"--id={physical_gpu}", + "--query-compute-apps=pid,used_gpu_memory", + "--format=csv,noheader,nounits", + ], + capture_output=True, + text=True, + timeout=10, + check=False, + ) + except (FileNotFoundError, subprocess.TimeoutExpired) as exc: + raise RuntimeError(f"nvidia-smi telemetry unavailable: {exc}") from exc + if result.returncode != 0: + detail = result.stderr.strip() or f"exit code {result.returncode}" + raise RuntimeError(f"nvidia-smi telemetry unavailable: {detail}") + + total = 0.0 + matched = 0 + for pid, used_mib in _parse_nvidia_smi_processes(result.stdout): + try: + belongs_to_server = os.getpgid(pid) == process_group + except ProcessLookupError: + continue + if belongs_to_server: + total += used_mib + matched += 1 + if matched == 0: + raise RuntimeError(f"GPU {physical_gpu} reports no compute process in server group {process_group}") + return total + + +class MemorySampler: + """Continuously sample only the API server's process group.""" + + def __init__(self, process_group: int, physical_gpu: int, interval: float = 0.25): + self.process_group = process_group + self.physical_gpu = physical_gpu + self.interval = interval + self._phase = "ready" + self._samples: list[MemorySample] = [] + self._last_error: str | None = None + self._lock = threading.Lock() + self._stop = threading.Event() + self._thread = threading.Thread(target=self._run, name="waypoint-memory-sampler", daemon=True) + + def start(self) -> None: + self._thread.start() + + def stop(self) -> None: + self._stop.set() + self._thread.join(timeout=max(15.0, self.interval * 4)) + if self._thread.is_alive(): + raise RuntimeError("memory sampler did not stop") + + def set_phase(self, phase: str) -> None: + with self._lock: + self._phase = phase + + def snapshot(self) -> tuple[list[MemorySample], str | None]: + with self._lock: + return list(self._samples), self._last_error + + def _run(self) -> None: + while not self._stop.is_set(): + with self._lock: + phase = self._phase + try: + host_pss_mib = _process_group_pss_mib(self.process_group) + gpu_mib = _process_group_gpu_mib(self.process_group, self.physical_gpu) + sample = MemorySample(time.monotonic(), phase, host_pss_mib, gpu_mib) + except (OSError, RuntimeError, ValueError) as exc: + with self._lock: + self._last_error = str(exc) + else: + with self._lock: + self._samples.append(sample) + self._last_error = None + self._stop.wait(self.interval) + + +def _wait_for_first_memory_sample( + sampler: MemorySampler, + proc: subprocess.Popen, + timeout: float = 30.0, +) -> None: + deadline = time.monotonic() + timeout + while time.monotonic() < deadline: + samples, _ = sampler.snapshot() + if samples: + return + if proc.poll() is not None: + raise RuntimeError(f"server exited with code {proc.returncode} before telemetry started") + time.sleep(0.1) + _, error = sampler.snapshot() + raise RuntimeError(f"memory telemetry unavailable after {timeout:.0f}s: {error or 'no samples'}") + + +def _stable_memory_plateau( + samples: list[MemorySample], + *, + count: int = 3, + tolerance_mib: float = 2.0, +) -> tuple[float, float] | None: + if len(samples) < count: + return None + tail = samples[-count:] + host = [sample.host_pss_mib for sample in tail] + gpu = [sample.gpu_mib for sample in tail] + if max(host) - min(host) > tolerance_mib or max(gpu) - min(gpu) > tolerance_mib: + return None + return max(host), max(gpu) + + +def _wait_for_quiescent_memory( + sampler: MemorySampler, + phase: str, + timeout: float = 10.0, +) -> tuple[float, float]: + sampler.set_phase(phase) + deadline = time.monotonic() + timeout + while time.monotonic() < deadline: + samples, _ = sampler.snapshot() + plateau = _stable_memory_plateau([sample for sample in samples if sample.phase == phase]) + if plateau is not None: + return plateau + time.sleep(0.1) + _, error = sampler.snapshot() + detail = f"; last telemetry error: {error}" if error else "" + raise RuntimeError(f"memory did not reach a stable {phase!r} plateau within {timeout:.0f}s{detail}") + + +def _summarize_wave_memory( + samples: list[MemorySample], + phase: str, + quiet: tuple[float, float], +) -> WaveMemory: + wave_samples = [sample for sample in samples if sample.phase == phase] + if not wave_samples: + raise RuntimeError(f"memory telemetry captured no samples during {phase}") + return WaveMemory( + phase=phase, + peak_host_pss_mib=max(sample.host_pss_mib for sample in wave_samples), + peak_gpu_mib=max(sample.gpu_mib for sample in wave_samples), + quiet_host_pss_mib=quiet[0], + quiet_gpu_mib=quiet[1], + ) + + +def _bounded_memory_failures( + measured: list[WaveMemory], + *, + host_growth_mib: float, + gpu_growth_mib: float, +) -> list[str]: + if len(measured) < 2: + return ["bounded-memory check needs at least two measured waves after warmup"] + baseline = measured[0] + failures = [] + for wave in measured[1:]: + host_growth = wave.quiet_host_pss_mib - baseline.quiet_host_pss_mib + gpu_growth = wave.quiet_gpu_mib - baseline.quiet_gpu_mib + if host_growth > host_growth_mib: + failures.append( + f"{wave.phase} quiescent host PSS grew {host_growth:.1f} MiB " + f"from {baseline.phase} (limit {host_growth_mib:.1f} MiB)" + ) + if gpu_growth > gpu_growth_mib: + failures.append( + f"{wave.phase} quiescent GPU memory grew {gpu_growth:.1f} MiB " + f"from {baseline.phase} (limit {gpu_growth_mib:.1f} MiB)" + ) + return failures + + +def _free_port() -> int: + """A loopback port nothing holds. The box is shared and the server binds + before it can report a conflict, so a fixed default collides.""" + import socket + + with socket.socket() as sock: + sock.bind(("127.0.0.1", 0)) + return sock.getsockname()[1] + + +def _shutdown(proc: subprocess.Popen) -> None: + """Let the API parent shut down its children, then kill a hung group.""" + if proc.poll() is not None: + return + try: + proc.send_signal(signal.SIGINT) + proc.wait(timeout=60) + return + except ProcessLookupError: + return + except subprocess.TimeoutExpired: + pass + + try: + os.killpg(proc.pid, signal.SIGKILL) + proc.wait(timeout=30) + except (ProcessLookupError, subprocess.TimeoutExpired): + pass + + +def _server_command( + config: Path, + port: int, + workdir: Path, + log_level: str, + request_timeout: float, + cache_dir: Path | None, + enable_nvtx: bool = False, +) -> list[str]: + command = [ + sys.executable, + str(REPO / "mstar/api_server/entrypoint.py"), + "--config", + str(config), + "--port", + str(port), + "--host", + "127.0.0.1", + "--socket-path-prefix", + str(workdir / "sock"), + "--upload-dir", + str(workdir / "uploads"), + "--tensor-comm-protocol", + "SHM", + "--log-level", + log_level, + "--timeout", + str(request_timeout), + ] + if cache_dir is not None: + command.extend(("--cache-dir", str(cache_dir))) + if enable_nvtx: + command.append("--enable-nvtx") + return command + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--config", type=Path, default=DEFAULT_CONFIG) + parser.add_argument("--variant", choices=sorted(VARIANTS), required=True) + parser.add_argument( + "--source", + choices=("local", "hub"), + default="local", + help="local paths (default) or the registry's variant-specific Hub repositories", + ) + parser.add_argument( + "--checkpoint-dir", + type=Path, + help="checkpoint override; defaults to the selected variant under the checkpoint root", + ) + parser.add_argument("--ae-path", type=Path, help="local TAEHV override") + parser.add_argument("--cache-dir", type=Path, help="Hugging Face download cache") + parser.add_argument("--seed-image", type=Path, default=DEFAULT_ROOT / "seed/default.jpg") + parser.add_argument( + "--steps", + "--frames", + dest="steps", + type=int, + default=8, + help="generated latent steps; each step streams four RGB frames", + ) + parser.add_argument("--port", type=int, default=0, help="0 picks a free one") + parser.add_argument("--request-id", type=str, default="waypoint-serve-rollout") + parser.add_argument("--seed", type=int, default=112464007) + parser.add_argument("--worlds", type=int, default=1) + parser.add_argument( + "--concurrent-waves", + type=int, + default=0, + help="run the two-world isolation gate for this many waves (minimum 2)", + ) + parser.add_argument("--measure-memory", action="store_true") + parser.add_argument( + "--physical-gpu", + type=int, + help="physical nvidia-smi GPU index; required with --measure-memory", + ) + parser.add_argument("--host-growth-mib", type=float, default=128.0) + parser.add_argument("--gpu-growth-mib", type=float, default=64.0) + parser.add_argument("--startup-timeout", type=float, default=900.0) + parser.add_argument("--request-timeout", type=float, default=900.0) + parser.add_argument("--log", type=Path, default=Path("/tmp/waypoint_server.log")) + parser.add_argument("--log-level", type=str, default="INFO") + parser.add_argument( + "--enable-nvtx", + action="store_true", + help="enable server NVTX ranges for an external CUDA profiler", + ) + args = parser.parse_args() + + if args.worlds < 1: + parser.error("--worlds must be positive") + if args.concurrent_waves < 0: + parser.error("--concurrent-waves cannot be negative") + if args.concurrent_waves == 1: + parser.error("--concurrent-waves must be 0 or at least 2") + if args.concurrent_waves and args.worlds != 2: + parser.error("the concurrent isolation gate requires exactly --worlds 2") + if args.measure_memory and args.concurrent_waves < 3: + parser.error("--measure-memory needs at least 3 concurrent waves (one warm, two measured)") + if args.measure_memory and args.physical_gpu is None: + parser.error("--measure-memory requires --physical-gpu") + if args.physical_gpu is not None and args.physical_gpu < 0: + parser.error("--physical-gpu cannot be negative") + if args.host_growth_mib < 0 or args.gpu_growth_mib < 0: + parser.error("memory growth limits cannot be negative") + if args.source == "hub" and (args.checkpoint_dir is not None or args.ae_path is not None): + parser.error("--source hub cannot be combined with --checkpoint-dir or --ae-path") + + variant = VARIANTS[args.variant] + if args.source == "local": + checkpoint_dir = args.checkpoint_dir or variant.checkpoint_dir + ae_path = args.ae_path or DEFAULT_ROOT / "taehv1_5" + weight_source = str(checkpoint_dir) + else: + checkpoint_dir = None + ae_path = None + weight_source = f"registry Hub mapping for {variant.model_variant}" + port = args.port or _free_port() + url = f"http://127.0.0.1:{port}" + workdir = Path(tempfile.mkdtemp(prefix="waypoint-serve-")) + config = _run_config( + args.config, + variant, + checkpoint_dir, + ae_path, + workdir / "run.yaml", + worlds=args.worlds, + ) + seed = _seed_png(args.seed_image, variant, workdir / "seed.png") + path = str(REPO) + server_log_level = "DEBUG" if args.concurrent_waves else args.log_level + print( + f"variant {args.variant} ({variant.width}x{variant.height})\n" + f"weights {weight_source}\nworlds {args.worlds}\nconfig {config}\n" + f"seed {seed}\nlog {args.log}" + ) + + server_command = _server_command( + config, + port, + workdir, + server_log_level, + args.request_timeout, + args.cache_dir, + args.enable_nvtx, + ) + + # Its own process group, so a hung run is killed together with the + # conductor and worker processes it spawned. + with args.log.open("wb") as log: + proc = subprocess.Popen( + server_command, + cwd=str(REPO), + stdout=log, + stderr=subprocess.STDOUT, + start_new_session=True, + # The worktree is not an installed package and the conductor and + # worker are spawned, not forked, so the path has to be inherited. + env={**os.environ, "PYTHONUNBUFFERED": "1", "PYTHONPATH": path}, + ) + + failures: list[str] = [] + sampler: MemorySampler | None = None + try: + client = MStarClient(url, timeout=args.request_timeout) + started = time.time() + _wait_for_health(client, proc, args.startup_timeout) + print(f"server ready after {time.time() - started:.1f}s") + + if args.measure_memory: + sampler = MemorySampler(proc.pid, args.physical_gpu) + sampler.start() + _wait_for_first_memory_sample(sampler, proc) + + if not args.concurrent_waves: + runs: list[list[VideoFrameChunk]] = [] + for attempt in (1, 2): + rid = f"{args.request_id}-{attempt}" + print(f"--- request {attempt} (request_id={rid} seed={args.seed}) ---") + started = time.time() + chunks = _rollout(client, seed, args.steps, rid, args.seed) + print(f" {len(chunks)} chunks in {time.time() - started:.1f}s") + runs.append(chunks) + failures += [ + f"request {attempt}: {failure}" + for failure in _check(chunks, args.steps, variant.height, variant.width) + ] + + first, second = (_video_bytes(run) for run in runs) + if not first: + failures.append("no video came back, so the repeat proves nothing") + elif first != second: + failures.append( + "the repeated request differs from the first, so per-request state " + "outlived its request (the ring, the AE session, or both)" + ) + else: + print(f"repeat is byte-identical over {len(first)} bytes of video") + else: + baseline_specs = ( + RolloutSpec("A", f"{args.request_id}-solo-a", args.seed), + RolloutSpec("B", f"{args.request_id}-solo-b", args.seed + 1), + ) + baselines: dict[str, bytes] = {} + for spec in baseline_specs: + print(f"--- solo {spec.label} (request_id={spec.request_id} seed={spec.rng_seed}) ---") + log_offset = args.log.stat().st_size + chunks = _rollout(client, seed, args.steps, spec.request_id, spec.rng_seed) + failures += [ + f"solo {spec.label}: {failure}" + for failure in _check(chunks, args.steps, variant.height, variant.width) + ] + baselines[spec.label] = _video_bytes(chunks) + _wait_for_cleanup( + args.log, + (spec.request_id,), + proc, + args.request_timeout, + offset=log_offset, + ) + + if not all(baselines.values()): + failures.append("a solo baseline returned no video") + elif baselines["A"] == baselines["B"]: + failures.append("distinct solo seeds produced identical baselines, so world swaps are invisible") + + wave_memory: list[WaveMemory] = [] + interleaved_waves = 0 + for wave in range(1, args.concurrent_waves + 1): + phase = f"concurrent-wave-{wave}" + specs = ( + RolloutSpec("A", f"{args.request_id}-wave-{wave}-a", args.seed), + RolloutSpec("B", f"{args.request_id}-wave-{wave}-b", args.seed + 1), + ) + if sampler is not None: + sampler.set_phase(phase) + print(f"--- {phase}: {specs[0].request_id} + {specs[1].request_id} ---") + log_offset = args.log.stat().st_size + chunks_by_label = _concurrent_rollouts( + lambda: MStarClient(url, timeout=args.request_timeout), + seed, + args.steps, + specs, + ) + for spec in specs: + chunks = chunks_by_label[spec.label] + failures += [ + f"{phase} {spec.label}: {failure}" + for failure in _check(chunks, args.steps, variant.height, variant.width) + ] + actual = _video_bytes(chunks) + if actual != baselines[spec.label]: + failures.append(f"{phase} {spec.label} differs byte-for-byte from its solo baseline") + + rids = tuple(spec.request_id for spec in specs) + _wait_for_cleanup( + args.log, + rids, + proc, + args.request_timeout, + offset=log_offset, + ) + wave_log = _read_log_since(args.log, log_offset) + count_failure = _execution_count_failure(wave_log, rids, args.steps) + if count_failure is not None: + failures.append(f"{phase}: {count_failure}") + schedule_failure = _interleaving_failure(wave_log, rids) + if schedule_failure is None: + interleaved_waves += 1 + else: + print(f" {phase} was serialized; another wave must prove A/B/A") + + if sampler is not None: + quiet = _wait_for_quiescent_memory(sampler, f"{phase}-quiet") + samples, _ = sampler.snapshot() + summary = _summarize_wave_memory(samples, phase, quiet) + wave_memory.append(summary) + warm = " (warmup, excluded)" if wave == 1 else "" + print( + f" memory{warm}: peak host={summary.peak_host_pss_mib:.1f} MiB " + f"gpu={summary.peak_gpu_mib:.1f} MiB; quiet " + f"host={summary.quiet_host_pss_mib:.1f} MiB " + f"gpu={summary.quiet_gpu_mib:.1f} MiB" + ) + + if interleaved_waves == 0: + failures.append( + "no concurrent wave contained an A/B/A or B/A/B DiT rollout execution order" + ) + + if sampler is not None: + failures += _bounded_memory_failures( + wave_memory[1:], + host_growth_mib=args.host_growth_mib, + gpu_growth_mib=args.gpu_growth_mib, + ) + finally: + try: + if sampler is not None: + sampler.stop() + finally: + _shutdown(proc) + + for failure in failures: + print(f"FAIL {failure}") + print("PASS" if not failures else f"{len(failures)} failure(s)") + return 1 if failures else 0 + + +if __name__ == "__main__": + raise SystemExit(main()) From 51ea63b5d3f219da3231654c2668d5af4c40ad2e Mon Sep 17 00:00:00 2001 From: garv Date: Sun, 13 Sep 2026 01:15:20 +0000 Subject: [PATCH 06/29] nvtx: instrument the result-delivery path for 720p streaming attribution The api_server process emitted no NVTX at all: APIServer was constructed without enable_nvtx, so the flag only ever reached the conductor. Traces therefore ended at worker.send_outputs, leaving ~90ms of the 116.9ms 720p inter-chunk gap unattributed. Adds: apiserver.b64encode / json_dumps / chunk_available / yield_line, dataworker.get_tensor / postprocess / queue_output, cg.replay.slot, and benchmark.stream / await_chunk / chunk_arrival to anchor the client clock. All gated on the existing --enable-nvtx flag. --- mstar/api_server/data_worker.py | 28 ++++++++++++++-- mstar/api_server/entrypoint.py | 49 ++++++++++++++++++++++++---- mstar/engine/cuda_graph_runner.py | 7 ++++ test/waypoint/benchmark_streaming.py | 17 ++++++++++ 4 files changed, 91 insertions(+), 10 deletions(-) diff --git a/mstar/api_server/data_worker.py b/mstar/api_server/data_worker.py index 2e5054022..21fc00eb0 100644 --- a/mstar/api_server/data_worker.py +++ b/mstar/api_server/data_worker.py @@ -27,6 +27,7 @@ from mstar.communication.tensors import NameToTensorList, create_tensor_communication_manager from mstar.model.base import Model from mstar.profile.format import InputInfo, RxInfo, TxInfo +from mstar.utils import profiler from mstar.utils.ipc_format import ( AbortRequest, ConductorMessage, @@ -100,7 +101,8 @@ def __init__( socket_path_prefix: str = "/tmp/mstar", tensor_comm_protocol: CommProtocol = CommProtocol.RDMA, tcp_transfer_device="", - enable_prof: bool=False + enable_prof: bool=False, + enable_nvtx: bool=False, ): self.request_input_queue = queue.Queue() self.result_tensor_input_queue = queue.Queue() @@ -150,7 +152,8 @@ def __init__( communicator=self.communicator, tensor_manager=self.tensor_manager, model=model, - enable_prof=enable_prof + enable_prof=enable_prof, + enable_nvtx=enable_nvtx, ) ) self.thread.start() @@ -302,7 +305,8 @@ def __init__( tensor_manager, device: str = "cpu", model: Model | None = None, - enable_prof: bool=False + enable_prof: bool=False, + enable_nvtx: bool=False, ): self.in_queue = in_queue self.result_tensor_queue = result_tensor_queue @@ -322,6 +326,10 @@ def __init__( self.device = device self.model = model self.enable_prof = enable_prof + # This thread turns each output tensor into client-ready bytes. At 720p + # that is an 11 MiB SHM read plus a postprocess copy per engine step, + # downstream of the last worker-side NVTX range. + self.enable_nvtx = enable_nvtx self.in_flight_requests = set() self.tensor_uuid_to_metadata_per_request = {} @@ -613,14 +621,24 @@ def _process_read_tensors(self): request_id, loop_indices, ) + if self.enable_nvtx: + profiler.range_push(f"dataworker.get_tensor.{modality}") tensor = self.tensor_manager.get_tensor( request_id=request_id, uuid=tensor_info.uuid ) + if self.enable_nvtx: + profiler.range_pop() + profiler.range_push(f"dataworker.postprocess.{modality}") postprocessed = self.model.postprocess( tensor, modality, request_kwargs=self.request_model_kwargs.get(request_id), ) + if self.enable_nvtx: + profiler.range_pop() + profiler.mark( + f"dataworker.postprocessed.bytes[{len(postprocessed)}]" + ) chunk_metadata = self.tensor_uuid_to_metadata_per_request[request_id][ tensor_info.uuid] or {} @@ -658,6 +676,8 @@ def _process_read_tensors(self): f"expected {expected_bytes} bytes, got {len(postprocessed)}" ) + if self.enable_nvtx: + profiler.range_push("dataworker.queue_output") self._queue_completed_output( request_id, sequence, @@ -668,6 +688,8 @@ def _process_read_tensors(self): metadata=chunk_metadata, ), ) + if self.enable_nvtx: + profiler.range_pop() except Exception as exc: # noqa: BLE001 — must reach the client self._fail_request( request_id, exc, f"{modality} output postprocessing", diff --git a/mstar/api_server/entrypoint.py b/mstar/api_server/entrypoint.py index 58cf9652d..fe37b0466 100644 --- a/mstar/api_server/entrypoint.py +++ b/mstar/api_server/entrypoint.py @@ -30,6 +30,7 @@ from mstar.model.registry import HF_MODELS from mstar.profile.display import pretty_print_profile from mstar.profile.format import OutputInfo, RequestProfile, RequestTiming +from mstar.utils import profiler from mstar.utils.exitcode import describe_exitcode from mstar.utils.logging_config import quiet_noisy_loggers from mstar.utils.orphan import watch_parent @@ -171,6 +172,15 @@ class PendingRequest: error_status: int = 500 +def _chunk_to_ndjson_payload(chunk: ResultChunk) -> str: + """Serialize one result chunk as an NDJSON line.""" + return json.dumps({ + "modality": chunk.modality, + "data": base64.b64encode(chunk.data).decode("ascii"), + "metadata": chunk.metadata, + }) + "\n" + + class DeadConductorError(RuntimeError): """The conductor process exited before the workers finished setup (it exits when a worker dies during init), so the server must not bind.""" @@ -191,11 +201,18 @@ def __init__( model_name: str = "dummy", log_stats: bool = False, log_stats_file: str | None = None, + enable_nvtx: bool = False, ): self.upload_dir = Path(upload_dir) self.upload_dir.mkdir(parents=True, exist_ok=True) self.timeout_seconds = timeout_seconds + # The result-delivery path runs on this process, not the worker's, so its + # cost is invisible to worker-side markers. Streaming a 720p chunk means + # base64-encoding 11 MiB into 14.7 MiB of ASCII and copying that again + # through json.dumps, once per engine step. + self.enable_nvtx = enable_nvtx + # Per-request profiling: when enabled, a RequestProfile is collected for # each request and pretty-printed when the request finishes. ``log_stats_file`` # (optional) appends the report to a file instead of only stdout. @@ -215,7 +232,8 @@ def __init__( socket_path_prefix=socket_path_prefix, tensor_comm_protocol=tensor_comm_protocol, tcp_transfer_device=tcp_transfer_device, - enable_prof=self.log_stats + enable_prof=self.log_stats, + enable_nvtx=enable_nvtx, ) # Concurrent request tracking @@ -641,6 +659,8 @@ async def iter_result_chunks(self, request_id: str): done = True for chunk in new_chunks: + if self.enable_nvtx: + profiler.mark("apiserver.chunk_available") yield chunk if done: @@ -681,15 +701,29 @@ async def iter_result_chunks(self, request_id: str): async def async_stream_results(self, request_id: str): """Yield NDJSON lines as result chunks arrive (``/generate`` format).""" async for chunk in self.iter_result_chunks(request_id): - yield self._chunk_to_ndjson(chunk) - - @staticmethod - def _chunk_to_ndjson(chunk: ResultChunk) -> str: - return json.dumps({ + line = self._chunk_to_ndjson(chunk) + if self.enable_nvtx: + profiler.mark(f"apiserver.yield_line.bytes[{len(line)}]") + yield line + + def _chunk_to_ndjson(self, chunk: ResultChunk) -> str: + if not self.enable_nvtx: + return _chunk_to_ndjson_payload(chunk) + + # Split rather than wrapped as one range: the question these markers + # answer is which of the two full passes over the payload dominates. + profiler.range_push(f"apiserver.b64encode.bytes[{len(chunk.data)}]") + encoded = base64.b64encode(chunk.data).decode("ascii") + profiler.range_pop() + + profiler.range_push(f"apiserver.json_dumps.chars[{len(encoded)}]") + line = json.dumps({ "modality": chunk.modality, - "data": base64.b64encode(chunk.data).decode("ascii"), + "data": encoded, "metadata": chunk.metadata, }) + "\n" + profiler.range_pop() + return line # ---------------------------------------------------------- # Non-streaming helper @@ -1085,6 +1119,7 @@ def main(argv: list[str] | None = None): tcp_transfer_device=args.tcp_transfer_device, log_stats=log_stats, log_stats_file=args.log_stats_file, + enable_nvtx=args.enable_nvtx, ) # Spawn conductor in a separate process diff --git a/mstar/engine/cuda_graph_runner.py b/mstar/engine/cuda_graph_runner.py index a31cc43b0..a667b865e 100644 --- a/mstar/engine/cuda_graph_runner.py +++ b/mstar/engine/cuda_graph_runner.py @@ -17,6 +17,7 @@ ) from mstar.engine.resources import BucketKey, CGSlotSpec, Resource, SlotLease, StepContext, StepRunner from mstar.model.submodule_base import ModelInputsFromEngine, NodeInputs, NodeSubmodule +from mstar.utils import profiler logger = logging.getLogger(__name__) @@ -773,7 +774,13 @@ def _stage(self, lease: SlotLease, preprocessed: dict[str, Any]) -> None: def _replay(self, lease: SlotLease) -> dict: slot = self.slot_for(lease) + # The launch, not the GPU work: replay is async, so this range measures + # enqueue cost only. GPU-side duration comes from --cuda-graph-trace. + if self._enable_nvtx: + profiler.range_push(f"cg.replay.slot[{lease.slot}]") slot.graph.replay() + if self._enable_nvtx: + profiler.range_pop() return slot.static_outputs def run_forward( diff --git a/test/waypoint/benchmark_streaming.py b/test/waypoint/benchmark_streaming.py index 868cf600b..6437c526e 100644 --- a/test/waypoint/benchmark_streaming.py +++ b/test/waypoint/benchmark_streaming.py @@ -43,6 +43,7 @@ import serve_rollout as rollout # noqa: E402 from mstar.client import MStarClient, VideoFrameChunk # noqa: E402 +from mstar.utils import profiler # noqa: E402 @dataclass(frozen=True) @@ -176,6 +177,7 @@ def _measure_stream( stall_threshold_seconds: float, clock: Callable[[], float] = time.perf_counter, sleep: Callable[[float], None] = time.sleep, + enable_nvtx: bool = False, ) -> tuple[dict, list[str]]: """Consume one stream while retaining only timings and an incremental hash.""" stream = client.stream( @@ -188,6 +190,8 @@ def _measure_stream( seed=rng_seed, ) iterator = iter(stream) + if enable_nvtx: + profiler.range_push(f"benchmark.stream[{request_id}]") started = clock() observations: list[ChunkObservation] = [] failures: list[str] = [] @@ -196,11 +200,22 @@ def _measure_stream( while True: try: + # The span the sustained ratio is built from: SDK decode plus the + # blocking socket read, ending at the instant `arrived` is stamped. + if enable_nvtx: + profiler.range_push(f"benchmark.await_chunk[{len(observations)}]") event = next(iterator) + if enable_nvtx: + profiler.range_pop() except StopIteration: + if enable_nvtx: + profiler.range_pop() # the await range + profiler.range_pop() # benchmark.stream completed = clock() break arrived = clock() + if enable_nvtx: + profiler.mark(f"benchmark.chunk_arrival[{len(observations)}]") if not isinstance(event, VideoFrameChunk): failures.append( f"stream event {len(observations)} was {type(event).__name__}, not VideoFrameChunk" @@ -513,6 +528,7 @@ def _run_benchmark(args: argparse.Namespace) -> dict: rng_seed=args.seed, consumer_pause_seconds=0.0, stall_threshold_seconds=stall_threshold, + enable_nvtx=args.enable_nvtx, ) failures.extend(f"warmup: {failure}" for failure in warmup_failures) rollout._wait_for_cleanup( @@ -540,6 +556,7 @@ def _run_benchmark(args: argparse.Namespace) -> dict: rng_seed=args.seed, consumer_pause_seconds=pause, stall_threshold_seconds=stall_threshold, + enable_nvtx=args.enable_nvtx, ) failures.extend(f"{name}: {failure}" for failure in stream_failures) rollout._wait_for_cleanup( From f0ad4a215786071423ea624c35a7e6ee2af04062 Mon Sep 17 00:00:00 2001 From: garv Date: Sun, 13 Sep 2026 23:50:19 +0000 Subject: [PATCH 07/29] stream: send video frames as raw bytes instead of base64 NDJSON NDJSON has to base64 each chunk so it fits on one line. At 720p that turns 11 MB into 14.7 MB, and json.dumps then scans every character of it. That is more CPU than the 66.67 ms frame budget allows, so the event loop stalls and the socket jams on backpressure. Binary framing sends a JSON header with nbytes, then the payload untouched. Nothing has to scan for a delimiter, so nothing needs escaping or encoding. NDJSON stays the default; clients opt in via Accept. Also lands the client-side NVTX ranges used to measure this, gated on enable_nvtx. --- mstar/api_server/entrypoint.py | 90 ++++++++++- mstar/client/client.py | 124 +++++++++++++++- mstar/client/media.py | 74 +++++++++ test/modular/test_binary_framing.py | 223 ++++++++++++++++++++++++++++ test/modular/test_client_sdk.py | 73 +++++++++ 5 files changed, 571 insertions(+), 13 deletions(-) create mode 100644 test/modular/test_binary_framing.py diff --git a/mstar/api_server/entrypoint.py b/mstar/api_server/entrypoint.py index fe37b0466..0f89cc07d 100644 --- a/mstar/api_server/entrypoint.py +++ b/mstar/api_server/entrypoint.py @@ -42,6 +42,12 @@ }) STREAMING_ONLY_MODALITIES = frozenset({"video_frame"}) +NDJSON_STREAM_MEDIA_TYPE = "application/x-ndjson" +# Opt-in framing for raw binary payloads, requested via ``Accept``. Duplicated +# rather than shared with ``mstar.client.media`` so the SDK keeps its stdlib-only +# import contract; ``test_binary_framing.py`` asserts the two stay equal. +BINARY_STREAM_MEDIA_TYPE = "application/vnd.mstar.frames" + # Extension-based modality detection for uploaded files. _EXT_TO_MODALITY: dict[str, str] = {} for _mod, _exts in { @@ -181,6 +187,26 @@ def _chunk_to_ndjson_payload(chunk: ResultChunk) -> str: }) + "\n" +def _chunk_to_binary_frame(chunk: ResultChunk) -> tuple[bytes, bytes]: + """Serialize one result chunk as a header line plus its untouched payload. + + ``nbytes`` lets the reader frame by length instead of by delimiter, and no + delimiter means no escaping — which is the only reason the NDJSON form has + to base64 the payload. A 720p video_frame chunk costs two full passes over + ~14.7 MB in that form (base64, then ``json.dumps`` escape-scanning every + character it just produced); here the payload is handed on by reference. + + ``json.dumps`` escapes control characters, so the header can never contain + a raw newline and the reader's line split is always unambiguous. + """ + header = json.dumps({ + "modality": chunk.modality, + "nbytes": len(chunk.data), + "metadata": chunk.metadata, + }, separators=(",", ":")) + return header.encode("utf-8") + b"\n", chunk.data + + class DeadConductorError(RuntimeError): """The conductor process exited before the workers finished setup (it exits when a worker dies during init), so the server must not bind.""" @@ -698,13 +724,57 @@ async def iter_result_chunks(self, request_id: str): if not finished: self.abort_request(request_id) - async def async_stream_results(self, request_id: str): - """Yield NDJSON lines as result chunks arrive (``/generate`` format).""" + def async_stream_results(self, request_id: str, binary: bool = False): + """Yield the serialized body of ``/generate`` one piece at a time. + + ``binary`` selects the length-framed form negotiated through ``Accept``. + The default stays NDJSON, so a client that did not negotiate — including + the Rust frontend, which never reads ``Accept`` — sees today's bytes. + + Deliberately a plain ``def`` returning the chosen async generator rather + than an ``async def`` delegating to it: the branch is per-request, not + per-chunk, and this keeps both bodies flat. + """ + if binary: + return self._stream_binary(request_id) + return self._stream_ndjson(request_id) + + async def _stream_ndjson(self, request_id: str): async for chunk in self.iter_result_chunks(request_id): line = self._chunk_to_ndjson(chunk) - if self.enable_nvtx: - profiler.mark(f"apiserver.yield_line.bytes[{len(line)}]") - yield line + if not self.enable_nvtx: + yield line + continue + profiler.mark(f"apiserver.yield_line.bytes[{len(line)}]") + # Spans the handoff to Starlette/uvicorn: chunked-transfer framing + # and the socket writes for ~14.7 MB, plus any transport + # backpressure. The generator resumes only once that is done, so + # this range is the server's share of the client's blocking read. + profiler.range_push(f"apiserver.socket_write.bytes[{len(line)}]") + try: + yield line + finally: + profiler.range_pop() + + async def _stream_binary(self, request_id: str): + async for chunk in self.iter_result_chunks(request_id): + header, payload = _chunk_to_binary_frame(chunk) + # Two yields rather than one concatenation: joining them would copy + # the whole payload to prepend ~100 bytes, which is the class of + # work this framing exists to remove. + yield header + if not self.enable_nvtx: + yield payload + continue + profiler.mark(f"apiserver.yield_frame.bytes[{len(payload)}]") + # Same range name as the NDJSON path on purpose: it is the column + # the gap budget in STREAMING_GAP_BUDGET.md is built from, so the + # two protocols stay directly comparable in one analyzer run. + profiler.range_push(f"apiserver.socket_write.bytes[{len(payload)}]") + try: + yield payload + finally: + profiler.range_pop() def _chunk_to_ndjson(self, chunk: ResultChunk) -> str: if not self.enable_nvtx: @@ -959,10 +1029,14 @@ async def generate( ) if streaming: + # Substring match, not RFC 7231 q-value parsing: the value is a + # private vendor type that appears in no other media range, and a + # client that does not ask for it keeps the historical NDJSON body. + binary = BINARY_STREAM_MEDIA_TYPE in request.headers.get("accept", "") return StreamingResponse( - api_server.async_stream_results(request_id), - media_type="application/x-ndjson", - headers={"Cache-Control": "no-cache"}, + api_server.async_stream_results(request_id, binary=binary), + media_type=BINARY_STREAM_MEDIA_TYPE if binary else NDJSON_STREAM_MEDIA_TYPE, + headers={"Cache-Control": "no-cache", "Vary": "Accept"}, ) chunks = await api_server.collect_results(request_id, request) diff --git a/mstar/client/client.py b/mstar/client/client.py index d03e09d12..a91ad9f8d 100644 --- a/mstar/client/client.py +++ b/mstar/client/client.py @@ -22,7 +22,12 @@ import requests -from mstar.client.media import parse_ndjson_line +from mstar.client.media import ( + BINARY_STREAM_MEDIA_TYPE, + NDJSON_STREAM_MEDIA_TYPE, + iter_binary_frames, + parse_ndjson_line, +) from mstar.client.types import ( AudioBuffer, AudioChunk, @@ -42,16 +47,42 @@ MediaItem = "str | bytes | Path | tuple[str, bytes]" +def _load_nvtx(): + """Return ``(range_push, range_pop)``, importing torch only on demand. + + The SDK's dependency contract is stdlib + ``requests`` (+ ``numpy``), so + the profiler import cannot happen at module scope. Only a caller that + explicitly asks for NVTX pays for it, and such a caller is by definition + running under a CUDA profiler already. + """ + from mstar.utils.profiler import range_pop, range_push + + return range_push, range_pop + + class MStarClient: def __init__( self, base_url: str = "http://localhost:8000", timeout: float = 600.0, session: requests.Session | None = None, + enable_nvtx: bool = False, + prefer_binary: bool = True, ): self.base_url = base_url.rstrip("/") self.timeout = timeout self._session = session or requests.Session() + # Ask for length-framed binary payloads, but decide how to parse from + # the response's Content-Type. A server that does not implement the + # framing — an older build, or the Rust frontend, neither of which reads + # ``Accept`` — answers NDJSON and the historical path handles it. Set + # False to force NDJSON, which the streaming benchmark uses to A/B. + self._prefer_binary = prefer_binary + # Splits the client's share of the streaming gap into the blocking + # socket read and the decode of the line it returns. Without this the + # whole of both lands in the caller's "waiting for a chunk" range and + # is indistinguishable from server time. + self._nvtx = _load_nvtx() if enable_nvtx else None # ------------------------------------------------------------------ # Core @@ -193,11 +224,65 @@ def _coerce_file(kind: str, idx: int, item) -> tuple[str, bytes]: return item[0], bytes(item[1]) raise TypeError(f"Unsupported {kind} item type: {type(item)!r}") + def _stream_headers(self) -> dict[str, str]: + if not self._prefer_binary: + return {} + return { + "Accept": f"{BINARY_STREAM_MEDIA_TYPE}, {NDJSON_STREAM_MEDIA_TYPE};q=0.9", + # Compressing ~11 MB of near-incompressible RGB per chunk would put + # back the byte-proportional CPU pass this framing exists to remove. + "Accept-Encoding": "identity", + } + + def _stream_binary(self, resp) -> Iterator[StreamEvent]: + """Consume a length-framed response body off the raw socket.""" + encoding = resp.headers.get("content-encoding", "").lower() + if encoding and encoding != "identity": + # ``resp.raw.read`` hands back undecoded bytes, so a compressed body + # would surface as an unreadable header. Fail with the cause named. + raise RuntimeError( + f"Binary frame stream arrived with Content-Encoding {encoding!r}; " + "only 'identity' can be read from the raw socket" + ) + frames = iter_binary_frames(resp.raw) + if self._nvtx is None: + for parsed in frames: + yield self._to_event(parsed) + return + + range_push, range_pop = self._nvtx + while True: + # The blocking socket read plus frame reassembly. Counterpart of + # ``client.iter_lines`` on the NDJSON path; there is no decode step + # to measure after it, which is the point. + range_push("client.read_frame") + try: + parsed = next(frames) + except StopIteration: + range_pop() + break + range_pop() + range_push("client.to_event") + event = self._to_event(parsed) + range_pop() + yield event + def _stream(self, url, data, files) -> Iterator[StreamEvent]: with self._session.post( - url, data=data, files=files or None, stream=True, timeout=self.timeout + url, + data=data, + files=files or None, + stream=True, + timeout=self.timeout, + headers=self._stream_headers(), ) as resp: resp.raise_for_status() + # Branch on what the server actually sent, not on what was asked + # for: that is what makes the negotiation safe against servers that + # ignore ``Accept`` entirely. + if resp.headers.get("content-type", "").startswith(BINARY_STREAM_MEDIA_TYPE): + yield from self._stream_binary(resp) + return # ``decode_unicode=True`` only yields ``str`` when ``resp.encoding`` # is set, and that is derived from the Content-Type charset. The # server streams ``application/x-ndjson`` without one, so default to @@ -207,16 +292,45 @@ def _stream(self, url, data, files) -> Iterator[StreamEvent]: # Raw RGB frame events are multi-megabyte NDJSON lines. Requests' # 512-byte default repeatedly concatenates the growing partial # line and becomes quadratic at 720p, so read them in large slabs. - for line in resp.iter_lines( + lines = resp.iter_lines( chunk_size=_STREAM_READ_CHUNK_SIZE, decode_unicode=True, - ): + ) + if self._nvtx is None: + for line in lines: + if not isinstance(line, str): + continue + parsed = parse_ndjson_line(line) + if parsed is None: + continue + yield self._to_event(parsed) + return + + range_push, range_pop = self._nvtx + while True: + # Socket read, UTF-8 decode and line reassembly. At 720p one + # line is ~14.7 MB of ASCII, so this is where transport time + # and the client's own copying both land. + range_push("client.iter_lines") + try: + line = next(lines) + except StopIteration: + range_pop() + break + range_pop() if not isinstance(line, str): continue + # json.loads over that line plus the base64 decode back to + # the original 11 MiB of RGB. + range_push(f"client.parse_ndjson.chars[{len(line)}]") parsed = parse_ndjson_line(line) + range_pop() if parsed is None: continue - yield self._to_event(parsed) + range_push("client.to_event") + event = self._to_event(parsed) + range_pop() + yield event @staticmethod def _to_event(parsed: dict) -> StreamEvent: diff --git a/mstar/client/media.py b/mstar/client/media.py index 1a51fc448..db9f6ecb9 100644 --- a/mstar/client/media.py +++ b/mstar/client/media.py @@ -10,6 +10,15 @@ import io import json import wave +from collections.abc import Iterator + +NDJSON_STREAM_MEDIA_TYPE = "application/x-ndjson" +# Opt-in framing for raw binary payloads, requested via ``Accept``. Duplicated +# rather than imported from the server so this module keeps its stdlib-only +# contract; ``test_binary_framing.py`` asserts the two stay equal. +BINARY_STREAM_MEDIA_TYPE = "application/vnd.mstar.frames" + +_FRAME_HEADER_READ_SIZE = 64 * 1024 def pcm16_to_wav_bytes(pcm: bytes, sample_rate: int, num_channels: int = 1) -> bytes: @@ -45,3 +54,68 @@ def parse_ndjson_line(line: str) -> dict | None: "bytes": base64.b64decode(data) if data else b"", "metadata": msg.get("metadata") or {}, } + + +def iter_binary_frames(raw, read_size: int = _FRAME_HEADER_READ_SIZE) -> Iterator[dict]: + """Decode a length-framed binary stream into the same dicts as NDJSON. + + Each frame is a one-line JSON header (``modality``, ``nbytes``, + ``metadata``) followed by exactly ``nbytes`` of untouched payload. Framing + by length rather than by delimiter is what lets the payload travel raw: at + 720p the NDJSON form spends ~85 ms per chunk base64-ing 11 MB and scanning + the result for characters that need escaping, against a 66.67 ms budget. + + Yields ``{"modality", "bytes", "metadata"}``, matching + :func:`parse_ndjson_line`, so both protocols share one event path. + + ``raw`` is anything with ``read(n) -> bytes``: ``requests``' ``resp.raw`` in + production, a ``BytesIO`` in tests. ``read_size`` only bounds the header + scan; payload reads ask for exactly what is outstanding. + """ + buf = bytearray() + while True: + newline = buf.find(b"\n") + while newline < 0: + block = raw.read(read_size) + if not block: + if buf: + raise RuntimeError( + f"Binary stream ended mid-header after {len(buf)} bytes" + ) + return # Clean end: the server closed between frames. + buf += block + newline = buf.find(b"\n") + + # Unlike NDJSON, a malformed header is not recoverable by skipping the + # line: without ``nbytes`` there is no way to find the next frame, so + # this raises rather than returning None. + header = json.loads(bytes(buf[:newline])) + del buf[: newline + 1] + if "modality" not in header and "error" in header: + raise RuntimeError(f"Server stream failed: {header['error']}") + + nbytes = header.get("nbytes") or 0 + parts: list[bytes] = [] + buffered = min(len(buf), nbytes) + if buffered: + parts.append(bytes(buf[:buffered])) + del buf[:buffered] + have = buffered + while have < nbytes: + block = raw.read(nbytes - have) + if not block: + raise RuntimeError( + f"Binary stream ended {nbytes - have} bytes short of the " + f"{nbytes}-byte {header.get('modality')!r} payload" + ) + parts.append(block) + have += len(block) + + yield { + "modality": header.get("modality"), + # Must be ``bytes``: VideoFrameChunk rejects ``bytearray``. The join + # is the only copy of the payload on this path, and a socket read + # that satisfied the frame in one go skips even that. + "bytes": parts[0] if len(parts) == 1 else b"".join(parts), + "metadata": header.get("metadata") or {}, + } diff --git a/test/modular/test_binary_framing.py b/test/modular/test_binary_framing.py new file mode 100644 index 000000000..f9f20f43d --- /dev/null +++ b/test/modular/test_binary_framing.py @@ -0,0 +1,223 @@ +"""Binary frame streaming for ``/generate`` (no live server, no GPU). + +The NDJSON form base64s the payload so it can live inside a JSON string, which +at 720p costs ~85 ms of CPU per chunk against a 66.67 ms budget. Binary framing +sends a one-line JSON header carrying ``nbytes`` and then the payload untouched. +These tests pin the framing, the reader's tolerance for short socket reads, and +— most importantly — that an un-negotiated request still gets the old bytes. +""" + +import base64 +import io +import json + +import pytest +from fastapi.testclient import TestClient + +from mstar.api_server import entrypoint +from mstar.api_server.entrypoint import ( + BINARY_STREAM_MEDIA_TYPE, + NDJSON_STREAM_MEDIA_TYPE, + _chunk_to_binary_frame, + _chunk_to_ndjson_payload, +) +from mstar.api_server.request_types import ResultChunk +from mstar.client.client import MStarClient +from mstar.client.media import BINARY_STREAM_MEDIA_TYPE as CLIENT_BINARY_MEDIA_TYPE +from mstar.client.media import iter_binary_frames + + +def _chunks(): + return [ + ResultChunk( + request_id="r", + modality="video_frame", + # Spans every byte value, including b"\n" and bytes that are not + # valid UTF-8 — precisely what base64 existed to work around. + data=bytes(range(256)) * 64, + metadata={"frame_index": i, "width": 8, "height": 8}, + ) + for i in range(3) + ] + + +def _wire(chunks): + return b"".join(b"".join(_chunk_to_binary_frame(c)) for c in chunks) + + +class _Dribble: + """A reader that returns at most ``limit`` bytes, like a real socket.""" + + def __init__(self, data: bytes, limit: int = 7): + self._data = data + self._pos = 0 + self._limit = limit + + def read(self, size: int) -> bytes: + end = self._pos + min(size, self._limit) + out = self._data[self._pos:end] + self._pos += len(out) + return out + + +def test_media_type_constants_agree(): + """The server and the SDK duplicate this string; drift would silently + disable negotiation and leave 720p slow with no failing test.""" + assert BINARY_STREAM_MEDIA_TYPE == CLIENT_BINARY_MEDIA_TYPE + + +def test_binary_frames_round_trip(): + chunks = _chunks() + frames = list(iter_binary_frames(io.BytesIO(_wire(chunks)))) + + assert len(frames) == len(chunks) + for frame, chunk in zip(frames, chunks, strict=True): + assert frame["bytes"] == chunk.data + assert frame["modality"] == chunk.modality + assert frame["metadata"] == chunk.metadata + + +def test_binary_frames_yield_bytes_not_bytearray(): + """``VideoFrameChunk.__post_init__`` rejects ``bytearray`` outright.""" + (frame,) = iter_binary_frames(io.BytesIO(_wire(_chunks()[:1]))) + assert type(frame["bytes"]) is bytes + + +def test_binary_frames_survive_short_reads(): + chunks = _chunks() + frames = list(iter_binary_frames(_Dribble(_wire(chunks)))) + assert [f["bytes"] for f in frames] == [c.data for c in chunks] + + +def test_binary_frames_reject_truncated_payload(): + wire = _wire(_chunks()) + with pytest.raises(RuntimeError, match="ended 10 bytes short"): + list(iter_binary_frames(io.BytesIO(wire[:-10]))) + + +def test_binary_frames_reject_truncated_header(): + header, _ = _chunk_to_binary_frame(_chunks()[0]) + with pytest.raises(RuntimeError, match="ended mid-header"): + list(iter_binary_frames(io.BytesIO(header[:20]))) + + +def test_binary_frames_end_cleanly_on_empty_stream(): + assert list(iter_binary_frames(io.BytesIO(b""))) == [] + + +def test_binary_frame_header_is_always_one_line(): + """The reader splits the header on the first newline, so metadata that + contains one must not be able to forge a frame boundary.""" + chunk = ResultChunk( + request_id="r", + modality="text", + data=b"payload\nwith\nnewlines", + metadata={"note": "line\nbreak", "unicode": "café é"}, + ) + header, payload = _chunk_to_binary_frame(chunk) + + assert header.count(b"\n") == 1 and header.endswith(b"\n") + (frame,) = iter_binary_frames(io.BytesIO(header + payload)) + assert frame["bytes"] == chunk.data + assert frame["metadata"] == chunk.metadata + + +def test_binary_frames_raise_on_top_level_error_envelope(): + """Mirrors ``parse_ndjson_line``: a header with no ``modality`` but an + ``error`` key is a failure envelope, not a zero-length chunk.""" + wire = json.dumps({"error": "bridge failed"}).encode() + b"\n" + with pytest.raises(RuntimeError, match="Server stream failed: bridge failed"): + list(iter_binary_frames(io.BytesIO(wire))) + + +def test_binary_error_chunk_reaches_the_sdk_error_path(): + chunk = ResultChunk( + request_id="r", modality="error", data=b"capture failed", metadata={"status": 500} + ) + (frame,) = iter_binary_frames(io.BytesIO(_wire([chunk]))) + with pytest.raises(RuntimeError, match=r"Server stream failed \(status 500\): capture failed"): + MStarClient._to_event(frame) + + +# ---------------------------------------------------------------------- +# End to end through the real FastAPI route (no model, no GPU) +# ---------------------------------------------------------------------- + + +class _ChunkServer: + """Stand-in for ``APIServer`` that replays canned chunks. + + Borrows the real serialization methods so the route, the framing and the + NDJSON fallback are all the production code paths. + """ + + enable_nvtx = False + async_stream_results = entrypoint.APIServer.async_stream_results + _stream_ndjson = entrypoint.APIServer._stream_ndjson + _stream_binary = entrypoint.APIServer._stream_binary + _chunk_to_ndjson = entrypoint.APIServer._chunk_to_ndjson + + def __init__(self, chunks): + self._chunks = chunks + + def submit_request(self, **kwargs): + return None + + async def iter_result_chunks(self, request_id): + for chunk in self._chunks: + yield chunk + + +def _post(monkeypatch, chunks, headers=None): + monkeypatch.setattr(entrypoint, "api_server", _ChunkServer(chunks)) + return TestClient(entrypoint.app).post( + "/generate", + data={"output_modalities": "video_frame", "streaming": "true"}, + headers=headers or {}, + ) + + +def test_generate_streams_binary_frames_when_negotiated(monkeypatch): + chunks = _chunks() + response = _post(monkeypatch, chunks, {"Accept": BINARY_STREAM_MEDIA_TYPE}) + + assert response.status_code == 200 + assert response.headers["content-type"].startswith(BINARY_STREAM_MEDIA_TYPE) + assert response.headers["vary"] == "Accept" + frames = list(iter_binary_frames(io.BytesIO(response.content))) + assert [f["bytes"] for f in frames] == [c.data for c in chunks] + assert len(response.content) < len(b"".join( + _chunk_to_ndjson_payload(c).encode() for c in chunks + )) + + +def test_generate_defaults_to_unchanged_ndjson(monkeypatch): + """Regression gate on the default path: an un-negotiated request must get + byte-identical output to what the server produced before this change.""" + chunks = _chunks() + response = _post(monkeypatch, chunks) + + assert response.headers["content-type"].startswith(NDJSON_STREAM_MEDIA_TYPE) + expected = "".join(_chunk_to_ndjson_payload(c) for c in chunks).encode() + assert response.content == expected + + +def test_generate_ignores_unrelated_accept_headers(monkeypatch): + response = _post(monkeypatch, _chunks(), {"Accept": "application/json, */*"}) + assert response.headers["content-type"].startswith(NDJSON_STREAM_MEDIA_TYPE) + + +def test_generate_binary_and_ndjson_deliver_identical_payloads(monkeypatch): + """The property the GPU benchmark checks with SHA-256, asserted here for + free: switching protocol must not change a single delivered byte.""" + chunks = _chunks() + binary = _post(monkeypatch, chunks, {"Accept": BINARY_STREAM_MEDIA_TYPE}) + ndjson = _post(monkeypatch, chunks) + + from_binary = [f["bytes"] for f in iter_binary_frames(io.BytesIO(binary.content))] + from_ndjson = [ + base64.b64decode(json.loads(line)["data"]) + for line in ndjson.content.decode().splitlines() + if line + ] + assert from_binary == from_ndjson == [c.data for c in chunks] diff --git a/test/modular/test_client_sdk.py b/test/modular/test_client_sdk.py index f216863cf..e96abc056 100644 --- a/test/modular/test_client_sdk.py +++ b/test/modular/test_client_sdk.py @@ -130,3 +130,76 @@ def test_audiobuffer_wav_bytes(): pcm = np.array([0, 16000, -16000], dtype=" Date: Mon, 14 Sep 2026 02:40:02 +0000 Subject: [PATCH 08/29] waypoint: capture the DiT prime step behind capture_dit_prime Startup p95 -7.6% at 720p and -14.2% at 360p for 0.12 MB more graph memory. --- docs/waypoint/DECISIONS.md | 15 +- docs/waypoint/MVP_IMPLEMENTATION_STATUS.md | 4 +- docs/waypoint/OPTIMIZATION_BACKLOG.md | 40 ++++- docs/waypoint/VALIDATION.md | 59 ++++++- .../prime-capture-2026-09-14-360p-off.json | 163 ++++++++++++++++++ .../prime-capture-2026-09-14-360p-on.json | 163 ++++++++++++++++++ ...rime-capture-2026-09-14-720p-off-arm1.json | 163 ++++++++++++++++++ ...rime-capture-2026-09-14-720p-off-arm2.json | 163 ++++++++++++++++++ ...prime-capture-2026-09-14-720p-on-arm1.json | 163 ++++++++++++++++++ ...prime-capture-2026-09-14-720p-on-arm2.json | 163 ++++++++++++++++++ mstar/model/waypoint/config.py | 15 +- mstar/model/waypoint/submodules.py | 42 +++-- mstar/model/waypoint/waypoint_model.py | 2 + test/modular/test_waypoint_checkpoint.py | 8 +- test/modular/test_waypoint_gpu.py | 158 ++++++++++++++++- test/modular/test_waypoint_shell.py | 35 +++- .../test_waypoint_streaming_benchmark.py | 20 +++ test/waypoint/benchmark_streaming.py | 71 ++++++++ 18 files changed, 1408 insertions(+), 39 deletions(-) create mode 100644 docs/waypoint/baselines/prime-capture-2026-09-14-360p-off.json create mode 100644 docs/waypoint/baselines/prime-capture-2026-09-14-360p-on.json create mode 100644 docs/waypoint/baselines/prime-capture-2026-09-14-720p-off-arm1.json create mode 100644 docs/waypoint/baselines/prime-capture-2026-09-14-720p-off-arm2.json create mode 100644 docs/waypoint/baselines/prime-capture-2026-09-14-720p-on-arm1.json create mode 100644 docs/waypoint/baselines/prime-capture-2026-09-14-720p-on-arm2.json diff --git a/docs/waypoint/DECISIONS.md b/docs/waypoint/DECISIONS.md index d46445da8..206449b78 100644 --- a/docs/waypoint/DECISIONS.md +++ b/docs/waypoint/DECISIONS.md @@ -30,17 +30,20 @@ ## WP-003: Optional CUDA Graph Acceleration - **Status:** Supersedes the required-capture policy, 2026-09-11 -- **Decision:** `cuda_graph=True` attempts capture for encoder prime, steady DiT - rollout, decoder initialization, and steady decoder execution. Capture failure - falls back to eager execution; `cuda_graph=False` declares no capture buckets. - `compile_dit` independently controls the two outer DiT regions. +- **Decision:** `cuda_graph=True` attempts capture for encoder prime, DiT prime, + steady DiT rollout, decoder initialization, and steady decoder execution. Capture + failure falls back to eager execution; `cuda_graph=False` declares no capture + buckets. `compile_dit` independently controls the two outer DiT regions. - **Reason:** CUDA graphs are an acceleration mechanism, not part of the model's numerical contract. The engine already supports eager fallback, and Waypoint's ring, mask planning, and functional AE state have eager execution paths. - **Constraint:** The masked FlexAttention primitive remains compiled because bare eager `flex_attention` ignores this BlockMask's block-index visibility data. -- **Exception:** The one-time DiT prime/cache pass is compiled with - `fullgraph=True` only when `compile_dit=True`, and remains uncaptured. +- **Amendment:** `PRIME-GRAPH-001` promoted the one-time DiT prime/cache pass from + compiled-only to captured on 2026-09-14. `capture_dit_prime` defaults to `True` + and is subordinate to `cuda_graph`; setting it `False` keeps the uncaptured prime + reachable as the startup-latency control arm. `compile_dit` still independently + controls that pass's `fullgraph=True` region either way. ## WP-004: Internal Prime Is Not User Output diff --git a/docs/waypoint/MVP_IMPLEMENTATION_STATUS.md b/docs/waypoint/MVP_IMPLEMENTATION_STATUS.md index 849b46755..50335dca7 100644 --- a/docs/waypoint/MVP_IMPLEMENTATION_STATUS.md +++ b/docs/waypoint/MVP_IMPLEMENTATION_STATUS.md @@ -19,10 +19,10 @@ unmerged entries, conflict artifacts, or staged files. |---|---|---|---| | Startup/config | Passed | Both artifact paths, the TAEHV runtime, and the DiT manifest validate before device allocation; tensor completeness and TAEHV architecture validate during loading before admission; variant-specific Hub mapping prevents cross-variant weights; Python 3.12/uv resolves and builds the pin; registry-selected 360p Hub startup passed | None for scripted MVP | | Request semantics | Passed | Positive bounded `num_steps`, exact validated action count, required seed, internal idle prime, action zero preserved; full 360p/720p eight-step streams emitted exactly 32 generated frames from index zero | None for scripted MVP | -| DiT execution | Integrated | Runtime tables materialize after load; `compile_dit` independently selects compiled or eager denoise/cache regions; optional 128-token and 512-token rollout graphs captured on H100 | Record a full server graph-off run | +| DiT execution | Integrated | Runtime tables materialize after load; `compile_dit` independently selects compiled or eager denoise/cache regions; optional 128-token and 512-token rollout and prime graphs captured on H100 | Record a full server graph-off run | | Mask planning | Passed | One fixed-address local/global block mask per slot; immutable device visibility tables remove per-step allocations; multi-wrap/dilation/world parity passes; full-size 360p and 720p profiles each found 16/16 steady DiT graph replays and zero blocking CUDA calls | None for scripted MVP | | Encoder/decoder | Integrated | Pure tensor encoder; nine explicit histories; real-weight FP32 parity; optional encoder/init/steady graphs captured and served at both resolutions on H100; graph-free declarations use eager forwards | Record a full server graph-off run | -| Optional capture policy | Integrated | `cuda_graph` controls declaration independently of `compile_dit`; all Waypoint buckets use normal eager fallback on capture failure; all four graph-enabled buckets previously captured at both resolutions | Record graph-off and injected capture-failure server runs | +| Optional capture policy | Integrated | `cuda_graph` controls declaration independently of `compile_dit`; all Waypoint buckets use normal eager fallback on capture failure; all five graph-enabled buckets captured at both resolutions, with `capture_dit_prime` gating the fifth | Record graph-off and injected capture-failure server runs | | Frame protocol | Passed | Python streaming-only `video_frame`, canonical RGB24 metadata, immutable zero-copy SDK view, named-byte upload, explicit stream errors, ordered async-read delivery, and live 360p/720p SDK streams pass | Rust frontend support is deferred and outside the scripted MVP | | End to end | Passed | Local 720p and registry-Hub 360p normal `EngineManager` paths captured all four buckets; both resolutions passed sequential and full-size two-world interleaved deterministic streams, exact counts, cleanup, repeated slot reuse, and bounded server memory | None for scripted MVP | | Streaming viability | Baseline complete | `STREAM-001` records captured 16-step baseline and matched slow-consumer runs for both resolutions, including typed-stream correctness and server process-group memory | A later decision may define a viability threshold from these measurements | diff --git a/docs/waypoint/OPTIMIZATION_BACKLOG.md b/docs/waypoint/OPTIMIZATION_BACKLOG.md index c82e457ad..f0eb714bb 100644 --- a/docs/waypoint/OPTIMIZATION_BACKLOG.md +++ b/docs/waypoint/OPTIMIZATION_BACKLOG.md @@ -92,14 +92,48 @@ at 360p and 3.462 seconds at 720p for 3.750 seconds deliberately injected, with ## PRIME-GRAPH-001: DiT Prime Capture -- **Evidence:** MVP deliberately compiles but does not capture the one-time DiT - prime/cache pass. +- **Status:** Closed 2026-09-14. The DiT now declares `prime` alongside `rollout`, + so a graph-enabled server captures five buckets instead of four, the new one being + `dit: prime[bs=1,tokens=512]`. Gated by `capture_dit_prime`, default `True` and + subordinate to `cuda_graph`; `False` restores the compiled eager prime and is the + control arm below. +- **Evidence:** Startup TTFF measured by `benchmark_streaming.py --startup-repeats`, + one sample per world-slot reuse cycle. At 360p, 20 samples per arm: p50 63.10 -> + 55.13 ms (-12.6%), p95 67.72 -> 58.12 ms (-14.2%), peak GPU 4626 -> 4424 MiB. At + 720p, four arms position-balanced (see the measurement caveat): p50 142.53 -> + 141.95 ms (-0.4%), p95 167.24 -> 154.55 ms (-7.6%), peak GPU 5576 -> 5476 MiB. + The DiT graph runner's static buffers grow 5 -> 10 entries, 0.13 -> 0.25 MB, and + `post_warmup_validate` still passes, so the ring is left clean by capture. Every + arm reported `correctness.passed`, zero stalls, and an unchanged payload SHA-256 + (`cc84686681d6...` at 360p, `bdd8ed5605d3...` at 720p, the latter matching the + 2026-09-13 binary-framing record). Raw artifacts: + `baselines/prime-capture-2026-09-14-360p-{on,off}.json` and + `baselines/prime-capture-2026-09-14-720p-{on,off}-arm{1,2}.json`. + An nsys trace attributes the win to the prime node itself: the + `worker[worker_0].node[dit].graph_walk[prime]` range falls from 55.86 ms host / + 1743 kernels / 13.6 ms GPU uncaptured to 6.13 ms host / 27 kernels / 0.033 ms GPU + captured, and `check_nsys_replay.py --rollout-range + "worker[worker_0].node[dit].graph_walk[prime]" --expected-forwards 3` goes from + `graph_replays=0 sync_or_blocking_calls=1` (6 `cudaMalloc` and 2 `cudaFree` inside + the forward) to `graph_replays=3 sync_or_blocking_calls=0`. The steady rollout + range is unchanged at `forwards=33 graph_replays=33 sync_or_blocking_calls=0`. +- **Measurement caveat:** Arm order dominates this benchmark. Identical + `capture_dit_prime=False` code measured p50 149.56 / p95 194.31 ms as a job's first + arm and p50 135.50 / p95 140.16 ms as its second, a 14.06 / 54.15 ms swing at an + unchanged 130.0 ms floor. A first blocked A/B ran capture-on first at 720p and + reported an apparent +5.93 ms p50 / +17.44 ms p95 regression, entirely inside that + swing; a reversed-order repeat inverted the sign. The 720p figures above are the + mean of both positions per arm and supersede the blocked result. Note also that + nsys charges per launch, so the profiled kernel-count gap flatters capture relative + to unprofiled wall time. - **Expected benefit:** Lower request startup latency. - **Dependency:** Required steady graphs and stable prime inputs/state addresses. - **Proposed benchmark:** Admission-to-first-frame latency and graph memory with and without prime capture across repeated world-slot reuse. - **Completion criterion:** Capture reduces p50/p95 startup latency without state - leakage or disproportionate graph memory. + leakage or disproportionate graph memory. Met: p95 falls at both resolutions and + p50 falls at 360p, 720p p50 is a 0.4% tie, no arm leaked state, and graph memory + grew by 0.12 MB with no peak-GPU growth. ## ENCODED-VIDEO-001: Encoded Video Output diff --git a/docs/waypoint/VALIDATION.md b/docs/waypoint/VALIDATION.md index 1690bc76f..a8e5942d4 100644 --- a/docs/waypoint/VALIDATION.md +++ b/docs/waypoint/VALIDATION.md @@ -14,13 +14,13 @@ exact command, environment, artifact location, and result when closing a row. | NUM-001 | Live reference-compatible parity | Passed at 360p and 720p | Native-checkpoint same-process tables, conditioner, every DiT stage, five passes, ring writes, 41 rollout latents, functional TAEHV state, and pixels passed with zero tolerance on H100 at both resolutions. | | REQ-001 | Request and prime semantics | Passed | CPU tests reject non-positive steps and wrong action counts and prove idle prime preserves action zero; live sequential and interleaved 360p/720p runs emitted no seed frames and exact generated counts. | | GRAPH-001 | Optional DiT compile | Passed | Post-load table materialization, `fullgraph=True` construction, and bounded compiled/eager equivalence are tested; both full-size variants compiled on H100. | -| GRAPH-002 | Optional capture and eager fallback | CPU mode-selection coverage added; existing capture path passed on H100 | `cuda_graph=False` declares no buckets; attempted captures are optional and use the engine's eager fallback on failure. All four graph-enabled buckets previously captured at both resolutions. A full server graph-off run remains to be recorded. | +| GRAPH-002 | Optional capture and eager fallback | CPU mode-selection coverage added; existing capture path passed on H100 | `cuda_graph=False` declares no buckets; attempted captures are optional and use the engine's eager fallback on failure. All five graph-enabled buckets captured at both resolutions, the fifth being the `dit: prime` bucket added by `PRIME-GRAPH-001`; `capture_dit_prime=False` declares four and serves prime eagerly. A full server graph-off run remains to be recorded. | | MASK-001 | Planned masks and replay sync | Passed | Tests cover one staged local/global mask per geometry and graph slot; full 360p and 720p profiles each prove 16/16 graph replay and zero blocking CUDA calls inside steady DiT forwards. | | AE-001 | Functional TAEHV execution paths | Passed | Nine-history state, fixed graph interfaces, isolation, cleanup, real-weight parity, optional capture declarations, and full-size eight-step streams pass at both resolutions. | | FRAME-001 | Typed RGB protocol | Passed | Server/SDK tests cover metadata, contiguous RGB24 bytes, zero-copy NumPy shape, indexing, errors, non-streaming rejection, ordered delivery, and live 360p/720p typed consumption. | | E2E-360 | 360p normal serving path | Passed | Registry-selected Hub source + `EngineManager`, all buckets, SDK stream, exact counts, deterministic concurrent worlds, cleanup, slot reuse, and bounded memory passed. | | E2E-720 | 720p normal serving path | Passed | Local source + `EngineManager`, all buckets, typed SDK stream, exact counts, byte-identical sequential reuse, full-size two-world interleaving, cleanup, bounded memory, and full eight-step profiler soak passed. | -| WORLD-001 | World isolation and reuse | Passed | Full server two-world DiT execution interleaved at both resolutions, reproduced distinct solo baselines byte-for-byte, cleaned every request, reused slots, and had -2.0/-49.5 MiB host PSS and 0/0 MiB GPU quiescent growth after warmup at 360p/720p. | +| WORLD-001 | World isolation and reuse | Passed | Full server two-world DiT execution interleaved at both resolutions, reproduced distinct solo baselines byte-for-byte, cleaned every request, reused slots, and had -2.0/-49.5 MiB host PSS and 0/0 MiB GPU quiescent growth after warmup at 360p/720p. Re-run 2026-09-14 with the `dit: prime` graph live: same verdict at both resolutions, 0 MiB GPU growth. | | STREAM-001 | Post-MVP streaming baseline | Passed without a release threshold | Captured 16-step baseline and slow-consumer runs at both resolutions record TTFF, sustained media/wall ratio, p50/p95 gaps, jitter, stalls, backpressure, host PSS, and GPU memory in retained JSON artifacts. | ## Historical Evidence @@ -92,6 +92,14 @@ end-to-end gates. | 2026-09-11 | Post-rebase Python API/SDK/worker and native Rust checks | Historical: 51 Python tests and 16 Rust wire tests passed; `cargo check --locked` passed | Python raw-frame handling remains in scope. The Waypoint-specific Rust changes covered by this run were later reverted. Socket tests ran outside the restricted sandbox. | | 2026-09-11 | `pytest -q test/modular/test_waypoint_packaging.py test/modular/test_waypoint_checkpoint.py test/modular/test_video_frame_protocol.py` | 73 passed, 2 warnings | Post-fix CLI/alias/metadata/pinned-TAEHV contracts and the complete Python raw-frame protocol gate. | | 2026-09-12 | `PYTHONPATH=. pytest -q test/modular/test_video_frame_protocol.py test/modular/test_client_sdk.py test/modular/test_api_completion_guard.py` after reverting Waypoint-specific Rust frontend changes | 45 passed, 2 existing FastAPI deprecation warnings | Confirms the supported Python server/SDK frame path is unaffected; `rust/server/src/main.rs` and `test/rust/test_rust_frontend.py` have no remaining Waypoint diff. | +| 2026-09-14 | `PYTHONPATH=. pytest -q` over `test_waypoint_shell.py`, `test_waypoint_checkpoint.py`, `test_waypoint_dit.py`, `test_waypoint_components.py`, `test_waypoint_streaming_benchmark.py`, `test_cuda_graph_capture.py`; CPU-only sandbox | 210 passed; `ruff check` clean on all 8 changed files | `PRIME-GRAPH-001` contracts: both DiT walks declared in `[rollout, prime]` order, `capture_dit_prime=False` declares rollout only, one shared static-input family, new `--startup-repeats`/`--startup-steps` CLI validation and percentile math | +| 2026-09-14 | `WAYPOINT_GPU_TESTS=1 pytest -q test/modular/test_waypoint_gpu.py`; H100 80GB, slurm job 6027 | 13 passed (10 pre-existing, 3 new) | Prime replay equals the uncaptured prime by ring snapshot; the prime graph returns its own static input buffer; prime-then-rollout through both graphs is bit-exact against eager, with anti-vacuity assertions on each | +| 2026-09-14 | `pytest -q test/modular/test_piecewise_config_signature.py test/modular/test_cuda_graph_capture.py`; H100, slurm job 6027 | 11 passed | Capture-policy and piecewise-signature contracts unchanged by the new bucket | +| 2026-09-14 | `benchmark_streaming.py --variant {360p,720p} --protocol binary --steps 16 --startup-repeats 20`, `capture_dit_prime` default vs `false`; H100, slurm job 6028 | Five capture lines with the flag on, four with it off, at both resolutions. 360p startup p50 63.10 -> 55.13 ms and p95 67.72 -> 58.12 ms; 720p blocked result superseded by job 6033 | `PRIME-GRAPH-001` startup A/B. Payload SHA-256 identical per resolution across arms, `correctness.passed` true, zero stalls | +| 2026-09-14 | Same benchmark at 720p with the arms reversed, 30 samples per arm; H100, slurm job 6033 | Identical capture-off code moved p50 14.06 ms and p95 54.15 ms by run position alone. Position-balanced over both jobs: p50 142.53 -> 141.95 ms, p95 167.24 -> 154.55 ms, peak GPU 5576 -> 5476 MiB | Controls the arm-ordering confound that made the first blocked 720p A/B read as a regression | +| 2026-09-14 | `nsys profile` + `check_nsys_replay.py --rollout-range "worker[worker_0].node[dit].graph_walk[prime]" --expected-forwards 3`, capture on and off; H100, slurm jobs 6029 and 6030 | On: `forwards=3 graph_replays=3 sync_or_blocking_calls=0`. Off: `forwards=3 graph_replays=0 sync_or_blocking_calls=1` with 6 `cudaMalloc` and 2 `cudaFree` inside the forward | Prime replay contract, and proof the gate discriminates. Same traces hold the steady rollout range at `forwards=33 graph_replays=33 sync_or_blocking_calls=0` | +| 2026-09-14 | `serve_rollout.py --variant {720p,360p} --source hub --steps 8 --worlds 2 --concurrent-waves 4 --measure-memory --physical-gpu 0`, `capture_dit_prime` default; H100, slurm job 6035 | PASS at both resolutions, exit 0, five capture lines each including `dit: prime`. 720p peak host 6465.7 MiB / GPU 6380.0 MiB, quiet 6465.8 / 6380.0. 360p peak 6227.6 / 4916.0, quiet 6232.9 / 4916.0 | `WORLD-001` re-run with the prime graph live: four interleaved two-world waves reproduce the solo baselines byte for byte, every request cleaned, slots reused, 0 MiB GPU growth | +| 2026-09-14 | `pytest -q test/modular/test_waypoint_reference_equivalence.py test/modular/test_waypoint_pixel_equivalence.py`; H100, slurm job 6035 | 19 skipped, not run | Not runnable on this host: these fixtures root at `/mnt/storage/garv901/waypoint-1.5-1B`, which does not exist here, and no oracle frames are present. Not a coverage gap for `PRIME-GRAPH-001` -- both files construct the model directly with `capture=False` and never build a graph. Numerical coverage for the prime bucket comes from `test_waypoint_gpu.py` bit-exactness and the unchanged payload SHA-256 across every benchmark arm | ## GPU Reproduction Commands @@ -189,6 +197,53 @@ needed network access to resolve the pinned TAEHV source archive. A later clean Python 3.12/uv resolution and separate real pinned artifact build closed CKPT-003; the archive is now intentionally installed outside the index-safe project metadata. +### 2026-09-14 DiT prime capture (PRIME-GRAPH-001) + +One H100 80GB, Hub weights from the offline HF cache, upstream `default.jpg` as +the seed. Allocation: `sbatch --partition=team1 --gres=gpu:1 --cpus-per-task=32 +--mem=200G`, `--physical-gpu 0`, `MSTAR_ENGINE_STEP_SYNC=0`, `HF_HUB_OFFLINE=1`. + +The control arm is a copy of `configs/waypoint.yaml` with `capture_dit_prime: +false` added; the default arm passes no `--config`. The `--protocol binary` flag +in the commands below ships with the binary-framing change, not with this one. + +```bash +# startup A/B, run once per arm per resolution (jobs 6028 and 6033) +PYTHONPATH=. python3 test/waypoint/benchmark_streaming.py \ + --variant 720p --source hub --cache-dir "$HF_CACHE" --seed-image "$SEED" \ + --physical-gpu 0 --protocol binary \ + --steps 16 --warmup-steps 1 --slow-consumer-delay 0.25 \ + --startup-repeats 20 --startup-steps 1 \ + --startup-timeout 1800 --request-timeout 1200 \ + --artifact 720p-on.json --log 720p-on-server.log +# ... and the same with `--config ` for the off arm. +# Job 6033 repeated 720p with the arms swapped and 30 samples each; run both +# orders, because arm position moves this metric more than the change does. + +# prime replay contract, capture on and off (jobs 6029 and 6030) +PYTHONPATH=. nsys profile \ + --trace=cuda,nvtx,osrt --sample=none --cpuctxsw=none \ + --trace-fork-before-exec=true --cuda-graph-trace=graph --force-overwrite=true \ + -o prime-720p-on python3 test/waypoint/benchmark_streaming.py \ + --variant 720p --source hub --cache-dir "$HF_CACHE" --seed-image "$SEED" \ + --physical-gpu 0 --protocol binary --enable-nvtx \ + --steps 16 --warmup-steps 1 --slow-consumer-delay 0.25 \ + --artifact prime-720p-on.json --log prime-720p-on-server.log + +nsys export -t sqlite -f true -o prime-720p-on.sqlite prime-720p-on.nsys-rep +python3 test/waypoint/check_nsys_replay.py prime-720p-on.sqlite \ + --rollout-range "worker[worker_0].node[dit].graph_walk[prime]" \ + --expected-forwards 3 # warmup + baseline + slow_consumer each prime once +python3 test/waypoint/check_nsys_replay.py prime-720p-on.sqlite \ + --expected-forwards 33 # steady rollout, unchanged control +``` + +Retained artifacts: `baselines/prime-capture-2026-09-14-360p-{on,off}.json` and +`baselines/prime-capture-2026-09-14-720p-{on,off}-arm{1,2}.json`. The `-arm1` and +`-arm2` suffixes record which position each arm ran in, which is load-bearing for +reading the 720p numbers. Results are in `OPTIMIZATION_BACKLOG.md` +`## PRIME-GRAPH-001`. + ## End-to-End Acceptance Checklist - Both local checkpoint paths and the registry hub ID start through normal model diff --git a/docs/waypoint/baselines/prime-capture-2026-09-14-360p-off.json b/docs/waypoint/baselines/prime-capture-2026-09-14-360p-off.json new file mode 100644 index 000000000..d4700c3ef --- /dev/null +++ b/docs/waypoint/baselines/prime-capture-2026-09-14-360p-off.json @@ -0,0 +1,163 @@ +{ + "backpressure": { + "configured_consumer_pause_seconds": 0.25, + "injected_pause_seconds": 3.75, + "observed_request_wall_increase_seconds": 3.4970633819466457, + "payloads_match": true, + "peak_gpu_memory_change_mib": 0.0, + "peak_host_pss_change_mib": 14.05078125, + "sustained_media_to_wall_ratio_change": -2.9669949113960556, + "time_to_first_frame_change_seconds": -9.566196240484715e-05, + "wall_increase_beyond_injected_pause_seconds": -0.2529366180533543 + }, + "benchmark": "waypoint_streaming_viability", + "configuration": { + "memory_sample_interval_seconds": 0.1, + "physical_gpu": 0, + "rng_seed": 112464007, + "server_command": [ + "/shared/home/garv901-55613a/mstar-worktrees/graphapi-testing/.venv/bin/python", + "/shared/home/garv901-55613a/waypoint-int/mstar/mstar/api_server/entrypoint.py", + "--config", + "/tmp/waypoint-stream-benchmark-z80dtfmy/run.yaml", + "--port", + "47577", + "--host", + "127.0.0.1", + "--socket-path-prefix", + "/tmp/waypoint-stream-benchmark-z80dtfmy/sock", + "--upload-dir", + "/tmp/waypoint-stream-benchmark-z80dtfmy/uploads", + "--tensor-comm-protocol", + "SHM", + "--log-level", + "INFO", + "--timeout", + "1200.0", + "--cache-dir", + "/shared/home/garv901-55613a/waypoint-int/_ckpt/hf/hub" + ], + "server_log": "/tmp/primegraph/360p-off-server.log", + "slow_consumer_delay_seconds": 0.25, + "stall_threshold_seconds": 0.26666666666666666, + "steps": 16, + "stream_protocol": "binary", + "warmup_steps": 1, + "weight_source": "registry Hub mapping for waypoint-1.5-1b-360p" + }, + "correctness": { + "failures": [], + "passed": true + }, + "created_at_utc": "2026-09-14T01:21:10.964069+00:00", + "geometry": { + "fps": 60.0, + "height": 360, + "width": 640 + }, + "metric_definitions": { + "backpressure": "delta between an unpaused stream and an identical stream paused between SDK reads", + "jitter_population_stddev": "population standard deviation of inter-chunk gaps", + "memory": "PSS and nvidia-smi GPU process memory summed over the server process group only", + "stall": "inter-chunk gap strictly greater than stall_threshold_seconds", + "startup_latency_seconds": "time_to_first_frame_seconds over --startup-repeats short streams, each after a full request cleanup, so every sample reuses a world slot", + "sustained_media_to_wall_ratio": "media seconds in chunks after the first divided by first-to-last chunk arrival time", + "time_to_first_frame_seconds": "request iterator start to first fully decoded SDK VideoFrameChunk" + }, + "model_variant": "waypoint-1.5-1b-360p", + "release_threshold": null, + "runs": { + "baseline": { + "chunk_count": 16, + "consumer": { + "injected_pause_seconds": 0.0, + "pause_count": 0, + "pause_seconds": 0.0 + }, + "frame_count": 64, + "generated_media_seconds": 1.0666666666666667, + "inter_chunk_gap_seconds": { + "jitter_population_stddev": 0.0014653687131390544, + "maximum": 0.022965203039348125, + "mean": 0.02064180220477283, + "p50": 0.020365420961752534, + "p95": 0.022909664339385925, + "sample_count": 15 + }, + "memory": { + "peak_gpu_mib": 4626.0, + "peak_host_pss_mib": 3365.46484375, + "phase": "baseline", + "quiet_gpu_mib": 4626.0, + "quiet_host_pss_mib": 3373.3798828125, + "sample_count": 3 + }, + "overall_media_to_wall_ratio": 2.8254469531764714, + "payload_bytes": 44236800, + "payload_sha256": "cc84686681d6dca8b4860971fb80794f2a66a784d3ca544df3615ab2bb43a47e", + "request_id": "waypoint-streaming-benchmark-baseline", + "request_wall_seconds": 0.3775213919579983, + "stalls": { + "count": 0, + "longest_seconds": null, + "threshold_seconds": 0.26666666666666666, + "total_excess_seconds": 0 + }, + "sustained_media_to_wall_ratio": 3.2296921560100937, + "time_to_first_frame_seconds": 0.06580381095409393 + }, + "slow_consumer": { + "chunk_count": 16, + "consumer": { + "injected_pause_seconds": 3.75, + "pause_count": 15, + "pause_seconds": 0.25 + }, + "frame_count": 64, + "generated_media_seconds": 1.0666666666666667, + "inter_chunk_gap_seconds": { + "jitter_population_stddev": 0.0006347077181201878, + "maximum": 0.25593707989901304, + "mean": 0.2537775634632756, + "p50": 0.2535740720340982, + "p95": 0.2547811881522648, + "sample_count": 15 + }, + "memory": { + "peak_gpu_mib": 4626.0, + "peak_host_pss_mib": 3379.515625, + "phase": "slow-consumer", + "quiet_gpu_mib": 4626.0, + "quiet_host_pss_mib": 3375.9853515625, + "sample_count": 16 + }, + "overall_media_to_wall_ratio": 0.2752983168288572, + "payload_bytes": 44236800, + "payload_sha256": "cc84686681d6dca8b4860971fb80794f2a66a784d3ca544df3615ab2bb43a47e", + "request_id": "waypoint-streaming-benchmark-slow-consumer", + "request_wall_seconds": 3.874584773904644, + "stalls": { + "count": 0, + "longest_seconds": null, + "threshold_seconds": 0.26666666666666666, + "total_excess_seconds": 0 + }, + "sustained_media_to_wall_ratio": 0.262697244614038, + "time_to_first_frame_seconds": 0.06570814899168909 + } + }, + "schema_version": 1, + "server": { + "startup_seconds": 87.15073139511514 + }, + "startup_latency_seconds": { + "maximum": 0.07032173802144825, + "mean": 0.06389240985154174, + "minimum": 0.0596659219590947, + "p50": 0.06310040800599381, + "p95": 0.06771635722834617, + "sample_count": 20 + }, + "status": "completed", + "variant": "360p" +} diff --git a/docs/waypoint/baselines/prime-capture-2026-09-14-360p-on.json b/docs/waypoint/baselines/prime-capture-2026-09-14-360p-on.json new file mode 100644 index 000000000..9f2fe6fc1 --- /dev/null +++ b/docs/waypoint/baselines/prime-capture-2026-09-14-360p-on.json @@ -0,0 +1,163 @@ +{ + "backpressure": { + "configured_consumer_pause_seconds": 0.25, + "injected_pause_seconds": 3.75, + "observed_request_wall_increase_seconds": 3.492240247898735, + "payloads_match": true, + "peak_gpu_memory_change_mib": 0.0, + "peak_host_pss_change_mib": 15.8671875, + "sustained_media_to_wall_ratio_change": -2.9372519154651013, + "time_to_first_frame_change_seconds": 0.0014183248858898878, + "wall_increase_beyond_injected_pause_seconds": -0.2577597521012649 + }, + "benchmark": "waypoint_streaming_viability", + "configuration": { + "memory_sample_interval_seconds": 0.1, + "physical_gpu": 0, + "rng_seed": 112464007, + "server_command": [ + "/shared/home/garv901-55613a/mstar-worktrees/graphapi-testing/.venv/bin/python", + "/shared/home/garv901-55613a/waypoint-int/mstar/mstar/api_server/entrypoint.py", + "--config", + "/tmp/waypoint-stream-benchmark-tj31iler/run.yaml", + "--port", + "41227", + "--host", + "127.0.0.1", + "--socket-path-prefix", + "/tmp/waypoint-stream-benchmark-tj31iler/sock", + "--upload-dir", + "/tmp/waypoint-stream-benchmark-tj31iler/uploads", + "--tensor-comm-protocol", + "SHM", + "--log-level", + "INFO", + "--timeout", + "1200.0", + "--cache-dir", + "/shared/home/garv901-55613a/waypoint-int/_ckpt/hf/hub" + ], + "server_log": "/tmp/primegraph/360p-on-server.log", + "slow_consumer_delay_seconds": 0.25, + "stall_threshold_seconds": 0.26666666666666666, + "steps": 16, + "stream_protocol": "binary", + "warmup_steps": 1, + "weight_source": "registry Hub mapping for waypoint-1.5-1b-360p" + }, + "correctness": { + "failures": [], + "passed": true + }, + "created_at_utc": "2026-09-14T01:19:25.040156+00:00", + "geometry": { + "fps": 60.0, + "height": 360, + "width": 640 + }, + "metric_definitions": { + "backpressure": "delta between an unpaused stream and an identical stream paused between SDK reads", + "jitter_population_stddev": "population standard deviation of inter-chunk gaps", + "memory": "PSS and nvidia-smi GPU process memory summed over the server process group only", + "stall": "inter-chunk gap strictly greater than stall_threshold_seconds", + "startup_latency_seconds": "time_to_first_frame_seconds over --startup-repeats short streams, each after a full request cleanup, so every sample reuses a world slot", + "sustained_media_to_wall_ratio": "media seconds in chunks after the first divided by first-to-last chunk arrival time", + "time_to_first_frame_seconds": "request iterator start to first fully decoded SDK VideoFrameChunk" + }, + "model_variant": "waypoint-1.5-1b-360p", + "release_threshold": null, + "runs": { + "baseline": { + "chunk_count": 16, + "consumer": { + "injected_pause_seconds": 0.0, + "pause_count": 0, + "pause_seconds": 0.0 + }, + "frame_count": 64, + "generated_media_seconds": 1.0666666666666667, + "inter_chunk_gap_seconds": { + "jitter_population_stddev": 0.0021165463763351705, + "maximum": 0.025491400039754808, + "mean": 0.020832174667157234, + "p50": 0.0208142320625484, + "p95": 0.024783308710902927, + "sample_count": 15 + }, + "memory": { + "peak_gpu_mib": 4424.0, + "peak_host_pss_mib": 3269.25390625, + "phase": "baseline", + "quiet_gpu_mib": 4424.0, + "quiet_host_pss_mib": 3282.44921875, + "sample_count": 3 + }, + "overall_media_to_wall_ratio": 2.8917383232634135, + "payload_bytes": 44236800, + "payload_sha256": "cc84686681d6dca8b4860971fb80794f2a66a784d3ca544df3615ab2bb43a47e", + "request_id": "waypoint-streaming-benchmark-baseline", + "request_wall_seconds": 0.3688669400289655, + "stalls": { + "count": 0, + "longest_seconds": null, + "threshold_seconds": 0.26666666666666666, + "total_excess_seconds": 0 + }, + "sustained_media_to_wall_ratio": 3.2001779810232374, + "time_to_first_frame_seconds": 0.05451832804828882 + }, + "slow_consumer": { + "chunk_count": 16, + "consumer": { + "injected_pause_seconds": 3.75, + "pause_count": 15, + "pause_seconds": 0.25 + }, + "frame_count": 64, + "generated_media_seconds": 1.0666666666666667, + "inter_chunk_gap_seconds": { + "jitter_population_stddev": 0.0006765666966317799, + "maximum": 0.2555718390503898, + "mean": 0.2535567043349147, + "p50": 0.25334677298087627, + "p95": 0.25509403366595507, + "sample_count": 15 + }, + "memory": { + "peak_gpu_mib": 4424.0, + "peak_host_pss_mib": 3285.12109375, + "phase": "slow-consumer", + "quiet_gpu_mib": 4424.0, + "quiet_host_pss_mib": 3285.12109375, + "sample_count": 22 + }, + "overall_media_to_wall_ratio": 0.27625927350625007, + "payload_bytes": 44236800, + "payload_sha256": "cc84686681d6dca8b4860971fb80794f2a66a784d3ca544df3615ab2bb43a47e", + "request_id": "waypoint-streaming-benchmark-slow-consumer", + "request_wall_seconds": 3.8611071879277006, + "stalls": { + "count": 0, + "longest_seconds": null, + "threshold_seconds": 0.26666666666666666, + "total_excess_seconds": 0 + }, + "sustained_media_to_wall_ratio": 0.2629260655581359, + "time_to_first_frame_seconds": 0.05593665293417871 + } + }, + "schema_version": 1, + "server": { + "startup_seconds": 87.09111319098156 + }, + "startup_latency_seconds": { + "maximum": 0.06129706697538495, + "mean": 0.05519236869295128, + "minimum": 0.05124405003152788, + "p50": 0.05512793594971299, + "p95": 0.058123435318702836, + "sample_count": 20 + }, + "status": "completed", + "variant": "360p" +} diff --git a/docs/waypoint/baselines/prime-capture-2026-09-14-720p-off-arm1.json b/docs/waypoint/baselines/prime-capture-2026-09-14-720p-off-arm1.json new file mode 100644 index 000000000..2a61e17b3 --- /dev/null +++ b/docs/waypoint/baselines/prime-capture-2026-09-14-720p-off-arm1.json @@ -0,0 +1,163 @@ +{ + "backpressure": { + "configured_consumer_pause_seconds": 0.25, + "injected_pause_seconds": 3.75, + "observed_request_wall_increase_seconds": 3.434869210002944, + "payloads_match": true, + "peak_gpu_memory_change_mib": 0.0, + "peak_host_pss_change_mib": 31.943359375, + "sustained_media_to_wall_ratio_change": -1.376907450061608, + "time_to_first_frame_change_seconds": -0.007874322938732803, + "wall_increase_beyond_injected_pause_seconds": -0.3151307899970561 + }, + "benchmark": "waypoint_streaming_viability", + "configuration": { + "memory_sample_interval_seconds": 0.1, + "physical_gpu": 0, + "rng_seed": 112464007, + "server_command": [ + "/shared/home/garv901-55613a/mstar-worktrees/graphapi-testing/.venv/bin/python", + "/shared/home/garv901-55613a/waypoint-int/mstar/mstar/api_server/entrypoint.py", + "--config", + "/tmp/waypoint-stream-benchmark-2f1a82ka/run.yaml", + "--port", + "48929", + "--host", + "127.0.0.1", + "--socket-path-prefix", + "/tmp/waypoint-stream-benchmark-2f1a82ka/sock", + "--upload-dir", + "/tmp/waypoint-stream-benchmark-2f1a82ka/uploads", + "--tensor-comm-protocol", + "SHM", + "--log-level", + "INFO", + "--timeout", + "1200.0", + "--cache-dir", + "/shared/home/garv901-55613a/waypoint-int/_ckpt/hf/hub" + ], + "server_log": "/tmp/primegraph/rev-720p-off-server.log", + 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"media seconds in chunks after the first divided by first-to-last chunk arrival time", + "time_to_first_frame_seconds": "request iterator start to first fully decoded SDK VideoFrameChunk" + }, + "model_variant": "waypoint-1.5-1b-720p", + "release_threshold": null, + "runs": { + "baseline": { + "chunk_count": 16, + "consumer": { + "injected_pause_seconds": 0.0, + "pause_count": 0, + "pause_seconds": 0.0 + }, + "frame_count": 64, + "generated_media_seconds": 1.0666666666666667, + "inter_chunk_gap_seconds": { + "jitter_population_stddev": 0.013385453313035183, + "maximum": 0.06719237205106765, + "mean": 0.041069517067323126, + "p50": 0.04316837911028415, + "p95": 0.06126766155939548, + "sample_count": 15 + }, + "memory": { + "peak_gpu_mib": 5656.0, + "peak_host_pss_mib": 3606.158203125, + "phase": "baseline", + "quiet_gpu_mib": 5656.0, + "quiet_host_pss_mib": 3627.25390625, + "sample_count": 4 + }, + "overall_media_to_wall_ratio": 1.342067821066469, + "payload_bytes": 176947200, + 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"overall_media_to_wall_ratio": 0.2524448861182096, + "payload_bytes": 176947200, + "payload_sha256": "bdd8ed5605d329ea704820b0c017c3ea0b0a70122286611a1d999b19e327dde3", + "request_id": "waypoint-streaming-benchmark-slow-consumer", + "request_wall_seconds": 4.225344720063731, + "stalls": { + "count": 6, + "longest_seconds": 0.2825224169064313, + "threshold_seconds": 0.26666666666666666, + "total_excess_seconds": 0.052089675888419174 + }, + "sustained_media_to_wall_ratio": 0.24841580051943601, + "time_to_first_frame_seconds": 0.1904655440011993 + } + }, + "schema_version": 1, + "server": { + "startup_seconds": 80.10912929603364 + }, + "startup_latency_seconds": { + "maximum": 0.15587666200008243, + "mean": 0.1418924984172918, + "minimum": 0.1319633589591831, + "p50": 0.14247635047649965, + "p95": 0.15150842317962088, + "sample_count": 30 + }, + "status": "completed", + "variant": "720p" +} diff --git a/mstar/model/waypoint/config.py b/mstar/model/waypoint/config.py index c052040e1..885488154 100644 --- a/mstar/model/waypoint/config.py +++ b/mstar/model/waypoint/config.py @@ -136,12 +136,19 @@ class WaypointConfig: # primitive, which stays compiled for correctness (engine/resources/attn/flex.py). compile_dit: bool = True - # Attempt fixed-shape CUDA graph capture for the encoder prime, steady DiT - # rollout, and decoder prime/rollout paths. An optimization: disabled - # declares no buckets, and a failed capture falls back to eager submodule - # forwards. + # Attempt fixed-shape CUDA graph capture for the encoder prime, DiT prime, + # steady DiT rollout, and decoder prime/rollout paths. An optimization: + # disabled declares no buckets, and a failed capture falls back to eager + # submodule forwards. cuda_graph: bool = True + # Also capture the one-time DiT prime/cache pass. Subordinate to + # ``cuda_graph``: disabled leaves the steady rollout graph alone and serves + # prime through the compiled eager forward. On by default since + # PRIME-GRAPH-001 measured lower startup p95 at both resolutions; False is + # that A/B's control arm and stays reachable. + capture_dit_prime: bool = True + # Guard rails the ported modules assert against, kept here so a drifting # checkpoint fails loudly at construction rather than silently mis-serving. _supported_rope_impls: tuple[str, ...] = field( diff --git a/mstar/model/waypoint/submodules.py b/mstar/model/waypoint/submodules.py index 2ce39fd07..272ee126d 100644 --- a/mstar/model/waypoint/submodules.py +++ b/mstar/model/waypoint/submodules.py @@ -391,11 +391,14 @@ def forward( def get_cuda_graph_configs( self, device: torch.device, tp_world_size: int = 1 ) -> list[CudaGraphConfig]: - """The optional steady-rollout graph; the one-time prime stays uncaptured. - - The DiT compiles its reference-shaped denoise/cache regions internally. - Compiling this wrapper would fuse across their boundary, while capturing - prime would spend graph memory on one cache-only forward per request. + """Both walks, as optional captures. + + The DiT compiles its reference-shaped denoise/cache regions internally; + compiling this wrapper would fuse across their boundary. Prime is one + cache-only forward per request, but it is on the admission-to-first-frame + path and its inputs are the rollout template with ``noise`` renamed, so + the capture costs one static-input family and reuses the pool the rollout + graph already sized. """ del tp_world_size # no sharded nodes; the ring and the mask do not shard if not self.config.cuda_graph: @@ -417,15 +420,26 @@ def template(latent_key: str) -> NodeInputs: input_seq_len=self.config.tokens_per_frame, ) - return [BatchedCudaGraphConfig( - capture_graph_walk=ROLLOUT_WALK, - single_request_inputs=template("noise"), - capture_batch_sizes=[1], - capture_forward_method="forward_batched", - # The DiT compiles its two reference-shaped fullgraph regions - # itself. Compiling this wrapper would fuse across their boundary. - compile=False, - )] + # Rollout first, and the order is load-bearing: both captures share one + # graph pool and rollout's five forwards are a superset of prime's one, + # so the pool is sized once and prime reuses its freed blocks. + # ``prepare_for_capture``'s sort is stable and both specs are + # (bs=1, tokens_per_frame), so declaration order is capture order. + walks = [(ROLLOUT_WALK, "noise")] + if self.config.capture_dit_prime: + walks.append((PRIME_WALK, "latent")) + return [ + BatchedCudaGraphConfig( + capture_graph_walk=walk, + single_request_inputs=template(latent_key), + capture_batch_sizes=[1], + capture_forward_method="forward_batched", + # The DiT compiles its two reference-shaped fullgraph regions + # itself. Compiling this wrapper would fuse across their boundary. + compile=False, + ) + for walk, latent_key in walks + ] # ------------------------------------------------------------------ # step tail diff --git a/mstar/model/waypoint/waypoint_model.py b/mstar/model/waypoint/waypoint_model.py index d0b2458e9..a08a13618 100644 --- a/mstar/model/waypoint/waypoint_model.py +++ b/mstar/model/waypoint/waypoint_model.py @@ -128,6 +128,7 @@ def __init__( reference_compat: bool | None = None, compile_dit: bool | None = None, cuda_graph: bool | None = None, + capture_dit_prime: bool | None = None, full_global_ring: bool | None = None, checkpoint_revision: str | None = None, ae_revision: str | None = None, @@ -153,6 +154,7 @@ def __init__( "reference_compat": reference_compat, "compile_dit": compile_dit, "cuda_graph": cuda_graph, + "capture_dit_prime": capture_dit_prime, "full_global_ring": full_global_ring, }.items() if value is not None } diff --git a/test/modular/test_waypoint_checkpoint.py b/test/modular/test_waypoint_checkpoint.py index 4c3476a32..4ed31fcb1 100644 --- a/test/modular/test_waypoint_checkpoint.py +++ b/test/modular/test_waypoint_checkpoint.py @@ -93,23 +93,27 @@ def test_serving_defaults_to_reference_compatible_optimized_execution(): assert config.reference_compat is True assert config.compile_dit is True assert config.cuda_graph is True + assert config.capture_dit_prime is True @pytest.mark.parametrize("compile_dit", [False, True]) @pytest.mark.parametrize("cuda_graph", [False, True]) +@pytest.mark.parametrize("capture_dit_prime", [False, True]) @pytest.mark.parametrize("reference_compat", [False, True]) def test_execution_and_numerical_modes_are_independent( - compile_dit, cuda_graph, reference_compat, + compile_dit, cuda_graph, capture_dit_prime, reference_compat, ): config = replace( waypoint_1_5_1b_720p(), compile_dit=compile_dit, cuda_graph=cuda_graph, + capture_dit_prime=capture_dit_prime, reference_compat=reference_compat, ) config.validate_supported_deployment() assert config.compile_dit is compile_dit assert config.cuda_graph is cuda_graph + assert config.capture_dit_prime is capture_dit_prime assert config.reference_compat is reference_compat @@ -118,10 +122,12 @@ def test_model_constructor_threads_all_execution_modes_independently(): skip_weight_loading=True, compile_dit=False, cuda_graph=False, + capture_dit_prime=False, reference_compat=False, ) assert model.config.compile_dit is False assert model.config.cuda_graph is False + assert model.config.capture_dit_prime is False assert model.config.reference_compat is False diff --git a/test/modular/test_waypoint_gpu.py b/test/modular/test_waypoint_gpu.py index 590c7bce0..590c8010a 100644 --- a/test/modular/test_waypoint_gpu.py +++ b/test/modular/test_waypoint_gpu.py @@ -448,6 +448,13 @@ def captured(): static_noise = torch.zeros(1, 1, *config.latent_shape, dtype=DTYPE, device=DEVICE) static_frame = torch.zeros((), dtype=torch.int64, device=DEVICE) + static_latent = torch.zeros(1, 1, *config.latent_shape, dtype=DTYPE, device=DEVICE) + + # One pool for both graphs, as ``CudaGraphRunner`` does, and rollout first: + # its five forwards are a superset of prime's one, so the pool is sized once + # and prime reuses the blocks rollout freed. + pool = torch.cuda.graphs.graph_pool_handle() + stream = torch.cuda.Stream() stream.wait_stream(torch.cuda.current_stream()) with torch.cuda.stream(stream), torch.no_grad(): @@ -455,15 +462,26 @@ def captured(): dit.generate_frame( static_noise, static_frame, mouse=mouse, button=button, scroll=scroll ) + for _ in range(3): + dit.append_frame( + static_latent, static_frame, mouse=mouse, button=button, scroll=scroll + ) torch.cuda.current_stream().wait_stream(stream) graph = torch.cuda.CUDAGraph() - with torch.cuda.graph(graph), torch.no_grad(): + with torch.cuda.graph(graph, pool=pool), torch.no_grad(): static_out = dit.generate_frame( static_noise, static_frame, mouse=mouse, button=button, scroll=scroll ) torch.cuda.synchronize() + prime_graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(prime_graph, pool=pool), torch.no_grad(): + prime_out = dit.append_frame( + static_latent, static_frame, mouse=mouse, button=button, scroll=scroll + ) + torch.cuda.synchronize() + # Warmup and capture wrote real frames into the ring and left the dummy rid # holding a world. Asserted here rather than in a test because this is the # one place the contamination is unambiguous -- every test below scrubs on @@ -474,6 +492,7 @@ def captured(): return { "config": config, "dit": dit, "kv": kv, "attn": attn, "graph": graph, "noise": static_noise, "frame": static_frame, "out": static_out, + "prime_graph": prime_graph, "latent": static_latent, "prime_out": prime_out, "controls": (mouse, button, scroll), } @@ -509,6 +528,39 @@ def eager_frame(captured, rid: str, frame: int, stream: str) -> torch.Tensor: return latent +def replay_prime(captured, rid: str, stream: str) -> torch.Tensor: + """Prime one world through the captured graph. Prime always sits at frame 0. + + The latent stands in for the VAE encoder's output; ``noise_for`` only has to + be a deterministic function of ``stream`` here, not real pixels. + """ + admit_frame(captured["kv"], rid, 0, captured["attn"]) + captured["latent"].copy_(noise_for(captured["config"], stream, 0)) + captured["frame"].fill_(0) + captured["prime_graph"].replay() + torch.cuda.synchronize() + latent = captured["prime_out"].clone() + captured["kv"].commit(_step(rid, 0), _ctx(rid)) + return latent + + +def eager_prime(captured, rid: str, stream: str) -> torch.Tensor: + """The same prime through the same compiled ``_cache_pass``, uncaptured -- + the control replay must be compared against, for the reason ``eager_frame`` + gives.""" + admit_frame(captured["kv"], rid, 0, captured["attn"]) + mouse, button, scroll = captured["controls"] + with torch.no_grad(): + latent = captured["dit"].append_frame( + noise_for(captured["config"], stream, 0), + torch.tensor(0, dtype=torch.int64, device=DEVICE), + mouse=mouse, button=button, scroll=scroll, + ).clone() + torch.cuda.synchronize() + captured["kv"].commit(_step(rid, 0), _ctx(rid)) + return latent + + def test_capture_replays_fixed_address_planned_masks(captured): """The manual CUDA gate uses the served preplanned-mask path.""" attn = captured["attn"] @@ -667,6 +719,110 @@ def test_two_worlds_interleaved_match_the_same_rollouts_run_alone(captured): scrub(kv, "both_a", "both_b") +# --------------------------------------------------------------------------- +# A.6 -- the prime graph +# --------------------------------------------------------------------------- + + +def test_prime_replay_matches_the_uncaptured_prime(captured): + """Replaying prime writes the ring the compiled cache pass writes. + + The ring *is* prime's whole product -- ``append_frame`` returns its input + untouched -- so a latent comparison alone would pass on a graph that did + nothing at all. + """ + kv = captured["kv"] + scrub(kv, "capture") + + kv.ingest_request("eager_p") + eager_prime(captured, "eager_p", "P") + eager_ring = world_snapshot(kv, kv.world_of("eager_p")) + scrub(kv, "eager_p") + + kv.ingest_request("graph_p") + replay_prime(captured, "graph_p", "P") + graph_ring = world_snapshot(kv, kv.world_of("graph_p")) + + assert_worlds_equal(eager_ring, graph_ring, "primed by replay vs uncaptured") + assert any(written.any() for _, written in graph_ring), ( + "priming made nothing visible; the comparison above is between two " + "empty rings and would pass on a graph that never ran" + ) + scrub(kv, "graph_p") + + # A second, different latent must land somewhere else, or the replay is + # reproducing capture-time state rather than reading its input buffer. + kv.ingest_request("other_p") + replay_prime(captured, "other_p", "Q") + other_ring = world_snapshot(kv, kv.world_of("other_p")) + assert not torch.equal(graph_ring[0][0], other_ring[0][0]), ( + "two different seed latents primed the same ring bytes" + ) + scrub(kv, "other_p") + + +def test_the_prime_graph_returns_its_own_static_input_buffer(captured): + """Prime's output aliases its input, and that is the contract downstream. + + ``append_frame`` hands the settled latent straight back, so the captured + output is the captured input buffer. The consumer -- the VAE decoder, next + in the same ``Sequential`` -- must therefore copy before the following prime + stages over it, which it does: the engine stages every captured node's + inputs through ``copy_``. Pinned here so adding a ``.clone()`` to + ``append_frame`` is a decision and not an accident. + """ + assert captured["prime_out"].data_ptr() == captured["latent"].data_ptr() + + staged = noise_for(captured["config"], "R", 0) + captured["latent"].copy_(staged) + assert torch.equal(captured["prime_out"], staged), ( + "the output view did not follow its input buffer, so it is a copy made " + "at capture time and every replay would return stale bytes" + ) + + +def test_prime_then_rollout_through_both_graphs_matches_eager(captured): + """The integration claim, and the shared-pool gate. + + Priming through one graph and then rolling out through the other must be + bit-identical to the same sequence through the uncaptured compiled regions. + If the two captures aliased each other in the shared pool, the rollout would + read latents the prime graph had since overwritten. + """ + kv = captured["kv"] + frames = 8 + scrub(kv, "capture") + + kv.ingest_request("eager_pr") + eager_prime(captured, "eager_pr", "S") + want = [eager_frame(captured, "eager_pr", f, "S") for f in range(1, frames + 1)] + want_ring = world_snapshot(kv, kv.world_of("eager_pr")) + scrub(kv, "eager_pr") + + kv.ingest_request("graph_pr") + replay_prime(captured, "graph_pr", "S") + got = [replay_frame(captured, "graph_pr", f, "S") for f in range(1, frames + 1)] + got_ring = world_snapshot(kv, kv.world_of("graph_pr")) + + for frame, (a, b) in enumerate(zip(want, got, strict=True), start=1): + assert torch.equal(a, b), ( + f"frame {frame}: priming through the graph changed the rollout by " + f"{(a.float() - b.float()).abs().max().item():.3e}" + ) + assert_worlds_equal(want_ring, got_ring, "primed rollout, replay vs uncaptured") + scrub(kv, "graph_pr") + + # Without this, a prime graph that replayed nothing would pass: the rollout + # would simply start from an empty world in both arms. + kv.ingest_request("unprimed") + unprimed = [replay_frame(captured, "unprimed", f, "S") for f in range(1, frames + 1)] + assert not torch.equal(got[0], unprimed[0]), ( + "the first rolled-out frame is the same with and without priming; the " + "prime graph wrote nothing the rollout could read" + ) + scrub(kv, "unprimed") + + # --------------------------------------------------------------------------- # B -- what the BlockMask rebuild costs # --------------------------------------------------------------------------- diff --git a/test/modular/test_waypoint_shell.py b/test/modular/test_waypoint_shell.py index f800afed8..c96aff3d1 100644 --- a/test/modular/test_waypoint_shell.py +++ b/test/modular/test_waypoint_shell.py @@ -846,11 +846,14 @@ def test_binding_without_a_declared_resource_fails_at_bind(submodule): # --------------------------------------------------------------------------- -def test_only_the_steady_dit_rollout_is_an_optional_capture(submodule): - """The one-time prime/cache pass is compiled internally but uncaptured.""" +def test_both_dit_walks_are_optional_captures(submodule, config): + """Prime and rollout both capture; the declaration order is capture order.""" configs = submodule.get_cuda_graph_configs(torch.device("meta")) - assert len(configs) == 1 - assert configs[0].capture_graph_walk == ROLLOUT_WALK + # Rollout first: the two share one graph pool and rollout's five forwards + # are a superset of prime's one, so rollout sizes the pool. The runner's + # largest-first sort is stable and both specs are (1, tokens_per_frame), + # so this list order is the order they are captured in. + assert [cfg.capture_graph_walk for cfg in configs] == [ROLLOUT_WALK, PRIME_WALK] for cfg in configs: assert cfg.compile is False # One live world again: the bucket cannot be wider than it. @@ -858,10 +861,30 @@ def test_only_the_steady_dit_rollout_is_an_optional_capture(submodule): # The v1 engine always dispatches batched; a submodule captured on bare # `forward` is captured on a method that never runs. assert cfg.capture_forward_method == "forward_batched" + assert cfg.single_request_inputs.input_seq_len == config.tokens_per_frame + + rollout, prime = (cfg.single_request_inputs.tensor_inputs for cfg in configs) + assert "noise" in rollout and "latent" not in rollout + assert "latent" in prime and "noise" not in prime + # The two templates are one shape with the frame tensor renamed. A + # divergence here is a second static-input family for no reason. + assert set(rollout) - {"noise"} == set(prime) - {"latent"} + assert rollout["noise"].shape == prime["latent"].shape + assert rollout["noise"].dtype == prime["latent"].dtype + assert submodule.disable_torch_compile is True + +def test_dit_prime_capture_can_be_declined_on_its_own(config): + """The A/B control arm: prime off leaves the steady rollout graph alone.""" + no_prime = dataclasses.replace(config, capture_dit_prime=False) + with torch.device("meta"): + dit = WaypointDiT(no_prime) + dit.cast_serving_dtypes() + submodule = WaypointDitSubmodule(dit, no_prime) + + configs = submodule.get_cuda_graph_configs(torch.device("meta")) + assert [cfg.capture_graph_walk for cfg in configs] == [ROLLOUT_WALK] assert "noise" in configs[0].single_request_inputs.tensor_inputs - assert "latent" not in configs[0].single_request_inputs.tensor_inputs - assert submodule.disable_torch_compile is True def test_dit_declares_no_capture_when_cuda_graph_is_disabled(config): diff --git a/test/modular/test_waypoint_streaming_benchmark.py b/test/modular/test_waypoint_streaming_benchmark.py index bb7e15a7b..7505a1c46 100644 --- a/test/modular/test_waypoint_streaming_benchmark.py +++ b/test/modular/test_waypoint_streaming_benchmark.py @@ -155,11 +155,31 @@ def stream(self, **kwargs): assert len(client.kwargs["actions"]) == 2 +def test_startup_latency_is_none_until_samples_are_asked_for(benchmark): + """The key is always present, so a consumer never has to guess the shape.""" + assert benchmark["_startup_latency_metrics"]([]) is None + + +def test_startup_latency_summarizes_every_sample(benchmark): + metrics = benchmark["_startup_latency_metrics"]([0.40, 0.10, 0.20, 0.30]) + + assert metrics == { + "sample_count": 4, + "p50": pytest.approx(0.25), + "p95": pytest.approx(0.385), + "mean": pytest.approx(0.25), + "minimum": 0.10, + "maximum": 0.40, + } + + @pytest.mark.parametrize( "extra, message", [ (["--steps", "0"], "--steps must be positive"), (["--warmup-steps", "-1"], "--warmup-steps cannot be negative"), + (["--startup-repeats", "-1"], "--startup-repeats cannot be negative"), + (["--startup-steps", "0"], "--startup-steps must be positive"), (["--slow-consumer-delay", "-0.1"], "--slow-consumer-delay cannot be negative"), (["--stall-threshold", "0"], "--stall-threshold must be positive"), (["--memory-sample-interval", "0"], "--memory-sample-interval must be positive"), diff --git a/test/waypoint/benchmark_streaming.py b/test/waypoint/benchmark_streaming.py index 6437c526e..4d27dad56 100644 --- a/test/waypoint/benchmark_streaming.py +++ b/test/waypoint/benchmark_streaming.py @@ -165,6 +165,20 @@ def _validate_chunk( return failures +def _startup_latency_metrics(samples: Sequence[float]) -> dict | None: + """Time-to-first-frame across repeated world-slot reuse, or None if unmeasured.""" + if not samples: + return None + return { + "sample_count": len(samples), + "p50": _percentile(samples, 0.50), + "p95": _percentile(samples, 0.95), + "mean": statistics.fmean(samples), + "minimum": min(samples), + "maximum": max(samples), + } + + def _measure_stream( client: MStarClient, seed_image: Path, @@ -357,6 +371,14 @@ def _human_summary(result: dict, artifact: Path) -> str: f"{memory['quiet_host_pss_mib']:.1f} MiB, GPU={memory['peak_gpu_mib']:.1f}/" f"{memory['quiet_gpu_mib']:.1f} MiB" ) + startup = result["startup_latency_seconds"] + if startup is not None: + lines.append( + f" startup TTFF over {startup['sample_count']} reused slots: " + f"p50/p95={_format_number(startup['p50'])}/" + f"{_format_number(startup['p95'])}s " + f"mean={_format_number(startup['mean'])}s" + ) backpressure = result["backpressure"] lines.extend( [ @@ -388,6 +410,13 @@ def _build_parser() -> argparse.ArgumentParser: ) parser.add_argument("--steps", type=int, default=16) parser.add_argument("--warmup-steps", type=int, default=1) + parser.add_argument( + "--startup-repeats", + type=int, + default=0, + help="short streams measured before the baseline, for startup p50/p95", + ) + parser.add_argument("--startup-steps", type=int, default=1) parser.add_argument("--slow-consumer-delay", type=float, default=0.25) parser.add_argument( "--stall-threshold", @@ -421,6 +450,10 @@ def _parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: parser.error("--steps must be positive") if args.warmup_steps < 0: parser.error("--warmup-steps cannot be negative") + if args.startup_repeats < 0: + parser.error("--startup-repeats cannot be negative") + if args.startup_steps <= 0: + parser.error("--startup-steps must be positive") if args.slow_consumer_delay < 0: parser.error("--slow-consumer-delay cannot be negative") if args.stall_threshold is not None and args.stall_threshold <= 0: @@ -500,6 +533,7 @@ def _run_benchmark(args: argparse.Namespace) -> dict: sampler: rollout.MemorySampler | None = None failures: list[str] = [] runs: dict[str, dict] = {} + startup_ttffs: list[float] = [] startup_started = time.perf_counter() try: client = MStarClient(url, timeout=args.request_timeout) @@ -536,6 +570,38 @@ def _run_benchmark(args: argparse.Namespace) -> dict: ) rollout._wait_for_quiescent_memory(sampler, "warmup-quiet") + # Startup samples: short streams, each followed by a full cleanup, so + # every TTFF is measured against a reused world slot rather than a + # freshly warmed one. This is the population the prime-capture A/B + # compares. + for index in range(args.startup_repeats): + phase = f"startup-{index:02d}" + _wait_for_phase_sample(sampler, proc, phase) + request_id = f"{args.request_id}-{phase}" + metrics, startup_failures = _measure_stream( + client, + seed_image, + variant, + num_steps=args.startup_steps, + request_id=request_id, + rng_seed=args.seed, + consumer_pause_seconds=0.0, + stall_threshold_seconds=stall_threshold, + enable_nvtx=args.enable_nvtx, + ) + failures.extend(f"{phase}: {failure}" for failure in startup_failures) + rollout._wait_for_cleanup( + args.log, (request_id,), proc, args.request_timeout + ) + ttff = metrics["time_to_first_frame_seconds"] + if ttff is not None: + startup_ttffs.append(ttff) + if args.startup_repeats: + print( + f"startup: {len(startup_ttffs)}/{args.startup_repeats} samples, " + f"p50={_format_number(_percentile(startup_ttffs, 0.50))}s" + ) + for name, pause in ( ("baseline", 0.0), ("slow_consumer", args.slow_consumer_delay), @@ -603,6 +669,7 @@ def _run_benchmark(args: argparse.Namespace) -> dict: "server_log": str(args.log), }, "server": {"startup_seconds": startup_seconds}, + "startup_latency_seconds": _startup_latency_metrics(startup_ttffs), "runs": runs, "backpressure": _backpressure_metrics( runs["baseline"], @@ -615,6 +682,10 @@ def _run_benchmark(args: argparse.Namespace) -> dict: "time_to_first_frame_seconds": ( "request iterator start to first fully decoded SDK VideoFrameChunk" ), + "startup_latency_seconds": ( + "time_to_first_frame_seconds over --startup-repeats short streams, " + "each after a full request cleanup, so every sample reuses a world slot" + ), "sustained_media_to_wall_ratio": ( "media seconds in chunks after the first divided by first-to-last chunk arrival time" ), From 8c2405d01ac2a892b7f6204938f06f5ffe34df56 Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Wed, 16 Sep 2026 19:30:41 +0000 Subject: [PATCH 09/29] speculation: handle loop-external inputs; enable async execution for waypoint Loop-external inputs (re-injected into ready_signals each iteration) now count as ready for same-node speculation. Waypoint fuses TAEHV decode into the DiT step and runs the rollout loop async with a clock self-edge; 720p payload SHA unchanged. --- .gitignore | 7 +- WAYPOINT_PROGRESS.md | 332 --------------- configs/waypoint.yaml | 9 +- docs/waypoint/DECISIONS.md | 86 ---- docs/waypoint/MVP_IMPLEMENTATION_STATUS.md | 144 ------- docs/waypoint/OPTIMIZATION_BACKLOG.md | 186 -------- docs/waypoint/PORT_PLAN.md | 64 --- docs/waypoint/VALIDATION.md | 256 ----------- .../prime-capture-2026-09-14-360p-off.json | 163 ------- .../prime-capture-2026-09-14-360p-on.json | 163 ------- ...rime-capture-2026-09-14-720p-off-arm1.json | 163 ------- ...rime-capture-2026-09-14-720p-off-arm2.json | 163 ------- ...prime-capture-2026-09-14-720p-on-arm1.json | 163 ------- ...prime-capture-2026-09-14-720p-on-arm2.json | 163 ------- .../reference-parity-2026-09-11-360p.json | 62 --- .../baselines/streaming-2026-09-11-360p.json | 153 ------- .../baselines/streaming-2026-09-11-720p.json | 151 ------- mstar/graph/base.py | 13 +- mstar/model/waypoint/submodules.py | 360 ++++++++-------- mstar/model/waypoint/waypoint_model.py | 119 +++--- mstar/worker/worker.py | 9 + .../test_speculation_external_inputs.py | 56 +++ test/modular/test_waypoint_checkpoint.py | 32 +- test/modular/test_waypoint_shell.py | 397 ++++++++++++------ 24 files changed, 606 insertions(+), 2808 deletions(-) delete mode 100644 WAYPOINT_PROGRESS.md delete mode 100644 docs/waypoint/DECISIONS.md delete mode 100644 docs/waypoint/MVP_IMPLEMENTATION_STATUS.md delete mode 100644 docs/waypoint/OPTIMIZATION_BACKLOG.md delete mode 100644 docs/waypoint/PORT_PLAN.md delete mode 100644 docs/waypoint/VALIDATION.md delete mode 100644 docs/waypoint/baselines/prime-capture-2026-09-14-360p-off.json delete mode 100644 docs/waypoint/baselines/prime-capture-2026-09-14-360p-on.json delete mode 100644 docs/waypoint/baselines/prime-capture-2026-09-14-720p-off-arm1.json delete mode 100644 docs/waypoint/baselines/prime-capture-2026-09-14-720p-off-arm2.json delete mode 100644 docs/waypoint/baselines/prime-capture-2026-09-14-720p-on-arm1.json delete mode 100644 docs/waypoint/baselines/prime-capture-2026-09-14-720p-on-arm2.json delete mode 100644 docs/waypoint/baselines/reference-parity-2026-09-11-360p.json delete mode 100644 docs/waypoint/baselines/streaming-2026-09-11-360p.json delete mode 100644 docs/waypoint/baselines/streaming-2026-09-11-720p.json create mode 100644 test/modular/test_speculation_external_inputs.py diff --git a/.gitignore b/.gitignore index b701a991d..9b10c77fd 100644 --- a/.gitignore +++ b/.gitignore @@ -31,7 +31,6 @@ CLAUDE.md AGENTS.md .claude_scratch/ -# Waypoint port records are release artifacts, even when a developer's shared -# bare-worktree exclude hides docs/waypoint locally. -!docs/waypoint/ -!docs/waypoint/** +# Waypoint port records (plans, validation logs, baseline numbers) stay local. +docs/waypoint/ +WAYPOINT_PROGRESS.md diff --git a/WAYPOINT_PROGRESS.md b/WAYPOINT_PROGRESS.md deleted file mode 100644 index 0b3349e50..000000000 --- a/WAYPOINT_PROGRESS.md +++ /dev/null @@ -1,332 +0,0 @@ -# Waypoint Port Progress - -The active plan and durable records are in `docs/waypoint/`. This file preserves -the earlier investigation log, including failed approaches and measured results. -Its old phase numbers and "done" labels describe those experiments; they do not -close the current scripted-streaming MVP gates. - -## Current MVP Status - -| Phase | Scope | State | -|---|---|---| -| 1 | Documentation and baseline | Recorded; durable plan/decision/validation/backlog records added | -| 2 | Startup and configuration | Passed, including clean install and registry-selected Hub startup | -| 3 | Request and numerical correctness | Passed, including 41-step same-process parity | -| 4 | DiT and attention execution | Passed, including required full-size capture and host-sync-free steady replay | -| 5 | Encoder and decoder CUDA graphs | Passed, including real-weight parity and full-size eight-step streams | -| 6 | Streaming frame protocol | Passed through Python/Rust server and typed SDK at both resolutions | -| 7 | End-to-end 360p/720p MVP gate | Passed: local/Hub, sequential/interleaved, cleanup/reuse, bounded memory | -| 8 | Streaming viability harness | Baseline passed at 360p/720p; threshold remains a later decision | - -The earlier baseline was **242 CPU tests green** at `HEAD` `0b88001a`. It is -historical, not a claim about the current dirty tree. Current commands and results -belong in `docs/waypoint/VALIDATION.md`. - -## Historical Investigation Log - -### Wave 1 — three parallel streams, no file overlap - -| stream | phase | owns | -|---|---|---| -| A | 7 | `test/modular/test_waypoint_gpu.py` (new only) | -| B | 8 | `pyproject.toml`, `test/waypoint/record_oracle.py`, checkpoint download | -| C | 10 + 11a | `configs/waypoint.yaml`, `submodules.py`, `waypoint_model.py`, `test_waypoint_shell.py` | - -Split this way because the three touch disjoint files. - -Checkpoints download **outside the worktree** (`../checkpoints/`) so they cannot -enter the diff. - -### Wave 2 — phase 9, launched once B's oracle landed - -| stream | phase | owns | -|---|---|---| -| D | 9 | `test/modular/test_waypoint_reference_equivalence.py` (new only) | - -Runs alongside A and C, which still own their own files. D compares **eager to -eager**: the oracle was recorded with the 4+1 driver unrolled to capture per-pass -outputs, so it carries the reference's arithmetic but not its kernel selection. - -### Box constraints - -Only **GPU 2** is usable. 0, 1 and 3 each hold ~72–75 GB of another job, so anything -CUDA must run under `CUDA_VISIBLE_DEVICES=2` and no test may assume a device count. -torch is 2.9.1+cu128; 4.4 TB free on `/mnt/storage`. A and D now share GPU 2 — the -oracle run peaked at 18 GiB, so there is headroom, but a CUDA OOM in either is -contention before it is a bug. - -### Reported results - -**Phase 7.** `test/modular/test_waypoint_gpu.py`, 10 tests green (~22 s warm, 93 s -cold), prose 26.9%. A.1 **failed as shipped** and is fixed (below). A.3/A.4/A.5 pass -bit-exact. A.2 does not — bounded instead, at 4 bf16 ulp of frame peak against a -worst measured 2.35 over 120 comparisons; localized to inductor holding bf16 -pointwise intermediates in fp32 across a fusion, which moves compiled *toward* an -fp32 reference (0.0168) and away from eager (0.0252), and does not compound across -20 frames. Flex compiled-vs-eager and the GEMMs are 0.0. Also fixed the -`compile_dit` and `369 keys` docstrings the two agents flagged. - -**Phase 8.** `tensordict==0.10.0` and `taehv 0.1.0` installed (torch untouched at -2.9.1+cu128); both declared under a new `[waypoint]` extra. Checkpoints at -`../checkpoints/{Waypoint-1.5-1B,taehv1_5}` (11 GB / 22 MB). `build_waypoint_dit` -loads the real `model.safetensors` clean — 174 params, 1.282 B, the 2 fp32 -`NoiseConditioner` params intact. Oracle recorded to `../oracle` (9.2 GB, 41 -frames, 62 s): `test/waypoint/record_oracle.py`, world_engine only. - -**Phase 8, re-recorded.** The first cut ran eager and was wrong: eager -`flex_attention` ignores the `BlockMask` block index lists, so every pass attended -over unwritten ring slots (rel 0.68 against the compiled reference). State now -comes from `engine._denoise_pass`/`_cache_pass` verbatim. It cannot be instrumented -— making the per-pass output an extra output of that region, or splitting it into -five, moves the latent ~1 bf16 ULP and compounds — so `dit_out` comes from frozen -shadow passes, and every run checks they leave the state alone. - -Two findings Phase 9 depends on. **The reference is not bit-reproducible across -processes**: two processes running it alone disagree by 1 ULP at layer 0, compounding -to 11.6 on peak 20.4 by layer 23; latent drifts 0.03 → 0.38 over 6 frames. Not -cudagraphs; `max_autotune` is one source, not the only one. So nothing supports a -bit-exact assertion — `../oracle/repro/` is a second independent recording so the -floor is measurable from artifacts. **And a port that runs its denoise passes as -separate compiled regions cannot match the reference's latent**, for the same -fusion reason. The port is not such a port — `compile_regions()` compiles the same -two outer regions the reference does — but any future refactor that splits them -inherits a ~1 bf16 ULP floor at frame 1. - -**Why phase 9 survives this.** Its bit-exactness is an *in-process* claim: the -parity file drives the reference live and uses the oracle only for inputs (noise, -controls, seed latent) and kernel-independent bookkeeping (`written`, live buckets). -Where it starts both sides from an oracle ring snapshot, both get the same bytes, so -cross-process drift cannot enter. Verified by reading the file, not assumed. What -the finding *does* constrain is **L4**: the oracle's stored `latent` and `pixels` -are not bit-exact targets — pixels drift up to 72/255 across processes — so L4 must -either drive the reference live the same way, or assert against the floor measured -from `../oracle/repro/`. - -**The parity claim, stated exactly.** Port == reference, bit-exact, *when both run -in one process with the attention kernel shared and `reference_compat=True`*. That -is narrower than "the port matches the reference", and it is the strongest claim the -reference's own non-determinism permits. - -**Phase 9.** `test/modular/test_waypoint_reference_equivalence.py`, 10 tests, prose -26.8%. Result: **the port is not bit-equivalent to the reference, because the port -is more numerically correct than the reference.** As served, every layer diverges — -L1 frame 0 maxabs 9.06e-01 (rel 1.38e-01), first divergent stage `cond`, then -`rope`, then the blocks; L2 diverges at pass 0; L3 at frame 0. `written` masks are -**equal at every frame**, so ring bookkeeping is correct and only arithmetic -differs. The control is what makes this conclusive rather than a guess: injecting -the reference's three tables plus its cached LUT gives **0/30 divergent stages** and -a **41-frame rollout bit-exact** on latent, ring bytes and `written`, at every frame -and layer. Every tolerance in the file is exactly 0.0, justified by that control. - -**`WaypointConfig.reference_compat`** (the phase-9 implementation). At the time of -this experiment its default was `False`; WP-001 has since superseded that choice -and makes it `True` for serving. On, it bf16-round-trips the three tables where they are built and serves -the reference's batch-5 sigma LUT; off, the exact path is unchanged. Threads through -5 lines of `dit.py`; `submodules.py` and `waypoint_model.py` needed nothing. -**Flag on ⇒ 0.0 everywhere**: three tables, the LUT at all 5 sigmas, 30 stages, -both forwards, all 5 pass outputs, and the 41-frame rollout on latent, ring bytes -and `written` at every frame × layer. The port builds its own batch-5 table from its -own quantized `freq` and matches the reference bit-for-bit, so the flag is -self-contained rather than borrowing reference state — the hand-injection helper is -deleted. Parity file 5 failed/5 passed → **14 passed**; 277 passed across the -waypoint + ring + flex + GPU set; ruff clean. - -The five failing tests became `[exact]` / `[reference_compat]` pairs. The exact side -asserts *characterised* divergence, not magnitudes — the strongest being -`torch.equal(bf16_roundtrip(port_table), reference_table)`, a zero-tolerance -identity that survives an oracle re-record and goes red if the divergence ever stops -being `NoCastModule`'s cast. Nothing got weaker; `written`-mask equality and that -identity are new. Verified independently: no `allclose`/`atol`/`rtol`/`approx` in -either parity file. - -**Phase 10 + 11a.** `components/taehv.py` (port of `ae.py`'s -`ChunkedStreamingTAEHV` + `load_taehv`, all `taehv` imports deferred), the two VAE -submodules, the rewired walks, and `configs/waypoint.yaml`. Walks are now -`prime: vae_encoder → dit → vae_decoder → EMIT` and -`rollout: Loop { dit → vae_decoder → EMIT }`; the DiT node's contract is unchanged -and `get_node_resources` stays DiT-only. Streaming state is one -`ChunkedStreamingTAEHV` per request per AE node in `PerRequestState.kwargs`, dropped -by the engine's own `cleanup_request` — no cleanup code on either node. `taehv` -landed mid-task, so the port was checked against `world_engine/src/ae.py` directly: -**encode and decode bit-identical in fp32 and bf16**, including the moved -`.div(255)`. Tests still run on a fake `taehv`, and one pins that the tree imports -with the package absent. 253 passed, 3 skipped; ruff clean. - -Config is one `node_groups` entry with `[vae_encoder, dit, vae_decoder]` on rank 0, -not wan22's split — a worker boundary inside the rollout loop would put a process -hop between the DiT and an order-dependent decoder. - -## Problems hit - -- **Wave 1 was killed mid-run and produced nothing.** The parent process exited - while all three streams were still working; none had written a file. Verified on - disk: no `test_waypoint_gpu.py`, no `test/waypoint/`, no `configs/waypoint.yaml`, - no `../checkpoints/`, and neither `tensordict` nor `taehv` installed. All three - resumed from their saved transcripts with the GPU constraint added — their - exploration survived, their output did not. -- **`pip install taehv` from the pinned URL silently installs nothing.** pip here - is 22.0.2 (Ubuntu system pip) and cannot parse `Metadata-Version: 2.4`, which - modern setuptools emits for taehv's PEP 639 `license = "MIT"`. It falls back to - project name `unknown`, rejects the URL on a name mismatch, and installing the - extracted directory instead produces an empty `UNKNOWN-0.0.0` wheel with no - `taehv.py` in it — and uninstalls any other `UNKNOWN-0.0.0` on the box on the - way past. Fixed by building the wheel directly with the system setuptools - (`setuptools.build_meta.build_wheel`) and installing that. uv, which the - reference uses, does not hit this. -- **`fullgraph=True` did not hold on the shipped code.** `_denoise_pass` had - `zip(sigmas, sigmas.diff(), strict=False)`; Dynamo rejects a ragged `zip` under - `fullgraph` regardless of `strict`, with `UserError: zip() has one argument of - len differing from others`. Localized by monkeypatching a zip-free pass, which - compiled clean. Fixed to `zip(sigmas[:-1], sigmas.diff(), strict=True)` — same - four iterations, same values, bit-identical in eager. `capture_scalar_outputs` - is **not** needed and the test asserts it stays unset. The oracle's own unrolled - driver still uses `strict=False`, which is fine: it is never compiled. -- **Gate B costs ~6× the old estimate.** 120 eager `BlockMask` rebuilds at real - 720P = **14.31 ms/frame**, of which the block-alignment `torch.equal` sync is - 4.38 ms — against 40.8 ms/frame steady state, so it is ~35% of frame time. The - `(layer, frame)` cache would cut it to 24 rebuilds = 2.86 ms, **saving 11.45 - ms/frame**. The plan gated this fix on the measurement; the measurement says do - it. Deferred until stream D finishes so it cannot contaminate parity debugging. -- **`compile_dit`'s docstring was wrong** — it claimed compilation is "a - throughput knob only" that "does NOT govern attention correctness." It is a - **capture prerequisite**: eager capture dies with - `cudaErrorStreamCaptureInvalidated` at `make_block_mask`'s device-to-host sync. - Corrected in `config.py`, and shorter than what it replaced. -- **The reference's own buffer handling is lossy.** `NoCastModule._apply` - (`world_engine/src/model/nn.py:11-24`) casts fp32→bf16 and then casts *the result* - back to fp32. Parameters recover because `load_state_dict` refills them - afterwards; **non-persistent derived buffers never do**. So the served reference - runs on bf16-quantized `denoise_step_emb.freq` (1.80e-03), `rope_angles.xy` - (1.88e-02) and `rope_angles.inv_t` (1.78e-04). Not cosmetic: `freq` is multiplied - by `sigma*1000`, so 1.8e-3 relative is a RoPE phase error up to ~1.8 rad. The - reference warns about it itself. Verified in the source before acting on it. -- **The oracle was recorded under eager attention and is partly unusable.** - `record_oracle.py` calls `engine.model(...)` directly (lines 264, 270, 354), - bypassing the reference's two `@torch.compile` regions. Eager `flex_attention` - ignores a `BlockMask`'s block index lists and attends **unwritten ring slots** — - a property this port already pins. Oracle vs reference-compiled is maxabs 5.6406 - on peak 8.25 (rel 0.684). So `dit_out`, `committed_kv`, and the `latent`/`pixels` - that decode from them are **not valid parity targets**; inputs, `ctx`, noise, - `written` masks and the live-bucket pattern still are. Phase 9 worked around it by - driving the reference live with attention rebound to the port's own - `flex_attention_masked`, so the kernel could not be the variable. **Being - re-recorded through the compiled path** — L4 pixel parity depends on it. -- **The decode-ordering comment was wrong, and the test that caught it stands.** - `enable_async_scheduling=False` does *not* serialize dit→decoder; it only - disables speculation (`Worker._can_speculate`). What actually stops the DiT - running twice before a decode is that `NodeManager.pop_ready_nodes` **removes** a - node from the ready set when it schedules it, and the DiT's persisted controller - streams are re-injected only at the loop's iteration boundary. Surfaced by a loop - test asserting `ready_node_names == {vae_decoder}` and failing. Comments and test - now say the real reason. -- **Latent bug in `mstar/graph/base.py` — not waypoint's, not currently reachable.** - `GraphStateRegistry.mark_entity_complete` tracks `_num_completed_entities` as a - *count*, not a set, despite a "no-ops if already done" docstring. Marking one - entity complete twice inside a 2-node loop body satisfies - `_num_completed == _num_managed`, fires `complete_iter()`, and `reset_for_iter` - then silently discards the latent queued in the other node's `ready_signals`. - Single-node loop bodies (wan22, cosmos3) cannot hit it; waypoint is held off it - only by the pop semantics above. Any future multi-node loop body is one re-ingest - away. **Reported, not fixed — shared engine code, outside this port's scope.** -- **The `369 keys` in `weight_loader.py`'s key map is stale** — the shipped - checkpoint has **393**. Prose only; nothing computes from it, and the loader's - completeness contract passes on the real file. -- **Stream C's in-flight work fails 6 tests in `test_waypoint_shell.py`** - (`KeyError: 'vae_encoder'`, `NameError: WorkerGraphIO`) — the Phase 10 VAE nodes - do not exist yet. Not caused by the phase 8 dependency changes: the only tracked - phase 8 edit is +17 lines of `[project.optional-dependencies]`, and the other - 217 tests in the ring/flex/waypoint set stay green. - -## Historical decisions - -These record the choices made on 2026-09-10 after phase 9. The first choice was -superseded by WP-001 on 2026-09-11; it remains here to explain the implementation -and measurements that followed it. - -- **Superseded: the port ships the mathematically exact tables by default.** The - reference serves bf16-quantized RoPE - and conditioner tables because of the lossy `NoCastModule._apply` round-trip - above. Rather than be bug-compatible, the port stays exact and gains a - reference-compat flag alongside the existing `full_global_ring`, which is already - there "to restore the reference's allocation for an A/B parity run". Flag on ⇒ - bit-exact over 41 frames, which validates every other line of the port; flag off ⇒ - exact-table serving differed from the reference by one characterised difference. - Rejected: bug-compatibility (ships a ~1.8 rad phase defect the reference's authors - appear not to have intended) and dropping bit-exactness as the gate (leaves no - zero for a future regression to fail against). -- **`patch_cached_noise_conditioning` sits behind the same flag.** It is measurably - **not** a no-op — the planning assumption fell the wrong way. The cause is the - batch shape, not the LUT: the reference builds the table by evaluating the fp32 - MLP on all 5 sigmas at once (M=5 ⇒ TF32 tensor-core GEMM under - `float32_matmul_precision('high')`), while serving one sigma at a time is an M=1 - GEMV that stays exact fp32. Measured batch5-vs-batch1: **3.125e-02 @ `high`, 0.0 @ - `highest`**, 3.125e-02 @ `medium`. Per-sigma the patch is off the live path by - 1.5625e-02, except sigma 0.75 at 3.125e-02. Only the `CachedDenoiseStepEmb` half - diverges — **`CachedCondHead` measures 0.0 at all five sigmas and needs no compat - treatment**. Serving keeps the exact per-sigma GEMV; parity runs reproduce the - reference. One flag, so tables and LUT can never disagree about which model is - being served. - -Three independent measurements converge on the M=5 TF32 story — phase 8's -calibration probe (`high` vs `highest` = 0.03125 on the conditioner), phase 7's pin -test (all precisions identical at the served M=1 shape), and phase 9's direct -batch5-vs-batch1 comparison. Phase 8 read its 0.03125 as only proving the probe was -live; it was also the signal, unrecognised at the time. - -## Assumptions made - -Carried in from planning, to be confirmed or killed by the work: - -- ~~**`patch_cached_noise_conditioning` is a numerical no-op.**~~ **Killed by phase - 9.** The reasoning was that the LUT is built by running the same fp32 island on - bf16 sigmas and rounding to bf16, which is what the port's live path returns. That - missed the batch shape entirely. See *Decisions taken* above — a good example of - why the plan said to test this rather than assert it. -- **The patched reference is the reference.** `world_engine.py:84` applies - `apply_inference_patches` unconditionally, so parity targets the patched model. -- **Same-torch parity.** The reference pins `torch==2.11.0`; this env has 2.9.1. - Both sides run at 2.9.1 so the comparison means something; this deviates from the - reference's pin deliberately. -- **The two matmul-precision settings differ, but not observably** — mstar sets - `'high'`, the reference `'medium'`. Settled by phase 8: the oracle records under - `'high'` (serving's value, set *after* importing world_engine, which sets - `'medium'` at import), and a calibration probe run at record time measures - `'high'` and `'medium'` as bit-identical on this build — 0.0 on both a 4096² - fp32 GEMM and on the NoiseConditioner LUT. `'highest'` differs from both (0.104 - and 0.03125), which is what shows the probe is live. So the flag cannot explain - a phase 9 mismatch on this box; it can on another. Phase 7 reached the same - answer by a second, independent route: at the served shape (B=N=1) the - `NoiseConditioner` matmuls are GEMVs (M=1), where all three settings are - bit-identical. Its pin test asserts both the setting and the reason it does not - bite, with an N=8 precondition that fails if the flag ever stops being live. -- **Historical implementation: `reference_compat` + `compile_dit` needed one eager frame first.** The sigma LUT - is built on first forward and cached per device, because it needs loaded weights — - the same contract `compile_regions`' docstring already states for the RoPE and - token-grid tables. An operational constraint on the parity configuration only; - the exact-table default at that time had no LUT. The active plan instead requires - post-load table materialization before compile/warmup. -- **Both LUTs are built under the same `float32_matmul_precision`.** At `'highest'` - the batch-5 GEMM stops rounding and the compat conditioner would converge on the - exact one; a test asserts the exact path still differs, so that surfaces red - rather than silently. The fixture ordering that guarantees it is commented. -- **The port's global-ring compaction touches only slots the reference never - addresses** — the reference's live region is `[0, port ring_len)` plus scratch - `[L, capacity)`, with `[8192, 65536)` dead. Not taken on faith: phase 9's - `test_the_port_compacts_only_slots_the_reference_never_addresses` proves it - against the oracle's `written` masks, which survive the oracle's eager-attention - defect. -- **VAE nodes use `disable_autocast = True`, not the fp32-island mixin.** TAEHV is - uniformly bf16, so the recorded island set would be empty, and - `EngineManager.build` skips the blanket cast entirely under `disable_autocast` — - stronger than restoring dtypes after a cast that already rounded. The only such - mixin is wan22's, and this port deliberately does not import across models. -- **The emitted payload is raw uint8 RGB** `[temporal_compression, H, W, 3]` in C - order, no container: a per-step mp4 would be an unplayable fragment. -- **A 1-frame seed clip is repeated to fill `temporal_compression`**, matching - `gen_sample.py`'s `seed_frame_x4`. Anything but 1 or exactly - `temporal_compression` is refused at the API boundary. Aspect ratio is checked, - resolution is not — the AE resizes 16:9 input onto its own grid. -- **The noise must be supplied, not reproduced.** The reference draws bf16 on - device unseeded; the port draws fp32 from a seeded CPU generator. The oracle - saves the exact noise tensor per frame so phase 9 feeds the port the same draw — - without that there is nothing bit-exact to compare. diff --git a/configs/waypoint.yaml b/configs/waypoint.yaml index 186b0da8f..cf9b9fd60 100644 --- a/configs/waypoint.yaml +++ b/configs/waypoint.yaml @@ -30,10 +30,9 @@ resources: # max_concurrent_requests; the two are checked against each other. num_worlds: 1 -# 1.28B DiT plus a ~7M-parameter TAEHV, all on rank 0. One group, not wan22's -# split: the rollout Loop body is dit -> vae_decoder, and the decoder is a -# streaming model whose frames must be decoded exactly once in emission order, -# so there is nothing to gain from putting a worker boundary inside the loop. +# 1.28B DiT plus a ~7M-parameter TAEHV, all on rank 0. One group: the rollout +# Loop body is a single dit node (the TAEHV decode is fused into its forward), +# so there is no separate decoder to put a worker boundary in front of. node_groups: - - node_names: [vae_encoder, dit, vae_decoder] + - node_names: [vae_encoder, dit] ranks: [0] diff --git a/docs/waypoint/DECISIONS.md b/docs/waypoint/DECISIONS.md deleted file mode 100644 index 206449b78..000000000 --- a/docs/waypoint/DECISIONS.md +++ /dev/null @@ -1,86 +0,0 @@ -# Waypoint Decision Record - -## WP-001: Reference-Compatible Numerics by Default - -- **Status:** Accepted, 2026-09-11 -- **Decision:** `reference_compat=True` is the serving default. Setting it to - `False` selects experimental exact-table arithmetic. -- **Evidence:** A live same-process 41-frame run previously measured zero - difference for reference-compatible tables, conditioner, DiT stages, five-pass - output, ring writes, and rollout latents. Exact-table arithmetic intentionally - diverges because the released reference BF16-round-trips derived FP32 tables and - builds its sigma table with a batch-5 operation. -- **Consequence:** Release validation targets reference-compatible mode. Exact - mode remains useful for investigation but cannot satisfy the reference parity - gate. - -## WP-002: Resolve and Validate Before Allocation - -- **Status:** Accepted, 2026-09-11 -- **Decision:** Resolve local paths before considering a string to be a Hugging - Face ID. Download only root native safetensors plus `config.yaml`; resolve - `taehv1_5.pth` separately. Validate the manifest's architecture, supported - geometry, scheduler, temporal compression, and FPS before device allocation; - validate tensor completeness and the pinned TAEHV runtime architecture while - loading, before request admission. -- **Reason:** The upstream repository also contains redundant transformer and VAE - weights. Downloading the whole snapshot wastes several GiB and allows partial or - incompatible snapshots to fail late. - -## WP-003: Optional CUDA Graph Acceleration - -- **Status:** Supersedes the required-capture policy, 2026-09-11 -- **Decision:** `cuda_graph=True` attempts capture for encoder prime, DiT prime, - steady DiT rollout, decoder initialization, and steady decoder execution. Capture - failure falls back to eager execution; `cuda_graph=False` declares no capture - buckets. `compile_dit` independently controls the two outer DiT regions. -- **Reason:** CUDA graphs are an acceleration mechanism, not part of the model's - numerical contract. The engine already supports eager fallback, and Waypoint's - ring, mask planning, and functional AE state have eager execution paths. -- **Constraint:** The masked FlexAttention primitive remains compiled because bare - eager `flex_attention` ignores this BlockMask's block-index visibility data. -- **Amendment:** `PRIME-GRAPH-001` promoted the one-time DiT prime/cache pass from - compiled-only to captured on 2026-09-14. `capture_dit_prime` defaults to `True` - and is subordinate to `cuda_graph`; setting it `False` keeps the uncaptured prime - reachable as the startup-latency control arm. `compile_dit` still independently - controls that pass's `fullgraph=True` region either way. - -## WP-004: Internal Prime Is Not User Output - -- **Status:** Accepted; end-to-end validation passed -- **Decision:** Prime with an internal idle action, preserve user action zero for - the first generated latent, initialize all model state, and emit no reconstructed - seed frames. -- **Consequence:** A request with `num_steps=N` supplies exactly `N` actions and - emits exactly `4*N` RGB frames indexed from zero. - -## WP-005: Typed Streaming RGB Frames - -- **Status:** Accepted; live 360p and 720p validation passed -- **Decision:** Waypoint emits only the `video_frame` modality in streaming mode. - Payloads are contiguous RGB24 with width, height, FPS, pixel format, frame index, - and frame count metadata. -- **Consequence:** The OpenAI encoded-video endpoint is not a Waypoint transport. - Non-streaming `video_frame` requests fail before execution. The scripted MVP - supports only the Python frontend; `--rust-frontend` support is deferred. - -## WP-006: TAEHV Source Pin and Installer Floor - -- **Status:** Accepted -- **Decision:** TAEHV is installed separately from the index-safe `waypoint` extra - and pinned to upstream commit `7dc60ec6601af2e668e31bc70acc4cb3665e4c22`. - Direct URLs cannot appear in metadata published to PyPI. Supported installation - uses `uv>=0.4.0` or `pip>=24.3`; absence of TAEHV must produce an actionable - error containing its exact pinned archive command. -- **Reason:** Old pip releases misread the source package's Metadata-Version 2.4 - metadata and can install an empty `UNKNOWN` wheel. - -## WP-007: Streaming Benchmark Follows the MVP - -- **Status:** Accepted; first captured baseline recorded -- **Decision:** The first trustworthy captured run establishes a baseline. It does - not invent a release threshold. A later decision record sets a viability - threshold from the `STREAM-001` measurements. -- **Evidence:** Captured 16-step baseline and matched slow-consumer runs passed at - 360p and 720p. The durable JSON artifacts and summarized measurements are in - `OPTIMIZATION_BACKLOG.md`. diff --git a/docs/waypoint/MVP_IMPLEMENTATION_STATUS.md b/docs/waypoint/MVP_IMPLEMENTATION_STATUS.md deleted file mode 100644 index 50335dca7..000000000 --- a/docs/waypoint/MVP_IMPLEMENTATION_STATUS.md +++ /dev/null @@ -1,144 +0,0 @@ -# Waypoint MVP Implementation Status - -This is the live implementation report for the current completion pass. It is -separate from `WAYPOINT_PROGRESS.md`, which remains the historical investigation -log. Durable decisions, acceptance gates, and deferred work remain in the other -files in this directory. - -## Current Snapshot - -Last updated: 2026-09-11. - -The branch was fetched and rebased onto `origin/main` at `9ef65097`. Its four -Waypoint commits now sit directly above the four new upstream packaging, ragged -attention, API-validation, and sampler commits. The pre-rebase tracked patch ID -and all 25 restored untracked file blobs matched the safety stash; there are no -unmerged entries, conflict artifacts, or staged files. - -| Area | Implementation | Current evidence | Remaining gate | -|---|---|---|---| -| Startup/config | Passed | Both artifact paths, the TAEHV runtime, and the DiT manifest validate before device allocation; tensor completeness and TAEHV architecture validate during loading before admission; variant-specific Hub mapping prevents cross-variant weights; Python 3.12/uv resolves and builds the pin; registry-selected 360p Hub startup passed | None for scripted MVP | -| Request semantics | Passed | Positive bounded `num_steps`, exact validated action count, required seed, internal idle prime, action zero preserved; full 360p/720p eight-step streams emitted exactly 32 generated frames from index zero | None for scripted MVP | -| DiT execution | Integrated | Runtime tables materialize after load; `compile_dit` independently selects compiled or eager denoise/cache regions; optional 128-token and 512-token rollout and prime graphs captured on H100 | Record a full server graph-off run | -| Mask planning | Passed | One fixed-address local/global block mask per slot; immutable device visibility tables remove per-step allocations; multi-wrap/dilation/world parity passes; full-size 360p and 720p profiles each found 16/16 steady DiT graph replays and zero blocking CUDA calls | None for scripted MVP | -| Encoder/decoder | Integrated | Pure tensor encoder; nine explicit histories; real-weight FP32 parity; optional encoder/init/steady graphs captured and served at both resolutions on H100; graph-free declarations use eager forwards | Record a full server graph-off run | -| Optional capture policy | Integrated | `cuda_graph` controls declaration independently of `compile_dit`; all Waypoint buckets use normal eager fallback on capture failure; all five graph-enabled buckets captured at both resolutions, with `capture_dit_prime` gating the fifth | Record graph-off and injected capture-failure server runs | -| Frame protocol | Passed | Python streaming-only `video_frame`, canonical RGB24 metadata, immutable zero-copy SDK view, named-byte upload, explicit stream errors, ordered async-read delivery, and live 360p/720p SDK streams pass | Rust frontend support is deferred and outside the scripted MVP | -| End to end | Passed | Local 720p and registry-Hub 360p normal `EngineManager` paths captured all four buckets; both resolutions passed sequential and full-size two-world interleaved deterministic streams, exact counts, cleanup, repeated slot reuse, and bounded server memory | None for scripted MVP | -| Streaming viability | Baseline complete | `STREAM-001` records captured 16-step baseline and matched slow-consumer runs for both resolutions, including typed-stream correctness and server process-group memory | A later decision may define a viability threshold from these measurements | - -## Latest Validation - -- After reverting the out-of-scope Waypoint Rust frontend changes, the Python - frame protocol, SDK, and API guard selection passed: **45 passed, 2 existing - FastAPI deprecation warnings**. -- Consolidated Waypoint, startup, frame/SDK, graph-policy, attention/ring, GPU, - and live-parity CPU selection: **385 passed, 41 skipped, 4 warnings**. -- On H100 (`CUDA_VISIBLE_DEVICES=2`), the reduced random-weight graph suite passed - **10 tests** covering capture/replay, planned masks, interleaving, and - world-slot reuse. -- The 720p full-checkpoint same-process parity suite passed **14 tests**. Its - 41-frame reference-compatible rollout was bit exact through tables, - conditioning, all DiT stages, five-pass output, ring writes, and rollout - latents. The touched suite was rerun after the rebase: **14 passed in 56.98s**. -- The dedicated 360p same-process gate passed on the published 360p checkpoint: - **1 passed in 37.79s** on H100. Zero-tolerance comparisons covered all three - derived tables, five conditioner rows, all 30 DiT stages at frozen and - committing sigmas, 201 passes across 41 latent frames, every ring write, the - full-size BF16 functional encoder, all nine explicit decoder histories, and - 164 decoded 640x360 RGB frames. The retained report is - `baselines/reference-parity-2026-09-11-360p.json`. -- The real-checkpoint pixel suite passed **5 tests**, including TAEHV seed - encode/decode and reference pixel/order checks. -- A normal local 720p registry and `EngineManager` server run captured all four - graph-enabled buckets under the earlier required-capture policy. Two sequential eight-step SDK requests each emitted eight - typed chunks and exactly 32 1280x720 RGB24 frames; all 88,473,600 output bytes - were identical across slot reuse. The SDK's NDJSON reader now uses 1 MiB input - chunks, avoiding quadratic buffering of each 14.7 MB base64 response line. -- The distinct 360p checkpoint was selectively downloaded from its published - Hub repository: only `config.yaml` and `model.safetensors`. Its local SHA-256 - matched Hub metadata, and the variant-specific manifest preflight passed. -- A normal 360p registry and `EngineManager` run through `MStarClient` captured - all four graph-enabled paths under the earlier required-capture policy. Two sequential one-step requests each returned one - typed 2,764,800-byte chunk containing four 640x360 RGB24 frames from index - zero; the repeat was byte-identical. Startup took 223.5 seconds and requests - took 29.7 and 6.2 seconds. -- Real pinned TAEHV test on a reduced spatial grid: functional encoder, decoder - initialization, decoder steady output, and all nine histories are bit-identical - to the upstream streaming scheduler in FP32. -- `ruff check` over all changed implementation and focused test files: passed. -- `python3 -m compileall` over the changed Python surfaces: passed. -- `git diff --check`: passed. -- An optional full `test/modular` run required redirecting FlashInfer's cache - to `/tmp`; it reached 31% but then stopped producing output for several - minutes and was interrupted. The focused 16-file gate above was rerun cleanly - afterward, so this incomplete broad run is not counted as validation. -- The server log exposed a spurious `rollout_loop` stop signal during the prime - walk. The graph runtime ignored it, but it was a model lifecycle bug; the stop - hook now returns no signal outside rollout and its regression test passes. -- SDK error/upload regressions plus frame protocol tests pass: **26 passed**. -- Historical Rust validation remains recorded in `VALIDATION.md`, but the - Waypoint-specific Rust guard and tests were reverted after the Rust frontend - was removed from the scripted MVP scope. -- `uv 0.11.13` with Python 3.12 resolved the index-safe `.[waypoint]` - dependencies in dry-run mode. A separate `--no-deps` install then built the - pinned TAEHV revision as a real 21,947-byte `taehv.py` package exporting - `TAEHV`; this closes the installer-floor check without downloading a second - CUDA/PyTorch stack or placing a PyPI-incompatible direct URL in `m-star` - metadata. -- Async result reads now retain notification sequence and loop snapshots, buffer - out-of-order completions, and assign frame indices only in emission order. A - deliberately reversed-completion regression test passes. -- Flex mask planning now reads immutable per-geometry device lookup tables; a - repeat-plan test forbids fresh `torch.tensor` staging and checks all mask/table - addresses remain stable. -- Latest combined protocol, shell, ring, and Flex resource gate: **199 passed, - 3 skipped**; Ruff and Python compilation passed. -- A registry-selected 360p Hub deployment captured all required buckets, served - distinct deterministic solo baselines, and reproduced them byte-for-byte over - two concurrent two-world waves with actual A/B/A DiT scheduling. Every request - executed eight rollout steps and emitted 32 frames. A three-wave memory run - measured -2.0 MiB quiescent server PSS growth and 0 MiB GPU growth after warmup. -- A local 720p deployment repeated the full-size two-world gate over three - concurrent waves. All 48 chunks matched their distinct solo baselines, worker - execution interleaved, every request cleaned up, and measured quiescent growth - after warmup was -49.5 MiB host PSS and 0 MiB GPU memory. -- Reproducible Nsight validation now scopes CUDA calls to the nested steady DiT - `engine.forward` ranges. Full 360p and 720p eight-step traces each reported - **16 forwards, 16 graph replays, 0 synchronization or blocking calls**. -- The post-MVP streaming harness passed 16-step captured runs at both resolutions. - Baseline TTFF / sustained media ratio / p50-p95 gap were **0.148s / 3.268x / - 0.020-0.027s** at 360p and **0.465s / 0.570x / 0.112-0.142s** at 720p, with - no baseline stalls. GPU memory was flat under slow-reader backpressure, payload - hashes matched, and the raw JSON artifacts are retained under - `docs/waypoint/baselines/`. - -## Completed This Pass - -- Startup/configuration, graph/mask, frame-protocol, and end-to-end audits - completed in parallel, followed by coordinator integration review. -- Mandatory DiT compilation, TAEHV dependency preflight, exact action mapping, - real-weight functional AE parity, and the planned-mask CUDA test path were - tightened during those audits. -- Rebased the four committed Waypoint changes onto `origin/main` at `9ef65097` - and restored the full tracked/untracked working tree without content loss. -- Integrated upstream's PyPI packaging: Waypoint's extra is index-safe, both - alias packages forward it, the CLI resolves its packaged default config, and - pinned TAEHV remains an explicit separate install with actionable errors. -- Preserved upstream malformed-`model_kwargs` handling. The Python frontend - rejects `video_frame` input as HTTP 400; Rust frontend parity is deferred. -- No changes were staged or committed. -- Closed the separate 360p numerical gate with the native 360p Hub weights. - Inputs use the canonical seed/action script and seeded CPU-fp32 noise recipe, - resized/generated directly at 360p. The existing stored oracle remains a 720p - artifact and was deliberately not reused as a 360p numerical target. - -## Release Boundary - -The scripted streaming MVP gate and its post-MVP measurement phase are complete: -both supported resolutions start through the normal registry and `EngineManager`, -all four graph-enabled paths have captured successfully, server output is consumed -as typed SDK frames, and interleaved world-slot reuse remains deterministic and -memory-bounded. `STREAM-001` supplies the first captured baseline without inventing -a release threshold. CUDA graph capture is now optional; graph-off and injected -capture-failure full-server runs remain to be recorded. diff --git a/docs/waypoint/OPTIMIZATION_BACKLOG.md b/docs/waypoint/OPTIMIZATION_BACKLOG.md deleted file mode 100644 index f0eb714bb..000000000 --- a/docs/waypoint/OPTIMIZATION_BACKLOG.md +++ /dev/null @@ -1,186 +0,0 @@ -# Waypoint Optimization Backlog - -An item may remain deferred only when it records evidence, expected benefit, -dependency, proposed benchmark, and completion criterion. These items are outside -the scripted streaming MVP unless a gate promotes one. - -## STREAM-001: Streaming Viability Baseline and Threshold - -- **Status:** Captured baseline complete on 2026-09-11; threshold decision deferred. -- **Evidence:** Normal server startup captured all four graph-enabled paths for - each run. The 16-step baseline and matched slow-consumer stream passed at both - resolutions with identical payload hashes. Raw artifacts: - `baselines/streaming-2026-09-11-360p.json` and - `baselines/streaming-2026-09-11-720p.json`. -- **Expected benefit:** Quantifies user-visible startup latency, sustained delivery, - stalls, backpressure, and memory behavior before setting a release threshold. -- **Dependency:** Satisfied: Phases 1-7 pass with all four graph-enabled paths - captured. -- **Proposed benchmark:** Measure time to first frame, generated-media-time divided - by wall time, p50/p95 inter-chunk gap, jitter, stall count/duration, slow-consumer - backpressure, peak GPU memory, and peak host PSS for 360p and 720p. -- **Completion criterion:** Baseline satisfied by the results below. A later - decision defines thresholds; no threshold is inferred from one run. - -| Variant | TTFF | Sustained media/wall | Gap p50 / p95 | Baseline stalls | Peak host PSS | GPU memory | -|---|---:|---:|---:|---:|---:|---:| -| 360p | 0.148 s | 3.268x | 0.020 / 0.027 s | 0 | 3291.5 MiB | 4648 MiB | -| 720p | 0.465 s | 0.570x | 0.112 / 0.142 s | 0 | 3687.3 MiB | 5756 MiB | - -The slow consumer paused 0.25 seconds between 15 reads. It added 3.717 seconds -at 360p and 3.462 seconds at 720p for 3.750 seconds deliberately injected, with -0 MiB GPU growth and byte-identical output. Its host peak changed by +60.4 MiB at -360p and +7.1 MiB at 720p. These are observations, not release limits. - -## INTERACTIVE-001: Interactive Sessions - -- **Evidence:** The MVP input is a complete action script and has no session - lifetime or reconnect contract. -- **Expected benefit:** Enables long-lived controllable worlds rather than fixed - offline scripts. -- **Dependency:** Stable scripted cleanup, world-slot reuse, and streaming protocol. -- **Proposed benchmark:** Reconnect, cancellation, idle timeout, and one-hour - session soak with deterministic action traces. -- **Completion criterion:** A documented session state machine passes lifecycle and - soak tests without state leakage. - -## ACTION-INGRESS-001: Live Action Ingress - -- **Evidence:** Actions are validated as a fixed list before execution; no - bidirectional live ingress or timing policy exists. -- **Expected benefit:** Allows real-time control while frames are generated. -- **Dependency:** `INTERACTIVE-001` and an explicit late/missing-action policy. -- **Proposed benchmark:** Timestamped actions under latency, reordering, loss, and - backpressure with output/action correlation checks. -- **Completion criterion:** Every generated latent consumes exactly one documented - live action under normal and degraded transport tests. - -## BATCH-001: True Request Batching - -- **Evidence:** MVP graph slots isolate worlds but do not establish a shared batched - DiT/AE execution path. -- **Expected benefit:** Higher throughput under concurrent scripted requests. -- **Dependency:** Correct interleaved worlds and measurements showing launch or - occupancy headroom. -- **Proposed benchmark:** Throughput, tail latency, graph memory, and parity at batch - sizes 1, 2, 4, and 8 for each resolution. -- **Completion criterion:** A selected batch policy improves throughput without - parity drift or unacceptable p95 latency. - -## QUANT-001: Weight Quantization - -- **Evidence:** The parity baseline uses checkpoint-native BF16; no quantized error - or speed/memory data exists. -- **Expected benefit:** Lower GPU memory and potentially higher DiT throughput. -- **Dependency:** Stable BF16 end-to-end baseline and quality evaluation corpus. -- **Proposed benchmark:** Layer/rollout error, pixel metrics, action consistency, - memory, and media-time/wall-time for candidate formats. -- **Completion criterion:** A format meets an explicitly recorded quality bound and - materially improves memory or throughput. - -## DECODER-PLACEMENT-001: Decoder on Another GPU - -- **Evidence:** Decoder order is stateful and the MVP keeps DiT and decoder in one - worker group; transfer and scheduling costs are unmeasured. -- **Expected benefit:** Overlap decode with DiT work and reduce rank-0 pressure. -- **Dependency:** Typed frame streaming, decoder graph state transfer, and correct - multi-worker loop ordering. -- **Proposed benchmark:** Compare colocated and split placement for throughput, - inter-chunk gaps, transfer time, and memory. -- **Completion criterion:** Split placement is parity-preserving and wins a recorded - performance target without ordering failures. - -## PRIME-GRAPH-001: DiT Prime Capture - -- **Status:** Closed 2026-09-14. The DiT now declares `prime` alongside `rollout`, - so a graph-enabled server captures five buckets instead of four, the new one being - `dit: prime[bs=1,tokens=512]`. Gated by `capture_dit_prime`, default `True` and - subordinate to `cuda_graph`; `False` restores the compiled eager prime and is the - control arm below. -- **Evidence:** Startup TTFF measured by `benchmark_streaming.py --startup-repeats`, - one sample per world-slot reuse cycle. At 360p, 20 samples per arm: p50 63.10 -> - 55.13 ms (-12.6%), p95 67.72 -> 58.12 ms (-14.2%), peak GPU 4626 -> 4424 MiB. At - 720p, four arms position-balanced (see the measurement caveat): p50 142.53 -> - 141.95 ms (-0.4%), p95 167.24 -> 154.55 ms (-7.6%), peak GPU 5576 -> 5476 MiB. - The DiT graph runner's static buffers grow 5 -> 10 entries, 0.13 -> 0.25 MB, and - `post_warmup_validate` still passes, so the ring is left clean by capture. Every - arm reported `correctness.passed`, zero stalls, and an unchanged payload SHA-256 - (`cc84686681d6...` at 360p, `bdd8ed5605d3...` at 720p, the latter matching the - 2026-09-13 binary-framing record). Raw artifacts: - `baselines/prime-capture-2026-09-14-360p-{on,off}.json` and - `baselines/prime-capture-2026-09-14-720p-{on,off}-arm{1,2}.json`. - An nsys trace attributes the win to the prime node itself: the - `worker[worker_0].node[dit].graph_walk[prime]` range falls from 55.86 ms host / - 1743 kernels / 13.6 ms GPU uncaptured to 6.13 ms host / 27 kernels / 0.033 ms GPU - captured, and `check_nsys_replay.py --rollout-range - "worker[worker_0].node[dit].graph_walk[prime]" --expected-forwards 3` goes from - `graph_replays=0 sync_or_blocking_calls=1` (6 `cudaMalloc` and 2 `cudaFree` inside - the forward) to `graph_replays=3 sync_or_blocking_calls=0`. The steady rollout - range is unchanged at `forwards=33 graph_replays=33 sync_or_blocking_calls=0`. -- **Measurement caveat:** Arm order dominates this benchmark. Identical - `capture_dit_prime=False` code measured p50 149.56 / p95 194.31 ms as a job's first - arm and p50 135.50 / p95 140.16 ms as its second, a 14.06 / 54.15 ms swing at an - unchanged 130.0 ms floor. A first blocked A/B ran capture-on first at 720p and - reported an apparent +5.93 ms p50 / +17.44 ms p95 regression, entirely inside that - swing; a reversed-order repeat inverted the sign. The 720p figures above are the - mean of both positions per arm and supersede the blocked result. Note also that - nsys charges per launch, so the profiled kernel-count gap flatters capture relative - to unprofiled wall time. -- **Expected benefit:** Lower request startup latency. -- **Dependency:** Required steady graphs and stable prime inputs/state addresses. -- **Proposed benchmark:** Admission-to-first-frame latency and graph memory with and - without prime capture across repeated world-slot reuse. -- **Completion criterion:** Capture reduces p50/p95 startup latency without state - leakage or disproportionate graph memory. Met: p95 falls at both resolutions and - p50 falls at 360p, 720p p50 is a 0.4% tie, no arm leaked state, and graph memory - grew by 0.12 MB with no peak-GPU growth. - -## ENCODED-VIDEO-001: Encoded Video Output - -- **Evidence:** Per-step encoded fragments are not independently playable and the - MVP protocol intentionally emits raw RGB frames. -- **Expected benefit:** Lower network bandwidth and direct media playback. -- **Dependency:** Session-aware muxing, cancellation/finalization semantics, and - separate API design from the MVP `video_frame` modality. -- **Proposed benchmark:** End-to-end latency, bandwidth, seek/playability, encoder - load, and cancellation integrity for candidate codecs/containers. -- **Completion criterion:** A complete playable stream meets a separately recorded - latency/bandwidth target and never exposes broken fragments. - -## NOISE-001: GPU or Stateless Noise - -- **Evidence:** Current deterministic parity supplies CPU FP32 noise then casts; - device/stateless generation would change reproducibility and possibly bytes. -- **Expected benefit:** Avoid host generation/copy and simplify graph inputs. -- **Dependency:** A documented seed mapping and a new numerical baseline. -- **Proposed benchmark:** Generation/copy time, replay behavior, determinism across - world slots, and rollout parity/quality. -- **Completion criterion:** Deterministic request-to-noise mapping and a measured - performance win pass long interleaved rollouts. - -## GRAPH-MEM-001: CUDA Graph Memory Reduction - -- **Evidence:** Four graph-enabled paths across two resolutions can duplicate pools - and staging buffers. Full-server peaks are now measured at 4648 MiB for 360p and - 5756 MiB for 720p, but per-bucket pool and staging attribution remains open. -- **Expected benefit:** More world slots or lower deployment GPU requirements. -- **Dependency:** Complete optional-capture implementation and memory attribution. -- **Proposed benchmark:** Per-bucket private-pool/staging bytes, peak allocated and - reserved memory, and reuse across sequential/interleaved requests. -- **Completion criterion:** A change reduces measured graph memory without capture - fallback, address instability, or parity drift. - -## HOTSPOT-001: Measured Runtime Hotspots - -- **Evidence:** Mask rebuild was historically measured at 14.31 ms/frame. Final - full-size captured traces now prove host-sync-free steady DiT replay, while the - 720p streaming baseline remains below real-time at 0.570x sustained media time; - detailed hotspot attribution is still open. -- **Expected benefit:** Direct optimization effort toward the dominant final-path - cost. -- **Dependency:** `STREAM-001` traces with synchronized attribution outside timed - replay. -- **Proposed benchmark:** GPU/CPU trace of startup and steady state, ranked by frame - time and memory traffic for both resolutions. -- **Completion criterion:** Each promoted hotspot gets its own evidence-backed item; - close this placeholder when the final trace has no untracked material hotspot. diff --git a/docs/waypoint/PORT_PLAN.md b/docs/waypoint/PORT_PLAN.md deleted file mode 100644 index cf128a1c1..000000000 --- a/docs/waypoint/PORT_PLAN.md +++ /dev/null @@ -1,64 +0,0 @@ -# Waypoint MVP Port Plan - -This is the active completion plan for the scripted Waypoint MVP. Historical -experiments and measurements remain in `WAYPOINT_PROGRESS.md`; a historical -"done" label there is evidence about that experiment, not an MVP release gate. - -## MVP Contract - -- Serve both `waypoint-1.5-1b-360p` and `waypoint-1.5-1b-720p`. -- Use reference-compatible arithmetic by default. Exact-table arithmetic remains - an explicitly selected experimental mode. -- Attempt CUDA graphs by default for encoder prime, steady DiT rollout, decoder - initialization, and steady decoder execution. Allow explicit graph-free - execution and eager fallback when capture fails. -- Accept exactly one validated action per generated latent step. The internal idle - prime action does not consume action zero. -- Prime encoder, DiT cache, and decoder state without emitting reconstructed seed - frames. -- Stream contiguous RGB24 frames only. Exactly `4 * num_steps` frames are emitted, - starting at frame index zero. - -## Phases - -| Phase | Scope | Completion gate | -|---|---|---| -| 1 | Documentation and baseline | Current diff/test baseline recorded; plan, decisions, validation, and backlog exist. | -| 2 | Startup and configuration | Local/HF sources resolve before allocation; downloads are selective; config/checkpoint facts and TAEHV dependency fail clearly when invalid. | -| 3 | Request and numerical correctness | Positive steps, exact action count, internal prime semantics, and live parity through pixels are tested. | -| 4 | DiT and attention execution | Runtime tables are post-load; optional full-graph DiT compilation is independent of optional CUDA graph capture; masks are planned/staged once; captured and eager paths are supported. | -| 5 | Encoder and decoder graphs | Tensor-only fixed-shape state; encoder prime and decoder init/steady paths support optional capture with eager fallback, equivalence, reuse, and cleanup tests. | -| 6 | Streaming frame protocol | `video_frame` events and SDK `VideoFrameChunk` validate typed metadata and expose a zero-copy `[N,H,W,3]` NumPy view; non-streaming use is rejected. | -| 7 | End-to-end MVP gate | Registry and `EngineManager` runs pass for local/HF sources, 360p/720p, sequential/interleaved worlds, cleanup, bounded memory, and exact frame counts. | -| 8 | Post-MVP streaming viability | Only after Phase 7: collect latency, throughput, gap/jitter/stall, backpressure, and memory baselines under `STREAM-001`. | - -## Execution Rules - -1. Preserve unrelated working-tree changes. Do not stage, commit, or revert. -2. Treat artifact resolution and manifest validation as pre-allocation contracts; - tensor/runtime architecture and request validation must pass before admission. -3. Keep CUDA graph capture optional. `cuda_graph=False` skips capture, and failed - capture attempts fall back to eager execution without changing numerical mode. -4. Validate numerical parity live in one process where reproducibility permits. - Stored cross-process artifacts establish a measured floor, not a bit-exact - oracle. -5. Update `VALIDATION.md` with the command, environment, result, and evidence for - every completed gate. Record any deferral in `OPTIMIZATION_BACKLOG.md` with all - required fields. - -## Parallel Ownership - -Startup/configuration, ring/Flex/DiT, and server/SDK protocol can proceed in -parallel when their file sets do not overlap. Functional TAEHV state, generic -required-capture support, and isolated tests form the second wave. One integration -owner then changes Waypoint model wiring and YAML. Numerical, graph, server, and -SDK gates run only after integration. - -## Current State - -Phases 1-8 are complete. Local and registry-selected Hub startup, live -same-process numerical parity, all required CUDA captures, fixed-address mask and -TAEHV state, typed SDK streaming at both resolutions, two-world interleaving, -cleanup/reuse, and bounded server memory have passed. `STREAM-001` now records the -first captured 360p/720p latency, pacing, backpressure, and memory measurements; -as decided in WP-007, they establish a baseline and do not set a release threshold. diff --git a/docs/waypoint/VALIDATION.md b/docs/waypoint/VALIDATION.md deleted file mode 100644 index a8e5942d4..000000000 --- a/docs/waypoint/VALIDATION.md +++ /dev/null @@ -1,256 +0,0 @@ -# Waypoint Validation Ledger - -This ledger distinguishes historical evidence from the active MVP gate. Add the -exact command, environment, artifact location, and result when closing a row. - -## Active Gates - -| ID | Gate | State | Required evidence | -|---|---|---|---| -| CFG-001 | 360p and 720p supported config facts | Passed | CPU tests cover variant geometry, scheduler, FPS, temporal assumptions, explicit model kwargs, and registry construction; both variants started on H100 from their own manifests. | -| CKPT-001 | Local checkpoint resolution | Passed | Valid, missing, partial, cross-variant, and incompatible local checkpoint tests plus both published manifests. | -| CKPT-002 | Hub checkpoint resolution | Passed | Mocked and real selective downloads resolve only native safetensors plus `config.yaml`; registry-selected 360p Hub startup completed without a local model or AE override. | -| CKPT-003 | TAEHV resolution and dependency pin | Passed | Local/HF single-file tests, actual local weights, missing/empty-runtime preflight, index-safe Python 3.12 dependency resolution, and a separate real pinned-revision TAEHV build/install pass. | -| NUM-001 | Live reference-compatible parity | Passed at 360p and 720p | Native-checkpoint same-process tables, conditioner, every DiT stage, five passes, ring writes, 41 rollout latents, functional TAEHV state, and pixels passed with zero tolerance on H100 at both resolutions. | -| REQ-001 | Request and prime semantics | Passed | CPU tests reject non-positive steps and wrong action counts and prove idle prime preserves action zero; live sequential and interleaved 360p/720p runs emitted no seed frames and exact generated counts. | -| GRAPH-001 | Optional DiT compile | Passed | Post-load table materialization, `fullgraph=True` construction, and bounded compiled/eager equivalence are tested; both full-size variants compiled on H100. | -| GRAPH-002 | Optional capture and eager fallback | CPU mode-selection coverage added; existing capture path passed on H100 | `cuda_graph=False` declares no buckets; attempted captures are optional and use the engine's eager fallback on failure. All five graph-enabled buckets captured at both resolutions, the fifth being the `dit: prime` bucket added by `PRIME-GRAPH-001`; `capture_dit_prime=False` declares four and serves prime eagerly. A full server graph-off run remains to be recorded. | -| MASK-001 | Planned masks and replay sync | Passed | Tests cover one staged local/global mask per geometry and graph slot; full 360p and 720p profiles each prove 16/16 graph replay and zero blocking CUDA calls inside steady DiT forwards. | -| AE-001 | Functional TAEHV execution paths | Passed | Nine-history state, fixed graph interfaces, isolation, cleanup, real-weight parity, optional capture declarations, and full-size eight-step streams pass at both resolutions. | -| FRAME-001 | Typed RGB protocol | Passed | Server/SDK tests cover metadata, contiguous RGB24 bytes, zero-copy NumPy shape, indexing, errors, non-streaming rejection, ordered delivery, and live 360p/720p typed consumption. | -| E2E-360 | 360p normal serving path | Passed | Registry-selected Hub source + `EngineManager`, all buckets, SDK stream, exact counts, deterministic concurrent worlds, cleanup, slot reuse, and bounded memory passed. | -| E2E-720 | 720p normal serving path | Passed | Local source + `EngineManager`, all buckets, typed SDK stream, exact counts, byte-identical sequential reuse, full-size two-world interleaving, cleanup, bounded memory, and full eight-step profiler soak passed. | -| WORLD-001 | World isolation and reuse | Passed | Full server two-world DiT execution interleaved at both resolutions, reproduced distinct solo baselines byte-for-byte, cleaned every request, reused slots, and had -2.0/-49.5 MiB host PSS and 0/0 MiB GPU quiescent growth after warmup at 360p/720p. Re-run 2026-09-14 with the `dit: prime` graph live: same verdict at both resolutions, 0 MiB GPU growth. | -| STREAM-001 | Post-MVP streaming baseline | Passed without a release threshold | Captured 16-step baseline and slow-consumer runs at both resolutions record TTFF, sustained media/wall ratio, p50/p95 gaps, jitter, stalls, backpressure, host PSS, and GPU memory in retained JSON artifacts. | - -## Historical Evidence - -The earlier investigation reported the following. These results are retained as -diagnostic evidence and must not be read as completion of the active graph or -end-to-end gates. - -- A 41-frame same-process run with `reference_compat=True` reported bit-exact DiT - state and ring bookkeeping. Cross-process reference runs were not bit - reproducible, with pixel drift reported as high as 72/255. -- CPU TAEHV component comparisons reported bit-identical FP32 and BF16 encode and - decode results against the reference implementation. -- Earlier GPU tests reported full-graph compilation and stable DiT capture, while - compiled versus eager output was bounded rather than bit-exact due to BF16 - fusion behavior. -- Mask reconstruction was measured at 14.31 ms per 720p frame in the eager test, - motivating planned per-frame/per-slot local and global masks. - -## Validation Record - -| Date | Command/environment | Result | Scope | -|---|---|---|---| -| 2026-09-11 | Historical baseline copied from `WAYPOINT_PROGRESS.md` | 242 CPU tests reported green before subsequent uncommitted work | Not a current-tree result | -| 2026-09-11 | `pytest -q test/modular/test_waypoint_checkpoint.py` | 21 passed | Config and local/mocked-HF resolver contracts | -| 2026-09-11 | `pytest -q test/modular/test_waypoint_weight_loader.py` | 61 passed | Existing synthetic checkpoint loading | -| 2026-09-11 | `PYTHONPATH=. pytest -q test/modular/test_waypoint_reference_compat.py` | 11 passed | Default/experimental numerical modes | -| 2026-09-11 | Focused Waypoint execution-mode and capture-runner selection (7 files) | 255 passed, 9 skipped | Independent `cuda_graph`, `compile_dit`, and `reference_compat` modes; optional declarations and generic eager fallback. CUDA-only cases skipped because CUDA was unavailable in the sandbox. | -| 2026-09-11 | `PYTHONPATH=. pytest -q test/modular/test_waypoint_dit.py test/modular/test_waypoint_components.py` | 56 passed, 2 warnings | Existing CPU DiT/component contracts | -| 2026-09-11 | Direct resolver call on local Waypoint and TAEHV checkpoints | Historically accepted 720p weights for both variants; superseded | Exposed the stale shared-weight assumption. Exact variant geometry validation now rejects this pairing. | -| 2026-09-11 | `ruff check` on changed Waypoint Python/tests | Passed | Static checks | -| 2026-09-11 | `PYTHONPATH=. pytest -q test/modular/test_waypoint_checkpoint.py` | 27 passed | Config, resolver, dependency preflight, normal registry/engine construction, and startup ordering | -| 2026-09-11 | `PYTHONPATH=. pytest -q test/modular/test_waypoint_shell.py` | 62 passed | Request/prime, graph declarations, functional AE state, and YAML contracts | -| 2026-09-11 | `PYTHONPATH=. pytest -q test/modular/test_video_frame_protocol.py test/modular/test_cuda_graph_capture.py` | 22 passed, 9 skipped, 2 warnings | Typed frame protocol and required-capture CPU contracts; nine real CUDA cases skipped because CUDA was unavailable | -| 2026-09-11 | `pytest -q test/modular/test_waypoint_taehv_equivalence.py` with local pinned weights | 1 passed | Bit-exact FP32 encoder, decoder init/steady pixels, and all nine histories against upstream `StreamingTAEHV` | -| 2026-09-11 | Consolidated 16-file Waypoint/startup/protocol/graph/resource test selection | 345 passed, 41 skipped, 4 warnings | All then-current CPU-verifiable integration contracts; CUDA and live-reference cases skipped | -| 2026-09-11 | `FLASHINFER_WORKSPACE_BASE=/tmp/waypoint-flashinfer pytest -q test/modular` | Interrupted after 31% when no output was produced for several minutes | Optional broad regression run; earlier output included failures that could not be attributed because the run did not finish, so this is not passing evidence | -| 2026-09-11 | `ruff check ...`; `python3 -m compileall -q ...`; `git diff --check` | Passed | Changed Python/static formatting and syntax checks | -| 2026-09-11 | Default-sandbox torch/NVML probe | CUDA unavailable, zero devices; `nvidia-smi` driver failure | Sandbox-only limitation; later escalated runs reached physical GPU 2. | -| 2026-09-11 | `cargo check --locked` in `rust/server` | Toolchain blocked: Cargo 1.75 cannot parse lockfile v4 | Rust route source and tests added; build requires a current Cargo/rustfmt environment | -| 2026-09-11 | `pytest -q test/rust/test_rust_frontend.py` | Suite skipped because built Rust frontend is unavailable | Native route behavior still needs execution after a toolchain build | -| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 pytest -q test/modular/test_waypoint_gpu.py` | 10 passed in 84.44s | Required capture/replay, planned masks, long rollout, two-world interleaving, cleanup, and slot reuse on H100. | -| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. WAYPOINT_GPU_TESTS=1 pytest -q test/modular/test_waypoint_gpu.py` after optional-capture change | 10 passed in 20.70s | Graph-enabled capture/replay, compiled/eager DiT equivalence, planned masks, interleaving, cleanup, and slot reuse on H100. | -| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 pytest -q test/modular/test_waypoint_reference_equivalence.py` | 14 passed in 54.85s | Full-checkpoint same-process parity, including the 41-frame zero-difference gate through planned masks and ring writes. | -| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 pytest -q test/modular/test_waypoint_pixel_equivalence.py` | 5 passed in 32.89s | Real TAEHV seed encode/decode, output order, VAE output, and reference pixels. | -| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. WAYPOINT_360P_PARITY_REPORT=/tmp/waypoint-360p-reference-parity.json pytest -q -s test/modular/test_waypoint_360p_reference_equivalence.py` | 1 passed, 3 expected dtype-preservation warnings in 37.79s; every asserted maximum difference was zero | Native revision `35acd20...`; 3 derived tables, 5 conditioner rows, 30 stages at 2 sigmas, 201 passes, 41 latents, 24 ring layers per frame, full-size BF16 encoder, 9 decoder histories per frame, and 164 RGB frames on physical H100 GPU 2. Live same-process/eager scope with a shared corrected masked-attention kernel; the 720p stored oracle was not used. Raw report `/tmp/waypoint-360p-reference-parity.json`; retained report `baselines/reference-parity-2026-09-11-360p.json`. | -| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. pytest -q test/modular/test_waypoint_reference_equivalence.py` after adding the variant-specific checkpoint argument | 14 passed, 3 expected dtype-preservation warnings in 56.98s | Post-rebase/touched-file 720p numerical regression; the default checkpoint behavior remains unchanged. | -| 2026-09-11 | Normal 720p `serve_rollout.py --steps 1`; log `/tmp/waypoint_server_720.log` | Passed: four required captures; two byte-identical 1280x720 RGB24 requests | Local registry/`EngineManager`, exact four-frame output, cleanup and sole-world reuse. This predated typed-SDK harness conversion. | -| 2026-09-11 | Hugging Face metadata query + selective `snapshot_download` for `Overworld/Waypoint-1.5-1B-360P` | Passed; revision `35acd20e649fe79c1c1002df456696408547202d`, safetensors SHA-256 `a2cdccb5...c8101d9` | Confirms distinct 360p weights and downloads only `config.yaml` plus `model.safetensors`. | -| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 serve_rollout.py --variant 360p --steps 1`; log `/tmp/waypoint_server_360_sdk.log` | Passed: startup 223.5s; requests 29.7s/6.2s; byte-identical 2,764,800-byte chunks | Normal registry/`EngineManager`, four required captures, typed SDK, four 640x360 frames from index zero, cleanup and reuse. | -| 2026-09-11 | Startup/shell/SDK/frame selection after variant and SDK fixes | 136 passed, 2 warnings | Variant-specific repositories/manifests, prime stop guard, SDK uploads/errors, harness geometry, and frame protocol. | -| 2026-09-11 | Rust 1.98.1: locked `cargo check`, release build, and `cargo test` with target/cache under `/tmp` | Passed; 4 Rust unit tests | Native server compiles against the repository lockfile without changing the system Rust installation. | -| 2026-09-11 | `MSTAR_SERVER_BIN=/tmp/waypoint-rust-target/release/mstar-server pytest -q test/rust/test_rust_frontend.py` outside socket sandbox | Historical: 16 passed in 4.32s | This validated the former Rust raw-frame guards. Those Waypoint-specific changes were later reverted when `--rust-frontend` was removed from the MVP scope. | -| 2026-09-11 | Python 3.12 + uv 0.11.13: `uv pip install --dry-run --torch-backend=auto -e '.[waypoint]'` under the original direct-URL metadata | Resolved 88 packages; packaging layout later superseded | Proved dependency compatibility, but the TAEHV URL was subsequently moved out of project metadata because PyPI rejects direct-URL requirements. The current extra retains the index-hosted dependencies, including `tensordict==0.10.0`. | -| 2026-09-11 | Python 3.12 + uv: install pinned TAEHV URL with `--no-deps --target /tmp/waypoint-taehv-install` | Built/installed `taehv==0.1.0`; module exports `TAEHV` | Verifies Metadata-Version 2.4 packaging and non-empty artifact at the pinned upstream revision. | -| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 serve_rollout.py --variant 720p --steps 8` with local checkpoints; log `/tmp/waypoint_server_720_sdk_soak_fixed.log` | Passed: startup 43.1s; 8 typed chunks/32 frames per request; 88,473,600 bytes byte-identical across reuse | Full local 720p registry/`EngineManager` SDK soak and all four required buckets. | -| 2026-09-11 | Exact 360p Hub memory command below; log `/tmp/waypoint_server_360_hub_interleaved.log` | Serving checks passed; measured quiescent host growth -2.0 MiB and GPU growth 0 MiB; superseded parser falsely reported zero schedules | Registry-selected Hub source, exact typed streams, deterministic two-world waves, cleanup/reuse, and bounded server memory. Parser was corrected and rerun below. | -| 2026-09-11 | `CUDA_VISIBLE_DEVICES=2 serve_rollout.py --variant 360p --source hub --steps 8 --worlds 2 --concurrent-waves 2`; log `/tmp/waypoint_server_360_hub_interleaved_pass8.log` | Passed: exact 8 DiT executions per request, observed A/B/A interleaving, byte-exact solo/concurrent output, all cleanup markers | Corrected full registry-Hub 360p two-world gate. | -| 2026-09-11 | Exact unfiltered Nsight CUDA/NVTX commands below, `MSTAR_ENGINE_STEP_SYNC=0`, two sequential 8-step SDK requests per variant; `/tmp/waypoint-mask-{360,720}-full.nsys-rep` | Each variant: 16 steady forwards, exactly 16 graph replays, 0 synchronization/blocking calls | `check_nsys_replay.py` scopes API inspection to nested steady DiT `engine.forward` ranges and rejects anything other than one graph launch per forward, synchronize, blocking memcpy, or synchronous malloc/free. | -| 2026-09-11 | `PYTHONPATH=. pytest -q test/modular/test_waypoint_profiler.py`; exact report checks below | 4 tests passed; both real profiles passed | Reproducible profiler-query contract plus real 360p/720p evidence. | -| 2026-09-11 | Final consolidated 16-file Waypoint/startup/protocol/graph/resource selection | 385 passed, 41 skipped, 4 warnings in 41.19s | Current-tree CPU-verifiable MVP contracts; count includes 40 tests added since the earlier 345-pass row. | -| 2026-09-11 | `PYTHONPATH=. pytest -q test/modular/test_waypoint_streaming_benchmark.py test/modular/test_waypoint_profiler.py` | 15 passed | Streaming metric/orchestration and profiler-query contracts. | -| 2026-09-11 | Exact 360p benchmark command below | Passed; TTFF 0.148s, sustained 3.268x, p50/p95 0.020/0.027s, 0 baseline stalls, 3291.5 MiB peak PSS, 4648 MiB GPU | Registry-Hub captured baseline; 64 typed frames; slow-reader payload hash matched and GPU delta was 0 MiB. Artifact `baselines/streaming-2026-09-11-360p.json`. | -| 2026-09-11 | Exact 720p benchmark command below | Passed; TTFF 0.465s, sustained 0.570x, p50/p95 0.112/0.142s, 0 baseline stalls, 3687.3 MiB peak PSS, 5756 MiB GPU | Local captured baseline; 64 typed frames; slow-reader payload hash matched and GPU delta was 0 MiB. Artifact `baselines/streaming-2026-09-11-720p.json`. | -| 2026-09-11 | Exact 720p two-world command below; log `/tmp/waypoint_server_720_interleaved_memory.log` | Passed; startup 135.3s, exact 8 DiT executions/request, observed interleaving, deterministic output, all cleanup, -49.5 MiB host PSS/0 MiB GPU quiescent growth | Full local 720p three-wave world isolation, reuse, and bounded-memory gate. | -| 2026-09-11 | `git fetch --prune origin`; stash tracked/untracked work; `git rebase origin/main`; restore with `git stash apply` | Passed; branch is four commits above `origin/main` at `9ef65097`, with no unmerged entries | Range-diff found three patch-identical commits and one expected Bagel import-context merge; stable patch ID and all 25 untracked blobs matched the safety stash. | -| 2026-09-11 | Upstream/resource overlap and Waypoint CPU audit selections | 66 passed, 70 skipped; 279 passed, 12 skipped; 27 passed, 29 skipped | One TAEHV guidance assertion failed in the broad run, was corrected, and passed in the post-fix gate. | -| 2026-09-11 | Isolated sdist/wheel build after PyPI integration fixes | Passed; 125 `Requires-Dist` entries, zero direct URLs, Waypoint extra and default config present | Confirms the separately installed TAEHV pin does not make published metadata invalid. | -| 2026-09-11 | Post-rebase Python API/SDK/worker and native Rust checks | Historical: 51 Python tests and 16 Rust wire tests passed; `cargo check --locked` passed | Python raw-frame handling remains in scope. The Waypoint-specific Rust changes covered by this run were later reverted. Socket tests ran outside the restricted sandbox. | -| 2026-09-11 | `pytest -q test/modular/test_waypoint_packaging.py test/modular/test_waypoint_checkpoint.py test/modular/test_video_frame_protocol.py` | 73 passed, 2 warnings | Post-fix CLI/alias/metadata/pinned-TAEHV contracts and the complete Python raw-frame protocol gate. | -| 2026-09-12 | `PYTHONPATH=. pytest -q test/modular/test_video_frame_protocol.py test/modular/test_client_sdk.py test/modular/test_api_completion_guard.py` after reverting Waypoint-specific Rust frontend changes | 45 passed, 2 existing FastAPI deprecation warnings | Confirms the supported Python server/SDK frame path is unaffected; `rust/server/src/main.rs` and `test/rust/test_rust_frontend.py` have no remaining Waypoint diff. | -| 2026-09-14 | `PYTHONPATH=. pytest -q` over `test_waypoint_shell.py`, `test_waypoint_checkpoint.py`, `test_waypoint_dit.py`, `test_waypoint_components.py`, `test_waypoint_streaming_benchmark.py`, `test_cuda_graph_capture.py`; CPU-only sandbox | 210 passed; `ruff check` clean on all 8 changed files | `PRIME-GRAPH-001` contracts: both DiT walks declared in `[rollout, prime]` order, `capture_dit_prime=False` declares rollout only, one shared static-input family, new `--startup-repeats`/`--startup-steps` CLI validation and percentile math | -| 2026-09-14 | `WAYPOINT_GPU_TESTS=1 pytest -q test/modular/test_waypoint_gpu.py`; H100 80GB, slurm job 6027 | 13 passed (10 pre-existing, 3 new) | Prime replay equals the uncaptured prime by ring snapshot; the prime graph returns its own static input buffer; prime-then-rollout through both graphs is bit-exact against eager, with anti-vacuity assertions on each | -| 2026-09-14 | `pytest -q test/modular/test_piecewise_config_signature.py test/modular/test_cuda_graph_capture.py`; H100, slurm job 6027 | 11 passed | Capture-policy and piecewise-signature contracts unchanged by the new bucket | -| 2026-09-14 | `benchmark_streaming.py --variant {360p,720p} --protocol binary --steps 16 --startup-repeats 20`, `capture_dit_prime` default vs `false`; H100, slurm job 6028 | Five capture lines with the flag on, four with it off, at both resolutions. 360p startup p50 63.10 -> 55.13 ms and p95 67.72 -> 58.12 ms; 720p blocked result superseded by job 6033 | `PRIME-GRAPH-001` startup A/B. Payload SHA-256 identical per resolution across arms, `correctness.passed` true, zero stalls | -| 2026-09-14 | Same benchmark at 720p with the arms reversed, 30 samples per arm; H100, slurm job 6033 | Identical capture-off code moved p50 14.06 ms and p95 54.15 ms by run position alone. Position-balanced over both jobs: p50 142.53 -> 141.95 ms, p95 167.24 -> 154.55 ms, peak GPU 5576 -> 5476 MiB | Controls the arm-ordering confound that made the first blocked 720p A/B read as a regression | -| 2026-09-14 | `nsys profile` + `check_nsys_replay.py --rollout-range "worker[worker_0].node[dit].graph_walk[prime]" --expected-forwards 3`, capture on and off; H100, slurm jobs 6029 and 6030 | On: `forwards=3 graph_replays=3 sync_or_blocking_calls=0`. Off: `forwards=3 graph_replays=0 sync_or_blocking_calls=1` with 6 `cudaMalloc` and 2 `cudaFree` inside the forward | Prime replay contract, and proof the gate discriminates. Same traces hold the steady rollout range at `forwards=33 graph_replays=33 sync_or_blocking_calls=0` | -| 2026-09-14 | `serve_rollout.py --variant {720p,360p} --source hub --steps 8 --worlds 2 --concurrent-waves 4 --measure-memory --physical-gpu 0`, `capture_dit_prime` default; H100, slurm job 6035 | PASS at both resolutions, exit 0, five capture lines each including `dit: prime`. 720p peak host 6465.7 MiB / GPU 6380.0 MiB, quiet 6465.8 / 6380.0. 360p peak 6227.6 / 4916.0, quiet 6232.9 / 4916.0 | `WORLD-001` re-run with the prime graph live: four interleaved two-world waves reproduce the solo baselines byte for byte, every request cleaned, slots reused, 0 MiB GPU growth | -| 2026-09-14 | `pytest -q test/modular/test_waypoint_reference_equivalence.py test/modular/test_waypoint_pixel_equivalence.py`; H100, slurm job 6035 | 19 skipped, not run | Not runnable on this host: these fixtures root at `/mnt/storage/garv901/waypoint-1.5-1B`, which does not exist here, and no oracle frames are present. Not a coverage gap for `PRIME-GRAPH-001` -- both files construct the model directly with `capture=False` and never build a graph. Numerical coverage for the prime bucket comes from `test_waypoint_gpu.py` bit-exactness and the unchanged payload SHA-256 across every benchmark arm | - -## GPU Reproduction Commands - -All commands ran from the repository root on physical H100 GPU 2. The multiline -forms below are the exact invocations represented by the compact ledger rows. - -```bash -env CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. \ - WAYPOINT_360P_PARITY_REPORT=/tmp/waypoint-360p-reference-parity.json \ - pytest -q -s test/modular/test_waypoint_360p_reference_equivalence.py - -env CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. python3 test/waypoint/serve_rollout.py \ - --variant 360p --source hub --cache-dir /tmp/waypoint-hf-cache \ - --steps 8 --worlds 2 --concurrent-waves 3 --measure-memory --physical-gpu 2 \ - --startup-timeout 1200 --request-timeout 600 \ - --log /tmp/waypoint_server_360_hub_interleaved.log - -env CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. python3 test/waypoint/serve_rollout.py \ - --variant 360p --source hub --cache-dir /tmp/waypoint-hf-cache \ - --steps 8 --worlds 2 --concurrent-waves 2 \ - --startup-timeout 1200 --request-timeout 600 \ - --log /tmp/waypoint_server_360_hub_interleaved_pass8.log - -env CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. python3 test/waypoint/serve_rollout.py \ - --variant 720p \ - --checkpoint-dir /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/Waypoint-1.5-1B \ - --ae-path /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/taehv1_5 \ - --seed-image /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/seed/default.jpg \ - --steps 8 --worlds 2 --concurrent-waves 3 --measure-memory --physical-gpu 2 \ - --startup-timeout 1200 --request-timeout 900 \ - --log /tmp/waypoint_server_720_interleaved_memory.log -``` - -The profiler commands differed only in variant/source and output prefix: - -```bash -env CUDA_VISIBLE_DEVICES=2 MSTAR_ENGINE_STEP_SYNC=0 PYTHONPATH=. nsys profile \ - --trace=cuda,nvtx --sample=none --cpuctxsw=none \ - --trace-fork-before-exec=true --cuda-graph-trace=graph --force-overwrite=true \ - -o /tmp/waypoint-mask-360-full python3 test/waypoint/serve_rollout.py \ - --variant 360p \ - --checkpoint-dir /tmp/waypoint-hf-cache/models--Overworld--Waypoint-1.5-1B-360P/snapshots/35acd20e649fe79c1c1002df456696408547202d \ - --ae-path ../../checkpoints/taehv1_5 --seed-image ../../checkpoints/seed/default.jpg \ - --steps 8 --enable-nvtx --startup-timeout 1200 --request-timeout 600 \ - --log /tmp/waypoint_mask_360_full_server.log - -env CUDA_VISIBLE_DEVICES=2 MSTAR_ENGINE_STEP_SYNC=0 PYTHONPATH=. nsys profile \ - --trace=cuda,nvtx --sample=none --cpuctxsw=none \ - --trace-fork-before-exec=true --cuda-graph-trace=graph --force-overwrite=true \ - -o /tmp/waypoint-mask-720-full python3 test/waypoint/serve_rollout.py \ - --variant 720p \ - --checkpoint-dir /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/Waypoint-1.5-1B \ - --ae-path /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/taehv1_5 \ - --seed-image /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/seed/default.jpg \ - --steps 8 --enable-nvtx --startup-timeout 1200 --request-timeout 900 \ - --log /tmp/waypoint_mask_720_full_server.log - -nsys export -t sqlite -f true -o /tmp/waypoint-mask-360-full.sqlite \ - /tmp/waypoint-mask-360-full.nsys-rep -nsys export -t sqlite -f true -o /tmp/waypoint-mask-720-full.sqlite \ - /tmp/waypoint-mask-720-full.nsys-rep -python3 test/waypoint/check_nsys_replay.py \ - /tmp/waypoint-mask-360-full.sqlite --expected-forwards 16 -python3 test/waypoint/check_nsys_replay.py \ - /tmp/waypoint-mask-720-full.sqlite --expected-forwards 16 -``` - -Trace SHA-256 values are -`b82080765b36ca0da72fcd665153230869c60a83d51de11b33c13b910e159da9` -(360p) and -`a97777e4c375bce8b54a6b702d9ac01111f3db62eedf71a5c75d750d1af307ff` -(720p). - -```bash -env CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. python3 test/waypoint/benchmark_streaming.py \ - --variant 360p --source hub --cache-dir /tmp/waypoint-hf-cache \ - --physical-gpu 2 --steps 16 --warmup-steps 1 --slow-consumer-delay 0.25 \ - --startup-timeout 1200 --request-timeout 900 \ - --artifact /tmp/waypoint-streaming-360p.json \ - --log /tmp/waypoint-streaming-360p-server.log - -env CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. python3 test/waypoint/benchmark_streaming.py \ - --variant 720p \ - --checkpoint-dir /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/Waypoint-1.5-1B \ - --ae-path /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/taehv1_5 \ - --seed-image /mnt/storage/garv901/waypoint-1.5-1B/checkpoints/seed/default.jpg \ - --physical-gpu 2 --steps 16 --warmup-steps 1 --slow-consumer-delay 0.25 \ - --startup-timeout 1200 --request-timeout 900 \ - --artifact /tmp/waypoint-streaming-720p.json \ - --log /tmp/waypoint-streaming-720p-server.log -``` - -`uv run` could not be used for the initial CPU pass because the then-current extra -needed network access to resolve the pinned TAEHV source archive. A later clean -Python 3.12/uv resolution and separate real pinned artifact build closed CKPT-003; -the archive is now intentionally installed outside the index-safe project metadata. - -### 2026-09-14 DiT prime capture (PRIME-GRAPH-001) - -One H100 80GB, Hub weights from the offline HF cache, upstream `default.jpg` as -the seed. Allocation: `sbatch --partition=team1 --gres=gpu:1 --cpus-per-task=32 ---mem=200G`, `--physical-gpu 0`, `MSTAR_ENGINE_STEP_SYNC=0`, `HF_HUB_OFFLINE=1`. - -The control arm is a copy of `configs/waypoint.yaml` with `capture_dit_prime: -false` added; the default arm passes no `--config`. The `--protocol binary` flag -in the commands below ships with the binary-framing change, not with this one. - -```bash -# startup A/B, run once per arm per resolution (jobs 6028 and 6033) -PYTHONPATH=. python3 test/waypoint/benchmark_streaming.py \ - --variant 720p --source hub --cache-dir "$HF_CACHE" --seed-image "$SEED" \ - --physical-gpu 0 --protocol binary \ - --steps 16 --warmup-steps 1 --slow-consumer-delay 0.25 \ - --startup-repeats 20 --startup-steps 1 \ - --startup-timeout 1800 --request-timeout 1200 \ - --artifact 720p-on.json --log 720p-on-server.log -# ... and the same with `--config ` for the off arm. -# Job 6033 repeated 720p with the arms swapped and 30 samples each; run both -# orders, because arm position moves this metric more than the change does. - -# prime replay contract, capture on and off (jobs 6029 and 6030) -PYTHONPATH=. nsys profile \ - --trace=cuda,nvtx,osrt --sample=none --cpuctxsw=none \ - --trace-fork-before-exec=true --cuda-graph-trace=graph --force-overwrite=true \ - -o prime-720p-on python3 test/waypoint/benchmark_streaming.py \ - --variant 720p --source hub --cache-dir "$HF_CACHE" --seed-image "$SEED" \ - --physical-gpu 0 --protocol binary --enable-nvtx \ - --steps 16 --warmup-steps 1 --slow-consumer-delay 0.25 \ - --artifact prime-720p-on.json --log prime-720p-on-server.log - -nsys export -t sqlite -f true -o prime-720p-on.sqlite prime-720p-on.nsys-rep -python3 test/waypoint/check_nsys_replay.py prime-720p-on.sqlite \ - --rollout-range "worker[worker_0].node[dit].graph_walk[prime]" \ - --expected-forwards 3 # warmup + baseline + slow_consumer each prime once -python3 test/waypoint/check_nsys_replay.py prime-720p-on.sqlite \ - --expected-forwards 33 # steady rollout, unchanged control -``` - -Retained artifacts: `baselines/prime-capture-2026-09-14-360p-{on,off}.json` and -`baselines/prime-capture-2026-09-14-720p-{on,off}-arm{1,2}.json`. The `-arm1` and -`-arm2` suffixes record which position each arm ran in, which is load-bearing for -reading the 720p numbers. Results are in `OPTIMIZATION_BACKLOG.md` -`## PRIME-GRAPH-001`. - -## End-to-End Acceptance Checklist - -- Both local checkpoint paths and the registry hub ID start through normal model - construction without downloading unrelated repository assets. -- Graph-enabled runs either capture their declared buckets or fall back eagerly; - graph-disabled runs declare no buckets. -- 360p and 720p requests each emit contiguous RGB24 chunks with complete metadata. -- The first emitted index is zero and the total is exactly `4 * num_steps`. -- Sequential and interleaved worlds preserve deterministic action mapping. -- Teardown releases request state and repeated world-slot reuse remains bounded. diff --git a/docs/waypoint/baselines/prime-capture-2026-09-14-360p-off.json b/docs/waypoint/baselines/prime-capture-2026-09-14-360p-off.json deleted file mode 100644 index d4700c3ef..000000000 --- a/docs/waypoint/baselines/prime-capture-2026-09-14-360p-off.json +++ /dev/null @@ -1,163 +0,0 @@ -{ - "backpressure": { - "configured_consumer_pause_seconds": 0.25, - "injected_pause_seconds": 3.75, - "observed_request_wall_increase_seconds": 3.4970633819466457, - "payloads_match": true, - "peak_gpu_memory_change_mib": 0.0, - "peak_host_pss_change_mib": 14.05078125, - "sustained_media_to_wall_ratio_change": -2.9669949113960556, - "time_to_first_frame_change_seconds": -9.566196240484715e-05, - "wall_increase_beyond_injected_pause_seconds": -0.2529366180533543 - }, - "benchmark": "waypoint_streaming_viability", - "configuration": { - "memory_sample_interval_seconds": 0.1, - "physical_gpu": 0, - "rng_seed": 112464007, - "server_command": [ - "/shared/home/garv901-55613a/mstar-worktrees/graphapi-testing/.venv/bin/python", - "/shared/home/garv901-55613a/waypoint-int/mstar/mstar/api_server/entrypoint.py", - "--config", - "/tmp/waypoint-stream-benchmark-z80dtfmy/run.yaml", - "--port", - "47577", - "--host", - "127.0.0.1", - "--socket-path-prefix", - "/tmp/waypoint-stream-benchmark-z80dtfmy/sock", - "--upload-dir", - "/tmp/waypoint-stream-benchmark-z80dtfmy/uploads", - "--tensor-comm-protocol", - "SHM", - "--log-level", - "INFO", - "--timeout", - "1200.0", - "--cache-dir", - "/shared/home/garv901-55613a/waypoint-int/_ckpt/hf/hub" - ], - "server_log": "/tmp/primegraph/360p-off-server.log", - "slow_consumer_delay_seconds": 0.25, - "stall_threshold_seconds": 0.26666666666666666, - "steps": 16, - "stream_protocol": "binary", - "warmup_steps": 1, - "weight_source": "registry Hub mapping for waypoint-1.5-1b-360p" - }, - "correctness": { - "failures": [], - "passed": true - }, - "created_at_utc": "2026-09-14T01:21:10.964069+00:00", - "geometry": { - "fps": 60.0, - "height": 360, - "width": 640 - }, - "metric_definitions": { - "backpressure": "delta between an unpaused stream and an identical stream paused between SDK reads", - "jitter_population_stddev": "population standard deviation of inter-chunk gaps", - "memory": "PSS and nvidia-smi GPU process memory summed over the server process group only", - "stall": "inter-chunk gap strictly greater than stall_threshold_seconds", - "startup_latency_seconds": "time_to_first_frame_seconds over --startup-repeats short streams, each after a full request cleanup, so every sample reuses a world slot", - "sustained_media_to_wall_ratio": "media seconds in chunks after the first divided by first-to-last chunk arrival time", - "time_to_first_frame_seconds": "request iterator start to first fully decoded SDK VideoFrameChunk" - }, - "model_variant": "waypoint-1.5-1b-360p", - "release_threshold": null, - "runs": { - "baseline": { - "chunk_count": 16, - "consumer": { - "injected_pause_seconds": 0.0, - "pause_count": 0, - "pause_seconds": 0.0 - }, - "frame_count": 64, - "generated_media_seconds": 1.0666666666666667, - "inter_chunk_gap_seconds": { - "jitter_population_stddev": 0.0014653687131390544, - "maximum": 0.022965203039348125, - "mean": 0.02064180220477283, - "p50": 0.020365420961752534, - "p95": 0.022909664339385925, - "sample_count": 15 - }, - "memory": { - "peak_gpu_mib": 4626.0, - "peak_host_pss_mib": 3365.46484375, - "phase": "baseline", - "quiet_gpu_mib": 4626.0, - "quiet_host_pss_mib": 3373.3798828125, - "sample_count": 3 - }, - "overall_media_to_wall_ratio": 2.8254469531764714, - "payload_bytes": 44236800, - "payload_sha256": "cc84686681d6dca8b4860971fb80794f2a66a784d3ca544df3615ab2bb43a47e", - "request_id": "waypoint-streaming-benchmark-baseline", - "request_wall_seconds": 0.3775213919579983, - "stalls": { - "count": 0, - "longest_seconds": null, - "threshold_seconds": 0.26666666666666666, - "total_excess_seconds": 0 - }, - "sustained_media_to_wall_ratio": 3.2296921560100937, - "time_to_first_frame_seconds": 0.06580381095409393 - }, - "slow_consumer": { - "chunk_count": 16, - "consumer": { - "injected_pause_seconds": 3.75, - "pause_count": 15, - "pause_seconds": 0.25 - }, - "frame_count": 64, - "generated_media_seconds": 1.0666666666666667, - "inter_chunk_gap_seconds": { - "jitter_population_stddev": 0.0006347077181201878, - "maximum": 0.25593707989901304, - "mean": 0.2537775634632756, - "p50": 0.2535740720340982, - "p95": 0.2547811881522648, - "sample_count": 15 - }, - "memory": { - "peak_gpu_mib": 4626.0, - "peak_host_pss_mib": 3379.515625, - "phase": "slow-consumer", - "quiet_gpu_mib": 4626.0, - "quiet_host_pss_mib": 3375.9853515625, - "sample_count": 16 - }, - "overall_media_to_wall_ratio": 0.2752983168288572, - "payload_bytes": 44236800, - "payload_sha256": "cc84686681d6dca8b4860971fb80794f2a66a784d3ca544df3615ab2bb43a47e", - "request_id": "waypoint-streaming-benchmark-slow-consumer", - "request_wall_seconds": 3.874584773904644, - "stalls": { - "count": 0, - "longest_seconds": null, - "threshold_seconds": 0.26666666666666666, - "total_excess_seconds": 0 - }, - "sustained_media_to_wall_ratio": 0.262697244614038, - "time_to_first_frame_seconds": 0.06570814899168909 - } - }, - "schema_version": 1, - "server": { - "startup_seconds": 87.15073139511514 - }, - "startup_latency_seconds": { - "maximum": 0.07032173802144825, - "mean": 0.06389240985154174, - "minimum": 0.0596659219590947, - "p50": 0.06310040800599381, - "p95": 0.06771635722834617, - "sample_count": 20 - }, - "status": "completed", - "variant": "360p" -} diff --git a/docs/waypoint/baselines/prime-capture-2026-09-14-360p-on.json b/docs/waypoint/baselines/prime-capture-2026-09-14-360p-on.json deleted file mode 100644 index 9f2fe6fc1..000000000 --- a/docs/waypoint/baselines/prime-capture-2026-09-14-360p-on.json +++ /dev/null @@ -1,163 +0,0 @@ -{ - "backpressure": { - "configured_consumer_pause_seconds": 0.25, - "injected_pause_seconds": 3.75, - "observed_request_wall_increase_seconds": 3.492240247898735, - "payloads_match": true, - "peak_gpu_memory_change_mib": 0.0, - "peak_host_pss_change_mib": 15.8671875, - "sustained_media_to_wall_ratio_change": -2.9372519154651013, - "time_to_first_frame_change_seconds": 0.0014183248858898878, - "wall_increase_beyond_injected_pause_seconds": -0.2577597521012649 - }, - "benchmark": "waypoint_streaming_viability", - "configuration": { - "memory_sample_interval_seconds": 0.1, - "physical_gpu": 0, - "rng_seed": 112464007, - "server_command": [ - "/shared/home/garv901-55613a/mstar-worktrees/graphapi-testing/.venv/bin/python", - "/shared/home/garv901-55613a/waypoint-int/mstar/mstar/api_server/entrypoint.py", - "--config", - "/tmp/waypoint-stream-benchmark-tj31iler/run.yaml", - "--port", - "41227", - "--host", - "127.0.0.1", - "--socket-path-prefix", - "/tmp/waypoint-stream-benchmark-tj31iler/sock", - "--upload-dir", - "/tmp/waypoint-stream-benchmark-tj31iler/uploads", - "--tensor-comm-protocol", - "SHM", - "--log-level", - "INFO", - "--timeout", - "1200.0", - "--cache-dir", - "/shared/home/garv901-55613a/waypoint-int/_ckpt/hf/hub" - ], - "server_log": "/tmp/primegraph/360p-on-server.log", - "slow_consumer_delay_seconds": 0.25, - "stall_threshold_seconds": 0.26666666666666666, - "steps": 16, - "stream_protocol": "binary", - "warmup_steps": 1, - "weight_source": "registry Hub mapping for waypoint-1.5-1b-360p" - }, - "correctness": { - "failures": [], - "passed": true - }, - "created_at_utc": "2026-09-14T01:19:25.040156+00:00", - "geometry": { - "fps": 60.0, - "height": 360, - "width": 640 - }, - "metric_definitions": { - "backpressure": "delta between an unpaused stream and an identical stream paused between SDK reads", - "jitter_population_stddev": "population standard deviation of inter-chunk gaps", - "memory": "PSS and nvidia-smi GPU process memory summed over the server process group only", - "stall": "inter-chunk gap strictly greater than stall_threshold_seconds", - "startup_latency_seconds": "time_to_first_frame_seconds over --startup-repeats short streams, each after a full request cleanup, so every sample reuses a world slot", - "sustained_media_to_wall_ratio": "media seconds in chunks after the first divided by first-to-last chunk arrival time", - "time_to_first_frame_seconds": "request iterator start to first fully decoded SDK VideoFrameChunk" - }, - "model_variant": "waypoint-1.5-1b-360p", - "release_threshold": null, - "runs": { - "baseline": { - "chunk_count": 16, - "consumer": { - "injected_pause_seconds": 0.0, - "pause_count": 0, - "pause_seconds": 0.0 - }, - "frame_count": 64, - "generated_media_seconds": 1.0666666666666667, - "inter_chunk_gap_seconds": { - "jitter_population_stddev": 0.0021165463763351705, - "maximum": 0.025491400039754808, - "mean": 0.020832174667157234, - "p50": 0.0208142320625484, - "p95": 0.024783308710902927, - "sample_count": 15 - }, - "memory": { - "peak_gpu_mib": 4424.0, - "peak_host_pss_mib": 3269.25390625, - "phase": "baseline", - "quiet_gpu_mib": 4424.0, - "quiet_host_pss_mib": 3282.44921875, - "sample_count": 3 - }, - "overall_media_to_wall_ratio": 2.8917383232634135, - "payload_bytes": 44236800, - "payload_sha256": "cc84686681d6dca8b4860971fb80794f2a66a784d3ca544df3615ab2bb43a47e", - "request_id": "waypoint-streaming-benchmark-baseline", - "request_wall_seconds": 0.3688669400289655, - "stalls": { - "count": 0, - "longest_seconds": null, - "threshold_seconds": 0.26666666666666666, - "total_excess_seconds": 0 - }, - "sustained_media_to_wall_ratio": 3.2001779810232374, - "time_to_first_frame_seconds": 0.05451832804828882 - }, - "slow_consumer": { - "chunk_count": 16, - "consumer": { - "injected_pause_seconds": 3.75, - "pause_count": 15, - "pause_seconds": 0.25 - }, - "frame_count": 64, - "generated_media_seconds": 1.0666666666666667, - "inter_chunk_gap_seconds": { - "jitter_population_stddev": 0.0006765666966317799, - "maximum": 0.2555718390503898, - "mean": 0.2535567043349147, - "p50": 0.25334677298087627, - "p95": 0.25509403366595507, - "sample_count": 15 - }, - "memory": { - "peak_gpu_mib": 4424.0, - "peak_host_pss_mib": 3285.12109375, - "phase": "slow-consumer", - "quiet_gpu_mib": 4424.0, - "quiet_host_pss_mib": 3285.12109375, - "sample_count": 22 - }, - "overall_media_to_wall_ratio": 0.27625927350625007, - "payload_bytes": 44236800, - "payload_sha256": "cc84686681d6dca8b4860971fb80794f2a66a784d3ca544df3615ab2bb43a47e", - "request_id": "waypoint-streaming-benchmark-slow-consumer", - "request_wall_seconds": 3.8611071879277006, - "stalls": { - "count": 0, - 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"payloads_match": true, - "peak_gpu_memory_change_mib": 0.0, - "peak_host_pss_change_mib": 31.943359375, - "sustained_media_to_wall_ratio_change": -1.376907450061608, - "time_to_first_frame_change_seconds": -0.007874322938732803, - "wall_increase_beyond_injected_pause_seconds": -0.3151307899970561 - }, - "benchmark": "waypoint_streaming_viability", - "configuration": { - "memory_sample_interval_seconds": 0.1, - "physical_gpu": 0, - "rng_seed": 112464007, - "server_command": [ - "/shared/home/garv901-55613a/mstar-worktrees/graphapi-testing/.venv/bin/python", - "/shared/home/garv901-55613a/waypoint-int/mstar/mstar/api_server/entrypoint.py", - "--config", - "/tmp/waypoint-stream-benchmark-2f1a82ka/run.yaml", - "--port", - "48929", - "--host", - "127.0.0.1", - "--socket-path-prefix", - "/tmp/waypoint-stream-benchmark-2f1a82ka/sock", - "--upload-dir", - "/tmp/waypoint-stream-benchmark-2f1a82ka/uploads", - "--tensor-comm-protocol", - "SHM", - "--log-level", - "INFO", - "--timeout", - "1200.0", - 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"peak_gpu_mib": 5396.0, - "peak_host_pss_mib": 3522.330078125, - "phase": "baseline", - "quiet_gpu_mib": 5396.0, - "quiet_host_pss_mib": 3543.431640625, - "sample_count": 5 - }, - "overall_media_to_wall_ratio": 1.4145152826837752, - "payload_bytes": 176947200, - "payload_sha256": "bdd8ed5605d329ea704820b0c017c3ea0b0a70122286611a1d999b19e327dde3", - "request_id": "waypoint-streaming-benchmark-baseline", - "request_wall_seconds": 0.7540863500908017, - "stalls": { - "count": 0, - "longest_seconds": null, - "threshold_seconds": 0.26666666666666666, - "total_excess_seconds": 0 - }, - "sustained_media_to_wall_ratio": 1.6473387974948124, - "time_to_first_frame_seconds": 0.13760872301645577 - }, - "slow_consumer": { - "chunk_count": 16, - "consumer": { - "injected_pause_seconds": 3.75, - "pause_count": 15, - "pause_seconds": 0.25 - }, - "frame_count": 64, - "generated_media_seconds": 1.0666666666666667, - "inter_chunk_gap_seconds": { - "jitter_population_stddev": 0.006705657149242373, - "maximum": 0.2825224169064313, - "mean": 0.2683672557352111, - "p50": 0.26421670499257743, - "p95": 0.27946688475785775, - "sample_count": 15 - }, - "memory": { - "peak_gpu_mib": 5396.0, - "peak_host_pss_mib": 3554.025390625, - "phase": "slow-consumer", - "quiet_gpu_mib": 5396.0, - "quiet_host_pss_mib": 3554.013671875, - "sample_count": 23 - }, - "overall_media_to_wall_ratio": 0.2524448861182096, - "payload_bytes": 176947200, - "payload_sha256": "bdd8ed5605d329ea704820b0c017c3ea0b0a70122286611a1d999b19e327dde3", - "request_id": "waypoint-streaming-benchmark-slow-consumer", - "request_wall_seconds": 4.225344720063731, - "stalls": { - "count": 6, - "longest_seconds": 0.2825224169064313, - "threshold_seconds": 0.26666666666666666, - "total_excess_seconds": 0.052089675888419174 - }, - "sustained_media_to_wall_ratio": 0.24841580051943601, - "time_to_first_frame_seconds": 0.1904655440011993 - } - }, - "schema_version": 1, - "server": { - "startup_seconds": 80.10912929603364 - }, - "startup_latency_seconds": { - "maximum": 0.15587666200008243, - "mean": 0.1418924984172918, - "minimum": 0.1319633589591831, - "p50": 0.14247635047649965, - "p95": 0.15150842317962088, - "sample_count": 30 - }, - "status": "completed", - "variant": "720p" -} diff --git a/docs/waypoint/baselines/reference-parity-2026-09-11-360p.json b/docs/waypoint/baselines/reference-parity-2026-09-11-360p.json deleted file mode 100644 index 61e1a47db..000000000 --- a/docs/waypoint/baselines/reference-parity-2026-09-11-360p.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "checkpoint": "/tmp/waypoint-hf-cache/models--Overworld--Waypoint-1.5-1B-360P/snapshots/35acd20e649fe79c1c1002df456696408547202d", - "checkpoint_revision": "35acd20e649fe79c1c1002df456696408547202d", - "checkpoint_sha256": "a2cdccb5eb074afc48a1c99b0868cebef38cf944d4b366aed2a866f50c8101d9", - "controls": "canonical 40-action record_oracle.py sequence", - "decoded_rgb_frames_including_internal_prime": 164, - "denoise_and_commit_passes": 201, - "derived_tables": [ - "denoise_step_emb.freq", - "rope_angles.xy", - "rope_angles.inv_t" - ], - "device": "NVIDIA H100 80GB HBM3", - "device_index_visible": 0, - "dtype": "torch.bfloat16", - "elapsed_pytest_seconds": 37.79, - "generated_latent_frames": 40, - "maximum_absolute_differences": { - "conditioner": 0.0, - "decoder_histories": 0.0, - "encoder": 0.0, - "latents": 0.0, - "passes": 0.0, - "pixels": 0, - "ring_kv": 0.0, - "stages": 0.0, - "tables": 0.0 - }, - "noise": { - "seed": 42, - "source": "seeded CPU fp32 then cast to CUDA BF16" - }, - "python": "3.10.12", - "reference_compat": true, - "reference_source": "/mnt/storage/garv901/waypoint-1.5-1B/world_engine", - "rollout_latent_frames": 41, - "scheduler_sigmas": [ - 1.0, - 0.9, - 0.75, - 0.3, - 0.0 - ], - "scope": { - "attention": "shared mstar flex_attention_masked kernel", - "dit_execution": "eager on both sides", - "reference": "live same-process world_engine", - "stored_oracle": "not used; existing artifacts belong to the 720p checkpoint", - "taehv": "upstream streaming state versus functional explicit nine-history path" - }, - "seed_sha256": "c61c9393311d7281f793d86329dca343e12c93bf0409980a186eb39269cf6862", - "stage_probes": { - "sigmas": [ - 1.0, - 0.0 - ], - "stages_per_probe": 30 - }, - "status": "passed", - "torch": "2.9.1+cu128", - "variant": "waypoint-1.5-1b-360p" -} diff --git a/docs/waypoint/baselines/streaming-2026-09-11-360p.json b/docs/waypoint/baselines/streaming-2026-09-11-360p.json deleted file mode 100644 index c9145f4e8..000000000 --- a/docs/waypoint/baselines/streaming-2026-09-11-360p.json +++ /dev/null @@ -1,153 +0,0 @@ -{ - "backpressure": { - "configured_consumer_pause_seconds": 0.25, - "injected_pause_seconds": 3.75, - "observed_request_wall_increase_seconds": 3.7167182420380414, - "payloads_match": true, - "peak_gpu_memory_change_mib": 0.0, - "peak_host_pss_change_mib": 60.3642578125, - "sustained_media_to_wall_ratio_change": -3.021464358453587, - "time_to_first_frame_change_seconds": -0.038335707038640976, - "wall_increase_beyond_injected_pause_seconds": -0.03328175796195865 - }, - "benchmark": "waypoint_streaming_viability", - "configuration": { - "memory_sample_interval_seconds": 0.1, - "physical_gpu": 2, - "rng_seed": 112464007, - "server_command": [ - "/usr/bin/python3", - "/mnt/storage/garv901/waypoint-1.5-1B/mstar/rp2/mstar/api_server/entrypoint.py", - "--config", - "/dev/shm/garv901/tmp/waypoint-stream-benchmark-4_9f01b8/run.yaml", - "--port", - "47481", - "--host", - "127.0.0.1", - "--socket-path-prefix", - "/dev/shm/garv901/tmp/waypoint-stream-benchmark-4_9f01b8/sock", - "--upload-dir", - "/dev/shm/garv901/tmp/waypoint-stream-benchmark-4_9f01b8/uploads", - "--tensor-comm-protocol", - "SHM", - "--log-level", - "INFO", - "--timeout", - "900.0", - "--cache-dir", - "/tmp/waypoint-hf-cache" - ], - "server_log": "/tmp/waypoint-streaming-360p-server.log", - "slow_consumer_delay_seconds": 0.25, - "stall_threshold_seconds": 0.26666666666666666, - "steps": 16, - "warmup_steps": 1, - "weight_source": "registry Hub mapping for waypoint-1.5-1b-360p" - }, - "correctness": { - "failures": [], - "passed": true - }, - "created_at_utc": "2026-09-11T03:50:26.393848+00:00", - "geometry": { - "fps": 60.0, - "height": 360, - "width": 640 - }, - "metric_definitions": { - "backpressure": "delta between an unpaused stream and an identical stream paused between SDK reads", - "jitter_population_stddev": "population standard deviation of inter-chunk gaps", - "memory": "PSS and nvidia-smi GPU process memory summed over the server process group only", - "stall": "inter-chunk gap strictly greater than stall_threshold_seconds", - "sustained_media_to_wall_ratio": "media seconds in chunks after the first divided by first-to-last chunk arrival time", - "time_to_first_frame_seconds": "request iterator start to first fully decoded SDK VideoFrameChunk" - }, - "model_variant": "waypoint-1.5-1b-360p", - "release_threshold": null, - "runs": { - "baseline": { - "chunk_count": 16, - "consumer": { - "injected_pause_seconds": 0.0, - "pause_count": 0, - "pause_seconds": 0.0 - }, - "frame_count": 64, - "generated_media_seconds": 1.0666666666666667, - "inter_chunk_gap_seconds": { - "jitter_population_stddev": 0.003996953245955496, - "maximum": 0.031120519153773785, - "mean": 0.02040156687920292, - "p50": 0.019661725964397192, - "p95": 0.02699114484712481, - "sample_count": 15 - }, - "memory": { - "peak_gpu_mib": 4648.0, - "peak_host_pss_mib": 3291.5068359375, - "phase": "baseline", - "quiet_gpu_mib": 4648.0, - "quiet_host_pss_mib": 3338.77734375, - "sample_count": 2 - }, - "overall_media_to_wall_ratio": 2.341649833374671, - "payload_bytes": 44236800, - "payload_sha256": "9943036eb0cc954fd7bd2545d4b0980a1f5ded6435206c59a2d098d69ca4ae44", - "request_id": "waypoint-streaming-benchmark-baseline", - "request_wall_seconds": 0.45551928877830505, - "stalls": { - "count": 0, - "longest_seconds": null, - "threshold_seconds": 0.26666666666666666, - "total_excess_seconds": 0 - }, - "sustained_media_to_wall_ratio": 3.2677228695912444, - "time_to_first_frame_seconds": 0.14788120286539197 - }, - "slow_consumer": { - "chunk_count": 16, - "consumer": { - "injected_pause_seconds": 3.75, - "pause_count": 15, - "pause_seconds": 0.25 - }, - "frame_count": 64, - "generated_media_seconds": 1.0666666666666667, - "inter_chunk_gap_seconds": { - "jitter_population_stddev": 0.006550622140463195, - "maximum": 0.2819611048325896, - "mean": 0.2707182235394915, - "p50": 0.2681501042097807, - "p95": 0.2819179646205157, - "sample_count": 15 - }, - "memory": { - "peak_gpu_mib": 4648.0, - "peak_host_pss_mib": 3351.87109375, - "phase": "slow-consumer", - "quiet_gpu_mib": 4648.0, - "quiet_host_pss_mib": 3341.478515625, - "sample_count": 8 - }, - "overall_media_to_wall_ratio": 0.2556581831183425, - "payload_bytes": 44236800, - "payload_sha256": "9943036eb0cc954fd7bd2545d4b0980a1f5ded6435206c59a2d098d69ca4ae44", - "request_id": "waypoint-streaming-benchmark-slow-consumer", - "request_wall_seconds": 4.172237530816346, - "stalls": { - "count": 8, - "longest_seconds": 0.2819611048325896, - "threshold_seconds": 0.26666666666666666, - "total_excess_seconds": 0.07224301105986042 - }, - "sustained_media_to_wall_ratio": 0.2462585111376573, - "time_to_first_frame_seconds": 0.109545495826751 - } - }, - "schema_version": 1, - "server": { - "startup_seconds": 43.09219791833311 - }, - "status": "completed", - "variant": "360p" -} diff --git a/docs/waypoint/baselines/streaming-2026-09-11-720p.json b/docs/waypoint/baselines/streaming-2026-09-11-720p.json deleted file mode 100644 index 47275c6f6..000000000 --- a/docs/waypoint/baselines/streaming-2026-09-11-720p.json +++ /dev/null @@ -1,151 +0,0 @@ -{ - "backpressure": { - "configured_consumer_pause_seconds": 0.25, - "injected_pause_seconds": 3.75, - "observed_request_wall_increase_seconds": 3.4616449642926455, - "payloads_match": true, - "peak_gpu_memory_change_mib": 0.0, - "peak_host_pss_change_mib": 7.0703125, - "sustained_media_to_wall_ratio_change": -0.3815909607693788, - "time_to_first_frame_change_seconds": -0.08133614482358098, - "wall_increase_beyond_injected_pause_seconds": -0.28835503570735455 - }, - "benchmark": "waypoint_streaming_viability", - "configuration": { - "memory_sample_interval_seconds": 0.1, - "physical_gpu": 2, - "rng_seed": 112464007, - "server_command": [ - "/usr/bin/python3", - "/mnt/storage/garv901/waypoint-1.5-1B/mstar/rp2/mstar/api_server/entrypoint.py", - "--config", - "/dev/shm/garv901/tmp/waypoint-stream-benchmark-lqroa06h/run.yaml", - "--port", - "56069", - "--host", - "127.0.0.1", - "--socket-path-prefix", - "/dev/shm/garv901/tmp/waypoint-stream-benchmark-lqroa06h/sock", - "--upload-dir", - "/dev/shm/garv901/tmp/waypoint-stream-benchmark-lqroa06h/uploads", - "--tensor-comm-protocol", - "SHM", - "--log-level", - "INFO", - "--timeout", - "900.0" - ], - "server_log": "/tmp/waypoint-streaming-720p-server.log", - "slow_consumer_delay_seconds": 0.25, - "stall_threshold_seconds": 0.26666666666666666, - "steps": 16, - "warmup_steps": 1, - "weight_source": "/mnt/storage/garv901/waypoint-1.5-1B/checkpoints/Waypoint-1.5-1B" - }, - "correctness": { - "failures": [], - "passed": true - }, - "created_at_utc": "2026-09-11T03:51:53.049194+00:00", - "geometry": { - "fps": 60.0, - "height": 720, - "width": 1280 - }, - "metric_definitions": { - "backpressure": "delta between an unpaused stream and an identical stream paused between SDK reads", - "jitter_population_stddev": "population standard deviation of inter-chunk gaps", - "memory": "PSS and nvidia-smi GPU process memory summed over the server process group only", - "stall": "inter-chunk gap strictly greater than stall_threshold_seconds", - "sustained_media_to_wall_ratio": "media seconds in chunks after the first divided by first-to-last chunk arrival time", - "time_to_first_frame_seconds": "request iterator start to first fully decoded SDK VideoFrameChunk" - }, - "model_variant": "waypoint-1.5-1b-720p", - "release_threshold": null, - "runs": { - "baseline": { - "chunk_count": 16, - "consumer": { - "injected_pause_seconds": 0.0, - "pause_count": 0, - "pause_seconds": 0.0 - }, - "frame_count": 64, - "generated_media_seconds": 1.0666666666666667, - "inter_chunk_gap_seconds": { - "jitter_population_stddev": 0.016008292004214712, - "maximum": 0.14418772095814347, - "mean": 0.11690171121930083, - "p50": 0.11206015711650252, - "p95": 0.14245065064169465, - "sample_count": 15 - }, - "memory": { - "peak_gpu_mib": 5756.0, - "peak_host_pss_mib": 3687.2998046875, - "phase": "baseline", - "quiet_gpu_mib": 5756.0, - "quiet_host_pss_mib": 3645.1083984375, - "sample_count": 3 - }, - "overall_media_to_wall_ratio": 0.47871936597732684, - "payload_bytes": 176947200, - "payload_sha256": "facfdd2c70e27c0c675e72e944fc3e24cfec997010257c2c9b4ceb80a1bb7701", - "request_id": "waypoint-streaming-benchmark-baseline", - "request_wall_seconds": 2.2281669438816607, - "stalls": { - "count": 0, - "longest_seconds": null, - "threshold_seconds": 0.26666666666666666, - "total_excess_seconds": 0 - }, - "sustained_media_to_wall_ratio": 0.5702796475032248, - "time_to_first_frame_seconds": 0.46520866593346 - }, - "slow_consumer": { - "chunk_count": 16, - "consumer": { - "injected_pause_seconds": 3.75, - "pause_count": 15, - "pause_seconds": 0.25 - }, - "frame_count": 64, - "generated_media_seconds": 1.0666666666666667, - "inter_chunk_gap_seconds": { - "jitter_population_stddev": 0.01187843873988182, - "maximum": 0.38108016178011894, - "mean": 0.3533156535277764, - "p50": 0.3503798511810601, - "p95": 0.3789634692016989, - "sample_count": 15 - }, - "memory": { - "peak_gpu_mib": 5756.0, - "peak_host_pss_mib": 3694.3701171875, - "phase": "slow-consumer", - "quiet_gpu_mib": 5756.0, - "quiet_host_pss_mib": 3666.2431640625, - "sample_count": 9 - }, - "overall_media_to_wall_ratio": 0.1874695831569112, - "payload_bytes": 176947200, - "payload_sha256": "facfdd2c70e27c0c675e72e944fc3e24cfec997010257c2c9b4ceb80a1bb7701", - "request_id": "waypoint-streaming-benchmark-slow-consumer", - "request_wall_seconds": 5.689811908174306, - "stalls": { - "count": 15, - "longest_seconds": 0.38108016178011894, - "threshold_seconds": 0.26666666666666666, - "total_excess_seconds": 1.2997348029166464 - }, - "sustained_media_to_wall_ratio": 0.18868868673384595, - "time_to_first_frame_seconds": 0.383872521109879 - } - }, - "schema_version": 1, - "server": { - "startup_seconds": 42.095929856877774 - }, - "status": "completed", - "variant": "720p" -} diff --git a/mstar/graph/base.py b/mstar/graph/base.py index 1cdcc8b58..20c6801dc 100644 --- a/mstar/graph/base.py +++ b/mstar/graph/base.py @@ -325,8 +325,19 @@ def is_ready_for_speculation( if allow_streaming: needed_inputs = needed_inputs - self._streaming_inputs if check_next_iter: + # Loop-external inputs are re-injected unchanged into ready_signals + # every iteration (Loop.ingest_external_input / complete_iter) and + # never routed through ready_next_iter. Count them here so a + # same-node next-iteration speculation isn't blocked on inputs + # that are guaranteed to reappear. + carried_names = { + name for name, edge in self.ready_signals.ready_inputs.items() + if edge._persist_for_loop + } return needed_inputs.issubset( - self.ready_next_iter.ready_names | self.speculative_signals.ready_names + self.ready_next_iter.ready_names + | self.speculative_signals.ready_names + | carried_names ) return needed_inputs.issubset( self.ready_signals.ready_names | self.speculative_signals.ready_names diff --git a/mstar/model/waypoint/submodules.py b/mstar/model/waypoint/submodules.py index 272ee126d..3cbfe5842 100644 --- a/mstar/model/waypoint/submodules.py +++ b/mstar/model/waypoint/submodules.py @@ -108,8 +108,39 @@ def forward_batched( } -class WaypointDitSubmodule(_SingleRequestMixin, NodeSubmodule): - """The world DiT: one latent frame per engine step.""" +class _FunctionalAeMixin: + """Shared fixed-shape facts for the captured functional AE paths.""" + + @property + def ae_dtype(self) -> torch.dtype: + """The dtype the weights are in, read live: an input scaled into a dtype + the convs are not in faults on the first layer.""" + return next(self.taehv.parameters()).dtype + + @property + def encoded_size(self) -> tuple[int, int]: + return encoded_size_for_latent( + self.config.latent_height, self.config.latent_width + ) + + @property + def pixel_size(self) -> tuple[int, int]: + return pixel_size_for_latent( + self.config.latent_height, self.config.latent_width + ) + + +class WaypointDitSubmodule(_SingleRequestMixin, _FunctionalAeMixin, NodeSubmodule): + """The world DiT: one latent frame per engine step, TAEHV-decoded in the + same forward. + + The decode is fused in rather than left on its own node so that a + same-worker speculative N+1 (``GraphNode.enable_async_scheduling``) can + start while N's frame is still going out: a separate decoder node + would decode frame N only after N+1 was already queued, adding a frame of + latency to every step it was meant to hide. See ``WaypointModel``'s + module docstring for the resulting two-node graph. + """ # ``WaypointConfig.compile_dit`` exclusively controls the two deliberate # full-graph regions. Do not let the engine independently compile this @@ -122,10 +153,27 @@ class WaypointDitSubmodule(_SingleRequestMixin, NodeSubmodule): # those fp32 islands to bf16. disable_autocast = True - def __init__(self, dit: WaypointDiT, config: WaypointConfig): + def __init__(self, dit: WaypointDiT, taehv: torch.nn.Module, config: WaypointConfig): super().__init__() + validate_taehv_architecture(taehv) self.dit = dit + self.taehv = taehv self.config = config + # The decoder node this replaces was captured with the engine's + # ``compile=True``, i.e. ``torch.compile(mode="max-autotune-no-cudagraphs", + # fullgraph=False, dynamic=False)`` (``CudaGraphRunner``), and only when + # graphs were on. Same options, same gate: Inductor's mode decides which + # GEMM/conv kernels the decode runs, and a different mode changes the low + # bits of every pixel (the 720p payload SHA in VALIDATION.md is the gate). + # Wrapping just the decode call keeps the engine from compiling across + # the denoise/decode boundary (this wrapper stays ``disable_torch_compile``). + self._decode_latent = ( + torch.compile( + decode_latent, mode="max-autotune-no-cudagraphs", + fullgraph=False, dynamic=False, + ) + if config.cuda_graph else decode_latent + ) def bind_node_resources(self, resources: dict) -> None: """Require both resources before letting the bind reach the layers.""" @@ -148,21 +196,46 @@ def prepare_inputs( fwd_info: CurrentForwardPassInfo, inputs: NameToTensorList, **kwargs, - ) -> NodeInputs: + ) -> NodeInputs | None: """This frame's row: the ring clock, its controller slice, and either - the noise to denoise from (rollout) or the latent to prime with. + the noise to denoise from (rollout) or the latent to prime with. Also + the nine decoder histories the fused decode reads and writes. Runs on the host, outside any captured region — which is the whole reason the noise is drawn here. A captured region cannot call the RNG, and ``cuda_graph_runner``'s dummy metadata hardcodes ``random_seed=0``, so a forward that seeded itself would draw the capture-time dummy's noise forever. + + ``inputs.get("clock")`` — the rollout node's loop-back to itself — is + never read. Its only job is to sit in ``input_names`` so + ``GraphNode.is_ready_for_speculation`` can propose "dit, next iter" as + a same-node speculation target; the value carries nothing, since + ``frame_pos``/``rollout_step`` already live in host state. """ device = self.get_device() dtype = self.dit.dtype # The clock is per request and lives on the host; frame 0 is the first # frame of the session, priming included. state = self.request_state(fwd_info.request_id) + + if graph_walk == ROLLOUT_WALK: + requested = int(fwd_info.step_metadata.get("num_steps", 0) or 0) + rollout_step = int(state.get("rollout_step", 0)) + if requested and rollout_step >= requested: + # Async scheduling (enable_async_scheduling=True) can dispatch + # iteration N+1 before this request's check_stop(N) has + # registered the loop's finish signal. Veto it here, before any + # tensor work: None makes the engine skip the forward, and the + # overshoot never commits a frame into the ring, which has no + # undo. Mirrors Wan22DitSubmodule.prepare_inputs. + logger.info( + "Waypoint dit: skipping async-overshoot rollout step %d " + "(request %s runs %d steps)", + rollout_step, fwd_info.request_id, requested, + ) + return None + frame_pos = int(state.get("frame_pos", 0)) if graph_walk == PRIME_WALK: @@ -191,6 +264,11 @@ def prepare_inputs( else: raise ValueError(f"Unknown Waypoint graph walk: {graph_walk!r}") + tensor_inputs.update({ + f"{DECODER_HISTORY_PREFIX}{idx}": value + for idx, value in enumerate(self._history_state(fwd_info.request_id)) + }) + return NodeInputs( tensor_inputs=tensor_inputs, input_seq_len=self.config.tokens_per_frame, @@ -283,6 +361,37 @@ def _idle_controller( torch.zeros((1, 1, 1), dtype=dtype, device=device), ) + def _zero_histories(self, device: torch.device) -> tuple[torch.Tensor, ...]: + """Fresh zero-valued histories, shaped for this config's latent grid. + + Takes a device rather than a real latent: nothing here needs a value, + only the shape ``initial_decoder_histories`` derives from one, and this + method runs both from a request's first ``prepare_inputs`` (before the + dit has produced anything this session) and from the capture template. + """ + seed_latent = torch.zeros( + (1, *self.config.latent_shape), dtype=self.ae_dtype, device=device, + ) + return initial_decoder_histories(self.taehv, seed_latent) + + def _history_state(self, request_id: str) -> tuple[torch.Tensor, ...]: + """The request's nine decoder histories, seeded to zero on first use. + + Seeding happens on the first call any request makes — prime, since + prime always runs first — because the real values only exist inside + this fixed-shape state, not off any graph edge: there is no decoder + node's ``prepare_inputs`` to seed them anymore. + """ + state = self.request_state(request_id) + histories = tuple( + state.get(f"{DECODER_HISTORY_PREFIX}{idx}") for idx in range(9) + ) + if any(value is None for value in histories): + histories = self._zero_histories(self.get_device()) + for idx, value in enumerate(histories): + state.add(f"{DECODER_HISTORY_PREFIX}{idx}", value) + return histories + # ------------------------------------------------------------------ # declare_step # ------------------------------------------------------------------ @@ -360,14 +469,19 @@ def forward( **kwargs, ) -> NameToTensorList: """One frame. ``rollout`` denoises it from noise, ``prime`` appends a - real one; both commit to the ring and both return ``latent``. + real one; both commit to the ring, decode it with TAEHV in the same + call, and return the decoded frame, the updated histories, and the + clock passthrough. ``engine_inputs`` is read for nothing at all here, on purpose: under capture it is the dummy request's forever. The ring and the attention backend come off ``self.node_resources``, which the DiT and its 24 attention layers resolved once at ``bind_node_resources`` time. """ - del engine_inputs, kwargs + del engine_inputs + histories = tuple(kwargs.pop(f"{DECODER_HISTORY_PREFIX}{idx}") for idx in range(9)) + if kwargs: + raise TypeError(f"unexpected dit inputs: {sorted(kwargs)}") # The graph boundary owns [1]; the model owns [] — ``_pos_ids`` # asserts rank 0 and int64. This reshape is the entire seam. pos = frame_pos.reshape(()) @@ -382,7 +496,27 @@ def forward( ) else: raise ValueError(f"Unknown Waypoint graph walk: {graph_walk!r}") - return {"latent": [out]} + + frames, updated = self._decode_latent( + self.taehv, + out.squeeze(1), + histories, + output_size=self.pixel_size, + initialize=graph_walk == PRIME_WALK, + ) + result: NameToTensorList = { + "video_output": [frames], + # [1], not []: the loop-back edge to next iteration's "clock" + # input, whose only job is to be a name in ready_signals (see + # prepare_inputs). Harmless on prime too, whose node declares no + # outputs at all. + "clock": [frame_pos], + } + result.update({ + f"{DECODER_HISTORY_PREFIX}{idx}": [value] + for idx, value in enumerate(updated) + }) + return result # ------------------------------------------------------------------ # capture @@ -407,16 +541,26 @@ def get_cuda_graph_configs( frame = (1, 1, *self.config.latent_shape) def template(latent_key: str) -> NodeInputs: + tensor_inputs = { + latent_key: torch.zeros(frame, dtype=dtype, device=device), + "frame_pos": torch.zeros(1, dtype=torch.int64, device=device), + "mouse": torch.zeros((1, 1, 2), dtype=dtype, device=device), + "button": torch.zeros( + (1, 1, self.config.n_buttons), dtype=dtype, device=device + ), + "scroll": torch.zeros((1, 1, 1), dtype=dtype, device=device), + } + # The fused decode's histories, exactly as prepare_inputs builds + # them — this is what makes + # test_prepared_shapes_and_dtypes_are_the_capture_template_exactly + # hold. "clock" is deliberately absent from both: it is never read + # as an input (see prepare_inputs), only produced as an output. + tensor_inputs.update({ + f"{DECODER_HISTORY_PREFIX}{idx}": value + for idx, value in enumerate(self._zero_histories(device)) + }) return NodeInputs( - tensor_inputs={ - latent_key: torch.zeros(frame, dtype=dtype, device=device), - "frame_pos": torch.zeros(1, dtype=torch.int64, device=device), - "mouse": torch.zeros((1, 1, 2), dtype=dtype, device=device), - "button": torch.zeros( - (1, 1, self.config.n_buttons), dtype=dtype, device=device - ), - "scroll": torch.zeros((1, 1, 1), dtype=dtype, device=device), - }, + tensor_inputs=tensor_inputs, input_seq_len=self.config.tokens_per_frame, ) @@ -453,20 +597,33 @@ def postprocess( inputs: NodeInputs | None = None, **kwargs, ): - """Advance the ring clock by exactly one committed frame. + """Advance the ring clock by exactly one committed frame, and copy the + fused decode's updated histories into the request's stable tensors. Both walks commit — ``append_frame`` runs the cache pass alone and ``generate_frame`` runs it after the four denoise passes — so both - advance. Metadata only: no ``.item()``, nothing read off ``outputs``. + advance. The clock advance is metadata only: no ``.item()``. The + history copy is a device ``copy_`` into the same fixed-address tensors + ``prepare_inputs`` reads back next call — no ``.item()``, no sync. The clock lives on the host because it has to be readable *before* the forward that uses it; the device tensor is derived from it in ``prepare_inputs``, never the other way round. """ - del outputs, inputs, kwargs + del inputs, kwargs state = self.request_state(request_id) state.add("frame_pos", int(state.get("frame_pos", 0)) + 1) if request_info.graph_walk == ROLLOUT_WALK: state.add("rollout_step", int(state.get("rollout_step", 0)) + 1) + for idx in range(9): + key = f"{DECODER_HISTORY_PREFIX}{idx}" + values = outputs.get(key) + if not values: + raise RuntimeError(f"fused dit+decode returned no {key}") + target = state.get(key) + if target is None: + state.add(key, values[0].clone()) + else: + target.copy_(values[0]) def check_stop( self, @@ -481,9 +638,10 @@ def check_stop( that iteration — so N frames means firing at ``k == N - 1``, i.e. ``k + 1 >= N``. Mirrors ``Wan22DitSubmodule.check_stop``; the ``>=`` rather than ``==`` keeps it firing if the deferred count ever reads past - N. The rollout node runs with async scheduling OFF (see - ``WaypointModel``), because an overshoot frame here is not a wasted - forward — it commits garbage into the ring, and there is no undo. + N. The rollout node runs with async scheduling ON (see + ``WaypointModel``): an overshoot iteration this signal is too late to + stop is not a wasted forward, it is vetoed instead in + ``prepare_inputs`` before it ever commits garbage into the ring. """ del request_id, outputs if request_info.graph_walk != ROLLOUT_WALK: @@ -507,28 +665,6 @@ def cleanup_request(self, request_id: str): super().cleanup_request(request_id) -class _FunctionalAeMixin: - """Shared fixed-shape facts for the captured functional AE paths.""" - - @property - def ae_dtype(self) -> torch.dtype: - """The dtype the weights are in, read live: an input scaled into a dtype - the convs are not in faults on the first layer.""" - return next(self.taehv.parameters()).dtype - - @property - def encoded_size(self) -> tuple[int, int]: - return encoded_size_for_latent( - self.config.latent_height, self.config.latent_width - ) - - @property - def pixel_size(self) -> tuple[int, int]: - return pixel_size_for_latent( - self.config.latent_height, self.config.latent_width - ) - - class WaypointVaeEncoderSubmodule(_SingleRequestMixin, _FunctionalAeMixin, NodeSubmodule): """TAEHV encoder: ``temporal_compression`` raw frames -> one latent frame. @@ -603,137 +739,3 @@ def get_cuda_graph_configs( capture_forward_method="forward_batched", compile=True, )] - - -class WaypointVaeDecoderSubmodule(_SingleRequestMixin, _FunctionalAeMixin, NodeSubmodule): - """TAEHV decoder: one latent frame -> ``temporal_compression`` RGB frames. - - ``latent`` ``[1, 1, 32, 32, 64]`` from the dit -> ``video_output`` - ``[4, 720, 1280, 3]`` uint8, one message per engine step. - - Every latent the world commits must reach this node exactly once and in - order, the priming frame included: the temporal memory advances per call, so - a duplicate, gap or reorder shifts the whole stream with nothing raised. - ``enable_async_scheduling=False`` on both rollout nodes is half of what - holds that; the other half is the loop's own iteration boundary. - """ - - disable_torch_compile = True - disable_autocast = True - - def __init__(self, taehv: torch.nn.Module, config: WaypointConfig): - super().__init__() - validate_taehv_architecture(taehv) - self.taehv = taehv - self.config = config - - def _history_state(self, request_id: str, latent: torch.Tensor) -> tuple[torch.Tensor, ...]: - state = self.request_state(request_id) - histories = tuple( - state.get(f"{DECODER_HISTORY_PREFIX}{idx}") for idx in range(9) - ) - if any(value is None for value in histories): - histories = initial_decoder_histories(self.taehv, latent) - for idx, value in enumerate(histories): - state.add(f"{DECODER_HISTORY_PREFIX}{idx}", value) - return histories - - def prepare_inputs( - self, - graph_walk: str, - fwd_info: CurrentForwardPassInfo, - inputs: NameToTensorList, - **kwargs, - ) -> NodeInputs: - del graph_walk, kwargs - latent = inputs["latent"][0] - histories = self._history_state(fwd_info.request_id, latent.squeeze(1)) - tensor_inputs = {"latent": latent} - tensor_inputs.update({ - f"{DECODER_HISTORY_PREFIX}{idx}": value - for idx, value in enumerate(histories) - }) - return NodeInputs(tensor_inputs=tensor_inputs, input_seq_len=1) - - def forward( - self, - graph_walk: str, - engine_inputs: ModelInputsFromEngine, - latent: torch.Tensor, - **kwargs, - ) -> NameToTensorList: - """Decode this step and return its updated fixed-shape histories.""" - del engine_inputs - # [B, 1, C, h, w] -> [B, C, h, w]: the frame axis is the dit's, and the - # AE takes one latent per call. - histories = tuple( - kwargs.pop(f"{DECODER_HISTORY_PREFIX}{idx}") for idx in range(9) - ) - if kwargs: - raise TypeError(f"unexpected decoder inputs: {sorted(kwargs)}") - frames, updated = decode_latent( - self.taehv, - latent.squeeze(1), - histories, - output_size=self.pixel_size, - initialize=graph_walk == PRIME_WALK, - ) - out: NameToTensorList = {"video_output": [frames]} - out.update({ - f"{DECODER_HISTORY_PREFIX}{idx}": [value] - for idx, value in enumerate(updated) - }) - return out - - def _capture_template(self, device: torch.device) -> NodeInputs: - latent = torch.zeros( - (1, 1, *self.config.latent_shape), - dtype=self.ae_dtype, - device=device, - ) - histories = initial_decoder_histories(self.taehv, latent.squeeze(1)) - tensors = {"latent": latent} - tensors.update({ - f"{DECODER_HISTORY_PREFIX}{idx}": value - for idx, value in enumerate(histories) - }) - return NodeInputs(tensor_inputs=tensors, input_seq_len=1) - - def get_cuda_graph_configs( - self, device: torch.device, tp_world_size: int = 1 - ) -> list[CudaGraphConfig]: - del tp_world_size - if not self.config.cuda_graph: - return [] - return [ - BatchedCudaGraphConfig( - capture_graph_walk=walk, - single_request_inputs=self._capture_template(device), - capture_batch_sizes=[1], - capture_forward_method="forward_batched", - compile=True, - ) - for walk in (PRIME_WALK, ROLLOUT_WALK) - ] - - def postprocess( - self, - request_id: str, - request_info: CurrentForwardPassInfo, - outputs: dict[str, list[torch.Tensor]], - inputs: NodeInputs | None = None, - **kwargs, - ) -> None: - """Copy graph outputs into the request's stable history tensors.""" - del request_info, inputs, kwargs - state = self.request_state(request_id) - for idx in range(9): - key = f"{DECODER_HISTORY_PREFIX}{idx}" - values = outputs.get(key) - if not values: - raise RuntimeError(f"captured decoder returned no {key}") - target = state.get(key) - if target is None: - state.add(key, values[0].clone()) - else: - target.copy_(values[0]) diff --git a/mstar/model/waypoint/waypoint_model.py b/mstar/model/waypoint/waypoint_model.py index a08a13618..a4a11ddfc 100644 --- a/mstar/model/waypoint/waypoint_model.py +++ b/mstar/model/waypoint/waypoint_model.py @@ -1,19 +1,26 @@ """WaypointModel: Waypoint-1.5-1B interactive video world model. -Architecture (three nodes): +Architecture (two nodes): vae_encoder - TAEHV encode. The seed clip (``temporal_compression`` raw frames) into the one latent frame it stands for. dit - the 1.28B world DiT. One engine step is one latent frame: four frozen Euler denoise passes plus one committing cache - pass, all inside a single ``forward``. - vae_decoder - TAEHV decode. One latent frame back into its raw frames. + pass, then a TAEHV decode of that frame, all inside a single + ``forward``. The decode is fused in (rather than left on its + own node) so a same-worker speculative N+1 can start while + N's frame is still going out to the client — a separate + decoder node would decode frame N only after N+1 was + already queued. Graph walks (2): - prime - vae_encoder -> dit.append_frame -> vae_decoder. Seeds the world - and decoder state from a real frame, advances the ring clock by - one, and emits nothing. - rollout - Loop("rollout_loop") over dit.generate_frame -> vae_decoder -> - client; one latent frame per iteration, emitted as it lands. + prime - vae_encoder -> dit.append_frame(+decode). Seeds the world and + decoder state from a real frame, advances the ring clock by one, + and emits nothing (the dit's rollout instance is a separate + ``GraphNode`` with ``outputs=[]``). + rollout - Loop("rollout_loop") over a single dit.generate_frame(+decode) + node with a self loop-back ("clock") and + ``enable_async_scheduling=True``; one latent frame decoded and + emitted per iteration. **Prime decodes as well as encodes**, and not for symmetry: the functional decoder's first call spends ``frames_to_trim`` of temporal memory priming @@ -85,7 +92,6 @@ ROLLOUT_LOOP_NAME, ROLLOUT_WALK, WaypointDitSubmodule, - WaypointVaeDecoderSubmodule, WaypointVaeEncoderSubmodule, ) @@ -93,7 +99,6 @@ DIT_NODE = "dit" VAE_ENCODER_NODE = "vae_encoder" -VAE_DECODER_NODE = "vae_decoder" # The scripted action stream, one row per frame. Named once because the walk # declarations, the initial-args validation and the per-walk edge builder all @@ -259,12 +264,9 @@ def _emit_frames(self) -> GraphEdge: ) def get_graph_walk_graphs(self) -> dict[str, GraphSection]: - # -- prime: encode the seed clip, commit it to the world, initialize the - # -- decoder state, and discard the reconstructed seed frames. - # -- - # -- Both `latent` edges carry that name because both endpoints call it - # -- that; a section keys edges on (name, next_node), so they are two - # -- edges and not one. + # -- prime: encode the seed clip, commit it to the world, decode it to + # -- initialize the fused decoder's state, and discard the + # -- reconstructed seed frames (outputs=[] below). prime = Sequential([ GraphNode( name=VAE_ENCODER_NODE, @@ -274,58 +276,43 @@ def get_graph_walk_graphs(self) -> dict[str, GraphSection]: GraphNode( name=DIT_NODE, input_names={"latent", *_CONTROLLER_STREAMS}, - outputs=[GraphEdge(next_node=VAE_DECODER_NODE, name="latent")], - ), - # Advance the decoder's nine temporal histories, but do not expose - # reconstructed seed frames. Client frame zero is generated. - GraphNode( - name=VAE_DECODER_NODE, - input_names={"latent"}, outputs=[], ), ]) - # -- rollout: one frame per iteration, emitted as it lands. - # -- - # -- No loop-back edges, and that is not an omission: everything that - # -- crosses a frame boundary is the ring (an engine resource at a fixed - # -- address), the host-side frame_pos, or the decoder's streaming - # -- state. The controller streams are loop-external, re-injected every - # -- iteration, and the submodule slices the current frame's row out. + # -- rollout: one frame decoded and emitted per iteration, from a + # -- single node. # -- - # -- EMIT_TO_CLIENT sits on the decoder node, one message per iteration, - # -- rather than on Loop.accumulated_outputs: a client that only sees - # -- frames after the rollout ends has no world to interact with. + # -- The "clock" self loop-back is what makes the dit a same-node + # -- speculation target (GraphNode.is_ready_for_speculation / + # -- WorkerGraphIO.ingest_for_speculation): it carries no information + # -- of its own (the submodule ignores its value; frame_pos and + # -- rollout_step live in host state), it only has to be a name the + # -- loop re-injects every iteration. enable_async_scheduling=True lets + # -- the worker build iteration N+1 while N is still on the GPU; the + # -- overshoot that can result — N+1 dispatched before check_stop(N) + # -- registers the loop's finish signal — is vetoed host-side in + # -- WaypointDitSubmodule.prepare_inputs before it can commit a frame + # -- into the ring, which has no undo. # -- - # -- check_stop ends the loop, but the loop's registry calls - # -- complete_iter only once *every* entity in the section is done, so - # -- the final frame is decoded before the loop closes. + # -- There is no separate decoder node left to order against: the + # -- decode happens inside the same forward as the denoise passes, so + # -- a frame is decoded and emitted exactly once, when its dit + # -- iteration runs. rollout = Loop( name=ROLLOUT_LOOP_NAME, - section=Sequential([ - GraphNode( - name=DIT_NODE, - input_names=set(_CONTROLLER_STREAMS), - outputs=[GraphEdge(next_node=VAE_DECODER_NODE, name="latent")], - # Speculation would dispatch iteration N+1 before - # check_stop's decision on N landed. The overshoot forward - # *commits a frame into the ring* and there is no undo; the - # next real rollout would inherit it. - enable_async_scheduling=False, - ), - GraphNode( - name=VAE_DECODER_NODE, - input_names={"latent"}, - outputs=[self._emit_frames()], - # And here for the decoder's own reason: it is streaming, so - # frames must be decoded exactly once in emission order, and - # a speculative decode of a frame that may not stand is a - # reorder of a stream that cannot be reordered. - enable_async_scheduling=False, - ), - ]), + section=GraphNode( + name=DIT_NODE, + input_names=set(_CONTROLLER_STREAMS) | {"clock"}, + outputs=[ + GraphEdge(next_node=DIT_NODE, name="clock"), + self._emit_frames(), + ], + enable_async_scheduling=True, + ), # Ceiling only; the request's num_steps stops the loop early via - # WaypointDitSubmodule.check_stop. + # WaypointDitSubmodule.check_stop (and the overshoot veto above + # catches what check_stop is too late for under async scheduling). max_iters=self.config.max_frames, outputs=[], accumulated_outputs=[], @@ -576,7 +563,7 @@ def postprocess( raise ValueError(f"Unsupported modality for Waypoint: {modality!r}") if output.dtype != torch.uint8: raise ValueError( - f"the vae_decoder emits uint8 RGB frames; got {output.dtype}." + f"the dit's fused decode emits uint8 RGB frames; got {output.dtype}." ) return output.detach().cpu().contiguous().numpy().tobytes() @@ -677,7 +664,7 @@ def _walk_inputs( ) -> list[GraphEdge]: """The external edges seeding one walk. Both walks read the controller streams at the dit; only ``prime`` reads the seed clip, and it reads it - at the vae_encoder.""" + at the vae_encoder; only ``rollout`` seeds the dit's self loop-back.""" inputs = [ GraphEdge(next_node=DIT_NODE, name=name, persist=True) for name in _CONTROLLER_STREAMS @@ -689,6 +676,10 @@ def _walk_inputs( next_node=VAE_ENCODER_NODE, name="image_inputs", persist=True ), ) + else: + # Sent empty, like wan22's denoise loop-back edges: the dit + # submodule never reads its value, only its name (F4 pattern). + inputs.append(GraphEdge(next_node=DIT_NODE, name="clock")) for edge in inputs: edge.tensor_info = signals.get(edge.name, []) return inputs @@ -765,7 +756,7 @@ def _create_submodule( nodes, which makes the engine run that node without real computation.""" if self.skip_weight_loading: return None - if node_name in {DIT_NODE, VAE_ENCODER_NODE, VAE_DECODER_NODE}: + if node_name in {DIT_NODE, VAE_ENCODER_NODE}: self._resolve_checkpoints() if node_name == DIT_NODE: from mstar.model.waypoint.weight_loader import build_waypoint_dit @@ -773,11 +764,9 @@ def _create_submodule( dit = build_waypoint_dit( self.config, self.checkpoint_dir, device=device, ) - return WaypointDitSubmodule(dit, self.config) + return WaypointDitSubmodule(dit, self._taehv_weights(device), self.config) if node_name == VAE_ENCODER_NODE: return WaypointVaeEncoderSubmodule(self._taehv_weights(device), self.config) - if node_name == VAE_DECODER_NODE: - return WaypointVaeDecoderSubmodule(self._taehv_weights(device), self.config) logger.warning("Waypoint has no submodule for node %r; running it dummy.", node_name) return None diff --git a/mstar/worker/worker.py b/mstar/worker/worker.py index a156fa7d0..e810291af 100644 --- a/mstar/worker/worker.py +++ b/mstar/worker/worker.py @@ -1577,6 +1577,15 @@ def _get_input_tensors( ) -> NameToTensorList: inputs = node.ready_next_iter.ready_inputs if check_next_iter \ else node.ready_signals.ready_inputs + if check_next_iter: + # Carry over loop-external inputs sitting in ready_signals (see + # GraphNode.is_ready_for_speculation): they are re-injected + # unchanged every iteration and never land in ready_next_iter. + carried = { + name: edge for name, edge in node.ready_signals.ready_inputs.items() + if edge._persist_for_loop and name not in inputs + } + inputs = {**carried, **inputs} tensors = {} for input_name, edge in inputs.items(): tensors[input_name] = [ diff --git a/test/modular/test_speculation_external_inputs.py b/test/modular/test_speculation_external_inputs.py new file mode 100644 index 000000000..293bb53a0 --- /dev/null +++ b/test/modular/test_speculation_external_inputs.py @@ -0,0 +1,56 @@ +"""Regression test for the same-node speculation gate with loop-external inputs. + +Before the fix, ``GraphNode.is_ready_for_speculation(check_next_iter=True)`` +required every input name to be in ``ready_next_iter`` or +``speculative_signals``. Loop-external inputs (e.g. wan22's ``text_embeds_*``, +waypoint's controller streams) never land in ``ready_next_iter`` — they're +re-injected unchanged into ``ready_signals`` every iteration +(``Loop.ingest_external_input`` / ``complete_iter``) — so a same-node loop +node with any external input could never be proposed as a same-node +speculation target. See ``docs`` for the plan (F3) this fixes. +""" + +from mstar.graph.base import GraphEdge, GraphNode, Loop, SpeculativeNodeInfo +from mstar.graph.graph_io import WorkerGraphIO + + +def _wan22_shaped_loop(): + """A wan22-shaped rollout loop: one node, 2 external (persisted) inputs + plus 2 loop-back inputs to itself.""" + dit = GraphNode( + name="dit", + input_names={"text", "lat", "t"}, + outputs=[ + GraphEdge(next_node="dit", name="lat"), + GraphEdge(next_node="dit", name="t"), + ], + ) + return dit, Loop(name="L", section=dit, max_iters=10, outputs=[]) + + +def test_loop_external_persisted_input_satisfies_next_iter_speculation(): + dit, loop = _wan22_shaped_loop() + io = WorkerGraphIO(loop) + for name in ("text", "lat", "t"): + io.ingest_input(GraphEdge(next_node="dit", name=name, persist=True)) + + ready = io.ingest_for_speculation(dit.outputs, "dit") + + assert ready == [ + SpeculativeNodeInfo(node_name="dit", is_new_loop_iter=True, loop_name="L") + ] + + +def test_top_level_external_input_without_persist_for_loop_is_not_speculation_ready(): + # A node with an external input outside of any Loop never gets + # `_persist_for_loop` set (that flag is only set by + # `Loop.ingest_external_input`), so it must still fail the next-iter gate. + top = GraphNode( + name="top", + input_names={"a", "b"}, + outputs=[GraphEdge(next_node="top", name="b")], + ) + io = WorkerGraphIO(top) + io.ingest_input(GraphEdge(next_node="top", name="a", persist=True)) + + assert not top.is_ready_for_speculation(check_next_iter=True) diff --git a/test/modular/test_waypoint_checkpoint.py b/test/modular/test_waypoint_checkpoint.py index 4ed31fcb1..04da75bf1 100644 --- a/test/modular/test_waypoint_checkpoint.py +++ b/test/modular/test_waypoint_checkpoint.py @@ -32,7 +32,6 @@ ) from mstar.model.waypoint.waypoint_model import ( DIT_NODE, - VAE_DECODER_NODE, VAE_ENCODER_NODE, WaypointModel, ) @@ -308,6 +307,22 @@ def build_dit(config, checkpoint_dir, device): calls.append(("allocate", checkpoint_dir, device)) return torch.nn.Linear(1, 1) + class MemBlock(torch.nn.Module): + """The class name, not the weights, is what + ``validate_taehv_architecture`` reads off ``encoder``/``decoder``.""" + + def load_taehv(ae_uri, cache_dir=None): + calls.append(("load_taehv", ae_uri, {"cache_dir": cache_dir})) + stub = torch.nn.Module() + stub.patch_size = 2 + stub.latent_channels = 32 + stub.t_downscale = 4 + stub.t_upscale = 4 + stub.frames_to_trim = 3 + stub.encoder = torch.nn.ModuleList([MemBlock() for _ in range(9)]) + stub.decoder = torch.nn.ModuleList([MemBlock() for _ in range(9)]) + return stub + monkeypatch.setattr( "mstar.model.waypoint.checkpoint.require_taehv_runtime", lambda: calls.append(("runtime",)), @@ -322,6 +337,9 @@ def build_dit(config, checkpoint_dir, device): monkeypatch.setattr( "mstar.model.waypoint.weight_loader.build_waypoint_dit", build_dit, ) + monkeypatch.setattr( + "mstar.model.waypoint.components.taehv.load_taehv", load_taehv, + ) model = WaypointModel( **HF_MODELS["waypoint"], variant=variant, @@ -333,7 +351,13 @@ def build_dit(config, checkpoint_dir, device): model.get_submodule(DIT_NODE) - assert [entry[0] for entry in calls] == ["runtime", "waypoint", "taehv", "allocate"] + # The dit's fused decode needs TAEHV weights too now (Step 2): allocation + # still runs first, and the AE load trails it rather than gating it, since + # ``_taehv_weights`` is evaluated as part of the same return statement, + # after ``build_waypoint_dit`` already ran. + assert [entry[0] for entry in calls] == [ + "runtime", "waypoint", "taehv", "allocate", "load_taehv", + ] assert calls[1][1:] == ( expected_source, {"cache_dir": "/cache", "revision": "dit-rev"}, @@ -401,7 +425,7 @@ def test_shipped_config_builds_through_registry_and_engine_manager_without_netwo model.get_worker_graphs(str(config_path)) manager = EngineManager.build( - node_names={DIT_NODE, VAE_ENCODER_NODE, VAE_DECODER_NODE}, + node_names={DIT_NODE, VAE_ENCODER_NODE}, device=torch.device("cpu"), model_config=model_config, parallel_groups=WorkerParallelGroups(num_workers=1, global_rank=0), @@ -413,7 +437,7 @@ def test_shipped_config_builds_through_registry_and_engine_manager_without_netwo model=model, ) try: - assert manager.node_names == {DIT_NODE, VAE_ENCODER_NODE, VAE_DECODER_NODE} + assert manager.node_names == {DIT_NODE, VAE_ENCODER_NODE} assert manager.engine._autocast_dtype is torch.bfloat16 assert model.config.reference_compat is True assert model.config.compile_dit is True diff --git a/test/modular/test_waypoint_shell.py b/test/modular/test_waypoint_shell.py index c96aff3d1..d27cc8052 100644 --- a/test/modular/test_waypoint_shell.py +++ b/test/modular/test_waypoint_shell.py @@ -57,13 +57,17 @@ RingKVStep, ) from mstar.engine.resources.runner import topo_sort -from mstar.graph.base import GraphEdge, Loop, Sequential +from mstar.graph.base import GraphEdge, Loop, Sequential, SpeculativeNodeInfo from mstar.graph.graph_io import WorkerGraphIO from mstar.graph.special_destinations import EMIT_TO_CLIENT from mstar.model.submodule_base import ModelInputsFromEngine from mstar.model.waypoint.components.attention import WaypointAttention from mstar.model.waypoint.components.dit import WaypointDiT -from mstar.model.waypoint.config import waypoint_1_5_1b_360p, waypoint_1_5_1b_720p +from mstar.model.waypoint.config import ( + WaypointConfig, + waypoint_1_5_1b_360p, + waypoint_1_5_1b_720p, +) from mstar.model.waypoint.submodules import ( ATTN_RESOURCE, KV_RESOURCE, @@ -71,12 +75,10 @@ ROLLOUT_LOOP_NAME, ROLLOUT_WALK, WaypointDitSubmodule, - WaypointVaeDecoderSubmodule, WaypointVaeEncoderSubmodule, ) from mstar.model.waypoint.waypoint_model import ( DIT_NODE, - VAE_DECODER_NODE, VAE_ENCODER_NODE, WaypointModel, ) @@ -101,11 +103,17 @@ def submodule(config): things under test, and a stub would assert them against itself. Meta also keeps ``prepare_inputs``' shapes and dtypes honest -- they are computed the same way on meta as on cuda -- while allocating nothing. + + The taehv here is the same fake used by the VAE section below (defined + later in this module; fixtures resolve names at call time, so the forward + reference is fine): the fused decode only needs its structural facts + (nine MemBlock histories, ``frames_to_trim``) to build shapes, not real + weights. """ with torch.device("meta"): dit = WaypointDiT(config) dit.cast_serving_dtypes() - return WaypointDitSubmodule(dit, config) + return WaypointDitSubmodule(dit, _FakeTaehv(), config) class _HostOnlyDit(torch.nn.Module): @@ -130,7 +138,17 @@ def dtype(self) -> torch.dtype: @pytest.fixture def host_submodule(config): - return WaypointDitSubmodule(_HostOnlyDit(), config) + return WaypointDitSubmodule(_HostOnlyDit(), _FakeTaehv(), config) + + +def _history_outputs(submodule: WaypointDitSubmodule) -> dict: + """A plausible ``outputs`` dict for the nine decoder histories, for tests + that call ``postprocess`` directly (bypassing ``forward``) to isolate the + clock bookkeeping it also does.""" + return { + f"decoder_history_{idx}": [value] + for idx, value in enumerate(submodule._zero_histories(submodule.get_device())) + } def _fwd_info( @@ -246,31 +264,28 @@ def test_attention_resolves_after_the_cache_it_names(model): def test_prime_encodes_commits_and_initializes_decoder_without_emitting(model): - """The seed frame advances decoder state. Encoding it and dropping the latent would - prime the world correctly and still corrupt every emitted frame: the - streaming decoder spends its first call on ``frames_to_trim`` of temporal - memory, so the first *rollout* frame would pay for it and the whole stream - would sit one priming short of the world it came from. Silently.""" + """The seed frame advances decoder state through the dit's fused decode. + Encoding it and dropping the latent would prime the world correctly and + still corrupt every emitted frame: the functional decoder spends its first + call on ``frames_to_trim`` of temporal memory, so the first *rollout* frame + would pay for it and the whole stream would sit one priming short of the + world it came from. Silently.""" walks = model.get_graph_walk_graphs() assert set(walks) == {PRIME_WALK, ROLLOUT_WALK} - assert model.nodes == [DIT_NODE, VAE_DECODER_NODE, VAE_ENCODER_NODE] + assert model.nodes == [DIT_NODE, VAE_ENCODER_NODE] prime = walks[PRIME_WALK] assert isinstance(prime, Sequential) - assert [s.name for s in prime.sections] == [ - VAE_ENCODER_NODE, DIT_NODE, VAE_DECODER_NODE, - ] - encoder, dit, decoder = prime.sections + assert [s.name for s in prime.sections] == [VAE_ENCODER_NODE, DIT_NODE] + encoder, dit = prime.sections assert encoder.input_names == {"image_inputs"} assert [(e.name, e.next_node) for e in encoder.outputs] == [("latent", DIT_NODE)] - # The dit node's contract is unchanged by the nodes bracketing it. + # The dit node's contract is unchanged by the node bracketing it. assert dit.input_names == {"latent", "mouse", "button", "scroll"} - assert [(e.name, e.next_node) for e in dit.outputs] == [("latent", VAE_DECODER_NODE)] - assert decoder.input_names == {"latent"} - assert decoder.outputs == [] + # Nothing to route: the decode happens inside this forward, and the + # reconstructed seed frames it produces are internal-only. + assert dit.outputs == [] - # The two `latent` edges are distinct because a section keys on - # (name, next_node); collapsing them would route the seed latent past the dit. io = prime.get_inputs_outputs() assert io.ext_inputs == { ("image_inputs", VAE_ENCODER_NODE), @@ -285,88 +300,130 @@ def test_the_rollout_loop_decodes_and_emits_every_iteration(model, config): assert rollout.name == ROLLOUT_LOOP_NAME # what check_stop's signal is keyed by assert rollout.max_iters == config.max_frames - section = rollout.section - assert isinstance(section, Sequential) - dit, decoder = section.sections - assert (dit.name, decoder.name) == (DIT_NODE, VAE_DECODER_NODE) - assert [(e.name, e.next_node) for e in dit.outputs] == [("latent", VAE_DECODER_NODE)] - # Emitted from inside the loop, one frame per iteration -- an interactive - # world model whose frames only arrive after the rollout ends has no world - # to interact with. - assert [e.next_node for e in decoder.outputs] == [EMIT_TO_CLIENT] + dit = rollout.section + assert dit.name == DIT_NODE + assert dit.input_names == {"mouse", "button", "scroll", "clock"} + assert {(e.name, e.next_node) for e in dit.outputs} == { + ("clock", DIT_NODE), ("video_output", EMIT_TO_CLIENT), + } assert rollout.accumulated_outputs == [] - # An overshoot iteration is not a wasted forward: the dit commits a frame - # into the ring, and a speculative decode is a reorder of a stream that - # cannot be reordered. - assert dit.enable_async_scheduling is False - assert decoder.enable_async_scheduling is False - # The controller streams stay loop-external; the dit->decoder latent does - # not become one, or the conductor would re-inject a stale frame. + # The "clock" self loop-back is what makes the dit a same-node + # speculation target (see the test below); async scheduling can now + # dispatch iteration N+1 while N is still running. An overshoot iteration + # is vetoed host-side in WaypointDitSubmodule.prepare_inputs, not + # prevented by keeping this off. + assert dit.enable_async_scheduling is True + # The controller streams stay loop-external; "clock" is the only + # loop-back, or the conductor would try to re-inject it every walk step. assert rollout._external_inputs == { ("mouse", DIT_NODE), ("button", DIT_NODE), ("scroll", DIT_NODE), } + assert rollout._loop_back_inputs == {("clock", DIT_NODE)} + + +def test_the_clock_loop_back_makes_dit_a_same_node_speculation_target(model): + """The generic readiness fix (``GraphNode.is_ready_for_speculation``, + ``Worker._get_input_tensors``) only pays off if the graph actually gives + the dit a self-edge to speculate on. Once the loop-external controller + streams are ready and the "clock" loop-back has been ingested (empty, as + ``WaypointModel._walk_inputs`` sends it for iteration 0), + ``ingest_for_speculation`` must propose the dit as ready for its own next + iteration -- exactly the wan22-shaped case F3/Step 1 fixed, now over the + real Waypoint graph. + """ + rollout = model.get_graph_walk_graphs()[ROLLOUT_WALK] + wgio = WorkerGraphIO(rollout) + dit = wgio.get_node(DIT_NODE) + for name in ("mouse", "button", "scroll"): + wgio.ingest_input(GraphEdge(next_node=DIT_NODE, name=name, persist=True)) + wgio.ingest_input(GraphEdge(next_node=DIT_NODE, name="clock")) + assert wgio.ready_node_names == {DIT_NODE} + + assert wgio.ingest_for_speculation(dit.outputs, DIT_NODE) == [ + SpeculativeNodeInfo(node_name=DIT_NODE, is_new_loop_iter=True, loop_name=ROLLOUT_LOOP_NAME) + ] -def test_every_committed_frame_is_decoded_once_including_the_last(model): - """Driven through ``WorkerGraphIO``, because what is under test is the order - the worker runs these in, not the order they are declared in. +def test_the_rollout_loop_closes_after_exactly_num_steps_frames(model): + """Driven through ``WorkerGraphIO``: what is under test is the loop's own + iteration/finish-signal bookkeeping for the new single-node shape, not the + order two nodes run in (there is only one node now, so nothing can decode + a frame twice, skip one, or take one out of turn -- that guarantee moved + into the dit's own forward being one atomic step). - The decoder is order-dependent and its memory advances per call, so a latent - decoded twice, skipped, or taken out of turn shifts every frame after it - with nothing raised. Two things have to hold. The decode runs between its - own dit pass and the next one -- which it does because scheduling *pops* a - node off the ready set, and the dit's controller streams are only - re-injected at the iteration boundary. And the stop signal, which fires - during the dit's postprocess, closes the loop only after that iteration's - decode: ``LoopStateRegistry`` calls ``complete_iter`` once every entity is - finished, so the finish cannot short-circuit the decoder. + ``register_loop_finish_signal`` is what ``WaypointDitSubmodule.check_stop`` + does; it fires during postprocess of the loop's last iteration, so the + frame that iteration produced must still be emitted before the loop + reports done. The actual overshoot guard for a speculative iteration + dispatched before that signal lands is a *separate* mechanism -- the + host-side veto in ``prepare_inputs`` -- checked below. """ + num_steps = 3 rollout = model.get_graph_walk_graphs()[ROLLOUT_WALK] wgio = WorkerGraphIO(rollout) - decoder = wgio.get_node(VAE_DECODER_NODE) for name in ("mouse", "button", "scroll"): wgio.ingest_input(GraphEdge(next_node=DIT_NODE, name=name, persist=True)) + wgio.ingest_input(GraphEdge(next_node=DIT_NODE, name="clock")) - emitted, decoded = [], [] - - def run(node_name: str, latent_id: int | None = None) -> None: - """One scheduling round: pop, execute, route. The pop is what - ``NodeManager.pop_ready_nodes`` does, and it is the whole reason the dit - cannot be picked twice in an iteration.""" - assert node_name in wgio.ready_node_names - wgio.ready_node_names.discard(node_name) - queued = decoder.ready_signals.ready_inputs.get("latent") - if queued is not None: - decoded.append(queued.latent_id) - for edge in wgio.mark_node_complete(node_name).output_edges: + emitted = [] + for step in range(num_steps): + assert wgio.ready_node_names == {DIT_NODE} + wgio.ready_node_names.discard(DIT_NODE) + if step == num_steps - 1: + wgio.register_loop_finish_signal(ROLLOUT_LOOP_NAME) # what check_stop does + completion = wgio.mark_node_complete(DIT_NODE) + # ``WorkerGraphIO.mark_node_complete`` has already stripped anything in + # ``completion.filtered_signals`` (e.g. the "clock" loop-back, once the + # final iteration's completion filters it out) from ``output_edges``; + # what is left to route is exactly the caller's job. + for edge in completion.output_edges: if edge.next_node == EMIT_TO_CLIENT: emitted.append(edge.name) continue - if edge.next_node == VAE_DECODER_NODE: - # A fresh edge per iteration: the node's declared outputs are - # one reused object, so identity is the only way to tell which - # frame's latent the decoder actually consumed. - edge = GraphEdge(next_node=edge.next_node, name=edge.name) - edge.latent_id = latent_id wgio.ingest_input(edge) + assert rollout.is_done == (step == num_steps - 1) - for frame in range(3): - assert wgio.ready_node_names == {DIT_NODE} - run(DIT_NODE, latent_id=frame) - assert wgio.ready_node_names == {VAE_DECODER_NODE} - run(VAE_DECODER_NODE) - assert rollout.is_done is False - assert rollout.curr_iter == frame + 1 + assert emitted == ["video_output"] * num_steps - wgio.register_loop_finish_signal(ROLLOUT_LOOP_NAME) # what check_stop does - run(DIT_NODE, latent_id=3) - assert rollout.is_done is False, "the loop closed before the frame was decoded" - assert wgio.ready_node_names == {VAE_DECODER_NODE} - run(VAE_DECODER_NODE) - assert rollout.is_done is True - assert decoded == [0, 1, 2, 3], "a latent was skipped, repeated or reordered" - assert emitted == ["video_output"] * 4, "one emit per committed frame" +def test_the_overshoot_veto_fires_only_past_num_steps(submodule): + """The other half of the guarantee above: a speculative iteration built + from the state the last real one left behind must never reach a forward + once the request's ``num_steps`` is spent, and must never fire during + prime (which has no ``rollout_step`` to overshoot). + """ + num_steps = 3 + rid = "overshoot" + submodule.request_states.pop(rid, None) + inputs = _controller_stream(submodule.config, frames=num_steps) + + for expected_step in range(num_steps): + prepared = submodule.prepare_inputs( + ROLLOUT_WALK, _fwd_info(request_id=rid, num_steps=num_steps), inputs + ) + assert prepared is not None, f"step {expected_step} must not be vetoed" + submodule.postprocess( + rid, _fwd_info(request_id=rid, num_steps=num_steps), + {"video_output": [torch.zeros(1)], **_history_outputs(submodule)}, + ) + + vetoed = submodule.prepare_inputs( + ROLLOUT_WALK, _fwd_info(request_id=rid, num_steps=num_steps), inputs + ) + assert vetoed is None, "an async-overshoot iteration must be vetoed, not run" + + # Prime never overshoots: it has no num_steps to compare rollout_step + # against, regardless of how far along the rollout counter is. + prime_inputs = { + **_controller_stream(submodule.config, frames=1), + "latent": [torch.zeros((1, 1, *submodule.config.latent_shape))], + } + assert submodule.prepare_inputs( + PRIME_WALK, _fwd_info(request_id=rid, graph_walk=PRIME_WALK, num_steps=num_steps), + prime_inputs, + ) is not None + + submodule.cleanup_request(rid) # --------------------------------------------------------------------------- @@ -508,7 +565,8 @@ def test_prime_is_idle_and_rollout_zero_receives_action_zero( assert int(prime.tensor_inputs["frame_pos"][0]) == 0 assert float(prime.tensor_inputs["scroll"][0, 0, 0]) == 0.0 host_submodule.postprocess( - rid, _fwd_info(request_id=rid, graph_walk=PRIME_WALK), {} + rid, _fwd_info(request_id=rid, graph_walk=PRIME_WALK), + _history_outputs(host_submodule), ) seen = [] @@ -520,7 +578,9 @@ def test_prime_is_idle_and_rollout_zero_receives_action_zero( int(node_inputs.tensor_inputs["frame_pos"][0]), float(node_inputs.tensor_inputs["scroll"][0, 0, 0]), )) - host_submodule.postprocess(rid, _fwd_info(request_id=rid), {}) + host_submodule.postprocess( + rid, _fwd_info(request_id=rid), _history_outputs(host_submodule) + ) assert seen == [(1, 1.0), (2, 2.0)] with pytest.raises(IndexError, match="action index 2"): @@ -565,7 +625,9 @@ def test_declare_step_carries_the_same_clock_prepare_inputs_reads( node_inputs.tensor_inputs["frame_pos"][0] ) - host_submodule.postprocess(rid, _fwd_info(request_id=rid), {}) + host_submodule.postprocess( + rid, _fwd_info(request_id=rid), _history_outputs(host_submodule) + ) host_submodule.cleanup_request(rid) @@ -590,7 +652,9 @@ def test_declare_step_names_a_clock_for_every_request_in_the_batch(host_submodul for i, rid in enumerate(("a", "b", "c")): host_submodule.request_states.pop(rid, None) for _ in range(i * 2): - host_submodule.postprocess(rid, _fwd_info(request_id=rid), {}) + host_submodule.postprocess( + rid, _fwd_info(request_id=rid), _history_outputs(host_submodule) + ) step = host_submodule.declare_step( graph_walk=ROLLOUT_WALK, request_ids=["a", "b", "c"], inputs=[], @@ -647,7 +711,7 @@ def _write_config(tmp_path, name: str, **extra) -> str: "model": "waypoint", "max_seq_len": 512, "node_groups": [ - {"node_names": [VAE_ENCODER_NODE, DIT_NODE, VAE_DECODER_NODE], "ranks": [0]} + {"node_names": [VAE_ENCODER_NODE, DIT_NODE], "ranks": [0]} ], **extra, } @@ -747,13 +811,14 @@ def test_get_worker_graphs_accepts_a_deployment_inside_its_pool( } -def test_the_shipped_config_serializes_all_three_nodes_onto_one_rank(model): +def test_the_shipped_config_serializes_both_nodes_onto_one_rank(model): """``configs/waypoint.yaml`` is the deployment and has to pass its own gate. - All three nodes in one group, on rank 0: a node missing from - ``node_groups`` has no rank to run on and the split fails there, and a - worker boundary inside the rollout loop would put a process hop between the - dit and a decoder whose frames must arrive in order. + Both nodes in one group, on rank 0: a node missing from ``node_groups`` + has no rank to run on and the split fails there. There is no decoder node + left to put a worker boundary in front of -- its decode is fused into the + dit's own forward -- so a rank split inside the rollout loop is no longer + even expressible. """ path = pathlib.Path(__file__).resolve().parents[2] / "configs" / "waypoint.yaml" @@ -762,10 +827,8 @@ def test_the_shipped_config_serializes_all_three_nodes_onto_one_rank(model): assert {walk for g in graphs for walk in g.graph_walks} == {PRIME_WALK, ROLLOUT_WALK} assert {tuple(g.ranks) for g in graphs} == {(0,)} by_walk = {walk: g for g in graphs for walk in g.graph_walks} - assert set(by_walk[PRIME_WALK].section.get_nodes()) == { - VAE_ENCODER_NODE, DIT_NODE, VAE_DECODER_NODE, - } - assert set(by_walk[ROLLOUT_WALK].section.get_nodes()) == {DIT_NODE, VAE_DECODER_NODE} + assert set(by_walk[PRIME_WALK].section.get_nodes()) == {VAE_ENCODER_NODE, DIT_NODE} + assert set(by_walk[ROLLOUT_WALK].section.get_nodes()) == {DIT_NODE} def test_get_worker_graphs_warns_about_worlds_no_request_can_reach( @@ -880,7 +943,7 @@ def test_dit_prime_capture_can_be_declined_on_its_own(config): with torch.device("meta"): dit = WaypointDiT(no_prime) dit.cast_serving_dtypes() - submodule = WaypointDitSubmodule(dit, no_prime) + submodule = WaypointDitSubmodule(dit, _FakeTaehv(), no_prime) configs = submodule.get_cuda_graph_configs(torch.device("meta")) assert [cfg.capture_graph_walk for cfg in configs] == [ROLLOUT_WALK] @@ -892,7 +955,7 @@ def test_dit_declares_no_capture_when_cuda_graph_is_disabled(config): with torch.device("meta"): dit = WaypointDiT(eager_config) dit.cast_serving_dtypes() - eager = WaypointDitSubmodule(dit, eager_config) + eager = WaypointDitSubmodule(dit, _FakeTaehv(), eager_config) assert eager.get_cuda_graph_configs(torch.device("meta")) == [] @@ -987,9 +1050,41 @@ def encoder(taehv_weights, ae_config): return WaypointVaeEncoderSubmodule(taehv_weights, ae_config) +class _FakeDit(torch.nn.Module): + """Stands in for the DiT in the fused-decode tests below. + + What is under test there is TAEHV history bookkeeping riding along inside + ``WaypointDitSubmodule`` -- isolation across requests, fixed addresses + across walks, cleanup -- not the denoiser, so this returns a deterministic + latent shaped like the real one instead of running 24 attention layers on + CPU. The real DiT has its own coverage above and in ``test_waypoint_dit.py``. + """ + + def __init__(self, config: WaypointConfig, dtype: torch.dtype = torch.bfloat16): + super().__init__() + self.config = config + self.marker = torch.nn.Parameter(torch.zeros(1, dtype=dtype)) + + @property + def dtype(self) -> torch.dtype: + return self.marker.dtype + + def generate_frame(self, noise, pos, *, mouse, button, scroll): + del pos, mouse, button, scroll + return noise.reshape(1, 1, *self.config.latent_shape).to(self.dtype) + + def append_frame(self, latent, pos, *, mouse, button, scroll): + del pos, mouse, button, scroll + return latent.reshape(1, 1, *self.config.latent_shape).to(self.dtype) + + @pytest.fixture def decoder(taehv_weights, ae_config): - return WaypointVaeDecoderSubmodule(taehv_weights, ae_config) + """A ``WaypointDitSubmodule`` over a fake dit: the fused node under test, + minus the denoiser. Named ``decoder`` still, because every test below + exercises the TAEHV history half of this node, the half the standalone + decoder node used to own.""" + return WaypointDitSubmodule(_FakeDit(ae_config), taehv_weights, ae_config) def _seed_clip(ae_config, value: int = 200) -> torch.Tensor: @@ -1005,11 +1100,23 @@ def _engine_inputs( ) -def _decode(decoder, latent, *, request_id="r0", graph_walk=ROLLOUT_WALK): - info = _fwd_info(request_id, graph_walk=graph_walk) - prepared = decoder.prepare_inputs( - graph_walk, info, {"latent": [latent]} - ) +def _decode( + decoder, *, request_id="r0", graph_walk=ROLLOUT_WALK, + seed_latent: torch.Tensor | None = None, num_steps: int = 8, +): + """Drive the fused submodule through one real step of its own + ``prepare_inputs`` / ``forward`` / ``postprocess`` cycle -- the same + sequence the engine runs -- rather than poking ``taehv`` directly, since + what several tests below check is that this cycle seeds, threads and + advances the nine histories correctly across requests and walks. + """ + info = _fwd_info(request_id, graph_walk=graph_walk, num_steps=num_steps) + if graph_walk == PRIME_WALK: + inputs = {"latent": [seed_latent]} + else: + inputs = _controller_stream(decoder.config, frames=num_steps) + prepared = decoder.prepare_inputs(graph_walk, info, inputs) + assert prepared is not None, "must not be vetoed inside num_steps" outputs = decoder.forward( graph_walk, _engine_inputs(request_id, graph_walk), @@ -1046,48 +1153,49 @@ def test_the_encoder_emits_the_dit_s_priming_latent(encoder, ae_config): assert latent.dtype == torch.bfloat16 -def test_the_decoder_turns_one_latent_into_one_raw_clip(decoder, ae_config): - latent = torch.zeros( +def test_the_fused_decode_turns_one_frame_into_one_raw_clip(decoder, ae_config): + out = _decode(decoder, graph_walk=PRIME_WALK, seed_latent=torch.zeros( (1, 1, ae_config.channels, *ae_config.latent_shape[1:]), dtype=torch.bfloat16 - ) - - out = _decode(decoder, latent) + )) frames = out["video_output"][0] assert frames.shape == (ae_config.temporal_compression, 360, 640, 3) assert frames.dtype == torch.uint8 + decoder.cleanup_request("r0") -def test_decoder_prime_and_steady_state_use_fixed_tensor_histories(decoder, ae_config): +def test_prime_and_rollout_use_the_same_fixed_tensor_histories(decoder, ae_config): latent = torch.full( - (1, 1, ae_config.channels, *ae_config.latent_shape[1:]), dtype=torch.bfloat16 - , fill_value=0.125 + (1, 1, ae_config.channels, *ae_config.latent_shape[1:]), 0.125, dtype=torch.bfloat16, ) - _decode(decoder, latent, graph_walk=PRIME_WALK) + _decode(decoder, graph_walk=PRIME_WALK, seed_latent=latent, num_steps=1) state = decoder.request_state("r0") keys = [f"decoder_history_{idx}" for idx in range(9)] assert set(state.tensors) == set(keys) addresses = [state[key].data_ptr() for key in keys] - _decode(decoder, latent * 2, graph_walk=ROLLOUT_WALK) + _decode(decoder, graph_walk=ROLLOUT_WALK, num_steps=8) assert [state[key].data_ptr() for key in keys] == addresses assert all(state[key].dtype == torch.bfloat16 for key in keys) + decoder.cleanup_request("r0") -def test_decoder_histories_are_isolated_interleaved_and_cleaned_up(decoder, ae_config): +def test_fused_decode_histories_are_isolated_interleaved_and_cleaned_up(decoder, ae_config): latent = torch.full( - (1, 1, ae_config.channels, *ae_config.latent_shape[1:]), dtype=torch.bfloat16 - , fill_value=0.125 + (1, 1, ae_config.channels, *ae_config.latent_shape[1:]), 0.125, dtype=torch.bfloat16, ) - first = _decode(decoder, latent, request_id="a", graph_walk=PRIME_WALK) - second = _decode(decoder, latent, request_id="b", graph_walk=PRIME_WALK) + first = _decode(decoder, request_id="a", graph_walk=PRIME_WALK, seed_latent=latent, num_steps=1) + second = _decode(decoder, request_id="b", graph_walk=PRIME_WALK, seed_latent=latent, num_steps=1) assert torch.equal(first["video_output"][0], second["video_output"][0]) before_b = { key: value.clone() for key, value in decoder.request_state("b").tensors.items() } + # A generous num_steps: what is under test is address/isolation stability + # across interleaved requests, not the overshoot veto (covered on its own + # above), so nothing here should be able to trip it. for _ in range(20): - _decode(decoder, latent * 2, request_id="a") - _decode(decoder, latent * 3, request_id="b") + _decode(decoder, request_id="a", graph_walk=ROLLOUT_WALK, num_steps=100) + _decode(decoder, request_id="b", graph_walk=ROLLOUT_WALK, num_steps=100) assert all( not torch.equal(before_b[key], value) for key, value in decoder.request_state("b").tensors.items() @@ -1100,36 +1208,49 @@ def test_decoder_histories_are_isolated_interleaved_and_cleaned_up(decoder, ae_c decoder.cleanup_request("a") assert "a" not in decoder.request_states - restarted = _decode(decoder, latent, request_id="a", graph_walk=PRIME_WALK) + restarted = _decode(decoder, request_id="a", graph_walk=PRIME_WALK, seed_latent=latent, num_steps=1) assert torch.equal(restarted["video_output"][0], first["video_output"][0]) + decoder.cleanup_request("a") + decoder.cleanup_request("b") -def test_the_encoder_and_decoder_share_only_weights( +def test_the_encoder_and_the_fused_decode_share_only_weights( encoder, decoder, taehv_weights, ae_config ): - """Encoder prime is stateless; only decoder histories survive a request.""" + """Encoder prime is stateless; only the fused decode's histories survive a + request.""" image = encoder.prepare_inputs( PRIME_WALK, _fwd_info(), {"image_inputs": [_seed_clip(ae_config)]} ).tensor_inputs["image"] latent = encoder.forward(PRIME_WALK, _engine_inputs(), image)["latent"][0] - _decode(decoder, latent, graph_walk=PRIME_WALK) + _decode(decoder, graph_walk=PRIME_WALK, seed_latent=latent, num_steps=1) assert encoder.taehv is decoder.taehv is taehv_weights assert encoder.request_states == {} assert len(decoder.request_state("r0").tensors) == 9 + decoder.cleanup_request("r0") def test_ae_graphs_are_compiled_for_capture_but_remain_optional(encoder, decoder): encoder_configs = encoder.get_cuda_graph_configs(torch.device("cpu")) - decoder_configs = decoder.get_cuda_graph_configs(torch.device("cpu")) + fused_configs = decoder.get_cuda_graph_configs(torch.device("cpu")) assert [cfg.capture_graph_walk for cfg in encoder_configs] == [PRIME_WALK] - assert {cfg.capture_graph_walk for cfg in decoder_configs} == { + assert {cfg.capture_graph_walk for cfg in fused_configs} == { PRIME_WALK, ROLLOUT_WALK, } - for node, configs in ((encoder, encoder_configs), (decoder, decoder_configs)): - assert configs - assert all(cfg.compile for cfg in configs) - assert node.disable_torch_compile is True - assert node.disable_autocast is True + assert encoder_configs + assert all(cfg.compile for cfg in encoder_configs) + assert encoder.disable_torch_compile is True + assert encoder.disable_autocast is True + + # The fused dit+decode node compiles its own two reference-shaped regions + # (WaypointDiT.compile_regions, gated by config.compile_dit); letting the + # engine also compile this wrapper would fuse across that boundary (see + # test_both_dit_walks_are_optional_captures), so its captures stay + # uncompiled regardless. + assert fused_configs + assert all(cfg.compile is False for cfg in fused_configs) + assert decoder.disable_torch_compile is True + assert decoder.disable_autocast is True def test_ae_nodes_declare_no_capture_when_cuda_graph_is_disabled( @@ -1137,10 +1258,10 @@ def test_ae_nodes_declare_no_capture_when_cuda_graph_is_disabled( ): eager_config = dataclasses.replace(ae_config, cuda_graph=False) encoder = WaypointVaeEncoderSubmodule(taehv_weights, eager_config) - decoder = WaypointVaeDecoderSubmodule(taehv_weights, eager_config) + fused = WaypointDitSubmodule(_FakeDit(eager_config), taehv_weights, eager_config) assert encoder.get_cuda_graph_configs(torch.device("cpu")) == [] - assert decoder.get_cuda_graph_configs(torch.device("cpu")) == [] + assert fused.get_cuda_graph_configs(torch.device("cpu")) == [] def test_the_shell_builds_without_the_taehv_package(monkeypatch): @@ -1153,9 +1274,9 @@ def test_the_shell_builds_without_the_taehv_package(monkeypatch): unweighted = WaypointModel(skip_weight_loading=True) assert set(unweighted.get_graph_walk_graphs()) == {PRIME_WALK, ROLLOUT_WALK} - assert unweighted.nodes == [DIT_NODE, VAE_DECODER_NODE, VAE_ENCODER_NODE] + assert unweighted.nodes == [DIT_NODE, VAE_ENCODER_NODE] assert unweighted.get_node_resources() - for node in (VAE_ENCODER_NODE, VAE_DECODER_NODE): + for node in (VAE_ENCODER_NODE, DIT_NODE): assert unweighted.get_submodule(node) is None From d881b43e4771b7e2a27cc2ef5bf49ff6783437be Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Wed, 16 Sep 2026 22:55:52 +0000 Subject: [PATCH 10/29] cache CondHead modulation per sigma, removing 720 cond_proj GEMVs/step --- mstar/model/waypoint/components/dit.py | 38 ++++++++++++++++++++--- mstar/model/waypoint/components/layers.py | 32 +++++++++++++++++-- test/modular/test_waypoint_dit.py | 29 +++++++++++++++++ 3 files changed, 92 insertions(+), 7 deletions(-) diff --git a/mstar/model/waypoint/components/dit.py b/mstar/model/waypoint/components/dit.py index 30300bab4..571d5675a 100644 --- a/mstar/model/waypoint/components/dit.py +++ b/mstar/model/waypoint/components/dit.py @@ -91,14 +91,16 @@ def forward( v1: Tensor | None, *, commit: bool, + cond_idx: int, ) -> tuple[Tensor, Tensor]: """``x`` ``[B, N*T, D]``, ``cond``/``ctrl_emb`` ``[B, N, D]`` (per frame) -> ``(x, v1)``. ``v1`` is layer 0's pre-lerp V, threaded down the stack. + ``cond_idx`` is the ``scheduler_sigmas`` slot, for the cond_head cache. Only ``f_pos`` reaches this far -- the RoPE angles are built once at the root -- so the scalar ring clock is passed rather than the whole bundle. """ - s0, b0, g0, s1, b1, g1 = self.cond_head(cond) + s0, b0, g0, s1, b1, g1 = self.cond_head(cond, cond_idx) residual = x x = ada_rmsnorm(x, s0, b0) @@ -236,8 +238,27 @@ def materialize_runtime_tables(self, device: torch.device | str) -> "WaypointDiT self.rope_angles.materialize(device) self.denoise_step_emb.materialize(device) self._sigma_schedule(device, self.dtype) + self._materialize_cond_cache(device) return self + def _materialize_cond_cache(self, device: torch.device) -> None: + """Fold every block's cond_head GEMMs into a per-sigma gather. + + The six modulation tensors depend only on the sigma-indexed cond, and + sigma is one of ``scheduler_sigmas``. Each cond is built at M=1, exactly + as ``forward`` receives it, so the cached rows are bit-identical to the + live projection they replace (bf16 GEMV in, bf16 gather out). Runs after + weight load, before ``compile_regions``, on the same footing as the + conditioner LUT. + """ + sigmas = self._sigma_schedule(device, self.dtype) + with torch.no_grad(): + conds = torch.cat( + [self.denoise_step_emb(s.view(1, 1)) for s in sigmas], dim=0 + ) # [S, 1, D], one M=1 embedding per scheduled sigma + for block in self.blocks: + block.cond_head.build_cache(conds) + # ---- Positions --------------------------------------------------------- def _pos_ids(self, frame_pos: Tensor) -> WaypointPosIds: @@ -266,6 +287,7 @@ def forward( button: Tensor, scroll: Tensor, commit: bool, + cond_idx: int, ) -> Tensor: """One pass over one latent frame; returns the rectified-flow velocity. @@ -275,7 +297,9 @@ def forward( ``commit`` says whether this pass keeps its K/V: False for the four denoise passes, True for the fifth. An argument rather than resource - state -- all five passes sit inside one engine step. + state -- all five passes sit inside one engine step. ``cond_idx`` is + this pass's ``scheduler_sigmas`` slot; it selects the cached modulation + row and must match ``sigma``. """ B, N, C, H, W = x.shape ph, pw = self.patch @@ -308,7 +332,8 @@ def forward( v1 = None # layer 0's pre-lerp V, threaded through all 24 blocks for block in self.blocks: h, v1 = block( - h, pos_ids.f_pos, rope_angles, cond, ctrl_emb, v1, commit=commit + h, pos_ids.f_pos, rope_angles, cond, ctrl_emb, v1, + commit=commit, cond_idx=cond_idx, ) # silu sits BETWEEN the adaLN norm and the unpatchify projection @@ -359,7 +384,9 @@ def _denoise_pass( # 5 sigmas, 4 diffs: the trailing 0.0 exists only to produce the last # step size. Sliced, not zipped ragged -- dynamo rejects a ragged zip # under fullgraph. - for step_sigma, step_dsigma in zip(sigmas[:-1], sigmas.diff(), strict=True): + for cond_idx, (step_sigma, step_dsigma) in enumerate( + zip(sigmas[:-1], sigmas.diff(), strict=True) + ): v = self( x, sigma.fill_(step_sigma), @@ -368,6 +395,7 @@ def _denoise_pass( button=button, scroll=scroll, commit=False, + cond_idx=cond_idx, ) # fp32 accumulate, back to the latent dtype: the add in bf16 loses # the small late steps. @@ -395,6 +423,8 @@ def _cache_pass( button=button, scroll=scroll, commit=True, + # sigma=0 is the trailing schedule entry, the committing pass's slot. + cond_idx=len(self.config.scheduler_sigmas) - 1, ) def generate_frame( diff --git a/mstar/model/waypoint/components/layers.py b/mstar/model/waypoint/components/layers.py index 5eb1c16c0..8a32bcfdb 100644 --- a/mstar/model/waypoint/components/layers.py +++ b/mstar/model/waypoint/components/layers.py @@ -333,9 +333,35 @@ def __init__(self, config: WaypointConfig): nn.Linear(config.d_model, config.d_model, bias=False) for _ in range(self.n_cond) ) - def forward(self, cond: torch.Tensor) -> tuple[torch.Tensor, ...]: - """``cond`` ``[B, N, D]`` -> six ``[B, N, D]`` tensors, in block order - ``(s0, b0, g0, s1, b1, g1)``.""" + def _project(self, cond: torch.Tensor) -> tuple[torch.Tensor, ...]: + """The live head: ``cond`` ``[B, N, D]`` -> six ``[B, N, D]`` tensors, in + block order ``(s0, b0, g0, s1, b1, g1)``.""" cond = cond + self.bias_in if self.bias_in is not None else cond h = F.silu(cond) return tuple(p(h) for p in self.cond_proj) + + def build_cache(self, conds: torch.Tensor) -> None: + """Precompute the six modulation tensors for every scheduled sigma. + + ``conds`` is ``[S, 1, D]`` -- one embedding per ``scheduler_sigmas`` + entry, each built at M=1 exactly as ``forward`` receives it, so a cached + row is bit-identical to the live ``_project`` it replaces. Switches + ``forward`` to a gather; idempotent enough to rebuild. + """ + with torch.no_grad(): + rows = [ + torch.stack(self._project(conds[i : i + 1]), dim=0) + for i in range(conds.size(0)) + ] + self._cache = torch.stack(rows, dim=0) + + def forward(self, cond: torch.Tensor, cond_idx: int) -> tuple[torch.Tensor, ...]: + """``cond`` ``[B, N, D]``, ``cond_idx`` the ``scheduler_sigmas`` slot -> + six ``[B, N, D]`` tensors ``(s0, b0, g0, s1, b1, g1)``. + + After ``build_cache`` the six come from the cache row ``cond_idx`` and + ``cond`` is unused + """ + if self._cache is not None: + return tuple(self._cache[cond_idx].unbind(0)) + return self._project(cond) diff --git a/test/modular/test_waypoint_dit.py b/test/modular/test_waypoint_dit.py index 9f3903419..8a0c2a8af 100644 --- a/test/modular/test_waypoint_dit.py +++ b/test/modular/test_waypoint_dit.py @@ -455,6 +455,35 @@ def test_generate_frame_is_four_frozen_denoise_passes_then_one_commit(): assert (kv.resets, kv.states) == (0, []) +def test_cond_head_cache_is_bit_exact_and_replaces_the_live_projection(): + """``materialize_runtime_tables`` folds every block's six cond_proj GEMMs + into a per-sigma gather. The cache is a pure precompute of the same M=1 + projection ``forward`` runs, so a cached frame must match a live one + bit-for-bit -- two seed-identical DiTs over fresh (empty) rings, one + materialized and one not, must return the same latent.""" + config = reduced_config() + noise, mouse, button, scroll = frame_inputs(config) + fp = torch.tensor(0, dtype=torch.int64) + + live, _ = bound_dit(config, seed=0) + cached, _ = bound_dit(config, seed=0) + cached.materialize_runtime_tables("cpu") + + assert all(b.cond_head._cache is None for b in live.blocks), "live path must not cache" + n_cond = cached.blocks[0].cond_head.n_cond + for block in cached.blocks: + assert block.cond_head._cache is not None, "materialize must build every block's cache" + assert tuple(block.cond_head._cache.shape) == ( + len(config.scheduler_sigmas), n_cond, 1, 1, config.d_model, + ) + + with torch.no_grad(): + x_live = live.generate_frame(noise, fp, mouse=mouse, button=button, scroll=scroll) + x_cached = cached.generate_frame(noise, fp, mouse=mouse, button=button, scroll=scroll) + + assert torch.equal(x_live, x_cached), "cond_head cache changed the frame; it must be bit-exact" + + def test_the_ring_only_moves_on_the_committing_pass(): """The behavioural half of the same invariant, measured on the ring itself.""" config = reduced_config() From 0eab4daa7fb3f9892ffbd59f734e74576f069359 Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Wed, 16 Sep 2026 23:27:24 +0000 Subject: [PATCH 11/29] draw frame noise on device, dropping the prepare_inputs H2D sync. _frame_noise drew fp32 on a CPU generator and copied to device. That pageable H2D was a blocking copy --- mstar/model/waypoint/components/layers.py | 3 +++ mstar/model/waypoint/submodules.py | 23 ++++++++++++++--------- test/waypoint/record_oracle.py | 11 +++++++---- 3 files changed, 24 insertions(+), 13 deletions(-) diff --git a/mstar/model/waypoint/components/layers.py b/mstar/model/waypoint/components/layers.py index 8a32bcfdb..ee7a95c44 100644 --- a/mstar/model/waypoint/components/layers.py +++ b/mstar/model/waypoint/components/layers.py @@ -332,6 +332,9 @@ def __init__(self, config: WaypointConfig): self.cond_proj = nn.ModuleList( nn.Linear(config.d_model, config.d_model, bias=False) for _ in range(self.n_cond) ) + # ``[S, 6, 1, 1, D]`` after ``build_cache``; None keeps ``forward`` on + # the live projection until the runtime tables are materialized. + self._cache: torch.Tensor | None = None def _project(self, cond: torch.Tensor) -> tuple[torch.Tensor, ...]: """The live head: ``cond`` ``[B, N, D]`` -> six ``[B, N, D]`` tensors, in diff --git a/mstar/model/waypoint/submodules.py b/mstar/model/waypoint/submodules.py index 3cbfe5842..fa7b22a4c 100644 --- a/mstar/model/waypoint/submodules.py +++ b/mstar/model/waypoint/submodules.py @@ -295,19 +295,24 @@ def _frame_noise( ) -> torch.Tensor: """``[1, 1, C, H, W]`` of fresh noise for this frame. - Drawn fp32 on a CPU generator and cast, rather than bf16 straight onto - the device: a CPU draw is reproducible across devices, which is what - makes "same seed, same frame, same tensor" a testable claim. The - reference draws bf16 on device and unseeded, so there is no - bit-exactness here to preserve — only the distribution. + Drawn straight onto the device. Determinism is now per-GPU: + a CUDA generator reproduces run-to-run on the same arch + torch build, + not against a CPU draw or another arch. Runs in ``prepare_inputs``, outside any + captured region, so this is a normal stream-ordered kernel launch. """ - generator = torch.Generator(device="cpu").manual_seed( + shape = (1, 1, *self.config.latent_shape) + if device.type == "meta": + # Shape-only builds (meta-device shell tests) have no RNG to seed. + return torch.empty(shape, device=device, dtype=dtype) + generator = torch.Generator(device=device).manual_seed( _frame_seed(request_seed, frame_pos) ) - noise = torch.randn( - (1, 1, *self.config.latent_shape), generator=generator, dtype=torch.float32 + return torch.randn( + shape, + generator=generator, + device=device, + dtype=dtype, ) - return noise.to(device=device, dtype=dtype) def _controller_slice( self, diff --git a/test/waypoint/record_oracle.py b/test/waypoint/record_oracle.py index 99b166cc0..e06cfd12e 100644 --- a/test/waypoint/record_oracle.py +++ b/test/waypoint/record_oracle.py @@ -66,10 +66,13 @@ Noise is an input, not model behaviour, and the reference draws it unseeded (``torch.randn(..., device=cuda, dtype=bf16)``), which no oracle can reproduce. -This draws fp32 from a seeded CPU generator and casts, matching how the port -draws it (``mstar/model/waypoint/submodules.py::_frame_noise``), saves both -tensors, and records the substitution. Seeding also makes the run re-recordable, -which is what lets ``--ring-snapshot-frames`` be narrowed by default. +The port now draws the same way — bf16 straight onto the device, but from a +seeded per-frame ``Generator`` (``mstar/model/waypoint/submodules.py::_frame_noise``). +This recorder instead draws fp32 from a seeded CPU generator, casts, and injects +that tensor into both sides, so the substitution stays device-independent and +re-recordable regardless of how the port draws — which is what lets +``--ring-snapshot-frames`` be narrowed by default. It saves both tensors and +records the substitution. Usage: From 4ff18e4b71e122b22f998ad7fc606a0c9fa3e343 Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Thu, 17 Sep 2026 22:03:29 +0000 Subject: [PATCH 12/29] attn: default flex backend to FLASH (FA3-class sm90 CuTe kernel), 1.79x/1.74x faster per call at 720p/360p The Triton flex kernel runs one 4-warp CTA per SM (~27% of tensor peak on H100). The sm90 kernel from the flash-attn-4 CuTe package runs 3 warpgroups per CTA with TMA loads and softmax/GEMM overlap across warpgroups, so the same ring-masked attention gets far more parallelism per SM. Ring-full median per call: 720p 129.4 -> 72.3 us (12.5 -> 7.0 ms/step, 2.62 -> 3.10x realtime), 360p 35.1 -> 20.2 us (3.46 -> 2.03 ms/step, 4.19 -> 4.99x). MSTAR_FLEX_BACKEND=TRITON restores the previous kernel bit-for-bit. flash-attn-4 is installed separately (docs/installation.rst). flashinfer FA3 paged prefill was only 1.12x at batch 1; revisit it once batch size > 1 is in play. --- docs/installation.rst | 36 ++++++++++++++++--- mstar/engine/resources/attn/flex.py | 54 ++++++++++++++++++++++++++--- pyproject.toml | 4 +++ 3 files changed, 85 insertions(+), 9 deletions(-) diff --git a/docs/installation.rst b/docs/installation.rst index 58751a433..5fe60289d 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -98,7 +98,9 @@ Model families and some output formats need extra packages, exposed as pip *extr - Index-hosted Waypoint dependencies: ``huggingface-hub``, ``safetensors``, and ``tensordict`` for reference validation. The pinned TAEHV implementation must be installed separately as shown below; keeping its direct URL out of - package metadata allows ``m-star`` to be published on PyPI. + package metadata allows ``m-star`` to be published on PyPI. **Also needs** + ``flash-attn-4``, which is installed separately — + see `flash-attn-4 (Waypoint, FA3 sm90 kernel)`_. * - ``.[audio]`` - ``soundfile`` — only needed to return **non-WAV** audio containers (mp3/flac/…) from the OpenAI/SDK audio surfaces. WAV/PCM output works without it. @@ -164,13 +166,15 @@ The GPU model families depend on: autoregressive backbones (every model with a ``KV_CACHE`` node runs attention through it). - **flash-attn** — used by Qwen3-Omni. **Not installed by any extra**; install it separately (see `flash-attn (Qwen3-Omni)`_). +- **flash-attn-4** — Waypoint's flex-attention ``FLASH`` backend. **Not installed by any + extra**; install it separately (see `flash-attn-4 (Waypoint, FA3 sm90 kernel)`_). - **mooncake-transfer-engine** — RDMA tensor transport for multi-GPU, disaggregated deployments. Single-node deployments can use shared-memory (``SHM``) or ``TCP`` transport instead (see :doc:`serving`). -Apart from ``flash-attn``, these are installed by the extras above. Your installed ``torch`` -must match your system CUDA toolkit — ``--torch-backend=auto`` handles that for you (next -section). +Apart from ``flash-attn`` and ``flash-attn-4``, these are installed by the extras above. Your +installed ``torch`` must match your system CUDA toolkit — ``--torch-backend=auto`` handles +that for you (next section). flash-attn (Qwen3-Omni) ----------------------- @@ -253,6 +257,30 @@ Three things to get right: FLASH_ATTN_CUDA_ARCHS="90" uv pip install flash-attn==2.8.3.post1 --no-build-isolation python -c "import flash_attn; print(flash_attn.__version__)" +flash-attn-4 (Waypoint, FA3 sm90 kernel) +---------------------------------------- + +Waypoint's DiT attention runs torch flex-attention with the ``FLASH`` backend by +default. That backend needs the **flash-attn-4** package, which provides +``flash_attn.cute`` — the CuTe DSL rewrite of flash-attn; on H100 it runs the +FA3-style sm90 kernel with TMA and warpgroup specialisation. It is **not on +PyPI** as of 2026-09-17, and it is **not** pulled in by any extra. + +Install it from the upstream repo's ``flash_attn/cute`` subdirectory: + +.. code-block:: bash + + git clone https://github.com/Dao-AILab/flash-attention + uv pip install --torch-backend=auto ./flash-attention/flash_attn/cute + +This is pure Python plus ``nvidia-cutlass-dsl`` — there is no CUDA extension to +build. Its kernels are JIT-compiled on first use, which adds roughly a minute to +the first server startup. + +If ``flash-attn-4`` isn't installed, set ``MSTAR_FLEX_BACKEND=TRITON`` to fall +back to the previous Triton flex kernel. It is correct but slower — about 1.8x +per attention call at 720p. + Matching your CUDA toolkit -------------------------- diff --git a/mstar/engine/resources/attn/flex.py b/mstar/engine/resources/attn/flex.py index 3a96ad955..3d7f4f9ff 100644 --- a/mstar/engine/resources/attn/flex.py +++ b/mstar/engine/resources/attn/flex.py @@ -11,6 +11,8 @@ 128" a real constraint here rather than a convenience. """ +import os + import torch from torch import Tensor from torch.nn.attention.flex_attention import ( @@ -28,6 +30,20 @@ __all__ = ["FlexAttentionManager", "flex_attention_masked", "make_block_mask"] +# Backend switch, read once at import. "FLASH" is the default. Setting +# MSTAR_FLEX_BACKEND=TRITON is the rollback switch: it restores the previous +# Triton flex kernel bit-for-bit; see the comment block below for why FLASH +# needs a non-trivial mask_mod. FLASH requires the flash-attn-4 package +# (flash_attn.cute) -- see docs/installation.rst. Torch itself raises "CUTE +# flash attention library is not available" if it's missing, so there's no +# extra check here. +_ALLOWED_FLEX_BACKENDS = ("TRITON", "FLASH") +_FLEX_BACKEND = os.environ.get("MSTAR_FLEX_BACKEND", "FLASH") +if _FLEX_BACKEND not in _ALLOWED_FLEX_BACKENDS: + raise ValueError( + f"MSTAR_FLEX_BACKEND must be one of {_ALLOWED_FLEX_BACKENDS}, got {_FLEX_BACKEND!r}" + ) + # CORRECTNESS, not speed. Our BlockMask carries a NO-OP `mask_mod`: we pass # `mask_mod=None` to `from_kv_blocks` and it substitutes `noop_mask`, so @@ -45,9 +61,37 @@ # @torch.compile(fullgraph=True) -- compilation is load-bearing for the *result* # there too, not just the throughput. # -# So the compile is pinned here rather than left to the caller: correctness must -# not depend on whether someone set `WaypointConfig.compile_dit`. -flex_attention_masked = torch.compile(flex_attention, dynamic=False) +# So the compile is pinned here (below, after the backend selection) rather than +# left to the caller: correctness must not depend on whether someone set +# `WaypointConfig.compile_dit`. + + +# FLASH needs a non-trivial `mask_mod`, unlike the TRITON rollback path above. +# `from_kv_blocks(..., mask_mod=None)` substitutes `noop_mask`, +# whose traced graph is a shapeless `aten.full` -- `is_trivial_mask_graph` +# (torch/_inductor/kernel/flex/flex_flash_attention.py:205-216) treats exactly +# that graph as "no block mask" and sets `needs_block_mask=False` (line 394), +# so the FLASH template attends densely over the whole KV instead of the +# visible blocks -> wrong output. +def _flash_mask_mod(b, h, q_idx, kv_idx): + return kv_idx >= 0 + + +_MASK_MOD = _flash_mask_mod if _FLEX_BACKEND == "FLASH" else None +_FLASH_KERNEL_OPTIONS = {"BACKEND": "FLASH"} + + +def _flash_flex_attention(q, k, v, *, block_mask, enable_gqa): + return flex_attention( + q, k, v, block_mask=block_mask, enable_gqa=enable_gqa, + kernel_options=_FLASH_KERNEL_OPTIONS, + ) + + +flex_attention_masked = torch.compile( + _flash_flex_attention if _FLEX_BACKEND == "FLASH" else flex_attention, + dynamic=False, +) def make_block_mask(q_len: int, kv_len: int, written: Tensor) -> BlockMask: @@ -109,7 +153,7 @@ def make_block_mask(q_len: int, kv_len: int, written: Tensor) -> BlockMask: full_kv_num_blocks, full_kv_indices, BLOCK_SIZE=block_size, - mask_mod=None, + mask_mod=_MASK_MOD, seq_lengths=(q_len, kv_len), compute_q_blocks=False, ) @@ -133,7 +177,7 @@ def _empty_block_mask(q_len: int, kv_len: int, device: torch.device) -> BlockMas full_kv_num_blocks, full_kv_indices, BLOCK_SIZE=block_size, - mask_mod=None, + mask_mod=_MASK_MOD, seq_lengths=(q_len, kv_len), compute_q_blocks=False, ) diff --git a/pyproject.toml b/pyproject.toml index 4052fe251..37d594ffb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -196,6 +196,8 @@ waypoint = [ # so the parity tests import it. Serving does not — components/dit.py uses a # NamedTuple to keep tensordict out of the serving path. "tensordict==0.10.0", + # flash-attn-4 (flash_attn.cute, the default flex attention backend) is not + # on PyPI; install it separately, see docs/installation.rst. ] vjepa2_ac = [ @@ -250,6 +252,8 @@ all = [ "tensordict==0.10.0", # flash-attn is intentionally omitted (Qwen3-Omni needs it) — install it # separately, see docs/installation.rst. + # flash-attn-4 (waypoint's default flex attention backend) is also installed + # separately, see docs/installation.rst. "jiwer", ] From 20965ec1ae744a75602fe690300b8ef4051cd87b Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Sat, 19 Sep 2026 19:25:51 +0000 Subject: [PATCH 13/29] waypoint: batch concurrent rollout streams per DiT step --- configs/waypoint.yaml | 2 + mstar/engine/cuda_graph_runner.py | 35 +- mstar/engine/resources/attn/flex.py | 60 ++- mstar/engine/resources/kv/config.py | 12 + mstar/engine/resources/kv/ring/cache.py | 48 +- mstar/engine/resources/kv/ring/manager.py | 100 +++-- mstar/model/waypoint/components/attention.py | 2 +- mstar/model/waypoint/components/dit.py | 27 +- mstar/model/waypoint/components/taehv.py | 7 +- mstar/model/waypoint/config.py | 6 + mstar/model/waypoint/submodules.py | 135 ++++-- mstar/model/waypoint/waypoint_model.py | 28 +- test/modular/test_batch_sweep_summary.py | 166 +++++++ test/modular/test_cuda_graph_capture.py | 70 +++ test/modular/test_flex_attention_resource.py | 159 ++++++- test/modular/test_ring_kv_resource.py | 147 +++++-- test/modular/test_video_frame_protocol.py | 55 +++ test/modular/test_waypoint_components.py | 76 +++- test/modular/test_waypoint_dit.py | 20 +- test/modular/test_waypoint_gpu.py | 130 +++++- test/modular/test_waypoint_shell.py | 151 ++++++- .../test_waypoint_streaming_benchmark.py | 167 +++++++ test/waypoint/benchmark_streaming.py | 416 +++++++++++++++++- test/waypoint/serve_rollout.py | 246 +++++++++-- 24 files changed, 1977 insertions(+), 288 deletions(-) create mode 100644 test/modular/test_batch_sweep_summary.py diff --git a/configs/waypoint.yaml b/configs/waypoint.yaml index cf9b9fd60..0622b7e84 100644 --- a/configs/waypoint.yaml +++ b/configs/waypoint.yaml @@ -11,6 +11,8 @@ model_kwargs: compile_dit: true cuda_graph: true full_global_ring: false + # Rows per rollout step; must be <= resources.kv.num_worlds below. + step_batch_size: 1 # The world state is a ring, not a paged KV cache, so nothing here is sized in # sequence positions; max_seq_len only satisfies the conductor's config check diff --git a/mstar/engine/cuda_graph_runner.py b/mstar/engine/cuda_graph_runner.py index a667b865e..9cff5d0a3 100644 --- a/mstar/engine/cuda_graph_runner.py +++ b/mstar/engine/cuda_graph_runner.py @@ -445,7 +445,8 @@ def prepare() -> dict[str, Any]: for key, value in list(static_inputs.items()): if isinstance(value, torch.Tensor): static_inputs[key] = self._intern_static_buffer( - spec.config_idx, key, value, seq_len=spec.num_tokens, + spec.config_idx, key, value, + seq_len=spec.num_tokens, batch_size=spec.bs, ) static_input_keys = tuple( key for key, value in static_inputs.items() @@ -523,12 +524,26 @@ def _dummy_engine_inputs( ) @staticmethod - def _seq_dim(value: torch.Tensor, seq_len: int) -> int: - """Index of the first dim whose size matches ``seq_len``, else 0. - - Used to bring the (bucket-varying) seq dim to the front for shared-buffer - interning: most inputs are seq-leading (returns 0), but mrope-style ids - carry seq in a later dim (e.g. ``[3, seq]`` → 1).""" + def _seq_dim(value: torch.Tensor, seq_len: int, batch_size: int | None = None) -> int: + """Index of the dim that varies with this bucket, else 0. + + Dim 0 is checked first against both accepted sizes — the flattened + token count (``seq_len``) and the row count (``batch_size``) — since + that is where every real per-bucket-varying Waypoint input already + lives (``preprocess`` concatenates rows on dim 0). Only a tensor with + something else on dim 0 falls through to the later-dim scan, which + exists for mrope-style ids that carry seq in a later dim (e.g. + ``[3, seq]`` → 1). + + Checking dim 0 first, rather than folding ``batch_size`` into the same + scan, matters: a fixed-width tensor unrelated to batching can collide + with ``seq_len`` on a later dim (Waypoint's ``button`` is ``[B, 1, + 256]``, and 360p's bs=2 bucket also has 256 tokens) — that used to get + hoisted before dim 0's real, smaller ``batch_size`` match was ever + checked. + """ + if value.shape and value.shape[0] in (seq_len, batch_size): + return 0 for dim, size in enumerate(value.shape): if size == seq_len: return dim @@ -536,7 +551,7 @@ def _seq_dim(value: torch.Tensor, seq_len: int) -> int: def _intern_static_buffer( self, config_idx: int, key: str, value: torch.Tensor, - seq_len: int | None = None, + seq_len: int | None = None, batch_size: int | None = None, ) -> torch.Tensor: """Return a slice view into the shared buffer for (config_idx, key). @@ -544,6 +559,8 @@ def _intern_static_buffer( largest-first) bucket's shape; smaller buckets reslice its leading dim. If ``seq_len`` is given, the seq dim is moved to the front for storage and back on return, so the captured forward sees the original layout. + ``batch_size`` — the bucket's row count — disambiguates dim 0 from a + coincidental same-size match elsewhere; see `_seq_dim`. """ buf_key = (config_idx, key) if seq_len is None: @@ -554,7 +571,7 @@ def _intern_static_buffer( # — and the buffer is shared, so its layout cannot vary anyway seq_dim = self._static_buffer_seq_dims[buf_key] else: - seq_dim = self._seq_dim(value, seq_len) + seq_dim = self._seq_dim(value, seq_len, batch_size) self._static_buffer_seq_dims[buf_key] = seq_dim stored = value.movedim(seq_dim, 0) if seq_dim != 0 else value shared = self._shared_static_buffers.get(buf_key) diff --git a/mstar/engine/resources/attn/flex.py b/mstar/engine/resources/attn/flex.py index 3d7f4f9ff..755acbb48 100644 --- a/mstar/engine/resources/attn/flex.py +++ b/mstar/engine/resources/attn/flex.py @@ -82,6 +82,12 @@ def _flash_mask_mod(b, h, q_idx, kv_idx): def _flash_flex_attention(q, k, v, *, block_mask, enable_gqa): + # Batching hands this a ring k/v at batch 1 alongside q (and the + # BlockMask) at batch B. TRITON broadcasts Bkv=1 over Bq natively + # but FLASH's flash_attn.cute path does not. + if k.size(0) == 1 and q.size(0) > 1: + k = k.expand(q.size(0), *k.shape[1:]) + v = v.expand(q.size(0), *v.shape[1:]) return flex_attention( q, k, v, block_mask=block_mask, enable_gqa=enable_gqa, kernel_options=_FLASH_KERNEL_OPTIONS, @@ -159,15 +165,15 @@ def make_block_mask(q_len: int, kv_len: int, written: Tensor) -> BlockMask: ) -def _empty_block_mask(q_len: int, kv_len: int, device: torch.device) -> BlockMask: +def _empty_block_mask(q_len: int, kv_len: int, device: torch.device, batch: int) -> BlockMask: """Allocate a fixed-address mask whose visible prefix is staged in plan.""" block_size = _DEFAULT_SPARSE_BLOCK_SIZE q_blocks, kv_blocks = q_len // block_size, kv_len // block_size full_kv_num_blocks = torch.zeros( - (1, 1, q_blocks), dtype=torch.int32, device=device + (batch, 1, q_blocks), dtype=torch.int32, device=device ) full_kv_indices = torch.zeros( - (1, 1, q_blocks, kv_blocks), dtype=torch.int32, device=device + (batch, 1, q_blocks, kv_blocks), dtype=torch.int32, device=device ) kv_num_blocks = torch.zeros_like(full_kv_num_blocks) kv_indices = torch.zeros_like(full_kv_indices) @@ -211,7 +217,7 @@ def __init__( self._device = device self._dtype = dtype self._kv_config = kv_config - self._planned_masks: dict[tuple[int, tuple[int, int, int]], BlockMask] = {} + self._planned_masks: dict[tuple[int, tuple[int, int, int], int], BlockMask] = {} self._visibility_tables: dict[ tuple[int, int, int], tuple[Tensor, Tensor, int] ] = {} @@ -253,23 +259,28 @@ def _geometry(layer) -> tuple[int, int, int]: return (layer.ring_frames, layer.ring_buckets, layer.pinned_dilation) def _mask_for( - self, slot: int, geometry: tuple[int, int, int], *, create: bool = False, + self, + slot: int, + geometry: tuple[int, int, int], + batch: int, + *, + create: bool = False, ) -> BlockMask: - key = (slot, geometry) + key = (slot, geometry, batch) mask = self._planned_masks.get(key) if mask is None and create: ring_frames, _, _ = geometry capacity = (ring_frames + 1) * self._kv_config.tokens_per_frame - capacity *= self._kv_config.num_worlds + capacity *= self._kv_config.total_worlds mask = _empty_block_mask( - self._kv_config.tokens_per_frame, capacity, self._device + self._kv_config.tokens_per_frame, capacity, self._device, batch ) self._planned_masks[key] = mask self._visibility_table_for(geometry) if mask is None: raise RuntimeError( - f"FlexAttention mask for slot={slot}, geometry={geometry} was not " - "allocated before CUDA graph capture" + f"FlexAttention mask for slot={slot}, geometry={geometry}, " + f"batch={batch} was not allocated before CUDA graph capture" ) return mask @@ -294,11 +305,11 @@ def _visibility_table_for( self._kv_config.tokens_per_frame // _DEFAULT_SPARSE_BLOCK_SIZE ) total_blocks = ( - (ring_frames + 1) * blocks_per_frame * self._kv_config.num_worlds + (ring_frames + 1) * blocks_per_frame * self._kv_config.total_worlds ) counts: list[list[int]] = [] rows: list[list[list[int]]] = [] - for world_idx in range(self._kv_config.num_worlds): + for world_idx in range(self._kv_config.total_worlds): world_counts = [] world_rows = [] for frame_pos in range(2 * period): @@ -323,9 +334,9 @@ def build_cuda_graph_buffers( ) -> None: del max_bs, max_seq_len geometries = {self._geometry(layer) for layer in self._kv_config.layers} - for slot in {spec.slot for spec in slots}: + for slot, bs in {(spec.slot, spec.bs) for spec in slots}: for geometry in geometries: - self._mask_for(slot, geometry, create=True) + self._mask_for(slot, geometry, bs, create=True) def _visible_blocks_for( self, @@ -362,13 +373,10 @@ def _stage( plan: RingPlan, ) -> None: counts, indices, period = self._visibility_table_for(geometry) - phase = ( - plan.frame_pos - if plan.frame_pos < period - else period + plan.frame_pos % period - ) - mask.full_kv_num_blocks.copy_(counts[plan.world_idx, phase]) - mask.full_kv_indices.copy_(indices[plan.world_idx, phase]) + for b, (w, f) in enumerate(zip(plan.world_idx, plan.frame_pos)): + phase = f if f < period else period + f % period + mask.full_kv_num_blocks[b].copy_(counts[w, phase]) + mask.full_kv_indices[b].copy_(indices[w, phase]) def plan(self, step: AttentionStep, ctx: StepContext) -> None: """Stage one local/global visibility mask for this frame and slot.""" @@ -386,9 +394,14 @@ def plan(self, step: AttentionStep, ctx: StepContext) -> None: f"got {type(ring_plan).__name__}" ) self._active_slot = ctx.slot + # The padded width, not the real row count: the graph baked the address + # of the mask captured at the bucket size, and a replay padded below that + # bucket must stage into that same buffer. `ring_plan` already carries one + # (world, frame) per padded row, padding rows included. + B = len(ctx.padded_request_ids) geometries = {self._geometry(layer) for layer in self._kv_config.layers} for geometry in geometries: - mask = self._mask_for(ctx.slot, geometry, create=not ctx.capture) + mask = self._mask_for(ctx.slot, geometry, B, create=not ctx.capture) self._stage(mask, geometry, ring_plan) def attend( @@ -411,10 +424,11 @@ def attend( ``[B, H_q, T, D]``. """ if self._active_slot is None or layer_idx is None: + assert q.size(0) == 1, "the make_block_mask fallback only supports B == 1" block_mask = make_block_mask(q.size(-2), k.size(-2), visible) else: geometry = self._geometry(self._kv_config.layers[layer_idx]) - block_mask = self._mask_for(self._active_slot, geometry) + block_mask = self._mask_for(self._active_slot, geometry, q.size(0)) # `flex_attention_masked`, never bare `flex_attention`: with a no-op # `mask_mod` the eager path ignores the block mask entirely and attends # to unwritten ring slots. See the note at its definition. diff --git a/mstar/engine/resources/kv/config.py b/mstar/engine/resources/kv/config.py index 7ef8d3ed0..39f6469e6 100644 --- a/mstar/engine/resources/kv/config.py +++ b/mstar/engine/resources/kv/config.py @@ -123,6 +123,18 @@ class RingKVConfig(KVConfig): # How many worlds are resident at once. NOT a batch. num_worlds: int = 1 + @property + def total_worlds(self) -> int: + """Resident worlds plus one shared scratch world for padding rows. + + A replay padded to its capture bucket parks the dummy tail on this world + (see ``RingKVManager.plan``); it is never handed to a request, so + resident capacity stays ``num_worlds`` and the deployment knob keeps its + meaning. The ring buffer and the flex mask both size on this count so the + padding world is a real, addressable span. + """ + return self.num_worlds + 1 + def __post_init__(self): super().__post_init__() if len(self.layers) != self.num_layers: diff --git a/mstar/engine/resources/kv/ring/cache.py b/mstar/engine/resources/kv/ring/cache.py index 5737c554e..ccba8d201 100644 --- a/mstar/engine/resources/kv/ring/cache.py +++ b/mstar/engine/resources/kv/ring/cache.py @@ -144,8 +144,8 @@ def upsert( *, build_visibility: bool = True, ) -> tuple[Tensor, Tensor, Tensor]: - """``kv`` is ``[2, 1, H_kv, tokens_per_frame, D]`` for exactly one frame - of one world; + """``kv`` is ``[2, 1, H_kv, B*tokens_per_frame, D]``, one frame for + each of ``B`` worlds; ``commit`` writes the frame into its ring slot; without it the frame lands in that world's scratch tail only, visible to itself and to nothing later. @@ -155,36 +155,48 @@ def upsert( tokens = self.tokens_per_frame if not torch.compiler.is_compiling(): + # Shape-checked before indexing into it: a malformed frame_pos + # (e.g. the old [] scalar) must raise this message, not an + # IndexError out of `frame_pos.shape[0]` below. torch._check( - kv.size(3) == tokens, - lambda: f"ring cache expects exactly one frame per upsert; got {kv.size(3)} tokens", + frame_pos.ndim == 1 and frame_pos.dtype == torch.int64, + lambda: f"frame_pos must be a [B] int64 tensor; got {tuple(frame_pos.shape)} " + f"{frame_pos.dtype}", ) + B = frame_pos.shape[0] + + if not torch.compiler.is_compiling(): torch._check( - frame_pos.ndim == 0 and frame_pos.dtype == torch.int64, - lambda: f"frame_pos must be a [] int64 tensor; got {tuple(frame_pos.shape)} " - f"{frame_pos.dtype}", + kv.size(3) == B * tokens, + lambda: f"ring cache expects exactly one frame per world; got " + f"{kv.size(3)} tokens for B={B}", ) torch._check( - tuple(world_idx.shape) == (1,) and world_idx.dtype == torch.int64, - lambda: f"world_idx must be a [1] int64 tensor; got " - f"{tuple(world_idx.shape)} {world_idx.dtype}", + world_idx.shape == frame_pos.shape and world_idx.dtype == torch.int64, + lambda: f"world_idx must be a {list(frame_pos.shape)} int64 tensor matching " + f"frame_pos; got {tuple(world_idx.shape)} {world_idx.dtype}", ) - world_base = world_idx * self.capacity + world_base = world_idx * self.capacity # [B] bucket = (frame_pos + (self.pinned_dilation - 1)) // self.pinned_dilation - slot = bucket % self.ring_buckets - ring_idx = self.frame_offsets + slot * tokens + world_base - current_idx = self._current_base + world_base - ring_scatter(self.kv, self.written, current_idx, kv, False) + slot = bucket % self.ring_buckets # [B] + ring_idx = self.frame_offsets[None] + (slot * tokens + world_base)[:, None] # [B, T] + current_idx = self._current_base[None] + world_base[:, None] # [B, T] + ring_scatter(self.kv, self.written, current_idx.flatten(), kv, False) - write_step = frame_pos.remainder(self.pinned_dilation) == 0 + write_step = frame_pos.remainder(self.pinned_dilation) == 0 # [B] mask_written = self._mask_written if build_visibility: + torch._check( + B == 1, + lambda: "ring cache's fallback visibility row only supports B == 1; " + "the engine path passes build_visibility=False.", + ) mask_written.copy_(self.written) mask_written &= self._world_of_slot == world_idx - mask_written[ring_idx] = mask_written[ring_idx] & ~write_step + mask_written[ring_idx[0]] = mask_written[ring_idx[0]] & ~write_step if commit: - dst = torch.where(write_step, ring_idx, current_idx) + dst = torch.where(write_step[:, None], ring_idx, current_idx).flatten() ring_scatter(self.kv, self.written, dst, kv, True) k, v = self.kv.unbind(0) diff --git a/mstar/engine/resources/kv/ring/manager.py b/mstar/engine/resources/kv/ring/manager.py index cd7f84da2..6062e354d 100644 --- a/mstar/engine/resources/kv/ring/manager.py +++ b/mstar/engine/resources/kv/ring/manager.py @@ -28,11 +28,12 @@ class RingPlan(NamedTuple): - """Host facts downstream resources need to stage this fixed ring view.""" + """Host facts downstream resources need to stage this fixed ring view, one + entry per row of the step batch, in ``ctx.request_ids`` order.""" - request_id: str - world_idx: int - frame_pos: int + request_ids: tuple[str, ...] + world_idx: tuple[int, ...] + frame_pos: tuple[int, ...] class RingKVManager(AttentionResource): @@ -50,9 +51,12 @@ def __init__( self.device = torch.device(device) self.dtype = dtype + # ``total_worlds`` == ``num_worlds`` + 1: the extra world is the shared + # scratch that ``plan`` parks a replay's padding tail on. It is never in + # ``_free_worlds``, so no request is admitted to it. self.layers = [ LayerRingCache( - num_worlds=config.num_worlds, + num_worlds=config.total_worlds, n_kv_heads=config.num_kv_heads, ring_frames=layer.ring_frames, ring_buckets=layer.ring_buckets, @@ -67,9 +71,16 @@ def __init__( self._worlds: dict[str, int] = {} self._free_worlds: set[int] = set(range(config.num_worlds)) + # The one world past the resident pool; padding rows write here and it is + # never claimed, so a padding write never reaches a real world's history. + self._padding_world: int = config.num_worlds self._known_rids: set[str] = set() self._last_frames: dict[str, int] = {} - self._static_world_idx = torch.zeros(1, dtype=torch.int64, device=self.device) + # Sized to num_worlds, the largest B any step can carry (B_max <= + # num_worlds is enforced at config load). + self._static_world_idx = torch.zeros( + config.num_worlds, dtype=torch.int64, device=self.device + ) @classmethod def build(cls, spec: KVSpec, info: EngineResourceInfo) -> "RingKVManager": @@ -125,26 +136,31 @@ def upsert( commit: bool, build_visibility: bool = True, ) -> tuple[Tensor, Tensor, Tensor]: - """Write one frame's K/V for ``layer_idx`` and return what to attend to. + """Write one step's K/V for ``layer_idx`` and return what to attend to. - ``k``/``v`` are ``[1, H_kv, tokens_per_frame, D]``. ``k`` is already - RoPE'd and RMS-normed and ``v`` is post value-residual lerp: the cache - stores post-RoPE keys, so replayed history is never re-rotated. + ``k``/``v`` are ``[B, H_kv, tokens_per_frame, D]``, one frame per + resident world in the step batch. ``k`` is already RoPE'd and + RMS-normed and ``v`` is post value-residual lerp: the cache stores + post-RoPE keys, so replayed history is never re-rotated. Returns ``(k_all, v_all, visible)``, the first two spanning the whole buffer, every resident world and the third a ``[total_slots]`` bool row that is False everywhere outside the calling request's own world. ``frame_pos`` and ``commit`` are both arguments rather than resource - state, for the same reason. ``frame_pos`` is the ``[]`` int64 ring clock - -- not a slot id -- and alone determines the slot written and the + state, for the same reason. ``frame_pos`` is the ``[B]`` int64 ring + clock -- not a slot id -- and alone determines the slot written and the visibility row; """ # `layer_idx` is Python-level (it indexes a list of differently-shaped # rings), so indexing on it is graph-safe. - kv = torch.stack([k, v], dim=0) + B = k.size(0) + # [2, H_kv, B, T, D] contiguous, then folded into the ring's [2, 1, + # H_kv, B*T, D] layout -- the one copy this already needed. + kv = torch.stack([k.transpose(0, 1), v.transpose(0, 1)], dim=0) + kv = kv.view(2, 1, k.size(1), B * k.size(2), k.size(3)) return self.layers[layer_idx].upsert( - kv, frame_pos, commit, self._static_world_idx, + kv, frame_pos, commit, self._static_world_idx[:B], build_visibility=build_visibility, ) @@ -245,15 +261,13 @@ def admit(self, step: ResourceStep, ctx: StepContext) -> AdmitOutcome: # not be able to take a world from the real request in the same batch. rids = list(ctx.request_ids) - if len({*rids}) > 1: + if len(set(rids)) != len(rids): return AdmitOutcome( ok=False, ready=False, reason=AdmitRuntimeError( - f"ring KV {self.name!r} was handed a batch of {len(set(rids))} " - f"requests ({sorted(set(rids))}); one step advances one world, " - "so max_batch_size must be 1. This is a step-batch limit, not " - "a ring limit -- the ring holds " - f"{self.num_worlds} worlds and they take turns across steps." + f"ring KV {self.name!r} was handed a batch naming " + f"{sorted({rid for rid in rids if rids.count(rid) > 1})} more " + "than once; a step batches distinct worlds, one row per request." ), ) @@ -324,23 +338,37 @@ def admit(self, step: ResourceStep, ctx: StepContext) -> AdmitOutcome: return ADMIT_OK def plan(self, step: ResourceStep, ctx: StepContext) -> RingPlan: - """Stage this step's world index. Its ring addresses stay in the graph.""" - - rids = {*ctx.request_ids} - if len(rids) != 1: - raise ValueError( - f"ring KV {self.name!r} can stage one world index per step and " - f"this step names {sorted(rids)}. `admit` refuses a mixed batch " - "for the same reason; reaching here means it was bypassed." - ) - rid = rids.pop() - world_idx = self._require_world(rid, "plan") - # `fill_`, not a `copy_` from a fresh host tensor: same in-place write - # through the address the graph baked, without allocating a staging - # tensor 24 times a second. - self._static_world_idx.fill_(world_idx) + """Stage the padded batch's world indices, one per row. Ring addresses + stay in the graph. + + A replay pads the batch to its capture bucket with dummy rids that hold + no world. Those rows still write and attend (their output is dropped + downstream), so every one is parked on ``_padding_world`` -- the shared + scratch world outside the resident pool. The flex mask scopes each row to + its own world, so a padding row's read and its commit both stay on that + world and never reach a real request's history; no request is ever + admitted to it, so the tail it leaves behind is never read. + """ frames = self._step_frames(step) - return RingPlan(rid, world_idx, frames[rid]) + real_rids = tuple(ctx.request_ids) + padded_rids = tuple(ctx.padded_request_ids) + world_idx = [] + frame_pos = [] + for b, rid in enumerate(real_rids): + world = self._require_world(rid, "plan") + # `fill_`, not a `copy_` from a fresh host tensor: same in-place + # write through the address the graph baked, without allocating a + # staging tensor 24 times a second. + self._static_world_idx[b].fill_(world) + world_idx.append(world) + frame_pos.append(frames[rid]) + for b in range(len(real_rids), len(padded_rids)): + self._static_world_idx[b].fill_(self._padding_world) + world_idx.append(self._padding_world) + # Frame 0's visibility is scratch-only, so the padding row attends + # exactly one (garbage, dropped) block and never an unwritten slot. + frame_pos.append(0) + return RingPlan(padded_rids, tuple(world_idx), tuple(frame_pos)) def commit(self, step: ResourceStep, ctx: StepContext) -> None: """Record the frame each request just committed. Metadata only.""" diff --git a/mstar/model/waypoint/components/attention.py b/mstar/model/waypoint/components/attention.py index d70d175cc..f7f147044 100644 --- a/mstar/model/waypoint/components/attention.py +++ b/mstar/model/waypoint/components/attention.py @@ -93,7 +93,7 @@ def forward( ) -> tuple[Tensor, Tensor]: """``x`` ``[B, N*T, D]`` -> ``(out [B, N*T, D], v1 [B, H_kv, N*T, d_head])``. - ``frame_pos`` is the ``[]`` int64 ring clock; ``v1`` is ``None`` at + ``frame_pos`` is the ``[B]`` int64 ring clock; ``v1`` is ``None`` at layer 0 and layer 0's pre-lerp V thereafter. Both stay arguments rather than cursors the KV resource keeps: a cursor that drifts from the caller does not raise, it rewrites history. diff --git a/mstar/model/waypoint/components/dit.py b/mstar/model/waypoint/components/dit.py index 571d5675a..19579adb2 100644 --- a/mstar/model/waypoint/components/dit.py +++ b/mstar/model/waypoint/components/dit.py @@ -46,10 +46,10 @@ class WaypointPosIds(NamedTuple): - """The four position streams for one frame. + """The four position streams for one frame, per row of the step batch. - ``f_pos`` is the ``[]`` int64 ring clock (the KV resource's ``upsert`` takes - the scalar); ``t_pos`` is the ``[B, T]`` RoPE time coordinate + ``f_pos`` is the ``[B]`` int64 ring clock (the KV resource's ``upsert`` + takes it); ``t_pos`` is the ``[B, T]`` RoPE time coordinate ``f_pos * ts_mult``; ``y_pos``/``x_pos`` are ``[B, T]`` token-grid coordinates, ``row = i // width`` and ``col = i % width``. """ @@ -98,7 +98,7 @@ def forward( ``cond_idx`` is the ``scheduler_sigmas`` slot, for the cond_head cache. Only ``f_pos`` reaches this far -- the RoPE angles are built once at the - root -- so the scalar ring clock is passed rather than the whole bundle. + root -- so the ``[B]`` ring clock is passed rather than the whole bundle. """ s0, b0, g0, s1, b1, g1 = self.cond_head(cond, cond_idx) @@ -265,15 +265,18 @@ def _pos_ids(self, frame_pos: Tensor) -> WaypointPosIds: """Build one frame's position streams from the ring clock.""" if not torch.compiler.is_compiling(): torch._check( - frame_pos.ndim == 0 and frame_pos.dtype == torch.int64, - lambda: f"frame_pos must be a [] int64 tensor; got {tuple(frame_pos.shape)} " + frame_pos.ndim == 1 and frame_pos.dtype == torch.int64, + lambda: f"frame_pos must be a [B] int64 tensor; got {tuple(frame_pos.shape)} " f"{frame_pos.dtype}", ) y_pos, x_pos = self._grid.get(frame_pos.device) + B = frame_pos.shape[0] # A no-op at ts_mult == 1, kept because it is the only place the two # clocks are related; deleting it drifts silently at another fps. - t_pos = (frame_pos * self.config.ts_mult).reshape(1, 1).expand(1, y_pos.numel()) - return WaypointPosIds(f_pos=frame_pos, t_pos=t_pos, y_pos=y_pos[None], x_pos=x_pos[None]) + t_pos = (frame_pos * self.config.ts_mult)[:, None].expand(B, y_pos.numel()) + return WaypointPosIds( + f_pos=frame_pos, t_pos=t_pos, y_pos=y_pos[None].expand(B, -1), x_pos=x_pos[None].expand(B, -1) + ) # ---- One forward ------------------------------------------------------- @@ -291,8 +294,8 @@ def forward( ) -> Tensor: """One pass over one latent frame; returns the rectified-flow velocity. - ``x`` ``[B, N, C, H, W]`` latent (B == N == 1), ``sigma`` ``[B, N]``, - ``frame_pos`` ``[]`` int64 ring clock, controller inputs ``[B, N, 2]`` / + ``x`` ``[B, N, C, H, W]`` latent (N == 1), ``sigma`` ``[B, N]``, + ``frame_pos`` ``[B]`` int64 ring clock, controller inputs ``[B, N, 2]`` / ``[B, N, n_buttons]`` / ``[B, N, 1]``. Returns ``[B, N, C, H, W]``. ``commit`` says whether this pass keeps its K/V: False for the four @@ -310,9 +313,9 @@ def forward( Hp * Wp == self.config.tokens_per_frame, f"{Hp} * {Wp} != {self.config.tokens_per_frame}", ) - # One frame per call, batch 1: the ring cache indexes a single frame per + # One frame per call per row: the ring cache indexes a single frame per # upsert and the whole driver is built on that. - torch._assert(B == 1 and N == 1, "WaypointDiT.forward supports B == 1, N == 1") + torch._assert(N == 1, "WaypointDiT.forward supports N == 1") pos_ids = self._pos_ids(frame_pos) # Keyword arguments on purpose: a silent x/y swap on a non-square grid diff --git a/mstar/model/waypoint/components/taehv.py b/mstar/model/waypoint/components/taehv.py index 6bd89c396..d7ee26829 100644 --- a/mstar/model/waypoint/components/taehv.py +++ b/mstar/model/waypoint/components/taehv.py @@ -306,11 +306,12 @@ def decode_latent( "TAEHV decoder returned " f"{decoded.shape[1]} frames for one latent; expected {expected_frames}." ) + B, num_frames = decoded.shape[:2] decoded = F.interpolate( - decoded[0], size=output_size, mode="bilinear", align_corners=False - )[None] + decoded.flatten(0, 1), size=output_size, mode="bilinear", align_corners=False + ).unflatten(0, (B, num_frames)) frames = (decoded.clamp(0, 1) * 255).round().to(torch.uint8) - frames = frames.squeeze(0).permute(0, 2, 3, 1)[..., :3].contiguous() + frames = frames.permute(0, 1, 3, 4, 2)[..., :3].contiguous() return frames, state diff --git a/mstar/model/waypoint/config.py b/mstar/model/waypoint/config.py index 885488154..55d3b3475 100644 --- a/mstar/model/waypoint/config.py +++ b/mstar/model/waypoint/config.py @@ -149,6 +149,11 @@ class WaypointConfig: # that A/B's control arm and stays reachable. capture_dit_prime: bool = True + # Rows carried per rollout step; one per resident world sharing the DiT + # forward. Must be <= `resources.kv.num_worlds` (checked at YAML-load time + # in waypoint_model.py, where num_worlds is known). + step_batch_size: int = 1 + # Guard rails the ported modules assert against, kept here so a drifting # checkpoint fails loudly at construction rather than silently mis-serving. _supported_rope_impls: tuple[str, ...] = field( @@ -196,6 +201,7 @@ def __post_init__(self) -> None: "inference_fps": self.inference_fps, "temporal_compression": self.temporal_compression, "max_frames": self.max_frames, + "step_batch_size": self.step_batch_size, } invalid = [name for name, value in positive_ints.items() if type(value) is not int or value <= 0] if invalid: diff --git a/mstar/model/waypoint/submodules.py b/mstar/model/waypoint/submodules.py index fa7b22a4c..d0116e472 100644 --- a/mstar/model/waypoint/submodules.py +++ b/mstar/model/waypoint/submodules.py @@ -61,6 +61,26 @@ def _frame_seed(request_seed: int, frame_pos: int) -> int: return z & (_U64 >> 1) +def _rollout_capture_batch_sizes(step_batch_size: int) -> list[int]: + """Powers of two up to ``step_batch_size``, with ``step_batch_size`` itself. + + Capturing every size ``1..B`` makes startup linear in B: each bucket is a new + static shape, so the DiT re-traces both fullgraph regions and the decode + re-tunes its convs for it. A geometric set makes startup grow with ``log B`` + instead; a step of ``n`` rows replays the smallest bucket ``>= n`` and pads + the tail with dummy rows the ring parks on spare worlds (see + ``RingKVManager.plan``). This is what every other batched model already does + -- the engine default ``CAPTURE_BATCH_SIZES`` is the same geometric set. + """ + sizes = [] + bs = 1 + while bs < step_batch_size: + sizes.append(bs) + bs *= 2 + sizes.append(step_batch_size) + return sizes + + class _SingleRequestMixin: """Serve one request per step, through the engine's batched entry point. @@ -72,18 +92,10 @@ class _SingleRequestMixin: every batch with ``running_batched=True`` — so a submodule that only defines ``forward`` never runs. - **The cap is on the step, not on the node.** It used to be both: the ring - held one live world, so a second request in the batch had nowhere to put its - history and neither did a second request anywhere on the node. The ring now - holds ``num_worlds`` of them and they interleave freely across steps; what - is left here is the honest wan22 statement — the *step* is not batched yet. - The DiT's driver still asserts ``B == 1``, ``_pos_ids`` still hardcodes the - leading 1, and ``capture_batch_sizes`` is still ``[1]``, so a batched step - has nowhere to go until those lift together. - - ``max_batch_size`` is what the micro scheduler reads and chunks on; the - assert is the backstop, and ``RingKVManager.admit`` refusing a mixed batch - is the one below that. + Only ``WaypointVaeEncoderSubmodule`` uses this now: prime's encode is once + per request, off the steady-state path, so it stays capped at 1. The dit + node's step is batched (see ``WaypointDitSubmodule.max_batch_size`` and + ``forward_batched``); this mixin no longer describes it. """ def max_batch_size(self, graph_walk: str): @@ -130,7 +142,7 @@ def pixel_size(self) -> tuple[int, int]: ) -class WaypointDitSubmodule(_SingleRequestMixin, _FunctionalAeMixin, NodeSubmodule): +class WaypointDitSubmodule(_FunctionalAeMixin, NodeSubmodule): """The world DiT: one latent frame per engine step, TAEHV-decoded in the same forward. @@ -186,6 +198,25 @@ def bind_node_resources(self, resources: dict) -> None: ) super().bind_node_resources(resources) + def max_batch_size(self, graph_walk: str) -> int: + """Both walks carry up to ``step_batch_size`` rows: one per resident + world sharing the forward. Prime rows batch too, when several + requests are admitted in the same step. + """ + return self.config.step_batch_size + + def can_batch(self, batch, model_inputs) -> bool: + """Batch concurrent rows into one forward, matching every other batched + model. A captured lease replays batched regardless of this flag; it is + the eager fallback (graphs off, or a shape with no captured bucket) that + would otherwise run one forward per request. Rows are independent -- + ``preprocess`` concatenates on the batch dim and every resource is + scoped per row -- so any admitted set (capped by ``max_batch_size``) is + batchable. + """ + del batch, model_inputs + return True + # ------------------------------------------------------------------ # prepare_inputs / preprocess # ------------------------------------------------------------------ @@ -280,11 +311,22 @@ def preprocess( engine_inputs: ModelInputsFromEngine, inputs: list[NodeInputs], ) -> dict: - assert len(inputs) == 1, ( - f"WaypointDitSubmodule does not batch a step; preprocess got " - f"{len(inputs)} rows (max_batch_size should have capped it at 1)" - ) - return super().preprocess(graph_walk, engine_inputs, inputs) + """Concatenate every row's tensors along the batch dim. + + Each row already carries a leading 1 (``frame_pos [1]``, + ``noise``/``latent`` ``[1, 1, C, H, W]``, ``mouse``/``button``/``scroll`` + ``[1, 1, *]``, the nine histories ``[1, C, h, w]``), in + ``engine_inputs.request_ids`` order (``inputs[i]`` pairs positionally + with row ``i`` — the row-order invariant every batched resource below + this node relies on). ``len(inputs) == 1`` returns the row unchanged, + no copy, which is what B=1 ran before this method concatenated anything. + """ + if len(inputs) == 1: + return inputs[0].tensor_inputs + return { + key: torch.cat([row.tensor_inputs[key] for row in inputs], dim=0) + for key in inputs[0].tensor_inputs + } def _frame_noise( self, @@ -487,9 +529,7 @@ def forward( histories = tuple(kwargs.pop(f"{DECODER_HISTORY_PREFIX}{idx}") for idx in range(9)) if kwargs: raise TypeError(f"unexpected dit inputs: {sorted(kwargs)}") - # The graph boundary owns [1]; the model owns [] — ``_pos_ids`` - # asserts rank 0 and int64. This reshape is the entire seam. - pos = frame_pos.reshape(()) + pos = frame_pos if graph_walk == ROLLOUT_WALK: out = self.dit.generate_frame( @@ -511,7 +551,7 @@ def forward( ) result: NameToTensorList = { "video_output": [frames], - # [1], not []: the loop-back edge to next iteration's "clock" + # [B], not []: the loop-back edge to next iteration's "clock" # input, whose only job is to be a name in ready_signals (see # prepare_inputs). Harmless on prime too, whose node declares no # outputs at all. @@ -523,6 +563,32 @@ def forward( }) return result + def forward_batched( + self, + graph_walk: str, + engine_inputs: ModelInputsFromEngine, + **kwargs, + ) -> dict[str, NameToTensorList]: + """Run the batched ``forward`` once, then split its rows back out. + + Row ``i`` of every output tensor belongs to + ``engine_inputs.request_ids[i]`` — the same row-order invariant + ``preprocess`` concatenated on the way in. ``video_output[i]`` is + ``[F, H, W, 3]``, exactly what ``postprocess`` consumes today; + ``clock[i:i+1]`` and each history row stay ``[1, ...]``, matching what + ``prepare_inputs`` builds for the next step. + """ + out = self.forward(graph_walk, engine_inputs=engine_inputs, **kwargs) + # Each value is a one-element list holding the batched tensor; index + # the tensor's rows, not the list. + return { + rid: { + key: [value[0][i]] if key == "video_output" else [value[0][i : i + 1]] + for key, value in out.items() + } + for i, rid in enumerate(engine_inputs.request_ids) + } + # ------------------------------------------------------------------ # capture # ------------------------------------------------------------------ @@ -569,19 +635,32 @@ def template(latent_key: str) -> NodeInputs: input_seq_len=self.config.tokens_per_frame, ) - # Rollout first, and the order is load-bearing: both captures share one - # graph pool and rollout's five forwards are a superset of prime's one, - # so the pool is sized once and prime reuses its freed blocks. - # ``prepare_for_capture``'s sort is stable and both specs are - # (bs=1, tokens_per_frame), so declaration order is capture order. + # Rollout listed first, but what actually orders capture is + # ``prepare_for_capture``'s ``(bs, num_tokens)`` sort, descending: the + # largest bucket (bs=step_batch_size) captures before every smaller one, + # sizing the shared graph pool once at its biggest allocation. Prime and + # rollout share the bucket set and, per bs, the same num_tokens, so each + # prime bucket ties the same-size rollout bucket on that key; the sort is + # stable, so rollout's earlier position here keeps it captured first at + # every tie, and prime reuses freed blocks all the way down. + batch_sizes = _rollout_capture_batch_sizes(self.config.step_batch_size) walks = [(ROLLOUT_WALK, "noise")] if self.config.capture_dit_prime: + # Prime captures the same geometric buckets as rollout. A prime batch + # of several requests admitted in one step replays the smallest + # bucket >= its size and pads the tail on the ring's scratch world + # (see ``RingKVManager.plan``), the same idiom rollout uses -- so the + # first multi-request prime replays a captured graph instead of + # falling to a runtime eager re-trace. ``caps_eager_batch_size`` stays + # the default True: a prime batch is capped at the largest captured + # bucket, which is ``step_batch_size``, exactly where admission caps + # it anyway. walks.append((PRIME_WALK, "latent")) return [ BatchedCudaGraphConfig( capture_graph_walk=walk, single_request_inputs=template(latent_key), - capture_batch_sizes=[1], + capture_batch_sizes=batch_sizes, capture_forward_method="forward_batched", # The DiT compiles its two reference-shaped fullgraph regions # itself. Compiling this wrapper would fuse across their boundary. diff --git a/mstar/model/waypoint/waypoint_model.py b/mstar/model/waypoint/waypoint_model.py index a4a11ddfc..ab726f08d 100644 --- a/mstar/model/waypoint/waypoint_model.py +++ b/mstar/model/waypoint/waypoint_model.py @@ -39,9 +39,9 @@ ``num_worlds`` and ``max_batch_size`` are separate numbers and stay separate. ``num_worlds`` is how many sessions are *resident* (one ring span each, folded into the token dimension by ``LayerRingCache``); ``max_batch_size`` is how many -share one *forward step*, and is still 1, so resident worlds take turns across -steps rather than batching. Raising the second is the next cut and does not -change the layout chosen for the first. +share one *forward step*, set by ``step_batch_size`` (<= ``num_worlds``), so up +to that many resident worlds batch into one step instead of each taking a +separate turn. """ import logging @@ -135,6 +135,7 @@ def __init__( cuda_graph: bool | None = None, capture_dit_prime: bool | None = None, full_global_ring: bool | None = None, + step_batch_size: int | None = None, checkpoint_revision: str | None = None, ae_revision: str | None = None, ): @@ -161,6 +162,7 @@ def __init__( "cuda_graph": cuda_graph, "capture_dit_prime": capture_dit_prime, "full_global_ring": full_global_ring, + "step_batch_size": step_batch_size, }.items() if value is not None } self.config: WaypointConfig = replace(config, **overrides) @@ -226,7 +228,8 @@ def get_node_resources(self) -> list[NodeResourceSpec]: # business assuming the box; a deployment raises it under # ``resources: {kv: {num_worlds: N}}`` and raises # ``max_concurrent_requests`` with it (see ``get_worker_graphs``). - # Not the step batch — that is ``max_batch_size``, still 1. + # Not the step batch — that is ``max_batch_size``, set by + # ``step_batch_size`` (<= num_worlds). num_worlds=1, ) # Logged, not merely allocated: this declaration is worth ~816 MiB per @@ -336,11 +339,11 @@ def get_worker_graphs(self, config_path: str) -> list[WorkerGraph]: what changed is that the accepted value is a range rather than the single number 1. - ``max_batch_size = 1`` does NOT cover this, and that is still true with - N worlds. It caps how many requests share one *step*; N admitted - rollouts alternate steps, which is now the intended shape — each holds - its own world and the BlockMask keeps them apart — but it says nothing - about how many may exist, which is the thing the pool bounds. + ``max_batch_size`` does NOT cover this, independent of its value. It + caps how many requests share one *step* (``step_batch_size``, <= + ``num_worlds``); worlds beyond that batch still alternate steps — each + holds its own world and the BlockMask keeps them apart — but it says + nothing about how many may exist, which is the thing the pool bounds. A limit *below* ``num_worlds`` is legal and only wasteful: it allocates rings (~816 MiB each at 720P) for worlds no request can ever reach, so @@ -393,6 +396,13 @@ def get_worker_graphs(self, config_path: str) -> list[WorkerGraph]: "and can never be filled.", num_worlds, limit, num_worlds - limit, ) + if self.config.step_batch_size > num_worlds: + raise ValueError( + f"`step_batch_size: {self.config.step_batch_size}` exceeds " + f"`resources.{KV_RESOURCE}.num_worlds: {num_worlds}` in " + f"{config_path}. A step cannot batch more rows than there are " + "resident worlds to supply them." + ) return super().get_worker_graphs(config_path) # ------------------------------------------------------------------ diff --git a/test/modular/test_batch_sweep_summary.py b/test/modular/test_batch_sweep_summary.py new file mode 100644 index 000000000..607dc4235 --- /dev/null +++ b/test/modular/test_batch_sweep_summary.py @@ -0,0 +1,166 @@ +"""Onset logic for _tools/batch_sweep_summary.py, the pure-stdlib summarizer +for the concurrent-stream batch_sweep.sh sweep. Lives outside this repo (see +CLAUDE.md for _tools/ conventions) so it is loaded by absolute path.""" + +from __future__ import annotations + +import json +import runpy +from pathlib import Path + +SUMMARY_SCRIPT = Path("/shared/home/garv901-55613a/waypoint-int/_tools/batch_sweep_summary.py") + + +def _solo_baseline_artifact(*, ttff_s, gap_p50_s, sustained, fps, gpu_peak_mib): + frame_count = 40 + wall_seconds = frame_count / fps + return { + "status": "completed", + "server": {"startup_seconds": 5.0}, + "runs": { + "baseline": { + "time_to_first_frame_seconds": ttff_s, + "inter_chunk_gap_seconds": { + "p50": gap_p50_s, + "p95": gap_p50_s + 0.001, + "maximum": gap_p50_s + 0.002, + }, + "sustained_media_to_wall_ratio": sustained, + "stalls": {"count": 0}, + "on_time_chunk_fraction": 1.0, + "frame_count": frame_count, + "request_wall_seconds": wall_seconds, + "memory": {"peak_gpu_mib": gpu_peak_mib}, + } + }, + } + + +def _concurrent_artifact( + *, ttff_p50_ms, gap_p50_median_ms, aggregate_fps, all_realtime, delivery_bound, gpu_peak_mib, + streams=2, realtime_count=None, on_time_chunk_fraction_min=1.0, +): + if realtime_count is None: + realtime_count = streams if all_realtime else max(streams - 1, 0) + return { + "status": "completed", + "concurrent": { + "gpu_peak_mib": gpu_peak_mib, + "streams": streams, + "realtime_count": realtime_count, + "on_time_chunk_fraction_min": on_time_chunk_fraction_min, + "ttff_ms": {"p50": ttff_p50_ms, "p95": ttff_p50_ms + 5.0}, + "gap_ms": { + "p50_median": gap_p50_median_ms, + "p95_worst": gap_p50_median_ms + 5.0, + "max_worst": gap_p50_median_ms + 10.0, + }, + "sustained_min": 1.1, + "aggregate_fps": aggregate_fps, + "all_realtime": all_realtime, + "delivery_bound": delivery_bound, + "server": { + "startup_seconds": 5.0, + "step_spacing_ms": {"p50": 50.0, "p95": 55.0}, + "rows_per_step_histogram": {"2": 30}, + }, + }, + } + + +def _write_sweep(tmp_path: Path) -> Path: + """B=1..16 grid with a distinct, deliberately placed onset per metric: + gap regression at B=4, TTFF regression and fps-gain flattening at B=8 + (B=8 is also where delivery_bound flips True), and all_realtime failing + only at B=16. max realtime B should land on B=4 (the largest B that is + both realtime and not delivery-bound).""" + artifacts = { + 1: _solo_baseline_artifact(ttff_s=0.050, gap_p50_s=0.020, sustained=1.2, fps=10.0, gpu_peak_mib=1000.0), + 2: _concurrent_artifact( + ttff_p50_ms=55.0, gap_p50_median_ms=21.0, aggregate_fps=19.0, + all_realtime=True, delivery_bound=False, gpu_peak_mib=1500.0, streams=2, + ), + 4: _concurrent_artifact( + ttff_p50_ms=58.0, gap_p50_median_ms=23.0, aggregate_fps=27.0, + all_realtime=True, delivery_bound=False, gpu_peak_mib=2000.0, streams=4, + ), + 8: _concurrent_artifact( + ttff_p50_ms=65.0, gap_p50_median_ms=30.0, aggregate_fps=29.0, + all_realtime=True, delivery_bound=True, gpu_peak_mib=3000.0, streams=8, + ), + 16: _concurrent_artifact( + ttff_p50_ms=70.0, gap_p50_median_ms=40.0, aggregate_fps=29.5, + all_realtime=False, delivery_bound=False, gpu_peak_mib=4000.0, + streams=16, realtime_count=15, on_time_chunk_fraction_min=0.9, + ), + } + for b, artifact in artifacts.items(): + (tmp_path / f"b{b}.json").write_text(json.dumps(artifact)) + return tmp_path + + +def test_summary_table_and_onsets_over_a_synthetic_sweep(tmp_path, capsys): + out_dir = _write_sweep(tmp_path) + module = runpy.run_path(str(SUMMARY_SCRIPT)) + + rows, notes = module["_load_rows"](out_dir) + assert notes == [] + assert [row["b"] for row in rows] == [1, 2, 4, 8, 16] + # B=1 falls back to runs.baseline: gap p50 0.020s -> 20ms, fps 40/4s = 10. + assert rows[0]["gap_p50_median_ms"] == 20.0 + assert rows[0]["aggregate_fps"] == 10.0 + assert rows[0]["delivery_bound"] is False + # Viability columns: how many streams stayed realtime, and the worst + # stream's per-chunk in-budget fraction. + by_b = {row["b"]: row for row in rows} + assert by_b[1]["realtime_count"] == 1 and by_b[1]["streams"] == 1 + assert by_b[16]["realtime_count"] == 15 and by_b[16]["streams"] == 16 + assert by_b[16]["on_time_chunk_fraction_min"] == 0.9 + + onsets = module["_onsets"](rows) + assert "gap p50_median regression onset (>1.10x B=1): B=4" in onsets + assert "TTFF p50 regression onset (>1.25x B=1): B=8" in onsets + assert "aggregate fps gain onset (<10% over previous B): B=8" in onsets + assert "first B with all_realtime=false: B=16" in onsets + assert "max realtime B (all_realtime and not delivery_bound): B=4" in onsets + + exit_code = module["main"]([str(SUMMARY_SCRIPT), str(out_dir)]) + assert exit_code == 0 + summary_path = out_dir / "summary.md" + assert summary_path.exists() + table = summary_path.read_text() + assert "| B |" in table + assert "realtime streams" in table + assert "on-time chunk % (min)" in table + assert "15/16" in table + assert "90.0" in table + assert "Regression onsets" in table + printed = capsys.readouterr().out + assert "max realtime B" in printed + + +def test_summary_skips_error_status_files_gracefully(tmp_path, capsys): + out_dir = _write_sweep(tmp_path) + (out_dir / "b32.json").write_text( + json.dumps({"status": "error", "error": "RuntimeError: server never became healthy"}) + ) + module = runpy.run_path(str(SUMMARY_SCRIPT)) + + rows, notes = module["_load_rows"](out_dir) + assert [row["b"] for row in rows] == [1, 2, 4, 8, 16] + assert len(notes) == 1 + assert "b32" in notes[0] and "error" in notes[0] + + +def test_summary_reports_missing_out_dir_without_a_b1_baseline(tmp_path): + module = runpy.run_path(str(SUMMARY_SCRIPT)) + artifact = _concurrent_artifact( + ttff_p50_ms=55.0, gap_p50_median_ms=21.0, aggregate_fps=19.0, + all_realtime=True, delivery_bound=False, gpu_peak_mib=1500.0, + ) + (tmp_path / "b2.json").write_text(json.dumps(artifact)) + + rows, notes = module["_load_rows"](tmp_path) + assert notes == [] + onsets = module["_onsets"](rows) + assert onsets[0] == "no B=1 row: gap/TTFF onsets relative to B=1 cannot be computed" diff --git a/test/modular/test_cuda_graph_capture.py b/test/modular/test_cuda_graph_capture.py index 3c17e6c05..5fdc61a1c 100644 --- a/test/modular/test_cuda_graph_capture.py +++ b/test/modular/test_cuda_graph_capture.py @@ -220,3 +220,73 @@ def test_single_slot_runners_still_register(): (bucket,) = runner._buckets.values() assert bucket.slots == ["decode:slot0"] assert runner.declared == [], "nothing to pre-plan with a single slot" + + +class _InternRunner: + """`_intern_static_buffer` bound onto the three fields it touches.""" + + _seq_dim = staticmethod(CudaGraphRunner._seq_dim) + _intern_static_buffer = CudaGraphRunner._intern_static_buffer + + def __init__(self): + self._shared_static_buffers = {} + self._static_buffer_seq_dims = {} + self._capture_clone_bytes_naive = 0 + + +def test_button_shares_its_buffer_once_batch_size_disambiguates_the_axis(): + """Waypoint 360p at bs=2: the bucket's token count is 2*128 = 256, which + is also ``n_buttons``. Passing the real call site's ``batch_size`` lets + ``_seq_dim`` settle on dim 0 (bs=2 there) before it ever scans for a + ``seq_len`` match, so button reslices the shared buffer like every other + per-row tensor instead of falling back to a private allocation.""" + runner = _InternRunner() + big = torch.arange(2 * 256, dtype=torch.float32).reshape(2, 1, 256) + small = -torch.arange(256, dtype=torch.float32).reshape(1, 1, 256) + + shared_view = runner._intern_static_buffer(0, "button", big, seq_len=256, batch_size=2) + assert shared_view.shape == (2, 1, 256) + assert torch.equal(shared_view, big) + + resliced = runner._intern_static_buffer(0, "button", small, seq_len=128, batch_size=1) + + assert runner._static_buffer_seq_dims[(0, "button")] == 0 + assert resliced.shape == (1, 1, 256) + assert resliced.data_ptr() == shared_view.data_ptr() + assert torch.equal(resliced, small) + # the reslice writes through the shared buffer — row 0 now reads back + # as `small`, which is the whole point of sharing rather than cloning + assert torch.equal(shared_view[:1], small) + + +def test_a_smaller_bucket_that_shrinks_off_the_hoisted_axis_raises(): + """Without a batch size to disambiguate — a caller that only knows the + flattened token count — a fixed-width tensor can still coincidentally + match ``seq_len`` on a non-batch dim (here: button's n_buttons=256 lines + up with the bs=2 bucket's token count), hoisting the wrong axis. The bs=1 + bucket then shrinks along dim 0, not the hoisted one, and can't reslice + the shared buffer. This stays a hard failure rather than a silent private + allocation; the real fix is giving `_seq_dim` enough information (the + batch size) to never hoist the wrong axis in the first place — see + `test_button_shares_its_buffer_once_batch_size_disambiguates_the_axis`.""" + runner = _InternRunner() + big = torch.arange(2 * 256, dtype=torch.float32).reshape(2, 1, 256) + small = -torch.arange(256, dtype=torch.float32).reshape(1, 1, 256) + + runner._intern_static_buffer(0, "button", big, seq_len=256) + with pytest.raises(RuntimeError, match="captures must be largest-first"): + runner._intern_static_buffer(0, "button", small, seq_len=128) + + +def test_a_smaller_bucket_along_the_hoisted_axis_still_reslices_the_shared_buffer(): + runner = _InternRunner() + big = torch.arange(3 * 8, dtype=torch.float32).reshape(3, 8) + small = torch.zeros(3, 4) + + shared_view = runner._intern_static_buffer(0, "mrope", big, seq_len=8) + resliced = runner._intern_static_buffer(0, "mrope", small, seq_len=4) + + assert runner._static_buffer_seq_dims[(0, "mrope")] == 1 + assert resliced.shape == (3, 4) + assert resliced.data_ptr() == shared_view.data_ptr() + assert torch.equal(shared_view[:, :4], small) diff --git a/test/modular/test_flex_attention_resource.py b/test/modular/test_flex_attention_resource.py index c30cb3ddc..9e698162f 100644 --- a/test/modular/test_flex_attention_resource.py +++ b/test/modular/test_flex_attention_resource.py @@ -20,13 +20,13 @@ load-bearing and the argument for it needs re-deriving rather than the test being deleted. -A third section is not a failure but a *derisk*, and is labelled as one: the -world pool folds N worlds into the token axis of one ring, so the shape a -batched step would take is B queries against a stride-0 ``expand`` of a single -K/V with the per-world isolation moved into the BlockMask's leading dim. Nothing -ships that path today (the step batch is 1), but if it does not hold bit-exactly -then batching a step is not free and the design owes a different answer, so the -two properties it rests on are asserted rather than assumed. +A third section is a *derisk* that now backs a shipped path: the world pool +folds N worlds into the token axis of one ring, so a batched step is B queries +against a stride-0 ``expand`` of a single K/V with the per-world isolation +moved into the BlockMask's leading dim. If this ever stopped holding +bit-exactly, batching a step would not be free and the design would owe a +different answer, so the two properties it rests on are asserted rather than +assumed. Checkpoint-free, and CPU except where a test is parametrized over the device: ``torch.compile(flex_attention)`` works on CPU in torch 2.9, at a few seconds of @@ -56,6 +56,8 @@ ) from mstar.engine.resources.attn.base import AttentionManager from mstar.engine.resources.attn.flex import ( + _FLEX_BACKEND, + _MASK_MOD, FlexAttentionManager, flex_attention_masked, make_block_mask, @@ -234,17 +236,17 @@ def test_plan_stages_one_reused_mask_per_geometry_and_slot(monkeypatch): manager = build_attention(AttnBackend.FLEX, config) ctx = StepContext( request_ids=("r",), graph_walk="rollout", slot=1, capture=False, - plan_results={"kv": RingPlan("r", 1, 5)}, + plan_results={"kv": RingPlan(("r",), (1,), (5,))}, ) manager.plan(AttentionStep(), ctx) assert manager.needs_token_visibility is False assert len(manager._planned_masks) == 2 - local = manager._mask_for(1, manager._geometry(config.layers[0])) - assert local is manager._mask_for(1, manager._geometry(config.layers[1])) - global_mask = manager._mask_for(1, manager._geometry(config.layers[2])) - assert global_mask is manager._mask_for(1, manager._geometry(config.layers[3])) + local = manager._mask_for(1, manager._geometry(config.layers[0]), 1) + assert local is manager._mask_for(1, manager._geometry(config.layers[1]), 1) + global_mask = manager._mask_for(1, manager._geometry(config.layers[2]), 1) + assert global_mask is manager._mask_for(1, manager._geometry(config.layers[3]), 1) assert local is not global_mask # One block per frame. World 1 starts after world 0's five-block span. @@ -268,7 +270,7 @@ def test_plan_stages_one_reused_mask_per_geometry_and_slot(monkeypatch): "mask planning must not allocate a new staging tensor" ), ) - ctx.plan_results["kv"] = RingPlan("r", 1, 6) + ctx.plan_results["kv"] = RingPlan(("r",), (1,), (6,)) manager.plan(AttentionStep(), ctx) assert addresses == { key: (value.full_kv_num_blocks.data_ptr(), value.full_kv_indices.data_ptr()) @@ -324,7 +326,7 @@ def test_planned_masks_match_ring_visibility_across_wraps_and_worlds(): for cache in caches: *_, visible = cache.upsert( kv, - torch.tensor(frame, dtype=torch.int64), + torch.tensor([frame], dtype=torch.int64), True, torch.tensor([world], dtype=torch.int64), ) @@ -337,21 +339,64 @@ def test_planned_masks_match_ring_visibility_across_wraps_and_worlds(): graph_walk="rollout", slot=0, capture=False, - plan_results={"kv": RingPlan(f"r{world}", world, frame)}, + plan_results={"kv": RingPlan((f"r{world}",), (world,), (frame,))}, ) manager.plan(AttentionStep(), ctx) for layer, expected_blocks in zip(layers, expected, strict=True): - mask = manager._mask_for(0, manager._geometry(layer)) + mask = manager._mask_for(0, manager._geometry(layer), 1) count = int(mask.full_kv_num_blocks[0, 0, 0]) assert mask.full_kv_indices[0, 0, 0, :count].tolist() == expected_blocks +def test_stage_writes_each_rows_own_visibility_at_its_own_world_and_frame(): + """A two-row plan at different worlds and frame_pos: `_stage` must write + row b from `_visibility_table_for(geometry)[w_b, phase_b]`, not row 0's + world/frame for every row and not swap the two.""" + config = RingKVConfig( + num_layers=4, + num_kv_heads=N_KV_HEADS, + head_dim=D_HEAD, + num_qo_heads=N_QO_HEADS, + tokens_per_frame=TPF, + num_worlds=2, + layers=( + RingKVLayerConfig(4, 4, 1), + RingKVLayerConfig(4, 4, 1), + RingKVLayerConfig(4, 2, 2), + RingKVLayerConfig(4, 2, 2), + ), + ) + manager = build_attention(AttnBackend.FLEX, config) + rows = ((0, 5), (1, 3)) # (world, frame_pos), distinct on both axes + ctx = StepContext( + request_ids=("r0", "r1"), graph_walk="rollout", slot=0, capture=False, + plan_results={ + "kv": RingPlan(("r0", "r1"), tuple(w for w, _ in rows), tuple(f for _, f in rows)), + }, + ) + + manager.plan(AttentionStep(), ctx) + + for layer in (config.layers[0], config.layers[2]): + geometry = manager._geometry(layer) + mask = manager._mask_for(0, geometry, 2) + counts, indices, period = manager._visibility_table_for(geometry) + for row, (world, frame) in enumerate(rows): + phase = frame if frame < period else period + frame % period + count = int(mask.full_kv_num_blocks[row, 0, 0]) + assert count == int(counts[world, phase]) + assert ( + mask.full_kv_indices[row, 0, 0, :count].tolist() + == indices[world, phase, :count].tolist() + ) + + def test_capture_plan_requires_preallocated_mask_addresses(): manager = build_attention(AttnBackend.FLEX, ring_config()) ctx = StepContext( request_ids=("r",), graph_walk="rollout", slot=0, capture=True, - plan_results={"kv": RingPlan("r", 0, 0)}, + plan_results={"kv": RingPlan(("r",), (0,), (0,))}, ) with pytest.raises(RuntimeError, match="not allocated before CUDA graph capture"): manager.plan(AttentionStep(), ctx) @@ -557,13 +602,16 @@ def folded_block_mask(rows: list[torch.Tensor], q_len: int) -> "object": zeros_n = torch.zeros((b, 1, q_blocks), dtype=torch.int32, device=device) zeros_i = torch.zeros((b, 1, q_blocks, kv_blocks), dtype=torch.int32, device=device) + # Not mask_mod=None: under FLASH that substitutes noop_mask, whose trivial + # graph makes inductor attend densely and drop the block list entirely -- + # the same trap documented above `_flash_mask_mod` in flex.py. return BlockMask.from_kv_blocks( zeros_n, zeros_i, full_kv_num_blocks, full_kv_indices, BLOCK_SIZE=BLOCK, - mask_mod=None, + mask_mod=_MASK_MOD, seq_lengths=(q_len, FOLDED_KV), compute_q_blocks=False, ) @@ -573,11 +621,16 @@ def folded_ring(device, seed: int = 0x5EED): """A folded ring with every world's span filled with distinct noise, and B queries. Distinct per span is the point: an output that is invariant to a neighbour's bytes has actually been isolated, rather than reading zeros that - happened to contribute nothing.""" + happened to contribute nothing. + + bf16 on cuda: FLASH (flash_attn.cute) only accepts fp16/bf16/fp8, matching + the DTYPE the GPU tests compile with. fp32 on cpu, where flex_attention_masked + is the TRITON/eager path and has no such restriction.""" + dtype = torch.bfloat16 if torch.device(device).type == "cuda" else torch.float32 gen = torch.Generator().manual_seed(seed) - k = torch.randn(1, N_KV_HEADS, FOLDED_KV, D_HEAD, generator=gen).to(device) - v = torch.randn(1, N_KV_HEADS, FOLDED_KV, D_HEAD, generator=gen).to(device) - q = torch.randn(WORLDS, N_QO_HEADS, TPF, D_HEAD, generator=gen).to(device) + k = torch.randn(1, N_KV_HEADS, FOLDED_KV, D_HEAD, generator=gen, dtype=dtype).to(device) + v = torch.randn(1, N_KV_HEADS, FOLDED_KV, D_HEAD, generator=gen, dtype=dtype).to(device) + q = torch.randn(WORLDS, N_QO_HEADS, TPF, D_HEAD, generator=gen, dtype=dtype).to(device) return q, k, v @@ -685,3 +738,65 @@ def run(k_ring, v_ring): "no world moved at all, so the rewrite landed nowhere the mask reads " "and this test is vacuous" ) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="needs a GPU") +@pytest.mark.skipif( + _FLEX_BACKEND != "FLASH", + reason="guards the batch-1-to-batch-B expand that only exists on the FLASH path", +) +def test_flash_batched_step_does_not_copy_the_unexpanded_ring(): + """FLASH is handed an *un-expanded* batch-1 ring here (unlike the stride-0 + test above, which expands before calling) so ``_flash_flex_attention``'s own + ``k.expand``/``v.expand`` is what runs. If that ever turned into a real + copy -- inside ``flex_attention_masked``'s compile, or inside + ``flash_attn.cute``'s ``maybe_contiguous`` -- this is the test that would + catch it, since correctness (the equality check below and in the stride-0 + test) does not: a copy of the expand produces the same numbers. + + Sized so a copy cannot hide under warmup/compile noise. With + B=4, N_QO_HEADS=4, BLOCK=128, D_HEAD=32, bf16: + output_bytes = 4 * 4 * 128 * 32 * 2 = 131_072 + threshold = 2 * output_bytes + 1 MiB = 1_310_720 bytes (~1.25 MiB) + A materialized copy of k and v (each ``[1, N_KV_HEADS, KV_LEN, D_HEAD]``, + KV_LEN = 64 * BLOCK = 8192) to batch 4 would add + B * (k.numel() + v.numel()) * 2 = 4 * (2*8192*32 + 2*8192*32) * 2 + = 8_388_608 bytes (8 MiB) -- ~6.4x the threshold. + """ + device = torch.device("cuda") + B = 4 + KV_LEN = 64 * BLOCK + + gen = torch.Generator().manual_seed(0x51DE) + k = torch.randn(1, N_KV_HEADS, KV_LEN, D_HEAD, generator=gen, dtype=torch.bfloat16).to(device) + v = torch.randn(1, N_KV_HEADS, KV_LEN, D_HEAD, generator=gen, dtype=torch.bfloat16).to(device) + q = torch.randn(B, N_QO_HEADS, BLOCK, D_HEAD, generator=gen, dtype=torch.bfloat16).to(device) + written = torch.ones(KV_LEN, dtype=torch.bool, device=device) + block_mask = make_block_mask(BLOCK, KV_LEN, written) + + # Warmup: pay the one-time torch.compile allocation before measuring. + warmup = flex_attention_masked(q, k, v, block_mask=block_mask, enable_gqa=True) + del warmup + torch.cuda.synchronize() + torch.cuda.reset_peak_memory_stats() + baseline = torch.cuda.memory_allocated() + + batched = flex_attention_masked(q, k, v, block_mask=block_mask, enable_gqa=True) + torch.cuda.synchronize() + growth = torch.cuda.max_memory_allocated() - baseline + + output_bytes = B * N_QO_HEADS * BLOCK * D_HEAD * 2 + threshold = 2 * output_bytes + (1 << 20) + copy_bytes = B * (k.numel() + v.numel()) * 2 + assert growth < threshold, ( + f"FLASH allocated {growth} bytes for a batch-{B} step against a " + f"batch-1 ring (threshold {threshold} bytes); a materialized copy " + f"would add ~{copy_bytes} bytes, so this looks like a hidden copy" + ) + + manager = build_attention(AttnBackend.FLEX, ring_config()) + for w in range(B): + serial = manager.attend(q[w : w + 1], k, v, written, enable_gqa=True) + assert torch.equal(batched[w : w + 1], serial), ( + f"row {w} in a batch of {B} differs from the same row served alone" + ) diff --git a/test/modular/test_ring_kv_resource.py b/test/modular/test_ring_kv_resource.py index cf55e3603..1fbbe674b 100644 --- a/test/modular/test_ring_kv_resource.py +++ b/test/modular/test_ring_kv_resource.py @@ -114,9 +114,10 @@ def _step(*rids: str, frame: int = 0) -> RingKVStep: def _open(kv: RingKVManager, *rids: str, frame: int = 0) -> None: - """Register and admit each rid in turn, one step each — the shape the - engine actually produces, since ``max_batch_size`` is 1 and each request - gets its own step.""" + """Register and admit each rid in its own step, one world claimed per + call — the ownership tests are about who holds a world, not step + batching, so they build it up one rid at a time regardless of + ``step_batch_size``.""" for rid in rids: kv.ingest_request(rid) outcome = kv.admit(_step(rid, frame=frame), _ctx(rid)) @@ -124,8 +125,8 @@ def _open(kv: RingKVManager, *rids: str, frame: int = 0) -> None: def _frame(kv: RingKVManager, gen: torch.Generator) -> tuple[torch.Tensor, torch.Tensor]: - """One frame's K and V. Dim 0 is 1 and stays 1: it is FlexAttention's batch - dim, and worlds live in the token dim, not here.""" + """One row's K and V for a single-row (B=1) step. Dim 0 is the step-batch + dim, worlds live in the token dim, not here.""" shape = (1, kv.config.num_kv_heads, kv.tokens_per_frame, kv.config.head_dim) return ( torch.randn(shape, generator=gen), @@ -144,7 +145,7 @@ def _rollout( the resource lifecycle alongside it and need the two to agree.""" gen = torch.Generator().manual_seed(seed) for f in range(start, start + frames): - frame_pos = torch.tensor(f, dtype=torch.int64) + frame_pos = torch.tensor([f], dtype=torch.int64) for commit in (False, False, False, False, True): for layer_idx in range(len(kv.layers)): k, v = _frame(kv, gen) @@ -226,7 +227,9 @@ def test_num_worlds_is_the_one_yaml_tunable(): assert config.num_worlds == 4 kv = _manager(config) assert kv.num_worlds == 4 - assert kv.total_slots(0) == 4 * kv.capacity(0) + # 4 resident worlds plus the one shared padding world the ring parks a + # replay's dummy tail on. + assert kv.total_slots(0) == (4 + 1) * kv.capacity(0) @pytest.mark.parametrize("bad", [0, -1, True, 1.0, 1.9, "2"]) @@ -507,7 +510,7 @@ def test_remove_request_releases_the_world_and_the_registration(): def test_supports_preplan_stays_false(): """It keeps `CudaGraphRunner._num_slots` at 1. Two slots exist so a plan for step N+1 can write buffers replay N is not reading; the only thing planned - here is one `[1]` world index, so the second slot would be an identical + here is one `[B]` world index, so the second slot would be an identical graph at double the capture cost — and the only reason `_static_world_idx` would have to become one buffer per slot.""" assert _manager().supports_preplan is False @@ -524,34 +527,62 @@ def test_plan_stages_the_world_index_in_place_as_a_device_tensor(): buffer that existed at capture, so a rebind leaves every replay reading the orphaned original. - Hence both halves below: the staged value is a `[1]` int64 device tensor, - and `plan` writes *through* it rather than replacing it. + Hence both halves below: the staged value is a ``[num_worlds]`` int64 + device tensor (row ``b`` holds row ``b``'s world once staged), and `plan` + writes *through* it rather than replacing it. """ kv = _manager(_ring_config(num_worlds=4)) _open(kv, "a", "b") staged = kv._static_world_idx - assert staged.shape == (1,) and staged.dtype == torch.int64 + assert staged.shape == (4,) and staged.dtype == torch.int64 kv.plan(_step("b"), _ctx("b")) assert kv._static_world_idx is staged, "plan rebound the buffer capture baked" assert staged.data_ptr() == kv._static_world_idx.data_ptr() - assert int(staged) == kv.world_of("b") + assert int(staged[0]) == kv.world_of("b") kv.plan(_step("a"), _ctx("a")) - assert int(staged) == kv.world_of("a") + assert int(staged[0]) == kv.world_of("a") -def test_plan_refuses_a_batch_it_cannot_stage(): - """One world index per step, so one request per step. `admit` refuses a - mixed batch first; reaching here means it was bypassed, and staging one of - the two rids arbitrarily would run the other request's frame into the wrong - world.""" +def test_plan_stages_a_batch_of_distinct_rids(): + """A step can batch worlds each claimed on its own admit -- `admit` no + longer refuses a same-step batch of distinct rids, and `plan` stages every + row, in `ctx.request_ids` order, rather than just the first.""" kv = _manager(_ring_config(num_worlds=2)) _open(kv, "a", "b") - with pytest.raises(ValueError, match="one world index per step"): - kv.plan(_step("a", "b"), _ctx("a", "b")) + result = kv.plan(_step("a", "b"), _ctx("a", "b")) + + assert result.request_ids == ("a", "b") + assert result.world_idx == (kv.world_of("a"), kv.world_of("b")) + assert result.frame_pos == (0, 0) + assert kv._static_world_idx[:2].tolist() == list(result.world_idx) + + +def test_plan_parks_padding_rows_on_the_shared_padding_world(): + """A replay padded past its real rows stages the dummy tail on the padding + world -- the one world past the resident pool -- so a padding write never + lands in a resident request's history. Real rows keep their own world; the + padding rows all share ``_padding_world``, and no admit ever hands it out.""" + kv = _manager(_ring_config(num_worlds=4)) + _open(kv, "a", "b") + free_before = set(kv._free_worlds) + + ctx = _ctx("a", "b") + ctx.set_padded_rids(("a", "b", "__pad0__", "__pad1__")) + result = kv.plan(_step("a", "b"), ctx) + + pad = kv._padding_world + assert pad == 4, "the padding world is the one index past the resident pool" + assert pad not in kv._free_worlds and kv.layers[0].num_worlds == 5 + assert result.request_ids == ("a", "b", "__pad0__", "__pad1__") + assert result.world_idx == (kv.world_of("a"), kv.world_of("b"), pad, pad) + assert result.frame_pos == (0, 0, 0, 0) + assert kv._static_world_idx[:4].tolist() == list(result.world_idx) + # Padding never touched the pool: no free world was consumed for the tail. + assert set(kv._free_worlds) == free_before def test_plan_refuses_a_request_holding_no_world(): @@ -561,18 +592,17 @@ def test_plan_refuses_a_request_holding_no_world(): kv.plan(_step("a"), _ctx("a")) -def test_admit_refuses_a_mixed_batch_naming_the_step_limit(): - """A step advances one world. The message has to say which number capped it - — this is `max_batch_size`, not the ring, and a reader who reads it as a - ring limit raises `num_worlds` and sees nothing change.""" +def test_admit_refuses_a_batch_naming_the_same_request_twice(): + """A step batches distinct worlds, one row per request; naming the same + rid twice would try to stage two rows into the same world.""" kv = _manager(_ring_config(num_worlds=4)) - _open(kv, "a", "b") + kv.ingest_request("a") - outcome = kv.admit(_step("a", "b"), _ctx("a", "b")) + outcome = kv.admit(_step("a", "a"), _ctx("a", "a")) assert not outcome.ok assert type(outcome.reason) is AdmitRuntimeError - assert "max_batch_size" in outcome.reason.message + assert "'a'" in outcome.reason.message # ── the ring clock ────────────────────────────────────────────────────── @@ -669,6 +699,26 @@ def test_each_world_runs_its_own_clock(): assert not kv.admit(_step("b", frame=3), _ctx("b")).ok +def test_the_clock_check_fires_per_rid_inside_a_batched_admit(): + """Two rids in one `admit` call, not two: the continuity check must still + catch a bad clock on either row of a batch, and a good row ahead of it in + `ctx.request_ids` order must not paper over it.""" + kv = _manager(_ring_config(num_worlds=2)) + _open(kv, "a", "b") + _drive(kv, "a", 0) + _drive(kv, "b", 0) + + step = RingKVStep(frames=(("a", 1), ("b", 5))) # b skips ahead + outcome = kv.admit(step, _ctx("a", "b")) + + assert not outcome.ok + assert "'b'" in outcome.reason.message + assert "declares frame 5" in outcome.reason.message + + # the same two rids with both clocks valid still admits as a batch. + assert kv.admit(RingKVStep(frames=(("a", 1), ("b", 1))), _ctx("a", "b")).ok + + def test_a_step_that_declares_no_clock_for_an_admitted_request_is_refused(): """`RingKVStep` has no shape that declines to answer, so this is only reachable by hand — which is the point. The continuity check is the only @@ -1094,9 +1144,9 @@ def test_visible_is_the_layers_scratch_buffer_and_must_be_read_immediately(): gen = torch.Generator().manual_seed(3) k, v = _frame(kv, gen) - _, _, visible0 = kv.upsert(k, v, 0, torch.tensor(0, dtype=torch.int64), commit=True) + _, _, visible0 = kv.upsert(k, v, 0, torch.tensor([0], dtype=torch.int64), commit=True) snapshot = visible0.clone() - _, _, visible1 = kv.upsert(k, v, 0, torch.tensor(1, dtype=torch.int64), commit=True) + _, _, visible1 = kv.upsert(k, v, 0, torch.tensor([1], dtype=torch.int64), commit=True) assert visible1 is visible0 assert not torch.equal(snapshot, visible1), ( @@ -1113,10 +1163,13 @@ def test_visible_hides_the_slot_this_frame_is_about_to_overwrite(): gen = torch.Generator().manual_seed(4) for f in range(RING_FRAMES + 1): k, v = _frame(kv, gen) - _, _, visible = kv.upsert(k, v, 0, torch.tensor(f, dtype=torch.int64), commit=True) + _, _, visible = kv.upsert(k, v, 0, torch.tensor([f], dtype=torch.int64), commit=True) slot = (f % RING_FRAMES) * TPF assert not bool(visible[slot : slot + TPF].any()), f"frame {f} sees its own slot" - assert bool(visible[kv.layers[0].ring_len :].all()), "scratch must stay visible" + # World 0's scratch only: the buffer now holds a padding world past it, + # whose slots are (correctly) not visible to this row. + cap = kv.layers[0].capacity + assert bool(visible[kv.layers[0].ring_len : cap].all()), "scratch must stay visible" def test_upsert_returns_the_whole_buffer_and_delegates_by_layer(): @@ -1131,10 +1184,11 @@ def test_upsert_returns_the_whole_buffer_and_delegates_by_layer(): for layer_idx in range(N_LAYERS): k_all, v_all, visible = kv.upsert( - k, v, layer_idx, torch.tensor(0, dtype=torch.int64), commit=True + k, v, layer_idx, torch.tensor([0], dtype=torch.int64), commit=True ) total = kv.total_slots(layer_idx) - assert total == 3 * kv.capacity(layer_idx) + # 3 resident worlds + 1 shared padding world. + assert total == (3 + 1) * kv.capacity(layer_idx) assert k_all.shape[-2] == total and v_all.shape[-2] == total assert visible.shape == (total,) assert k_all.shape[0] == 1, "the world dim is folded into tokens, not dim 0" @@ -1157,7 +1211,7 @@ def test_planned_attention_skips_per_upsert_visibility_reconstruction(): k, v, 0, - torch.tensor(0, dtype=torch.int64), + torch.tensor([0], dtype=torch.int64), commit=False, build_visibility=False, ) @@ -1178,7 +1232,7 @@ def test_frozen_passes_leave_the_ring_byte_identical(): for _ in range(4): for layer_idx in range(N_LAYERS): k, v = _frame(kv, gen) - kv.upsert(k, v, layer_idx, torch.tensor(0, dtype=torch.int64), commit=False) + kv.upsert(k, v, layer_idx, torch.tensor([0], dtype=torch.int64), commit=False) for layer, snapshot in zip(kv.layers, before, strict=True): assert torch.equal(layer.kv[:, :, :, : layer.ring_len], snapshot[:, :, :, : layer.ring_len]) @@ -1239,7 +1293,7 @@ def test_the_flat_ring_matches_one_ring_per_world(num_worlds, pinned_dilation): for _ in range(20 * num_worlds): w = int(torch.randint(0, num_worlds, (1,), generator=order).item()) - frame_pos = torch.tensor(clocks[w], dtype=torch.int64) + frame_pos = torch.tensor([clocks[w]], dtype=torch.int64) for commit in (False, False, False, False, True): kv = torch.randn(2, 1, N_KV_HEADS, TPF, D_HEAD, generator=gens[w]) _, _, flat_vis = flat.upsert(kv, frame_pos, commit, _w(w)) @@ -1312,10 +1366,13 @@ def test_worlds_interleave_without_reaching_each_other(): world = flat.world_of(rid) for i, (layer, ref) in enumerate(zip(flat.layers, solo[rid].layers, strict=True)): lo, hi = layer.world_span(world) - assert torch.equal(layer.kv[:, :, :, lo:hi], ref.kv), ( + # The solo manager holds a padding world too, so compare against its + # world span, not its whole buffer. + ref_lo, ref_hi = ref.world_span(solo[rid].world_of(rid)) + assert torch.equal(layer.kv[:, :, :, lo:hi], ref.kv[:, :, :, ref_lo:ref_hi]), ( f"{rid} layer {i}: interleaving changed what the world holds" ) - assert torch.equal(layer.written[lo:hi], ref.written), ( + assert torch.equal(layer.written[lo:hi], ref.written[ref_lo:ref_hi]), ( f"{rid} layer {i}: interleaving changed what the world can see" ) @@ -1327,7 +1384,7 @@ def test_worlds_interleave_without_reaching_each_other(): for layer_idx in range(N_LAYERS): k, v = _frame(flat, gen) _, _, visible = flat.upsert( - k, v, layer_idx, torch.tensor(clocks[rid], dtype=torch.int64), commit=False + k, v, layer_idx, torch.tensor([clocks[rid]], dtype=torch.int64), commit=False ) lo, hi = flat.layers[layer_idx].world_span(world) seen = visible.clone() @@ -1368,7 +1425,7 @@ def test_the_captured_world_index_is_read_at_replay_not_baked_at_capture(): static_k = torch.zeros(1, N_KV_HEADS, TPF, D_HEAD, dtype=torch.float32, device="cuda") static_v = torch.zeros_like(static_k) - static_frame = torch.zeros((), dtype=torch.int64, device="cuda") + static_frame = torch.zeros(1, dtype=torch.int64, device="cuda") stream = torch.cuda.Stream() stream.wait_stream(torch.cuda.current_stream()) @@ -1455,7 +1512,7 @@ def test_the_bucket_rounding_is_unobservable_off_write_steps(): # step, this is the call that would write through it. World 1, not 0, so # a placeholder address that forgot the world offset lands somewhere # this test can see. - _, _, visible = layer.upsert(kv, torch.tensor(f, dtype=torch.int64), True, _w(1)) + _, _, visible = layer.upsert(kv, torch.tensor([f], dtype=torch.int64), True, _w(1)) lo, hi = layer.world_span(1) if f % d: @@ -1499,7 +1556,7 @@ def drive(kv, schedule): traces resident at once.""" gen = torch.Generator().manual_seed(37) for f in range(3 * RING_FRAMES): - frame_pos = torch.tensor(f, dtype=torch.int64) + frame_pos = torch.tensor([f], dtype=torch.int64) for commit in schedule: for layer_idx in range(N_LAYERS): k, v = _frame(kv, gen) @@ -1558,7 +1615,7 @@ def test_upsert_refuses_a_world_index_that_is_not_the_staged_shape(bad): kv = torch.zeros(2, 1, N_KV_HEADS, TPF, D_HEAD) with pytest.raises(RuntimeError, match="world_idx must be a"): - layer.upsert(kv, torch.tensor(0, dtype=torch.int64), True, bad) + layer.upsert(kv, torch.tensor([0], dtype=torch.int64), True, bad) def test_a_world_index_out_of_range_is_caught_on_the_host_paths(): @@ -1570,7 +1627,9 @@ def test_a_world_index_out_of_range_is_caught_on_the_host_paths(): layer = _manager(_ring_config(num_worlds=2)).layers[0] assert layer.world_span(1) == (layer.capacity, 2 * layer.capacity) - for bad in (-1, 2): + # The layer allocates one world past the pool (the shared padding scratch), + # so index 2 is that valid world and 3 is the first out-of-range one. + for bad in (-1, 3): with pytest.raises(IndexError, match="out of range"): layer.world_span(bad) with pytest.raises(IndexError, match="out of range"): diff --git a/test/modular/test_video_frame_protocol.py b/test/modular/test_video_frame_protocol.py index 71c027420..437cc42e3 100644 --- a/test/modular/test_video_frame_protocol.py +++ b/test/modular/test_video_frame_protocol.py @@ -487,6 +487,7 @@ def test_rollout_harness_variant_controls_config_and_checkpoint_default( assert generated["model_kwargs"] == { "variant": model_variant, "compile_dit": True, + "step_batch_size": 1, "checkpoint_dir": "custom/checkpoint", "ae_path": "custom/ae", } @@ -528,6 +529,7 @@ def test_rollout_harness_hub_config_omits_local_overrides_and_forwards_cache(tmp assert generated["model_kwargs"] == { "compile_dit": True, "variant": "waypoint-1.5-1b-360p", + "step_batch_size": 1, } assert generated["max_concurrent_requests"] == 2 assert generated["resources"]["kv"]["num_worlds"] == 2 @@ -650,6 +652,19 @@ def test_rollout_harness_requires_worker_schedule_interleaving_and_cleanup_marke serial, ("rid-a", "rid-b"), 3 ) + three_way = "\n".join( + [ + "DEBUG Executing: dit graph_walk=rollout ('rid-a', 'rid-b', 'rid-c')", + "DEBUG Executing: dit graph_walk=rollout ('rid-a', 'rid-b', 'rid-c')", + "DEBUG Executing: dit graph_walk=rollout ('rid-a', 'rid-b', 'rid-c')", + "INFO Waypoint dit: skipping async-overshoot rollout step 2 (request rid-a runs 2 steps)", + "INFO Waypoint dit: skipping async-overshoot rollout step 2 (request rid-b runs 2 steps)", + "INFO Waypoint dit: skipping async-overshoot rollout step 2 (request rid-c runs 2 steps)", + ] + ) + assert interleaving_failure(three_way, ("rid-a", "rid-b", "rid-c")) is None + assert harness["_execution_count_failure"](three_way, ("rid-a", "rid-b", "rid-c"), 2) is None + def test_rollout_harness_parses_and_filters_memory_telemetry(monkeypatch): harness = runpy.run_path(str(Path(__file__).parents[1] / "waypoint" / "serve_rollout.py")) @@ -750,3 +765,43 @@ def test_waypoint_emits_only_generated_raw_frame_chunks(): num_steps=1, actions=[{}], ) + + +def test_rollout_harness_batched_tolerance_gate_allows_bounded_psnr_drift(): + harness = runpy.run_path(str(Path(__file__).parents[1] / "waypoint" / "serve_rollout.py")) + batched_tolerance_failure = harness["_batched_tolerance_failure"] + chunk_size = 12 + base = bytes([100] * chunk_size) + + def shifted(delta): + return bytes((b + delta) & 0xFF for b in base) + + # (a) identical bytes -> pass + identical = base * 2 + assert batched_tolerance_failure(identical, identical, chunk_size) is None + + # (b) chunk 0 identical, chunk 1 off by +1 everywhere (PSNR 48.1) -> within both floors + expected_b = base * 2 + actual_b = base + shifted(1) + assert batched_tolerance_failure(actual_b, expected_b, chunk_size) is None + + # (c) chunk 1 off by +10 everywhere (PSNR 28.1) -> below the 38 dB early floor + actual_c = base + shifted(10) + failure_c = batched_tolerance_failure(actual_c, expected_b, chunk_size) + assert failure_c is not None + assert "chunk 1" in failure_c + assert "38.0" in failure_c + + # (d) chunk 0 identical, chunks 1-4 off by +1, chunk 5 off by +20 (PSNR 22.1) + expected_d = base * 6 + actual_d = base + shifted(1) * 4 + shifted(20) + failure_d = batched_tolerance_failure(actual_d, expected_d, chunk_size) + assert failure_d is not None + assert "chunk 5" in failure_d + assert "25.0" in failure_d + + # (e) length mismatch + failure_e = batched_tolerance_failure(base, base * 2, chunk_size) + assert failure_e is not None + assert str(len(base)) in failure_e + assert str(len(base) * 2) in failure_e diff --git a/test/modular/test_waypoint_components.py b/test/modular/test_waypoint_components.py index c2453c2c1..6464ea36e 100644 --- a/test/modular/test_waypoint_components.py +++ b/test/modular/test_waypoint_components.py @@ -321,7 +321,7 @@ def test_ring_slot_rotation(kind, dilation, frames, expected_slots): last wrote it, so the expected list is the whole history at once.""" cache = make_cache(ring_frames=16, ring_buckets=16, dilation=dilation) for f in frames: - upsert(cache, frame_kv(f), torch.tensor(f, dtype=torch.int64), commit=True) + upsert(cache, frame_kv(f), torch.tensor([f], dtype=torch.int64), commit=True) assert ring_slot_values(cache) == [float(v) for v in expected_slots], kind assert bool(cache.written[: cache.ring_len].all()) @@ -331,14 +331,14 @@ def test_frozen_passes_leave_the_ring_byte_identical(): would corrupt the world state permanently, and nothing would raise.""" cache = make_cache(ring_frames=4, ring_buckets=4, dilation=1) for f in range(4): - upsert(cache, frame_kv(f), torch.tensor(f, dtype=torch.int64), commit=True) + upsert(cache, frame_kv(f), torch.tensor([f], dtype=torch.int64), commit=True) ring_before = cache.kv[:, :, :, : cache.ring_len].clone() written_before = cache.written.clone() scratch_before = cache.kv[:, :, :, cache.ring_len :].clone() for pass_idx in range(4): # the four Euler steps, each a different noisy x - upsert(cache, frame_kv(100 + pass_idx), torch.tensor(4, dtype=torch.int64), commit=False) + upsert(cache, frame_kv(100 + pass_idx), torch.tensor([4], dtype=torch.int64), commit=False) assert torch.equal(cache.kv[:, :, :, : cache.ring_len], ring_before), ( "a frozen pass wrote the ring; that is amnesia, not a cache miss" @@ -360,12 +360,12 @@ def test_mask_hides_the_slot_this_frame_is_about_to_overwrite(): checked to agree over a whole 4+1 frame.""" cache = make_cache(ring_frames=4, ring_buckets=4, dilation=1) for f in range(4): - upsert(cache, frame_kv(f), torch.tensor(f, dtype=torch.int64), commit=True) + upsert(cache, frame_kv(f), torch.tensor([f], dtype=torch.int64), commit=True) assert set(range(5)) == visible_blocks( make_block_mask(TPF, cache.capacity, cache.written) ), "precondition: the whole ring plus scratch is written" - fp = torch.tensor(4, dtype=torch.int64) # slot 0 is about to be reused + fp = torch.tensor([4], dtype=torch.int64) # slot 0 is about to be reused for commit in (False, False, False, False, True): _, _, visible = upsert(cache, frame_kv(4), fp, commit=commit) assert visible_blocks(make_block_mask(TPF, cache.capacity, visible)) == {1, 2, 3, 4}, ( @@ -373,22 +373,52 @@ def test_mask_hides_the_slot_this_frame_is_about_to_overwrite(): ) +def test_upsert_batches_two_worlds_like_two_sequential_calls(): + """``upsert`` at B=2 (one row each for worlds 0 and 1) is bit-exact to + running the same two frames one world at a time: the row dimension is + folded into the token dim by ``world_base``, so a batched call must not + touch a row that is not its own.""" + def new_cache() -> LayerRingCache: + return LayerRingCache( + num_worlds=2, n_kv_heads=1, ring_frames=4, ring_buckets=4, + d_head=8, tokens_per_frame=TPF, pinned_dilation=1, + dtype=torch.float32, device="cpu", + ) + + solo = new_cache() + upsert(solo, frame_kv(10.0), torch.tensor([2], dtype=torch.int64), commit=True, world=0) + upsert(solo, frame_kv(20.0), torch.tensor([3], dtype=torch.int64), commit=True, world=1) + + batched = new_cache() + kv = torch.cat([frame_kv(10.0), frame_kv(20.0)], dim=3) + batched.upsert( + kv, + torch.tensor([2, 3], dtype=torch.int64), + True, + torch.tensor([0, 1], dtype=torch.int64), + build_visibility=False, + ) + + assert torch.equal(batched.kv, solo.kv) + assert torch.equal(batched.written, solo.written) + + def test_global_layer_commits_nothing_on_non_dilation_frames(): """``torch.where(write_step, ring_idx, current_idx)`` redirects the commit onto the scratch slot it just wrote.""" cache = make_cache(ring_frames=4, ring_buckets=4, dilation=8) - upsert(cache, frame_kv(0), torch.tensor(0, dtype=torch.int64), commit=True) + upsert(cache, frame_kv(0), torch.tensor([0], dtype=torch.int64), commit=True) ring_before = cache.kv[:, :, :, : cache.ring_len].clone() written_before = cache.written.clone() for f in range(1, 8): # the 7 non-committing frames of every 8 - upsert(cache, frame_kv(f), torch.tensor(f, dtype=torch.int64), commit=True) + upsert(cache, frame_kv(f), torch.tensor([f], dtype=torch.int64), commit=True) assert torch.equal(cache.kv[:, :, :, : cache.ring_len], ring_before) assert torch.equal(cache.written, written_before) assert cache.kv[0, 0, 0, cache.ring_len, 0].item() == 7.0 # scratch has the latest - upsert(cache, frame_kv(8), torch.tensor(8, dtype=torch.int64), commit=True) + upsert(cache, frame_kv(8), torch.tensor([8], dtype=torch.int64), commit=True) assert ring_slot_values(cache)[:2] == [0.0, 8.0] @@ -437,7 +467,7 @@ def test_bucket_round_up_is_faithful_but_currently_unobservable(dilation): floor_cache = make_cache(ring_frames=4, ring_buckets=4, dilation=dilation) for f in range(24): - fp = torch.tensor(f, dtype=torch.int64) + fp = torch.tensor([f], dtype=torch.int64) for pass_idx in range(5): commit = pass_idx == 4 kv = frame_kv(f * 10 + pass_idx) @@ -453,19 +483,19 @@ def test_bucket_round_up_is_faithful_but_currently_unobservable(dilation): def test_upsert_rejects_a_wrong_shaped_frame_or_clock(): cache = make_cache(ring_frames=4, ring_buckets=4, dilation=1) - fp = torch.tensor(0, dtype=torch.int64) - with pytest.raises(RuntimeError, match="exactly one frame per upsert"): + fp = torch.tensor([0], dtype=torch.int64) + with pytest.raises(RuntimeError, match="exactly one frame per world"): upsert(cache, frame_kv(0, tokens=TPF // 2), fp, commit=True) - with pytest.raises(RuntimeError, match=r"frame_pos must be a \[\] int64 tensor"): - upsert(cache, frame_kv(0), torch.tensor([0], dtype=torch.int64), commit=True) - with pytest.raises(RuntimeError, match=r"frame_pos must be a \[\] int64 tensor"): - upsert(cache, frame_kv(0), torch.tensor(0, dtype=torch.int32), commit=True) + with pytest.raises(RuntimeError, match=r"frame_pos must be a \[B\] int64 tensor"): + upsert(cache, frame_kv(0), torch.tensor(0, dtype=torch.int64), commit=True) + with pytest.raises(RuntimeError, match=r"frame_pos must be a \[B\] int64 tensor"): + upsert(cache, frame_kv(0), torch.tensor([0], dtype=torch.int32), commit=True) def test_reset_restores_a_fresh_ring(): cache = make_cache(ring_frames=4, ring_buckets=4, dilation=1) for f in range(4): - upsert(cache, frame_kv(f + 1), torch.tensor(f, dtype=torch.int64), commit=True) + upsert(cache, frame_kv(f + 1), torch.tensor([f], dtype=torch.int64), commit=True) cache.reset(0) assert not bool(cache.kv.any()) # The scratch tail stays permanently visible -- masking it removes @@ -493,7 +523,7 @@ def test_ring_state_is_a_deep_copy_and_is_specific_to_the_compaction_setting(): gen = torch.Generator().manual_seed(3) for layer in range(config.n_layers): frame = torch.randn(1, 1, TPF, config.d_head, generator=gen) - kv.upsert(frame, frame, layer, torch.tensor(0, dtype=torch.int64), commit=True) + kv.upsert(frame, frame, layer, torch.tensor([0], dtype=torch.int64), commit=True) state = kv.get_state("a") snapshot = [t.clone() for t, _ in state["layers"]] @@ -504,7 +534,13 @@ def test_ring_state_is_a_deep_copy_and_is_specific_to_the_compaction_setting(): assert all(torch.equal(a, b) for a, b in zip((t for t, _ in state["layers"]), snapshot, strict=True)) kv.load_state("a", state) - assert all(torch.equal(layer.kv, t) for layer, (t, _) in zip(kv.layers, state["layers"], strict=True)) + # Compare the request's world span, not the whole buffer: the ring now holds + # a padding world past the resident pool that get_state never captured. + world = kv.world_of("a") + assert all( + torch.equal(layer.kv[:, :, :, slice(*layer.world_span(world))], t) + for layer, (t, _) in zip(kv.layers, state["layers"], strict=True) + ) other = ring_manager(dataclasses.replace(config, full_global_ring=True)) other.ingest_request("a") @@ -571,7 +607,7 @@ def test_compacted_global_ring_addresses_all_sixteen_slots(): for j in range(16): f = 8 * j - upsert(cache, frame_kv(f), torch.tensor(f, dtype=torch.int64), commit=True) + upsert(cache, frame_kv(f), torch.tensor([f], dtype=torch.int64), commit=True) assert ring_slot_values(cache) == [float(8 * j) for j in range(16)] assert bool(cache.written[: cache.ring_len].all()), "a 2-bucket ring would leave 14 slots unwritten" @@ -588,7 +624,7 @@ def drive_ring(config: WaypointConfig, n_frames: int, seed: int = 7) -> list[tor gen = torch.Generator().manual_seed(seed) outputs = [] for f in range(n_frames): - fp = torch.tensor(f, dtype=torch.int64) + fp = torch.tensor([f], dtype=torch.int64) for pass_idx in range(5): for layer in range(config.n_layers): k = torch.randn(1, 1, TPF, config.d_head, generator=gen) diff --git a/test/modular/test_waypoint_dit.py b/test/modular/test_waypoint_dit.py index 8a0c2a8af..30dc77efa 100644 --- a/test/modular/test_waypoint_dit.py +++ b/test/modular/test_waypoint_dit.py @@ -428,7 +428,7 @@ def test_generate_frame_is_four_frozen_denoise_passes_then_one_commit(): try: with torch.no_grad(): dit.generate_frame( - noise, torch.tensor(0, dtype=torch.int64), + noise, torch.tensor([0], dtype=torch.int64), mouse=mouse, button=button, scroll=scroll, ) finally: @@ -463,7 +463,7 @@ def test_cond_head_cache_is_bit_exact_and_replaces_the_live_projection(): materialized and one not, must return the same latent.""" config = reduced_config() noise, mouse, button, scroll = frame_inputs(config) - fp = torch.tensor(0, dtype=torch.int64) + fp = torch.tensor([0], dtype=torch.int64) live, _ = bound_dit(config, seed=0) cached, _ = bound_dit(config, seed=0) @@ -489,7 +489,7 @@ def test_the_ring_only_moves_on_the_committing_pass(): config = reduced_config() dit, kv = bound_dit(config) noise, mouse, button, scroll = frame_inputs(config) - fp = torch.tensor(0, dtype=torch.int64) + fp = torch.tensor([0], dtype=torch.int64) ring_lens = [layer.ring_len for layer in kv.layers] before = [layer.kv[:, :, :, :n].clone() for layer, n in zip(kv.layers, ring_lens, strict=True)] @@ -530,7 +530,7 @@ def spy(*args, **kwargs): dit._denoise_pass = spy with torch.no_grad(): x0 = dit.generate_frame( - noise, torch.tensor(0, dtype=torch.int64), + noise, torch.tensor([0], dtype=torch.int64), mouse=mouse, button=button, scroll=scroll, ) @@ -555,7 +555,7 @@ def test_append_frame_is_the_committing_pass_alone(): try: with torch.no_grad(): out = dit.append_frame( - latent, torch.tensor(0, dtype=torch.int64), + latent, torch.tensor([0], dtype=torch.int64), mouse=mouse, button=button, scroll=scroll, ) finally: @@ -733,7 +733,7 @@ def test_layer_zero_v_reaches_every_block_in_the_dit(): latent, mouse, button, scroll = frame_inputs(config) with torch.no_grad(): dit.append_frame( - latent, torch.tensor(0, dtype=torch.int64), + latent, torch.tensor([0], dtype=torch.int64), mouse=mouse, button=button, scroll=scroll, ) @@ -868,7 +868,7 @@ def test_meta_built_model_generates_a_frame_in_the_serving_dtypes(): noise, mouse, button, scroll = frame_inputs(config, dtype=torch.bfloat16) with torch.no_grad(): x0 = dit.generate_frame( - noise, torch.tensor(0, dtype=torch.int64), + noise, torch.tensor([0], dtype=torch.int64), mouse=mouse, button=button, scroll=scroll, ) @@ -891,7 +891,7 @@ def test_two_frames_advance_the_ring_clock_together(): with torch.no_grad(): for f in range(2): dit.generate_frame( - noise, torch.tensor(f, dtype=torch.int64), + noise, torch.tensor([f], dtype=torch.int64), mouse=mouse, button=button, scroll=scroll, ) @@ -899,8 +899,8 @@ def test_two_frames_advance_the_ring_clock_together(): assert len(passes) == 10 assert [next(iter({u["frame_pos"] for u in group})) for group in passes] == [0] * 5 + [1] * 5 - pos = dit._pos_ids(torch.tensor(3, dtype=torch.int64)) - assert pos.f_pos.item() == 3 and pos.f_pos.ndim == 0 + pos = dit._pos_ids(torch.tensor([3], dtype=torch.int64)) + assert pos.f_pos.item() == 3 and pos.f_pos.ndim == 1 assert pos.t_pos.shape == (1, config.tokens_per_frame) assert bool((pos.t_pos == 3 * config.ts_mult).all()) assert bool((pos.y_pos == torch.arange(TPF).div(config.width, rounding_mode="floor")).all()) diff --git a/test/modular/test_waypoint_gpu.py b/test/modular/test_waypoint_gpu.py index 590c8010a..2c57a1b39 100644 --- a/test/modular/test_waypoint_gpu.py +++ b/test/modular/test_waypoint_gpu.py @@ -228,6 +228,30 @@ def admit_frame( attn.plan(AttentionStep(), ctx) +def _batch_ctx(*rids: str) -> StepContext: + return StepContext(request_ids=rids, graph_walk="rollout", slot=0, capture=False) + + +def _batch_step(frames: list[tuple[str, int]]) -> RingKVStep: + return RingKVStep(frames=tuple(frames)) + + +def admit_batch( + kv: RingKVManager, + frames: list[tuple[str, int]], + attn: AttentionManager, +) -> None: + """`admit_frame`'s multi-row form: one step naming every ``(rid, frame)`` + pair in ``frames``, in the row order the batched forward's inputs use.""" + ctx = _batch_ctx(*(rid for rid, _ in frames)) + step = _batch_step(frames) + outcome = kv.admit(step, ctx) + assert outcome.ok, f"batch {frames} refused: {outcome.reason}" + ring_plan = kv.plan(step, ctx) + ctx.plan_results["kv"] = ring_plan + attn.plan(AttentionStep(), ctx) + + def scrub(kv: RingKVManager, *rids: str) -> None: """Return every world to the pool and zero every ring. The tests share one captured graph, so each one starts from a ring that holds nothing.""" @@ -285,7 +309,7 @@ def test_compile_regions_holds_under_fullgraph(): with torch.no_grad(): out = dit.generate_frame( noise_for(config, "warm", 0), - torch.tensor(0, dtype=torch.int64, device=DEVICE), + torch.tensor([0], dtype=torch.int64, device=DEVICE), mouse=mouse, button=button, scroll=scroll, ) torch.cuda.synchronize() @@ -394,7 +418,7 @@ def rollout(compiled: bool): latents.append( dit.generate_frame( noise_for(config, "a2", frame), - torch.tensor(frame, dtype=torch.int64, device=DEVICE), + torch.tensor([frame], dtype=torch.int64, device=DEVICE), mouse=mouse, button=button, scroll=scroll, ).clone() ) @@ -446,7 +470,7 @@ def captured(): kv.ingest_request("capture") admit_frame(kv, "capture", 0, attn) static_noise = torch.zeros(1, 1, *config.latent_shape, dtype=DTYPE, device=DEVICE) - static_frame = torch.zeros((), dtype=torch.int64, device=DEVICE) + static_frame = torch.zeros(1, dtype=torch.int64, device=DEVICE) static_latent = torch.zeros(1, 1, *config.latent_shape, dtype=DTYPE, device=DEVICE) @@ -520,7 +544,7 @@ def eager_frame(captured, rid: str, frame: int, stream: str) -> torch.Tensor: with torch.no_grad(): latent = captured["dit"].generate_frame( noise_for(captured["config"], stream, frame), - torch.tensor(frame, dtype=torch.int64, device=DEVICE), + torch.tensor([frame], dtype=torch.int64, device=DEVICE), mouse=mouse, button=button, scroll=scroll, ).clone() torch.cuda.synchronize() @@ -553,7 +577,7 @@ def eager_prime(captured, rid: str, stream: str) -> torch.Tensor: with torch.no_grad(): latent = captured["dit"].append_frame( noise_for(captured["config"], stream, 0), - torch.tensor(0, dtype=torch.int64, device=DEVICE), + torch.tensor([0], dtype=torch.int64, device=DEVICE), mouse=mouse, button=button, scroll=scroll, ).clone() torch.cuda.synchronize() @@ -719,6 +743,102 @@ def test_two_worlds_interleaved_match_the_same_rollouts_run_alone(captured): scrub(kv, "both_a", "both_b") +def test_batched_step_matches_the_same_rollouts_run_one_row_at_a_time(): + """B=2 across two worlds, 8 frames each, matches the identical rollouts run + one row (B=1) at a time -- the row-order invariant BATCH-001 rests on. + + Same weights for both halves: run B=1 alternating first and snapshot every + frame and both rings, then scrub and run the same two streams again as a + genuinely batched B=2 forward per frame. A batched GEMM may pick a + different cuBLAS kernel at M=2T than at M=T, so that cross-run comparison + is bounded in bf16 ulp rather than exact -- but two rows of the SAME + batched call fed literally identical inputs share one kernel launch and + must be bit-exact. + """ + config = gpu_config() + frames = 8 + mouse, button, scroll = controls(config) + dit, kv, attn = build(config, seed=0, num_worlds=2) + dit.materialize_runtime_tables(DEVICE) + + # ---- B=1, alternating: each world through its own single-row forward. + for rid in ("w0", "w1"): + kv.ingest_request(rid) + solo_latents = {"w0": [], "w1": []} + with torch.no_grad(): + for frame in range(frames): + for stream, rid in (("s0", "w0"), ("s1", "w1")): + admit_frame(kv, rid, frame, attn) + out = dit.generate_frame( + noise_for(config, stream, frame), + torch.tensor([frame], dtype=torch.int64, device=DEVICE), + mouse=mouse, button=button, scroll=scroll, + ).clone() + solo_latents[rid].append(out) + kv.commit(_step(rid, frame), _ctx(rid)) + torch.cuda.synchronize() + solo_ring = {rid: world_snapshot(kv, kv.world_of(rid)) for rid in ("w0", "w1")} + scrub(kv, "w0", "w1") + + # ---- B=2, batched: both worlds' row for frame f in one forward. + for rid in ("w0", "w1"): + kv.ingest_request(rid) + mouse2 = mouse.expand(2, -1, -1).contiguous() + button2 = button.expand(2, -1, -1).contiguous() + scroll2 = scroll.expand(2, -1, -1).contiguous() + batched_latents = {"w0": [], "w1": []} + with torch.no_grad(): + for frame in range(frames): + batch_frames = [("w0", frame), ("w1", frame)] + admit_batch(kv, batch_frames, attn) + noise = torch.cat( + [noise_for(config, "s0", frame), noise_for(config, "s1", frame)], dim=0, + ) + frame_pos = torch.tensor([frame, frame], dtype=torch.int64, device=DEVICE) + out = dit.generate_frame( + noise, frame_pos, mouse=mouse2, button=button2, scroll=scroll2, + ).clone() + batched_latents["w0"].append(out[0:1]) + batched_latents["w1"].append(out[1:2]) + kv.commit(_batch_step(batch_frames), _batch_ctx("w0", "w1")) + torch.cuda.synchronize() + batch_ring = {rid: world_snapshot(kv, kv.world_of(rid)) for rid in ("w0", "w1")} + scrub(kv, "w0", "w1") + + for rid in ("w0", "w1"): + for frame, (want, got) in enumerate( + zip(solo_latents[rid], batched_latents[rid], strict=True) + ): + peak = want.float().abs().max().item() + deviation = (want.float() - got.float()).abs().max().item() + assert deviation <= COMPILE_TOL_ULP * BF16_EPS * peak, ( + f"world {rid} frame {frame}: batching diverged from the same row run " + f"alone by {deviation:.3e} ({deviation / peak / BF16_EPS:.2f} ulp of peak)" + ) + assert_worlds_equal(solo_ring[rid], batch_ring[rid], f"world {rid}, batched vs alone") + + # Two rows of the SAME batched call, fed literally identical inputs (same + # noise, same frame, same controls): must be bit-exact -- nothing about a + # row's own math may read another row's data or depend on its batch position. + for rid in ("id0", "id1"): + kv.ingest_request(rid) + identical_frames = [("id0", 0), ("id1", 0)] + admit_batch(kv, identical_frames, attn) + same_noise = noise_for(config, "dup", 0).expand(2, -1, -1, -1, -1).contiguous() + same_frame_pos = torch.tensor([0, 0], dtype=torch.int64, device=DEVICE) + with torch.no_grad(): + dup_out = dit.generate_frame( + same_noise, same_frame_pos, mouse=mouse2, button=button2, scroll=scroll2, + ) + torch.cuda.synchronize() + assert torch.equal(dup_out[0], dup_out[1]), ( + "identical inputs on two rows of the same batched call produced different " + "output; a row is reading something batch-position-dependent" + ) + kv.commit(_batch_step(identical_frames), _batch_ctx("id0", "id1")) + scrub(kv, "id0", "id1") + + # --------------------------------------------------------------------------- # A.6 -- the prime graph # --------------------------------------------------------------------------- diff --git a/test/modular/test_waypoint_shell.py b/test/modular/test_waypoint_shell.py index d27cc8052..add2b95cd 100644 --- a/test/modular/test_waypoint_shell.py +++ b/test/modular/test_waypoint_shell.py @@ -63,6 +63,7 @@ from mstar.model.submodule_base import ModelInputsFromEngine from mstar.model.waypoint.components.attention import WaypointAttention from mstar.model.waypoint.components.dit import WaypointDiT +from mstar.model.waypoint.components.taehv import decode_latent, initial_decoder_histories from mstar.model.waypoint.config import ( WaypointConfig, waypoint_1_5_1b_360p, @@ -76,6 +77,7 @@ ROLLOUT_WALK, WaypointDitSubmodule, WaypointVaeEncoderSubmodule, + _rollout_capture_batch_sizes, ) from mstar.model.waypoint.waypoint_model import ( DIT_NODE, @@ -464,6 +466,15 @@ def test_no_prepared_tensor_carries_tokens_per_frame_in_its_shape( told the real token count -- so the burden falls here. At 720P nothing collides, but the margin is thin: a 256-token-per-frame variant would put ``button``'s ``n_buttons = 256`` straight into the crosshairs. + + With ``step_batch_size > 1`` the runner's per-bucket count is + ``bs * tokens_per_frame``, and 360p at bs=2 does hit ``n_buttons``. That + case is handled on the runner side instead: ``_capture_one`` passes the + bucket's own batch size alongside its token count, so ``_seq_dim`` checks + dim 0 against both before it ever scans for a coincidental match, and + ``button`` shares its buffer like any other per-row tensor (see + ``test_cuda_graph_capture.py``, which also covers the case of a caller + that can't supply a batch size — that one is still a hard failure). """ inputs = _controller_stream(config, frames=4) inputs["latent"] = [torch.zeros((1, 1, *config.latent_shape))] @@ -904,6 +915,32 @@ def test_binding_without_a_declared_resource_fails_at_bind(submodule): submodule.bind_node_resources(partial) +# --------------------------------------------------------------------------- +# Batch size cap +# --------------------------------------------------------------------------- + + +def test_max_batch_size_is_step_batch_size_for_both_walks(config): + """Both walks carry up to ``step_batch_size`` rows -- prime rows batch + too, when several requests are admitted in the same step.""" + batched = dataclasses.replace(config, step_batch_size=4) + with torch.device("meta"): + dit = WaypointDiT(batched) + dit.cast_serving_dtypes() + submodule = WaypointDitSubmodule(dit, _FakeTaehv(), batched) + + assert submodule.max_batch_size(PRIME_WALK) == 4 + assert submodule.max_batch_size(ROLLOUT_WALK) == 4 + + +def test_dit_can_batch_is_true(submodule): + """The eager path must batch too. A captured lease replays batched + regardless, but with ``can_batch`` False a graphs-off (or uncaptured-shape) + multi-row step falls to one forward per request instead of a single batched + forward -- the one place the port used to diverge from the other models.""" + assert submodule.can_batch(batch=None, model_inputs=[]) is True + + # --------------------------------------------------------------------------- # Capture configs # --------------------------------------------------------------------------- @@ -926,6 +963,13 @@ def test_both_dit_walks_are_optional_captures(submodule, config): assert cfg.capture_forward_method == "forward_batched" assert cfg.single_request_inputs.input_seq_len == config.tokens_per_frame + rollout_cfg, prime_cfg = configs + # Both walks' captured buckets are a real ceiling on the eager batch size: + # prime now captures the same buckets rollout does, so a prime batch bigger + # than the largest captured bucket is refused, not run eager. + assert rollout_cfg.caps_eager_batch_size is True + assert prime_cfg.caps_eager_batch_size is True + rollout, prime = (cfg.single_request_inputs.tensor_inputs for cfg in configs) assert "noise" in rollout and "latent" not in rollout assert "latent" in prime and "noise" not in prime @@ -937,6 +981,42 @@ def test_both_dit_walks_are_optional_captures(submodule, config): assert submodule.disable_torch_compile is True +@pytest.mark.parametrize( + "step_batch_size, expected", + [ + (1, [1]), + (2, [1, 2]), + (3, [1, 2, 3]), + (4, [1, 2, 4]), + (6, [1, 2, 4, 6]), + (8, [1, 2, 4, 8]), + (16, [1, 2, 4, 8, 16]), + ], +) +def test_rollout_capture_batch_sizes_is_geometric(step_batch_size, expected): + """Powers of two up to B, then B itself -- so startup grows with log B, not + B. B that is itself a power of two ends on it once (no duplicate); a B that + is not appends the odd top bucket the padding path rounds up to.""" + assert _rollout_capture_batch_sizes(step_batch_size) == expected + + +def test_both_walks_capture_the_same_geometric_buckets(config): + """With ``step_batch_size > 1`` prime captures the same geometric buckets as + rollout and caps eagerly at the top of them, so a multi-request prime batch + replays a captured, padded graph rather than re-tracing eagerly.""" + batched = dataclasses.replace(config, step_batch_size=8) + with torch.device("meta"): + dit = WaypointDiT(batched) + dit.cast_serving_dtypes() + submodule = WaypointDitSubmodule(dit, _FakeTaehv(), batched) + + configs = submodule.get_cuda_graph_configs(torch.device("meta")) + assert [cfg.capture_graph_walk for cfg in configs] == [ROLLOUT_WALK, PRIME_WALK] + for cfg in configs: + assert cfg.capture_batch_sizes == [1, 2, 4, 8] + assert cfg.caps_eager_batch_size is True + + def test_dit_prime_capture_can_be_declined_on_its_own(config): """The A/B control arm: prime off leaves the steady rollout graph alone.""" no_prime = dataclasses.replace(config, capture_dit_prime=False) @@ -1159,11 +1239,80 @@ def test_the_fused_decode_turns_one_frame_into_one_raw_clip(decoder, ae_config): )) frames = out["video_output"][0] - assert frames.shape == (ae_config.temporal_compression, 360, 640, 3) + # forward() is the pre-split batched call (row B=1 here); the engine's + # forward_batched drops this leading dim per row before postprocess sees it. + assert frames.shape == (1, ae_config.temporal_compression, 360, 640, 3) assert frames.dtype == torch.uint8 decoder.cleanup_request("r0") +def test_forward_batched_hands_each_request_its_own_row(decoder, monkeypatch): + """``forward`` returns ``{key: [batched_tensor]}``; ``forward_batched`` must + index the tensor's rows, not the one-element list around it, and hand row + ``i`` to ``request_ids[i]``. ``video_output`` drops the batch dim (what + ``postprocess`` consumes); every other key keeps a leading 1 (what + ``prepare_inputs`` builds for the next step).""" + frames = torch.arange(2 * 4 * 2 * 2 * 3, dtype=torch.uint8).reshape(2, 4, 2, 2, 3) + clock = torch.tensor([5, 9]) + history = torch.arange(2 * 3 * 2 * 2, dtype=torch.float32).reshape(2, 3, 2, 2) + monkeypatch.setattr( + decoder, "forward", + lambda *a, **k: { + "video_output": [frames], "clock": [clock], "decoder_history_0": [history], + }, + ) + engine_inputs = ModelInputsFromEngine( + request_ids=["a", "b"], + per_request_info={rid: _fwd_info(rid, graph_walk=ROLLOUT_WALK) for rid in "ab"}, + ) + + out = decoder.forward_batched(ROLLOUT_WALK, engine_inputs=engine_inputs) + + assert set(out) == {"a", "b"} + for i, rid in enumerate("ab"): + assert torch.equal(out[rid]["video_output"][0], frames[i]) + assert torch.equal(out[rid]["clock"][0], clock[i : i + 1]) + assert torch.equal(out[rid]["decoder_history_0"][0], history[i : i + 1]) + assert all(isinstance(v[0], torch.Tensor) for v in out[rid].values()) + + +def test_decode_latent_batches_rows_independently(taehv_weights, ae_config): + """``decode_latent`` at B=2 equals two independent B=1 calls, row for row. + + MemBlock and TGrow (``_FakeTaehv``'s decoder) are convs/reshapes that never + mix across the batch dim, so nothing here should make row 1 depend on row + 0's latent -- the property the batched engine path now relies on. + """ + latent_shape = (1, ae_config.channels, *ae_config.latent_shape[1:]) + generator = torch.Generator().manual_seed(7) + latents = [ + torch.randn(latent_shape, generator=generator).to(torch.bfloat16) + for _ in range(2) + ] + histories = [initial_decoder_histories(taehv_weights, latent) for latent in latents] + + solo = [ + decode_latent( + taehv_weights, latent, history, output_size=(360, 640), initialize=True, + ) + for latent, history in zip(latents, histories) + ] + + batched_latent = torch.cat(latents, dim=0) + batched_histories = tuple( + torch.cat([histories[0][idx], histories[1][idx]], dim=0) for idx in range(9) + ) + batched_frames, batched_state = decode_latent( + taehv_weights, batched_latent, batched_histories, output_size=(360, 640), initialize=True, + ) + + assert batched_frames.shape == (2, ae_config.temporal_compression, 360, 640, 3) + for row, (solo_frames, solo_state) in enumerate(solo): + assert torch.equal(batched_frames[row], solo_frames[0]) + for idx in range(9): + assert torch.equal(batched_state[idx][row], solo_state[idx][0]) + + def test_prime_and_rollout_use_the_same_fixed_tensor_histories(decoder, ae_config): latent = torch.full( (1, 1, ae_config.channels, *ae_config.latent_shape[1:]), 0.125, dtype=torch.bfloat16, diff --git a/test/modular/test_waypoint_streaming_benchmark.py b/test/modular/test_waypoint_streaming_benchmark.py index 7505a1c46..2991f039f 100644 --- a/test/modular/test_waypoint_streaming_benchmark.py +++ b/test/modular/test_waypoint_streaming_benchmark.py @@ -223,3 +223,170 @@ def test_benchmark_cli_derives_stall_threshold_and_has_no_release_gate(benchmark action.dest.startswith("release") for action in benchmark["_build_parser"]()._actions ) + + +def test_streams_worlds_and_batch_default_to_one_without_the_new_flags(benchmark): + """--streams 1 (today's only mode) must keep worlds=batch=1, matching the + hardcoded worlds=1 _run_config call this replaced.""" + args = benchmark["_parse_args"](["--variant", "360p", "--physical-gpu", "2"]) + + assert args.streams == 1 + assert args.worlds == 1 + assert args.batch == 1 + + +def test_worlds_and_batch_default_to_streams(benchmark): + args = benchmark["_parse_args"]( + ["--variant", "360p", "--physical-gpu", "2", "--streams", "4"] + ) + + assert args.worlds == 4 + assert args.batch == 4 + + +@pytest.mark.parametrize( + "extra, message", + [ + (["--streams", "0"], "--streams must be positive"), + (["--streams", "4", "--worlds", "0"], "--worlds must be positive"), + (["--streams", "4", "--batch", "0"], "--batch must be positive"), + (["--streams", "4", "--worlds", "2", "--batch", "4"], "--batch must be <= --worlds"), + ], +) +def test_benchmark_cli_rejects_invalid_concurrent_stream_configuration( + benchmark, capsys, extra, message +): + with pytest.raises(SystemExit, match="2"): + benchmark["_parse_args"](["--variant", "360p", "--physical-gpu", "2", *extra]) + assert message in capsys.readouterr().err + + +def test_measure_stream_waits_on_start_barrier_before_opening_the_stream(benchmark, tmp_path): + """The request body (client.stream(...)) must be built before the barrier + wait, and consumption (the clock start) must not begin until after it.""" + variant = benchmark["rollout"].Variant("test", 2, 3, 1, "unused") + + def metadata(frame_index): + return { + "width": 3, + "height": 2, + "fps": 60.0, + "pixel_format": "rgb24", + "frame_index": frame_index, + "frame_count": 4, + } + + calls = [] + + class Barrier: + def wait(self, timeout=None): + calls.append("barrier_wait") + + class Client: + def stream(self, **kwargs): + calls.append("stream_called") + return iter([VideoFrameChunk(bytes([1]) * 72, metadata(0))]) + + clock_values = iter([10.0, 10.5, 10.6]) + metrics, failures = benchmark["_measure_stream"]( + Client(), + tmp_path / "seed.png", + variant, + num_steps=1, + request_id="rid", + rng_seed=1, + consumer_pause_seconds=0.0, + stall_threshold_seconds=0.4, + clock=lambda: next(clock_values), + sleep=lambda _seconds: None, + start_barrier=Barrier(), + ) + + assert calls == ["stream_called", "barrier_wait"] + assert failures == [] + assert metrics["chunk_count"] == 1 + + +def _chunk_stats(*, ttff_s, p50_s, p95_s, max_s, sustained, stalls): + return { + "time_to_first_frame_seconds": ttff_s, + "inter_chunk_gap_seconds": {"p50": p50_s, "p95": p95_s, "maximum": max_s}, + "sustained_media_to_wall_ratio": sustained, + "stalls": {"count": stalls}, + } + + +def test_stream_is_realtime_requires_sustained_gap_budget_and_no_stalls(benchmark): + is_realtime = benchmark["_stream_is_realtime"] + budget_s = benchmark["REALTIME_CHUNK_BUDGET_MS"] / 1000.0 + + healthy = _chunk_stats( + ttff_s=0.05, p50_s=0.05, p95_s=budget_s - 0.001, max_s=budget_s, sustained=1.05, stalls=0 + ) + assert is_realtime(healthy) is True + + under_sustained = {**healthy, "sustained_media_to_wall_ratio": 0.9} + assert is_realtime(under_sustained) is False + + over_budget = { + **healthy, + "inter_chunk_gap_seconds": {**healthy["inter_chunk_gap_seconds"], "p95": budget_s + 0.001}, + } + assert is_realtime(over_budget) is False + + stalled = {**healthy, "stalls": {"count": 1}} + assert is_realtime(stalled) is False + + +def test_concurrent_aggregate_computes_worst_median_realtime_and_delivery_bound(benchmark): + aggregate = benchmark["_concurrent_aggregate"] + budget_s = benchmark["REALTIME_CHUNK_BUDGET_MS"] / 1000.0 + + fast = _chunk_stats(ttff_s=0.05, p50_s=0.05, p95_s=0.06, max_s=0.07, sustained=1.2, stalls=0) + slow = _chunk_stats(ttff_s=0.20, p50_s=0.15, p95_s=budget_s + 0.01, max_s=0.20, sustained=0.8, stalls=1) + per_stream = [fast, slow] + + server_slack = {"step_spacing_ms": {"p50": 200.0, "p95": 220.0}} + result = aggregate(per_stream, aggregate_fps=8.0, server=server_slack) + + assert result["aggregate_fps"] == 8.0 + assert result["ttff_ms"]["p50"] == pytest.approx(125.0) + assert result["gap_ms"]["p50_worst"] == pytest.approx(150.0) + assert result["gap_ms"]["p95_worst"] == pytest.approx((budget_s + 0.01) * 1000.0) + assert result["gap_ms"]["max_worst"] == pytest.approx(200.0) + assert result["gap_ms"]["p50_median"] == pytest.approx((50.0 + 150.0) / 2) + assert result["sustained_min"] == pytest.approx(0.8) + assert result["stall_count_total"] == 1 + assert result["realtime_per_stream"] == [True, False] + assert result["all_realtime"] is False + assert result["server"] is server_slack + # gap p50_median (100ms) is not > 1.2x a 200ms server step: not delivery-bound. + assert result["delivery_bound"] is False + + server_fast = {"step_spacing_ms": {"p50": 50.0, "p95": 60.0}} + result_bound = aggregate(per_stream, aggregate_fps=8.0, server=server_fast) + # gap p50_median (100ms) > 1.2x a 50ms server step: clients are the bottleneck. + assert result_bound["delivery_bound"] is True + + +def test_concurrent_server_metrics_parses_rows_histogram_and_step_spacing(benchmark): + server_metrics = benchmark["_concurrent_server_metrics"] + log_text = "\n".join( + [ + "2026-09-17 21:38:40,000 DEBUG [worker-0] mstar.worker.worker: " + "Executing: dit graph_walk=rollout ('warmup-0',)", + "2026-09-17 21:38:40,500 DEBUG [worker-0] mstar.worker.worker: " + "Executing: dit graph_walk=rollout ('measured-0', 'measured-1')", + "2026-09-17 21:38:40,600 DEBUG [worker-0] mstar.worker.worker: " + "Executing: dit graph_walk=rollout ('measured-0', 'measured-1')", + "2026-09-17 21:38:40,800 DEBUG [worker-0] mstar.worker.worker: " + "Executing: dit graph_walk=rollout ('measured-0', 'measured-1')", + ] + ) + + result = server_metrics(log_text, {"measured-0", "measured-1"}, 12.3) + + assert result["rows_per_step_histogram"] == {2: 3} + assert result["step_spacing_ms"]["p50"] == pytest.approx(150.0) + assert result["step_spacing_ms"]["p95"] == pytest.approx(195.0) + assert result["startup_seconds"] == 12.3 diff --git a/test/waypoint/benchmark_streaming.py b/test/waypoint/benchmark_streaming.py index 4d27dad56..742da4f87 100644 --- a/test/waypoint/benchmark_streaming.py +++ b/test/waypoint/benchmark_streaming.py @@ -20,13 +20,17 @@ from __future__ import annotations import argparse +import ast +import concurrent.futures import hashlib import json import os +import re import statistics import subprocess import sys import tempfile +import threading import time from dataclasses import asdict, dataclass from datetime import datetime, timezone @@ -45,6 +49,16 @@ from mstar.client import MStarClient, VideoFrameChunk # noqa: E402 from mstar.utils import profiler # noqa: E402 +# One 4-frame chunk at 60 fps is the realtime delivery budget for a single +# stream (matches the chunk geometry _actions/_check assume elsewhere). +REALTIME_CHUNK_BUDGET_MS = 4 / 60 * 1000 + +# Worker DEBUG lines are formatted by logging.basicConfig(format="%(asctime)s +# %(levelname)s [worker_id] %(name)s: %(message)s") (mstar/conductor/conductor.py); +# %(asctime)s defaults to "2026-09-17 21:38:40,605". +_LOG_TIMESTAMP_RE = re.compile(r"^(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2},\d{3})") +_LOG_TIMESTAMP_FORMAT = "%Y-%m-%d %H:%M:%S,%f" + @dataclass(frozen=True) class ChunkObservation: @@ -100,6 +114,8 @@ def _stream_metrics( sustained_ratio = None stalls = [gap for gap in gaps if gap > stall_threshold_seconds] + realtime_budget_seconds = REALTIME_CHUNK_BUDGET_MS / 1000.0 + chunks_within_budget = sum(1 for gap in gaps if gap <= realtime_budget_seconds) return { "chunk_count": len(observations), "frame_count": total_frames, @@ -128,6 +144,9 @@ def _stream_metrics( gap - stall_threshold_seconds for gap in stalls ), }, + "on_time_chunk_fraction": ( + chunks_within_budget / len(gaps) if gaps else None + ), "consumer": { "pause_seconds": consumer_pause_seconds, "pause_count": consumer_pause_count, @@ -192,6 +211,7 @@ def _measure_stream( clock: Callable[[], float] = time.perf_counter, sleep: Callable[[float], None] = time.sleep, enable_nvtx: bool = False, + start_barrier: threading.Barrier | None = None, ) -> tuple[dict, list[str]]: """Consume one stream while retaining only timings and an incremental hash.""" stream = client.stream( @@ -204,6 +224,12 @@ def _measure_stream( seed=rng_seed, ) iterator = iter(stream) + # Lazy: crossing here means every stream in the wave has built its request + # body before any of them open the HTTP request, so the clock below starts + # from a synchronized release rather than staggered request construction + # (mirrors serve_rollout._rollout's start_barrier). + if start_barrier is not None: + start_barrier.wait(timeout=30) if enable_nvtx: profiler.range_push(f"benchmark.stream[{request_id}]") started = clock() @@ -344,6 +370,258 @@ def _backpressure_metrics( } +def _run_concurrent_wave( + client_factory: Callable[[], MStarClient], + seed_image: Path, + variant: rollout.Variant, + *, + num_steps: int, + request_ids: Sequence[str], + seeds: Sequence[int], + stall_threshold_seconds: float, + enable_nvtx: bool, +) -> tuple[list[tuple[dict, list[str]]], float, float]: + """Run ``len(request_ids)`` streams together, each opening only once every + thread has built its request body (mirrors serve_rollout._concurrent_rollouts). + + Returns (per-stream (metrics, failures) in ``request_ids`` order, wave + start, wave end) on the driver's own wall clock, for cross-stream + aggregate timing that does not require touching _stream_metrics. + """ + barrier = threading.Barrier(len(request_ids) + 1) + with concurrent.futures.ThreadPoolExecutor(max_workers=len(request_ids)) as executor: + futures = [ + executor.submit( + _measure_stream, + client_factory(), + seed_image, + variant, + num_steps=num_steps, + request_id=request_id, + rng_seed=seed, + consumer_pause_seconds=0.0, + stall_threshold_seconds=stall_threshold_seconds, + enable_nvtx=enable_nvtx, + start_barrier=barrier, + ) + for request_id, seed in zip(request_ids, seeds) + ] + barrier.wait(timeout=30) + wave_start = time.perf_counter() + results = [future.result() for future in futures] + wave_end = time.perf_counter() + return results, wave_start, wave_end + + +def _stream_is_realtime(metrics: dict) -> bool: + """A stream stayed realtime iff it sustained >=1x, its p95 inter-chunk gap + fit the one-chunk (4 frames @ 60 fps) delivery budget, and it never stalled.""" + sustained = metrics["sustained_media_to_wall_ratio"] + gap_p95 = metrics["inter_chunk_gap_seconds"]["p95"] + gap_p95_ms = gap_p95 * 1000.0 if gap_p95 is not None else None + return ( + sustained is not None + and sustained >= 1.0 + and gap_p95_ms is not None + and gap_p95_ms <= REALTIME_CHUNK_BUDGET_MS + and metrics["stalls"]["count"] == 0 + ) + + +def _concurrent_aggregate(per_stream: Sequence[dict], *, aggregate_fps: float | None, server: dict) -> dict: + """Cross-stream realtime summary built from each stream's _measure_stream + metrics dict (unmodified _stream_metrics output) plus the already-parsed + server-side rollout cadence.""" + ttff_ms = [ + metrics["time_to_first_frame_seconds"] * 1000.0 + for metrics in per_stream + if metrics["time_to_first_frame_seconds"] is not None + ] + gap_p50_ms = [ + metrics["inter_chunk_gap_seconds"]["p50"] * 1000.0 + for metrics in per_stream + if metrics["inter_chunk_gap_seconds"]["p50"] is not None + ] + gap_p95_ms = [ + metrics["inter_chunk_gap_seconds"]["p95"] * 1000.0 + for metrics in per_stream + if metrics["inter_chunk_gap_seconds"]["p95"] is not None + ] + gap_max_ms = [ + metrics["inter_chunk_gap_seconds"]["maximum"] * 1000.0 + for metrics in per_stream + if metrics["inter_chunk_gap_seconds"]["maximum"] is not None + ] + sustained = [ + metrics["sustained_media_to_wall_ratio"] + for metrics in per_stream + if metrics["sustained_media_to_wall_ratio"] is not None + ] + realtime_per_stream = [_stream_is_realtime(metrics) for metrics in per_stream] + realtime_count = sum(realtime_per_stream) + chunk_fractions = [ + metrics["on_time_chunk_fraction"] + for metrics in per_stream + if metrics["on_time_chunk_fraction"] is not None + ] + gap_p50_median = statistics.median(gap_p50_ms) if gap_p50_ms else None + server_step_p50 = server["step_spacing_ms"]["p50"] + + return { + "aggregate_fps": aggregate_fps, + "ttff_ms": {"p50": _percentile(ttff_ms, 0.50), "p95": _percentile(ttff_ms, 0.95)}, + "gap_ms": { + "p50_worst": max(gap_p50_ms) if gap_p50_ms else None, + "p95_worst": max(gap_p95_ms) if gap_p95_ms else None, + "max_worst": max(gap_max_ms) if gap_max_ms else None, + "p50_median": gap_p50_median, + }, + "sustained_min": min(sustained) if sustained else None, + "stall_count_total": sum(metrics["stalls"]["count"] for metrics in per_stream), + "realtime_per_stream": realtime_per_stream, + "realtime_count": realtime_count, + "realtime_fraction": ( + realtime_count / len(realtime_per_stream) if realtime_per_stream else None + ), + "all_realtime": all(realtime_per_stream) if realtime_per_stream else False, + "on_time_chunk_fraction_min": min(chunk_fractions) if chunk_fractions else None, + "on_time_chunk_fraction_mean": ( + statistics.fmean(chunk_fractions) if chunk_fractions else None + ), + "server": server, + "delivery_bound": ( + gap_p50_median is not None + and server_step_p50 is not None + and gap_p50_median > 1.2 * server_step_p50 + ), + } + + +def _dit_step_timestamps(log_text: str, request_ids: set[str]) -> list[float]: + """Wall-clock seconds (from each line's %(asctime)s prefix) of every + rollout DiT step whose batch includes at least one of ``request_ids``, in + log order. Same marker/parsing as serve_rollout._dit_schedule, extended + with the timestamp that function does not keep.""" + marker = "Executing: dit graph_walk=rollout " + timestamps = [] + for line in log_text.splitlines(): + if marker not in line: + continue + try: + batch = ast.literal_eval(line.split(marker, 1)[1].strip()) + except (SyntaxError, ValueError): + continue + if not isinstance(batch, (list, tuple)) or not any(rid in request_ids for rid in batch): + continue + match = _LOG_TIMESTAMP_RE.match(line) + if match is None: + continue + timestamps.append(datetime.strptime(match.group(1), _LOG_TIMESTAMP_FORMAT).timestamp()) + return timestamps + + +def _concurrent_server_metrics(log_text: str, request_ids: set[str], startup_seconds: float) -> dict: + """Rows-per-step histogram and step-to-step spacing for the measured + wave's DiT rollout executions, parsed from worker DEBUG log lines.""" + schedule = rollout._dit_schedule(log_text, request_ids) + histogram: dict[int, int] = {} + for batch in schedule: + histogram[len(batch)] = histogram.get(len(batch), 0) + 1 + + timestamps = sorted(_dit_step_timestamps(log_text, request_ids)) + spacing_ms = [(later - earlier) * 1000.0 for earlier, later in zip(timestamps, timestamps[1:])] + return { + "rows_per_step_histogram": histogram, + "step_spacing_ms": {"p50": _percentile(spacing_ms, 0.50), "p95": _percentile(spacing_ms, 0.95)}, + "startup_seconds": startup_seconds, + } + + +def _run_concurrent_phase( + client_factory: Callable[[], MStarClient], + seed_image: Path, + variant: rollout.Variant, + *, + streams: int, + worlds: int, + batch: int, + num_steps: int, + warmup_steps: int, + request_id_prefix: str, + rng_seed: int, + stall_threshold_seconds: float, + enable_nvtx: bool, + log_path: Path, + proc: subprocess.Popen, + request_timeout: float, + sampler: rollout.MemorySampler, + startup_seconds: float, +) -> tuple[dict, list[str]]: + """N-stream concurrent phase: a discarded warmup wave (captures/compiles + the batch-``streams`` CUDA graph bucket), then a measured wave whose + per-stream metrics and server-side rollout cadence decide whether every + stream stayed realtime under batch-``streams`` scheduling.""" + failures: list[str] = [] + + warmup_ids = [f"{request_id_prefix}-concurrent-warmup-{i}" for i in range(streams)] + warmup_seeds = [rng_seed + i for i in range(streams)] + print(f"concurrent warmup: {streams} streams, {warmup_steps} step(s) each") + _wait_for_phase_sample(sampler, proc, "concurrent-warmup") + warmup_results, _, _ = _run_concurrent_wave( + client_factory, + seed_image, + variant, + num_steps=warmup_steps, + request_ids=warmup_ids, + seeds=warmup_seeds, + stall_threshold_seconds=stall_threshold_seconds, + enable_nvtx=enable_nvtx, + ) + for request_id, (_, stream_failures) in zip(warmup_ids, warmup_results): + failures.extend(f"concurrent-warmup {request_id}: {failure}" for failure in stream_failures) + rollout._wait_for_cleanup(log_path, tuple(warmup_ids), proc, request_timeout) + + measured_ids = [f"{request_id_prefix}-concurrent-measured-{i}" for i in range(streams)] + measured_seeds = [rng_seed + i for i in range(streams)] + print(f"concurrent measured: {streams} streams, {num_steps} step(s) each") + _wait_for_phase_sample(sampler, proc, "concurrent-measured") + log_offset = log_path.stat().st_size + measured_results, wave_start, wave_end = _run_concurrent_wave( + client_factory, + seed_image, + variant, + num_steps=num_steps, + request_ids=measured_ids, + seeds=measured_seeds, + stall_threshold_seconds=stall_threshold_seconds, + enable_nvtx=enable_nvtx, + ) + per_stream = [] + for request_id, (metrics, stream_failures) in zip(measured_ids, measured_results): + failures.extend(f"concurrent-measured {request_id}: {failure}" for failure in stream_failures) + per_stream.append(metrics) + rollout._wait_for_cleanup(log_path, tuple(measured_ids), proc, request_timeout, offset=log_offset) + + total_frames = sum(metrics["frame_count"] for metrics in per_stream) + aggregate_fps = total_frames / (wave_end - wave_start) if wave_end > wave_start else None + + wave_log = rollout._read_log_since(log_path, log_offset) + server = _concurrent_server_metrics(wave_log, set(measured_ids), startup_seconds) + + samples, _ = sampler.snapshot() + measured_gpu_mib = [sample.gpu_mib for sample in samples if sample.phase == "concurrent-measured"] + + result = { + "streams": streams, + "worlds": worlds, + "batch": batch, + "per_stream": per_stream, + "gpu_peak_mib": max(measured_gpu_mib) if measured_gpu_mib else None, + **_concurrent_aggregate(per_stream, aggregate_fps=aggregate_fps, server=server), + } + return result, failures + + def _format_number(value: float | None, digits: int = 3) -> str: return "n/a" if value is None else f"{value:.{digits}f}" @@ -379,6 +657,37 @@ def _human_summary(result: dict, artifact: Path) -> str: f"{_format_number(startup['p95'])}s " f"mean={_format_number(startup['mean'])}s" ) + concurrent = result.get("concurrent") + if concurrent is not None: + lines.append( + f" concurrent: {concurrent['streams']} streams " + f"(worlds={concurrent['worlds']} batch={concurrent['batch']})" + ) + for idx, (stream, realtime) in enumerate( + zip(concurrent["per_stream"], concurrent["realtime_per_stream"]) + ): + gaps = stream["inter_chunk_gap_seconds"] + lines.append( + f" stream {idx}: TTFF={_format_number(stream['time_to_first_frame_seconds'])}s " + f"gap p50/p95/max={_format_number(gaps['p50'])}/{_format_number(gaps['p95'])}/" + f"{_format_number(gaps['maximum'])}s " + f"sustained={_format_number(stream['sustained_media_to_wall_ratio'])}x " + f"stalls={stream['stalls']['count']} " + f"realtime={'yes' if realtime else 'no'}" + ) + lines.append( + f" aggregate: fps={_format_number(concurrent['aggregate_fps'])} " + f"ttff p50/p95={_format_number(concurrent['ttff_ms']['p50'])}/" + f"{_format_number(concurrent['ttff_ms']['p95'])}ms " + f"gap p50_median/p95_worst={_format_number(concurrent['gap_ms']['p50_median'])}/" + f"{_format_number(concurrent['gap_ms']['p95_worst'])}ms " + f"sustained_min={_format_number(concurrent['sustained_min'])}x " + f"stalls_total={concurrent['stall_count_total']} " + f"all_realtime={concurrent['all_realtime']} " + f"delivery_bound={concurrent['delivery_bound']} " + f"gpu_peak={_format_number(concurrent['gpu_peak_mib'], 1)}MiB" + ) + backpressure = result["backpressure"] lines.extend( [ @@ -410,6 +719,20 @@ def _build_parser() -> argparse.ArgumentParser: ) parser.add_argument("--steps", type=int, default=16) parser.add_argument("--warmup-steps", type=int, default=1) + parser.add_argument( + "--streams", + type=int, + default=1, + help="concurrent streams; >1 runs the concurrent batching phase", + ) + parser.add_argument( + "--worlds", type=int, help="server world slots (kv.num_worlds); defaults to --streams" + ) + parser.add_argument( + "--batch", + type=int, + help="rows per rollout step (model_kwargs.step_batch_size); defaults to --streams", + ) parser.add_argument( "--startup-repeats", type=int, @@ -454,6 +777,18 @@ def _parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: parser.error("--startup-repeats cannot be negative") if args.startup_steps <= 0: parser.error("--startup-steps must be positive") + if args.streams <= 0: + parser.error("--streams must be positive") + if args.worlds is None: + args.worlds = args.streams + if args.batch is None: + args.batch = args.streams + if args.worlds <= 0: + parser.error("--worlds must be positive") + if args.batch <= 0: + parser.error("--batch must be positive") + if args.batch > args.worlds: + parser.error("--batch must be <= --worlds") if args.slow_consumer_delay < 0: parser.error("--slow-consumer-delay cannot be negative") if args.stall_threshold is not None and args.stall_threshold <= 0: @@ -503,14 +838,19 @@ def _run_benchmark(args: argparse.Namespace) -> dict: checkpoint_dir, ae_path, workdir / "run.yaml", - worlds=1, + worlds=args.worlds, + batch=args.batch, ) seed_image = rollout._seed_png(args.seed_image, variant, workdir / "seed.png") + # serve_rollout forces DEBUG for its own concurrent mode so the worker's + # per-step "Executing: dit graph_walk=..." lines are emitted; the + # concurrent phase below needs the same to parse rows/step and spacing. + server_log_level = "DEBUG" if args.streams > 1 else args.log_level server_command = rollout._server_command( config, port, workdir, - args.log_level, + server_log_level, args.request_timeout, args.cache_dir, args.enable_nvtx, @@ -534,6 +874,7 @@ def _run_benchmark(args: argparse.Namespace) -> dict: failures: list[str] = [] runs: dict[str, dict] = {} startup_ttffs: list[float] = [] + concurrent_result: dict | None = None startup_started = time.perf_counter() try: client = MStarClient(url, timeout=args.request_timeout) @@ -640,6 +981,32 @@ def _run_benchmark(args: argparse.Namespace) -> dict: failures.append( "slow-consumer payload differs from the identical-seed baseline" ) + + if args.streams > 1: + print( + f"concurrent: {args.streams} streams, {args.steps} steps each " + f"(worlds={args.worlds} batch={args.batch})" + ) + concurrent_result, concurrent_failures = _run_concurrent_phase( + client_factory=lambda: MStarClient(url, timeout=args.request_timeout), + seed_image=seed_image, + variant=variant, + streams=args.streams, + worlds=args.worlds, + batch=args.batch, + num_steps=args.steps, + warmup_steps=args.warmup_steps, + request_id_prefix=args.request_id, + rng_seed=args.seed, + stall_threshold_seconds=stall_threshold, + enable_nvtx=args.enable_nvtx, + log_path=args.log, + proc=proc, + request_timeout=args.request_timeout, + sampler=sampler, + startup_seconds=startup_seconds, + ) + failures.extend(concurrent_failures) finally: try: if sampler is not None: @@ -647,7 +1014,7 @@ def _run_benchmark(args: argparse.Namespace) -> dict: finally: rollout._shutdown(proc) - return { + result = { "schema_version": 1, "benchmark": "waypoint_streaming_viability", "created_at_utc": datetime.now(timezone.utc).isoformat(), @@ -691,6 +1058,11 @@ def _run_benchmark(args: argparse.Namespace) -> dict: ), "jitter_population_stddev": "population standard deviation of inter-chunk gaps", "stall": "inter-chunk gap strictly greater than stall_threshold_seconds", + "on_time_chunk_fraction": ( + f"per stream: fraction of chunks delivered on time (inter-chunk gap <= " + f"{REALTIME_CHUNK_BUDGET_MS:.1f}ms, one 4-frame/60fps chunk of playback); " + "the pacing pillar of streaming viability" + ), "memory": "PSS and nvidia-smi GPU process memory summed over the server process group only", "backpressure": ( "delta between an unpaused stream and an identical stream paused between SDK reads" @@ -698,6 +1070,44 @@ def _run_benchmark(args: argparse.Namespace) -> dict: }, } + if concurrent_result is not None: + result["concurrent"] = concurrent_result + result["metric_definitions"].update( + { + "concurrent.aggregate_fps": ( + "total frames delivered across streams / (last chunk arrival - earliest " + "stream start), wall-clock, over the measured concurrent wave" + ), + "concurrent.gap_ms.p50_median": "median across streams of each stream's own inter-chunk gap p50", + "concurrent.gap_ms.*_worst": ( + "largest across streams of each stream's own inter-chunk gap p50/p95/maximum" + ), + "concurrent.realtime_per_stream": ( + f"per stream: sustained ratio >= 1.0 and gap p95 <= {REALTIME_CHUNK_BUDGET_MS:.1f}ms " + "(one 4-frame/60fps chunk) and zero stalls" + ), + "concurrent.realtime_count": ( + "how many of the batch's streams stayed realtime (sum of realtime_per_stream); " + "realtime_fraction is that over the stream count" + ), + "concurrent.on_time_chunk_fraction_min": ( + "smallest per-stream on_time_chunk_fraction across the batch (mean is the average); " + "the worst stream's on-time chunk rate under batch-B scheduling" + ), + "concurrent.delivery_bound": ( + "client gap_ms.p50_median > 1.2x server.step_spacing_ms.p50: clients are " + "slower than the GPU produces steps, so the limit is delivery rather than compute" + ), + "concurrent.server.rows_per_step_histogram": ( + "count of measured-wave DiT rollout steps by batch row count" + ), + "concurrent.server.step_spacing_ms": ( + "gap between consecutive measured-wave DiT rollout step log timestamps" + ), + } + ) + return result + def _write_artifact(path: Path, result: dict) -> None: path.parent.mkdir(parents=True, exist_ok=True) diff --git a/test/waypoint/serve_rollout.py b/test/waypoint/serve_rollout.py index 975d53ba0..cf2471207 100644 --- a/test/waypoint/serve_rollout.py +++ b/test/waypoint/serve_rollout.py @@ -17,11 +17,11 @@ flight and keys the deferral on the rid alone, so reusing an id lets the first request's teardown land on the second and drop its in-flight reads. -``--concurrent-waves`` switches to the two-world isolation gate: two distinct -solo baselines are replayed concurrently through separate SDK clients, then -checked byte-for-byte across repeated world reuse. Optional memory sampling -tracks only the server process group and excludes the first concurrent wave as -allocator warmup. +``--concurrent-waves`` switches to the N-stream isolation gate, N = ``--worlds``: +N distinct solo baselines are replayed concurrently through separate SDK +clients, then checked byte-for-byte across repeated world reuse. Optional +memory sampling tracks only the server process group and excludes the first +concurrent wave as allocator warmup. Deployment details that the checked-in config cannot carry are supplied here rather than edited into it: @@ -57,6 +57,7 @@ from pathlib import Path from typing import Callable +import numpy as np import yaml REPO = Path(__file__).resolve().parents[2] @@ -130,14 +131,20 @@ def _run_config( ae_path: Path | None, out: Path, worlds: int = 1, + batch: int = 1, ) -> Path: """Build one deployment config without modifying the checked-in YAML.""" if worlds < 1: raise ValueError(f"worlds must be positive; got {worlds}") + if batch < 1: + raise ValueError(f"batch must be positive; got {batch}") + if batch > worlds: + raise ValueError(f"batch ({batch}) must be <= worlds ({worlds})") config = yaml.safe_load(base.read_text()) model_kwargs = { **(config.get("model_kwargs") or {}), "variant": variant.model_variant, + "step_batch_size": batch, } # Hub mode passes None for these and must not inherit a local override from # the base config: omitting checkpoint_dir is what exercises the registry's @@ -226,15 +233,24 @@ def _rollout( return chunks +def _wave_labels(n: int) -> tuple[str, ...]: + """Stream labels "A", "B", "C", ... for a wave of ``n`` concurrent streams.""" + if n < 1: + raise ValueError(f"n must be positive; got {n}") + if n > 26: + raise ValueError(f"concurrent isolation gate supports at most 26 streams; got {n}") + return tuple(chr(ord("A") + i) for i in range(n)) + + def _concurrent_rollouts( client_factory: Callable[[], MStarClient], seed: Path, num_steps: int, - specs: tuple[RolloutSpec, RolloutSpec], + specs: tuple[RolloutSpec, ...], ) -> dict[str, list[VideoFrameChunk]]: - """Start exactly two lazy SDK streams together, each on its own Session.""" - barrier = threading.Barrier(3) - with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor: + """Start ``len(specs)`` lazy SDK streams together, each on its own Session.""" + barrier = threading.Barrier(len(specs) + 1) + with concurrent.futures.ThreadPoolExecutor(max_workers=len(specs)) as executor: futures = { spec.label: executor.submit( _rollout, @@ -255,10 +271,79 @@ def _video_bytes(chunks: list[VideoFrameChunk]) -> bytes: return b"".join(chunk.data for chunk in chunks) -def _dit_schedule(log_text: str, request_ids: set[str]) -> list[str]: - """Extract single-request DiT rollout executions from worker DEBUG logs.""" +def _pixel_diff_summary(actual: bytes, expected: bytes, chunk_size: int) -> str: + """Summarize a byte-for-byte mismatch between two uint8 RGB24 video streams.""" + if len(actual) != len(expected): + return f"length mismatch: actual={len(actual)} bytes, expected={len(expected)} bytes" + max_abs_diff = 0 + num_differing = 0 + for a, b in zip(actual, expected): + diff = a - b if a > b else b - a + if diff: + num_differing += 1 + if diff > max_abs_diff: + max_abs_diff = diff + num_chunks = len(actual) // chunk_size + chunks_differing = sum( + 1 + for i in range(num_chunks) + if actual[i * chunk_size : (i + 1) * chunk_size] != expected[i * chunk_size : (i + 1) * chunk_size] + ) + fraction = num_differing / len(actual) if actual else 0.0 + return ( + f"max abs diff={max_abs_diff}, fraction of bytes differing={fraction:.6f}, " + f"chunks differing={chunks_differing}/{num_chunks}" + ) + + +# measured 2026-09-17, 360p, 16 steps: a 1-bf16-ulp noise perturbation of a +# solo run gives PSNR 45.6->43.4 dB over the first 4 frames and 29 dB by frame +# 15; floors sit ~5 dB and ~4 dB under that envelope. Provisional: one +# calibration, one variant. +EARLY_PSNR_FLOOR_DB = 38.0 +LATE_PSNR_FLOOR_DB = 25.0 + + +def _chunk_psnr_db(actual: bytes, expected: bytes, chunk_size: int) -> list[float]: + """Per-chunk PSNR (dB) between two same-length uint8 RGB24 video streams; inf where identical.""" + num_chunks = len(actual) // chunk_size + psnr = [] + for i in range(num_chunks): + a = np.frombuffer(actual[i * chunk_size : (i + 1) * chunk_size], dtype=np.uint8).astype(np.float64) + b = np.frombuffer(expected[i * chunk_size : (i + 1) * chunk_size], dtype=np.uint8).astype(np.float64) + mse = np.mean((a - b) ** 2) + psnr.append(float("inf") if mse == 0 else float(10 * np.log10(255.0**2 / mse))) + return psnr + + +def _batched_tolerance_failure(actual: bytes, expected: bytes, chunk_size: int) -> str | None: + """Tolerance gate for --batch > 1: chunks are allowed to differ, but must stay within the + PSNR envelope of a 1-bf16-ulp noise perturbation (see EARLY_PSNR_FLOOR_DB/LATE_PSNR_FLOOR_DB).""" + if len(actual) != len(expected): + return f"length mismatch: actual={len(actual)} bytes, expected={len(expected)} bytes" + psnr = _chunk_psnr_db(actual, expected, chunk_size) + first = next((i for i, p in enumerate(psnr) if p != float("inf")), None) + if first is None: + return None + for i in range(first, min(first + 4, len(psnr))): + if psnr[i] < EARLY_PSNR_FLOOR_DB: + return ( + f"chunk {i} psnr={psnr[i]:.1f}dB below early floor {EARLY_PSNR_FLOOR_DB}dB " + f"(first differing chunk={first})" + ) + for i in range(first, len(psnr)): + if psnr[i] < LATE_PSNR_FLOOR_DB: + return ( + f"chunk {i} psnr={psnr[i]:.1f}dB below late floor {LATE_PSNR_FLOOR_DB}dB " + f"(first differing chunk={first})" + ) + return None + + +def _dit_schedule(log_text: str, request_ids: set[str]) -> list[tuple[str, ...]]: + """Extract DiT rollout executions (any batch size) from worker DEBUG logs.""" marker = "Executing: dit graph_walk=rollout " - scheduled: list[str] = [] + scheduled: list[tuple[str, ...]] = [] for line in log_text.splitlines(): if marker not in line: continue @@ -266,41 +351,70 @@ def _dit_schedule(log_text: str, request_ids: set[str]) -> list[str]: batch = ast.literal_eval(line.split(marker, 1)[1].strip()) except (SyntaxError, ValueError): continue - if ( - isinstance(batch, (list, tuple)) - and len(batch) == 1 - and batch[0] in request_ids - ): - scheduled.append(batch[0]) + if not isinstance(batch, (list, tuple)): + continue + filtered = tuple(rid for rid in batch if rid in request_ids) + if filtered: + scheduled.append(filtered) return scheduled -def _interleaving_failure(log_text: str, request_ids: tuple[str, str]) -> str | None: - """Require an A/B/A or B/A/B DiT schedule, not just overlapping clients.""" +def _interleaving_failure(log_text: str, request_ids: tuple[str, ...]) -> str | None: + """Require a batched step, or some rid reappearing after a different rid in + the single-request DiT schedule, not just overlapping clients.""" scheduled = _dit_schedule(log_text, set(request_ids)) - compressed = [rid for i, rid in enumerate(scheduled) if i == 0 or rid != scheduled[i - 1]] - interleaved = any( - first == third and first != second - for first, second, third in zip(compressed, compressed[1:], compressed[2:], strict=False) - ) + if any(len(batch) > 1 for batch in scheduled): + return None + singles = [batch[0] for batch in scheduled if len(batch) == 1] + compressed = [rid for i, rid in enumerate(singles) if i == 0 or rid != singles[i - 1]] + interleaved = len(set(compressed)) != len(compressed) if interleaved: return None - counts = {rid: scheduled.count(rid) for rid in request_ids} + counts = {rid: sum(1 for batch in scheduled if rid in batch) for rid in request_ids} return ( - f"worker DEBUG schedule did not contain A/B/A interleaving for {request_ids}; DiT schedule counts were {counts}" + f"worker DEBUG schedule did not contain A/B/A-style interleaving for {request_ids}; " + f"DiT schedule counts were {counts}" ) +_OVERSHOOT_MARKER = "skipping async-overshoot rollout step" + + +def _vetoed_overshoots(log_text: str, request_ids: set[str]) -> dict[str, int]: + """Per rid, the async-overshoot iterations ``prepare_inputs`` vetoed. + + The worker logs ``Executing:`` before ``prepare_inputs`` runs, so the + schedule counts one iteration per request that never reached the GPU. + """ + counts = {rid: 0 for rid in request_ids} + for line in log_text.splitlines(): + if _OVERSHOOT_MARKER not in line: + continue + for rid in request_ids: + if f"(request {rid} runs" in line: + counts[rid] += 1 + return counts + + def _execution_count_failure( log_text: str, - request_ids: tuple[str, str], + request_ids: tuple[str, ...], num_steps: int, ) -> str | None: + """Every request ran its DiT step exactly ``num_steps`` times: scheduled + executions minus the vetoed overshoot iterations.""" scheduled = _dit_schedule(log_text, set(request_ids)) - counts = {rid: scheduled.count(rid) for rid in request_ids} + vetoed = _vetoed_overshoots(log_text, set(request_ids)) + counts = { + rid: sum(1 for batch in scheduled if rid in batch) - vetoed[rid] + for rid in request_ids + } if all(count == num_steps for count in counts.values()): return None - return f"expected {num_steps} DiT rollout executions per request; got {counts}" + return ( + f"expected {num_steps} DiT rollout forwards per request; got {counts} " + f"(vetoed overshoots {vetoed})" + ) _CLEANUP_MARKER = "Request cleanup complete:" @@ -710,11 +824,17 @@ def main() -> int: parser.add_argument("--request-id", type=str, default="waypoint-serve-rollout") parser.add_argument("--seed", type=int, default=112464007) parser.add_argument("--worlds", type=int, default=1) + parser.add_argument( + "--batch", + type=int, + default=1, + help="rows per rollout step (model_kwargs.step_batch_size); must be <= --worlds", + ) parser.add_argument( "--concurrent-waves", type=int, default=0, - help="run the two-world isolation gate for this many waves (minimum 2)", + help="run the N-stream isolation gate (N = --worlds) for this many waves (minimum 2)", ) parser.add_argument("--measure-memory", action="store_true") parser.add_argument( @@ -737,12 +857,16 @@ def main() -> int: if args.worlds < 1: parser.error("--worlds must be positive") + if args.batch < 1: + parser.error("--batch must be positive") + if args.batch > args.worlds: + parser.error("--batch must be <= --worlds") if args.concurrent_waves < 0: parser.error("--concurrent-waves cannot be negative") if args.concurrent_waves == 1: parser.error("--concurrent-waves must be 0 or at least 2") - if args.concurrent_waves and args.worlds != 2: - parser.error("the concurrent isolation gate requires exactly --worlds 2") + if args.concurrent_waves and args.worlds < 2: + parser.error("the concurrent isolation gate needs at least --worlds 2") if args.measure_memory and args.concurrent_waves < 3: parser.error("--measure-memory needs at least 3 concurrent waves (one warm, two measured)") if args.measure_memory and args.physical_gpu is None: @@ -773,6 +897,7 @@ def main() -> int: ae_path, workdir / "run.yaml", worlds=args.worlds, + batch=args.batch, ) seed = _seed_png(args.seed_image, variant, workdir / "seed.png") path = str(REPO) @@ -845,9 +970,10 @@ def main() -> int: else: print(f"repeat is byte-identical over {len(first)} bytes of video") else: - baseline_specs = ( - RolloutSpec("A", f"{args.request_id}-solo-a", args.seed), - RolloutSpec("B", f"{args.request_id}-solo-b", args.seed + 1), + labels = _wave_labels(args.worlds) + baseline_specs = tuple( + RolloutSpec(label, f"{args.request_id}-solo-{label.lower()}", args.seed + i) + for i, label in enumerate(labels) ) baselines: dict[str, bytes] = {} for spec in baseline_specs: @@ -869,20 +995,26 @@ def main() -> int: if not all(baselines.values()): failures.append("a solo baseline returned no video") - elif baselines["A"] == baselines["B"]: - failures.append("distinct solo seeds produced identical baselines, so world swaps are invisible") + else: + for i, label_i in enumerate(labels): + for label_j in labels[i + 1 :]: + if baselines[label_i] == baselines[label_j]: + failures.append( + f"distinct solo seeds produced identical baselines for {label_i} and " + f"{label_j}, so world swaps are invisible" + ) wave_memory: list[WaveMemory] = [] interleaved_waves = 0 for wave in range(1, args.concurrent_waves + 1): phase = f"concurrent-wave-{wave}" - specs = ( - RolloutSpec("A", f"{args.request_id}-wave-{wave}-a", args.seed), - RolloutSpec("B", f"{args.request_id}-wave-{wave}-b", args.seed + 1), + specs = tuple( + RolloutSpec(label, f"{args.request_id}-wave-{wave}-{label.lower()}", args.seed + i) + for i, label in enumerate(labels) ) if sampler is not None: sampler.set_phase(phase) - print(f"--- {phase}: {specs[0].request_id} + {specs[1].request_id} ---") + print(f"--- {phase}: {' + '.join(spec.request_id for spec in specs)} ---") log_offset = args.log.stat().st_size chunks_by_label = _concurrent_rollouts( lambda: MStarClient(url, timeout=args.request_timeout), @@ -897,8 +1029,34 @@ def main() -> int: for failure in _check(chunks, args.steps, variant.height, variant.width) ] actual = _video_bytes(chunks) - if actual != baselines[spec.label]: - failures.append(f"{phase} {spec.label} differs byte-for-byte from its solo baseline") + baseline = baselines[spec.label] + chunk_size = 4 * variant.height * variant.width * 3 + if args.batch == 1: + if actual != baseline: + failures.append(f"{phase} {spec.label} differs byte-for-byte from its solo baseline") + print( + f" {phase} {spec.label} pixel diff: " + f"{_pixel_diff_summary(actual, baseline, chunk_size)}" + ) + else: + tolerance_failure = _batched_tolerance_failure(actual, baseline, chunk_size) + if tolerance_failure is not None: + failures.append(f"{phase} {spec.label} {tolerance_failure}") + print( + f" {phase} {spec.label} pixel diff: " + f"{_pixel_diff_summary(actual, baseline, chunk_size)}" + ) + + if len(actual) == len(baseline): + psnr = _chunk_psnr_db(actual, baseline, chunk_size) + first = next((i for i, p in enumerate(psnr) if p != float("inf")), None) + else: + psnr, first = [], None + psnr_str = "[" + ", ".join("inf" if p == float("inf") else f"{p:.1f}" for p in psnr) + "]" + print( + f" {phase} {spec.label} first differing chunk={first if first is not None else 'none'} " + f"psnr/chunk={psnr_str}" + ) rids = tuple(spec.request_id for spec in specs) _wait_for_cleanup( From b643c9b41c09b1d1f9cf98ff7a046acb9b9b50af Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Sat, 19 Sep 2026 20:21:27 +0000 Subject: [PATCH 14/29] kv/ring: rename world vocabulary to session per review The ring KV cache called its per-request partition a "world", which is waypoint-specific jargon. Rename it to "session" across the KV resource layer and the code that drives it: num_worlds->num_sessions, world_idx->session_idx, world_span->session_span, world_base-> session_base, _free_worlds->_free_sessions, total_worlds->total_sessions, world_of->session_of and the remaining world_* helpers, plus the bare world/worlds nouns inside kv/ring, flex and the kv config. Used session_idx rather than the suggested span_idx: "span" already names the (lo, hi) KV tensor slice a session occupies (session_span), so span_idx there would be ambiguous. Left untouched: the distributed rank count (world_size / tp_world_size), the separate world-model / world_engine abstraction in model/waypoint, and the benchmark harness --worlds flag. --- configs/waypoint.yaml | 6 +- docs/adding_models.rst | 4 +- mstar/engine/resources/attn/flex.py | 34 +-- mstar/engine/resources/kv/config.py | 46 ++-- mstar/engine/resources/kv/ring/cache.py | 68 ++--- mstar/engine/resources/kv/ring/manager.py | 186 ++++++------- mstar/model/waypoint/config.py | 4 +- mstar/model/waypoint/ring_geometry.py | 16 +- mstar/model/waypoint/waypoint_model.py | 50 ++-- test/modular/test_flex_attention_resource.py | 14 +- test/modular/test_ring_kv_resource.py | 258 +++++++++--------- test/modular/test_video_frame_protocol.py | 4 +- test/modular/test_waypoint_components.py | 36 +-- test/modular/test_waypoint_dit.py | 4 +- test/modular/test_waypoint_gpu.py | 56 ++-- .../test_waypoint_reference_equivalence.py | 2 +- test/modular/test_waypoint_shell.py | 20 +- test/waypoint/benchmark_streaming.py | 2 +- test/waypoint/serve_rollout.py | 4 +- 19 files changed, 407 insertions(+), 407 deletions(-) diff --git a/configs/waypoint.yaml b/configs/waypoint.yaml index 0622b7e84..a5a01d3ad 100644 --- a/configs/waypoint.yaml +++ b/configs/waypoint.yaml @@ -11,7 +11,7 @@ model_kwargs: compile_dit: true cuda_graph: true full_global_ring: false - # Rows per rollout step; must be <= resources.kv.num_worlds below. + # Rows per rollout step; must be <= resources.kv.num_sessions below. step_batch_size: 1 # The world state is a ring, not a paged KV cache, so nothing here is sized in @@ -21,7 +21,7 @@ max_seq_len: 512 # The primary bound on the world pool. `WaypointModel.get_worker_graphs` # refuses to build unless this is a positive int no larger than -# `resources.kv.num_worlds`: a world is claimed at admit, by which point a +# `resources.kv.num_sessions`: a world is claimed at admit, by which point a # request the pool cannot hold can only be failed terminally, and the # conductor's FIFO admit queue that prevents that exists only when this is set. max_concurrent_requests: 1 @@ -30,7 +30,7 @@ resources: kv: # ~816 MiB of ring per world at 720P. Raise together with # max_concurrent_requests; the two are checked against each other. - num_worlds: 1 + num_sessions: 1 # 1.28B DiT plus a ~7M-parameter TAEHV, all on rank 0. One group: the rollout # Loop body is a single dit node (the TAEHV decode is fused into its forward), diff --git a/docs/adding_models.rst b/docs/adding_models.rst index 82b94d64b..95969e9ff 100644 --- a/docs/adding_models.rst +++ b/docs/adding_models.rst @@ -271,7 +271,7 @@ The spec types are: ``max_num_pages``, ``page_size`` and ``cpu_offload_pages`` fields. * - ``KVSpec(config=RingKVConfig(...))`` - A fixed-capacity frame ring. It adds ``tokens_per_frame``, one - ``RingKVLayerConfig`` per layer, and the deployment-tunable ``num_worlds``. + ``RingKVLayerConfig`` per layer, and the deployment-tunable ``num_sessions``. Ring storage is currently paired with FlexAttention. * - ``AttentionSpec(config=AttentionConfig(kv_cache=...))`` - Self-attention planned over the named cache. ``backend`` selects @@ -1224,7 +1224,7 @@ that a misspelled setting is never silently ignored: * - ``KVSpec`` with ``PagedKVConfig`` - ``max_num_pages``, ``page_size``, ``max_seq_len``, ``cpu_offload_pages`` * - ``KVSpec`` with ``RingKVConfig`` - - ``num_worlds`` + - ``num_sessions`` * - ``AttentionSpec`` - ``backend`` (``flashinfer`` / ``dense`` / ``flex``), ``flashinfer_backend`` (``auto`` / ``fa2`` / ``fa3``) diff --git a/mstar/engine/resources/attn/flex.py b/mstar/engine/resources/attn/flex.py index 755acbb48..5b6d0887d 100644 --- a/mstar/engine/resources/attn/flex.py +++ b/mstar/engine/resources/attn/flex.py @@ -201,7 +201,7 @@ class FlexAttentionManager(AttentionManager): drifting video. The FlashInfer alternative was measured at ~9% on the attention kernel, against the 2x that had motivated trying it. - Stateless across steps. The world state is the ring, and the ring belongs + Stateless across steps. The session state is the ring, and the ring belongs to the KV resource this one names in ``depends_on``; nothing here survives a call except the label/layer cursors the base class defines. """ @@ -271,7 +271,7 @@ def _mask_for( if mask is None and create: ring_frames, _, _ = geometry capacity = (ring_frames + 1) * self._kv_config.tokens_per_frame - capacity *= self._kv_config.total_worlds + capacity *= self._kv_config.total_sessions mask = _empty_block_mask( self._kv_config.tokens_per_frame, capacity, self._device, batch ) @@ -305,21 +305,21 @@ def _visibility_table_for( self._kv_config.tokens_per_frame // _DEFAULT_SPARSE_BLOCK_SIZE ) total_blocks = ( - (ring_frames + 1) * blocks_per_frame * self._kv_config.total_worlds + (ring_frames + 1) * blocks_per_frame * self._kv_config.total_sessions ) counts: list[list[int]] = [] rows: list[list[list[int]]] = [] - for world_idx in range(self._kv_config.total_worlds): - world_counts = [] - world_rows = [] + for session_idx in range(self._kv_config.total_sessions): + session_counts = [] + session_rows = [] for frame_pos in range(2 * period): visible = self._visible_blocks_for( - geometry, world_idx=world_idx, frame_pos=frame_pos + geometry, session_idx=session_idx, frame_pos=frame_pos ) - world_counts.append(len(visible)) - world_rows.append(visible + [0] * (total_blocks - len(visible))) - counts.append(world_counts) - rows.append(world_rows) + session_counts.append(len(visible)) + session_rows.append(visible + [0] * (total_blocks - len(visible))) + counts.append(session_counts) + rows.append(session_rows) table = ( torch.tensor(counts, dtype=torch.int32, device=self._device), @@ -342,14 +342,14 @@ def _visible_blocks_for( self, geometry: tuple[int, int, int], *, - world_idx: int, + session_idx: int, frame_pos: int, ) -> list[int]: tokens = self._kv_config.tokens_per_frame blocks_per_frame = tokens // _DEFAULT_SPARSE_BLOCK_SIZE ring_frames, ring_buckets, dilation = geometry capacity_blocks = (ring_frames + 1) * blocks_per_frame - world_base = world_idx * capacity_blocks + session_base = session_idx * capacity_blocks committed = range(0, frame_pos, dilation) slots = { @@ -360,9 +360,9 @@ def _visible_blocks_for( visible: list[int] = [] for ring_slot in sorted(slots): - start = world_base + ring_slot * blocks_per_frame + start = session_base + ring_slot * blocks_per_frame visible.extend(range(start, start + blocks_per_frame)) - scratch = world_base + ring_frames * blocks_per_frame + scratch = session_base + ring_frames * blocks_per_frame visible.extend(range(scratch, scratch + blocks_per_frame)) return visible @@ -373,7 +373,7 @@ def _stage( plan: RingPlan, ) -> None: counts, indices, period = self._visibility_table_for(geometry) - for b, (w, f) in enumerate(zip(plan.world_idx, plan.frame_pos)): + for b, (w, f) in enumerate(zip(plan.session_idx, plan.frame_pos)): phase = f if f < period else period + f % period mask.full_kv_num_blocks[b].copy_(counts[w, phase]) mask.full_kv_indices[b].copy_(indices[w, phase]) @@ -397,7 +397,7 @@ def plan(self, step: AttentionStep, ctx: StepContext) -> None: # The padded width, not the real row count: the graph baked the address # of the mask captured at the bucket size, and a replay padded below that # bucket must stage into that same buffer. `ring_plan` already carries one - # (world, frame) per padded row, padding rows included. + # (session, frame) per padded row, padding rows included. B = len(ctx.padded_request_ids) geometries = {self._geometry(layer) for layer in self._kv_config.layers} for geometry in geometries: diff --git a/mstar/engine/resources/kv/config.py b/mstar/engine/resources/kv/config.py index 39f6469e6..96d565009 100644 --- a/mstar/engine/resources/kv/config.py +++ b/mstar/engine/resources/kv/config.py @@ -50,7 +50,7 @@ def shard(self, num_shards: int) -> None: Idempotent because one KVConfig is shared by the KV resource and the attention resources planned against it, and each shards on construction. - ``num_shards`` is the instance world size (tp * sp): Ulysses SP + ``num_shards`` is the instance session size (tp * sp): Ulysses SP all-to-alls heads, so attention runs at head-degree tp*sp. """ from mstar.distributed.utils import divide @@ -120,20 +120,20 @@ class RingKVConfig(KVConfig): tokens_per_frame: int layers: tuple[RingKVLayerConfig, ...] - # How many worlds are resident at once. NOT a batch. - num_worlds: int = 1 + # How many sessions are resident at once. NOT a batch. + num_sessions: int = 1 @property - def total_worlds(self) -> int: - """Resident worlds plus one shared scratch world for padding rows. + def total_sessions(self) -> int: + """Resident sessions plus one shared scratch session for padding rows. - A replay padded to its capture bucket parks the dummy tail on this world + A replay padded to its capture bucket parks the dummy tail on this session (see ``RingKVManager.plan``); it is never handed to a request, so - resident capacity stays ``num_worlds`` and the deployment knob keeps its + resident capacity stays ``num_sessions`` and the deployment knob keeps its meaning. The ring buffer and the flex mask both size on this count so the - padding world is a real, addressable span. + padding session is a real, addressable span. """ - return self.num_worlds + 1 + return self.num_sessions + 1 def __post_init__(self): super().__post_init__() @@ -143,33 +143,33 @@ def __post_init__(self): f"{self.num_layers}; each layer's ring is declared separately." ) if ( - not isinstance(self.num_worlds, int) - or isinstance(self.num_worlds, bool) - or self.num_worlds < 1 + not isinstance(self.num_sessions, int) + or isinstance(self.num_sessions, bool) + or self.num_sessions < 1 ): raise ValueError( - f"num_worlds must be a positive int; got {self.num_worlds!r}. A node serving " - "zero worlds refuses every request at admit." + f"num_sessions must be a positive int; got {self.num_sessions!r}. A node serving " + "zero sessions refuses every request at admit." ) - def apply_yaml_overrides(self, num_worlds: int | None = None, **kwargs) -> None: - """``num_worlds`` only. Nothing else here is a deployment knob.""" + def apply_yaml_overrides(self, num_sessions: int | None = None, **kwargs) -> None: + """``num_sessions`` only. Nothing else here is a deployment knob.""" if kwargs: raise TypeError( "ring KV geometry is a checkpoint fact, not a deployment tunable; " f"got {sorted(kwargs)}" ) - if num_worlds is not None: + if num_sessions is not None: if ( - not isinstance(num_worlds, int) - or isinstance(num_worlds, bool) - or num_worlds < 1 + not isinstance(num_sessions, int) + or isinstance(num_sessions, bool) + or num_sessions < 1 ): raise ValueError( - f"num_worlds must be a positive int; got {num_worlds!r}. A node serving " - "zero worlds refuses every request at admit." + f"num_sessions must be a positive int; got {num_sessions!r}. A node serving " + "zero sessions refuses every request at admit." ) - self.num_worlds = num_worlds + self.num_sessions = num_sessions @dataclass diff --git a/mstar/engine/resources/kv/ring/cache.py b/mstar/engine/resources/kv/ring/cache.py index ccba8d201..10ca159e2 100644 --- a/mstar/engine/resources/kv/ring/cache.py +++ b/mstar/engine/resources/kv/ring/cache.py @@ -16,7 +16,7 @@ def ring_scatter( ``mstar::kv_scatter_nhd`` (``kv/cache.py``) is one: the forward reaches ``cache`` through an attribute chain, so dynamo lifts it as a graph attribute and AOTAutograd functionalizes the mutation into a copy of the - WHOLE ring. At 720P that is 816 MiB per world copied 120 times per frame + WHOLE ring. At 720P that is 816 MiB per session copied 120 times per frame (24 layers x 5 passes) -- a throughput collapse, not an error, so nothing tells you. Declaring the mutation keeps the write in place and in-graph, with no break @@ -42,19 +42,19 @@ def _ring_scatter_fake( class LayerRingCache: """One attention layer's ring: ``ring_frames`` frame slots of history plus - one scratch frame at the tail, times ``num_worlds`` resident worlds. + one scratch frame at the tail, times ``num_sessions`` resident sessions. - Storage is a single ``[2, 1, H_kv, num_worlds * capacity, D]`` tensor so + Storage is a single ``[2, 1, H_kv, num_sessions * capacity, D]`` tensor so that a commit is one ``index_copy_`` for K and V together and the read is one ``unbind(0)`` into two views -- no copy on the read path. - ``capacity`` is ONE world's token slots; ``total_slots`` is the token-dim + ``capacity`` is ONE session's token slots; ``total_slots`` is the token-dim length of the buffer. """ def __init__( self, - num_worlds: int, + num_sessions: int, n_kv_heads: int, ring_frames: int, ring_buckets: int, @@ -64,8 +64,8 @@ def __init__( dtype: torch.dtype, device: torch.device | str, ): - if num_worlds < 1: - raise ValueError(f"num_worlds must be >= 1; got {num_worlds}.") + if num_sessions < 1: + raise ValueError(f"num_sessions must be >= 1; got {num_sessions}.") if pinned_dilation < 1: raise ValueError(f"pinned_dilation must be >= 1; got {pinned_dilation}.") if not 1 <= ring_buckets <= ring_frames: @@ -79,29 +79,29 @@ def __init__( f"block size ({_DEFAULT_SPARSE_BLOCK_SIZE}); the BlockMask has no partial blocks." ) - self.num_worlds = num_worlds + self.num_sessions = num_sessions self.tokens_per_frame = tokens_per_frame self.ring_frames = ring_frames self.ring_buckets = ring_buckets self.pinned_dilation = pinned_dilation - # ring_len is the reference's `L`: one world's history region, scratch + # ring_len is the reference's `L`: one session's history region, scratch # excluded. self.ring_len = ring_frames * tokens_per_frame self.capacity = self.ring_len + tokens_per_frame - self.total_slots = num_worlds * self.capacity + self.total_slots = num_sessions * self.capacity self.kv = torch.zeros( 2, 1, n_kv_heads, self.total_slots, d_head, dtype=dtype, device=device ) written = torch.zeros(self.total_slots, dtype=torch.bool, device=device) - written.view(num_worlds, self.capacity)[:, self.ring_len :] = True + written.view(num_sessions, self.capacity)[:, self.ring_len :] = True self.written = written # Preallocated scratch for the per-call visibility mask. Allocating it # inside upsert would put a fresh buffer in the compiled region on every # one of the 120 upserts per frame. self._mask_written = torch.empty_like(written) - self._world_of_slot = ( + self._session_of_slot = ( torch.arange(self.total_slots, dtype=torch.long, device=device) // self.capacity ) @@ -110,27 +110,27 @@ def __init__( @property def memory_bytes(self) -> int: - """Resident bytes of KV storage, all worlds (the bool/index buffers are + """Resident bytes of KV storage, all sessions (the bool/index buffers are noise).""" return self.kv.numel() * self.kv.element_size() - def world_span(self, world_idx: int) -> tuple[int, int]: - """``[lo, hi)`` token slots owned by ``world_idx``. + def session_span(self, session_idx: int) -> tuple[int, int]: + """``[lo, hi)`` token slots owned by ``session_idx``. A host ``int`` here, unlike everywhere on the forward path: the two callers below are host-side lifecycle (a rollout ending, capture tearing down), never inside a captured region. """ - if not 0 <= world_idx < self.num_worlds: + if not 0 <= session_idx < self.num_sessions: raise IndexError( - f"world_idx {world_idx} out of range for {self.num_worlds} worlds." + f"session_idx {session_idx} out of range for {self.num_sessions} sessions." ) - lo = world_idx * self.capacity + lo = session_idx * self.capacity return lo, lo + self.capacity - def reset(self, world_idx: int) -> None: - """Drop ONE world's state and re-arm its scratch tail.""" - lo, hi = self.world_span(world_idx) + def reset(self, session_idx: int) -> None: + """Drop ONE session's state and re-arm its scratch tail.""" + lo, hi = self.session_span(session_idx) self.kv[:, :, :, lo:hi].zero_() self.written[lo:hi].zero_() self.written[lo + self.ring_len : hi].fill_(True) @@ -140,14 +140,14 @@ def upsert( kv: Tensor, frame_pos: Tensor, commit: bool, - world_idx: Tensor, + session_idx: Tensor, *, build_visibility: bool = True, ) -> tuple[Tensor, Tensor, Tensor]: """``kv`` is ``[2, 1, H_kv, B*tokens_per_frame, D]``, one frame for - each of ``B`` worlds; + each of ``B`` sessions; ``commit`` writes the frame into its ring slot; without it the frame - lands in that world's scratch tail only, visible to itself and to + lands in that session's scratch tail only, visible to itself and to nothing later. Returns ``(k, v, visible)`` @@ -168,19 +168,19 @@ def upsert( if not torch.compiler.is_compiling(): torch._check( kv.size(3) == B * tokens, - lambda: f"ring cache expects exactly one frame per world; got " + lambda: f"ring cache expects exactly one frame per session; got " f"{kv.size(3)} tokens for B={B}", ) torch._check( - world_idx.shape == frame_pos.shape and world_idx.dtype == torch.int64, - lambda: f"world_idx must be a {list(frame_pos.shape)} int64 tensor matching " - f"frame_pos; got {tuple(world_idx.shape)} {world_idx.dtype}", + session_idx.shape == frame_pos.shape and session_idx.dtype == torch.int64, + lambda: f"session_idx must be a {list(frame_pos.shape)} int64 tensor matching " + f"frame_pos; got {tuple(session_idx.shape)} {session_idx.dtype}", ) - world_base = world_idx * self.capacity # [B] + session_base = session_idx * self.capacity # [B] bucket = (frame_pos + (self.pinned_dilation - 1)) // self.pinned_dilation slot = bucket % self.ring_buckets # [B] - ring_idx = self.frame_offsets[None] + (slot * tokens + world_base)[:, None] # [B, T] - current_idx = self._current_base[None] + world_base[:, None] # [B, T] + ring_idx = self.frame_offsets[None] + (slot * tokens + session_base)[:, None] # [B, T] + current_idx = self._current_base[None] + session_base[:, None] # [B, T] ring_scatter(self.kv, self.written, current_idx.flatten(), kv, False) write_step = frame_pos.remainder(self.pinned_dilation) == 0 # [B] @@ -192,7 +192,7 @@ def upsert( "the engine path passes build_visibility=False.", ) mask_written.copy_(self.written) - mask_written &= self._world_of_slot == world_idx + mask_written &= self._session_of_slot == session_idx mask_written[ring_idx[0]] = mask_written[ring_idx[0]] & ~write_step if commit: @@ -205,8 +205,8 @@ def upsert( # layer's preallocated scratch, handed out by reference and overwritten # in place by the next `upsert` on this layer. A consumer that stashes # it and reads it later reads some *later* frame's visibility -- which - # is a mask off by one or more frames, and with worlds resident it can - # now also be another world's mask entirely. + # is a mask off by one or more frames, and with sessions resident it can + # now also be another session's mask entirely. # # When false, the value is intentionally stale and must be ignored by # the planned attention backend. The obligation on a fallback consumer: diff --git a/mstar/engine/resources/kv/ring/manager.py b/mstar/engine/resources/kv/ring/manager.py index 6062e354d..d235a94bd 100644 --- a/mstar/engine/resources/kv/ring/manager.py +++ b/mstar/engine/resources/kv/ring/manager.py @@ -32,12 +32,12 @@ class RingPlan(NamedTuple): entry per row of the step batch, in ``ctx.request_ids`` order.""" request_ids: tuple[str, ...] - world_idx: tuple[int, ...] + session_idx: tuple[int, ...] frame_pos: tuple[int, ...] class RingKVManager(AttentionResource): - """Per-layer ring caches holding ``num_worlds`` worlds, one per request.""" + """Per-layer ring caches holding ``num_sessions`` sessions, one per request.""" def __init__( self, @@ -51,12 +51,12 @@ def __init__( self.device = torch.device(device) self.dtype = dtype - # ``total_worlds`` == ``num_worlds`` + 1: the extra world is the shared + # ``total_sessions`` == ``num_sessions`` + 1: the extra session is the shared # scratch that ``plan`` parks a replay's padding tail on. It is never in - # ``_free_worlds``, so no request is admitted to it. + # ``_free_sessions``, so no request is admitted to it. self.layers = [ LayerRingCache( - num_worlds=config.total_worlds, + num_sessions=config.total_sessions, n_kv_heads=config.num_kv_heads, ring_frames=layer.ring_frames, ring_buckets=layer.ring_buckets, @@ -69,17 +69,17 @@ def __init__( for layer in config.layers ] - self._worlds: dict[str, int] = {} - self._free_worlds: set[int] = set(range(config.num_worlds)) - # The one world past the resident pool; padding rows write here and it is - # never claimed, so a padding write never reaches a real world's history. - self._padding_world: int = config.num_worlds + self._sessions: dict[str, int] = {} + self._free_sessions: set[int] = set(range(config.num_sessions)) + # The one session past the resident pool; padding rows write here and it is + # never claimed, so a padding write never reaches a real session's history. + self._padding_session: int = config.num_sessions self._known_rids: set[str] = set() self._last_frames: dict[str, int] = {} - # Sized to num_worlds, the largest B any step can carry (B_max <= - # num_worlds is enforced at config load). - self._static_world_idx = torch.zeros( - config.num_worlds, dtype=torch.int64, device=self.device + # Sized to num_sessions, the largest B any step can carry (B_max <= + # num_sessions is enforced at config load). + self._static_session_idx = torch.zeros( + config.num_sessions, dtype=torch.int64, device=self.device ) @classmethod @@ -107,24 +107,24 @@ def tokens_per_frame(self) -> int: return self.config.tokens_per_frame @property - def num_worlds(self) -> int: - """How many requests can hold a world here at once.""" - return self.config.num_worlds + def num_sessions(self) -> int: + """How many requests can hold a session here at once.""" + return self.config.num_sessions def capacity(self, layer_idx: int) -> int: - """Token slots ONE world owns in ``layer_idx``'s ring, scratch frame + """Token slots ONE session owns in ``layer_idx``'s ring, scratch frame included. ``total_slots`` is the whole buffer.""" return self.layers[layer_idx].capacity def total_slots(self, layer_idx: int) -> int: - """Token slots in ``layer_idx``'s buffer across every world -- the + """Token slots in ``layer_idx``'s buffer across every session -- the length of the KV view ``upsert`` returns and of its visibility row.""" return self.layers[layer_idx].total_slots - def world_of(self, rid: str) -> int | None: - """``rid``'s world index, or None if it holds none. Host-side + def session_of(self, rid: str) -> int | None: + """``rid``'s session index, or None if it holds none. Host-side introspection; the forward reads the staged tensor, never this.""" - return self._worlds.get(rid) + return self._sessions.get(rid) def upsert( self, @@ -139,13 +139,13 @@ def upsert( """Write one step's K/V for ``layer_idx`` and return what to attend to. ``k``/``v`` are ``[B, H_kv, tokens_per_frame, D]``, one frame per - resident world in the step batch. ``k`` is already RoPE'd and + resident session in the step batch. ``k`` is already RoPE'd and RMS-normed and ``v`` is post value-residual lerp: the cache stores post-RoPE keys, so replayed history is never re-rotated. Returns ``(k_all, v_all, visible)``, the first two spanning - the whole buffer, every resident world and the third a + the whole buffer, every resident session and the third a ``[total_slots]`` bool row that is False everywhere outside the calling - request's own world. + request's own session. ``frame_pos`` and ``commit`` are both arguments rather than resource state, for the same reason. ``frame_pos`` is the ``[B]`` int64 ring @@ -160,35 +160,35 @@ def upsert( kv = torch.stack([k.transpose(0, 1), v.transpose(0, 1)], dim=0) kv = kv.view(2, 1, k.size(1), B * k.size(2), k.size(3)) return self.layers[layer_idx].upsert( - kv, frame_pos, commit, self._static_world_idx[:B], + kv, frame_pos, commit, self._static_session_idx[:B], build_visibility=build_visibility, ) - def _reset_world(self, rid: str) -> None: - """Zero ``rid``'s world and drop its clock, leaving its claim in place.""" - world_idx = self._worlds.get(rid) - if world_idx is None: + def _reset_session(self, rid: str) -> None: + """Zero ``rid``'s session and drop its clock, leaving its claim in place.""" + session_idx = self._sessions.get(rid) + if session_idx is None: return for layer in self.layers: - layer.reset(world_idx) + layer.reset(session_idx) self._last_frames.pop(rid, None) - def _release_world(self, rid: str) -> None: - """Hand ``rid``'s world back to the pool. Zero it first.""" - self._reset_world(rid) - world_idx = self._worlds.pop(rid, None) - if world_idx is not None: - self._free_worlds.add(world_idx) + def _release_session(self, rid: str) -> None: + """Hand ``rid``'s session back to the pool. Zero it first.""" + self._reset_session(rid) + session_idx = self._sessions.pop(rid, None) + if session_idx is not None: + self._free_sessions.add(session_idx) @torch.no_grad() def get_state(self, rid: str) -> dict: - """Snapshot one request's world. Cloned, so the caller can hold it + """Snapshot one request's session. Cloned, so the caller can hold it across further rollout steps that mutate the rings in place. """ - world_idx = self._require_world(rid, "get_state") + session_idx = self._require_session(rid, "get_state") layers = [] for layer in self.layers: - lo, hi = layer.world_span(world_idx) + lo, hi = layer.session_span(session_idx) layers.append(( layer.kv[:, :, :, lo:hi].detach().clone(), layer.written[lo:hi].detach().clone(), @@ -197,40 +197,40 @@ def get_state(self, rid: str) -> dict: @torch.no_grad() def load_state(self, rid: str, state: dict) -> None: - """Restore one world's contents into the existing allocation.""" + """Restore one session's contents into the existing allocation.""" - world_idx = self._require_world(rid, "load_state") + session_idx = self._require_session(rid, "load_state") layers = state["layers"] if len(layers) != len(self.layers): raise ValueError( f"state has {len(layers)} layers, ring has {len(self.layers)}." ) for i, (layer, (kv, written)) in enumerate(zip(self.layers, layers, strict=True)): - lo, hi = layer.world_span(world_idx) + lo, hi = layer.session_span(session_idx) span = layer.kv[:, :, :, lo:hi] if tuple(kv.shape) != tuple(span.shape): raise ValueError( - f"layer {i} state shape {tuple(kv.shape)} != one world's ring " + f"layer {i} state shape {tuple(kv.shape)} != one session's ring " f"shape {tuple(span.shape)}." ) span.copy_(kv) layer.written[lo:hi].copy_(written) self._last_frames.pop(rid, None) - def _require_world(self, rid: str, what: str) -> int: - world_idx = self._worlds.get(rid) - if world_idx is None: + def _require_session(self, rid: str, what: str) -> int: + session_idx = self._sessions.get(rid) + if session_idx is None: raise KeyError( - f"ring KV {self.name!r} has no world for request {rid!r}; " + f"ring KV {self.name!r} has no session for request {rid!r}; " f"{what} is per request and a request that never admitted owns " "no span to read or write." ) - return world_idx + return session_idx # ---- Introspection ---------------------------------------------------- def memory_bytes(self) -> int: - """Total resident ring bytes across all layers and all worlds.""" + """Total resident ring bytes across all layers and all sessions.""" return sum(layer.memory_bytes for layer in self.layers) # ---- Resource lifecycle ----------------------------------------------- @@ -247,18 +247,18 @@ def _step_frames(self, step: ResourceStep) -> dict[str, int]: return dict(step.frames) def ingest_request(self, rid: str, overrides: ResourceReqConfig | None = None) -> None: - """Register ``rid``. Deliberately does not claim a world.""" + """Register ``rid``. Deliberately does not claim a session.""" del overrides # no per-request tunables: the ring geometry is fixed self._known_rids.add(rid) def admit(self, step: ResourceStep, ctx: StepContext) -> AdmitOutcome: - """Claim a world for this step's request, or refuse it terminally. + """Claim a session for this step's request, or refuse it terminally. Does not support eviction for now """ # `request_ids`, not `padded_request_ids` as the paged manager uses: a # padding row is a dummy rid a replay pads a bucket out to, and it must - # not be able to take a world from the real request in the same batch. + # not be able to take a session from the real request in the same batch. rids = list(ctx.request_ids) if len(set(rids)) != len(rids): @@ -267,15 +267,15 @@ def admit(self, step: ResourceStep, ctx: StepContext) -> AdmitOutcome: reason=AdmitRuntimeError( f"ring KV {self.name!r} was handed a batch naming " f"{sorted({rid for rid in rids if rids.count(rid) > 1})} more " - "than once; a step batches distinct worlds, one row per request." + "than once; a step batches distinct sessions, one row per request." ), ) frames = self._step_frames(step) - # Check every rid before claiming any world: a refused admit must not - # leave a world half-claimed by the first rid of a batch it rejected. - wanted = {rid for rid in rids if rid not in self._worlds} + # Check every rid before claiming any session: a refused admit must not + # leave a session half-claimed by the first rid of a batch it rejected. + wanted = {rid for rid in rids if rid not in self._sessions} for rid in sorted(wanted): if rid not in self._known_rids: return AdmitOutcome( @@ -284,20 +284,20 @@ def admit(self, step: ResourceStep, ctx: StepContext) -> AdmitOutcome: f"ring KV {self.name!r} was asked to admit request {rid!r}, " "which was never ingested. Worlds are handed back by " "`remove_request`, which only ever runs for a request the " - "engine opened -- so a world claimed here would never " + "engine opened -- so a session claimed here would never " "return to the pool and the node would lose capacity with " "nothing raised." ), ) - if len(wanted) > len(self._free_worlds): + if len(wanted) > len(self._free_sessions): return AdmitOutcome( ok=False, ready=False, reason=AdmitRuntimeError( - f"ring KV {self.name!r} holds all {self.num_worlds} of its " - f"worlds ({sorted(self._worlds)}); request(s) {sorted(wanted)} " + f"ring KV {self.name!r} holds all {self.num_sessions} of its " + f"sessions ({sorted(self._sessions)}); request(s) {sorted(wanted)} " "cannot be served concurrently. The rings are a fixed " "physical buffer and there is nothing to evict -- raise " - "`resources.kv.num_worlds` (and `max_concurrent_requests` " + "`resources.kv.num_sessions` (and `max_concurrent_requests` " "with it) to serve more." ), ) @@ -312,7 +312,7 @@ def admit(self, step: ResourceStep, ctx: StepContext) -> AdmitOutcome: f"clock for request {rid!r} (it names {sorted(frames)}). " "Every admitted request needs one: the continuity check is " "the only thing standing between a stalled clock and a " - "world that rewrites its own history." + "session that rewrites its own history." ), ) last = self._last_frames.get(rid) @@ -331,44 +331,44 @@ def admit(self, step: ResourceStep, ctx: StepContext) -> AdmitOutcome: ) for rid in rids: - if rid not in self._worlds: - world_idx = min(self._free_worlds) - self._free_worlds.remove(world_idx) - self._worlds[rid] = world_idx + if rid not in self._sessions: + session_idx = min(self._free_sessions) + self._free_sessions.remove(session_idx) + self._sessions[rid] = session_idx return ADMIT_OK def plan(self, step: ResourceStep, ctx: StepContext) -> RingPlan: - """Stage the padded batch's world indices, one per row. Ring addresses + """Stage the padded batch's session indices, one per row. Ring addresses stay in the graph. A replay pads the batch to its capture bucket with dummy rids that hold - no world. Those rows still write and attend (their output is dropped - downstream), so every one is parked on ``_padding_world`` -- the shared - scratch world outside the resident pool. The flex mask scopes each row to - its own world, so a padding row's read and its commit both stay on that - world and never reach a real request's history; no request is ever + no session. Those rows still write and attend (their output is dropped + downstream), so every one is parked on ``_padding_session`` -- the shared + scratch session outside the resident pool. The flex mask scopes each row to + its own session, so a padding row's read and its commit both stay on that + session and never reach a real request's history; no request is ever admitted to it, so the tail it leaves behind is never read. """ frames = self._step_frames(step) real_rids = tuple(ctx.request_ids) padded_rids = tuple(ctx.padded_request_ids) - world_idx = [] + session_idx = [] frame_pos = [] for b, rid in enumerate(real_rids): - world = self._require_world(rid, "plan") + session = self._require_session(rid, "plan") # `fill_`, not a `copy_` from a fresh host tensor: same in-place # write through the address the graph baked, without allocating a # staging tensor 24 times a second. - self._static_world_idx[b].fill_(world) - world_idx.append(world) + self._static_session_idx[b].fill_(session) + session_idx.append(session) frame_pos.append(frames[rid]) for b in range(len(real_rids), len(padded_rids)): - self._static_world_idx[b].fill_(self._padding_world) - world_idx.append(self._padding_world) + self._static_session_idx[b].fill_(self._padding_session) + session_idx.append(self._padding_session) # Frame 0's visibility is scratch-only, so the padding row attends # exactly one (garbage, dropped) block and never an unwritten slot. frame_pos.append(0) - return RingPlan(padded_rids, tuple(world_idx), tuple(frame_pos)) + return RingPlan(padded_rids, tuple(session_idx), tuple(frame_pos)) def commit(self, step: ResourceStep, ctx: StepContext) -> None: """Record the frame each request just committed. Metadata only.""" @@ -379,15 +379,15 @@ def commit(self, step: ResourceStep, ctx: StepContext) -> None: self._last_frames[rid] = frame def reset_request(self, rid: str, free: bool = False) -> None: - """Zero ``rid``'s world and release its claim. + """Zero ``rid``'s session and release its claim. ``free`` is ignored, there is no physical allocation to hand back. """ del free - self._release_world(rid) + self._release_session(rid) def remove_request(self, rid: str) -> None: - """The request is gone: drop its world, its claim, and its registration.""" - self._release_world(rid) + """The request is gone: drop its session, its claim, and its registration.""" + self._release_session(rid) self._known_rids.discard(rid) # `supports_preplan` stays the inherited False. @@ -396,23 +396,23 @@ def build_cuda_graph_buffers( self, slots: list[CGSlotSpec], max_bs: int, max_seq_len: int ) -> None: """No-op: every buffer a replay touches was allocated at ``build``, - ``_static_world_idx`` included. + ``_static_session_idx`` included. """ del slots, max_bs, max_seq_len def post_warmup_validate(self) -> None: - """Capture must leave every world exactly as it found it.""" + """Capture must leave every session exactly as it found it.""" - if self._worlds: + if self._sessions: raise RuntimeError( - f"ring KV {self.name!r} is still claimed by {sorted(self._worlds)} " + f"ring KV {self.name!r} is still claimed by {sorted(self._sessions)} " "after CUDA graph capture; a capture dummy rid was never reset, " - "and the world it holds is gone from the pool for good." + "and the session it holds is gone from the pool for good." ) - if len(self._free_worlds) != self.num_worlds: + if len(self._free_sessions) != self.num_sessions: raise RuntimeError( - f"ring KV {self.name!r} has {len(self._free_worlds)} of " - f"{self.num_worlds} worlds free after CUDA graph capture; a world " + f"ring KV {self.name!r} has {len(self._free_sessions)} of " + f"{self.num_sessions} sessions free after CUDA graph capture; a session " "was zeroed and never returned to the pool, so the node has " "silently lost concurrency." ) @@ -425,7 +425,7 @@ def post_warmup_validate(self) -> None: "declare the very next frame." ) for i, layer in enumerate(self.layers): - history = layer.written.view(layer.num_worlds, layer.capacity)[:, : layer.ring_len] + history = layer.written.view(layer.num_sessions, layer.capacity)[:, : layer.ring_len] if bool(layer.kv.any()) or bool(history.any()): raise RuntimeError( f"ring KV {self.name!r} layer {i} still holds capture-time " diff --git a/mstar/model/waypoint/config.py b/mstar/model/waypoint/config.py index 55d3b3475..be23fc8b5 100644 --- a/mstar/model/waypoint/config.py +++ b/mstar/model/waypoint/config.py @@ -150,8 +150,8 @@ class WaypointConfig: capture_dit_prime: bool = True # Rows carried per rollout step; one per resident world sharing the DiT - # forward. Must be <= `resources.kv.num_worlds` (checked at YAML-load time - # in waypoint_model.py, where num_worlds is known). + # forward. Must be <= `resources.kv.num_sessions` (checked at YAML-load time + # in waypoint_model.py, where num_sessions is known). step_batch_size: int = 1 # Guard rails the ported modules assert against, kept here so a drifting diff --git a/mstar/model/waypoint/ring_geometry.py b/mstar/model/waypoint/ring_geometry.py index 347f96b20..37cfdaf35 100644 --- a/mstar/model/waypoint/ring_geometry.py +++ b/mstar/model/waypoint/ring_geometry.py @@ -8,16 +8,16 @@ def ring_memory_bytes( - config: WaypointConfig, *, num_worlds: int = 1, dtype: torch.dtype = torch.bfloat16 + config: WaypointConfig, *, num_sessions: int = 1, dtype: torch.dtype = torch.bfloat16 ) -> list[int]: """Per-layer ring bytes for ``config``, computed without allocating anything (so it can be called on a laptop while sizing a deployment). - ``num_worlds`` is a straight multiplier: the world dimension is folded into + ``num_sessions`` is a straight multiplier: the world dimension is folded into the token axis, so N worlds is N times one world's slots and the per-slot arithmetic is untouched. That it is a multiplier is the whole reason - ``num_worlds`` is a deployment sizing knob and the geometry is not.""" - per_slot = 2 * num_worlds * config.n_kv_heads * config.d_head * dtype.itemsize + ``num_sessions`` is a deployment sizing knob and the geometry is not.""" + per_slot = 2 * num_sessions * config.n_kv_heads * config.d_head * dtype.itemsize return [per_slot * config.kv_capacity(i) for i in range(config.n_layers)] @@ -26,7 +26,7 @@ def _fmt_bytes(n: int) -> str: def describe_ring_memory( - config: WaypointConfig, *, num_worlds: int = 1, dtype: torch.dtype = torch.bfloat16 + config: WaypointConfig, *, num_sessions: int = 1, dtype: torch.dtype = torch.bfloat16 ) -> str: """Geometry and footprint of every ring, grouped local vs global, with the counterfactual under the opposite ``full_global_ring`` setting. @@ -35,11 +35,11 @@ def describe_ring_memory( line is the reference's allocation, 8/9ths of whose global storage is permanently unwritten. """ - per_layer = ring_memory_bytes(config, num_worlds=num_worlds, dtype=dtype) + per_layer = ring_memory_bytes(config, num_sessions=num_sessions, dtype=dtype) total = sum(per_layer) lines = [ - f"Waypoint ring KV variant={config.variant} worlds={num_worlds} dtype={dtype} " + f"Waypoint ring KV variant={config.variant} worlds={num_sessions} dtype={dtype} " f"full_global_ring={config.full_global_ring}" ] groups = ( @@ -61,7 +61,7 @@ def describe_ring_memory( lines.append(f" total {_fmt_bytes(total)} ({total} bytes)") other = dataclasses.replace(config, full_global_ring=not config.full_global_ring) - other_total = sum(ring_memory_bytes(other, num_worlds=num_worlds, dtype=dtype)) + other_total = sum(ring_memory_bytes(other, num_sessions=num_sessions, dtype=dtype)) delta = other_total - total lines.append( f" full_global_ring={other.full_global_ring} would use {_fmt_bytes(other_total)} " diff --git a/mstar/model/waypoint/waypoint_model.py b/mstar/model/waypoint/waypoint_model.py index ab726f08d..17da06859 100644 --- a/mstar/model/waypoint/waypoint_model.py +++ b/mstar/model/waypoint/waypoint_model.py @@ -36,10 +36,10 @@ or refuse it when they are all taken. That refusal is a backstop, though — see ``get_worker_graphs``. -``num_worlds`` and ``max_batch_size`` are separate numbers and stay separate. -``num_worlds`` is how many sessions are *resident* (one ring span each, folded +``num_sessions`` and ``max_batch_size`` are separate numbers and stay separate. +``num_sessions`` is how many sessions are *resident* (one ring span each, folded into the token dimension by ``LayerRingCache``); ``max_batch_size`` is how many -share one *forward step*, set by ``step_batch_size`` (<= ``num_worlds``), so up +share one *forward step*, set by ``step_batch_size`` (<= ``num_sessions``), so up to that many resident worlds batch into one step instead of each taking a separate turn. """ @@ -226,25 +226,25 @@ def get_node_resources(self) -> list[NodeResourceSpec]: # How many sessions this node holds resident. One by default # because a world is ~816 MiB of ring at 720P and a model has no # business assuming the box; a deployment raises it under - # ``resources: {kv: {num_worlds: N}}`` and raises + # ``resources: {kv: {num_sessions: N}}`` and raises # ``max_concurrent_requests`` with it (see ``get_worker_graphs``). # Not the step batch — that is ``max_batch_size``, set by - # ``step_batch_size`` (<= num_worlds). - num_worlds=1, + # ``step_batch_size`` (<= num_sessions). + num_sessions=1, ) # Logged, not merely allocated: this declaration is worth ~816 MiB per # world and nothing downstream prints it. The report carries the # counterfactual under the other ``full_global_ring`` setting, which is # the number you want *before* the engine commits to one of them. # - # `ring_config.num_worlds` is the DECLARED count, which is what this + # `ring_config.num_sessions` is the DECLARED count, which is what this # line can honestly report: `EngineManager.build` calls # `apply_yaml_overrides` on the specs after this hook returns, so a - # deployment's `num_worlds` has not landed yet. The reported total + # deployment's `num_sessions` has not landed yet. The reported total # scales linearly with it -- the world dim is folded into the token # axis -- so N worlds is N times the number below. logger.info( - "%s", describe_ring_memory(self.config, num_worlds=ring_config.num_worlds) + "%s", describe_ring_memory(self.config, num_sessions=ring_config.num_sessions) ) return [ KVSpec( @@ -341,11 +341,11 @@ def get_worker_graphs(self, config_path: str) -> list[WorkerGraph]: ``max_batch_size`` does NOT cover this, independent of its value. It caps how many requests share one *step* (``step_batch_size``, <= - ``num_worlds``); worlds beyond that batch still alternate steps — each + ``num_sessions``); worlds beyond that batch still alternate steps — each holds its own world and the BlockMask keeps them apart — but it says nothing about how many may exist, which is the thing the pool bounds. - A limit *below* ``num_worlds`` is legal and only wasteful: it allocates + A limit *below* ``num_sessions`` is legal and only wasteful: it allocates rings (~816 MiB each at 720P) for worlds no request can ever reach, so it is warned about rather than refused. @@ -360,46 +360,46 @@ def get_worker_graphs(self, config_path: str) -> list[WorkerGraph]: # ``apply_yaml_overrides``, read here for the same key, so the gate and # the allocation cannot disagree about how many worlds exist. overrides = (config.get("resources") or {}).get(KV_RESOURCE) or {} - num_worlds = overrides.get("num_worlds", 1) + num_sessions = overrides.get("num_sessions", 1) if ( - not isinstance(num_worlds, int) - or isinstance(num_worlds, bool) - or num_worlds < 1 + not isinstance(num_sessions, int) + or isinstance(num_sessions, bool) + or num_sessions < 1 ): raise ValueError( - f"Waypoint requires `resources.{KV_RESOURCE}.num_worlds` in " - f"{config_path} to be a positive int; got {num_worlds!r}." + f"Waypoint requires `resources.{KV_RESOURCE}.num_sessions` in " + f"{config_path} to be a positive int; got {num_sessions!r}." ) limit = config.get("max_concurrent_requests") if not isinstance(limit, int) or isinstance(limit, bool) or limit < 1: raise ValueError( f"Waypoint requires `max_concurrent_requests` in {config_path} to be " f"a positive int; got {limit!r}. The DiT node holds " - f"{num_worlds} live world(s) in a fixed ring buffer, and the " + f"{num_sessions} live world(s) in a fixed ring buffer, and the " "conductor's FIFO admit queue — which exists only when this key " "is set — is what keeps arrivals inside that pool. max_batch_size " "alone does not: it caps a step, not the number of requests on " "the node." ) - if limit > num_worlds: + if limit > num_sessions: raise ValueError( f"`max_concurrent_requests: {limit}` in {config_path} exceeds the " - f"{num_worlds} world(s) the ring is sized for. Every request past " + f"{num_sessions} world(s) the ring is sized for. Every request past " "the pool fails terminally at admit — there is nothing to evict. " - f"Set `resources.{KV_RESOURCE}.num_worlds` to {limit} to match, at " + f"Set `resources.{KV_RESOURCE}.num_sessions` to {limit} to match, at " "the cost of ~816 MiB of ring per world at 720P." ) - if limit < num_worlds: + if limit < num_sessions: logger.warning( "Waypoint ring is sized for %d worlds but max_concurrent_requests " "is %d: %d world(s) of ring (~816 MiB each at 720P) are allocated " "and can never be filled.", - num_worlds, limit, num_worlds - limit, + num_sessions, limit, num_sessions - limit, ) - if self.config.step_batch_size > num_worlds: + if self.config.step_batch_size > num_sessions: raise ValueError( f"`step_batch_size: {self.config.step_batch_size}` exceeds " - f"`resources.{KV_RESOURCE}.num_worlds: {num_worlds}` in " + f"`resources.{KV_RESOURCE}.num_sessions: {num_sessions}` in " f"{config_path}. A step cannot batch more rows than there are " "resident worlds to supply them." ) diff --git a/test/modular/test_flex_attention_resource.py b/test/modular/test_flex_attention_resource.py index 9e698162f..fa6366c77 100644 --- a/test/modular/test_flex_attention_resource.py +++ b/test/modular/test_flex_attention_resource.py @@ -225,7 +225,7 @@ def test_plan_stages_one_reused_mask_per_geometry_and_slot(monkeypatch): head_dim=D_HEAD, num_qo_heads=N_QO_HEADS, tokens_per_frame=TPF, - num_worlds=2, + num_sessions=2, layers=( RingKVLayerConfig(4, 4, 1), RingKVLayerConfig(4, 4, 1), @@ -300,13 +300,13 @@ def test_planned_masks_match_ring_visibility_across_wraps_and_worlds(): head_dim=D_HEAD, num_qo_heads=N_QO_HEADS, tokens_per_frame=TPF, - num_worlds=2, + num_sessions=2, layers=layers, ) manager = build_attention(AttnBackend.FLEX, config) caches = [ LayerRingCache( - num_worlds=config.num_worlds, + num_sessions=config.num_sessions, n_kv_heads=config.num_kv_heads, ring_frames=layer.ring_frames, ring_buckets=layer.ring_buckets, @@ -359,7 +359,7 @@ def test_stage_writes_each_rows_own_visibility_at_its_own_world_and_frame(): head_dim=D_HEAD, num_qo_heads=N_QO_HEADS, tokens_per_frame=TPF, - num_worlds=2, + num_sessions=2, layers=( RingKVLayerConfig(4, 4, 1), RingKVLayerConfig(4, 4, 1), @@ -556,7 +556,7 @@ def test_masked_dense_reference_is_not_trivially_satisfied(): FOLDED_KV = WORLDS * CAPACITY -def world_span(w: int) -> tuple[int, int]: +def session_span(w: int) -> tuple[int, int]: return w * CAPACITY, (w + 1) * CAPACITY @@ -565,7 +565,7 @@ def folded_visible_row(w: int, *, history: bool, device) -> torch.Tensor: always, its own history block if it has committed one, and nothing outside its span ever.""" row = torch.zeros(FOLDED_KV, dtype=torch.bool, device=device) - lo, hi = world_span(w) + lo, hi = session_span(w) row[hi - BLOCK : hi] = True # scratch: the frame being denoised if history: row[lo : lo + BLOCK] = True @@ -723,7 +723,7 @@ def run(k_ring, v_ring): for w in range(WORLDS): if w == kept: continue - lo, hi = world_span(w) + lo, hi = session_span(w) shape = (1, N_KV_HEADS, hi - lo, D_HEAD) k2[:, :, lo:hi] = torch.randn(shape, generator=gen).to(device) v2[:, :, lo:hi] = torch.randn(shape, generator=gen).to(device) diff --git a/test/modular/test_ring_kv_resource.py b/test/modular/test_ring_kv_resource.py index 1fbbe674b..fb1d6d32e 100644 --- a/test/modular/test_ring_kv_resource.py +++ b/test/modular/test_ring_kv_resource.py @@ -7,7 +7,7 @@ smoothly drifting video, or a request that hangs forever waiting on an evictor with nothing to evict. -``num_worlds`` worlds share one buffer per layer, folded into its token +``num_sessions`` worlds share one buffer per layer, folded into its token dimension (``kv/ring/cache.py``). Nothing physical separates them: ``upsert`` hands back K and V spanning every resident world and one bool row that is False outside the caller's own span. That row is the whole isolation mechanism, so the @@ -18,7 +18,7 @@ CPU-only and allocation-free at test scale: the geometry is 3 layers of 4 frames at 128 tokens, not 24 x 17 x 512. The one GPU test is the capture test, which -cannot be anything else — the property it pins (``world_idx`` is *read* at +cannot be anything else — the property it pins (``session_idx`` is *read* at replay, not baked at capture) only exists inside a CUDA graph. """ @@ -64,7 +64,7 @@ def _ring_config( *, - num_worlds: int = 1, + num_sessions: int = 1, ring_frames: int = RING_FRAMES, num_kv_heads: int = N_KV_HEADS, ) -> RingKVConfig: @@ -75,7 +75,7 @@ def _ring_config( num_kv_heads=num_kv_heads, head_dim=D_HEAD, tokens_per_frame=TPF, - num_worlds=num_worlds, + num_sessions=num_sessions, layers=tuple( RingKVLayerConfig( ring_frames=ring_frames, @@ -160,9 +160,9 @@ def _ptrs(kv: RingKVManager) -> list[tuple[int, int, int, int]]: def _world_view(layer: LayerRingCache) -> torch.Tensor: - """``written`` cut back into ``[num_worlds, capacity]``. A view, so it reads + """``written`` cut back into ``[num_sessions, capacity]``. A view, so it reads the live row rather than a snapshot of it.""" - return layer.written.view(layer.num_worlds, layer.capacity) + return layer.written.view(layer.num_sessions, layer.capacity) # ── spec dispatch ─────────────────────────────────────────────────────── @@ -212,7 +212,7 @@ def test_num_worlds_is_the_one_yaml_tunable(): """The counterweight to the test above, and the reason it is a whitelist rather than a blanket refusal. - `num_worlds` is a different kind of number from the geometry: how many + `num_sessions` is a different kind of number from the geometry: how many concurrent sessions this box holds resident (~816 MiB of ring each at 720P) is a sizing decision about the box, exactly like `max_num_pages` on the paged config. It changes what the node can *serve* and never what it @@ -222,11 +222,11 @@ def test_num_worlds_is_the_one_yaml_tunable(): config = _ring_config() spec = KVSpec(resource_key="kv", nodes={"dit"}, config=config) - apply_yaml_overrides([spec], {"resources": {"kv": {"num_worlds": 4}}}) + apply_yaml_overrides([spec], {"resources": {"kv": {"num_sessions": 4}}}) - assert config.num_worlds == 4 + assert config.num_sessions == 4 kv = _manager(config) - assert kv.num_worlds == 4 + assert kv.num_sessions == 4 # 4 resident worlds plus the one shared padding world the ring parks a # replay's dummy tail on. assert kv.total_slots(0) == (4 + 1) * kv.capacity(0) @@ -236,14 +236,14 @@ def test_num_worlds_is_the_one_yaml_tunable(): def test_invalid_num_worlds_is_refused_at_both_entry_points(bad): """A node sized for zero worlds refuses every request at admit — a deployment that boots, reports healthy, and serves nothing.""" - with pytest.raises(ValueError, match="num_worlds"): - _ring_config(num_worlds=bad) - with pytest.raises(ValueError, match="num_worlds"): - _ring_config().apply_yaml_overrides(num_worlds=bad) + with pytest.raises(ValueError, match="num_sessions"): + _ring_config(num_sessions=bad) + with pytest.raises(ValueError, match="num_sessions"): + _ring_config().apply_yaml_overrides(num_sessions=bad) def test_applying_num_worlds_does_not_rebaseline_the_head_counts(): - """`apply_yaml_overrides` validates `num_worlds` inline rather than + """`apply_yaml_overrides` validates `num_sessions` inline rather than re-running `__post_init__`, and that is load-bearing rather than tidiness. `KVConfig.__post_init__` snapshots `_unsharded_kv_heads` from the CURRENT @@ -258,7 +258,7 @@ def test_applying_num_worlds_does_not_rebaseline_the_head_counts(): sharded = config.num_kv_heads assert sharded == 4 - config.apply_yaml_overrides(num_worlds=4) + config.apply_yaml_overrides(num_sessions=4) config.shard(2) assert config.num_kv_heads == sharded, "shard() stopped being idempotent" @@ -322,7 +322,7 @@ def test_two_capture_configs_can_each_open_and_claim(): anything the manager could learn without a commit, the second warmup admit would refuse and capture would die here rather than in production. """ - kv = _manager(_ring_config(num_worlds=1)) + kv = _manager(_ring_config(num_sessions=1)) pool = DummyRowPool( prefix="dit", step_runner=SimpleNamespace(ingest_request=kv.ingest_request), @@ -362,7 +362,7 @@ def test_ingest_request_registers_without_claiming(): kv.ingest_request("b") kv.ingest_request("a") # idempotent: one NewRequest per partition - assert kv.world_of("a") is None and kv.world_of("b") is None + assert kv.session_of("a") is None and kv.session_of("b") is None # neither registration took a world, so either may still be admitted assert kv.admit(_step("b"), _ctx("b")).ok @@ -379,19 +379,19 @@ def test_admit_refuses_a_request_that_was_never_ingested(): With one world the distinction was invisible, because the single claim was always overwritten by whoever asked next. It is not invisible with a pool. """ - kv = _manager(_ring_config(num_worlds=4)) + kv = _manager(_ring_config(num_sessions=4)) outcome = kv.admit(_step("stranger"), _ctx("stranger")) assert not outcome.ok assert type(outcome.reason) is AdmitRuntimeError assert "never ingested" in outcome.reason.message - assert kv.world_of("stranger") is None - assert len(kv._free_worlds) == 4, "a refused admit still took a world" + assert kv.session_of("stranger") is None + assert len(kv._free_sessions) == 4, "a refused admit still took a world" -@pytest.mark.parametrize("num_worlds", [1, 2, 4]) -def test_the_n_plus_first_request_is_refused_with_a_terminal_reason(num_worlds): +@pytest.mark.parametrize("num_sessions", [1, 2, 4]) +def test_the_n_plus_first_request_is_refused_with_a_terminal_reason(num_sessions): """The pool is finite and exhaustion is terminal. The reason class is the whole point: `AllocationFailed` sends the scheduler to evict, but `supports_eviction` is False so nothing is evictable and the request spins; @@ -399,8 +399,8 @@ def test_the_n_plus_first_request_is_refused_with_a_terminal_reason(num_worlds): of failing. That reasoning does not change with the pool size — an exhausted ring is exhausted for the same reason at N=4 as at N=1. """ - kv = _manager(_ring_config(num_worlds=num_worlds)) - _open(kv, *[f"r{i}" for i in range(num_worlds)]) + kv = _manager(_ring_config(num_sessions=num_sessions)) + _open(kv, *[f"r{i}" for i in range(num_sessions)]) kv.ingest_request("extra") outcome = kv.admit(_step("extra"), _ctx("extra")) @@ -409,11 +409,11 @@ def test_the_n_plus_first_request_is_refused_with_a_terminal_reason(num_worlds): assert not outcome.ready assert type(outcome.reason) is AdmitRuntimeError assert not isinstance(outcome.reason, (AllocationFailed, RequestOffloading)) - assert f"all {num_worlds}" in outcome.reason.message + assert f"all {num_sessions}" in outcome.reason.message -@pytest.mark.parametrize("num_worlds", [2, 4]) -def test_concurrent_requests_get_distinct_worlds(num_worlds): +@pytest.mark.parametrize("num_sessions", [2, 4]) +def test_concurrent_requests_get_distinct_worlds(num_sessions): """The point of the whole change, and the thing that has no meaning at N=1. Two requests handed the same world index would share one span: each would @@ -422,18 +422,18 @@ def test_concurrent_requests_get_distinct_worlds(num_worlds): set, not against an expected assignment, because which index a request gets is allocator business; that it gets its own is not. """ - kv = _manager(_ring_config(num_worlds=num_worlds)) - rids = [f"r{i}" for i in range(num_worlds)] + kv = _manager(_ring_config(num_sessions=num_sessions)) + rids = [f"r{i}" for i in range(num_sessions)] _open(kv, *rids) - worlds = [kv.world_of(rid) for rid in rids] + worlds = [kv.session_of(rid) for rid in rids] assert None not in worlds - assert len(set(worlds)) == num_worlds, ( + assert len(set(worlds)) == num_sessions, ( f"worlds collided: {dict(zip(rids, worlds, strict=True))}" ) - assert set(worlds) == set(range(num_worlds)), "a world was skipped" - assert not kv._free_worlds + assert set(worlds) == set(range(num_sessions)), "a world was skipped" + assert not kv._free_sessions def test_admit_is_idempotent_for_the_holder(): @@ -442,7 +442,7 @@ def test_admit_is_idempotent_for_the_holder(): kv.ingest_request("a") assert all(kv.admit(_step("a"), _ctx("a")).ok for _ in range(4)) - assert kv.world_of("a") == 0 + assert kv.session_of("a") == 0 def test_a_refused_admit_leaves_every_previous_holder_in_place(): @@ -452,17 +452,17 @@ def test_a_refused_admit_leaves_every_previous_holder_in_place(): refusal that half-claimed would now do it to whichever holder happened to own the index it grabbed. """ - kv = _manager(_ring_config(num_worlds=2)) + kv = _manager(_ring_config(num_sessions=2)) _open(kv, "a", "b") - before = {rid: kv.world_of(rid) for rid in ("a", "b")} + before = {rid: kv.session_of(rid) for rid in ("a", "b")} kv.ingest_request("c") assert not kv.admit(_step("c"), _ctx("c")).ok - assert {rid: kv.world_of(rid) for rid in ("a", "b")} == before + assert {rid: kv.session_of(rid) for rid in ("a", "b")} == before assert kv.admit(_step("a"), _ctx("a")).ok assert kv.admit(_step("b"), _ctx("b")).ok - assert kv.world_of("c") is None + assert kv.session_of("c") is None @pytest.mark.parametrize("free", [False, True]) @@ -476,31 +476,31 @@ def test_reset_request_releases_one_world_and_only_one(free): dropped *the* claim, which was the same statement then and would now end every other rollout on the node every time any request was reset. """ - kv = _manager(_ring_config(num_worlds=2)) + kv = _manager(_ring_config(num_sessions=2)) _open(kv, "a", "b") - b_world = kv.world_of("b") + b_world = kv.session_of("b") kv.reset_request("a", free=free) - assert kv.world_of("a") is None - assert kv.world_of("b") == b_world, "resetting `a` released `b`'s world" + assert kv.session_of("a") is None + assert kv.session_of("b") == b_world, "resetting `a` released `b`'s world" kv.ingest_request("c") assert kv.admit(_step("c"), _ctx("c")).ok - assert kv.world_of("c") == 0, "the freed world was not the one handed on" + assert kv.session_of("c") == 0, "the freed world was not the one handed on" def test_remove_request_releases_the_world_and_the_registration(): """Both, and neither anyone else's. Dropping the registration is what makes the world genuinely returned rather than reserved for a rid the engine has already forgotten.""" - kv = _manager(_ring_config(num_worlds=2)) + kv = _manager(_ring_config(num_sessions=2)) _open(kv, "a", "b") - b_world = kv.world_of("b") + b_world = kv.session_of("b") kv.remove_request("a") - assert kv.world_of("a") is None - assert kv.world_of("b") == b_world, "removing `a` released `b`'s world" + assert kv.session_of("a") is None + assert kv.session_of("b") == b_world, "removing `a` released `b`'s world" # the registration went with it: `a` is a stranger again assert not kv.admit(_step("a"), _ctx("a")).ok kv.ingest_request("a") @@ -511,7 +511,7 @@ def test_supports_preplan_stays_false(): """It keeps `CudaGraphRunner._num_slots` at 1. Two slots exist so a plan for step N+1 can write buffers replay N is not reading; the only thing planned here is one `[B]` world index, so the second slot would be an identical - graph at double the capture cost — and the only reason `_static_world_idx` + graph at double the capture cost — and the only reason `_static_session_idx` would have to become one buffer per slot.""" assert _manager().supports_preplan is False @@ -527,75 +527,75 @@ def test_plan_stages_the_world_index_in_place_as_a_device_tensor(): buffer that existed at capture, so a rebind leaves every replay reading the orphaned original. - Hence both halves below: the staged value is a ``[num_worlds]`` int64 + Hence both halves below: the staged value is a ``[num_sessions]`` int64 device tensor (row ``b`` holds row ``b``'s world once staged), and `plan` writes *through* it rather than replacing it. """ - kv = _manager(_ring_config(num_worlds=4)) + kv = _manager(_ring_config(num_sessions=4)) _open(kv, "a", "b") - staged = kv._static_world_idx + staged = kv._static_session_idx assert staged.shape == (4,) and staged.dtype == torch.int64 kv.plan(_step("b"), _ctx("b")) - assert kv._static_world_idx is staged, "plan rebound the buffer capture baked" - assert staged.data_ptr() == kv._static_world_idx.data_ptr() - assert int(staged[0]) == kv.world_of("b") + assert kv._static_session_idx is staged, "plan rebound the buffer capture baked" + assert staged.data_ptr() == kv._static_session_idx.data_ptr() + assert int(staged[0]) == kv.session_of("b") kv.plan(_step("a"), _ctx("a")) - assert int(staged[0]) == kv.world_of("a") + assert int(staged[0]) == kv.session_of("a") def test_plan_stages_a_batch_of_distinct_rids(): """A step can batch worlds each claimed on its own admit -- `admit` no longer refuses a same-step batch of distinct rids, and `plan` stages every row, in `ctx.request_ids` order, rather than just the first.""" - kv = _manager(_ring_config(num_worlds=2)) + kv = _manager(_ring_config(num_sessions=2)) _open(kv, "a", "b") result = kv.plan(_step("a", "b"), _ctx("a", "b")) assert result.request_ids == ("a", "b") - assert result.world_idx == (kv.world_of("a"), kv.world_of("b")) + assert result.session_idx == (kv.session_of("a"), kv.session_of("b")) assert result.frame_pos == (0, 0) - assert kv._static_world_idx[:2].tolist() == list(result.world_idx) + assert kv._static_session_idx[:2].tolist() == list(result.session_idx) def test_plan_parks_padding_rows_on_the_shared_padding_world(): """A replay padded past its real rows stages the dummy tail on the padding world -- the one world past the resident pool -- so a padding write never lands in a resident request's history. Real rows keep their own world; the - padding rows all share ``_padding_world``, and no admit ever hands it out.""" - kv = _manager(_ring_config(num_worlds=4)) + padding rows all share ``_padding_session``, and no admit ever hands it out.""" + kv = _manager(_ring_config(num_sessions=4)) _open(kv, "a", "b") - free_before = set(kv._free_worlds) + free_before = set(kv._free_sessions) ctx = _ctx("a", "b") ctx.set_padded_rids(("a", "b", "__pad0__", "__pad1__")) result = kv.plan(_step("a", "b"), ctx) - pad = kv._padding_world + pad = kv._padding_session assert pad == 4, "the padding world is the one index past the resident pool" - assert pad not in kv._free_worlds and kv.layers[0].num_worlds == 5 + assert pad not in kv._free_sessions and kv.layers[0].num_sessions == 5 assert result.request_ids == ("a", "b", "__pad0__", "__pad1__") - assert result.world_idx == (kv.world_of("a"), kv.world_of("b"), pad, pad) + assert result.session_idx == (kv.session_of("a"), kv.session_of("b"), pad, pad) assert result.frame_pos == (0, 0, 0, 0) - assert kv._static_world_idx[:4].tolist() == list(result.world_idx) + assert kv._static_session_idx[:4].tolist() == list(result.session_idx) # Padding never touched the pool: no free world was consumed for the tail. - assert set(kv._free_worlds) == free_before + assert set(kv._free_sessions) == free_before def test_plan_refuses_a_request_holding_no_world(): kv = _manager() - with pytest.raises(KeyError, match="no world for request"): + with pytest.raises(KeyError, match="no session for request"): kv.plan(_step("a"), _ctx("a")) def test_admit_refuses_a_batch_naming_the_same_request_twice(): """A step batches distinct worlds, one row per request; naming the same rid twice would try to stage two rows into the same world.""" - kv = _manager(_ring_config(num_worlds=4)) + kv = _manager(_ring_config(num_sessions=4)) kv.ingest_request("a") outcome = kv.admit(_step("a", "a"), _ctx("a", "a")) @@ -679,7 +679,7 @@ def test_each_world_runs_its_own_clock(): and, worse under a hypothetical "just take the max", would let a lagging world skip forward into a slot it never wrote. """ - kv = _manager(_ring_config(num_worlds=3)) + kv = _manager(_ring_config(num_sessions=3)) _open(kv, "a", "b", "c") _drive(kv, "a", 0) @@ -703,7 +703,7 @@ def test_the_clock_check_fires_per_rid_inside_a_batched_admit(): """Two rids in one `admit` call, not two: the continuity check must still catch a bad clock on either row of a batch, and a good row ahead of it in `ctx.request_ids` order must not paper over it.""" - kv = _manager(_ring_config(num_worlds=2)) + kv = _manager(_ring_config(num_sessions=2)) _open(kv, "a", "b") _drive(kv, "a", 0) _drive(kv, "b", 0) @@ -858,10 +858,10 @@ def test_reset_request_zeroes_one_span_without_reallocating(): A reset that zeroed the whole buffer (which is what the single-world version did, indistinguishably) fails on `b`. """ - kv = _manager(_ring_config(num_worlds=2)) + kv = _manager(_ring_config(num_sessions=2)) before = _ptrs(kv) _open(kv, "a", "b") - assert (kv.world_of("a"), kv.world_of("b")) == (0, 1) + assert (kv.session_of("a"), kv.session_of("b")) == (0, 1) kv.plan(_step("b"), _ctx("b")) _rollout(kv, frames=6, seed=2) @@ -893,7 +893,7 @@ def test_remove_request_zeroes_one_span_without_reallocating(): rollout ending takes. The single-world version zeroed the whole buffer here: with one world that was the same statement, with N it ends every concurrent rollout on the node every time any one of them finishes.""" - kv = _manager(_ring_config(num_worlds=2)) + kv = _manager(_ring_config(num_sessions=2)) before = _ptrs(kv) _open(kv, "a", "b") @@ -913,11 +913,11 @@ def test_remove_request_zeroes_one_span_without_reallocating(): def test_a_reused_world_starts_empty(): - """The pairing `_release_world` exists to enforce: a world handed back to + """The pairing `_release_session` exists to enforce: a world handed back to the pool still holding a dead request's frames is handed to the next request as its history — neither empty nor its own, and attended to as real. There is no release path that does not zero first.""" - kv = _manager(_ring_config(num_worlds=1)) + kv = _manager(_ring_config(num_sessions=1)) _open(kv, "a") kv.plan(_step("a"), _ctx("a")) _rollout(kv, frames=5, seed=6) @@ -926,7 +926,7 @@ def test_a_reused_world_starts_empty(): kv.remove_request("a") _open(kv, "b") - assert kv.world_of("b") == 0, "this test needs the same index handed on" + assert kv.session_of("b") == 0, "this test needs the same index handed on" assert all(not bool(layer.kv.any()) for layer in kv.layers) for layer in kv.layers: assert not bool(layer.written[: layer.ring_len].any()) @@ -945,7 +945,7 @@ def test_post_warmup_validate_catches_capture_residue(): """NUM_WARMUP=2 plus the capture forward is three committing passes into whatever world the dummy held. Left there, the first real rollout handed that world attends to them as history.""" - kv = _manager(_ring_config(num_worlds=2)) + kv = _manager(_ring_config(num_sessions=2)) kv.post_warmup_validate() _rollout(kv, frames=1) @@ -957,7 +957,7 @@ def test_post_warmup_validate_catches_a_lingering_claim(): """A dummy rid still holding a world after capture is a world no request will ever get back: the node boots reporting healthy and serves one fewer session than it was sized for, forever.""" - kv = _manager(_ring_config(num_worlds=2)) + kv = _manager(_ring_config(num_sessions=2)) _open(kv, "dummy") with pytest.raises(RuntimeError, match="still claimed"): @@ -994,10 +994,10 @@ def test_state_is_refused_for_a_request_holding_no_world(call): it back would overwrite worlds the caller never asked about — so there is no unscoped spelling to fall back to, and a rid that owns nothing has to say so.""" - kv = _manager(_ring_config(num_worlds=2)) + kv = _manager(_ring_config(num_sessions=2)) _open(kv, "a") - with pytest.raises(KeyError, match="no world for request"): + with pytest.raises(KeyError, match="no session for request"): if call == "get_state": kv.get_state("ghost") else: @@ -1010,7 +1010,7 @@ def test_get_state_covers_one_world_and_is_cloned_not_aliased(): place, and an aliased snapshot silently tracks the live world instead. The span check is the multi-world half — a snapshot the size of the whole buffer would carry `b`'s history into `a`'s save file.""" - kv = _manager(_ring_config(num_worlds=2)) + kv = _manager(_ring_config(num_sessions=2)) _open(kv, "a", "b") kv.plan(_step("a"), _ctx("a")) _rollout(kv, frames=2) @@ -1037,7 +1037,7 @@ def test_load_state_copies_into_one_span_of_the_fixed_allocation(): a *span* rather than the whole buffer is the second half of that: every other resident world has to come through untouched, or restoring one session resets its neighbours.""" - kv = _manager(_ring_config(num_worlds=2)) + kv = _manager(_ring_config(num_sessions=2)) _open(kv, "a", "b") kv.plan(_step("a"), _ctx("a")) _rollout(kv, frames=5) @@ -1060,10 +1060,10 @@ def test_load_state_copies_into_one_span_of_the_fixed_allocation(): assert _ptrs(kv) == before for i, layer in enumerate(kv.layers): - lo, hi = layer.world_span(kv.world_of("a")) + lo, hi = layer.session_span(kv.session_of("a")) assert torch.equal(layer.kv[:, :, :, lo:hi], state["layers"][i][0]) assert torch.equal(layer.written[lo:hi], state["layers"][i][1]) - b_lo, b_hi = layer.world_span(kv.world_of("b")) + b_lo, b_hi = layer.session_span(kv.session_of("b")) assert torch.equal(layer.kv[:, :, :, b_lo:b_hi], b_kv[i][:, :, :, b_lo:b_hi]), ( "load_state reached another world's span" ) @@ -1075,8 +1075,8 @@ def test_a_state_is_portable_between_rings_of_different_widths(): part of it. This is not incidental: a node resized from 2 worlds to 4 between restarts must still be able to load the sessions it wrote, and a guard that compared against the whole buffer would refuse them all.""" - small = _manager(_ring_config(num_worlds=2)) - big = _manager(_ring_config(num_worlds=4)) + small = _manager(_ring_config(num_sessions=2)) + big = _manager(_ring_config(num_sessions=4)) _open(small, "a") small.plan(_step("a"), _ctx("a")) _rollout(small, frames=3, seed=13) @@ -1085,12 +1085,12 @@ def test_a_state_is_portable_between_rings_of_different_widths(): big.load_state("y", small.get_state("a")) for layer, (kv_t, w_t) in zip(big.layers, small.get_state("a")["layers"], strict=True): - lo, hi = layer.world_span(big.world_of("y")) + lo, hi = layer.session_span(big.session_of("y")) assert torch.equal(layer.kv[:, :, :, lo:hi], kv_t) assert torch.equal(layer.written[lo:hi], w_t) # and `x`'s world is still empty for layer in big.layers: - lo, hi = layer.world_span(big.world_of("x")) + lo, hi = layer.session_span(big.session_of("x")) assert not bool(layer.kv[:, :, :, lo:hi].any()) @@ -1178,7 +1178,7 @@ def test_upsert_returns_the_whole_buffer_and_delegates_by_layer(): the caller's world back out of it. `capacity` and `total_slots` are both named for this reason: using either where the other belongs is an off-by-N that produces a valid shape.""" - kv = _manager(_ring_config(num_worlds=3)) + kv = _manager(_ring_config(num_sessions=3)) gen = torch.Generator().manual_seed(5) k, v = _frame(kv, gen) @@ -1255,14 +1255,14 @@ def _w(idx: int) -> torch.Tensor: return torch.tensor([idx], dtype=torch.int64) -@pytest.mark.parametrize("num_worlds", [2, 4]) +@pytest.mark.parametrize("num_sessions", [2, 4]) @pytest.mark.parametrize("pinned_dilation", [1, 8]) -def test_the_flat_ring_matches_one_ring_per_world(num_worlds, pinned_dilation): +def test_the_flat_ring_matches_one_ring_per_world(num_sessions, pinned_dilation): """N worlds folded into one token axis are bit-identical to N separate single-world rings, span for span. This is the equivalence the whole layout rests on and the reason - `num_worlds` can be a deployment knob at all: a world's arithmetic must not + `num_sessions` can be a deployment knob at all: a world's arithmetic must not depend on how many neighbours it has. Three things are compared and all three are necessary — the K/V bytes (the write landed in the right slots), `written` (the bookkeeping did too), and the visibility row restricted to @@ -1270,7 +1270,7 @@ def test_the_flat_ring_matches_one_ring_per_world(num_worlds, pinned_dilation): single-world counterpart: everything OUTSIDE the span is False, which is isolation itself. - The own-world term is checked as `_world_of_slot == world_idx` computed from + The own-world term is checked as `_session_of_slot == session_idx` computed from the span arithmetic, not read back off the implementation, so a mask built from the wrong comparison cannot agree with it by construction. """ @@ -1279,27 +1279,27 @@ def test_the_flat_ring_matches_one_ring_per_world(num_worlds, pinned_dilation): d_head=D_HEAD, tokens_per_frame=TPF, pinned_dilation=pinned_dilation, dtype=torch.float32, device="cpu", ) - flat = LayerRingCache(num_worlds=num_worlds, **kwargs) - solo = [LayerRingCache(num_worlds=1, **kwargs) for _ in range(num_worlds)] - assert flat.total_slots == num_worlds * flat.capacity + flat = LayerRingCache(num_sessions=num_sessions, **kwargs) + solo = [LayerRingCache(num_sessions=1, **kwargs) for _ in range(num_sessions)] + assert flat.total_slots == num_sessions * flat.capacity assert all(s.capacity == flat.capacity for s in solo) # Independent clocks and a deliberately uneven interleave: with every world - # on the same frame, a mask that ignored `world_idx` entirely would still + # on the same frame, a mask that ignored `session_idx` entirely would still # hide the same slots and this test would pass against it. - gens = [torch.Generator().manual_seed(100 + w) for w in range(num_worlds)] - clocks = [3 * w for w in range(num_worlds)] + gens = [torch.Generator().manual_seed(100 + w) for w in range(num_sessions)] + clocks = [3 * w for w in range(num_sessions)] order = torch.Generator().manual_seed(41) - for _ in range(20 * num_worlds): - w = int(torch.randint(0, num_worlds, (1,), generator=order).item()) + for _ in range(20 * num_sessions): + w = int(torch.randint(0, num_sessions, (1,), generator=order).item()) frame_pos = torch.tensor([clocks[w]], dtype=torch.int64) for commit in (False, False, False, False, True): kv = torch.randn(2, 1, N_KV_HEADS, TPF, D_HEAD, generator=gens[w]) _, _, flat_vis = flat.upsert(kv, frame_pos, commit, _w(w)) _, _, solo_vis = solo[w].upsert(kv, frame_pos, commit, _w(0)) - lo, hi = flat.world_span(w) + lo, hi = flat.session_span(w) assert torch.equal(flat_vis[lo:hi], solo_vis), ( f"world {w} frame {clocks[w]}: visibility diverged from a solo ring" ) @@ -1316,8 +1316,8 @@ def test_the_flat_ring_matches_one_ring_per_world(num_worlds, pinned_dilation): assert torch.equal(flat_vis, flat_vis & own) clocks[w] += 1 - for w in range(num_worlds): - lo, hi = flat.world_span(w) + for w in range(num_sessions): + lo, hi = flat.session_span(w) assert torch.equal(flat.kv[:, :, :, lo:hi], solo[w].kv), f"world {w} ring bytes" assert torch.equal(flat.written[lo:hi], solo[w].written), f"world {w} written" @@ -1341,9 +1341,9 @@ def test_worlds_interleave_without_reaching_each_other(): starts = {"a": 0, "b": 5, "c": 11} frames = 40 - flat = _manager(_ring_config(num_worlds=3)) + flat = _manager(_ring_config(num_sessions=3)) _open(flat, *rids) - solo = {rid: _manager(_ring_config(num_worlds=1)) for rid in rids} + solo = {rid: _manager(_ring_config(num_sessions=1)) for rid in rids} for rid, mgr in solo.items(): _open(mgr, rid) @@ -1363,12 +1363,12 @@ def test_worlds_interleave_without_reaching_each_other(): clocks[rid] += 1 for rid in rids: - world = flat.world_of(rid) + world = flat.session_of(rid) for i, (layer, ref) in enumerate(zip(flat.layers, solo[rid].layers, strict=True)): - lo, hi = layer.world_span(world) + lo, hi = layer.session_span(world) # The solo manager holds a padding world too, so compare against its # world span, not its whole buffer. - ref_lo, ref_hi = ref.world_span(solo[rid].world_of(rid)) + ref_lo, ref_hi = ref.session_span(solo[rid].session_of(rid)) assert torch.equal(layer.kv[:, :, :, lo:hi], ref.kv[:, :, :, ref_lo:ref_hi]), ( f"{rid} layer {i}: interleaving changed what the world holds" ) @@ -1379,14 +1379,14 @@ def test_worlds_interleave_without_reaching_each_other(): # and the mask hid every other world completely, on the last step of each gen = torch.Generator().manual_seed(59) for rid in rids: - world = flat.world_of(rid) + world = flat.session_of(rid) flat.plan(_step(rid), _ctx(rid)) for layer_idx in range(N_LAYERS): k, v = _frame(flat, gen) _, _, visible = flat.upsert( k, v, layer_idx, torch.tensor([clocks[rid]], dtype=torch.int64), commit=False ) - lo, hi = flat.layers[layer_idx].world_span(world) + lo, hi = flat.layers[layer_idx].session_span(world) seen = visible.clone() seen[lo:hi] = False assert not bool(seen.any()), ( @@ -1404,7 +1404,7 @@ def test_the_captured_world_index_is_read_at_replay_not_baked_at_capture(): """The property the entire layout exists for, and the only test that can see it. - `world_idx` is a `[1]` int64 device tensor rather than a Python int for one + `session_idx` is a `[1]` int64 device tensor rather than a Python int for one reason: a host int — or a `kv[:, w]` view taken with one — has its value (or its `storage_offset`) folded into the graph at capture time, and every subsequent replay then serves whichever world capture happened to hold. @@ -1416,12 +1416,12 @@ def test_the_captured_world_index_is_read_at_replay_not_baked_at_capture(): tensor. Every other world, including the one capture used, has to be untouched. """ - kv = _manager(_ring_config(num_worlds=4), device="cuda") + kv = _manager(_ring_config(num_sessions=4), device="cuda") layer = kv.layers[0] _open(kv, "cap") kv.plan(_step("cap"), _ctx("cap")) - assert kv.world_of("cap") == 0, "this test needs capture to hold world 0" + assert kv.session_of("cap") == 0, "this test needs capture to hold world 0" static_k = torch.zeros(1, N_KV_HEADS, TPF, D_HEAD, dtype=torch.float32, device="cuda") static_v = torch.zeros_like(static_k) @@ -1442,12 +1442,12 @@ def test_the_captured_world_index_is_read_at_replay_not_baked_at_capture(): torch.cuda.synchronize() kv.reset_request("cap", free=True) - for world in range(layer.num_worlds): + for world in range(layer.num_sessions): layer.reset(world) # push the real request off world 0: three placeholders take 0, 1, 2 _open(kv, "pad0", "pad1", "pad2", "real") - world = kv.world_of("real") + world = kv.session_of("real") assert world == 3 kv.plan(_step("real"), _ctx("real")) @@ -1456,17 +1456,17 @@ def test_the_captured_world_index_is_read_at_replay_not_baked_at_capture(): graph.replay() torch.cuda.synchronize() - lo, hi = layer.world_span(world) + lo, hi = layer.session_span(world) span = layer.kv[:, :, :, lo:hi] assert bool((span == 1.5).any()), ( - "the replay wrote nothing into the world `plan` staged; `world_idx` was " + "the replay wrote nothing into the world `plan` staged; `session_idx` was " "baked at capture" ) assert bool(layer.written[lo : lo + TPF].all()), "the frame's ring slot went unmarked" - for other in range(kv.num_worlds): + for other in range(kv.num_sessions): if other == world: continue - o_lo, o_hi = layer.world_span(other) + o_lo, o_hi = layer.session_span(other) assert not bool(layer.kv[:, :, :, o_lo:o_hi].any()), ( f"the replay wrote into world {other}; it was staged to write world " f"{world}" @@ -1474,7 +1474,7 @@ def test_the_captured_world_index_is_read_at_replay_not_baked_at_capture(): assert not bool(layer.written[o_lo : o_lo + layer.ring_len].any()) # the visibility row the graph returns is the layer's scratch, restaged too assert bool(captured_visible[lo:hi].any()) - other_lo, other_hi = layer.world_span(0) + other_lo, other_hi = layer.session_span(0) assert not bool(captured_visible[other_lo:other_hi].any()), ( "the captured mask still shows the capture-time world" ) @@ -1497,7 +1497,7 @@ def test_the_bucket_rounding_is_unobservable_off_write_steps(): step, the off-write-step case is empty, and a version of this test that ran layer 0 would assert nothing at all; hence the counter at the end. """ - layer = _manager(_ring_config(num_worlds=2)).layers[N_LAYERS - 1] + layer = _manager(_ring_config(num_sessions=2)).layers[N_LAYERS - 1] d = layer.pinned_dilation assert d > 1, "this test needs a layer that has off-write-step frames" @@ -1514,7 +1514,7 @@ def test_the_bucket_rounding_is_unobservable_off_write_steps(): # this test can see. _, _, visible = layer.upsert(kv, torch.tensor([f], dtype=torch.int64), True, _w(1)) - lo, hi = layer.world_span(1) + lo, hi = layer.session_span(1) if f % d: off_steps += 1 expected = before_written.clone() @@ -1614,23 +1614,23 @@ def test_upsert_refuses_a_world_index_that_is_not_the_staged_shape(bad): layer = _manager().layers[0] kv = torch.zeros(2, 1, N_KV_HEADS, TPF, D_HEAD) - with pytest.raises(RuntimeError, match="world_idx must be a"): + with pytest.raises(RuntimeError, match="session_idx must be a"): layer.upsert(kv, torch.tensor([0], dtype=torch.int64), True, bad) def test_a_world_index_out_of_range_is_caught_on_the_host_paths(): - """Only on the host paths. The forward cannot check it — `world_idx` is a + """Only on the host paths. The forward cannot check it — `session_idx` is a device tensor there and comparing it would cost a sync per upsert — so an out-of-range index in the graph silently writes past the buffer's last world or wraps into another's. What keeps it in range is that `admit` is the only thing that ever produces one.""" - layer = _manager(_ring_config(num_worlds=2)).layers[0] + layer = _manager(_ring_config(num_sessions=2)).layers[0] - assert layer.world_span(1) == (layer.capacity, 2 * layer.capacity) + assert layer.session_span(1) == (layer.capacity, 2 * layer.capacity) # The layer allocates one world past the pool (the shared padding scratch), # so index 2 is that valid world and 3 is the first out-of-range one. for bad in (-1, 3): with pytest.raises(IndexError, match="out of range"): - layer.world_span(bad) + layer.session_span(bad) with pytest.raises(IndexError, match="out of range"): layer.reset(bad) diff --git a/test/modular/test_video_frame_protocol.py b/test/modular/test_video_frame_protocol.py index 437cc42e3..2d0753d82 100644 --- a/test/modular/test_video_frame_protocol.py +++ b/test/modular/test_video_frame_protocol.py @@ -493,7 +493,7 @@ def test_rollout_harness_variant_controls_config_and_checkpoint_default( } assert generated["max_seq_len"] == tokens assert generated["max_concurrent_requests"] == 2 - assert generated["resources"]["kv"]["num_worlds"] == 2 + assert generated["resources"]["kv"]["num_sessions"] == 2 def test_rollout_harness_hub_config_omits_local_overrides_and_forwards_cache(tmp_path): @@ -532,7 +532,7 @@ def test_rollout_harness_hub_config_omits_local_overrides_and_forwards_cache(tmp "step_batch_size": 1, } assert generated["max_concurrent_requests"] == 2 - assert generated["resources"]["kv"]["num_worlds"] == 2 + assert generated["resources"]["kv"]["num_sessions"] == 2 assert command[command.index("--cache-dir") + 1] == str(tmp_path / "hub-cache") assert "--enable-nvtx" in command diff --git a/test/modular/test_waypoint_components.py b/test/modular/test_waypoint_components.py index 6464ea36e..9f2ff13b2 100644 --- a/test/modular/test_waypoint_components.py +++ b/test/modular/test_waypoint_components.py @@ -121,7 +121,7 @@ def visible_blocks(block_mask) -> set[int]: return set(block_mask.full_kv_indices[0, 0, 0, :n].tolist()) -def ring_kv_spec(config: WaypointConfig, *, num_worlds: int = 1) -> KVSpec: +def ring_kv_spec(config: WaypointConfig, *, num_sessions: int = 1) -> KVSpec: """The ``RingKVConfig`` a ``WaypointConfig``'s geometry implies -- which is the bridge under test in most of section 3. @@ -140,7 +140,7 @@ def ring_kv_spec(config: WaypointConfig, *, num_worlds: int = 1) -> KVSpec: head_dim=config.d_head, num_qo_heads=config.n_heads, tokens_per_frame=config.tokens_per_frame, - num_worlds=num_worlds, + num_sessions=num_sessions, layers=tuple( RingKVLayerConfig( ring_frames=config.ring_frames(i), @@ -153,11 +153,11 @@ def ring_kv_spec(config: WaypointConfig, *, num_worlds: int = 1) -> KVSpec: ) -def ring_manager(config: WaypointConfig, *, num_worlds: int = 1) -> RingKVManager: +def ring_manager(config: WaypointConfig, *, num_sessions: int = 1) -> RingKVManager: """``config``'s rings, allocated. Through ``build(spec, info)`` and not the constructor: the spec is what picks ``RingKVManager`` over the paged one.""" return RingKVManager.build( - ring_kv_spec(config, num_worlds=num_worlds), + ring_kv_spec(config, num_sessions=num_sessions), EngineResourceInfo(device=torch.device("cpu"), kv_dtype=torch.float32), ) @@ -277,11 +277,11 @@ def test_a_fully_visible_ring_makes_eager_and_compiled_agree(): def make_cache(*, ring_frames: int, ring_buckets: int, dilation: int) -> LayerRingCache: """One world, because this section is about the ring *algorithm* — which slot a frame lands in, which slot it hides — and that is per world and - identical at any ``num_worlds``. The folded layout and its isolation are + identical at any ``num_sessions``. The folded layout and its isolation are pinned where they belong, in ``test_ring_kv_resource.py``; driving them again here would only make these tests slower to read.""" return LayerRingCache( - num_worlds=1, + num_sessions=1, n_kv_heads=1, ring_frames=ring_frames, ring_buckets=ring_buckets, @@ -296,7 +296,7 @@ def make_cache(*, ring_frames: int, ring_buckets: int, dilation: int) -> LayerRi def upsert(cache: LayerRingCache, kv, frame_pos, *, commit: bool, world: int = 0): """``LayerRingCache.upsert`` with the world index spelled out. - Not a default on ``upsert`` itself, deliberately. ``world_idx`` is a ``[1]`` + Not a default on ``upsert`` itself, deliberately. ``session_idx`` is a ``[1]`` int64 *device* tensor on the forward path and never a Python int — a host int is folded into the graph at capture and every replay then serves the capture-time world, silently. A default argument is exactly how a caller @@ -376,11 +376,11 @@ def test_mask_hides_the_slot_this_frame_is_about_to_overwrite(): def test_upsert_batches_two_worlds_like_two_sequential_calls(): """``upsert`` at B=2 (one row each for worlds 0 and 1) is bit-exact to running the same two frames one world at a time: the row dimension is - folded into the token dim by ``world_base``, so a batched call must not + folded into the token dim by ``session_base``, so a batched call must not touch a row that is not its own.""" def new_cache() -> LayerRingCache: return LayerRingCache( - num_worlds=2, n_kv_heads=1, ring_frames=4, ring_buckets=4, + num_sessions=2, n_kv_heads=1, ring_frames=4, ring_buckets=4, d_head=8, tokens_per_frame=TPF, pinned_dilation=1, dtype=torch.float32, device="cpu", ) @@ -427,18 +427,18 @@ def floor_bucket_upsert(cache: LayerRingCache, kv, frame_pos, commit: bool): instead of ``(f + d - 1) // d``. Everything else is statement-for-statement the same. Used only to A/B the round-up.""" tokens = cache.tokens_per_frame - world_idx = torch.tensor([0], dtype=torch.int64) - world_base = world_idx * cache.capacity + session_idx = torch.tensor([0], dtype=torch.int64) + session_base = session_idx * cache.capacity slot = (frame_pos // cache.pinned_dilation) % cache.ring_buckets - ring_idx = cache.frame_offsets + slot * tokens + world_base - current_idx = cache._current_base + world_base + ring_idx = cache.frame_offsets + slot * tokens + session_base + current_idx = cache._current_base + session_base cache.kv.index_copy_(3, current_idx, kv) write_step = frame_pos.remainder(cache.pinned_dilation) == 0 mask_written = torch.empty_like(cache.written) mask_written.copy_(cache.written) - mask_written &= cache._world_of_slot == world_idx + mask_written &= cache._session_of_slot == session_idx mask_written[ring_idx] = mask_written[ring_idx] & ~write_step if commit: @@ -484,7 +484,7 @@ def test_bucket_round_up_is_faithful_but_currently_unobservable(dilation): def test_upsert_rejects_a_wrong_shaped_frame_or_clock(): cache = make_cache(ring_frames=4, ring_buckets=4, dilation=1) fp = torch.tensor([0], dtype=torch.int64) - with pytest.raises(RuntimeError, match="exactly one frame per world"): + with pytest.raises(RuntimeError, match="exactly one frame per session"): upsert(cache, frame_kv(0, tokens=TPF // 2), fp, commit=True) with pytest.raises(RuntimeError, match=r"frame_pos must be a \[B\] int64 tensor"): upsert(cache, frame_kv(0), torch.tensor(0, dtype=torch.int64), commit=True) @@ -528,7 +528,7 @@ def test_ring_state_is_a_deep_copy_and_is_specific_to_the_compaction_setting(): state = kv.get_state("a") snapshot = [t.clone() for t, _ in state["layers"]] for layer in kv.layers: - layer.reset(kv.world_of("a")) + layer.reset(kv.session_of("a")) assert not any(layer.kv.any() for layer in kv.layers) # get_state must clone: the reset above must not have reached the snapshot. assert all(torch.equal(a, b) for a, b in zip((t for t, _ in state["layers"]), snapshot, strict=True)) @@ -536,9 +536,9 @@ def test_ring_state_is_a_deep_copy_and_is_specific_to_the_compaction_setting(): kv.load_state("a", state) # Compare the request's world span, not the whole buffer: the ring now holds # a padding world past the resident pool that get_state never captured. - world = kv.world_of("a") + world = kv.session_of("a") assert all( - torch.equal(layer.kv[:, :, :, slice(*layer.world_span(world))], t) + torch.equal(layer.kv[:, :, :, slice(*layer.session_span(world))], t) for layer, (t, _) in zip(kv.layers, state["layers"], strict=True) ) diff --git a/test/modular/test_waypoint_dit.py b/test/modular/test_waypoint_dit.py index 30dc77efa..3e0259f06 100644 --- a/test/modular/test_waypoint_dit.py +++ b/test/modular/test_waypoint_dit.py @@ -71,7 +71,7 @@ def reduced_config(**overrides) -> WaypointConfig: return WaypointConfig(**{**base, **overrides}) -def ring_kv_spec(config: WaypointConfig, *, num_worlds: int = 1) -> KVSpec: +def ring_kv_spec(config: WaypointConfig, *, num_sessions: int = 1) -> KVSpec: """The ``RingKVConfig`` a ``WaypointConfig``'s geometry implies. Hand-rolled here because the model does not declare its specs yet; when @@ -89,7 +89,7 @@ def ring_kv_spec(config: WaypointConfig, *, num_worlds: int = 1) -> KVSpec: head_dim=config.d_head, num_qo_heads=config.n_heads, tokens_per_frame=config.tokens_per_frame, - num_worlds=num_worlds, + num_sessions=num_sessions, layers=tuple( RingKVLayerConfig( ring_frames=config.ring_frames(i), diff --git a/test/modular/test_waypoint_gpu.py b/test/modular/test_waypoint_gpu.py index 2c57a1b39..978c762ad 100644 --- a/test/modular/test_waypoint_gpu.py +++ b/test/modular/test_waypoint_gpu.py @@ -99,7 +99,7 @@ def gpu_config(**overrides) -> WaypointConfig: return WaypointConfig(**{**base, **overrides}) -def _kv_spec(config: WaypointConfig, num_worlds: int) -> KVSpec: +def _kv_spec(config: WaypointConfig, num_sessions: int) -> KVSpec: return KVSpec( resource_key="kv", nodes={"dit"}, @@ -109,7 +109,7 @@ def _kv_spec(config: WaypointConfig, num_worlds: int) -> KVSpec: head_dim=config.d_head, num_qo_heads=config.n_heads, tokens_per_frame=config.tokens_per_frame, - num_worlds=num_worlds, + num_sessions=num_sessions, layers=tuple( RingKVLayerConfig( ring_frames=config.ring_frames(i), @@ -137,7 +137,7 @@ def forward(self, *args, **kwargs): raise NotImplementedError("binding stand-in; nothing here runs a step") -def build(config: WaypointConfig, *, seed: int = 0, num_worlds: int = 1): +def build(config: WaypointConfig, *, seed: int = 0, num_sessions: int = 1): """The serving build order -- meta, cast, ``to_empty``, retie -- with random weights, wired to a real ring and a real flex backend on the GPU. @@ -159,7 +159,7 @@ def build(config: WaypointConfig, *, seed: int = 0, num_worlds: int = 1): block.attn.v_lamb.fill_(0.25) dit.eval() - spec = _kv_spec(config, num_worlds) + spec = _kv_spec(config, num_sessions) info = EngineResourceInfo(device=DEVICE, kv_dtype=DTYPE) kv = RingKVManager.build(spec, info) attn = AttentionManager.build( @@ -259,15 +259,15 @@ def scrub(kv: RingKVManager, *rids: str) -> None: kv.reset_request(rid, free=True) kv.remove_request(rid) for layer in kv.layers: - for world in range(layer.num_worlds): + for world in range(layer.num_sessions): layer.reset(world) -def world_snapshot(kv: RingKVManager, world_idx: int): +def world_snapshot(kv: RingKVManager, session_idx: int): return [ ( - layer.kv[:, :, :, slice(*layer.world_span(world_idx))].clone(), - layer.written[slice(*layer.world_span(world_idx))].clone(), + layer.kv[:, :, :, slice(*layer.session_span(session_idx))].clone(), + layer.written[slice(*layer.session_span(session_idx))].clone(), ) for layer in kv.layers ] @@ -424,7 +424,7 @@ def rollout(compiled: bool): ) kv.commit(_step("r", frame), _ctx("r")) torch.cuda.synchronize() - return latents, world_snapshot(kv, kv.world_of("r")) + return latents, world_snapshot(kv, kv.session_of("r")) eager_latents, eager_ring = rollout(False) compiled_latents, compiled_ring = rollout(True) @@ -457,12 +457,12 @@ def captured(): static buffers a replay reads. Two worlds, so the interleave gate can use the same graph the single-world - gates do -- ``world_idx`` is staged by ``plan`` and read at replay, never + gates do -- ``session_idx`` is staged by ``plan`` and read at replay, never baked. Capture holds a dummy rid and its warmup frames land in the ring; every test scrubs on entry. """ config = gpu_config() - dit, kv, attn = build(config, seed=0, num_worlds=2) + dit, kv, attn = build(config, seed=0, num_sessions=2) dit.materialize_runtime_tables(DEVICE) dit.compile_regions() mouse, button, scroll = controls(config) @@ -633,14 +633,14 @@ def test_replay_matches_the_uncaptured_regions_over_two_ring_wraps(captured): uncaptured = [ eager_frame(captured, "uncaptured", f, "a3") for f in range(ROLLOUT_FRAMES) ] - uncaptured_ring = world_snapshot(kv, kv.world_of("uncaptured")) + uncaptured_ring = world_snapshot(kv, kv.session_of("uncaptured")) scrub(kv, "uncaptured") kv.ingest_request("replayed") replayed = [ replay_frame(captured, "replayed", f, "a3") for f in range(ROLLOUT_FRAMES) ] - replayed_ring = world_snapshot(kv, kv.world_of("replayed")) + replayed_ring = world_snapshot(kv, kv.session_of("replayed")) scrub(kv, "replayed") for frame, (want, got) in enumerate(zip(uncaptured, replayed, strict=True)): @@ -672,7 +672,7 @@ def test_a_second_rollout_starts_from_nothing(captured): kv.ingest_request("first") first = [replay_frame(captured, "first", f, "a4") for f in range(frames)] - first_ring = world_snapshot(kv, kv.world_of("first")) + first_ring = world_snapshot(kv, kv.session_of("first")) with pytest.raises(RuntimeError): kv.post_warmup_validate() scrub(kv, "first") @@ -680,7 +680,7 @@ def test_a_second_rollout_starts_from_nothing(captured): kv.ingest_request("second") second = [replay_frame(captured, "second", f, "a4") for f in range(frames)] - second_ring = world_snapshot(kv, kv.world_of("second")) + second_ring = world_snapshot(kv, kv.session_of("second")) for frame, (want, got) in enumerate(zip(first, second, strict=True)): assert torch.equal(want, got), ( @@ -699,12 +699,12 @@ def test_two_worlds_interleaved_match_the_same_rollouts_run_alone(captured): Nothing physical separates the worlds -- they share one buffer per layer, folded into the token dimension -- so the whole isolation mechanism is the visibility row, and a leak is silent. Both rollouts go through the *same* - captured graph, which is also the claim that ``world_idx`` is read at replay + captured graph, which is also the claim that ``session_idx`` is read at replay rather than baked at capture. """ kv = captured["kv"] frames = 10 - assert kv.num_worlds == 2 + assert kv.num_sessions == 2 scrub(kv, "capture") alone = {} @@ -712,14 +712,14 @@ def test_two_worlds_interleaved_match_the_same_rollouts_run_alone(captured): rid = f"alone{stream}" kv.ingest_request(rid) latents = [replay_frame(captured, rid, f, stream) for f in range(frames)] - alone[stream] = (latents, world_snapshot(kv, kv.world_of(rid))) + alone[stream] = (latents, world_snapshot(kv, kv.session_of(rid))) scrub(kv, rid) kv.ingest_request("both_a") kv.ingest_request("both_b") admit_frame(kv, "both_a", 0, captured["attn"]) admit_frame(kv, "both_b", 0, captured["attn"]) - assert {kv.world_of("both_a"), kv.world_of("both_b")} == {0, 1} + assert {kv.session_of("both_a"), kv.session_of("both_b")} == {0, 1} interleaved = {"A": [], "B": []} for frame in range(frames): @@ -735,7 +735,7 @@ def test_two_worlds_interleaved_match_the_same_rollouts_run_alone(captured): f"world {stream} frame {frame}: sharing the node changed the rollout by " f"{(want.float() - got.float()).abs().max().item():.3e}" ) - assert_worlds_equal(want_ring, world_snapshot(kv, kv.world_of(rid)), f"world {stream}") + assert_worlds_equal(want_ring, world_snapshot(kv, kv.session_of(rid)), f"world {stream}") assert not torch.equal(interleaved["A"][0], interleaved["B"][0]), ( "the two rollouts are identical; an isolation leak would be invisible" @@ -758,7 +758,7 @@ def test_batched_step_matches_the_same_rollouts_run_one_row_at_a_time(): config = gpu_config() frames = 8 mouse, button, scroll = controls(config) - dit, kv, attn = build(config, seed=0, num_worlds=2) + dit, kv, attn = build(config, seed=0, num_sessions=2) dit.materialize_runtime_tables(DEVICE) # ---- B=1, alternating: each world through its own single-row forward. @@ -777,7 +777,7 @@ def test_batched_step_matches_the_same_rollouts_run_one_row_at_a_time(): solo_latents[rid].append(out) kv.commit(_step(rid, frame), _ctx(rid)) torch.cuda.synchronize() - solo_ring = {rid: world_snapshot(kv, kv.world_of(rid)) for rid in ("w0", "w1")} + solo_ring = {rid: world_snapshot(kv, kv.session_of(rid)) for rid in ("w0", "w1")} scrub(kv, "w0", "w1") # ---- B=2, batched: both worlds' row for frame f in one forward. @@ -802,7 +802,7 @@ def test_batched_step_matches_the_same_rollouts_run_one_row_at_a_time(): batched_latents["w1"].append(out[1:2]) kv.commit(_batch_step(batch_frames), _batch_ctx("w0", "w1")) torch.cuda.synchronize() - batch_ring = {rid: world_snapshot(kv, kv.world_of(rid)) for rid in ("w0", "w1")} + batch_ring = {rid: world_snapshot(kv, kv.session_of(rid)) for rid in ("w0", "w1")} scrub(kv, "w0", "w1") for rid in ("w0", "w1"): @@ -856,12 +856,12 @@ def test_prime_replay_matches_the_uncaptured_prime(captured): kv.ingest_request("eager_p") eager_prime(captured, "eager_p", "P") - eager_ring = world_snapshot(kv, kv.world_of("eager_p")) + eager_ring = world_snapshot(kv, kv.session_of("eager_p")) scrub(kv, "eager_p") kv.ingest_request("graph_p") replay_prime(captured, "graph_p", "P") - graph_ring = world_snapshot(kv, kv.world_of("graph_p")) + graph_ring = world_snapshot(kv, kv.session_of("graph_p")) assert_worlds_equal(eager_ring, graph_ring, "primed by replay vs uncaptured") assert any(written.any() for _, written in graph_ring), ( @@ -874,7 +874,7 @@ def test_prime_replay_matches_the_uncaptured_prime(captured): # reproducing capture-time state rather than reading its input buffer. kv.ingest_request("other_p") replay_prime(captured, "other_p", "Q") - other_ring = world_snapshot(kv, kv.world_of("other_p")) + other_ring = world_snapshot(kv, kv.session_of("other_p")) assert not torch.equal(graph_ring[0][0], other_ring[0][0]), ( "two different seed latents primed the same ring bytes" ) @@ -916,13 +916,13 @@ def test_prime_then_rollout_through_both_graphs_matches_eager(captured): kv.ingest_request("eager_pr") eager_prime(captured, "eager_pr", "S") want = [eager_frame(captured, "eager_pr", f, "S") for f in range(1, frames + 1)] - want_ring = world_snapshot(kv, kv.world_of("eager_pr")) + want_ring = world_snapshot(kv, kv.session_of("eager_pr")) scrub(kv, "eager_pr") kv.ingest_request("graph_pr") replay_prime(captured, "graph_pr", "S") got = [replay_frame(captured, "graph_pr", f, "S") for f in range(1, frames + 1)] - got_ring = world_snapshot(kv, kv.world_of("graph_pr")) + got_ring = world_snapshot(kv, kv.session_of("graph_pr")) for frame, (a, b) in enumerate(zip(want, got, strict=True), start=1): assert torch.equal(a, b), ( diff --git a/test/modular/test_waypoint_reference_equivalence.py b/test/modular/test_waypoint_reference_equivalence.py index 2afa719c7..06fe16980 100644 --- a/test/modular/test_waypoint_reference_equivalence.py +++ b/test/modular/test_waypoint_reference_equivalence.py @@ -185,7 +185,7 @@ def _build_port(config, checkpoint: Path = CHECKPOINT): head_dim=config.d_head, num_qo_heads=config.n_heads, tokens_per_frame=config.tokens_per_frame, - num_worlds=1, + num_sessions=1, layers=tuple( RingKVLayerConfig( ring_frames=config.ring_frames(i), diff --git a/test/modular/test_waypoint_shell.py b/test/modular/test_waypoint_shell.py index add2b95cd..c92b828ad 100644 --- a/test/modular/test_waypoint_shell.py +++ b/test/modular/test_waypoint_shell.py @@ -218,7 +218,7 @@ def test_ring_geometry_is_copied_from_the_config_layer_for_layer(model, config): # One world declared here, because sizing is a deployment question and # `apply_yaml_overrides` runs after this hook. What is pinned is the # *default*: a node that never says otherwise serves one session. - assert ring.num_worlds == 1 + assert ring.num_sessions == 1 assert len(ring.layers) == config.n_layers for i, layer in enumerate(ring.layers): @@ -731,11 +731,11 @@ def _write_config(tmp_path, name: str, **extra) -> str: return str(path) -def _worlds(n: int) -> dict: +def _sessions(n: int) -> dict: """The ``resources:`` block a deployment writes to size the ring — the same one ``EngineManager.build`` feeds to ``apply_yaml_overrides``, which is why the gate reads it here rather than inventing its own key.""" - return {"resources": {KV_RESOURCE: {"num_worlds": n}}} + return {"resources": {KV_RESOURCE: {"num_sessions": n}}} @pytest.mark.parametrize("limit", [None, 0, -1, True, 1.0, "2"]) @@ -773,9 +773,9 @@ def test_get_worker_graphs_refuses_invalid_world_pool_size( tmp_path, f"invalid_worlds_{worlds}.yaml", max_concurrent_requests=1, - **_worlds(worlds), + **_sessions(worlds), ) - with pytest.raises(ValueError, match=r"resources\.kv\.num_worlds"): + with pytest.raises(ValueError, match=r"resources\.kv\.num_sessions"): model.get_worker_graphs(path) @@ -784,7 +784,7 @@ def test_get_worker_graphs_refuses_more_arrivals_than_worlds( model, tmp_path, limit, worlds ): """A queue longer than the pool is not a queue, it is a delayed failure: the - conductor admits ``limit`` requests, the ring hands out ``num_worlds``, and + conductor admits ``limit`` requests, the ring hands out ``num_sessions``, and the difference is a set of requests that reach ``admit`` and die there with an ``AdmitRuntimeError`` that no retry, eviction or reload can clear. @@ -794,7 +794,7 @@ def test_get_worker_graphs_refuses_more_arrivals_than_worlds( """ extra = {"max_concurrent_requests": limit} if worlds is not None: - extra |= _worlds(worlds) + extra |= _sessions(worlds) path = _write_config(tmp_path, f"over_{limit}_{worlds}.yaml", **extra) with pytest.raises(ValueError, match="exceeds the"): @@ -805,12 +805,12 @@ def test_get_worker_graphs_refuses_more_arrivals_than_worlds( def test_get_worker_graphs_accepts_a_deployment_inside_its_pool( model, tmp_path, limit, worlds ): - """``limit == num_worlds`` is the shape that should be written, at any size. + """``limit == num_sessions`` is the shape that should be written, at any size. The ``(1, None)`` case is the default deployment, which must keep working unchanged — the pool is a widening, not a migration.""" extra = {"max_concurrent_requests": limit} if worlds is not None: - extra |= _worlds(worlds) + extra |= _sessions(worlds) path = _write_config(tmp_path, f"ok_{limit}_{worlds}.yaml", **extra) graphs = model.get_worker_graphs(path) @@ -850,7 +850,7 @@ def test_get_worker_graphs_warns_about_worlds_no_request_can_reach( and never written, which is worth a line in the log rather than a failed boot: the deployment still serves correctly.""" path = _write_config( - tmp_path, "underused.yaml", max_concurrent_requests=2, **_worlds(8) + tmp_path, "underused.yaml", max_concurrent_requests=2, **_sessions(8) ) with caplog.at_level(logging.WARNING): diff --git a/test/waypoint/benchmark_streaming.py b/test/waypoint/benchmark_streaming.py index 742da4f87..e25198370 100644 --- a/test/waypoint/benchmark_streaming.py +++ b/test/waypoint/benchmark_streaming.py @@ -726,7 +726,7 @@ def _build_parser() -> argparse.ArgumentParser: help="concurrent streams; >1 runs the concurrent batching phase", ) parser.add_argument( - "--worlds", type=int, help="server world slots (kv.num_worlds); defaults to --streams" + "--worlds", type=int, help="server world slots (kv.num_sessions); defaults to --streams" ) parser.add_argument( "--batch", diff --git a/test/waypoint/serve_rollout.py b/test/waypoint/serve_rollout.py index cf2471207..2a66efb4e 100644 --- a/test/waypoint/serve_rollout.py +++ b/test/waypoint/serve_rollout.py @@ -9,7 +9,7 @@ By default the request is sent twice, under *different* ids and one explicit ``model_kwargs.seed``, so the two rollouts draw the same noise. Identical bytes the second time are what shows the first request left nothing behind: with -``num_worlds: 1`` a leaked world fails the second admission outright, and a +``num_sessions: 1`` a leaked world fails the second admission outright, and a leaked ``ChunkedStreamingTAEHV`` in ``PerRequestState.kwargs`` would resume the first rollout's stream and change the pixels. @@ -160,7 +160,7 @@ def _run_config( config["max_concurrent_requests"] = worlds resources = config["resources"] = config.get("resources") or {} kv = resources["kv"] = resources.get("kv") or {} - kv["num_worlds"] = worlds + kv["num_sessions"] = worlds out.write_text(yaml.safe_dump(config, sort_keys=False)) return out From 388ac1728b185e46bbaf92e68886aebe06c31a33 Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Tue, 22 Sep 2026 05:08:20 +0000 Subject: [PATCH 15/29] lint: satisfy ruff check (B905, W291, PLW0108, PLR1730) CI runs `ruff check --output-format=github .` and was failing on 12 findings across waypoint code: - B905 zip() without strict=: flex._stage, serve_rollout diff summary, test_waypoint_shell, and 5 sites in benchmark_streaming. Parallel arrays that must match length get strict=True; the adjacent-pairs zip(timestamps, timestamps[1:]) keeps its length-off-by-one intent with strict=False. - W291 trailing whitespace in the frame-noise docstring (submodules). - PLW0108 lambda: object() -> object for the graph_pool_handle stub (identical: both return a fresh object when called). - PLR1730 if/assign -> max(max_abs_diff, diff) in serve_rollout. --- mstar/engine/resources/attn/flex.py | 2 +- mstar/model/waypoint/submodules.py | 4 ++-- test/modular/test_cuda_graph_capture.py | 2 +- test/modular/test_waypoint_shell.py | 2 +- test/waypoint/benchmark_streaming.py | 10 +++++----- test/waypoint/serve_rollout.py | 5 ++--- 6 files changed, 12 insertions(+), 13 deletions(-) diff --git a/mstar/engine/resources/attn/flex.py b/mstar/engine/resources/attn/flex.py index 5b6d0887d..2392927be 100644 --- a/mstar/engine/resources/attn/flex.py +++ b/mstar/engine/resources/attn/flex.py @@ -373,7 +373,7 @@ def _stage( plan: RingPlan, ) -> None: counts, indices, period = self._visibility_table_for(geometry) - for b, (w, f) in enumerate(zip(plan.session_idx, plan.frame_pos)): + for b, (w, f) in enumerate(zip(plan.session_idx, plan.frame_pos, strict=True)): phase = f if f < period else period + f % period mask.full_kv_num_blocks[b].copy_(counts[w, phase]) mask.full_kv_indices[b].copy_(indices[w, phase]) diff --git a/mstar/model/waypoint/submodules.py b/mstar/model/waypoint/submodules.py index d0116e472..217c995e9 100644 --- a/mstar/model/waypoint/submodules.py +++ b/mstar/model/waypoint/submodules.py @@ -337,8 +337,8 @@ def _frame_noise( ) -> torch.Tensor: """``[1, 1, C, H, W]`` of fresh noise for this frame. - Drawn straight onto the device. Determinism is now per-GPU: - a CUDA generator reproduces run-to-run on the same arch + torch build, + Drawn straight onto the device. Determinism is now per-GPU: + a CUDA generator reproduces run-to-run on the same arch + torch build, not against a CPU draw or another arch. Runs in ``prepare_inputs``, outside any captured region, so this is a normal stream-ordered kernel launch. """ diff --git a/test/modular/test_cuda_graph_capture.py b/test/modular/test_cuda_graph_capture.py index 5fdc61a1c..1012179f1 100644 --- a/test/modular/test_cuda_graph_capture.py +++ b/test/modular/test_cuda_graph_capture.py @@ -31,7 +31,7 @@ def fake_cuda_runtime(monkeypatch): Stubbing them keeps these policy tests running where there is no GPU.""" monkeypatch.setattr(torch.cuda, "is_available", lambda: True) monkeypatch.setattr(torch.cuda, "memory_allocated", lambda device=None: 0) - monkeypatch.setattr(torch.cuda.graphs, "graph_pool_handle", lambda: object()) + monkeypatch.setattr(torch.cuda.graphs, "graph_pool_handle", object) class _Group: diff --git a/test/modular/test_waypoint_shell.py b/test/modular/test_waypoint_shell.py index c92b828ad..3b98839d7 100644 --- a/test/modular/test_waypoint_shell.py +++ b/test/modular/test_waypoint_shell.py @@ -1295,7 +1295,7 @@ def test_decode_latent_batches_rows_independently(taehv_weights, ae_config): decode_latent( taehv_weights, latent, history, output_size=(360, 640), initialize=True, ) - for latent, history in zip(latents, histories) + for latent, history in zip(latents, histories, strict=True) ] batched_latent = torch.cat(latents, dim=0) diff --git a/test/waypoint/benchmark_streaming.py b/test/waypoint/benchmark_streaming.py index e25198370..f6f30a17f 100644 --- a/test/waypoint/benchmark_streaming.py +++ b/test/waypoint/benchmark_streaming.py @@ -404,7 +404,7 @@ def _run_concurrent_wave( enable_nvtx=enable_nvtx, start_barrier=barrier, ) - for request_id, seed in zip(request_ids, seeds) + for request_id, seed in zip(request_ids, seeds, strict=True) ] barrier.wait(timeout=30) wave_start = time.perf_counter() @@ -529,7 +529,7 @@ def _concurrent_server_metrics(log_text: str, request_ids: set[str], startup_sec histogram[len(batch)] = histogram.get(len(batch), 0) + 1 timestamps = sorted(_dit_step_timestamps(log_text, request_ids)) - spacing_ms = [(later - earlier) * 1000.0 for earlier, later in zip(timestamps, timestamps[1:])] + spacing_ms = [(later - earlier) * 1000.0 for earlier, later in zip(timestamps, timestamps[1:], strict=False)] return { "rows_per_step_histogram": histogram, "step_spacing_ms": {"p50": _percentile(spacing_ms, 0.50), "p95": _percentile(spacing_ms, 0.95)}, @@ -577,7 +577,7 @@ def _run_concurrent_phase( stall_threshold_seconds=stall_threshold_seconds, enable_nvtx=enable_nvtx, ) - for request_id, (_, stream_failures) in zip(warmup_ids, warmup_results): + for request_id, (_, stream_failures) in zip(warmup_ids, warmup_results, strict=True): failures.extend(f"concurrent-warmup {request_id}: {failure}" for failure in stream_failures) rollout._wait_for_cleanup(log_path, tuple(warmup_ids), proc, request_timeout) @@ -597,7 +597,7 @@ def _run_concurrent_phase( enable_nvtx=enable_nvtx, ) per_stream = [] - for request_id, (metrics, stream_failures) in zip(measured_ids, measured_results): + for request_id, (metrics, stream_failures) in zip(measured_ids, measured_results, strict=True): failures.extend(f"concurrent-measured {request_id}: {failure}" for failure in stream_failures) per_stream.append(metrics) rollout._wait_for_cleanup(log_path, tuple(measured_ids), proc, request_timeout, offset=log_offset) @@ -664,7 +664,7 @@ def _human_summary(result: dict, artifact: Path) -> str: f"(worlds={concurrent['worlds']} batch={concurrent['batch']})" ) for idx, (stream, realtime) in enumerate( - zip(concurrent["per_stream"], concurrent["realtime_per_stream"]) + zip(concurrent["per_stream"], concurrent["realtime_per_stream"], strict=True) ): gaps = stream["inter_chunk_gap_seconds"] lines.append( diff --git a/test/waypoint/serve_rollout.py b/test/waypoint/serve_rollout.py index 2a66efb4e..3d59f3cca 100644 --- a/test/waypoint/serve_rollout.py +++ b/test/waypoint/serve_rollout.py @@ -277,12 +277,11 @@ def _pixel_diff_summary(actual: bytes, expected: bytes, chunk_size: int) -> str: return f"length mismatch: actual={len(actual)} bytes, expected={len(expected)} bytes" max_abs_diff = 0 num_differing = 0 - for a, b in zip(actual, expected): + for a, b in zip(actual, expected, strict=True): diff = a - b if a > b else b - a if diff: num_differing += 1 - if diff > max_abs_diff: - max_abs_diff = diff + max_abs_diff = max(max_abs_diff, diff) num_chunks = len(actual) // chunk_size chunks_differing = sum( 1 From 05e1b0d7ff87cbb8180af1b5e45a4473f0d95cac Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Tue, 22 Sep 2026 22:34:52 +0000 Subject: [PATCH 16/29] test/waypoint: prune benchmarking/profiling tooling and tidy equivalence gates Untrack (kept local) the benchmark/profiler/summary meta-tests and the nsys replay parser; no peer model port (wan22, orpheus, pi05, vjepa2, ...) commits benchmark/profiler pytest tests. Drops test_batch_sweep_summary.py (also a hardcoded out-of-repo _tools path), test_waypoint_streaming_benchmark.py, test_waypoint_profiler.py, and test/waypoint/check_nsys_replay.py from the tree. Share _load_reference and _seed_clip across the equivalence gates via test_waypoint_reference_equivalence.py instead of the 360p file re-implementing them, and skip the pixel tests that need the second 'repro' oracle recording when it is absent rather than hard-failing. --- test/modular/test_batch_sweep_summary.py | 166 -------- ...est_waypoint_360p_reference_equivalence.py | 46 +- .../test_waypoint_pixel_equivalence.py | 13 +- test/modular/test_waypoint_profiler.py | 117 ------ .../test_waypoint_reference_equivalence.py | 40 +- .../test_waypoint_streaming_benchmark.py | 392 ------------------ test/waypoint/check_nsys_replay.py | 175 -------- 7 files changed, 36 insertions(+), 913 deletions(-) delete mode 100644 test/modular/test_batch_sweep_summary.py delete mode 100644 test/modular/test_waypoint_profiler.py delete mode 100644 test/modular/test_waypoint_streaming_benchmark.py delete mode 100644 test/waypoint/check_nsys_replay.py diff --git a/test/modular/test_batch_sweep_summary.py b/test/modular/test_batch_sweep_summary.py deleted file mode 100644 index 607dc4235..000000000 --- a/test/modular/test_batch_sweep_summary.py +++ /dev/null @@ -1,166 +0,0 @@ -"""Onset logic for _tools/batch_sweep_summary.py, the pure-stdlib summarizer -for the concurrent-stream batch_sweep.sh sweep. Lives outside this repo (see -CLAUDE.md for _tools/ conventions) so it is loaded by absolute path.""" - -from __future__ import annotations - -import json -import runpy -from pathlib import Path - -SUMMARY_SCRIPT = Path("/shared/home/garv901-55613a/waypoint-int/_tools/batch_sweep_summary.py") - - -def _solo_baseline_artifact(*, ttff_s, gap_p50_s, sustained, fps, gpu_peak_mib): - frame_count = 40 - wall_seconds = frame_count / fps - return { - "status": "completed", - "server": {"startup_seconds": 5.0}, - "runs": { - "baseline": { - "time_to_first_frame_seconds": ttff_s, - "inter_chunk_gap_seconds": { - "p50": gap_p50_s, - "p95": gap_p50_s + 0.001, - "maximum": gap_p50_s + 0.002, - }, - "sustained_media_to_wall_ratio": sustained, - "stalls": {"count": 0}, - "on_time_chunk_fraction": 1.0, - "frame_count": frame_count, - "request_wall_seconds": wall_seconds, - "memory": {"peak_gpu_mib": gpu_peak_mib}, - } - }, - } - - -def _concurrent_artifact( - *, ttff_p50_ms, gap_p50_median_ms, aggregate_fps, all_realtime, delivery_bound, gpu_peak_mib, - streams=2, realtime_count=None, on_time_chunk_fraction_min=1.0, -): - if realtime_count is None: - realtime_count = streams if all_realtime else max(streams - 1, 0) - return { - "status": "completed", - "concurrent": { - "gpu_peak_mib": gpu_peak_mib, - "streams": streams, - "realtime_count": realtime_count, - "on_time_chunk_fraction_min": on_time_chunk_fraction_min, - "ttff_ms": {"p50": ttff_p50_ms, "p95": ttff_p50_ms + 5.0}, - "gap_ms": { - "p50_median": gap_p50_median_ms, - "p95_worst": gap_p50_median_ms + 5.0, - "max_worst": gap_p50_median_ms + 10.0, - }, - "sustained_min": 1.1, - "aggregate_fps": aggregate_fps, - "all_realtime": all_realtime, - "delivery_bound": delivery_bound, - "server": { - "startup_seconds": 5.0, - "step_spacing_ms": {"p50": 50.0, "p95": 55.0}, - "rows_per_step_histogram": {"2": 30}, - }, - }, - } - - -def _write_sweep(tmp_path: Path) -> Path: - """B=1..16 grid with a distinct, deliberately placed onset per metric: - gap regression at B=4, TTFF regression and fps-gain flattening at B=8 - (B=8 is also where delivery_bound flips True), and all_realtime failing - only at B=16. max realtime B should land on B=4 (the largest B that is - both realtime and not delivery-bound).""" - artifacts = { - 1: _solo_baseline_artifact(ttff_s=0.050, gap_p50_s=0.020, sustained=1.2, fps=10.0, gpu_peak_mib=1000.0), - 2: _concurrent_artifact( - ttff_p50_ms=55.0, gap_p50_median_ms=21.0, aggregate_fps=19.0, - all_realtime=True, delivery_bound=False, gpu_peak_mib=1500.0, streams=2, - ), - 4: _concurrent_artifact( - ttff_p50_ms=58.0, gap_p50_median_ms=23.0, aggregate_fps=27.0, - all_realtime=True, delivery_bound=False, gpu_peak_mib=2000.0, streams=4, - ), - 8: _concurrent_artifact( - ttff_p50_ms=65.0, gap_p50_median_ms=30.0, aggregate_fps=29.0, - all_realtime=True, delivery_bound=True, gpu_peak_mib=3000.0, streams=8, - ), - 16: _concurrent_artifact( - ttff_p50_ms=70.0, gap_p50_median_ms=40.0, aggregate_fps=29.5, - all_realtime=False, delivery_bound=False, gpu_peak_mib=4000.0, - streams=16, realtime_count=15, on_time_chunk_fraction_min=0.9, - ), - } - for b, artifact in artifacts.items(): - (tmp_path / f"b{b}.json").write_text(json.dumps(artifact)) - return tmp_path - - -def test_summary_table_and_onsets_over_a_synthetic_sweep(tmp_path, capsys): - out_dir = _write_sweep(tmp_path) - module = runpy.run_path(str(SUMMARY_SCRIPT)) - - rows, notes = module["_load_rows"](out_dir) - assert notes == [] - assert [row["b"] for row in rows] == [1, 2, 4, 8, 16] - # B=1 falls back to runs.baseline: gap p50 0.020s -> 20ms, fps 40/4s = 10. - assert rows[0]["gap_p50_median_ms"] == 20.0 - assert rows[0]["aggregate_fps"] == 10.0 - assert rows[0]["delivery_bound"] is False - # Viability columns: how many streams stayed realtime, and the worst - # stream's per-chunk in-budget fraction. - by_b = {row["b"]: row for row in rows} - assert by_b[1]["realtime_count"] == 1 and by_b[1]["streams"] == 1 - assert by_b[16]["realtime_count"] == 15 and by_b[16]["streams"] == 16 - assert by_b[16]["on_time_chunk_fraction_min"] == 0.9 - - onsets = module["_onsets"](rows) - assert "gap p50_median regression onset (>1.10x B=1): B=4" in onsets - assert "TTFF p50 regression onset (>1.25x B=1): B=8" in onsets - assert "aggregate fps gain onset (<10% over previous B): B=8" in onsets - assert "first B with all_realtime=false: B=16" in onsets - assert "max realtime B (all_realtime and not delivery_bound): B=4" in onsets - - exit_code = module["main"]([str(SUMMARY_SCRIPT), str(out_dir)]) - assert exit_code == 0 - summary_path = out_dir / "summary.md" - assert summary_path.exists() - table = summary_path.read_text() - assert "| B |" in table - assert "realtime streams" in table - assert "on-time chunk % (min)" in table - assert "15/16" in table - assert "90.0" in table - assert "Regression onsets" in table - printed = capsys.readouterr().out - assert "max realtime B" in printed - - -def test_summary_skips_error_status_files_gracefully(tmp_path, capsys): - out_dir = _write_sweep(tmp_path) - (out_dir / "b32.json").write_text( - json.dumps({"status": "error", "error": "RuntimeError: server never became healthy"}) - ) - module = runpy.run_path(str(SUMMARY_SCRIPT)) - - rows, notes = module["_load_rows"](out_dir) - assert [row["b"] for row in rows] == [1, 2, 4, 8, 16] - assert len(notes) == 1 - assert "b32" in notes[0] and "error" in notes[0] - - -def test_summary_reports_missing_out_dir_without_a_b1_baseline(tmp_path): - module = runpy.run_path(str(SUMMARY_SCRIPT)) - artifact = _concurrent_artifact( - ttff_p50_ms=55.0, gap_p50_median_ms=21.0, aggregate_fps=19.0, - all_realtime=True, delivery_bound=False, gpu_peak_mib=1500.0, - ) - (tmp_path / "b2.json").write_text(json.dumps(artifact)) - - rows, notes = module["_load_rows"](tmp_path) - assert notes == [] - onsets = module["_onsets"](rows) - assert onsets[0] == "no B=1 row: gap/TTFF onsets relative to B=1 cannot be computed" diff --git a/test/modular/test_waypoint_360p_reference_equivalence.py b/test/modular/test_waypoint_360p_reference_equivalence.py index d8715e577..b3ff3b3de 100644 --- a/test/modular/test_waypoint_360p_reference_equivalence.py +++ b/test/modular/test_waypoint_360p_reference_equivalence.py @@ -12,7 +12,6 @@ from __future__ import annotations -import hashlib import importlib.util import json import os @@ -32,8 +31,8 @@ _commit, _deviation, _divergent_stages, - _import_reference, _island_tables, + _load_reference, _new_request, _port_forward, _port_frame, @@ -42,6 +41,7 @@ _reference_importable, _reset, _ring_deviation, + _seed_clip, _stage_capture, _stage_modules, ) @@ -119,43 +119,6 @@ def _context( } -def _seed_clip() -> tuple[torch.Tensor, str]: - import cv2 - import numpy as np - - raw = SEED_IMAGE.read_bytes() - digest = hashlib.sha256(raw).hexdigest() - image = cv2.imdecode(np.frombuffer(raw, np.uint8), cv2.IMREAD_COLOR) - image = cv2.cvtColor(cv2.resize(image, (640, 360)), cv2.COLOR_BGR2RGB) - return torch.from_numpy(np.repeat(image[None], 4, axis=0)), digest - - -def _load_reference() -> dict: - WorldModel, StaticKVCache, patch_model = _import_reference() - torch.set_float32_matmul_precision("high") - cfg = WorldModel.load_config(str(CHECKPOINT)) - assert (cfg.tokens_per_frame, cfg.height, cfg.width) == (128, 8, 16) - model = WorldModel.from_pretrained(str(CHECKPOINT), cfg=cfg, device=DEVICE, dtype=DTYPE).eval() - islands = { - "freq": model.denoise_step_emb.freq.clone(), - "xy": model.transformer.rope_angles.xy.clone(), - "inv_t": model.transformer.rope_angles.inv_t.clone(), - } - bare_conditioner = model.denoise_step_emb - patch_model.apply_inference_patches(model) - patch_model.flex_attention = __import__( - "mstar.engine.resources.attn.flex", fromlist=["flex_attention_masked"] - ).flex_attention_masked - cache = StaticKVCache(cfg, batch_size=1, dtype=DTYPE).to(device=DEVICE) - return { - "cfg": cfg, - "model": model, - "kv": cache, - "islands": islands, - "bare_conditioner": bare_conditioner, - } - - def _reference_decoder_histories(session) -> tuple[torch.Tensor, ...]: return tuple(value for value in session.streaming_ae_model.decoder_memory if torch.is_tensor(value)) @@ -174,7 +137,8 @@ def test_360p_reference_compat_is_bit_exact_end_to_end(): assert ROLLOUT_FRAMES > 32, "the live gate must cross two 16-frame local windows" assert ROLLOUT_FRAMES <= len(CONTROL_SEQUENCE) + 1 - reference = _load_reference() + reference = _load_reference(CHECKPOINT) + assert (reference["cfg"].tokens_per_frame, reference["cfg"].height, reference["cfg"].width) == (128, 8, 16) config = replace(waypoint_1_5_1b_360p(), reference_compat=True, compile_dit=False) port = _build_port(config, CHECKPOINT) @@ -193,7 +157,7 @@ def test_360p_reference_compat_is_bit_exact_end_to_end(): print(f"conditioner sigma={value:<7.4f} maxabs={gap:.4e} rel={relative:.4e}") assert gap == 0.0, f"360p conditioner sigma={value} maxabs={gap:.4e} rel={relative:.4e}" - clip, seed_digest = _seed_clip() + clip, seed_digest = _seed_clip(SEED_IMAGE, (640, 360)) assert seed_digest == SEED_SHA256 ae = load_taehv(str(AE_CHECKPOINT)).to(device=DEVICE, dtype=DTYPE) from src.ae import ChunkedStreamingTAEHV as ReferenceTAEHV diff --git a/test/modular/test_waypoint_pixel_equivalence.py b/test/modular/test_waypoint_pixel_equivalence.py index 741bcef9f..893e64e96 100644 --- a/test/modular/test_waypoint_pixel_equivalence.py +++ b/test/modular/test_waypoint_pixel_equivalence.py @@ -22,7 +22,6 @@ from __future__ import annotations -import hashlib import os import sys from dataclasses import replace @@ -50,6 +49,7 @@ _reference_frame, _reference_importable, _reset, + _seed_clip, ) from mstar.model.waypoint.components.taehv import ChunkedStreamingTAEHV, load_taehv @@ -139,14 +139,7 @@ def oracle(): def seed_clip(): """The oracle's seed frame as ``[4, 720, 1280, 3]`` uint8, decoded and resized in ``record_oracle.load_seed_frame``'s order.""" - import cv2 - import numpy as np - - raw = SEED_IMAGE.read_bytes() - digest = hashlib.sha256(raw).hexdigest() - img = cv2.imdecode(np.frombuffer(raw, np.uint8), cv2.IMREAD_COLOR) - img = cv2.cvtColor(cv2.resize(img, (1280, 720)), cv2.COLOR_BGR2RGB) - return torch.from_numpy(np.repeat(img[None], 4, axis=0)), digest + return _seed_clip(SEED_IMAGE, (1280, 720)) # --------------------------------------------------------------------------- @@ -330,6 +323,7 @@ def _distances(left: torch.Tensor, right: torch.Tensor) -> torch.Tensor: return torch.stack([(right - row).flatten(1).norm(dim=1) for row in left]) +@pytest.mark.skipif(not (REPRO / "frames").is_dir(), reason=f"repro recording not at {REPRO}") def test_the_primed_frame_matches_the_oracle_exactly(rollout, oracle): """The one frame the oracle *is* a bit-exact target for. @@ -354,6 +348,7 @@ def test_the_primed_frame_matches_the_oracle_exactly(rollout, oracle): assert gap == 0, f"primed pixels differ from the oracle by maxabs={gap}/255" +@pytest.mark.skipif(not (REPRO / "frames").is_dir(), reason=f"repro recording not at {REPRO}") def test_the_emitted_stream_stays_aligned_with_the_oracle(rollout, oracle): """A decoder desync is a *shift*, and a shift is visible without a tolerance: every raw frame's nearest neighbour in the recording must be itself. Over diff --git a/test/modular/test_waypoint_profiler.py b/test/modular/test_waypoint_profiler.py deleted file mode 100644 index 3d6fe6db2..000000000 --- a/test/modular/test_waypoint_profiler.py +++ /dev/null @@ -1,117 +0,0 @@ -from __future__ import annotations - -import importlib.util -import sqlite3 -import sys -from pathlib import Path - -_CHECK_PATH = Path(__file__).parents[1] / "waypoint/check_nsys_replay.py" -_SPEC = importlib.util.spec_from_file_location("waypoint_nsys_check", _CHECK_PATH) -assert _SPEC is not None and _SPEC.loader is not None -_CHECK = importlib.util.module_from_spec(_SPEC) -sys.modules[_SPEC.name] = _CHECK -_SPEC.loader.exec_module(_CHECK) -DEFAULT_ROLLOUT_RANGE = _CHECK.DEFAULT_ROLLOUT_RANGE -inspect_replay = _CHECK.inspect_replay - - -def _profile_database(path: Path, apis: list[list[str]]) -> Path: - with sqlite3.connect(path) as connection: - connection.executescript( - """ - CREATE TABLE StringIds (id INTEGER PRIMARY KEY, value TEXT); - CREATE TABLE NVTX_EVENTS ( - start INTEGER, end INTEGER, globalTid INTEGER, - textId INTEGER, text TEXT - ); - CREATE TABLE CUPTI_ACTIVITY_KIND_RUNTIME ( - start INTEGER, end INTEGER, globalTid INTEGER, nameId INTEGER - ); - """ - ) - strings = {DEFAULT_ROLLOUT_RANGE, "engine.forward", *(api for row in apis for api in row)} - ids = {value: index for index, value in enumerate(sorted(strings), start=1)} - connection.executemany("INSERT INTO StringIds VALUES (?, ?)", ((ids[v], v) for v in ids)) - for index, forward_apis in enumerate(apis): - base = index * 100 - connection.execute( - "INSERT INTO NVTX_EVENTS VALUES (?, ?, ?, ?, NULL)", - (base, base + 90, 7, ids[DEFAULT_ROLLOUT_RANGE]), - ) - connection.execute( - "INSERT INTO NVTX_EVENTS VALUES (?, ?, ?, ?, NULL)", - (base + 10, base + 80, 7, ids["engine.forward"]), - ) - connection.executemany( - "INSERT INTO CUPTI_ACTIVITY_KIND_RUNTIME VALUES (?, ?, ?, ?)", - ( - (base + 20 + offset, base + 21 + offset, 7, ids[api]) - for offset, api in enumerate(forward_apis) - ), - ) - return path - - -def test_inspect_replay_accepts_graph_only_forwards(tmp_path: Path): - database = _profile_database( - tmp_path / "clean.sqlite", - [["cudaMemcpyAsync", "cudaGraphLaunch_v10000"] for _ in range(3)], - ) - - result = inspect_replay(database) - - assert result.forwards == 3 - assert result.graph_replays == 3 - assert result.sync_or_blocking_calls == 0 - assert result.offending_apis == () - - -def test_inspect_replay_reports_each_blocking_api(tmp_path: Path): - database = _profile_database( - tmp_path / "blocking.sqlite", - [ - ["cudaGraphLaunch_v10000", "cudaDeviceSynchronize"], - ["cudaGraphLaunch_v10000", "cudaMemcpy"], - ["cudaGraphLaunch_v10000", "cudaMalloc"], - ["cudaGraphLaunch_v10000", "cudaFree"], - ], - ) - - result = inspect_replay(database) - - assert result.forwards == 4 - assert result.graph_replays == 4 - assert result.sync_or_blocking_calls == 4 - assert result.offending_apis == ( - ("cudaDeviceSynchronize", 1), - ("cudaFree", 1), - ("cudaMalloc", 1), - ("cudaMemcpy", 1), - ) - - -def test_inspect_replay_reports_missing_graph_launch(tmp_path: Path): - database = _profile_database( - tmp_path / "missing-graph.sqlite", - [["cudaGraphLaunch_v10000"], ["cudaMemcpyAsync"]], - ) - - result = inspect_replay(database) - - assert result.forwards == 2 - assert result.graph_replays == 1 - - -def test_inspect_replay_rejects_multiple_graph_launches_in_one_forward(tmp_path: Path): - database = _profile_database( - tmp_path / "multiple-graphs.sqlite", - [ - ["cudaGraphLaunch_v10000"], - ["cudaGraphLaunch_v10000", "cudaGraphLaunch_v10000"], - ], - ) - - result = inspect_replay(database) - - assert result.forwards == 2 - assert result.graph_replays == 1 diff --git a/test/modular/test_waypoint_reference_equivalence.py b/test/modular/test_waypoint_reference_equivalence.py index 06fe16980..ac7fefe9c 100644 --- a/test/modular/test_waypoint_reference_equivalence.py +++ b/test/modular/test_waypoint_reference_equivalence.py @@ -36,6 +36,7 @@ from __future__ import annotations import contextlib +import hashlib import os import sys from dataclasses import replace @@ -133,21 +134,13 @@ def forward(self, *args, **kwargs): raise NotImplementedError("binding stand-in") -@pytest.fixture(scope="module") -def reference(): - """The served reference: inference patches applied, flex pinned to the port's - compiled kernel. The three fp32 island tables are captured *before* patching - -- ``CachedDenoiseStepEmb`` keeps no handle back to the module it replaces. - """ +def _load_reference(checkpoint: Path = CHECKPOINT) -> dict: + # Served reference: islands cloned pre-patch, flex pinned to the port's kernel, matmul precision 'high' for the reference's batch-5 sigma LUT (TF32, not 'highest'). WorldModel, StaticKVCache, patch_model = _import_reference() - # The oracle recorded at 'high'; its own calibration measured high and medium - # bit-identical for this model, and high vs highest differing (metadata.json). - # It also decides the reference's sigma LUT: that table is a batch-5 fp32 GEMM, - # which rounds through TF32 here and not at 'highest'. torch.set_float32_matmul_precision("high") - cfg = WorldModel.load_config(str(CHECKPOINT)) - model = WorldModel.from_pretrained(str(CHECKPOINT), cfg=cfg, device=DEVICE, dtype=DTYPE).eval() + cfg = WorldModel.load_config(str(checkpoint)) + model = WorldModel.from_pretrained(str(checkpoint), cfg=cfg, device=DEVICE, dtype=DTYPE).eval() islands = { "freq": model.denoise_step_emb.freq.clone(), "xy": model.transformer.rope_angles.xy.clone(), @@ -159,7 +152,7 @@ def reference(): patch_model.apply_inference_patches(model) patch_model.flex_attention = flex_attention_masked cache = StaticKVCache(cfg, batch_size=1, dtype=DTYPE).to(device=DEVICE) - yield { + return { "cfg": cfg, "model": model, "kv": cache, @@ -169,6 +162,27 @@ def reference(): } +def _seed_clip(image_path: Path, size: tuple[int, int]) -> tuple[torch.Tensor, str]: + # Recorder-order seed clip: decode image_path, resize to size (w, h), RGB, four-frame stack. + import cv2 + import numpy as np + + raw = image_path.read_bytes() + digest = hashlib.sha256(raw).hexdigest() + image = cv2.imdecode(np.frombuffer(raw, np.uint8), cv2.IMREAD_COLOR) + image = cv2.cvtColor(cv2.resize(image, size), cv2.COLOR_BGR2RGB) + return torch.from_numpy(np.repeat(image[None], 4, axis=0)), digest + + +@pytest.fixture(scope="module") +def reference(): + """The served reference: inference patches applied, flex pinned to the port's + compiled kernel. The three fp32 island tables are captured *before* patching + -- ``CachedDenoiseStepEmb`` keeps no handle back to the module it replaces. + """ + yield _load_reference() + + def _build_port(config, checkpoint: Path = CHECKPOINT): """Build a port from the checkpoint belonging to ``config``. diff --git a/test/modular/test_waypoint_streaming_benchmark.py b/test/modular/test_waypoint_streaming_benchmark.py deleted file mode 100644 index 2991f039f..000000000 --- a/test/modular/test_waypoint_streaming_benchmark.py +++ /dev/null @@ -1,392 +0,0 @@ -from __future__ import annotations - -import hashlib -import runpy -from pathlib import Path - -import pytest - -from mstar.client import VideoFrameChunk - - -@pytest.fixture(scope="module") -def benchmark(): - return runpy.run_path( - str(Path(__file__).parents[1] / "waypoint" / "benchmark_streaming.py") - ) - - -def test_streaming_metric_math_includes_pacing_jitter_and_stalls(benchmark): - observation = benchmark["ChunkObservation"] - metrics = benchmark["_stream_metrics"]( - [ - observation(1.0, 100, 0, 4, 4.0), - observation(2.0, 100, 4, 4, 4.0), - observation(4.0, 100, 8, 4, 4.0), - ], - request_wall_seconds=4.2, - stall_threshold_seconds=1.5, - consumer_pause_seconds=0.25, - consumer_pause_count=2, - payload_sha256="abc", - ) - - assert metrics["time_to_first_frame_seconds"] == 1.0 - assert metrics["generated_media_seconds"] == 3.0 - assert metrics["sustained_media_to_wall_ratio"] == pytest.approx(2 / 3) - assert metrics["overall_media_to_wall_ratio"] == pytest.approx(3 / 4.2) - assert metrics["inter_chunk_gap_seconds"] == { - "sample_count": 2, - "p50": 1.5, - "p95": 1.95, - "mean": 1.5, - "jitter_population_stddev": 0.5, - "maximum": 2.0, - } - assert metrics["stalls"] == { - "threshold_seconds": 1.5, - "count": 1, - "longest_seconds": 2.0, - "total_excess_seconds": 0.5, - } - assert metrics["consumer"]["injected_pause_seconds"] == 0.5 - - -def test_streaming_metric_math_handles_one_chunk_without_fake_gap(benchmark): - observation = benchmark["ChunkObservation"] - metrics = benchmark["_stream_metrics"]( - [observation(0.5, 72, 0, 4, 60.0)], - request_wall_seconds=0.6, - stall_threshold_seconds=0.25, - consumer_pause_seconds=0.0, - consumer_pause_count=0, - payload_sha256="def", - ) - - assert metrics["sustained_media_to_wall_ratio"] is None - assert metrics["inter_chunk_gap_seconds"]["sample_count"] == 0 - assert metrics["inter_chunk_gap_seconds"]["p50"] is None - assert metrics["inter_chunk_gap_seconds"]["jitter_population_stddev"] is None - assert metrics["stalls"]["count"] == 0 - - -def test_backpressure_summary_reports_deltas_without_a_threshold(benchmark): - baseline = { - "request_wall_seconds": 2.0, - "time_to_first_frame_seconds": 0.5, - "sustained_media_to_wall_ratio": 0.8, - "payload_sha256": "same", - "consumer": {"pause_seconds": 0.0, "injected_pause_seconds": 0.0}, - } - slow = { - "request_wall_seconds": 3.2, - "time_to_first_frame_seconds": 0.6, - "sustained_media_to_wall_ratio": 0.4, - "payload_sha256": "same", - "consumer": {"pause_seconds": 0.25, "injected_pause_seconds": 1.0}, - } - baseline_memory = {"peak_host_pss_mib": 100.0, "peak_gpu_mib": 1000.0} - slow_memory = {"peak_host_pss_mib": 112.0, "peak_gpu_mib": 1004.0} - - result = benchmark["_backpressure_metrics"]( - baseline, slow, baseline_memory, slow_memory - ) - - assert result["observed_request_wall_increase_seconds"] == pytest.approx(1.2) - assert result["wall_increase_beyond_injected_pause_seconds"] == pytest.approx(0.2) - assert result["peak_host_pss_change_mib"] == 12.0 - assert result["peak_gpu_memory_change_mib"] == 4.0 - assert result["payloads_match"] is True - assert "threshold" not in result - - -def test_measurement_loop_consumes_typed_chunks_and_pauses_only_between_them( - benchmark, tmp_path -): - variant = benchmark["rollout"].Variant("test", 2, 3, 1, "unused") - - def metadata(frame_index): - return { - "width": 3, - "height": 2, - "fps": 60.0, - "pixel_format": "rgb24", - "frame_index": frame_index, - "frame_count": 4, - } - - payloads = [bytes([1]) * 72, bytes([2]) * 72] - - class Client: - kwargs = None - - def stream(self, **kwargs): - self.kwargs = kwargs - return iter( - [ - VideoFrameChunk(payloads[0], metadata(0)), - VideoFrameChunk(payloads[1], metadata(4)), - ] - ) - - client = Client() - clock_values = iter([10.0, 10.5, 11.0, 11.1]) - pauses = [] - metrics, failures = benchmark["_measure_stream"]( - client, - tmp_path / "seed.png", - variant, - num_steps=2, - request_id="rid", - rng_seed=7, - consumer_pause_seconds=0.25, - stall_threshold_seconds=0.4, - clock=lambda: next(clock_values), - sleep=pauses.append, - ) - - assert failures == [] - assert pauses == [0.25] - assert metrics["time_to_first_frame_seconds"] == 0.5 - assert metrics["inter_chunk_gap_seconds"]["p95"] == 0.5 - assert metrics["consumer"]["pause_count"] == 1 - assert metrics["payload_sha256"] == hashlib.sha256(b"".join(payloads)).hexdigest() - assert client.kwargs["output_modalities"] == ("video_frame",) - assert len(client.kwargs["actions"]) == 2 - - -def test_startup_latency_is_none_until_samples_are_asked_for(benchmark): - """The key is always present, so a consumer never has to guess the shape.""" - assert benchmark["_startup_latency_metrics"]([]) is None - - -def test_startup_latency_summarizes_every_sample(benchmark): - metrics = benchmark["_startup_latency_metrics"]([0.40, 0.10, 0.20, 0.30]) - - assert metrics == { - "sample_count": 4, - "p50": pytest.approx(0.25), - "p95": pytest.approx(0.385), - "mean": pytest.approx(0.25), - "minimum": 0.10, - "maximum": 0.40, - } - - -@pytest.mark.parametrize( - "extra, message", - [ - (["--steps", "0"], "--steps must be positive"), - (["--warmup-steps", "-1"], "--warmup-steps cannot be negative"), - (["--startup-repeats", "-1"], "--startup-repeats cannot be negative"), - (["--startup-steps", "0"], "--startup-steps must be positive"), - (["--slow-consumer-delay", "-0.1"], "--slow-consumer-delay cannot be negative"), - (["--stall-threshold", "0"], "--stall-threshold must be positive"), - (["--memory-sample-interval", "0"], "--memory-sample-interval must be positive"), - ], -) -def test_benchmark_cli_rejects_invalid_measurement_configuration( - benchmark, capsys, extra, message -): - with pytest.raises(SystemExit, match="2"): - benchmark["_parse_args"]( - ["--variant", "360p", "--physical-gpu", "2", *extra] - ) - assert message in capsys.readouterr().err - - -def test_benchmark_cli_rejects_hub_with_local_overrides(benchmark, capsys): - with pytest.raises(SystemExit, match="2"): - benchmark["_parse_args"]( - [ - "--variant", - "720p", - "--physical-gpu", - "2", - "--source", - "hub", - "--checkpoint-dir", - "/tmp/checkpoint", - ] - ) - assert "--source hub cannot be combined" in capsys.readouterr().err - - -def test_benchmark_cli_derives_stall_threshold_and_has_no_release_gate(benchmark): - args = benchmark["_parse_args"]( - ["--variant", "360p", "--physical-gpu", "2"] - ) - - assert args.stall_threshold is None - assert benchmark["_resolve_stall_threshold"](args) == pytest.approx(4.0 / 15.0) - assert not any( - action.dest.startswith("release") - for action in benchmark["_build_parser"]()._actions - ) - - -def test_streams_worlds_and_batch_default_to_one_without_the_new_flags(benchmark): - """--streams 1 (today's only mode) must keep worlds=batch=1, matching the - hardcoded worlds=1 _run_config call this replaced.""" - args = benchmark["_parse_args"](["--variant", "360p", "--physical-gpu", "2"]) - - assert args.streams == 1 - assert args.worlds == 1 - assert args.batch == 1 - - -def test_worlds_and_batch_default_to_streams(benchmark): - args = benchmark["_parse_args"]( - ["--variant", "360p", "--physical-gpu", "2", "--streams", "4"] - ) - - assert args.worlds == 4 - assert args.batch == 4 - - -@pytest.mark.parametrize( - "extra, message", - [ - (["--streams", "0"], "--streams must be positive"), - (["--streams", "4", "--worlds", "0"], "--worlds must be positive"), - (["--streams", "4", "--batch", "0"], "--batch must be positive"), - (["--streams", "4", "--worlds", "2", "--batch", "4"], "--batch must be <= --worlds"), - ], -) -def test_benchmark_cli_rejects_invalid_concurrent_stream_configuration( - benchmark, capsys, extra, message -): - with pytest.raises(SystemExit, match="2"): - benchmark["_parse_args"](["--variant", "360p", "--physical-gpu", "2", *extra]) - assert message in capsys.readouterr().err - - -def test_measure_stream_waits_on_start_barrier_before_opening_the_stream(benchmark, tmp_path): - """The request body (client.stream(...)) must be built before the barrier - wait, and consumption (the clock start) must not begin until after it.""" - variant = benchmark["rollout"].Variant("test", 2, 3, 1, "unused") - - def metadata(frame_index): - return { - "width": 3, - "height": 2, - "fps": 60.0, - "pixel_format": "rgb24", - "frame_index": frame_index, - "frame_count": 4, - } - - calls = [] - - class Barrier: - def wait(self, timeout=None): - calls.append("barrier_wait") - - class Client: - def stream(self, **kwargs): - calls.append("stream_called") - return iter([VideoFrameChunk(bytes([1]) * 72, metadata(0))]) - - clock_values = iter([10.0, 10.5, 10.6]) - metrics, failures = benchmark["_measure_stream"]( - Client(), - tmp_path / "seed.png", - variant, - num_steps=1, - request_id="rid", - rng_seed=1, - consumer_pause_seconds=0.0, - stall_threshold_seconds=0.4, - clock=lambda: next(clock_values), - sleep=lambda _seconds: None, - start_barrier=Barrier(), - ) - - assert calls == ["stream_called", "barrier_wait"] - assert failures == [] - assert metrics["chunk_count"] == 1 - - -def _chunk_stats(*, ttff_s, p50_s, p95_s, max_s, sustained, stalls): - return { - "time_to_first_frame_seconds": ttff_s, - "inter_chunk_gap_seconds": {"p50": p50_s, "p95": p95_s, "maximum": max_s}, - "sustained_media_to_wall_ratio": sustained, - "stalls": {"count": stalls}, - } - - -def test_stream_is_realtime_requires_sustained_gap_budget_and_no_stalls(benchmark): - is_realtime = benchmark["_stream_is_realtime"] - budget_s = benchmark["REALTIME_CHUNK_BUDGET_MS"] / 1000.0 - - healthy = _chunk_stats( - ttff_s=0.05, p50_s=0.05, p95_s=budget_s - 0.001, max_s=budget_s, sustained=1.05, stalls=0 - ) - assert is_realtime(healthy) is True - - under_sustained = {**healthy, "sustained_media_to_wall_ratio": 0.9} - assert is_realtime(under_sustained) is False - - over_budget = { - **healthy, - "inter_chunk_gap_seconds": {**healthy["inter_chunk_gap_seconds"], "p95": budget_s + 0.001}, - } - assert is_realtime(over_budget) is False - - stalled = {**healthy, "stalls": {"count": 1}} - assert is_realtime(stalled) is False - - -def test_concurrent_aggregate_computes_worst_median_realtime_and_delivery_bound(benchmark): - aggregate = benchmark["_concurrent_aggregate"] - budget_s = benchmark["REALTIME_CHUNK_BUDGET_MS"] / 1000.0 - - fast = _chunk_stats(ttff_s=0.05, p50_s=0.05, p95_s=0.06, max_s=0.07, sustained=1.2, stalls=0) - slow = _chunk_stats(ttff_s=0.20, p50_s=0.15, p95_s=budget_s + 0.01, max_s=0.20, sustained=0.8, stalls=1) - per_stream = [fast, slow] - - server_slack = {"step_spacing_ms": {"p50": 200.0, "p95": 220.0}} - result = aggregate(per_stream, aggregate_fps=8.0, server=server_slack) - - assert result["aggregate_fps"] == 8.0 - assert result["ttff_ms"]["p50"] == pytest.approx(125.0) - assert result["gap_ms"]["p50_worst"] == pytest.approx(150.0) - assert result["gap_ms"]["p95_worst"] == pytest.approx((budget_s + 0.01) * 1000.0) - assert result["gap_ms"]["max_worst"] == pytest.approx(200.0) - assert result["gap_ms"]["p50_median"] == pytest.approx((50.0 + 150.0) / 2) - assert result["sustained_min"] == pytest.approx(0.8) - assert result["stall_count_total"] == 1 - assert result["realtime_per_stream"] == [True, False] - assert result["all_realtime"] is False - assert result["server"] is server_slack - # gap p50_median (100ms) is not > 1.2x a 200ms server step: not delivery-bound. - assert result["delivery_bound"] is False - - server_fast = {"step_spacing_ms": {"p50": 50.0, "p95": 60.0}} - result_bound = aggregate(per_stream, aggregate_fps=8.0, server=server_fast) - # gap p50_median (100ms) > 1.2x a 50ms server step: clients are the bottleneck. - assert result_bound["delivery_bound"] is True - - -def test_concurrent_server_metrics_parses_rows_histogram_and_step_spacing(benchmark): - server_metrics = benchmark["_concurrent_server_metrics"] - log_text = "\n".join( - [ - "2026-09-17 21:38:40,000 DEBUG [worker-0] mstar.worker.worker: " - "Executing: dit graph_walk=rollout ('warmup-0',)", - "2026-09-17 21:38:40,500 DEBUG [worker-0] mstar.worker.worker: " - "Executing: dit graph_walk=rollout ('measured-0', 'measured-1')", - "2026-09-17 21:38:40,600 DEBUG [worker-0] mstar.worker.worker: " - "Executing: dit graph_walk=rollout ('measured-0', 'measured-1')", - "2026-09-17 21:38:40,800 DEBUG [worker-0] mstar.worker.worker: " - "Executing: dit graph_walk=rollout ('measured-0', 'measured-1')", - ] - ) - - result = server_metrics(log_text, {"measured-0", "measured-1"}, 12.3) - - assert result["rows_per_step_histogram"] == {2: 3} - assert result["step_spacing_ms"]["p50"] == pytest.approx(150.0) - assert result["step_spacing_ms"]["p95"] == pytest.approx(195.0) - assert result["startup_seconds"] == 12.3 diff --git a/test/waypoint/check_nsys_replay.py b/test/waypoint/check_nsys_replay.py deleted file mode 100644 index 3fb3f64dc..000000000 --- a/test/waypoint/check_nsys_replay.py +++ /dev/null @@ -1,175 +0,0 @@ -#!/usr/bin/env python3 -"""Validate steady Waypoint DiT CUDA replay in an Nsight SQLite export. - -Export a report before running this check:: - - nsys export -t sqlite -f true -o trace.sqlite trace.nsys-rep - python3 test/waypoint/check_nsys_replay.py trace.sqlite \ - --expected-forwards 16 - -Only CUDA runtime calls nested inside the rollout ``engine.forward`` ranges are -examined. Synchronization in startup, output transfer, and postprocessing is -outside the steady DiT replay contract. -""" - -from __future__ import annotations - -import argparse -import sqlite3 -from dataclasses import dataclass -from pathlib import Path - -DEFAULT_ROLLOUT_RANGE = "worker[worker_0].node[dit].graph_walk[rollout]" - - -@dataclass(frozen=True) -class ReplayInspection: - forwards: int - graph_replays: int - sync_or_blocking_calls: int - offending_apis: tuple[tuple[str, int], ...] - - -_RANGE_CTE = """ -WITH nvtx AS ( - SELECT n.rowid AS id, n.start, n.end, n.globalTid, - coalesce(s.value, n.text) AS name - FROM NVTX_EVENTS AS n - LEFT JOIN StringIds AS s ON s.id = n.textId - WHERE n.end IS NOT NULL -), rollout AS ( - SELECT * FROM nvtx WHERE name = :rollout_range -), forwards AS ( - SELECT DISTINCT f.* - FROM nvtx AS f - JOIN rollout AS r - ON f.globalTid = r.globalTid - AND f.start >= r.start - AND f.end <= r.end - WHERE f.name = 'engine.forward' -), calls AS ( - SELECT f.id AS forward_id, s.value AS api - FROM forwards AS f - JOIN CUPTI_ACTIVITY_KIND_RUNTIME AS c - ON c.globalTid = f.globalTid - AND c.start >= f.start - AND c.end <= f.end - JOIN StringIds AS s ON s.id = c.nameId -), graph_launch_counts AS ( - SELECT forward_id, count(*) AS launches - FROM calls - WHERE api LIKE 'cudaGraphLaunch%' - GROUP BY forward_id -) -""" - -_BLOCKING_PREDICATE = """ -api LIKE '%Synchronize%' -OR (api LIKE 'cudaMemcpy%' AND api NOT LIKE '%Async%') -OR ( - (api LIKE 'cudaMalloc%' OR api LIKE 'cudaFree%') - AND api NOT LIKE '%Async%' -) -""" - - -def inspect_replay( - database: Path, - rollout_range: str = DEFAULT_ROLLOUT_RANGE, -) -> ReplayInspection: - if not database.is_file(): - raise ValueError(f"Nsight SQLite export does not exist: {database}") - - summary_query = ( - _RANGE_CTE - + """ -SELECT count(*) AS forwards, - sum(CASE WHEN coalesce(g.launches, 0) = 1 THEN 1 ELSE 0 END) AS graph_replays, - sum(EXISTS( - SELECT 1 FROM calls AS c - WHERE c.forward_id = f.id AND ( -""" - + _BLOCKING_PREDICATE - + """ - ) - )) AS sync_or_blocking_calls -FROM forwards AS f -LEFT JOIN graph_launch_counts AS g ON g.forward_id = f.id -""" - ) - offenders_query = ( - _RANGE_CTE - + """ -SELECT api, count(*) AS occurrences -FROM calls -WHERE -""" - + _BLOCKING_PREDICATE - + """ -GROUP BY api -ORDER BY api -""" - ) - - try: - with sqlite3.connect(database) as connection: - row = connection.execute(summary_query, {"rollout_range": rollout_range}).fetchone() - offenders = tuple( - (str(api), int(count)) - for api, count in connection.execute( - offenders_query, - {"rollout_range": rollout_range}, - ) - ) - except sqlite3.DatabaseError as exc: - raise ValueError(f"could not inspect Nsight SQLite export {database}: {exc}") from exc - - assert row is not None - return ReplayInspection( - forwards=int(row[0]), - graph_replays=int(row[1] or 0), - sync_or_blocking_calls=int(row[2] or 0), - offending_apis=offenders, - ) - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("database", type=Path, help="SQLite file produced by `nsys export -t sqlite`") - parser.add_argument("--expected-forwards", type=int, required=True) - parser.add_argument("--rollout-range", default=DEFAULT_ROLLOUT_RANGE) - args = parser.parse_args() - - if args.expected_forwards < 1: - parser.error("--expected-forwards must be positive") - try: - result = inspect_replay(args.database, args.rollout_range) - except ValueError as exc: - parser.error(str(exc)) - - print( - f"forwards={result.forwards} graph_replays={result.graph_replays} " - f"sync_or_blocking_calls={result.sync_or_blocking_calls}" - ) - failures = [] - if result.forwards != args.expected_forwards: - failures.append(f"expected {args.expected_forwards} forwards, found {result.forwards}") - if result.graph_replays != result.forwards: - failures.append( - f"only {result.graph_replays}/{result.forwards} forwards launched exactly one CUDA graph" - ) - if result.sync_or_blocking_calls: - detail = ", ".join(f"{api}={count}" for api, count in result.offending_apis) - failures.append( - f"{result.sync_or_blocking_calls} forwards contain blocking CUDA calls ({detail})" - ) - if failures: - for failure in failures: - print(f"FAIL: {failure}") - return 1 - print("PASS") - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) From 31a25465e5a96fa13041cced2c5c6532a0e7c976 Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Tue, 22 Sep 2026 23:21:23 +0000 Subject: [PATCH 17/29] waypoint: prune model comments to peer-port style Condense essay docstrings and drop historical notes, baked-in perf numbers, and references to local-only docs (VALIDATION.md, OPTIMIZATION_BACKLOG.md) across the model port, matching the terse functional comment style of peer ports (orpheus). Keeps the non-obvious why (numerics/perf hazards, ordering constraints, shape/dtype contracts). Comments and docstrings only; no code changed (AST-verified, py_compile clean). ~370 lines removed across 9 files. --- mstar/model/waypoint/components/attention.py | 17 +- mstar/model/waypoint/components/dit.py | 103 ++---- mstar/model/waypoint/components/layers.py | 69 ++-- mstar/model/waypoint/components/rope.py | 17 +- mstar/model/waypoint/components/taehv.py | 7 +- mstar/model/waypoint/config.py | 115 +++---- mstar/model/waypoint/submodules.py | 311 ++++++----------- mstar/model/waypoint/waypoint_model.py | 269 +++++---------- mstar/model/waypoint/weight_loader.py | 343 +++++++------------ 9 files changed, 438 insertions(+), 813 deletions(-) diff --git a/mstar/model/waypoint/components/attention.py b/mstar/model/waypoint/components/attention.py index f7f147044..a860b80e8 100644 --- a/mstar/model/waypoint/components/attention.py +++ b/mstar/model/waypoint/components/attention.py @@ -1,18 +1,9 @@ """Waypoint self-attention: fused QKV, value residual, OrthoRoPE, ring cache. -``WorldEngine.__init__`` applies ``apply_inference_patches`` unconditionally, so -the served reference module is the fused ``patch_model.MergedQKVAttn``. That -fused form, not three separate GEMMs, is the parity target. - -``qkv_proj.weight`` is ``cat([q, k, v], dim=0)`` — rows 0:2048 Q, 2048:3072 K, -3072:4096 V. GQA makes the slabs unequal, so a wrong order is a shape error for -Q but *not* between K and V: swapping those two loads cleanly and serves wrong -video. - -This module reaches the world state only through the two resources bound in -``bind_resources``. It never sees the ring, the slot arithmetic or the -``BlockMask`` — ``visible`` is a ``[capacity]`` bool row it hands straight back -to ``attend``. +The served reference is the fused ``patch_model.MergedQKVAttn``, not three +separate GEMMs, so that fused form is the parity target. ``qkv_proj.weight`` +is ``cat([q, k, v], dim=0)``; GQA makes the K/V slabs unequal, so swapping +them loads cleanly but serves wrong video. """ import torch diff --git a/mstar/model/waypoint/components/dit.py b/mstar/model/waypoint/components/dit.py index 19579adb2..485eb96f0 100644 --- a/mstar/model/waypoint/components/dit.py +++ b/mstar/model/waypoint/components/dit.py @@ -1,22 +1,10 @@ """The Waypoint-1.5 DiT: 24 blocks, the 4+1 pass driver, and the world clock. Five forwards per generated frame: four frozen Euler denoise passes over -``config.scheduler_sigmas``, then one committing pass at sigma=0. Only the last -writes the ring. The loop is plain Python here, not engine steps. - -Two clocks, threaded separately: ``f_pos`` drives buckets, slots and -visibility, ``t_pos = f_pos * config.ts_mult`` is the RoPE time coordinate. -They are numerically equal at this checkpoint's ``ts_mult == 1``; conflating -them is silent drift at any other serving fps. - -``cond_proj`` is physically shared by all 24 blocks, and ``retie_cond_proj()`` -is public because ``to_empty(device)`` silently un-ties it. Controller -conditioning is fused on 8 of 24 layers (``i % 3 == 0``); the other 16 carry no -``ctrl_mlpfusion`` submodule at all. - -The port collapses the reference's ``WorldModel``/``WorldDiT`` pair into one -module, so blocks live at ``blocks.{i}``, not ``transformer.blocks.{i}``. -Everything below that prefix keeps the reference's spelling. +``config.scheduler_sigmas``, then one committing pass at sigma=0 that writes +the ring. Two clocks are threaded separately: ``f_pos`` (buckets, slots, +visibility) and ``t_pos = f_pos * config.ts_mult`` (RoPE time); conflating +them is silent drift at any fps where ``ts_mult != 1``. """ from typing import NamedTuple @@ -93,12 +81,10 @@ def forward( commit: bool, cond_idx: int, ) -> tuple[Tensor, Tensor]: - """``x`` ``[B, N*T, D]``, ``cond``/``ctrl_emb`` ``[B, N, D]`` (per frame) - -> ``(x, v1)``. ``v1`` is layer 0's pre-lerp V, threaded down the stack. - ``cond_idx`` is the ``scheduler_sigmas`` slot, for the cond_head cache. - - Only ``f_pos`` reaches this far -- the RoPE angles are built once at the - root -- so the ``[B]`` ring clock is passed rather than the whole bundle. + """``x`` ``[B, N*T, D]``, ``cond``/``ctrl_emb`` ``[B, N, D]`` (per frame) -> + ``(x, v1)``. ``v1`` is layer 0's pre-lerp V, threaded down the stack; + ``cond_idx`` is the ``scheduler_sigmas`` slot for the cond_head cache. + Only ``f_pos`` reaches this far -- RoPE angles are built once at the root. """ s0, b0, g0, s1, b1, g1 = self.cond_head(cond, cond_idx) @@ -131,9 +117,8 @@ class WaypointDiT(nn.Module): dit.retie_cond_proj() # MUST follow to_empty load_weights_into(dit, ...) - The KV ring is NOT part of this module: it is derived state owned by an - engine resource bound after materialization, so no ``to_empty`` or - ``state_dict`` walk can leave it holding garbage. + The KV ring is derived state owned by an engine resource, bound after + materialization -- not part of this module. """ def __init__(self, config: WaypointConfig): @@ -178,7 +163,7 @@ def __init__(self, config: WaypointConfig): # No `bind_resources` here: the driver holds no resource handle, it passes # `commit` down to the layers, which own the only calls into the ring. - # ---- Build-time surface ------------------------------------------------ + # Build-time surface @property def dtype(self) -> torch.dtype: @@ -203,12 +188,9 @@ def retie_cond_proj(self) -> "WaypointDiT": """Alias blocks 1..23's six ``cond_proj`` matrices onto block 0's. Public, and separate from ``__init__``, because ``to_empty(device)`` - destroys the tying: ``Module._apply`` allocates per parameter with no - cross-module memo, so the 24 blocks come out holding 24 independent - copies. Nothing raises; the symptoms are +0.6B resident parameters and - 23 unfilled ``cond_proj`` sets. - - ``bias_in`` is deliberately NOT tied -- it is genuinely per-layer. + destroys the tying (``Module._apply`` has no cross-module memo, so + each block would get 24 independent copies). ``bias_in`` is + deliberately NOT tied -- it is genuinely per-layer. """ ref_proj = self.blocks[0].cond_head.cond_proj for block in self.blocks[1:]: @@ -244,12 +226,9 @@ def materialize_runtime_tables(self, device: torch.device | str) -> "WaypointDiT def _materialize_cond_cache(self, device: torch.device) -> None: """Fold every block's cond_head GEMMs into a per-sigma gather. - The six modulation tensors depend only on the sigma-indexed cond, and - sigma is one of ``scheduler_sigmas``. Each cond is built at M=1, exactly - as ``forward`` receives it, so the cached rows are bit-identical to the - live projection they replace (bf16 GEMV in, bf16 gather out). Runs after - weight load, before ``compile_regions``, on the same footing as the - conditioner LUT. + Each cond is built at M=1, exactly as ``forward`` receives it, so + cached rows are bit-identical to the live projection they replace. + Runs after weight load, before ``compile_regions``. """ sigmas = self._sigma_schedule(device, self.dtype) with torch.no_grad(): @@ -259,7 +238,7 @@ def _materialize_cond_cache(self, device: torch.device) -> None: for block in self.blocks: block.cond_head.build_cache(conds) - # ---- Positions --------------------------------------------------------- + # Positions def _pos_ids(self, frame_pos: Tensor) -> WaypointPosIds: """Build one frame's position streams from the ring clock.""" @@ -278,7 +257,7 @@ def _pos_ids(self, frame_pos: Tensor) -> WaypointPosIds: f_pos=frame_pos, t_pos=t_pos, y_pos=y_pos[None].expand(B, -1), x_pos=x_pos[None].expand(B, -1) ) - # ---- One forward ------------------------------------------------------- + # One forward def forward( self, @@ -294,15 +273,11 @@ def forward( ) -> Tensor: """One pass over one latent frame; returns the rectified-flow velocity. - ``x`` ``[B, N, C, H, W]`` latent (N == 1), ``sigma`` ``[B, N]``, - ``frame_pos`` ``[B]`` int64 ring clock, controller inputs ``[B, N, 2]`` / - ``[B, N, n_buttons]`` / ``[B, N, 1]``. Returns ``[B, N, C, H, W]``. - - ``commit`` says whether this pass keeps its K/V: False for the four - denoise passes, True for the fifth. An argument rather than resource - state -- all five passes sit inside one engine step. ``cond_idx`` is - this pass's ``scheduler_sigmas`` slot; it selects the cached modulation - row and must match ``sigma``. + ``x`` ``[B, N, C, H, W]`` (N == 1), ``sigma`` ``[B, N]``, ``frame_pos`` + ``[B]`` int64 ring clock. Returns ``[B, N, C, H, W]``. ``commit`` says + whether this pass keeps its K/V (False for the four denoise passes, + True for the fifth); ``cond_idx`` selects the cached modulation row + and must match ``sigma``. """ B, N, C, H, W = x.shape ph, pw = self.patch @@ -339,23 +314,22 @@ def forward( commit=commit, cond_idx=cond_idx, ) - # silu sits BETWEEN the adaLN norm and the unpatchify projection - # (reference world_model.py:348-352), not after it. + # silu sits BETWEEN the adaLN norm and the unpatchify projection, + # not after it (matches the reference). h = F.silu(self.out_norm(h, cond)) h = self.unpatchify(h) # [B, N*T, C*ph*pw] h = h.view(B, N, Hp, Wp, C, ph, pw).permute(0, 1, 4, 2, 5, 3, 6) return h.reshape(B, N, C, Hp * ph, Wp * pw) - # ---- The 4+1 driver ---------------------------------------------------- + # The 4+1 driver def _sigma_schedule(self, device: torch.device, dtype: torch.dtype) -> Tensor: - """The sigma table, memoized per (device, dtype) and resolved by the + """The sigma table, memoized per (device, dtype); resolved by the caller *outside* the compiled region -- materializing a tensor from a Python list inside ``fullgraph=True`` is a graph break. - The dtype is load-bearing: the reference builds this table in the serving - dtype and takes ``.diff()`` there, so the Euler step sizes are bf16 - differences of bf16 sigmas. fp32 changes two of the four steps. + The dtype is load-bearing: Euler step sizes are bf16 differences of + bf16 sigmas; fp32 changes two of the four steps. """ key = (device, dtype) schedule = self._sigma_cache.get(key) @@ -377,9 +351,9 @@ def _denoise_pass( """Four frozen Euler steps of the rectified-flow ODE. Returns the settled latent; **does not write the ring**. - ``commit=False`` matters: each step attends to a different noisy version - of the same frame, so none of them may keep its K/V. They still see - themselves, through the unconditional scratch write at the ring tail. + ``commit=False`` matters: each step attends to a different noisy + version of the same frame, so none may keep its K/V (they still see + themselves via the unconditional scratch write at the ring tail). """ # One reused sigma buffer, filled per step; a fresh allocation per step # would defeat cudagraph capture. @@ -444,12 +418,11 @@ def generate_frame( Five forwards: 4 frozen + 1 committing, all sharing one ``frame_pos``. The caller owns the ring clock and advances it once per committed frame. """ - # The .clone() is load-bearing, and must stay OUTSIDE the compiled - # region. Both passes run inside ONE CUDA-graph capture, so the cache - # pass allocates from the graph's private pool -- where the denoise - # pass's output buffer is a free block. The cache pass's first - # allocation can land on it and stomp the latent it is reading, at an - # address baked into the graph and repeated on every replay. + # The .clone() is load-bearing and must stay OUTSIDE the compiled + # region: both passes share one CUDA-graph capture, so without it the + # cache pass's first allocation can land on the denoise pass's freed + # output buffer and stomp the latent it is reading -- baked into the + # graph and repeated on every replay. sigmas = self._sigma_schedule(noise.device, noise.dtype) x0 = self._denoise_pass( noise, frame_pos, sigmas, mouse=mouse, button=button, scroll=scroll diff --git a/mstar/model/waypoint/components/layers.py b/mstar/model/waypoint/components/layers.py index ee7a95c44..8195c9172 100644 --- a/mstar/model/waypoint/components/layers.py +++ b/mstar/model/waypoint/components/layers.py @@ -1,20 +1,9 @@ """Stateless layer primitives for the Waypoint-1.5 DiT. Port of ``world_engine/src/model/nn.py`` plus the three conditioning modules -from ``world_engine/src/model/world_model.py``. Nothing here holds sequence -state and nothing here touches the KV ring. - -Parameter names are checkpoint keys: ``fc1``/``fc2``, ``bias_in``, ``cond_proj`` -and ``mlp`` are the reference's attribute names and the names ``weight_loader`` -remaps onto. mstar's shared ``components.mlp.MLP`` spells its projections -``linear_in``/``linear_out``, so it is not reused. - -``NoiseConditioner`` is an fp32 island: it runs its body under -``autocast(enabled=False)`` on ``.float()`` inputs and publishes -``FP32_MODULE_PATHS`` for ``WaypointDiT.cast_serving_dtypes()`` to re-pin after -the global bf16 cast. Its Fourier frequency table is derived state, held outside -the module tree so ``to_empty(device)`` cannot leave it uninitialized -- no -loader completeness check covers buffers. +from ``world_engine/src/model/world_model.py``. Parameter names track +checkpoint keys (``fc1``/``fc2``, ``bias_in``, ``cond_proj``, ``mlp``), not +mstar's usual ``components.mlp.MLP`` spelling, so this doesn't reuse it. """ import torch @@ -44,14 +33,10 @@ def _bf16_bits(x: torch.Tensor) -> torch.Tensor: class DeviceTableCache: """Per-device replicas of small derived fp32 tables (RoPE frequencies, - Fourier frequencies). + Fourier frequencies), held outside the module tree so they stay out of + ``state_dict``/``to_empty``'s reach and fp32 forever. - A pure function of the config, so neither checkpoint state nor something a - dtype cast should reach. Holding them here rather than as non-persistent - buffers keeps them out of ``state_dict``, out of ``to_empty``'s reach, and - fp32 forever. - - Callers MUST build the CPU tables with an explicit ``device="cpu"``: module + Callers MUST build the CPU tables with an explicit ``device="cpu"``: ``__init__`` runs under ``with torch.device("meta")``, where an ambient-device ``torch.arange`` produces a data-less meta tensor. """ @@ -148,10 +133,8 @@ class NoiseConditioner(nn.Module): fp32 island: ``FP32_MODULE_PATHS`` pins ``self.mlp`` back to fp32 after the serving bf16 cast, and the body runs under ``autocast(enabled=False)`` on a - ``.float()`` sigma. The four denoise sigmas are close together - (1.0, 0.9, 0.75, 0.3) and this embedding is the only thing that separates - them. ``* 1000`` scales [0, 1] into the Fourier basis's rotating range; - ``* 2**0.5`` restores unit variance after the sin/cos concat. + ``.float()`` sigma. ``* 1000`` scales [0, 1] into the Fourier basis's + rotating range; ``* 2**0.5`` restores unit variance after the sin/cos concat. """ def __init__( @@ -232,11 +215,10 @@ def _reference_lut( ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """Build (once per device) the reference's sigma table. - The batch shape is load-bearing: all ``S`` sigmas in one call is an M=S - GEMM, which under ``float32_matmul_precision('high')`` rounds through - TF32 where the served M=1 GEMV stays exact fp32. Built on first use - because it needs loaded weights, so an eager forward must warm it before - ``compile_regions``. + The batch shape is load-bearing: an M=S GEMM (all S sigmas in one + call) rounds through TF32 under ``float32_matmul_precision('high')``, + where the served M=1 GEMV stays exact fp32. Built on first use, so an + eager forward must warm it before ``compile_regions``. """ device = torch.device(device) if device not in self._lut: @@ -275,13 +257,11 @@ def forward(self, mouse: torch.Tensor, button: torch.Tensor, scroll: torch.Tenso class MLPFusion(nn.Module): """Fuses a per-frame conditioning vector into that frame's tokens. - The parameter tree is ``MLP(2*D, D, D)`` over ``cat([x, cond])`` -- one - ``[D, 2D]`` ``mlp.fc1`` for the loader to fill. The compute splits that - matrix instead (``chunk(2, dim=1)``) so ``cond`` broadcasts over the T tokens - of its frame rather than being repeated into a ``[B, N*T, 2D]`` concat. - - That split is compute-time only. Stored ``fc1_x``/``fc1_c`` parameters would - undo transform 7, whose job is to ``cat(dim=1)`` them into ``mlp.fc1``. + Parameter tree is ``MLP(2*D, D, D)`` over ``cat([x, cond])`` -- one + ``[D, 2D]`` ``mlp.fc1`` for the loader to fill. Compute splits that matrix + instead (``chunk(2, dim=1)``) so ``cond`` broadcasts over the T tokens of + its frame, compute-time only -- stored split parameters would undo the + loader's concat into ``mlp.fc1``. """ def __init__(self, config: WaypointConfig): @@ -307,18 +287,9 @@ class CondHead(nn.Module): block's six adaLN modulation tensors (scale/shift/gate for attention, then the same three for the MLP). - The per-layer/shared split is why the checkpoint is 1.86B on disk and 1.28B - resident: ``bias_in`` is genuinely per-layer (24 copies, present only for - ``noise_conditioning == "wan"``), while the six ``cond_proj`` Linears are - physically shared across all 24 blocks -- the DiT aliases blocks 1..23's - ``.weight`` onto block 0's and the loader drops the other 23 sets. - - The aliasing is established by ``WaypointDiT.retie_cond_proj``, which must run - after ``to_empty``; see its docstring for what un-ties it. - - The checkpoint spells this head as two half-heads, ``attn_cond_head`` - (indices 0..2) and ``mlp_cond_head`` (indices 3..5), with a ``bias_in`` on - each; the loader merges them and keeps the mlp one. + ``bias_in`` is genuinely per-layer; the six ``cond_proj`` Linears are + physically shared across all 24 blocks via ``WaypointDiT.retie_cond_proj`` + (must run after ``to_empty`` -- see its docstring). """ n_cond = 6 diff --git a/mstar/model/waypoint/components/rope.py b/mstar/model/waypoint/components/rope.py index 54aec931d..cad1494d5 100644 --- a/mstar/model/waypoint/components/rope.py +++ b/mstar/model/waypoint/components/rope.py @@ -1,15 +1,9 @@ """OrthoRoPE: Waypoint's orthogonal (x, y, t) rotary position embedding. ``d_xy`` and ``d_t`` count rotation *pairs*, not dims: at ``d_head == 64`` x -owns dims 0..15, y 16..31 and t 32..63, and nothing is left unrotated. The -three bands are disjoint, so the axes' phases add independently. - -Both classes are fp32 islands, made so by running the arithmetic in fp32 rather -than by pinning dtypes: they hold no parameter and no buffer, so neither -appears in ``layers.FP32_MODULE_PATHS``, and their frequency tables live in a -``DeviceTableCache`` outside the module tree. - -The cache stores post-RoPE keys, so replayed history is never re-rotated. +owns dims 0..15, y 16..31 and t 32..63, disjoint bands whose phases add +independently. The cache stores post-RoPE keys, so replayed history is never +re-rotated. """ import torch @@ -61,9 +55,8 @@ def forward( """``[B, T]`` integer position grids -> ``(cos, sin)``, each ``[B, 1, T, d_head // 2]`` fp32 with a broadcast head axis. - ``t_pos`` is the RoPE clock, not the ring clock ``f_pos``; they are - equal for this checkpoint (``ts_mult == 1``) and diverge at any other - serving fps. + ``t_pos`` is the RoPE clock, not the ring clock ``f_pos`` -- equal here + (``ts_mult == 1``), but they diverge at other serving fps. """ xy, inv_t = self._tables.get(x_pos.device) diff --git a/mstar/model/waypoint/components/taehv.py b/mstar/model/waypoint/components/taehv.py index d7ee26829..6e061eb24 100644 --- a/mstar/model/waypoint/components/taehv.py +++ b/mstar/model/waypoint/components/taehv.py @@ -38,10 +38,9 @@ def pixel_size_for_latent(latent_height: int, latent_width: int) -> tuple[int, i def load_taehv(ae_uri: str, cache_dir: str | None = None) -> nn.Module: """Load the shared weights module from a resolved local checkpoint. - Hub resolution belongs to ``checkpoint.resolve_taehv_checkpoint`` so only - the required file is downloaded and missing artifacts fail before allocation. - ``cache_dir`` remains accepted for compatibility with existing direct - callers, but is intentionally unused here. + Hub resolution belongs to ``checkpoint.resolve_taehv_checkpoint``, so only + the required file is downloaded. ``cache_dir`` is accepted for + compatibility with existing callers but is unused here. """ del cache_dir try: diff --git a/mstar/model/waypoint/config.py b/mstar/model/waypoint/config.py index be23fc8b5..81d9f72da 100644 --- a/mstar/model/waypoint/config.py +++ b/mstar/model/waypoint/config.py @@ -1,16 +1,10 @@ """Configuration for Waypoint-1.5 (autoregressive video world model). -The values here are facts of the published 720P and 360P checkpoint manifests, -hardcoded so that constructing the model never touches the network. The -reference reads those YAML files through OmegaConf with -``MODEL_CONFIG_DEFAULTS`` merged underneath; this dataclass is the merged result. - -One 1.28B DiT denoises exactly one latent frame per step and the KV cache IS the -world state, so attention geometry is per-layer heterogeneous (18 layers over a -16-frame local window, 6 over a 128-frame window at stride 8; see -``global_layers`` / ``ring_frames``) and every frame costs 5 forwards: 4 -non-committing Euler passes over ``scheduler_sigmas``, then 1 committing pass at -sigma=0 that writes the settled K/V into the ring. +Hardcodes the published 720P/360P checkpoint manifests so constructing the +model never touches the network. Attention geometry is per-layer heterogeneous +(local vs. global window; see ``global_layers`` / ``ring_frames``), and each +frame costs 4 non-committing Euler passes plus 1 committing pass that writes +the settled K/V into the ring. """ import math @@ -26,9 +20,8 @@ WAYPOINT_SCHEDULER_SIGMAS = (1.0, 0.9, 0.75, 0.3, 0.0) -# Geometry is the only manifest-field difference between the two deployment -# configs; the repositories still hold variant-specific weights. Startup -# validation reads this rather than a downloaded checkpoint. +# Only manifest field that differs between the two deployment configs; startup +# validation reads this instead of a downloaded checkpoint. WAYPOINT_VARIANT_GEOMETRY: dict[str, tuple[int, int, int]] = { WAYPOINT_VARIANT_720P: (512, 16, 32), WAYPOINT_VARIANT_360P: (128, 8, 16), @@ -45,7 +38,7 @@ class WaypointConfig: variant: str = WAYPOINT_VARIANT_720P - # ---- Transformer ------------------------------------------------------ + # Transformer n_layers: int = 24 n_heads: int = 32 n_kv_heads: int = 16 # GQA: 2 query heads per kv head @@ -53,36 +46,33 @@ class WaypointConfig: mlp_ratio: int = 4 channels: int = 32 # VAE latent channels - # ---- Token grid ------------------------------------------------------- + # Token grid # height/width are POST-patch token counts, not latent pixels: the latent - # frame is (height*patch[0], width*patch[1]) and patchify collapses it to - # tokens_per_frame tokens. The reference asserts tokens_per_frame == h*w. + # frame is (height*patch[0], width*patch[1]), collapsed to tokens_per_frame. tokens_per_frame: int = 512 height: int = 16 width: int = 32 patch: tuple[int, int] = (2, 2) - # ---- Attention geometry ----------------------------------------------- - # Layer i is "global" iff (i - global_attn_offset % period) % period == 0: - # {3, 7, 11, 15, 19, 23}. Local layers see local_window consecutive frames; - # global layers see global_window frames at stride global_pinned_dilation, - # i.e. 16 retained frames spanning 128 frames of history. + # Attention geometry + # Local layers see local_window consecutive frames; global layers see + # global_window frames at stride global_pinned_dilation (see global_layers + # for which layer indices are global). local_window: int = 16 global_window: int = 128 global_pinned_dilation: int = 8 global_attn_period: int = 4 global_attn_offset: int = -1 - # ---- RoPE ------------------------------------------------------------- + # RoPE # OrthoRoPE splits the head into disjoint axis slices: d_head//8 rotation - # PAIRS to x, d_head//8 to y, d_head//4 to t -- 8+8+16 = 32 pairs for - # d_head=64, so x owns dims 0-15, y 16-31, t 32-63 and nothing is left - # unrotated. The counts are pairs, not dims. + # pairs to x, d_head//8 to y, d_head//4 to t (8+8+16=32 pairs for d_head=64, + # so x owns dims 0-15, y 16-31, t 32-63, nothing left unrotated). rope_impl: str = "ortho" rope_nyquist_frac: float = 0.8 rope_theta: float = 10000.0 - # ---- Conditioning ----------------------------------------------------- + # Conditioning noise_conditioning: str = "wan" # WAN-style CondHead with a shared cond_proj value_residual: bool = True gated_attn: bool = False @@ -91,71 +81,58 @@ class WaypointConfig: ctrl_conditioning: bool = True ctrl_cond_dropout: float = 0.0 - # Controller conditioning is injected on layers where i % period == 0, i.e. - # 8 of 24 layers: {0, 3, 6, 9, 12, 15, 18, 21}. + # Injected on layers where i % period == 0 (8 of 24 layers). ctrl_conditioning_period: int = 3 n_buttons: int = 256 - # ---- Sampling --------------------------------------------------------- + # Sampling # 5 entries -> 4 Euler steps -> one separate committing pass at sigma=0. # Not a per-request knob: the reference's cached sigma/cond tables key on it. scheduler_sigmas: tuple[float, ...] = WAYPOINT_SCHEDULER_SIGMAS - # ---- Temporal --------------------------------------------------------- + # Temporal base_fps: int = 15 # fps the RoPE time axis was trained against inference_fps: int = 60 # raw video fps temporal_compression: int = 4 # raw frames per latent frame (TAEHV) max_frames: int = 512 # training-time rollout ceiling; not enforced here - # ---- VAE -------------------------------------------------------------- + # VAE taehv_ae: bool = True ae_uri: str = "Overworld-Models/taehv1_5" auto_aspect_ratio: bool = True - # ---- Port-local knobs (NOT checkpoint facts) -------------------------- - # The reference allocates global-layer ring storage as - # ``global_window * tokens_per_frame`` tokens but can only address - # ``global_window // global_pinned_dilation`` frame slots, so 7/8 of it is - # permanently unwritten and masked off. Compacting it is bit-exact and saves - # ~1.35 GiB; True restores the reference's allocation for an A/B parity run. + # Port-local knobs (not checkpoint facts) + # Reference over-allocates global-layer ring storage 8x (only + # global_window // global_pinned_dilation frame slots are ever addressed); + # compacting it is bit-exact. True restores the reference's allocation for + # an A/B parity run. full_global_ring: bool = False - # The reference's ``NoCastModule._apply`` casts every tensor it holds to the - # requested dtype and back, so its derived fp32 tables (``rope_angles.xy``/ - # ``inv_t``, ``denoise_step_emb.freq``) are served bf16-quantized — - # parameters recover from ``load_state_dict``, non-persistent buffers never - # do — and its cached sigma LUT rounds the same way through a TF32 batch-5 - # GEMM the served per-sigma GEMV avoids. True reproduces that rounding, which - # is what the live parity gate compares against; False serves exact tables - # and deliberately diverges from the released reference. + # Reference's NoCastModule casts held tensors to dtype and back, so derived + # fp32 tables (rope angles, sigma LUT) are served bf16-quantized. True + # reproduces that rounding (what the parity gate compares against); False + # serves exact tables and diverges from the released reference. reference_compat: bool = True - # torch.compile the two OUTER regions (denoise pass, cache pass), matching - # the reference's two @torch.compile(fullgraph=True, dynamic=False) sites. - # Independent of CUDA graph capture, and of the masked FlexAttention - # primitive, which stays compiled for correctness (engine/resources/attn/flex.py). + # torch.compile the two outer regions (denoise pass, cache pass), matching + # the reference. Independent of CUDA graph capture and of FlexAttention, + # which stays compiled for correctness regardless. compile_dit: bool = True - # Attempt fixed-shape CUDA graph capture for the encoder prime, DiT prime, - # steady DiT rollout, and decoder prime/rollout paths. An optimization: - # disabled declares no buckets, and a failed capture falls back to eager - # submodule forwards. + # Attempt fixed-shape CUDA graph capture for encoder/DiT/decoder prime and + # rollout paths; disabled or failed capture falls back to eager forwards. cuda_graph: bool = True # Also capture the one-time DiT prime/cache pass. Subordinate to - # ``cuda_graph``: disabled leaves the steady rollout graph alone and serves - # prime through the compiled eager forward. On by default since - # PRIME-GRAPH-001 measured lower startup p95 at both resolutions; False is - # that A/B's control arm and stays reachable. + # cuda_graph: disabled serves prime through the compiled eager forward. capture_dit_prime: bool = True - # Rows carried per rollout step; one per resident world sharing the DiT - # forward. Must be <= `resources.kv.num_sessions` (checked at YAML-load time - # in waypoint_model.py, where num_sessions is known). + # Rows carried per rollout step, one per resident world sharing the DiT + # forward. Must be <= resources.kv.num_sessions (checked at YAML-load time). step_batch_size: int = 1 - # Guard rails the ported modules assert against, kept here so a drifting - # checkpoint fails loudly at construction rather than silently mis-serving. + # Guard rails: a drifting checkpoint fails loudly here instead of silently + # mis-serving. _supported_rope_impls: tuple[str, ...] = field( default=("ortho",), repr=False, compare=False ) @@ -319,7 +296,7 @@ def validate_supported_deployment(self) -> None: + "." ) - # ---- Derived ---------------------------------------------------------- + # Derived @property def d_head(self) -> int: @@ -353,12 +330,10 @@ def latent_shape(self) -> tuple[int, int, int]: @property def ts_mult(self) -> int: - """RoPE time-axis stride per latent frame. + """RoPE time-axis stride per latent frame; here equal to 1. - ``base_fps // (inference_fps // temporal_compression)`` = 1 here, so the - RoPE clock ``t_pos`` and the ring-bucketing clock ``f_pos`` are equal. - They stay two separate values through the model: another inference_fps - separates them, and conflating them drifts silently rather than crashing. + Keeps the RoPE clock (``t_pos``) and ring-bucketing clock (``f_pos``) + in step; conflating them drifts silently rather than crashing. """ return self.base_fps // (self.inference_fps // self.temporal_compression) diff --git a/mstar/model/waypoint/submodules.py b/mstar/model/waypoint/submodules.py index 217c995e9..6bd87e315 100644 --- a/mstar/model/waypoint/submodules.py +++ b/mstar/model/waypoint/submodules.py @@ -25,7 +25,7 @@ ROLLOUT_WALK = "rollout" ROLLOUT_LOOP_NAME = "rollout_loop" -# Resource labels this node declares; +# Resource labels this node declares. KV_RESOURCE = "kv" ATTN_RESOURCE = "attn" @@ -39,38 +39,25 @@ def _frame_seed(request_seed: int, frame_pos: int) -> int: """A reproducible seed for one ``(request, frame)`` pair. - Stateless by construction: nothing here reads or advances a generator, so - frame k's noise is a pure function of the request's seed and the ring clock - and a resumed or re-run frame draws the identical tensor. A - ``torch.Generator`` advanced in place would work too, right up - until the state it accumulates — which ``get_state`` does not serialize — - made a resumed rollout diverge from the one it resumed. - - A splitmix64 finalizer rather than ``seed + frame_pos``: the cheap version - makes request seeds 0 and 1 share every frame's noise but the first, which - reads as "the sampler is broken" rather than "the seeds collided". - Re-seeding from ``request_seed`` alone is the other failure — every frame - gets identical noise and the video stops evolving. + Stateless by construction: a pure function of the request's seed and the + frame position, so a resumed or re-run frame draws the identical tensor. + A splitmix64 finalizer rather than ``seed + frame_pos`` avoids nearby + seeds sharing correlated noise. """ z = (request_seed + (frame_pos + 1) * _SPLITMIX_GAMMA) & _U64 z = ((z ^ (z >> 30)) * _SPLITMIX_MIX1) & _U64 z = ((z ^ (z >> 27)) * _SPLITMIX_MIX2) & _U64 z ^= z >> 31 - # manual_seed takes a signed 64-bit; keep it non-negative rather than - # relying on the accepted-range edge. + # manual_seed takes a signed 64-bit; keep it non-negative. return z & (_U64 >> 1) def _rollout_capture_batch_sizes(step_batch_size: int) -> list[int]: """Powers of two up to ``step_batch_size``, with ``step_batch_size`` itself. - Capturing every size ``1..B`` makes startup linear in B: each bucket is a new - static shape, so the DiT re-traces both fullgraph regions and the decode - re-tunes its convs for it. A geometric set makes startup grow with ``log B`` - instead; a step of ``n`` rows replays the smallest bucket ``>= n`` and pads - the tail with dummy rows the ring parks on spare worlds (see - ``RingKVManager.plan``). This is what every other batched model already does - -- the engine default ``CAPTURE_BATCH_SIZES`` is the same geometric set. + A geometric bucket set makes capture startup grow with ``log B`` instead + of linearly in B; a step of ``n`` rows replays the smallest bucket + ``>= n`` and pads the tail with dummy rows (see ``RingKVManager.plan``). """ sizes = [] bs = 1 @@ -84,18 +71,9 @@ def _rollout_capture_batch_sizes(step_batch_size: int) -> list[int]: class _SingleRequestMixin: """Serve one request per step, through the engine's batched entry point. - A copy of the wan22 idiom (``wan22/submodules.py``), deliberately not an - import: the two models share no other code and a cross-model dependency - here would make a wan22 refactor a Waypoint bug. - - The v1 engine always dispatches to ``forward_batched`` — the worker builds - every batch with ``running_batched=True`` — so a submodule that only defines - ``forward`` never runs. - - Only ``WaypointVaeEncoderSubmodule`` uses this now: prime's encode is once - per request, off the steady-state path, so it stays capped at 1. The dit - node's step is batched (see ``WaypointDitSubmodule.max_batch_size`` and - ``forward_batched``); this mixin no longer describes it. + The v1 engine always dispatches to ``forward_batched``, so a submodule + that only defines ``forward`` never runs. Copied from the wan22 idiom + rather than imported, since the two models share no other code. """ def max_batch_size(self, graph_walk: str): @@ -146,23 +124,18 @@ class WaypointDitSubmodule(_FunctionalAeMixin, NodeSubmodule): """The world DiT: one latent frame per engine step, TAEHV-decoded in the same forward. - The decode is fused in rather than left on its own node so that a - same-worker speculative N+1 (``GraphNode.enable_async_scheduling``) can - start while N's frame is still going out: a separate decoder node - would decode frame N only after N+1 was already queued, adding a frame of - latency to every step it was meant to hide. See ``WaypointModel``'s - module docstring for the resulting two-node graph. + The decode is fused in rather than left on its own node so a same-worker + speculative N+1 (``GraphNode.enable_async_scheduling``) can start while + N's frame is still going out. See ``WaypointModel``'s module docstring + for the resulting two-node graph. """ - # ``WaypointConfig.compile_dit`` exclusively controls the two deliberate - # full-graph regions. Do not let the engine independently compile this - # wrapper and fuse across those boundaries when the flag is disabled. + # Do not let the engine independently compile this wrapper and fuse + # across the DiT's own full-graph regions. disable_torch_compile = True - # Waypoint pins an explicit fp32 island list at build time. The model returns - # BF16 as the resource/allocation dtype, while this flag prevents - # EngineManager from blanket-casting the mixed-dtype module and dragging - # those fp32 islands to bf16. + # Waypoint pins an explicit fp32 island list at build time; this flag + # prevents EngineManager from blanket-casting those islands to bf16. disable_autocast = True def __init__(self, dit: WaypointDiT, taehv: torch.nn.Module, config: WaypointConfig): @@ -171,14 +144,11 @@ def __init__(self, dit: WaypointDiT, taehv: torch.nn.Module, config: WaypointCon self.dit = dit self.taehv = taehv self.config = config - # The decoder node this replaces was captured with the engine's - # ``compile=True``, i.e. ``torch.compile(mode="max-autotune-no-cudagraphs", - # fullgraph=False, dynamic=False)`` (``CudaGraphRunner``), and only when - # graphs were on. Same options, same gate: Inductor's mode decides which - # GEMM/conv kernels the decode runs, and a different mode changes the low - # bits of every pixel (the 720p payload SHA in VALIDATION.md is the gate). + # Matches the engine's own capture options (``compile=True`` -> + # ``torch.compile(mode="max-autotune-no-cudagraphs", fullgraph=False, + # dynamic=False)``) so Inductor picks the same kernels bit-for-bit. # Wrapping just the decode call keeps the engine from compiling across - # the denoise/decode boundary (this wrapper stays ``disable_torch_compile``). + # the denoise/decode boundary. self._decode_latent = ( torch.compile( decode_latent, mode="max-autotune-no-cudagraphs", @@ -200,20 +170,13 @@ def bind_node_resources(self, resources: dict) -> None: def max_batch_size(self, graph_walk: str) -> int: """Both walks carry up to ``step_batch_size`` rows: one per resident - world sharing the forward. Prime rows batch too, when several - requests are admitted in the same step. - """ + world sharing the forward.""" return self.config.step_batch_size def can_batch(self, batch, model_inputs) -> bool: - """Batch concurrent rows into one forward, matching every other batched - model. A captured lease replays batched regardless of this flag; it is - the eager fallback (graphs off, or a shape with no captured bucket) that - would otherwise run one forward per request. Rows are independent -- - ``preprocess`` concatenates on the batch dim and every resource is - scoped per row -- so any admitted set (capped by ``max_batch_size``) is - batchable. - """ + """Rows are independent (``preprocess`` concatenates on the batch dim + and every resource is scoped per row), so any admitted set is + batchable.""" del batch, model_inputs return True @@ -232,34 +195,27 @@ def prepare_inputs( the noise to denoise from (rollout) or the latent to prime with. Also the nine decoder histories the fused decode reads and writes. - Runs on the host, outside any captured region — which is the whole - reason the noise is drawn here. A captured region - cannot call the RNG, and ``cuda_graph_runner``'s dummy metadata - hardcodes ``random_seed=0``, so a forward that seeded itself would draw - the capture-time dummy's noise forever. - - ``inputs.get("clock")`` — the rollout node's loop-back to itself — is - never read. Its only job is to sit in ``input_names`` so - ``GraphNode.is_ready_for_speculation`` can propose "dit, next iter" as - a same-node speculation target; the value carries nothing, since - ``frame_pos``/``rollout_step`` already live in host state. + Runs on the host, outside any captured region, which is why the noise + is drawn here rather than in ``forward``: a captured region cannot + call the RNG. + + ``inputs.get("clock")`` is never read; it only has to be present in + ``input_names`` for ``GraphNode.is_ready_for_speculation`` to propose + the next iteration as a same-node speculation target. """ device = self.get_device() dtype = self.dit.dtype - # The clock is per request and lives on the host; frame 0 is the first - # frame of the session, priming included. + # The clock is per request and lives on the host. state = self.request_state(fwd_info.request_id) if graph_walk == ROLLOUT_WALK: requested = int(fwd_info.step_metadata.get("num_steps", 0) or 0) rollout_step = int(state.get("rollout_step", 0)) if requested and rollout_step >= requested: - # Async scheduling (enable_async_scheduling=True) can dispatch - # iteration N+1 before this request's check_stop(N) has - # registered the loop's finish signal. Veto it here, before any - # tensor work: None makes the engine skip the forward, and the - # overshoot never commits a frame into the ring, which has no - # undo. Mirrors Wan22DitSubmodule.prepare_inputs. + # Async scheduling can dispatch iteration N+1 before + # check_stop(N) registers the loop's finish signal. Veto here, + # before any tensor work: None skips the forward, so the + # overshoot never commits a frame into the ring. logger.info( "Waypoint dit: skipping async-overshoot rollout step %d " "(request %s runs %d steps)", @@ -270,8 +226,8 @@ def prepare_inputs( frame_pos = int(state.get("frame_pos", 0)) if graph_walk == PRIME_WALK: - # Prime is an internal cache operation. It has its own idle action - # and must not consume the client's action zero. + # Prime has its own idle action and must not consume the + # client's action zero. mouse, button, scroll = self._idle_controller(device, dtype) else: action_index = int(state.get("rollout_step", 0)) @@ -311,15 +267,11 @@ def preprocess( engine_inputs: ModelInputsFromEngine, inputs: list[NodeInputs], ) -> dict: - """Concatenate every row's tensors along the batch dim. - - Each row already carries a leading 1 (``frame_pos [1]``, - ``noise``/``latent`` ``[1, 1, C, H, W]``, ``mouse``/``button``/``scroll`` - ``[1, 1, *]``, the nine histories ``[1, C, h, w]``), in + """Concatenate every row's tensors along the batch dim, in ``engine_inputs.request_ids`` order (``inputs[i]`` pairs positionally - with row ``i`` — the row-order invariant every batched resource below + with row ``i`` -- the row-order invariant every batched resource below this node relies on). ``len(inputs) == 1`` returns the row unchanged, - no copy, which is what B=1 ran before this method concatenated anything. + no copy. """ if len(inputs) == 1: return inputs[0].tensor_inputs @@ -337,10 +289,9 @@ def _frame_noise( ) -> torch.Tensor: """``[1, 1, C, H, W]`` of fresh noise for this frame. - Drawn straight onto the device. Determinism is now per-GPU: - a CUDA generator reproduces run-to-run on the same arch + torch build, - not against a CPU draw or another arch. Runs in ``prepare_inputs``, outside any - captured region, so this is a normal stream-ordered kernel launch. + Drawn straight onto the device. Determinism is per-GPU: a CUDA + generator reproduces run-to-run on the same arch + torch build, not + against a CPU draw or another arch. """ shape = (1, 1, *self.config.latent_shape) if device.type == "meta": @@ -366,16 +317,9 @@ def _controller_slice( """This frame's ``(mouse, button, scroll)``, each ``[1, 1, *]``. The request carries the whole scripted action stream as ``[1, F, *]`` - and one frame is sliced out per step (actions are materialized at - request time; interactive conditioning needs a - refillable mid-``Loop`` edge that does not exist yet). The stream is a - loop-external input, so the conductor re-injects the same tensor every - iteration and the request's rollout counter advances through it. Prime - owns a separate idle controller and never calls this method. - - The API boundary already validates exact stream length. Raising here is - a backstop against a scheduler/state bug; repeating the final row would - silently map multiple generated latents to one user action. + and one frame is sliced out per step; the conductor re-injects the + same loop-external tensor every iteration while the rollout counter + advances through it. Prime never calls this method. """ widths = {"mouse": 2, "button": self.config.n_buttons, "scroll": 1} out = [] @@ -411,10 +355,9 @@ def _idle_controller( def _zero_histories(self, device: torch.device) -> tuple[torch.Tensor, ...]: """Fresh zero-valued histories, shaped for this config's latent grid. - Takes a device rather than a real latent: nothing here needs a value, - only the shape ``initial_decoder_histories`` derives from one, and this - method runs both from a request's first ``prepare_inputs`` (before the - dit has produced anything this session) and from the capture template. + Takes a device rather than a real latent since only the shape + matters; used both from a request's first ``prepare_inputs`` and + from the capture template. """ seed_latent = torch.zeros( (1, *self.config.latent_shape), dtype=self.ae_dtype, device=device, @@ -422,13 +365,8 @@ def _zero_histories(self, device: torch.device) -> tuple[torch.Tensor, ...]: return initial_decoder_histories(self.taehv, seed_latent) def _history_state(self, request_id: str) -> tuple[torch.Tensor, ...]: - """The request's nine decoder histories, seeded to zero on first use. - - Seeding happens on the first call any request makes — prime, since - prime always runs first — because the real values only exist inside - this fixed-shape state, not off any graph edge: there is no decoder - node's ``prepare_inputs`` to seed them anymore. - """ + """The request's nine decoder histories, seeded to zero on first use + (prime, since prime always runs first).""" state = self.request_state(request_id) histories = tuple( state.get(f"{DECODER_HISTORY_PREFIX}{idx}") for idx in range(9) @@ -447,20 +385,11 @@ def _declared_frames(self, request_ids: list[str]) -> tuple[tuple[str, int], ... """Every request's ring clock, for ``RingKVStep``. Read off the same host ``state["frame_pos"]`` that ``prepare_inputs`` - derives the ``[1]`` device tensor from and that ``postprocess`` - advances — one source, so the number the step declares cannot drift - from the one the forward runs at. Read on the host and never off - ``inputs``: the ``frame_pos`` in there is a device tensor by then, and - an ``.item()`` on it would be a sync per step. - - One pair per rid, and no ``None`` anywhere in the return type. The - singular version this replaces returned ``None`` for any batch it could - not describe with one number, and ``RingKVManager``'s continuity check - — the only thing standing between a stalled clock and a world quietly - rewriting its own history — then did nothing for that step. There must - be no batch shape that switches it off, so there is no shape that - declines to answer: a batch this submodule cannot serve is refused for - being a batch, with every clock in it still named. + derives its device tensor from and ``postprocess`` advances, never off + ``inputs`` (a device tensor by then, so an ``.item()`` would sync per + step). One pair per rid, with no ``None`` in the return type: a batch + this submodule cannot describe is refused rather than silently + skipping ``RingKVManager``'s continuity check. """ return tuple( (rid, int(self.request_state(rid).get("frame_pos", 0))) for rid in request_ids @@ -477,19 +406,12 @@ def declare_step( ) -> SubmoduleStep: """Name both resources so the runner drives their lifecycle. - Neither step carries segments and neither resource reserves anything: - a ring overwrites in place, so there is no span to admit and no page - table to plan. Declaring them anyway is not ceremony — ``admit`` is - where a request is handed one of the node's worlds (and refused when - they are all taken), and a node that declares no step is never admitted - at all. - - The one thing the KV step does carry is the ring clock, and it is a - ``RingKVStep`` rather than a ``KVStep`` so that it can. The clock has to - advance by exactly one per committed frame; declaring it here is what - lets ``RingKVManager.admit`` check that against the frame its ``commit`` - last recorded for that rid, at the one point per frame where both - numbers exist. A desynced clock rewrites history without raising. + Neither step carries segments or reserves anything: a ring overwrites + in place, so there is no span to admit. Declaring them is still + required for ``admit`` to hand a request one of the node's worlds. The + KV step carries the ring clock via ``RingKVStep``, letting + ``RingKVManager.admit`` check it against the frame its last ``commit`` + recorded for that rid. """ del graph_walk, inputs, slot_lease, piecewise_leases, kwargs return SubmoduleStep( @@ -520,10 +442,9 @@ def forward( call, and return the decoded frame, the updated histories, and the clock passthrough. - ``engine_inputs`` is read for nothing at all here, on purpose: under - capture it is the dummy request's forever. The ring and the - attention backend come off ``self.node_resources``, which the DiT and - its 24 attention layers resolved once at ``bind_node_resources`` time. + ``engine_inputs`` is unused here, on purpose: under capture it is the + dummy request's forever. The ring and attention backend come off + ``self.node_resources``, resolved once at ``bind_node_resources``. """ del engine_inputs histories = tuple(kwargs.pop(f"{DECODER_HISTORY_PREFIX}{idx}") for idx in range(9)) @@ -551,9 +472,8 @@ def forward( ) result: NameToTensorList = { "video_output": [frames], - # [B], not []: the loop-back edge to next iteration's "clock" - # input, whose only job is to be a name in ready_signals (see - # prepare_inputs). Harmless on prime too, whose node declares no + # Loop-back edge for next iteration's "clock" input; see + # prepare_inputs. Harmless on prime, whose node declares no # outputs at all. "clock": [frame_pos], } @@ -572,11 +492,8 @@ def forward_batched( """Run the batched ``forward`` once, then split its rows back out. Row ``i`` of every output tensor belongs to - ``engine_inputs.request_ids[i]`` — the same row-order invariant - ``preprocess`` concatenated on the way in. ``video_output[i]`` is - ``[F, H, W, 3]``, exactly what ``postprocess`` consumes today; - ``clock[i:i+1]`` and each history row stay ``[1, ...]``, matching what - ``prepare_inputs`` builds for the next step. + ``engine_inputs.request_ids[i]``, the same row-order invariant + ``preprocess`` used going in. """ out = self.forward(graph_walk, engine_inputs=engine_inputs, **kwargs) # Each value is a one-element list holding the batched tensor; index @@ -598,12 +515,8 @@ def get_cuda_graph_configs( ) -> list[CudaGraphConfig]: """Both walks, as optional captures. - The DiT compiles its reference-shaped denoise/cache regions internally; - compiling this wrapper would fuse across their boundary. Prime is one - cache-only forward per request, but it is on the admission-to-first-frame - path and its inputs are the rollout template with ``noise`` renamed, so - the capture costs one static-input family and reuses the pool the rollout - graph already sized. + Prime's inputs are the rollout template with ``noise`` renamed, so it + reuses the pool the rollout graph already sized. """ del tp_world_size # no sharded nodes; the ring and the mask do not shard if not self.config.cuda_graph: @@ -622,10 +535,8 @@ def template(latent_key: str) -> NodeInputs: "scroll": torch.zeros((1, 1, 1), dtype=dtype, device=device), } # The fused decode's histories, exactly as prepare_inputs builds - # them — this is what makes - # test_prepared_shapes_and_dtypes_are_the_capture_template_exactly - # hold. "clock" is deliberately absent from both: it is never read - # as an input (see prepare_inputs), only produced as an output. + # them. "clock" is absent: it is never read as an input, only + # produced as an output. tensor_inputs.update({ f"{DECODER_HISTORY_PREFIX}{idx}": value for idx, value in enumerate(self._zero_histories(device)) @@ -635,26 +546,17 @@ def template(latent_key: str) -> NodeInputs: input_seq_len=self.config.tokens_per_frame, ) - # Rollout listed first, but what actually orders capture is - # ``prepare_for_capture``'s ``(bs, num_tokens)`` sort, descending: the - # largest bucket (bs=step_batch_size) captures before every smaller one, - # sizing the shared graph pool once at its biggest allocation. Prime and - # rollout share the bucket set and, per bs, the same num_tokens, so each - # prime bucket ties the same-size rollout bucket on that key; the sort is - # stable, so rollout's earlier position here keeps it captured first at - # every tie, and prime reuses freed blocks all the way down. + # Rollout listed first: ``prepare_for_capture``'s ``(bs, num_tokens)`` + # sort is descending and stable, so rollout captures first at every + # size tie with prime and sizes the shared graph pool at its biggest + # allocation; prime reuses freed blocks going down. batch_sizes = _rollout_capture_batch_sizes(self.config.step_batch_size) walks = [(ROLLOUT_WALK, "noise")] if self.config.capture_dit_prime: - # Prime captures the same geometric buckets as rollout. A prime batch - # of several requests admitted in one step replays the smallest - # bucket >= its size and pads the tail on the ring's scratch world - # (see ``RingKVManager.plan``), the same idiom rollout uses -- so the - # first multi-request prime replays a captured graph instead of - # falling to a runtime eager re-trace. ``caps_eager_batch_size`` stays - # the default True: a prime batch is capped at the largest captured - # bucket, which is ``step_batch_size``, exactly where admission caps - # it anyway. + # Prime captures the same geometric buckets as rollout, so a + # multi-request prime batch replays a captured graph instead of + # falling back to a runtime eager re-trace (see + # ``RingKVManager.plan``). walks.append((PRIME_WALK, "latent")) return [ BatchedCudaGraphConfig( @@ -684,14 +586,10 @@ def postprocess( """Advance the ring clock by exactly one committed frame, and copy the fused decode's updated histories into the request's stable tensors. - Both walks commit — ``append_frame`` runs the cache pass alone and - ``generate_frame`` runs it after the four denoise passes — so both - advance. The clock advance is metadata only: no ``.item()``. The - history copy is a device ``copy_`` into the same fixed-address tensors - ``prepare_inputs`` reads back next call — no ``.item()``, no sync. - The clock lives on the host because it has to be readable *before* the - forward that uses it; the device tensor is derived from it in - ``prepare_inputs``, never the other way round. + Both walks commit, so both advance the clock. The clock advance is + metadata only, and the history copy is a device ``copy_`` into the + same fixed-address tensors ``prepare_inputs`` reads back next call -- + neither syncs. """ del inputs, kwargs state = self.request_state(request_id) @@ -717,15 +615,10 @@ def check_stop( ) -> set[str]: """Stop the rollout loop after exactly ``num_steps`` iterations. - While iteration k (0-based) is being postprocessed the loop counter - still reads k, and a stop registered here ends the loop at the end of - that iteration — so N frames means firing at ``k == N - 1``, i.e. - ``k + 1 >= N``. Mirrors ``Wan22DitSubmodule.check_stop``; the ``>=`` - rather than ``==`` keeps it firing if the deferred count ever reads past - N. The rollout node runs with async scheduling ON (see - ``WaypointModel``): an overshoot iteration this signal is too late to - stop is not a wasted forward, it is vetoed instead in - ``prepare_inputs`` before it ever commits garbage into the ring. + Iteration k (0-based) is still being postprocessed when the loop + counter reads k, so N frames means firing at ``k + 1 >= N``. Under + async scheduling an overshoot iteration this signal is too late to + stop is vetoed instead in ``prepare_inputs``. """ del request_id, outputs if request_info.graph_walk != ROLLOUT_WALK: @@ -737,15 +630,9 @@ def check_stop( return set() def cleanup_request(self, request_id: str): - """Drop the request's clock. The ring is NOT reset here. - - Releasing it is the engine's job — ``remove_request`` sweeps every - resource, and ``RingKVManager.remove_request`` is what drops the - ownership claim and zeroes the buffer. Doing it here as well would - double-free a claim the sweep is about to release, and doing it *only* - here would leave the ring held by a request the engine has already - forgotten. - """ + """Drop the request's clock. The ring itself is released by the + engine's ``remove_request`` sweep (``RingKVManager.remove_request``), + not here.""" super().cleanup_request(request_id) @@ -757,8 +644,8 @@ class WaypointVaeEncoderSubmodule(_SingleRequestMixin, _FunctionalAeMixin, NodeS """ disable_torch_compile = True - # The dit node's statement: the dtype layout is settled at load, and an - # engine-level cast would round it underneath the AE. + # Dtype layout is settled at load; an engine-level cast would round it + # underneath the AE. disable_autocast = True def __init__(self, taehv: torch.nn.Module, config: WaypointConfig): diff --git a/mstar/model/waypoint/waypoint_model.py b/mstar/model/waypoint/waypoint_model.py index 17da06859..24b75949f 100644 --- a/mstar/model/waypoint/waypoint_model.py +++ b/mstar/model/waypoint/waypoint_model.py @@ -1,47 +1,33 @@ """WaypointModel: Waypoint-1.5-1B interactive video world model. Architecture (two nodes): - vae_encoder - TAEHV encode. The seed clip (``temporal_compression`` raw + vae_encoder - TAEHV encode: the seed clip (``temporal_compression`` raw frames) into the one latent frame it stands for. dit - the 1.28B world DiT. One engine step is one latent frame: four frozen Euler denoise passes plus one committing cache - pass, then a TAEHV decode of that frame, all inside a single - ``forward``. The decode is fused in (rather than left on its - own node) so a same-worker speculative N+1 can start while - N's frame is still going out to the client — a separate - decoder node would decode frame N only after N+1 was - already queued. + pass, then a TAEHV decode of that frame, fused into a single + ``forward`` so a same-worker speculative N+1 can start while + N's frame is still going out. Graph walks (2): prime - vae_encoder -> dit.append_frame(+decode). Seeds the world and - decoder state from a real frame, advances the ring clock by one, - and emits nothing (the dit's rollout instance is a separate - ``GraphNode`` with ``outputs=[]``). + decoder state from a real frame and emits nothing. rollout - Loop("rollout_loop") over a single dit.generate_frame(+decode) node with a self loop-back ("clock") and ``enable_async_scheduling=True``; one latent frame decoded and emitted per iteration. -**Prime decodes as well as encodes**, and not for symmetry: the functional -decoder's first call spends ``frames_to_trim`` of temporal memory priming -itself, so a prime that only encoded would leave the first *rollout* frame -paying for it and every frame after that shifted against the world it came -from. Silently — drifting video, no exception. The reconstructed seed frames -are internal initialization output and are never sent to the client. - -The world state is the ring KV cache. It is an engine resource -(``get_node_resources`` below), not a model-owned buffer: a per-request ring in -``PerRequestState`` cannot survive CUDA-graph capture, and the resource -lifecycle is the only place that can hand a request one of the node's worlds — -or refuse it when they are all taken. That refusal is a backstop, though — see -``get_worker_graphs``. - -``num_sessions`` and ``max_batch_size`` are separate numbers and stay separate. -``num_sessions`` is how many sessions are *resident* (one ring span each, folded -into the token dimension by ``LayerRingCache``); ``max_batch_size`` is how many -share one *forward step*, set by ``step_batch_size`` (<= ``num_sessions``), so up -to that many resident worlds batch into one step instead of each taking a -separate turn. +Prime also decodes (not just encodes): the functional decoder's first call +spends ``frames_to_trim`` of temporal memory priming itself, so an +encode-only prime would leave every rollout frame quietly drifting from the +reference. The reconstructed seed frames are never sent to the client. + +The world state is the ring KV cache, an engine resource +(``get_node_resources`` below) rather than a model-owned buffer, since a +per-request ring cannot survive CUDA-graph capture. + +``num_sessions`` (resident worlds) and ``max_batch_size`` (rows sharing one +forward step, set by ``step_batch_size <= num_sessions``) are separate knobs. """ import logging @@ -100,10 +86,9 @@ DIT_NODE = "dit" VAE_ENCODER_NODE = "vae_encoder" -# The scripted action stream, one row per frame. Named once because the walk -# declarations, the initial-args validation and the per-walk edge builder all -# have to agree with WaypointDitSubmodule._controller_slice, which reads these -# names off the request's inputs dict. +# The scripted action stream, one row per frame. Named once since walk +# declarations, arg validation and the edge builder must all agree with +# WaypointDitSubmodule._controller_slice on these names. _CONTROLLER_STREAMS = ("mouse", "button", "scroll") _VARIANT_FACTORIES = { @@ -118,7 +103,7 @@ class WaypointModel(Model): PRIME_WALK = PRIME_WALK ROLLOUT_WALK = ROLLOUT_WALK - # Loop name — referenced by ``WaypointDitSubmodule.check_stop`` through + # Referenced by ``WaypointDitSubmodule.check_stop`` via # ``request_info.dynamic_loop_iter_counts[...]``. ROLLOUT_LOOP_NAME = ROLLOUT_LOOP_NAME @@ -144,10 +129,9 @@ def __init__( f"Waypoint variant {variant!r} is not implemented; known variants " f"are {sorted(_VARIANT_FACTORIES)}." ) - # The generic registry passes None so the selected variant chooses its - # published repository. Any explicit source remains authoritative, - # including a local path or a deliberately cross-variant Hub ID; the - # manifest preflight will reject it if its geometry is incompatible. + # None picks the variant's published repository; an explicit source + # (local path or cross-variant Hub ID) is authoritative and the + # manifest preflight rejects it if its geometry is incompatible. self.model_path_hf = ( WAYPOINT_VARIANT_HF_REPOS[variant] if model_path_hf is None @@ -167,17 +151,15 @@ def __init__( } self.config: WaypointConfig = replace(config, **overrides) # ``build_waypoint_dit`` never downloads: the caller resolves the local - # directory holding model.safetensors. ``cache_dir`` is where a - # snapshot lands if one is fetched out of band; the two are not the same - # thing and conflating them is how a half-downloaded repo gets loaded. + # directory holding model.safetensors. ``cache_dir`` is only where an + # out-of-band snapshot lands. self.checkpoint_dir = checkpoint_dir or self.model_path_hf - # The TAEHV weights ship in their own repo, so ``ae_path`` is a local - # override of ``config.ae_uri`` and not of ``checkpoint_dir``. + # TAEHV weights ship in their own repo, so ``ae_path`` overrides + # ``config.ae_uri``, not ``checkpoint_dir``. self.ae_uri = ae_path or self.config.ae_uri self.checkpoint_revision = checkpoint_revision self.ae_revision = ae_revision - # Dummy mode: get_submodule returns None for every node, so engines and - # tests run without weights, GPU or network. + # Dummy mode: get_submodule returns None for every node. self.skip_weight_loading = skip_weight_loading self._submodule_cache: dict[str, NodeSubmodule | None] = {} @@ -191,23 +173,15 @@ def __init__( def get_node_resources(self) -> list[NodeResourceSpec]: """The ring KV cache holding the world, and the FlexAttention over it. - The ring geometry is copied out of ``WaypointConfig`` layer by layer - rather than summarized: Waypoint's layers are not alike (the six global - layers hold 16 frames spaced 8 apart, the other eighteen hold 16 - consecutive frames), and ``ring_frames`` and ``ring_buckets`` are two - separate questions. They happen to agree on every layer of the - *compacted* 720P ring, which is exactly what makes deriving one from the - other look safe — flip ``full_global_ring`` back to the reference's - sizing and a global layer is 128 frames indexed by 16 buckets. - - FlexAttention rather than the paged FlashInfer default: a paged - kernel changes the accumulation order over the KV blocks, and a - mask-or-position bug in this model does not raise, it produces - plausible, smoothly drifting video. Bit-exactness against the reference - is the only check there is, so the kernel has to be the reference's. - - The attention spec names the cache by key, so ``depends_on`` orders the - two: the ring is built first and the attention resource resolves it. + Ring geometry is copied out of ``WaypointConfig`` layer by layer since + Waypoint's layers are not alike (six global layers hold 16 frames + spaced 8 apart, the other eighteen hold 16 consecutive frames), so + ``ring_frames`` and ``ring_buckets`` cannot be derived from one another. + + FlexAttention rather than the paged FlashInfer default: a paged kernel + changes the KV accumulation order, and a mask/position bug here would + not raise, it would just drift the video. Bit-exactness against the + reference is the only check there is. """ ring_config = RingKVConfig( num_layers=self.config.n_layers, @@ -223,26 +197,15 @@ def get_node_resources(self) -> list[NodeResourceSpec]: ) for i in range(self.config.n_layers) ), - # How many sessions this node holds resident. One by default - # because a world is ~816 MiB of ring at 720P and a model has no - # business assuming the box; a deployment raises it under - # ``resources: {kv: {num_sessions: N}}`` and raises - # ``max_concurrent_requests`` with it (see ``get_worker_graphs``). - # Not the step batch — that is ``max_batch_size``, set by - # ``step_batch_size`` (<= num_sessions). + # Resident session count, not the step batch (that's + # ``max_batch_size``/``step_batch_size``). Default 1; a deployment + # raises it via ``resources: {kv: {num_sessions: N}}`` along with + # ``max_concurrent_requests`` (see ``get_worker_graphs``). num_sessions=1, ) - # Logged, not merely allocated: this declaration is worth ~816 MiB per - # world and nothing downstream prints it. The report carries the - # counterfactual under the other ``full_global_ring`` setting, which is - # the number you want *before* the engine commits to one of them. - # - # `ring_config.num_sessions` is the DECLARED count, which is what this - # line can honestly report: `EngineManager.build` calls - # `apply_yaml_overrides` on the specs after this hook returns, so a - # deployment's `num_sessions` has not landed yet. The reported total - # scales linearly with it -- the world dim is folded into the token - # axis -- so N worlds is N times the number below. + # Logged since nothing downstream prints this ~816 MiB/world cost. + # Uses the DECLARED num_sessions: `apply_yaml_overrides` may still + # raise it after this hook returns. logger.info( "%s", describe_ring_memory(self.config, num_sessions=ring_config.num_sessions) ) @@ -267,9 +230,9 @@ def _emit_frames(self) -> GraphEdge: ) def get_graph_walk_graphs(self) -> dict[str, GraphSection]: - # -- prime: encode the seed clip, commit it to the world, decode it to - # -- initialize the fused decoder's state, and discard the - # -- reconstructed seed frames (outputs=[] below). + # prime: encode the seed clip, commit it to the world, decode it to + # init the fused decoder's state, and discard the reconstructed seed + # frames (outputs=[] below). prime = Sequential([ GraphNode( name=VAE_ENCODER_NODE, @@ -283,25 +246,15 @@ def get_graph_walk_graphs(self) -> dict[str, GraphSection]: ), ]) - # -- rollout: one frame decoded and emitted per iteration, from a - # -- single node. - # -- - # -- The "clock" self loop-back is what makes the dit a same-node - # -- speculation target (GraphNode.is_ready_for_speculation / - # -- WorkerGraphIO.ingest_for_speculation): it carries no information - # -- of its own (the submodule ignores its value; frame_pos and - # -- rollout_step live in host state), it only has to be a name the - # -- loop re-injects every iteration. enable_async_scheduling=True lets - # -- the worker build iteration N+1 while N is still on the GPU; the - # -- overshoot that can result — N+1 dispatched before check_stop(N) - # -- registers the loop's finish signal — is vetoed host-side in - # -- WaypointDitSubmodule.prepare_inputs before it can commit a frame - # -- into the ring, which has no undo. - # -- - # -- There is no separate decoder node left to order against: the - # -- decode happens inside the same forward as the denoise passes, so - # -- a frame is decoded and emitted exactly once, when its dit - # -- iteration runs. + # rollout: one frame decoded and emitted per iteration, from a single + # node. The "clock" self loop-back carries no data (frame_pos and + # rollout_step live in host state); it only makes the dit a same-node + # speculation target (GraphNode.is_ready_for_speculation). Under + # enable_async_scheduling=True the worker can build iteration N+1 + # before check_stop(N) registers the loop's finish signal; that + # overshoot is vetoed host-side in + # WaypointDitSubmodule.prepare_inputs before it can commit into the + # ring, which has no undo. rollout = Loop( name=ROLLOUT_LOOP_NAME, section=GraphNode( @@ -313,9 +266,8 @@ def get_graph_walk_graphs(self) -> dict[str, GraphSection]: ], enable_async_scheduling=True, ), - # Ceiling only; the request's num_steps stops the loop early via - # WaypointDitSubmodule.check_stop (and the overshoot veto above - # catches what check_stop is too late for under async scheduling). + # Ceiling only; num_steps stops the loop early via + # WaypointDitSubmodule.check_stop. max_iters=self.config.max_frames, outputs=[], accumulated_outputs=[], @@ -327,38 +279,22 @@ def get_worker_graphs(self, config_path: str) -> list[WorkerGraph]: """Refuse to build unless the deployment caps concurrency at the number of worlds the ring was sized for. - This is the **primary** gate on the world pool, not a nicety. A world is - claimed at ``admit``, i.e. once a batch has already been formed — by - then the only thing ``RingKVManager.admit`` can do about a request the - pool cannot hold is fail it terminally. What actually keeps arrivals - inside the pool is the conductor's FIFO admit queue, and that queue only - exists when ``max_concurrent_requests`` is set: the conductor drains - ``waiting_queue`` while ``len(self.requests) < max_concurrent_requests``, - so an unset value admits everything on arrival and every request past - the Nth dies at admit. Unset therefore stays fatal, exactly as before — - what changed is that the accepted value is a range rather than the - single number 1. - - ``max_batch_size`` does NOT cover this, independent of its value. It - caps how many requests share one *step* (``step_batch_size``, <= - ``num_sessions``); worlds beyond that batch still alternate steps — each - holds its own world and the BlockMask keeps them apart — but it says - nothing about how many may exist, which is the thing the pool bounds. - - A limit *below* ``num_sessions`` is legal and only wasteful: it allocates - rings (~816 MiB each at 720P) for worlds no request can ever reach, so - it is warned about rather than refused. - - Checked here because this hook is the only place a model sees the key: - the Conductor reads it out of the YAML itself and + The primary gate on the world pool: a world is claimed at ``admit``, + and the conductor's FIFO admit queue (only active when + ``max_concurrent_requests`` is set) is what keeps arrivals inside the + pool. ``max_batch_size``/``step_batch_size`` caps a step's row count, + not how many worlds may exist, so it does not substitute for this + check. + + Checked here because this hook is the only place a model sees the + key: the Conductor reads it from the YAML and ``api_server/entrypoint.py`` forwards only ``model_kwargs`` to ``Model.__init__``. """ with open(config_path, "r") as f: config = yaml.safe_load(f) or {} - # The same block ``EngineManager.build`` feeds to - # ``apply_yaml_overrides``, read here for the same key, so the gate and - # the allocation cannot disagree about how many worlds exist. + # Same block ``EngineManager.build`` feeds to + # ``apply_yaml_overrides``, so the gate and the allocation agree. overrides = (config.get("resources") or {}).get(KV_RESOURCE) or {} num_sessions = overrides.get("num_sessions", 1) if ( @@ -421,15 +357,12 @@ def process_prompt( clip, if any) as the edges the walk's first nodes consume. ``prompt`` is ignored: this checkpoint has ``prompt_conditioning=None`` - and carries no cross-attention, so a text prompt would have nowhere to - go. Raising on one would break clients that send an empty default. - - ``actions`` is a list of per-step dicts, exactly one per generated latent: - ``{"mouse": [dx, dy], "buttons": [id, ...], "scroll": s}``. The button - field is a set of pressed ids that gets one-hot scattered into - ``n_buttons`` columns, matching the reference's ``CtrlInput``; an - omitted field is that control's neutral value. Prime uses a separate - internal idle action and never consumes action zero. + and carries no cross-attention. + + ``actions`` is a list of per-step dicts, one per generated latent: + ``{"mouse": [dx, dy], "buttons": [id, ...], "scroll": s}``; buttons are + one-hot scattered into ``n_buttons`` columns and an omitted field is + that control's neutral value. Prime never consumes action zero. """ del prompt, input_modalities if output_modalities != ["video_frame"]: @@ -526,13 +459,9 @@ def process_prompt( def _seed_clip(self, image: torch.Tensor) -> torch.Tensor: """The prime walk's ``[temporal_compression, H, W, 3]`` uint8 clip. - One frame is repeated to fill it, the reference's way of seeding from a - still (``gen_sample.py``'s ``seed_frame_x4``). Fewer or more frames is - refused: the streaming encoder emits one latent per ``t_downscale``, so a - short clip buffers silently and a long one encodes twice. - - Resolution is not checked, only the aspect ratio -- the AE resizes 16:9 - input onto its own grid and decodes back to the variant's resolution. + A single frame is repeated to fill it (seeding from a still). Fewer or + more frames is refused since the streaming encoder emits one latent + per ``t_downscale``. Only the aspect ratio is checked, not resolution. """ frames = image if image.dim() == 4 else image.unsqueeze(0) if frames.dtype != torch.uint8 or frames.shape[-1] != 3: @@ -566,8 +495,8 @@ def postprocess( self, output: torch.Tensor, modality: str, request_kwargs: dict | None = None, ) -> bytes: """One step's frames as raw uint8 RGB bytes, - ``[temporal_compression, H, W, 3]`` in C order. No container: the emit is - per engine step, and a per-step mp4 is a fragment nothing plays.""" + ``[temporal_compression, H, W, 3]`` in C order. No container: the emit + is per engine step.""" del request_kwargs if modality != "video_frame": raise ValueError(f"Unsupported modality for Waypoint: {modality!r}") @@ -629,10 +558,9 @@ def get_initial_forward_pass_args( "Waypoint requires exactly one output modality, 'video_frame'; " f"got {output_modalities!r}." ) - # A backstop, not the primary guard: process_prompt already rejected a - # malformed request on the data worker, where a ValueError becomes a - # 400. A raise here runs at the conductor, whose main loop swallows it, - # so the client would hang instead. + # Backstop: process_prompt already rejects a malformed request (400). + # A raise here runs at the conductor, whose main loop swallows it, so + # the client would hang instead. for name in _CONTROLLER_STREAMS: if not input_signals.get(name): raise ValueError( @@ -736,11 +664,10 @@ def get_partition_forward_pass_args( def get_autocast_dtype(self): """Allocate BF16 resources while every node disables autocast. - The dtype layout is settled at build time — ``cast_serving_dtypes()`` - takes the meta module to bf16 and pins the fp32 islands back, and the AE - is built bf16 whole. The submodules' ``disable_autocast`` flags preserve - that mixed layout. Returning BF16 here is also the explicit ring-KV - allocation dtype; returning None silently allocated the ring in fp32. + The dtype layout is settled at build time (``cast_serving_dtypes()`` + pins fp32 islands after casting the rest to bf16); this is also the + ring-KV allocation dtype, and returning None would silently allocate + the ring in fp32. """ return torch.bfloat16 @@ -748,9 +675,8 @@ def get_submodule( self, node_name: str, device: str = "cpu", tp_group=None, autocast_dtype: torch.dtype | None = None, sp_group=None, ) -> torch.nn.Module | None: - # ``autocast_dtype``/``tp_group``/``sp_group`` exist for interface - # parity: weights load in the checkpoint's own dtypes and neither the - # ring nor the BlockMask shards yet. + # autocast_dtype/tp_group/sp_group exist for interface parity only: + # weights load in the checkpoint's own dtypes and nothing shards yet. if node_name in self._submodule_cache: return self._submodule_cache[node_name] submodule = self._create_submodule(node_name, device) @@ -783,11 +709,9 @@ def _create_submodule( def _resolve_checkpoints(self) -> None: """Resolve both artifacts and validate the DiT manifest before allocation. - The first requested node triggers this once. Resolving both together is - intentional: startup must fail on a missing AE before a multi-gigabyte - DiT has been allocated, even when the engine happens to ask for the DiT - node first. Tensor completeness and the pinned TAEHV runtime architecture - are then validated while loading, before request admission. + Triggered once by the first requested node. Resolved together so + startup fails on a missing AE before the multi-gigabyte DiT has been + allocated, even if the DiT node is requested first. """ if self._checkpoints_resolved: return @@ -802,9 +726,8 @@ def _resolve_checkpoints(self) -> None: resolve_waypoint_checkpoint, ) - # Dependency validation is part of the same preflight as both weight - # sources. In particular it must precede the DiT resolver: a missing or - # empty TAEHV install should not trigger a multi-GiB download first. + # Must precede the DiT resolver: a missing TAEHV install shouldn't + # trigger a multi-GiB download first. require_taehv_runtime() self.checkpoint_dir = str(resolve_waypoint_checkpoint( self.checkpoint_dir, @@ -821,9 +744,7 @@ def _resolve_checkpoints(self) -> None: def _taehv_weights(self, device: str) -> torch.nn.Module: """The AE weights, built once and shared by both VAE nodes: they differ - only in streaming state, which is per request and not held here. bf16 at - build is the reference's serving dtype, and with ``disable_autocast`` on - both nodes it is the dtype the convs actually run in.""" + only in per-request streaming state, not held here.""" if self._taehv is None: from mstar.model.waypoint.components.taehv import load_taehv diff --git a/mstar/model/waypoint/weight_loader.py b/mstar/model/waypoint/weight_loader.py index f10ea3a02..cab9a0375 100644 --- a/mstar/model/waypoint/weight_loader.py +++ b/mstar/model/waypoint/weight_loader.py @@ -1,54 +1,13 @@ -"""Weight loading for the native Waypoint-1.5-1B DiT (mstar loader pattern). - -``build_waypoint_dit`` constructs ``WaypointDiT`` on meta, casts it to the serving -dtypes while still on meta (so ``to_empty`` allocates storage in the final -dtypes), moves it to the device, **re-ties ``cond_proj``**, then streams the -safetensors shards through ``load_weights_into``. - -``retie_cond_proj()`` must follow ``to_empty``; skipping it leaves 23 blocks of -``cond_proj`` this loader never fills. - -The key map. Thirteen transforms sit between the checkpoint's 393 keys and this -module's 174 parameters: - -=== ============================================================== =========== -T0 ``transformer.blocks.{i}.`` -> ``blocks.{i}.`` prefix -T1 ``unpatchify.weight`` ``[D,C,ph,pw]`` -> ``[C*ph*pw,D]`` reshape -T2 ``unpatchify.bias`` ``[C]`` -> ``[C*ph*pw]`` reshape -T3 ``dit_mlp.{leaf}`` -> ``mlp.{leaf}``, 5-name allowlist rename -T4 ``{attn,mlp}_cond_head.bias_in`` -> ``cond_head.bias_in`` merge -T5 ``attn_cond_head.cond_proj.{j}`` -> ``cond_head..{j}`` rename -T6 ``mlp_cond_head.cond_proj.{j}`` -> ``cond_head..{j+3}`` rename -T7 ``ctrl_mlpfusion.fc1_{x,c}`` -> ``ctrl_mlpfusion.mlp.fc1`` fuse dim 1 -T8 ``ctrl_mlpfusion.fc2`` -> ``ctrl_mlpfusion.mlp.fc2`` rename -T9 ``cond_head.cond_proj.*`` for blocks 1..23 drop -T10 ``ctrl_cfg.null_emb`` drop -T11 ``attn.{q,k,v}_proj`` -> ``attn.qkv_proj`` fuse dim 0 -T12 any ``.cond_heads.`` key (note the plural) drop -=== ============================================================== =========== - -T0's source spelling is the reference's two-level ``WorldModel``/``WorldDiT`` -split, which ``components/dit.py`` collapses. Both spellings are accepted, as -are the canonical post-transform spellings the reference's own -``pop``/``setdefault`` transforms tolerate; which the shipped file uses could not -be established statically. - -Three things mstar's machinery does not give you. The fusions need a fan-in and -``name_remapper`` is ``str -> str|None``, so both go through ``StackedParamRule`` -with a ``_SliceShardLoader`` attached after ``to_empty``. ``load_weights_into`` -returns *target* names and q/k/v share one target, so the remapper tallies -``(target, shard_id)`` pairs. T1/T2 have no reshape hook, so they ride an adapter -over the shard iterator, which is also where the transcribed config facts -(``n_kv_heads``, ``patch``) are checked against the shapes on disk. - -Completeness is a hard contract: a key that reaches no parameter, a parameter no -key reached, a fused shard that never arrived, or two keys writing one slot all -raise. Explicitly dropped keys (T9/T10/T12) are expected and silent. - -The unconditional drops (T10/T12) run **before** the shape validation, because a -``.cond_heads.`` key ending in ``.k_proj.weight`` is a T12 drop and not a GQA -violation. T9's per-block ``cond_proj`` drop is not in that pre-filter: those -keys are what ``_CondProjTieCheck`` compares. +"""Weight loading for the native Waypoint DiT (mstar loader pattern). + +``build_waypoint_dit`` builds ``WaypointDiT`` on meta, casts it to the +serving dtypes, materializes it on the device, re-ties ``cond_proj`` (must +follow ``to_empty``, which un-aliases it), then streams the safetensors +shards through ``load_weights_into`` via ``remap_checkpoint_key``. The +fused q/k/v and MLP-fusion projections route through +``WAYPOINT_STACKED_PARAMS`` instead, since a name remapper can't express a +fan-in. Loading is a completeness contract: any unexpected, missing, or +duplicate-claimed key raises. """ from __future__ import annotations @@ -77,20 +36,16 @@ ] -# Which block's cond_proj set is the physical one. Not a knob: it records that -# WaypointDiT.retie_cond_proj hardcodes self.blocks[0], so any other value turns -# the six kept keys into unexpected-key failures and leaves the six real -# parameters unloaded. _assert_cond_proj_tied checks the tree still agrees. +# Which block's cond_proj is physical; must match WaypointDiT.retie_cond_proj's +# hardcoded blocks[0], or loading fails. _assert_cond_proj_tied checks this. COND_PROJ_SOURCE_BLOCK = 0 # Slot ids for the two port-side fusions. Order defines the layout. QKV_SHARD_IDS: tuple[str, ...] = ("q", "k", "v") CTRL_FC1_SHARD_IDS: tuple[str, ...] = ("x", "c") -# Fused-shard routing. The leading dots matter: without them ".v_proj" would -# also match inside "qkv_proj". Not LLAMA_STACKED_PARAMS, whose extra -# gate_proj/up_proj rules would be live substring matchers for parameters -# Waypoint does not have. +# Fused-shard routing; leading dots matter so ".v_proj" doesn't also match +# inside "qkv_proj". WAYPOINT_STACKED_PARAMS: list[StackedParamRule] = [ StackedParamRule(".qkv_proj", ".q_proj", "q"), StackedParamRule(".qkv_proj", ".k_proj", "k"), @@ -102,17 +57,17 @@ # The port's block prefix (``components/dit.py`` collapses WorldModel/WorldDiT). MODEL_BLOCK_PREFIX = "blocks." -# ``transformer.`` optional: T0. Accepts the reference's two-level spelling and -# the collapsed one. +# ``transformer.`` prefix is optional: accepts both the reference's two-level +# spelling and the collapsed one. _BLOCK_RE = re.compile(r"^(?:transformer\.)?blocks\.(\d+)\.(.+)$") # Legacy half-heads. j is range(3) on both sides; a j >= 3 is malformed and is # left unmapped so it surfaces as unexpected. _LEGACY_COND_PROJ_RE = re.compile(r"^(attn|mlp)_cond_head\.cond_proj\.(\d+)\.weight$") _COND_PROJ_RE = re.compile(r"^cond_head\.cond_proj\.(\d+)\.weight$") -# T3 is an allowlist, not a dit_mlp.* wildcard, so a future dit_mlp.* key -# surfaces instead of being absorbed. expert_*/router do not exist under -# moe=False; they are listed because the reference renames them. +# Allowlist, not a dit_mlp.* wildcard, so an unrecognized dit_mlp.* key +# surfaces as unexpected. expert_*/router don't exist under moe=False but +# are listed since the reference renames them. _DIT_MLP_LEAVES: tuple[str, ...] = ( "fc1.weight", "fc2.weight", @@ -121,56 +76,47 @@ "router.weight", ) -# T10. CFG.forward is a training-time dropout with no call site in the -# reference's WorldModel.forward, so the port has no tensor for it. Dropped -# explicitly rather than left unmatched, so unexpected-key accounting keeps -# no hole. +# CFG dropout is training-time only; the port has no tensor for it, so this +# key is dropped explicitly rather than left to surface as unexpected. _DROPPED_TOP_LEVEL_KEYS = frozenset({"ctrl_cfg.null_emb"}) -# T12. Substring filter, unconditional, note the plural. +# Substring filter, unconditional; note the plural ("cond_heads"). _COND_HEADS_FRAGMENT = ".cond_heads." # Half the CondHead slots come from each legacy half-head. _COND_PROJ_PER_HEAD = CondHead.n_cond // 2 -# T4 precedence over the three spellings that share cond_head.bias_in, lowest -# first, highest wins — the reference's pop/setdefault outcome: mlp beats attn, -# canonical beats both. The attn spelling is a FALLBACK, not a drop; which of the -# two the shipped file carries is unestablished, and dropping it unconditionally -# would leave 24 unloaded bias_in on an attn-only file. -# -# A rank rather than last-write-wins because this loader streams: the three -# spellings can arrive in any shard order and the resident weight must not depend -# on it. Arbitration lives in build_waypoint_dit, which sees every key. +# Precedence order for the three spellings sharing cond_head.bias_in, lowest +# first: mlp beats attn, canonical beats both (matches the reference's +# pop/setdefault outcome). Rank rather than last-write-wins because shards can +# arrive in any order; arbitration lives in build_waypoint_dit. _BIAS_IN_SPELLINGS: tuple[str, ...] = ( "attn_cond_head.bias_in", "mlp_cond_head.bias_in", "cond_head.bias_in", ) -# Reference init for the value-residual scalar, used by the no-checkpoint path. +# Reference init for the value-residual scalar (no-checkpoint path only). _V_LAMB_INIT = 0.5 -# Weight init std for the no-checkpoint path. Not a checkpoint fact; it only has -# to produce finite, sanely scaled activations for a structural smoke test. +# Weight init std for the no-checkpoint structural smoke-test path. _STRUCTURAL_INIT_STD = 0.02 # -------------------------------------------------------------------------- -# Name remapping (T0, T3-T6, T8-T12) +# Name remapping # -------------------------------------------------------------------------- def _remap_block_suffix(suffix: str, layer_idx: int) -> str | None: """Map one per-block checkpoint suffix to its parameter suffix, or ``None`` to drop it. ``suffix`` excludes the ``blocks.{i}.`` prefix.""" - # T4: both legacy spellings map to the one target; rank in build_waypoint_dit + # Both legacy spellings map to the one target; rank in build_waypoint_dit # decides which of the three writes. if suffix in ("attn_cond_head.bias_in", "mlp_cond_head.bias_in"): suffix = "cond_head.bias_in" - # T5/T6: identity index map for the attn head, +3 for the mlp head. Slots - # 0-2 drive the attention sublayer and 3-5 the MLP sublayer; swapping them - # is silent and numerically catastrophic. + # Identity index map for the attn head, +3 for the mlp head. Slots 0-2 + # drive attention, 3-5 drive MLP; swapping them is silent and catastrophic. legacy = _LEGACY_COND_PROJ_RE.match(suffix) if legacy is not None: head, j = legacy.group(1), int(legacy.group(2)) @@ -178,40 +124,39 @@ def _remap_block_suffix(suffix: str, layer_idx: int) -> str | None: slot = j if head == "attn" else j + _COND_PROJ_PER_HEAD suffix = f"cond_head.cond_proj.{slot}.weight" - # T3. for leaf in _DIT_MLP_LEAVES: if suffix == "dit_mlp." + leaf: suffix = "mlp." + leaf break - # T8. Guarded on fc2 alone, separately from T7's both-halves guard. + # Guarded on fc2 alone, separately from the fc1 fusion's both-halves guard. if suffix == "ctrl_mlpfusion.fc2.weight": suffix = "ctrl_mlpfusion.mlp.fc2.weight" - # T9. Runs last, on the post-T5/T6 name, so it catches both the legacy + # Runs last, on the post-remap name, so it catches both the legacy # half-head spellings and an already-canonical one. if _COND_PROJ_RE.match(suffix) is not None and layer_idx != COND_PROJ_SOURCE_BLOCK: return None - # T7 (fc1_x/fc1_c) and T11 (q/k/v_proj) are left alone: fan-ins, which a - # remapper cannot express. WAYPOINT_STACKED_PARAMS routes them. + # fc1_x/fc1_c and q/k/v_proj are left alone: fan-ins a remapper can't + # express. WAYPOINT_STACKED_PARAMS routes them instead. return suffix def _is_unconditionally_dropped(name: str) -> bool: - """T12 and T10 — the drops that depend on nothing but the key. + """Drops that depend on nothing but the key name. One predicate, two call sites, so the shard adapter's pre-filter and the - remapper cannot drift apart. T9 is not here even though it is also a drop: - those 138 keys are what ``_CondProjTieCheck`` has to see, so they survive the - stream filter and are dropped later, in the remapper. + remapper can't drift apart. The per-block cond_proj drop is not here: + those keys must survive to reach ``_CondProjTieCheck`` and are dropped + later, in the remapper. """ return _COND_HEADS_FRAGMENT in name or name in _DROPPED_TOP_LEVEL_KEYS def _bias_in_rank(name: str) -> int | None: - """T4 precedence rank of a per-block ``bias_in`` key, or ``None`` if the key - is not one. Higher wins; see ``_BIAS_IN_SPELLINGS``.""" + """Precedence rank of a per-block ``bias_in`` key, or ``None`` if not one. + Higher wins; see ``_BIAS_IN_SPELLINGS``.""" block = _BLOCK_RE.match(name) if block is None: return None @@ -222,49 +167,43 @@ def _bias_in_rank(name: str) -> int | None: def remap_checkpoint_key(name: str) -> str | None: - """Map one Waypoint checkpoint key to the native parameter path, or return - ``None`` for a key that is intentionally dropped (T9/T10/T12). + """Map one Waypoint checkpoint key to the native parameter path, or + ``None`` for a key that's intentionally dropped. - Pure function of the key — no model, no config; a key mapped to a name that - is not a parameter is the caller's problem. - - Not injective in exactly one place: all three T4 ``bias_in`` spellings map to - ``blocks.{i}.cond_head.bias_in``, and picking a winner needs the whole key - set, so ``build_waypoint_dit`` settles it. Everywhere else a second key - resolving to a claimed slot is a hard error. + Pure function of the key — no model, no config. Not injective in one + place: all three ``bias_in`` spellings map to + ``blocks.{i}.cond_head.bias_in``, and ``build_waypoint_dit`` picks the + winner since that needs the whole key set. """ - if _is_unconditionally_dropped(name): # T10/T12 + if _is_unconditionally_dropped(name): return None block = _BLOCK_RE.match(name) if block is None: - # Top-level keys are identity: denoise_step_emb.mlp.{fc1,fc2}.weight, - # ctrl_emb.mlp.{fc1,fc2}.weight, patchify.weight, unpatchify.{weight,bias}, - # out_norm.fc.weight. + # Top-level keys (patchify, unpatchify, denoise_step_emb, ctrl_emb, + # out_norm) pass through unchanged. return name layer_idx, suffix = int(block.group(1)), block.group(2) mapped = _remap_block_suffix(suffix, layer_idx) if mapped is None: return None - # T0. return f"{MODEL_BLOCK_PREFIX}{layer_idx}.{mapped}" # -------------------------------------------------------------------------- -# Tensor transforms and shape validation (T1, T2) over the shard stream +# Tensor transforms and shape validation over the shard stream # -------------------------------------------------------------------------- def _unpatchify_weight(tensor: torch.Tensor, config: WaypointConfig, key: str) -> torch.Tensor: - """T1. ``[D, C, ph, pw]`` conv kernel -> ``[C*ph*pw, D]`` Linear weight. - - ``permute(1, 2, 3, 0)`` then ``reshape``: the Linear's output feature axis is - ordered ``(c, ph, pw)`` with pw fastest, because ``WaypointDiT.forward`` - unpacks it as ``view(B, N, Hp, Wp, C, ph, pw)``. Dropping the permute, or - transposing ph/pw inside it, keeps the shape and silently reprojects every - output sub-pixel. ``reshape``, not ``view`` — the permuted tensor is not - contiguous. + """``[D, C, ph, pw]`` conv kernel -> ``[C*ph*pw, D]`` Linear weight. + + ``permute(1, 2, 3, 0)`` then ``reshape``: the output feature axis must be + ordered ``(c, ph, pw)`` with pw fastest to match ``WaypointDiT.forward``'s + ``view(B, N, Hp, Wp, C, ph, pw)``. Dropping the permute, or swapping + ph/pw, keeps the shape but silently reprojects every output sub-pixel. + ``reshape``, not ``view``: the permuted tensor isn't contiguous. """ ph, pw = config.patch if tensor.ndim == 4: @@ -283,7 +222,7 @@ def _unpatchify_weight(tensor: torch.Tensor, config: WaypointConfig, key: str) - return tensor.permute(1, 2, 3, 0).reshape(-1, d_model) # Already canonical (a checkpoint written post-transform). The reference's - # own ndim == 4 guard makes T1 idempotent the same way. + # own ndim == 4 guard makes this idempotent the same way. expected = (config.channels * ph * pw, config.d_model) if tuple(tensor.shape) != expected: raise RuntimeError( @@ -295,13 +234,11 @@ def _unpatchify_weight(tensor: torch.Tensor, config: WaypointConfig, key: str) - def _unpatchify_bias(tensor: torch.Tensor, config: WaypointConfig, key: str) -> torch.Tensor: - """T2. One learned bias per latent channel, repeated across the patch. + """One learned bias per latent channel, repeated across the patch. - ``[C] -> [C,1,1] -> expand(-1, ph, pw) -> reshape(-1)``. The expand target is - ``(C, ph, pw)`` so the flatten agrees with T1's row ordering; a - ``repeat(ph*pw)`` produces the same ``[128]`` shape with the bias on the - wrong sub-pixel, which integrates into a slow colour drift over a rollout - rather than failing. + ``[C] -> [C,1,1] -> expand(-1, ph, pw) -> reshape(-1)``, matching the + weight's row ordering. ``repeat(ph*pw)`` produces the same shape but puts + the bias on the wrong sub-pixel — a silent numerical bug, not a crash. """ ph, pw = config.patch if tensor.numel() == config.channels: @@ -343,10 +280,9 @@ def _check_attn_proj( ) -> None: """Validate an unfused q/k/v projection against the config's head counts. - This is the check that pins ``n_kv_heads``. It is worth doing explicitly even - though ``_SliceShardLoader`` would also catch it: a wrong ``n_kv_heads`` - reshapes attention without erroring anywhere downstream, and "shard 'k' shape - mismatch" does not tell a reader which config field to go look at. + Pins ``n_kv_heads`` explicitly: a wrong value silently reshapes attention + without erroring downstream, and a shard-shape-mismatch error wouldn't + say which config field to check. """ if tensor.ndim != 2 or tuple(tensor.shape) != (rows, config.d_model): raise RuntimeError( @@ -382,17 +318,13 @@ def _cond_proj_slot(key: str) -> tuple[int, int] | None: class _CondProjTieCheck: - """Confirms the checkpoint's 24 stored ``cond_proj`` sets agree before 23 of - them are dropped. - - The port keeps ``COND_PROJ_SOURCE_BLOCK``'s copy; the reference keeps block - 23's, because it loads all 24 into one shared tensor and the last write wins. - Nothing in the file or in either loader enforces that they match, so a - fine-tune that broke the tie would silently make the two serve different - video. Compares the matrices in full; whichever block arrives first for a - slot becomes that slot's reference, so the result does not depend on shard - ordering. Retains 6 x ``[2048, 2048]`` fp32 (96 MiB) for the load; - ``verify_cond_proj_tie=False`` is the escape hatch. + """Confirms the checkpoint's per-block ``cond_proj`` copies agree before + all but ``COND_PROJ_SOURCE_BLOCK``'s are dropped. + + Nothing else enforces this, so a fine-tune that broke the tie would + silently make the port and reference serve different video. Compares + matrices in full, independent of shard order; ``verify_cond_proj_tie=False`` + skips it. """ def __init__(self) -> None: @@ -404,8 +336,7 @@ def observe(self, key: str, tensor: torch.Tensor) -> None: if slot_info is None or tensor.ndim != 2: return block_idx, slot = slot_info - # fp32 on CPU: uniform, lossless from the checkpoint's bf16, and it keeps - # the retained set off the serving device. + # fp32 on CPU: uniform, lossless from bf16, and off the serving device. seen = tensor.detach().to(device="cpu", dtype=torch.float32) known = self._reference.get(slot) if known is None: @@ -421,22 +352,19 @@ def _adapt_checkpoint_stream( config: WaypointConfig, tie_check: _CondProjTieCheck | None, ) -> Iterator[tuple[str, torch.Tensor]]: - """Apply the two reshaping transforms and validate the transcribed config + """Apply the reshape transforms and validate the transcribed config facts, in one streaming pass over the shards. - T1/T2 live here rather than in the remapper because mstar has no reshape - hook: ``name_remapper`` sees names only, and ``weight_loader`` is per-target. - - Drops run before validation: the checks fire on key *suffixes*, so a key - T12 drops unconditionally can still end in ``.k_proj.weight`` - (``…blocks.0.cond_heads.0.k_proj.weight`` does) and a dropped key's shape is - not this model's business. + Lives here rather than in the remapper because mstar has no reshape + hook: ``name_remapper`` sees names only. Drops run before validation, + since a dropped key's shape (e.g. ``…cond_heads.0.k_proj.weight``) is not + this model's business. """ q_rows = config.n_heads * config.d_head kv_rows = config.n_kv_heads * config.d_head for key, tensor in weights: - if _is_unconditionally_dropped(key): # T10/T12, before anything reads a shape + if _is_unconditionally_dropped(key): # before anything reads a shape continue # Order matters: "unpatchify.weight".endswith("patchify.weight") is True, @@ -458,7 +386,7 @@ def _adapt_checkpoint_stream( # -------------------------------------------------------------------------- -# Fused-parameter shard loaders (T7, T11) +# Fused-parameter shard loaders # -------------------------------------------------------------------------- @@ -466,10 +394,9 @@ class _SliceShardLoader: """``param.weight_loader`` for a port-side fused parameter. Copies one checkpoint shard into its slice of the fused tensor. Neither - fusion target is a ``FusedColumnLinear`` — both are plain ``nn.Linear`` — so - neither carries a loader, and ``default_weight_loader`` asserts - ``loaded_shard_id is None``. ``ctrl_mlpfusion`` also concatenates along dim 1 - (the input-feature axis), which ``FusedColumnLinear`` does not do. + fusion target is a ``FusedColumnLinear`` (both are plain ``nn.Linear``), + so neither carries a loader and ``default_weight_loader`` won't accept a + shard id. """ def __init__(self, param_name: str, dim: int, layout: dict[str, tuple[int, int]]): @@ -484,9 +411,8 @@ def __call__( loaded_shard_id: str | int | None = None, ) -> None: if loaded_shard_id is None: - # A checkpoint already written in the fused spelling. Only reachable - # for ctrl_mlpfusion.mlp.fc1, whose fused form the reference also - # accepts; harmless and symmetric for qkv_proj. + # Checkpoint already in fused form; only reachable for + # ctrl_mlpfusion.mlp.fc1, which the reference also accepts fused. if tuple(param.data.shape) != tuple(loaded_weight.shape): raise RuntimeError( f"{self.param_name}: pre-fused checkpoint tensor has shape " @@ -517,8 +443,8 @@ def _attach_shard_loaders( """Install ``_SliceShardLoader`` on every fused parameter and return ``{param_name: required shard ids}``. - MUST run after ``to_empty(device)``: that reallocates the Parameter objects - and drops attached attributes along with the meta storage — the same reason + Must run after ``to_empty(device)``, which reallocates the Parameter + objects and drops attached attributes — the same reason ``FusedColumnLinear`` re-attaches its loaders from ``_apply``. """ q_rows = config.n_heads * config.d_head @@ -528,8 +454,8 @@ def _attach_shard_loaders( fused: dict[str, tuple[str, ...]] = {} for name, param in dit.named_parameters(): if name.endswith(".attn.qkv_proj.weight"): - # cat([q, k, v], dim=0), matching the split in components/attention.py. - # GQA makes the shards unequal, so a q/k swap raises but a k/v swap + # cat([q, k, v], dim=0), matching components/attention.py's split. + # GQA makes shards unequal: a q/k swap raises, but a k/v swap # loads cleanly and produces meaningless attention. dim, layout = 0, { "q": (0, q_rows), @@ -538,10 +464,9 @@ def _attach_shard_loaders( } expected_shape = (q_rows + 2 * kv_rows, d_model) elif name.endswith(".ctrl_mlpfusion.mlp.fc1.weight"): - # cat([fc1_x, fc1_c], dim=1) — x first. layers.MLPFusion splits it - # back with chunk(2, dim=1), whose low columns are the token half; - # the reverse order keeps the [2048, 4096] shape and applies - # controller conditioning to tokens and vice versa. + # cat([fc1_x, fc1_c], dim=1), x first — layers.MLPFusion splits it + # back with chunk(2, dim=1); swapping the order swaps which half + # gets token vs. controller conditioning. dim, layout = 1, {"x": (0, d_model), "c": (d_model, d_model)} expected_shape = (d_model, 2 * d_model) else: @@ -567,11 +492,9 @@ def _attach_shard_loaders( def parameter_census(dit: WaypointDiT) -> tuple[int, int, int]: """``(deduplicated tensors, deduplicated numel, raw state_dict numel)``. - ``named_parameters()`` deduplicates aliased Parameters; ``state_dict()`` does - not, so the gap between the two counts is exactly the tied ``cond_proj``. For - the 720P checkpoint this is ``(174, 1_281_958_040, 1_860_771_992)``; counting - off the checkpoint gives 2,048 more in each, because that carries - ``ctrl_cfg.null_emb`` ``[1, 1, 2048]``, which the port drops (T10). + ``named_parameters()`` deduplicates aliased Parameters; ``state_dict()`` + does not, so the gap between the two counts is exactly the tied + ``cond_proj``. """ params = dict(dit.named_parameters()) return ( @@ -584,12 +507,10 @@ def parameter_census(dit: WaypointDiT) -> tuple[int, int, int]: def _assert_cond_proj_tied(dit: WaypointDiT, config: WaypointConfig) -> None: """Fail if ``retie_cond_proj()`` did not take. - ``named_parameters()`` deduplicates aliased Parameters, so a correctly tied - model reports 6 ``cond_proj`` tensors and an un-tied one reports - ``6 * n_layers``. An un-tied model is otherwise silent: numerically correct, - 0.6B parameters heavier, and visible only as 138 unloaded parameters. The - count catches "never tied"; the resident/stored numel gap catches a partial - tie. Both bounds come from ``config``, not the 720P numbers. + An un-tied model is otherwise silent: numerically correct, just heavier + and missing loaded parameters. The tensor count catches "never tied"; + the numel gap between ``state_dict()`` and ``named_parameters()`` catches + a partial tie. """ tied = [name for name, _ in dit.named_parameters() if ".cond_head.cond_proj." in name] if len(tied) != CondHead.n_cond: @@ -599,9 +520,8 @@ def _assert_cond_proj_tied(dit: WaypointDiT, config: WaypointConfig) -> None: f"{config.n_layers} blocks). to_empty(device) un-ties them and " "retie_cond_proj() must be called after it, not before." ) - # COND_PROJ_SOURCE_BLOCK records which block retie_cond_proj aliases the - # others onto; if the two disagree, T9 drops the six keys the module keeps - # and keeps the six it drops. + # If COND_PROJ_SOURCE_BLOCK disagrees with which block retie_cond_proj + # aliases onto, the loader drops the keys the module keeps and vice versa. owner_prefix = f"{MODEL_BLOCK_PREFIX}{COND_PROJ_SOURCE_BLOCK}.cond_head.cond_proj." if not all(name.startswith(owner_prefix) for name in tied): raise RuntimeError( @@ -639,13 +559,11 @@ def _assert_expected_layout(dit: WaypointDiT) -> None: def _initialize_structurally(dit: WaypointDiT, seed: int = 0) -> None: """Fill a ``to_empty``-materialized model with finite values. - ``to_empty`` allocates *uninitialized* storage, which routinely contains NaN - and Inf bit patterns, so a "no checkpoint" model is unusable even for a shape - smoke test until something writes every parameter. This is not the - reference's init; only ``v_lamb`` = 0.5 and the zeroed 1-D tensors match it. - - Iterating ``named_parameters()`` writes each tied ``cond_proj`` once, which is - what makes the aliasing survive. + ``to_empty`` allocates uninitialized storage (often NaN/Inf), so this + isn't usable even for a shape smoke test until every parameter is + written. Not the reference's init — only ``v_lamb`` and the zeroed 1-D + tensors match it. Iterating ``named_parameters()`` (not ``state_dict()``) + keeps the tied ``cond_proj`` aliased. """ generators: dict[torch.device, torch.Generator] = {} with torch.no_grad(): @@ -679,14 +597,12 @@ def build_waypoint_dit( """Meta-build, materialize on ``device``, and load the checkpoint into a ready-to-serve (eval-mode) native Waypoint DiT. - ``config``'s transcribed ``n_kv_heads`` and ``patch`` are validated against - the shapes in the file. ``checkpoint_dir`` is a local directory the caller - has already resolved; nothing here downloads. ``skip_weight_loading`` builds - a randomly initialized structure for shape and plumbing work. + ``checkpoint_dir`` is a local directory the caller has already resolved; + nothing here downloads. ``skip_weight_loading`` builds a randomly + initialized structure for shape/plumbing work instead. - Raises ``RuntimeError`` on any completeness failure: an unexpected checkpoint - key, an unloaded parameter, a fused shard that never arrived, two keys - claiming one slot, or divergent ``cond_proj`` copies. + Raises ``RuntimeError`` on any completeness failure: an unexpected, + missing, or duplicate-claimed key, or divergent ``cond_proj`` copies. """ if not skip_weight_loading and checkpoint_dir is None: raise ValueError( @@ -718,26 +634,25 @@ def build_waypoint_dit( unexpected: list[str] = [] conflicts: list[str] = [] - # (target, shard_id) -> the checkpoint key that claimed it. load_weights_into's - # returned set holds target names only, so q, k and v collapse to one entry - # and a k_proj missing from every layer would still satisfy - # `set(params) - loaded`. + # (target, shard_id) -> claiming checkpoint key. load_weights_into's + # returned set holds target names only, so q/k/v collapse to one entry; + # `set(params) - loaded` alone wouldn't catch a missing k_proj. arrivals: dict[tuple[str, str | int | None], str] = {} - # T4's arbitrated collision: {target: (rank, winning checkpoint key)}. + # Arbitrated bias_in collision: {target: (rank, winning checkpoint key)}. bias_in_claims: dict[str, tuple[int, str]] = {} def remap(name: str) -> str | None: mapped = remap_checkpoint_key(name) if mapped is None: - return None # T9/T10/T12: expected, silent, not an unexpected key. + return None # Expected drop, not an unexpected key. target, shard_id = _apply_stacked(mapped, WAYPOINT_STACKED_PARAMS) if target not in params: unexpected.append(name) return None - # T4 is the one collision resolved rather than refused: three spellings - # legitimately share cond_head.bias_in and the reference picks between - # them (mlp > attn, canonical > both). Rank, not arrival order. + # The one collision resolved rather than refused: three spellings + # legitimately share cond_head.bias_in; rank decides the winner, not + # arrival order (see _BIAS_IN_SPELLINGS). rank = _bias_in_rank(name) if rank is not None: held = bias_in_claims.get(target) @@ -759,11 +674,11 @@ def remap(name: str) -> str | None: slot = target if shard_id is None else f"{target}[{shard_id}]" conflicts.append(f"{claimed_by} and {name} -> {slot}") - # The same "one slot, two writers" failure across the fused/unfused - # spellings, which (target, shard_id) cannot see: a pre-fused tensor - # claims (target, None) and a split shard claims (target, "q"), so they - # never collide, both write, and the survivor is a shard-order-dependent - # mix. Either spelling alone is fine; both together are refused. + # Same "two writers, one slot" failure across fused/unfused spellings, + # invisible to (target, shard_id): a pre-fused tensor claims (target, + # None) while a split shard claims (target, "q"), so they never + # collide directly. Either spelling alone is fine; both together are + # refused. if target in fused_shards: rival_slots = ( [(target, s) for s in fused_shards[target]] @@ -796,9 +711,9 @@ def remap(name: str) -> str | None: missing_shards = sorted( f"{name}[{shard_id}]" for name, shard_ids in fused_shards.items() - # A pre-fused tensor satisfies every shard of its target at once, and - # `remap` refuses a file carrying both spellings, so (name, None) here - # means the pre-fused tensor was the only writer. + # A pre-fused tensor satisfies every shard at once; `remap` refuses a + # file with both spellings, so (name, None) means it was the only + # writer. if (name, None) not in arrivals for shard_id in shard_ids if (name, shard_id) not in arrivals From 2451c644a064bb2598e91ec6f34fa958680c66b9 Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Tue, 22 Sep 2026 23:21:23 +0000 Subject: [PATCH 18/29] benchmark: add --protocol binary/ndjson A/B knob benchmark_streaming.py gains --protocol {binary,ndjson} (default binary) to force the base64 NDJSON path for A/B runs against the raw-bytes streaming format; wires enable_nvtx and prefer_binary through MStarClient and records stream_protocol in the report. --- test/waypoint/benchmark_streaming.py | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/test/waypoint/benchmark_streaming.py b/test/waypoint/benchmark_streaming.py index f6f30a17f..dd9201f60 100644 --- a/test/waypoint/benchmark_streaming.py +++ b/test/waypoint/benchmark_streaming.py @@ -763,6 +763,12 @@ def _build_parser() -> argparse.ArgumentParser: ) parser.add_argument("--log-level", default="INFO") parser.add_argument("--enable-nvtx", action="store_true") + parser.add_argument( + "--protocol", + choices=("binary", "ndjson"), + default="binary", + help="streaming wire format; 'ndjson' forces the base64 path for A/B runs", + ) return parser @@ -877,7 +883,12 @@ def _run_benchmark(args: argparse.Namespace) -> dict: concurrent_result: dict | None = None startup_started = time.perf_counter() try: - client = MStarClient(url, timeout=args.request_timeout) + client = MStarClient( + url, + timeout=args.request_timeout, + enable_nvtx=args.enable_nvtx, + prefer_binary=args.protocol == "binary", + ) rollout._wait_for_health(client, proc, args.startup_timeout) startup_seconds = time.perf_counter() - startup_started print(f"server ready after {startup_seconds:.1f}s") @@ -1025,6 +1036,7 @@ def _run_benchmark(args: argparse.Namespace) -> dict: "geometry": {"width": variant.width, "height": variant.height, "fps": 60.0}, "configuration": { "weight_source": weight_source, + "stream_protocol": args.protocol, "steps": args.steps, "warmup_steps": args.warmup_steps, "rng_seed": args.seed, From 330cfedf5c7e4847c835e9ce18963f7e27c5878b Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Wed, 23 Sep 2026 00:01:38 +0000 Subject: [PATCH 19/29] test/waypoint: prune comments/docstrings to peer-port style Comment/docstring-only cleanup across the Waypoint test suite, oracle/serving scripts, and config, matching peer model ports (wan22, pi05, orpheus). No code changed - verified by comparing docstring-stripped ASTs against HEAD. - test/modular/test_waypoint_*.py: trim narrative docstrings, drop dead-doc references left by the model-folder prune (T*/PARAM_TREE/S* labels), and remove verbatim duplicates between module and per-test docstrings; keep WHY-only comments, the F1-F4 regression catalog, and cross-referenced banners. - test/waypoint/record_oracle.py: 81-line module docstring -> 38, keeping the artifact layout, numerics/reproducibility contract, and usage. - test/waypoint/{serve_rollout,benchmark_streaming}.py: tighten docstrings; keep the isolation-gate invariants and protocol rationale. - configs/waypoint.yaml: essays -> terse invariants (values unchanged). - .gitignore: revert the branch's additions (.claude_scratch/, docs/waypoint/, WAYPOINT_PROGRESS.md) to keep main's .gitignore unpolluted. --- .gitignore | 5 - configs/waypoint.yaml | 20 +- test/modular/test_waypoint_checkpoint.py | 7 +- test/modular/test_waypoint_components.py | 206 ++++------ test/modular/test_waypoint_dit.py | 145 +++---- test/modular/test_waypoint_gpu.py | 181 ++++----- .../test_waypoint_pixel_equivalence.py | 6 +- .../test_waypoint_reference_equivalence.py | 7 +- test/modular/test_waypoint_shell.py | 383 +++++++----------- test/modular/test_waypoint_weight_loader.py | 132 +++--- test/waypoint/benchmark_streaming.py | 24 +- test/waypoint/record_oracle.py | 113 ++---- test/waypoint/serve_rollout.py | 64 ++- 13 files changed, 522 insertions(+), 771 deletions(-) diff --git a/.gitignore b/.gitignore index 9b10c77fd..5b5cc3a84 100644 --- a/.gitignore +++ b/.gitignore @@ -29,8 +29,3 @@ mstar/worker/ASYNC_REDESIGN.md # local AI-assistant context (kept local, not published — cf. vllm-omni) CLAUDE.md AGENTS.md -.claude_scratch/ - -# Waypoint port records (plans, validation logs, baseline numbers) stay local. -docs/waypoint/ -WAYPOINT_PROGRESS.md diff --git a/configs/waypoint.yaml b/configs/waypoint.yaml index a5a01d3ad..687e692f6 100644 --- a/configs/waypoint.yaml +++ b/configs/waypoint.yaml @@ -1,10 +1,5 @@ model: "waypoint" -# Checkpoint selection, numerical behavior, and execution mode are explicit inputs. -# Local `checkpoint_dir` / `ae_path` overrides can be added here without changing -# the registry entry. The exact-table path is experimental and must be opted into -# with `reference_compat: false`. `compile_dit` and `cuda_graph` are independent; -# failed CUDA graph capture falls back to the selected DiT execution mode. model_kwargs: variant: "waypoint-1.5-1b-720p" reference_compat: true @@ -14,16 +9,11 @@ model_kwargs: # Rows per rollout step; must be <= resources.kv.num_sessions below. step_batch_size: 1 -# The world state is a ring, not a paged KV cache, so nothing here is sized in -# sequence positions; max_seq_len only satisfies the conductor's config check -# and matches one frame's token count. +# The world state is a ring (not a paged KV cache); max_seq_len only satisfies the +# conductor's config check and matches one frame's token count. max_seq_len: 512 -# The primary bound on the world pool. `WaypointModel.get_worker_graphs` -# refuses to build unless this is a positive int no larger than -# `resources.kv.num_sessions`: a world is claimed at admit, by which point a -# request the pool cannot hold can only be failed terminally, and the -# conductor's FIFO admit queue that prevents that exists only when this is set. +# Primary bound on the world pool; must be a positive int <= resources.kv.num_sessions. max_concurrent_requests: 1 resources: @@ -32,9 +22,7 @@ resources: # max_concurrent_requests; the two are checked against each other. num_sessions: 1 -# 1.28B DiT plus a ~7M-parameter TAEHV, all on rank 0. One group: the rollout -# Loop body is a single dit node (the TAEHV decode is fused into its forward), -# so there is no separate decoder to put a worker boundary in front of. +# Single-GPU colocated: vae_encoder + dit on rank 0 (TAEHV decode is fused into the dit forward). node_groups: - node_names: [vae_encoder, dit] ranks: [0] diff --git a/test/modular/test_waypoint_checkpoint.py b/test/modular/test_waypoint_checkpoint.py index 04da75bf1..3fe5a428e 100644 --- a/test/modular/test_waypoint_checkpoint.py +++ b/test/modular/test_waypoint_checkpoint.py @@ -351,10 +351,9 @@ def load_taehv(ae_uri, cache_dir=None): model.get_submodule(DIT_NODE) - # The dit's fused decode needs TAEHV weights too now (Step 2): allocation - # still runs first, and the AE load trails it rather than gating it, since - # ``_taehv_weights`` is evaluated as part of the same return statement, - # after ``build_waypoint_dit`` already ran. + # AE load trails allocation rather than gating it: ``_taehv_weights`` is + # evaluated as part of the same return statement, after + # ``build_waypoint_dit`` already ran. assert [entry[0] for entry in calls] == [ "runtime", "waypoint", "taehv", "allocate", "load_taehv", ] diff --git a/test/modular/test_waypoint_components.py b/test/modular/test_waypoint_components.py index 9f2ff13b2..0dc3e7e0c 100644 --- a/test/modular/test_waypoint_components.py +++ b/test/modular/test_waypoint_components.py @@ -1,28 +1,24 @@ """Component-level contract tests for the Waypoint-1.5 port: the ring geometry its config implies, OrthoRoPE and the small layers. -The bar is the reference implementation at ``world_engine/src/``, not "the code -does what the code does". Every failure mode this file guards against is silent: -a wrong ring slot, a re-derived bucket count and a permuted controller concat all -produce plausible video and raise nothing. So the assertions are exact wherever -the reference is exact (bitwise for the compaction A/B, for the RoPE angle -tables, for the ring bytes after a frozen pass) and never widened to accommodate -the implementation. - -**The ring and the kernel are engine resources now**, imported below from -``mstar.engine.resources``. Their own contracts -- ownership, the capture -lifecycle, the ``visible`` aliasing hazard, the eager-``flex_attention`` trap -- -are pinned next door in ``test_ring_kv_resource.py`` and -``test_flex_attention_resource.py``. What stays here is the half those files -cannot see: that *Waypoint's config* produces the geometry the checkpoint was -trained against, and that the ring arithmetic matches the reference's tables. - -CPU-only and checkpoint-free by construction. Numeric work runs on a reduced but -structurally identical config (4 layers / 128 tokens per frame / d_head 32, one -global layer at stride 8); the real 720P config is used only where the assertion -is about geometry rather than activations. ``torch.compile(flex_attention)`` -works on CPU in torch 2.9, which is what makes the compaction A/B runnable -without a GPU -- it costs a few seconds of inductor time on first use. +Reference is ``world_engine/src/``. Failure modes here are silent (wrong ring +slot, re-derived bucket count, permuted controller concat all produce plausible +video and raise nothing), so assertions are exact wherever the reference is +exact (bitwise for the compaction A/B, the RoPE angle tables, the ring bytes +after a frozen pass). + +The ring and kernel are engine resources (``mstar.engine.resources``); their own +contracts -- ownership, capture lifecycle, the ``visible`` aliasing hazard, the +eager-``flex_attention`` trap -- are pinned in ``test_ring_kv_resource.py`` and +``test_flex_attention_resource.py``. This file covers that *Waypoint's config* +produces the geometry the checkpoint was trained against, and that the ring +arithmetic matches the reference's tables. + +CPU-only and checkpoint-free. Numeric work runs on a reduced but structurally +identical config (4 layers / 128 tokens per frame / d_head 32, one global layer +at stride 8); the real 720P config is used only for geometry assertions. +``torch.compile(flex_attention)`` works on CPU in torch 2.9, which is what makes +the compaction A/B runnable without a GPU. """ import dataclasses @@ -72,14 +68,14 @@ def reduced_config(**overrides) -> WaypointConfig: - """A 4-layer / 128-token-per-frame Waypoint whose *structure* is the 720P - model's: one global layer (index 3) at stride 8, one non-global period, GQA - live at 2 query heads over 1 KV head, controller fusion on ``i % 3 == 0``. + """A 4-layer / 128-token-per-frame Waypoint whose *structure* matches 720P: + one global layer (index 3) at stride 8, GQA at 2 query heads over 1 KV head, + controller fusion on ``i % 3 == 0``. Only the sizes shrink. ``global_window // global_pinned_dilation == 4`` addressable slots against a 32-frame reference allocation keeps the 8x - over-allocation the compaction deviation is about, while letting a test wrap the - global ring in 32 frames instead of 128. + over-allocation the compaction deviation is about, while letting a test wrap + the global ring in 32 frames instead of 128. """ base = { "n_layers": 4, @@ -122,14 +118,11 @@ def visible_blocks(block_mask) -> set[int]: def ring_kv_spec(config: WaypointConfig, *, num_sessions: int = 1) -> KVSpec: - """The ``RingKVConfig`` a ``WaypointConfig``'s geometry implies -- which is - the bridge under test in most of section 3. - - Hand-rolled because the model does not declare its specs yet; when - ``WaypointModel`` grows a ``get_node_resources`` this becomes a call to it. - Every field is read off the config rather than restated, so a geometry - change cannot leave these tests measuring a ring the model no longer asks - for. + """The ``RingKVConfig`` a ``WaypointConfig``'s geometry implies. + + Hand-rolled because the model does not declare its specs yet. Every field is + read off the config rather than restated, so a geometry change cannot leave + these tests measuring a ring the model no longer asks for. """ return KVSpec( resource_key="kv", @@ -186,27 +179,16 @@ def waypoint_resources(config: WaypointConfig): # --------------------------------------------------------------------------- # 1. The BlockMask's shape -# -# The trap itself -- eager `flex_attention` ignoring a no-op `mask_mod` and -# blending every unwritten ring slot in -- is pinned at its owner in -# `test_flex_attention_resource.py`, along with the compiled-path regression -# guard and `make_block_mask`'s alignment checks. What stays here is the half -# those numeric tests cannot establish about themselves: the mask's structure, -# and the all-visible control that makes their divergence attributable to the -# mask rather than to the two kernels merely computing softmax differently. # --------------------------------------------------------------------------- def test_block_mask_is_full_blocks_only_and_carries_a_noop_mask_mod(): - """The whole trap follows from this: visibility is in the index lists and - nowhere else, so anything that re-derives the mask from ``mask_mod`` sees - "everything visible". - - Note the exact shape of the fact: ``make_block_mask`` passes - ``mask_mod=None``, and ``BlockMask.from_kv_blocks`` substitutes - ``flex_attention.noop_mask``. The hazard is often stated as the BlockMask - "carrying ``mask_mod=None``"; what it carries is the noop, which is the - same hazard. + """Visibility lives in the index lists, not ``mask_mod``: anything that + re-derives the mask from ``mask_mod`` sees "everything visible". + + ``make_block_mask`` passes ``mask_mod=None``; ``BlockMask.from_kv_blocks`` + substitutes ``flex_attention.noop_mask`` for it, which carries the same + hazard. """ written = torch.zeros(5 * BLOCK, dtype=torch.bool) written[0 * BLOCK : 1 * BLOCK] = True # one committed frame @@ -225,20 +207,14 @@ def test_block_mask_is_full_blocks_only_and_carries_a_noop_mask_mod(): def test_a_fully_visible_ring_makes_eager_and_compiled_agree(): - """The control for the eager-flex trap, and the reason the divergence next - door is a diagnosis rather than an observation. - - ``test_eager_flex_attention_does_not_honour_the_block_mask`` shows the two - kernels disagreeing on a partly-hidden row. On its own that is also what - two kernels with different softmax numerics would look like. Take the mask - out -- same q/k/v, every block visible -- and they agree to ~1e-07, which - leaves the mask as the only thing the disagreement can be attributed to. - Delete this and the ~1e-01 next door stops meaning "eager ignored the mask". + """Control for the eager-flex trap pinned in + ``test_flex_attention_resource.py``: with every block visible, eager and + compiled agree to ~1e-07, which is what makes the divergence there + attributable to the mask rather than to differing softmax numerics. The all-visible row has to be built by hand: a live Waypoint ring never emits one. The slot the current frame is about to overwrite is always - hidden (see ``test_mask_hides_the_slot_this_frame_is_about_to_overwrite``), - so the steady state is capacity minus exactly one block, forever. + hidden (see ``test_mask_hides_the_slot_this_frame_is_about_to_overwrite``). """ config = reduced_config() capacity = config.kv_capacity(0) @@ -275,11 +251,10 @@ def test_a_fully_visible_ring_makes_eager_and_compiled_agree(): def make_cache(*, ring_frames: int, ring_buckets: int, dilation: int) -> LayerRingCache: - """One world, because this section is about the ring *algorithm* — which - slot a frame lands in, which slot it hides — and that is per world and - identical at any ``num_sessions``. The folded layout and its isolation are - pinned where they belong, in ``test_ring_kv_resource.py``; driving them - again here would only make these tests slower to read.""" + """One world: this section is about the ring *algorithm* -- which slot a + frame lands in, which slot it hides -- which is per world and identical at + any ``num_sessions``. The folded layout and its isolation are pinned in + ``test_ring_kv_resource.py``.""" return LayerRingCache( num_sessions=1, n_kv_heads=1, @@ -296,12 +271,10 @@ def make_cache(*, ring_frames: int, ring_buckets: int, dilation: int) -> LayerRi def upsert(cache: LayerRingCache, kv, frame_pos, *, commit: bool, world: int = 0): """``LayerRingCache.upsert`` with the world index spelled out. - Not a default on ``upsert`` itself, deliberately. ``session_idx`` is a ``[1]`` - int64 *device* tensor on the forward path and never a Python int — a host - int is folded into the graph at capture and every replay then serves the - capture-time world, silently. A default argument is exactly how a caller - ends up not thinking about which world it writes, so the cache takes it - positionally and this helper is the only place the zero is written down. + Not a default on ``upsert`` itself: ``session_idx`` is a ``[1]`` int64 + *device* tensor on the forward path, never a Python int -- a host int gets + folded into the graph at capture and every replay then silently serves the + capture-time world. This helper is the only place the zero is written down. """ return cache.upsert(kv, frame_pos, commit, torch.tensor([world], dtype=torch.int64)) @@ -316,9 +289,8 @@ def upsert(cache: LayerRingCache, kv, frame_pos, *, commit: bool, world: int = 0 ], ) def test_ring_slot_rotation(kind, dilation, frames, expected_slots): - """Global commits land on frames 0, 8, 16, ... in slots - 0, 1, 2, ...; local slots cycle 0..15. The slot holds the frame index that - last wrote it, so the expected list is the whole history at once.""" + """The slot holds the frame index that last wrote it, so ``expected_slots`` + doubles as the whole write history.""" cache = make_cache(ring_frames=16, ring_buckets=16, dilation=dilation) for f in frames: upsert(cache, frame_kv(f), torch.tensor([f], dtype=torch.int64), commit=True) @@ -351,12 +323,11 @@ def test_frozen_passes_leave_the_ring_byte_identical(): def test_mask_hides_the_slot_this_frame_is_about_to_overwrite(): - """And it applies on frozen passes too, so all five passes of a frame see + """Applies on frozen passes too, so all five passes of a frame see byte-identical KV. - Asserted through ``make_block_mask`` rather than on the ``visible`` row - directly: the row is what ``upsert`` returns now, but what the kernel reads - is the block list built from it, and this is the only place the two are + Asserted through ``make_block_mask``, not the raw ``visible`` row: the + kernel reads the block list built from it, and this is where the two are checked to agree over a whole 4+1 frame.""" cache = make_cache(ring_frames=4, ring_buckets=4, dilation=1) for f in range(4): @@ -374,10 +345,9 @@ def test_mask_hides_the_slot_this_frame_is_about_to_overwrite(): def test_upsert_batches_two_worlds_like_two_sequential_calls(): - """``upsert`` at B=2 (one row each for worlds 0 and 1) is bit-exact to - running the same two frames one world at a time: the row dimension is - folded into the token dim by ``session_base``, so a batched call must not - touch a row that is not its own.""" + """``upsert`` at B=2 (one row each for worlds 0 and 1) is bit-exact to two + sequential single-world calls: ``session_base`` folds the row dim into the + token dim, so a batched call must not touch a row that isn't its own.""" def new_cache() -> LayerRingCache: return LayerRingCache( num_sessions=2, n_kv_heads=1, ring_frames=4, ring_buckets=4, @@ -450,18 +420,15 @@ def floor_bucket_upsert(cache: LayerRingCache, kv, frame_pos, commit: bool): @pytest.mark.parametrize("dilation", [1, 8]) def test_bucket_round_up_is_faithful_but_currently_unobservable(dilation): - """Flooring instead of rounding up is said to "rotate the entire history by - one slot". **That consequence does not hold** for any geometry this - checkpoint uses, and this test pins the real behaviour rather than the - claim. + """Flooring instead of rounding up is claimed to "rotate the entire history + by one slot"; that does not hold for any geometry this checkpoint uses, and + this test pins the real behaviour over the claim. ``ceil`` and ``floor`` agree on every committing frame - (``(8j + 7) // 8 == 8j // 8 == j``) and at ``dilation == 1`` they are equal - outright. They differ only where ``write_step`` is False -- and there - ``ring_idx`` feeds nothing but ``mask_written[ring_idx] &= ~write_step``, - which is the identity, and ``torch.where(write_step, ring_idx, current_idx)`` - picks the scratch index. So the ``+ dilation - 1`` is faithfully ported and - harmless, but it is not load-bearing. + (``(8j + 7) // 8 == 8j // 8 == j``), and outright at ``dilation == 1``. They + differ only where ``write_step`` is False, where ``ring_idx`` feeds only the + identity op ``mask_written[ring_idx] &= ~write_step`` -- so ``+ dilation - 1`` + is faithfully ported but not load-bearing. """ ceil_cache = make_cache(ring_frames=4, ring_buckets=4, dilation=dilation) floor_cache = make_cache(ring_frames=4, ring_buckets=4, dilation=dilation) @@ -505,16 +472,15 @@ def test_reset_restores_a_fresh_ring(): def test_ring_state_is_a_deep_copy_and_is_specific_to_the_compaction_setting(): - """The Waypoint half of the state round trip: a state saved from a compacted - deployment must not load into a ``full_global_ring`` one. - - The geometries differ only on the global layers (5 frames vs 33 here), so - every local layer would copy cleanly and only layer 3 would fail -- and if - the guard were a bare ``copy_`` instead of a shape check, a run where the - dims happened to broadcast would restore a silently replicated frame. The - generic clone/copy_/layer-count guarantees are pinned in - ``test_ring_kv_resource.py``; what is here is that the compaction flag is - part of a state's identity. + """A state saved from a compacted deployment must not load into a + ``full_global_ring`` one -- the compaction flag is part of a state's + identity. + + Geometries differ only on the global layers (5 vs 33 frames here), so only + layer 3 would fail; a bare ``copy_`` instead of a shape check would let dims + broadcast and silently restore a replicated frame. Generic + clone/copy_/layer-count guarantees are pinned in + ``test_ring_kv_resource.py``. """ config = reduced_config() kv = ring_manager(config) @@ -580,10 +546,9 @@ def test_720p_ring_geometry_matches_the_contract_table(): def test_ring_buckets_is_an_input_not_a_derivation(): - """The trap. The reference computes - ``num_buckets = (L // tpf) // dilation``. Against the compacted ring that - yields 2, not 16 -- a global layer would retain 2 frames instead of 16, with - no shape error and no exception.""" + """The trap: the reference derives ``num_buckets = (L // tpf) // dilation``, + which against the compacted ring yields 2, not 16 -- a global layer would + silently retain 2 frames instead of 16.""" config = waypoint_1_5_1b_720p() global_layer = 3 reference_derivation = config.ring_frames(global_layer) // config.pinned_dilation(global_layer) @@ -638,14 +603,13 @@ def drive_ring(config: WaypointConfig, n_frames: int, seed: int = 7) -> list[tor def test_compacted_and_full_global_rings_are_bitwise_identical(): - """``from_kv_blocks`` derives the visited list - from a *stable* descending argsort truncated to the visited count, so - dropping never-written blocks changes neither which blocks are attended nor - the order they accumulate in. Bit-equality is therefore the correct bar and - an ``allclose`` here would be hiding a real difference. + """``from_kv_blocks`` derives the visited list from a *stable* descending + argsort truncated to the visited count, so dropping never-written blocks + changes neither which blocks are attended nor their accumulation order -- + bit-equality is the correct bar here, not ``allclose``. 36 frames wraps the global ring's 4 addressable slots (stride 8) more than - once and the local rings nine times over. + once, and the local rings nine times over. """ compacted = reduced_config() full = dataclasses.replace(compacted, full_global_ring=True) @@ -707,9 +671,8 @@ def test_ortho_rope_angles_match_the_reference_construction_bitwise(): def test_the_bands_count_rotation_pairs_and_cover_every_head_dim(): - """``d_xy = d_head // 8`` and ``d_t = d_head // 4`` count rotation PAIRS. - 8 + 8 + 16 = 32 pairs = 64 dims -- nothing is unrotated. (An earlier - reading of this had the top half untouched.)""" + """``d_xy = d_head // 8`` and ``d_t = d_head // 4`` count rotation PAIRS: + 8 + 8 + 16 = 32 pairs = 64 dims -- nothing is unrotated.""" config = waypoint_1_5_1b_720p() d_head = config.d_head d_xy, d_t = d_head // 8, d_head // 4 @@ -933,10 +896,9 @@ def test_noise_conditioner_fourier_features_and_fp32_island(): def test_noise_conditioner_must_stay_fp32_to_serve_fp32_sigma(): - """Why ``FP32_MODULE_PATHS`` exists at all: after the global bf16 cast the - module's own body still upcasts sigma to fp32, so a bf16 ``mlp`` cannot - consume it. The failure is loud here, which is the good case -- the point of - the pin is that the fp32 island is not optional.""" + """Why ``FP32_MODULE_PATHS`` exists: after the global bf16 cast the module + still upcasts sigma to fp32, so a bf16 ``mlp`` can't consume it. The failure + is loud here -- the fp32 island is not optional.""" cond = NoiseConditioner(16).to(torch.bfloat16) with pytest.raises(RuntimeError): cond(torch.tensor([[1.0]])) diff --git a/test/modular/test_waypoint_dit.py b/test/modular/test_waypoint_dit.py index 3e0259f06..e00b6e038 100644 --- a/test/modular/test_waypoint_dit.py +++ b/test/modular/test_waypoint_dit.py @@ -1,27 +1,22 @@ """Contract tests for the Waypoint-1.5 DiT block and the 4+1 per-frame driver. -The bar is the reference implementation at ``world_engine/src/``. The failure -modes covered here all produce plausible video and raise nothing: a -denoise pass that commits to the ring, a value residual threaded the wrong way -round, a missing ``.clone()`` between the two passes of a frame, an fp32 island -that came back bf16, or a derived RoPE table left as whatever ``to_empty`` -happened to allocate. - -Sections: the 4+1 pass structure, the 720P structural facts, fp32 islands and -the meta build, value-residual ordering, and ``cond_proj`` tying across -``to_empty``. - -The model owns no cache. Everything below drives the DiT against the two engine -resources it is bound to -- ``RingKVManager`` and ``FlexAttentionManager``, built -here directly rather than through an engine -- so the ring geometry, the -visibility rows and the attention numerics are the served ones and a test can -assert on what the model *did* without changing what it computed. +Reference is ``world_engine/src/``. Failure modes here all produce plausible +video and raise nothing: a denoise pass that commits to the ring, a value +residual threaded the wrong way round, a missing ``.clone()`` between the two +passes of a frame, an fp32 island that came back bf16, or a derived RoPE table +left as whatever ``to_empty`` happened to allocate. + +The model owns no cache: tests drive the DiT against the two engine resources +it is bound to -- ``RingKVManager`` and ``FlexAttentionManager``, built here +directly rather than through an engine -- so the ring geometry, the visibility +rows and the attention numerics are the served ones and a test can assert on +what the model *did* without changing what it computed. CPU-only and checkpoint-free. The real 720P config is used only for structural -assertions, which are cheap because the module is built on ``torch.device("meta")`` -and never materialized -- a real 720P bf16 build is ~2.6 GB. Anything that runs -tensors through the model uses a reduced but structurally identical config -(4 layers, one global at stride 8, controller fusion on ``i % 3 == 0``, GQA live). +assertions, built on ``torch.device("meta")`` and never materialized -- a real +720P bf16 build is ~2.6 GB. Anything that runs tensors through the model uses a +reduced but structurally identical config (4 layers, one global at stride 8, +controller fusion on ``i % 3 == 0``, GQA live). """ import sys @@ -74,11 +69,9 @@ def reduced_config(**overrides) -> WaypointConfig: def ring_kv_spec(config: WaypointConfig, *, num_sessions: int = 1) -> KVSpec: """The ``RingKVConfig`` a ``WaypointConfig``'s geometry implies. - Hand-rolled here because the model does not declare its specs yet; when - ``WaypointModel`` grows a ``get_node_resources``, this becomes a call to it - and the duplication goes away. Every field is read off the config rather - than restated, so a geometry change cannot leave the tests measuring a ring - the model no longer asks for. + Hand-rolled because the model does not declare its specs yet. Every field + is read off the config rather than restated, so a geometry change cannot + leave the tests measuring a ring the model no longer asks for. """ return KVSpec( resource_key="kv", @@ -103,13 +96,10 @@ def ring_kv_spec(config: WaypointConfig, *, num_sessions: int = 1) -> KVSpec: def build_resources(config: WaypointConfig, dtype: torch.dtype = torch.float32): - """The two resources the DiT calls, built the way the engine builds them. - - Through ``Resource.build(spec, info)`` and ``AttentionManager.build``'s - factory, not the constructors: the spec is what picks ``RingKVManager`` over - the paged one and what cross-checks the flex backend against a ring config, - and a test that bypassed it would be driving a pairing the engine would - refuse. + """The two resources the DiT calls, built the way the engine builds them: + through ``Resource.build(spec, info)``/``AttentionManager.build``, not the + constructors, so the spec picks ``RingKVManager`` over the paged one and + cross-checks the flex backend against the ring config. """ kv_spec = ring_kv_spec(config) cpu = torch.device("cpu") @@ -126,12 +116,12 @@ def build_resources(config: WaypointConfig, dtype: torch.dtype = torch.float32): class DitNode(NodeSubmodule): - """Stands in for the node submodule that will own the DiT. + """Stands in for the node submodule that will own the DiT (structural + parity with phase 6: the DiT is a child of the node, not the node itself). - Structural parity with phase 6, where the DiT is a child of the node rather - than the node itself. ``bind_node_resources`` walks ``self.modules()`` and - skips ``self``, so binding through the node is what the real submodule does; - the 24 attention layers are the only consumers either way. + ``bind_node_resources`` walks ``self.modules()`` and skips ``self``, so + binding through the node is what the real submodule does; the 24 attention + layers are the only consumers either way. """ def __init__(self, dit: WaypointDiT): @@ -156,12 +146,9 @@ def bind_resources_on(dit: WaypointDiT, kv, attn) -> DitNode: class RecordingRingKV: """A real ``RingKVManager`` with every model-facing call logged. - Delegation rather than a stub: the ring geometry, the visibility rows and - (through the attention resource it is paired with) the numerics stay real, - so a test can assert on what the model *did* without changing what it - computed. Only the KV side is spied on -- the attention resource is passed - through untouched, because nothing here needs to know what the kernel was - handed, only what was committed to the world state. + Delegation, not a stub: ring geometry, visibility rows and (via the paired + attention resource) the numerics stay real. Only the KV side is spied on; + attention passes through untouched. """ def __init__(self, config: WaypointConfig, dtype: torch.dtype = torch.float32): @@ -219,8 +206,8 @@ def tokens_per_frame(self) -> int: def passes(self) -> list[list[dict]]: """The upsert log regrouped into forwards: one entry per layer, in - layer order, per pass. A misgrouping means the model did not visit every - layer exactly once per forward, which the assertion below catches.""" + layer order, per pass. A misgrouping means the model skipped or + repeated a layer.""" n = self.config.n_layers assert len(self.upserts) % n == 0, f"{len(self.upserts)} upserts is not a whole number of passes" grouped = [self.upserts[i : i + n] for i in range(0, len(self.upserts), n)] @@ -365,15 +352,13 @@ def test_config_rejects_unsupported_checkpoint_variants(): def test_binding_the_node_reaches_every_layer_and_the_driver_holds_nothing(): - """The attention layers are the *only* consumers of either resource. - - The driver holds neither: it decides which pass commits and passes that - down as an argument, so there is no handle on it to leave unbound. That is - what removes the half-bind trap this test used to guard -- binding on the - DiT itself, which ``bind_node_resources`` cannot reach (it walks - ``self.modules()`` and skips ``self``), used to leave ``dit.kv`` at None and - raise nothing until four Euler steps into a frame. Asserted rather than - assumed, because a future handle on the driver would silently bring it back. + """The attention layers are the *only* consumers of either resource; the + driver holds neither (``commit`` arrives as an argument). + + Binding on the DiT itself would silently fail: ``bind_node_resources`` + walks ``self.modules()`` and skips ``self``, so ``dit.kv`` would stay None + and raise nothing until four Euler steps into a frame. Asserted rather than + assumed, since a future handle on the driver would bring the trap back. """ config = reduced_config() with torch.device("meta"): @@ -442,25 +427,21 @@ def test_generate_frame_is_four_frozen_denoise_passes_then_one_commit(): passes = kv.passes() assert len(passes) == 5, "a generated frame costs exactly five forwards" - # Per pass, not per resource: `commit` arrives as an argument on every one - # of the n_layers upserts, so a single-element set per pass is also the - # assertion that no layer disagreed with the driver about which pass it was. + # Per pass, not per resource: a single-element set per pass also asserts no + # layer disagreed with the driver about which pass it was. commit_per_pass = [{u["commit"] for u in group} for group in passes] assert commit_per_pass == [{False}, {False}, {False}, {False}, {True}] # All five passes of a frame share one ring clock. assert {u["frame_pos"] for u in kv.upserts} == {0} - # ...and nothing else on the resource. Dropping the world and snapshotting - # it belong to the request lifecycle a level up; a driver that reset between - # frames would be starting a new rollout every frame, silently. + # ...and nothing else on the resource: dropping/snapshotting the world is a + # level up; resetting between frames would silently start a new rollout. assert (kv.resets, kv.states) == (0, []) def test_cond_head_cache_is_bit_exact_and_replaces_the_live_projection(): """``materialize_runtime_tables`` folds every block's six cond_proj GEMMs - into a per-sigma gather. The cache is a pure precompute of the same M=1 - projection ``forward`` runs, so a cached frame must match a live one - bit-for-bit -- two seed-identical DiTs over fresh (empty) rings, one - materialized and one not, must return the same latent.""" + into a per-sigma gather -- a pure precompute of the same M=1 projection + ``forward`` runs, so a cached frame must match a live one bit-for-bit.""" config = reduced_config() noise, mouse, button, scroll = frame_inputs(config) fp = torch.tensor([0], dtype=torch.int64) @@ -507,13 +488,12 @@ def test_the_ring_only_moves_on_the_committing_pass(): def test_generate_frame_clones_the_denoised_latent(): - """``x0 = self._denoise_pass(...).clone()`` -- the - ``.clone()`` is load-bearing. Both passes run inside one CUDA-graph capture, - so the cache pass allocates from the graph's private pool, and the denoise - pass's output buffer is a block in that pool that nothing downstream holds: - the cache pass's first allocation can land on it and stomp the latent it is - supposed to be reading, with the address baked into the graph. The copy must - land in caller-owned memory, i.e. outside the compiled region. + """``x0 = self._denoise_pass(...).clone()`` -- the ``.clone()`` is + load-bearing. Both passes run inside one CUDA-graph capture, so the denoise + pass's output buffer is a block in the graph's private pool that nothing + downstream holds: the cache pass's first allocation can land on it and + stomp the latent, with the address baked into the graph. The copy must land + in caller-owned memory, outside the compiled region. """ config = reduced_config() dit, _kv = bound_dit(config) @@ -593,14 +573,13 @@ def test_sigma_schedule_is_built_in_the_latent_dtype(): class CaptureKV: - """Records the exact ``(k, v)`` handed to the ring and hands them straight - back, so a test can inspect what would have been stored forever. - - Deliberately NOT a ``RingKVManager``, unlike ``RecordingRingKV`` above: what - these tests read is the frame the layer *submitted*, and a real ring returns - the whole capacity with that frame scattered into a slot the test would then - have to find. Returning ``(k, v, None)`` keeps the KV the layer built and - the KV the kernel sees the same tensor. + """Records the exact ``(k, v)`` handed to the ring and returns them + unchanged, so a test can inspect what would have been stored. + + Not a ``RingKVManager``: a real ring returns the whole capacity with the + frame scattered into a slot the test would have to find. Returning + ``(k, v, None)`` keeps the submitted tensor and the tensor the kernel sees + identical. """ def __init__(self): @@ -615,10 +594,8 @@ def upsert( class DenseAttn: - """The attention half of the pair above: plain SDPA over whatever - ``CaptureKV`` returned. ``visible`` is ``None`` and ignored -- there is no - ring here to be visible into, and these tests are about what enters the - cache, not about the mask.""" + """Plain SDPA over whatever ``CaptureKV`` returned. ``visible`` is ``None`` + and ignored: these tests are about what enters the cache, not the mask.""" requires_kv_write = False @@ -762,7 +739,7 @@ def test_fp32_module_paths_is_exactly_the_noise_conditioner(): def test_cast_serving_dtypes_leaves_one_fp32_island_on_720p(): """bf16 everywhere, then the islands back to fp32 -- run on the **meta** module so storage is later allocated directly in the serving - dtype. Nothing is materialized here; a real 720P build is ~2.6 GB.""" + dtype.""" with torch.device("meta"): dit = WaypointDiT(waypoint_1_5_1b_720p()) assert dit.cast_serving_dtypes() is dit diff --git a/test/modular/test_waypoint_gpu.py b/test/modular/test_waypoint_gpu.py index 978c762ad..f16243ce2 100644 --- a/test/modular/test_waypoint_gpu.py +++ b/test/modular/test_waypoint_gpu.py @@ -1,21 +1,15 @@ """GPU gates for the Waypoint port: fullgraph capture, compiled/eager parity, CUDA-graph replay, rollout isolation, and the BlockMask rebuild cost. -Checkpoint-free -- the weights are random and every claim here is a -self-consistency one. The geometry is reduced (4 layers, 128 tokens per frame) -but structurally identical to 720P: one global layer at a dilated stride, -controller fusion on ``i % 3 == 0``, GQA live, and local/global rings of -*different* capacity so a layer-indexing mistake cannot hide behind a uniform -buffer. - -Two facts drive the shape of this file: - - * ``compile_regions()`` is an optional execution mode, with all runtime tables - materialized first when selected. Planned masks are staged at fixed addresses - outside the compiled region and outside CUDA-graph replay. - * There is no eager reference mode. Both sides of every comparison run with - the ``flex_attention_masked`` compile on, because eager ``flex_attention`` - ignores the no-op ``mask_mod`` and reads unwritten ring slots. +Checkpoint-free -- weights are random and every claim is a self-consistency +one. The geometry is reduced (4 layers, 128 tokens per frame) but structurally +identical to 720P: one global layer at a dilated stride, controller fusion on +``i % 3 == 0``, GQA live, and local/global rings of *different* capacity so a +layer-indexing mistake can't hide behind a uniform buffer. + +There is no eager reference mode: both sides of every comparison run with the +``flex_attention_masked`` compile on, because eager ``flex_attention`` ignores +the no-op ``mask_mod`` and reads unwritten ring slots. Set ``WAYPOINT_GPU_TESTS=0`` to skip; a full run is a few minutes, most of it ``torch.compile``. @@ -142,8 +136,7 @@ def build(config: WaypointConfig, *, seed: int = 0, num_sessions: int = 1): weights, wired to a real ring and a real flex backend on the GPU. Parameters are filled from a CPU generator so two builds at the same seed - are bit-identical, which is what lets a test compare two independent - instances. + are bit-identical, letting a test compare two independent instances. """ with torch.device("meta"): dit = WaypointDiT(config) @@ -288,14 +281,9 @@ def test_compile_regions_holds_under_fullgraph(): """``fullgraph=True`` on both regions, which is what makes capture possible at all -- a break here would leave the driver un-capturable. - Derived tables are explicitly materialized after weight loading and before - compilation, matching the serving lifecycle. No eager model frame mutates - initialization state before the compiled call. - - ``torch._dynamo.config.capture_scalar_outputs`` is deliberately NOT set. - The reference sets it as a documented graph-break fix; this port compiles - clean without it, and setting it here would hide a future break behind an - unbacked symint. + ``torch._dynamo.config.capture_scalar_outputs`` is deliberately NOT set: + this port compiles clean without it, and setting it here would hide a + future break behind an unbacked symint. """ config = gpu_config() dit, kv, attn = build(config) @@ -325,10 +313,9 @@ def test_compile_regions_holds_under_fullgraph(): def test_flex_attention_is_bit_exact_in_and_out_of_a_compiled_region(): - """The attention kernel does not change when the caller is traced into the - same graph. This is the floor of the A.2 ladder: it makes any compiled/eager - difference in a frame attributable to the pointwise chains around - attention rather than to the kernel the ring is read through. + """The floor of the A.2 ladder: the attention kernel itself doesn't change + under tracing, so any compiled/eager frame difference is attributable to + the pointwise chains around it, not the kernel the ring is read through. """ gen = torch.Generator(device="cpu").manual_seed(7) q = torch.randn(1, 2, 128, 32, generator=gen).to(DEVICE, DTYPE) @@ -351,10 +338,9 @@ def attend(q, k, v, written): def test_the_gemms_are_bit_exact_compiled_vs_eager(): """Second rung: ``nn.Linear`` lowers to the same cuBLAS call either way, so - the projections are not the source of the drift either. What is left is the - fused pointwise chains -- ``rms_norm`` into RoPE, adaLN into the residual -- - where inductor keeps the intermediate in fp32 across a fusion while eager - round-trips it through bf16. + the drift is in the fused pointwise chains -- ``rms_norm`` into RoPE, + adaLN into the residual -- where inductor keeps the intermediate in fp32 + across a fusion while eager round-trips it through bf16. """ gen = torch.Generator(device="cpu").manual_seed(11) x = torch.randn(1, 128, 64, generator=gen).to(DEVICE, DTYPE) @@ -385,21 +371,16 @@ def normed(x): def test_compiled_matches_eager_to_four_bf16_ulp_of_peak(): - """NOT bit-exact, and the two rungs above say why: inductor's fp32-carrying - pointwise fusions. The gap is a fixed handful of bf16 quanta, and the bound - below is measured, not chosen -- worst 2.35 ulp of the frame peak over 120 - frame comparisons spanning six weight/noise seeds, so ``COMPILE_TOL_ULP`` - sits at 4 with ~1.7x headroom. - - Run over the full rollout because the interesting claim is that the gap does - not compound even though the ring feeds itself: the deviation at frame 19 is - the same size as at frame 0 (measured 3.4e-3 to 6.4e-3 of peak either way), - which a per-frame bound over 20 self-feeding frames is what catches. - - Both sides run the pinned ``flex_attention_masked`` compile; this model has - no eager reference mode. What stays exact is the structure: the two runs - write the same ring slots and hide the same ones, and a visibility row that - differed would be a slot bug rather than a rounding one. + """NOT bit-exact: inductor's fp32-carrying pointwise fusions leave a fixed + handful of bf16 quanta of drift. The bound is measured, not chosen -- + worst 2.35 ulp of peak over 120 frame comparisons across six seeds, so + ``COMPILE_TOL_ULP`` sits at 4 with ~1.7x headroom. + + Run over the full rollout to check the gap doesn't compound even though + the ring feeds itself: frame 19's deviation is the same size as frame 0's. + + Ring-slot visibility is checked separately since a mismatch there would + be a slot bug, not a rounding one. """ config = gpu_config() frames = ROLLOUT_FRAMES @@ -535,10 +516,9 @@ def replay_frame(captured, rid: str, frame: int, stream: str) -> torch.Tensor: def eager_frame(captured, rid: str, frame: int, stream: str) -> torch.Tensor: - """The same frame through the same compiled regions, uncaptured. This is the - control A.3 needs: compiled/eager parity is A.2's subject, so replaying must - be compared against the code the graph was captured from, not against a - differently-fused build of it.""" + """The same frame through the same compiled regions, uncaptured: the + control A.3 needs, since replaying must be compared against the code the + graph was captured from, not a differently-fused build of it.""" admit_frame(captured["kv"], rid, frame, captured["attn"]) mouse, button, scroll = captured["controls"] with torch.no_grad(): @@ -612,13 +592,12 @@ def test_capture_replays_fixed_address_planned_masks(captured): def test_replay_matches_the_uncaptured_regions_over_two_ring_wraps(captured): - """The gate capture exists for: 20 frames of replay against 20 frames of - the same compiled code, bit-exact on the emitted latents AND on the ring -- - the ring is the world state, and a latent check alone would pass on a - rollout whose history had quietly gone somewhere else. + """20 frames of replay against 20 frames of the same compiled code, + bit-exact on the emitted latents AND on the ring -- a latent check alone + would pass on a rollout whose history had quietly gone somewhere else. - Long enough to wrap both rings twice, so a slot that was only ever appended - to has to be overwritten and read back. + Long enough to wrap both rings twice, so a slot that was only ever + appended to has to be overwritten and read back. """ config = captured["config"] kv = captured["kv"] @@ -655,14 +634,14 @@ def test_replay_matches_the_uncaptured_regions_over_two_ring_wraps(captured): def test_a_second_rollout_starts_from_nothing(captured): - """Two rollouts in one process on the same fixed ring. The second is fed the - identical noise and must produce the identical frames -- so it saw neither - the first rollout's history nor the capture warmup's, both of which are - still physically in the buffer until something zeroes them. + """Two rollouts in one process on the same fixed ring: the second is fed + identical noise and must produce identical frames, so it saw neither the + first rollout's history nor the capture warmup's, both still physically + in the buffer until something zeroes them. ``post_warmup_validate`` is asserted to raise on the dirty ring the first - rollout leaves behind: a scrub that silently did nothing would make the - comparison vacuous, and this is what distinguishes the two. + rollout leaves behind -- a scrub that silently did nothing would make the + comparison below vacuous. """ kv = captured["kv"] frames = 8 @@ -698,8 +677,8 @@ def test_two_worlds_interleaved_match_the_same_rollouts_run_alone(captured): Nothing physical separates the worlds -- they share one buffer per layer, folded into the token dimension -- so the whole isolation mechanism is the - visibility row, and a leak is silent. Both rollouts go through the *same* - captured graph, which is also the claim that ``session_idx`` is read at replay + visibility row, and a leak is silent. Both go through the *same* captured + graph, which is also the claim that ``session_idx`` is read at replay rather than baked at capture. """ kv = captured["kv"] @@ -744,16 +723,13 @@ def test_two_worlds_interleaved_match_the_same_rollouts_run_alone(captured): def test_batched_step_matches_the_same_rollouts_run_one_row_at_a_time(): - """B=2 across two worlds, 8 frames each, matches the identical rollouts run - one row (B=1) at a time -- the row-order invariant BATCH-001 rests on. - - Same weights for both halves: run B=1 alternating first and snapshot every - frame and both rings, then scrub and run the same two streams again as a - genuinely batched B=2 forward per frame. A batched GEMM may pick a - different cuBLAS kernel at M=2T than at M=T, so that cross-run comparison - is bounded in bf16 ulp rather than exact -- but two rows of the SAME - batched call fed literally identical inputs share one kernel launch and - must be bit-exact. + """B=2 across two worlds, 8 frames each, matches the identical rollouts + run one row (B=1) at a time. + + A batched GEMM may pick a different cuBLAS kernel at M=2T than at M=T, so + the B=1-vs-B=2 comparison is bounded in bf16 ulp rather than exact -- but + two rows of the SAME batched call fed identical inputs share one kernel + launch and must be bit-exact. """ config = gpu_config() frames = 8 @@ -817,9 +793,8 @@ def test_batched_step_matches_the_same_rollouts_run_one_row_at_a_time(): ) assert_worlds_equal(solo_ring[rid], batch_ring[rid], f"world {rid}, batched vs alone") - # Two rows of the SAME batched call, fed literally identical inputs (same - # noise, same frame, same controls): must be bit-exact -- nothing about a - # row's own math may read another row's data or depend on its batch position. + # Two rows of the SAME batched call, fed identical inputs: must be + # bit-exact, or a row is reading another row's data or batch position. for rid in ("id0", "id1"): kv.ingest_request(rid) identical_frames = [("id0", 0), ("id1", 0)] @@ -882,14 +857,11 @@ def test_prime_replay_matches_the_uncaptured_prime(captured): def test_the_prime_graph_returns_its_own_static_input_buffer(captured): - """Prime's output aliases its input, and that is the contract downstream. - - ``append_frame`` hands the settled latent straight back, so the captured - output is the captured input buffer. The consumer -- the VAE decoder, next - in the same ``Sequential`` -- must therefore copy before the following prime - stages over it, which it does: the engine stages every captured node's - inputs through ``copy_``. Pinned here so adding a ``.clone()`` to - ``append_frame`` is a decision and not an accident. + """Prime's output aliases its input, and that is the contract downstream: + ``append_frame`` hands the settled latent straight back, so the consumer + must copy before the next prime stages over it (the engine does, via + ``copy_``). Pinned here so adding a ``.clone()`` to ``append_frame`` is a + decision, not an accident. """ assert captured["prime_out"].data_ptr() == captured["latent"].data_ptr() @@ -949,15 +921,14 @@ def test_prime_then_rollout_through_both_graphs_matches_eager(captured): def test_block_mask_rebuild_cost_at_720p(record_property): - """A measurement, not a bound. ``FlexAttentionManager.attend`` rebuilds the - mask on every call -- 24 layers x 5 passes = 120 times per frame -- and the - note there asks for a number before anyone caches it. - - Reported for the eager path, where the rebuild is host work. Under the - compiled regions it is traced into the graph instead, so a captured replay - pays device time and no host time at all. The block-alignment ``torch.equal`` - is timed separately because it is a full sync and it is also what blocks - capture; ``torch.compiler.is_compiling()`` is what switches it off. + """A measurement, not a bound: ``FlexAttentionManager.attend`` rebuilds + the mask on every call (24 layers x 5 passes = 120 times per frame). + + Reported for the eager path, where the rebuild is host work; under the + compiled regions it is traced into the graph instead, so a captured + replay pays device time and no host time at all. The block-alignment + ``torch.equal`` is timed separately since it's a full sync and also what + blocks capture; ``torch.compiler.is_compiling()`` switches it off. """ config = waypoint_1_5_1b_720p() kv_len = config.kv_capacity(0) @@ -1012,15 +983,15 @@ def measure(fn, repeats: int) -> float: def test_the_fp32_island_is_pinned_against_the_engine_matmul_precision(): - """``mstar/engine/__init__.py`` sets ``float32_matmul_precision`` process-wide - (the reference sets ``medium``), and ``NoiseConditioner`` is a deliberate - fp32 island -- so the setting decides what "fp32" means there. - - Pinned here so a change to the engine default fails loudly. The second half - is the reason it does not bite *today*: the model serves B == N == 1, so the - island's matmuls are ``[1, 512] @ [512, 8192]`` -- a GEMV, which uses no - tensor cores and is bit-identical under every setting. From N >= 2 the - setting reaches it, which is what a future batched step would walk into. + """``mstar/engine/__init__.py`` sets ``float32_matmul_precision`` + process-wide, and ``NoiseConditioner`` is a deliberate fp32 island -- so + the setting decides what "fp32" means there. Pinned here so a change to + the engine default fails loudly. + + It doesn't bite *today*: the model serves B == N == 1, so the island's + matmuls are ``[1, 512] @ [512, 8192]`` -- a GEMV, bit-identical under + every setting. From N >= 2 the setting reaches it, which a future batched + step would walk into. """ assert torch.get_float32_matmul_precision() == "high" diff --git a/test/modular/test_waypoint_pixel_equivalence.py b/test/modular/test_waypoint_pixel_equivalence.py index 893e64e96..20331f805 100644 --- a/test/modular/test_waypoint_pixel_equivalence.py +++ b/test/modular/test_waypoint_pixel_equivalence.py @@ -56,10 +56,8 @@ from mstar.model.waypoint.config import waypoint_1_5_1b_720p AE_CHECKPOINT = Path(os.environ.get("WAYPOINT_AE_CHECKPOINT", CHECKPOINT.parent / "taehv1_5")) -# The oracle's seed image, cached by test/waypoint/record_oracle.py. The digest -# is what makes the oracle comparisons below mean anything: a different image is -# a different world, and the port would then be compared against a recording of -# something else. +# The oracle's seed image, cached by record_oracle.py. Its digest matters: a +# different image means the port is compared against a recording of some other world. SEED_IMAGE = Path(os.environ.get("WAYPOINT_SEED_IMAGE", CHECKPOINT.parent / "seed/default.jpg")) SEED_SHA256 = "c61c9393311d7281f793d86329dca343e12c93bf0409980a186eb39269cf6862" diff --git a/test/modular/test_waypoint_reference_equivalence.py b/test/modular/test_waypoint_reference_equivalence.py index ac7fefe9c..fb2646f37 100644 --- a/test/modular/test_waypoint_reference_equivalence.py +++ b/test/modular/test_waypoint_reference_equivalence.py @@ -241,10 +241,9 @@ def ports(reference): sigma LUT is a fp32 GEMM whose result depends on ``float32_matmul_precision``, and the reference fixture is what sets it. """ - # This harness decomposes the reference's five passes in Python so it can - # compare every intermediate. Keep the port's outer driver eager too; - # Keep outer compilation off so this test isolates numerical compatibility; - # compilation and CUDA graph selection have separate execution-mode gates. + # compile_dit=False: compilation and CUDA graph selection have separate + # execution-mode gates, so keeping the port eager here isolates numerical + # compatibility. default = replace(waypoint_1_5_1b_720p(), compile_dit=False) exact = replace(default, reference_compat=False) return {False: _build_port(exact), True: _build_port(default)} diff --git a/test/modular/test_waypoint_shell.py b/test/modular/test_waypoint_shell.py index 3b98839d7..9ae238f41 100644 --- a/test/modular/test_waypoint_shell.py +++ b/test/modular/test_waypoint_shell.py @@ -1,39 +1,18 @@ """Contract tests for the Waypoint serving shell: the model, its node submodule, and the resources it declares. -Nothing here runs the DiT. The 4+1 driver, the ring numerics and the weight -remap have their own suites; what is under test here is the *shell* — the -handful of declarations and host-side hooks that sit between the engine and a -model that already works, every one of which fails silently when it is wrong: - - * a ring geometry summarized instead of copied (a global layer served with a - local layer's stride still produces smooth video, of the wrong world), - * a rank-0 ``frame_pos`` (``_intern_static_buffer`` reads - ``stored.shape[0]``), - * a per-step tensor whose shape happens to carry ``tokens_per_frame`` - (``_seq_dim`` hoists the matching dim to the front of a shared static - buffer), - * noise drawn from an advancing generator instead of ``(seed, frame_pos)`` - (a resumed rollout diverges from the one it resumed), - * an off-by-one stop that runs one frame past the request and commits it, - * a deployment whose ``max_concurrent_requests`` is unset or larger than the - ring's world pool, which is the *only* thing keeping arrivals inside a pool - that fails terminally when it is overrun, - * a resource that never reaches the 24 attention layers, - * a latent that reaches the streaming decoder twice, out of order, or not at - all -- the prime walk skipping its decode is the version of this the port - nearly shipped. - -CPU-only, checkpoint-free, and no engine. The real 720P config is used -throughout, because the numbers that collide are that config's numbers; the DiT -behind the submodule is built on ``torch.device("meta")`` and never -materialized, since a real 720P bf16 build is ~2.6 GB and none of these -assertions touch a weight. The two places that need a value read back go around -it: the noise draw takes an explicit device (which is why that helper takes -one), and the frame-clock test runs over ``_HostOnlyDit``, since what it -asserts is host bookkeeping the DiT is not part of. The VAE section is the -third: it runs on the 360P config and a fake ``taehv`` package, for the reasons -given there. +Not the DiT, the ring numerics, or the weight remap -- those have their own +suites. What's under test is the shell layer between the engine and a working +model: ring geometry, frame_pos rank, the ``_seq_dim`` collision, noise +statelessness, the off-by-one stop, admit-queue sizing, resource binding, and +decode ordering -- each of which fails silently when it is wrong. + +CPU-only, checkpoint-free, no engine. Uses the real 720P config since its +numbers are the ones that collide; the DiT is built on ``torch.device("meta")`` +and never materialized (a real 720P bf16 build is ~2.6 GB). Two exceptions read +a value back: the noise draw takes an explicit device, and the frame-clock +tests run over ``_HostOnlyDit``. The VAE section runs on the 360P config with a +fake ``taehv`` package. """ import dataclasses @@ -101,16 +80,12 @@ def submodule(config): """The real submodule over a meta-built DiT. Meta, not a stub: ``bind_node_resources`` walking ``self.modules()`` and - ``self.dit.dtype`` surviving ``cast_serving_dtypes`` are exactly two of the - things under test, and a stub would assert them against itself. Meta also - keeps ``prepare_inputs``' shapes and dtypes honest -- they are computed the - same way on meta as on cuda -- while allocating nothing. - - The taehv here is the same fake used by the VAE section below (defined - later in this module; fixtures resolve names at call time, so the forward - reference is fine): the fused decode only needs its structural facts - (nine MemBlock histories, ``frames_to_trim``) to build shapes, not real - weights. + ``self.dit.dtype`` surviving ``cast_serving_dtypes`` are under test here, + and a stub would assert them against itself. Meta also keeps + ``prepare_inputs``' shapes/dtypes honest while allocating nothing. + + ``_FakeTaehv`` (defined later; fixtures resolve names at call time) only + needs its structural facts to build shapes, not real weights. """ with torch.device("meta"): dit = WaypointDiT(config) @@ -119,14 +94,12 @@ def submodule(config): class _HostOnlyDit(torch.nn.Module): - """Stands in for the DiT in the tests that need to read a value back. - - ``prepare_inputs`` touches the DiT for exactly one thing -- ``.dtype`` -- - and ``get_device`` for one parameter, so what those tests exercise is the - submodule's host-side bookkeeping and nothing else. The meta build above - cannot be read back (``.item()`` raises on a meta tensor) and materializing - 720P to assert on a frame counter is 2.6 GB; a stub is the honest third - option, and it is confined to the two tests that say so. + """Stands in for the DiT in tests that need to read a value back. + + ``prepare_inputs`` only touches the DiT for ``.dtype`` and ``get_device`` + for one parameter, so this exercises host-side bookkeeping only. The meta + build above can't be read back (``.item()`` raises on a meta tensor), and + materializing 720P just to read a frame counter isn't worth the memory. """ def __init__(self, dtype: torch.dtype = torch.bfloat16): @@ -198,8 +171,7 @@ def test_declares_exactly_the_ring_and_the_flex_attention_over_it(model): assert isinstance(specs[0].config, RingKVConfig) # FLEX, not the FLASHINFER default: a paged kernel reassociates the - # accumulation over the KV blocks, and bit-exactness against the reference - # is the only correctness signal this model has. + # accumulation over KV blocks, breaking bit-exactness against the reference. assert specs[1].config.backend is AttnBackend.FLEX assert specs[1].config.kv_cache == KV_RESOURCE @@ -215,9 +187,8 @@ def test_ring_geometry_is_copied_from_the_config_layer_for_layer(model, config): assert ring.num_qo_heads == config.n_heads assert ring.head_dim == config.d_head assert ring.tokens_per_frame == config.tokens_per_frame - # One world declared here, because sizing is a deployment question and - # `apply_yaml_overrides` runs after this hook. What is pinned is the - # *default*: a node that never says otherwise serves one session. + # Sizing is a deployment question (`apply_yaml_overrides` runs after this + # hook); what's pinned here is the *default* of one session. assert ring.num_sessions == 1 assert len(ring.layers) == config.n_layers @@ -266,12 +237,10 @@ def test_attention_resolves_after_the_cache_it_names(model): def test_prime_encodes_commits_and_initializes_decoder_without_emitting(model): - """The seed frame advances decoder state through the dit's fused decode. - Encoding it and dropping the latent would prime the world correctly and - still corrupt every emitted frame: the functional decoder spends its first - call on ``frames_to_trim`` of temporal memory, so the first *rollout* frame - would pay for it and the whole stream would sit one priming short of the - world it came from. Silently.""" + """Encoding the seed and dropping the latent would prime the ring but not + the decoder: the functional decoder spends its first call on + ``frames_to_trim`` of temporal memory, so the first rollout frame would + silently pay for it instead.""" walks = model.get_graph_walk_graphs() assert set(walks) == {PRIME_WALK, ROLLOUT_WALK} assert model.nodes == [DIT_NODE, VAE_ENCODER_NODE] @@ -309,11 +278,9 @@ def test_the_rollout_loop_decodes_and_emits_every_iteration(model, config): ("clock", DIT_NODE), ("video_output", EMIT_TO_CLIENT), } assert rollout.accumulated_outputs == [] - # The "clock" self loop-back is what makes the dit a same-node - # speculation target (see the test below); async scheduling can now - # dispatch iteration N+1 while N is still running. An overshoot iteration - # is vetoed host-side in WaypointDitSubmodule.prepare_inputs, not - # prevented by keeping this off. + # The "clock" self loop-back makes the dit a same-node speculation target + # (see below), so async scheduling can dispatch iteration N+1 before N + # finishes; overshoot is vetoed host-side in prepare_inputs, not by this. assert dit.enable_async_scheduling is True # The controller streams stay loop-external; "clock" is the only # loop-back, or the conductor would try to re-inject it every walk step. @@ -324,14 +291,10 @@ def test_the_rollout_loop_decodes_and_emits_every_iteration(model, config): def test_the_clock_loop_back_makes_dit_a_same_node_speculation_target(model): - """The generic readiness fix (``GraphNode.is_ready_for_speculation``, - ``Worker._get_input_tensors``) only pays off if the graph actually gives - the dit a self-edge to speculate on. Once the loop-external controller - streams are ready and the "clock" loop-back has been ingested (empty, as - ``WaypointModel._walk_inputs`` sends it for iteration 0), - ``ingest_for_speculation`` must propose the dit as ready for its own next - iteration -- exactly the wan22-shaped case F3/Step 1 fixed, now over the - real Waypoint graph. + """``ingest_for_speculation`` must propose the dit as ready for its own + next iteration once the loop-external streams are ready and the empty + "clock" loop-back (iteration 0) has been ingested -- the self-edge this + graph shape depends on for speculation to fire at all. """ rollout = model.get_graph_walk_graphs()[ROLLOUT_WALK] wgio = WorkerGraphIO(rollout) @@ -347,18 +310,15 @@ def test_the_clock_loop_back_makes_dit_a_same_node_speculation_target(model): def test_the_rollout_loop_closes_after_exactly_num_steps_frames(model): - """Driven through ``WorkerGraphIO``: what is under test is the loop's own - iteration/finish-signal bookkeeping for the new single-node shape, not the - order two nodes run in (there is only one node now, so nothing can decode - a frame twice, skip one, or take one out of turn -- that guarantee moved - into the dit's own forward being one atomic step). - - ``register_loop_finish_signal`` is what ``WaypointDitSubmodule.check_stop`` - does; it fires during postprocess of the loop's last iteration, so the - frame that iteration produced must still be emitted before the loop - reports done. The actual overshoot guard for a speculative iteration - dispatched before that signal lands is a *separate* mechanism -- the - host-side veto in ``prepare_inputs`` -- checked below. + """Driven through ``WorkerGraphIO``: exercises the loop's own + iteration/finish-signal bookkeeping, not node ordering (there's only one + node, so nothing can decode a frame twice or skip one). + + ``register_loop_finish_signal`` (what ``check_stop`` calls) fires during + postprocess of the loop's last iteration, so that iteration's frame must + still be emitted before the loop reports done. The overshoot guard for a + speculative iteration dispatched before the signal lands is separate -- + the host-side veto in ``prepare_inputs``, checked below. """ num_steps = 3 rollout = model.get_graph_walk_graphs()[ROLLOUT_WALK] @@ -374,10 +334,8 @@ def test_the_rollout_loop_closes_after_exactly_num_steps_frames(model): if step == num_steps - 1: wgio.register_loop_finish_signal(ROLLOUT_LOOP_NAME) # what check_stop does completion = wgio.mark_node_complete(DIT_NODE) - # ``WorkerGraphIO.mark_node_complete`` has already stripped anything in - # ``completion.filtered_signals`` (e.g. the "clock" loop-back, once the - # final iteration's completion filters it out) from ``output_edges``; - # what is left to route is exactly the caller's job. + # mark_node_complete already stripped filtered_signals (e.g. the final + # iteration's "clock" loop-back) from output_edges; the rest is ours to route. for edge in completion.output_edges: if edge.next_node == EMIT_TO_CLIENT: emitted.append(edge.name) @@ -389,10 +347,9 @@ def test_the_rollout_loop_closes_after_exactly_num_steps_frames(model): def test_the_overshoot_veto_fires_only_past_num_steps(submodule): - """The other half of the guarantee above: a speculative iteration built - from the state the last real one left behind must never reach a forward - once the request's ``num_steps`` is spent, and must never fire during - prime (which has no ``rollout_step`` to overshoot). + """The other half of the guarantee above: a speculative iteration must + never reach a forward once ``num_steps`` is spent, and never fires + during prime (no ``rollout_step`` to overshoot). """ num_steps = 3 rid = "overshoot" @@ -435,10 +392,9 @@ def test_the_overshoot_veto_fires_only_past_num_steps(submodule): @pytest.mark.parametrize("walk", [PRIME_WALK, ROLLOUT_WALK]) def test_no_prepared_tensor_is_rank_zero(submodule, config, walk): - """A 0-dim tensor reaches ``_intern_static_buffer``, which - reads ``stored.shape[0]``, and the worker's output fanout reads - ``dims[0]`` -- both IndexError at capture, i.e. at warmup, far from the - line that made the tensor.""" + """A 0-dim tensor reaches ``_intern_static_buffer`` (reads + ``stored.shape[0]``) and the worker's output fanout (reads ``dims[0]``) + -- both IndexError at capture/warmup, far from the line that made it.""" inputs = _controller_stream(config, frames=4) inputs["latent"] = [torch.zeros((1, 1, *config.latent_shape))] node_inputs = submodule.prepare_inputs(walk, _fwd_info(graph_walk=walk), inputs) @@ -457,24 +413,14 @@ def test_no_prepared_tensor_is_rank_zero(submodule, config, walk): def test_no_prepared_tensor_carries_tokens_per_frame_in_its_shape( submodule, config, walk ): - """``CudaGraphRunner._seq_dim`` finds the dim equal to - ``input_seq_len`` and hoists it to the front of a shared static buffer; a - tensor that carries that number for an unrelated reason gets silently - transposed under replay. - - ``input_seq_len`` is honestly ``tokens_per_frame`` (512) -- the scheduler is - told the real token count -- so the burden falls here. At 720P nothing - collides, but the margin is thin: a 256-token-per-frame variant would put - ``button``'s ``n_buttons = 256`` straight into the crosshairs. - - With ``step_batch_size > 1`` the runner's per-bucket count is - ``bs * tokens_per_frame``, and 360p at bs=2 does hit ``n_buttons``. That - case is handled on the runner side instead: ``_capture_one`` passes the - bucket's own batch size alongside its token count, so ``_seq_dim`` checks - dim 0 against both before it ever scans for a coincidental match, and - ``button`` shares its buffer like any other per-row tensor (see - ``test_cuda_graph_capture.py``, which also covers the case of a caller - that can't supply a batch size — that one is still a hard failure). + """``CudaGraphRunner._seq_dim`` finds the dim equal to ``input_seq_len`` + and hoists it to the front of a shared static buffer; a tensor carrying + that number for an unrelated reason gets silently transposed under replay. + + At 720P nothing collides, but the margin is thin: a 256-token-per-frame + variant would put ``button``'s ``n_buttons=256`` in the crosshairs. With + ``step_batch_size > 1``, 360p at bs=2 does collide with ``n_buttons``; + that case is handled runner-side instead (see ``test_cuda_graph_capture.py``). """ inputs = _controller_stream(config, frames=4) inputs["latent"] = [torch.zeros((1, 1, *config.latent_shape))] @@ -489,11 +435,10 @@ def test_no_prepared_tensor_carries_tokens_per_frame_in_its_shape( def test_prepared_shapes_and_dtypes_are_the_capture_template_exactly(submodule, config): - """``_capture_one`` bakes the config's template and replay re-stages only - what ``preprocess`` returned into it. A key, shape or dtype that differs - between the two is either a stale-address read or a silent eager fallback, - depending on which way it differs -- so they are asserted against each - other rather than against a literal.""" + """``_capture_one`` bakes the config's template; replay re-stages only + what ``preprocess`` returns into it. A mismatched key, shape, or dtype is + either a stale-address read or a silent eager fallback -- asserted + against each other rather than a literal for that reason.""" templates = { cfg.capture_graph_walk: cfg.single_request_inputs for cfg in submodule.get_cuda_graph_configs(torch.device("meta")) @@ -522,10 +467,10 @@ def test_prepared_shapes_and_dtypes_are_the_capture_template_exactly(submodule, def test_noise_is_a_pure_function_of_seed_and_frame_pos(submodule): - """Nothing about the draw may depend on how many - frames have already been drawn: a generator advanced in place accumulates - state that ``get_state`` does not serialize, so a resumed rollout would - diverge from the one it resumed and no assertion anywhere would fire.""" + """The draw must not depend on how many frames were drawn before it: a + generator advanced in place accumulates state ``get_state`` doesn't + serialize, so a resumed rollout would silently diverge from the one it + resumed.""" device, dtype = torch.device("cpu"), torch.float32 first = submodule._frame_noise(4242, 7, device, dtype) # Two intervening draws: if the helper carried a generator, these would @@ -546,9 +491,9 @@ def test_noise_differs_across_frames_and_across_seeds(submodule, config): # noise and the video stops evolving. assert not torch.equal(frames[a], frames[b]), f"frames {a} and {b} match" - # Adjacent seeds must not share frame k. `seed + frame_pos` would make seeds - # 0 and 1 agree on every frame but the first, which reads as a broken - # sampler rather than as a seed collision -- hence the splitmix finalizer. + # Adjacent seeds must not share frame k: `seed + frame_pos` would make + # seeds 0 and 1 agree on every frame but the first (hence the splitmix + # finalizer). assert not torch.equal( submodule._frame_noise(0, 3, device, dtype), submodule._frame_noise(1, 3, device, dtype), @@ -606,13 +551,13 @@ def test_prime_is_idle_and_rollout_zero_receives_action_zero( def test_declare_step_carries_the_same_clock_prepare_inputs_reads( host_submodule, config ): - """One source for the clock, not two. `RingKVManager.admit` checks the - declared frame against the one its `commit` last recorded, so a step that - declared a *different* number from the one the forward runs at would refuse - valid frames and pass desynced ones -- the check inverted. + """One source for the clock, not two: `RingKVManager.admit` checks the + declared frame against `commit`'s last-recorded one, so a mismatch would + refuse valid frames and pass desynced ones -- the check inverted. - Read on the host, off `PerRequestState`: the `frame_pos` in `NodeInputs` is - a `[1]` device tensor by then and reading it back would be a sync per step. + Read on the host, off `PerRequestState`, rather than off `NodeInputs`' + `frame_pos` (a `[1]` device tensor by then, so reading it back would sync + every step). """ rid = "declare" host_submodule.request_states.pop(rid, None) @@ -644,17 +589,10 @@ def test_declare_step_carries_the_same_clock_prepare_inputs_reads( def test_declare_step_names_a_clock_for_every_request_in_the_batch(host_submodule): - """No batch shape declines to answer. - - The singular ``frame_pos: int | None`` this replaces returned ``None`` - whenever the batch was not one request, and ``RingKVManager``'s continuity - check — the only thing standing between a stalled clock and a world quietly - rewriting its own history — then did nothing for that step. The reasoning - was that ``admit`` refuses such a batch anyway, which was true and is still - true; the problem is that it made the check's coverage depend on a second, - unrelated refusal staying in place. It does not any more: a batch this - submodule cannot serve is refused for *being a batch*, with every clock in - it still named. + """No batch shape declines to answer: ``RingKVManager``'s continuity check + -- the only thing standing between a stalled clock and a world quietly + rewriting its own history -- must get a clock for every request in the + batch, not just batches of one. The clocks below are genuinely different, so a declaration that broadcast one request's frame across the batch fails here rather than passing on a @@ -744,20 +682,17 @@ def test_get_worker_graphs_refuses_a_deployment_with_no_admit_queue( ): """The pool is finite, and this is the primary gate on it. - The conductor only forms a FIFO admit queue when ``max_concurrent_requests`` - is set: it drains ``waiting_queue`` while ``len(self.requests) < - max_concurrent_requests``, so an unset value admits every request on arrival - and everything past the Nth dies terminally at ``RingKVManager.admit`` — - which sees the batch far too late to queue it. + The conductor only forms a FIFO admit queue when + ``max_concurrent_requests`` is set; unset, every request is admitted on + arrival and anything past the Nth dies terminally at + ``RingKVManager.admit``, too late to queue. - ``max_batch_size = 1`` does not cover this and never did. It caps how many - requests share one *step*; N admitted rollouts alternating steps is now the - intended shape, but it says nothing about how many may exist at once, which - is the thing the world pool bounds. + ``max_batch_size = 1`` doesn't cover this -- it caps requests per step, + not how many may exist at once. - ``True`` is in the list because ``isinstance(True, int)`` is ``True`` in - Python: a YAML ``max_concurrent_requests: true`` would otherwise read as the - number 1 and silently serialize a node sized for eight. + ``True`` is in the parametrize list because ``isinstance(True, int)`` is + ``True`` in Python, so ``max_concurrent_requests: true`` would otherwise + silently read as 1. """ extra = {} if limit is None else {"max_concurrent_requests": limit} path = _write_config(tmp_path, f"reject_{limit}.yaml", **extra) @@ -783,14 +718,14 @@ def test_get_worker_graphs_refuses_invalid_world_pool_size( def test_get_worker_graphs_refuses_more_arrivals_than_worlds( model, tmp_path, limit, worlds ): - """A queue longer than the pool is not a queue, it is a delayed failure: the - conductor admits ``limit`` requests, the ring hands out ``num_sessions``, and - the difference is a set of requests that reach ``admit`` and die there with - an ``AdmitRuntimeError`` that no retry, eviction or reload can clear. - - The ``worlds=None`` case is the one a deployment writes by accident: raising - ``max_concurrent_requests`` without touching ``resources`` at all, which is - the shape every pre-pool config already has. + """A queue longer than the pool is a delayed failure: the conductor admits + ``limit`` requests, the ring hands out ``num_sessions``, and the + difference dies at ``admit`` with an ``AdmitRuntimeError`` no retry, + eviction, or reload can clear. + + ``worlds=None`` is the accidental case: raising + ``max_concurrent_requests`` without touching ``resources``, the shape + every pre-pool config already has. """ extra = {"max_concurrent_requests": limit} if worlds is not None: @@ -823,13 +758,12 @@ def test_get_worker_graphs_accepts_a_deployment_inside_its_pool( def test_the_shipped_config_serializes_both_nodes_onto_one_rank(model): - """``configs/waypoint.yaml`` is the deployment and has to pass its own gate. + """``configs/waypoint.yaml`` is the deployment and must pass its own gate. Both nodes in one group, on rank 0: a node missing from ``node_groups`` - has no rank to run on and the split fails there. There is no decoder node - left to put a worker boundary in front of -- its decode is fused into the - dit's own forward -- so a rank split inside the rollout loop is no longer - even expressible. + has no rank to run on. There's no decoder node to put a worker boundary + in front of -- decode is fused into the dit's forward -- so a rank split + inside the rollout loop isn't expressible any more. """ path = pathlib.Path(__file__).resolve().parents[2] / "configs" / "waypoint.yaml" @@ -845,10 +779,9 @@ def test_the_shipped_config_serializes_both_nodes_onto_one_rank(model): def test_get_worker_graphs_warns_about_worlds_no_request_can_reach( model, tmp_path, caplog ): - """Legal, and only wasteful — so a warning and not a refusal. Each - unreachable world is ~816 MiB of ring at 720P that is allocated, zeroed, - and never written, which is worth a line in the log rather than a failed - boot: the deployment still serves correctly.""" + """Legal, only wasteful -- a warning, not a refusal. Each unreachable + world is ~816 MiB of ring at 720P allocated and never written, worth a + log line rather than a failed boot.""" path = _write_config( tmp_path, "underused.yaml", max_concurrent_requests=2, **_sessions(8) ) @@ -865,11 +798,10 @@ def test_get_worker_graphs_warns_about_worlds_no_request_can_reach( def test_bind_reaches_the_dit_and_all_twenty_four_attention_layers(submodule): - """One bind on the submodule has to reach every caller: the 24 attention - layers hold their own references and call ``upsert``/``attend`` directly. - Anything left unbound raises ``NoneType has no attribute ...`` mid-forward - -- or, if warmup gets there first, inside a capture, where it poisons the - graph instead of failing a request.""" + """One bind has to reach every caller: the 24 attention layers hold their + own references and call ``upsert``/``attend`` directly. Anything left + unbound raises mid-forward -- or, during warmup, poisons the capture + instead of failing a request.""" kv, attn = object(), object() submodule.bind_node_resources({KV_RESOURCE: kv, ATTN_RESOURCE: attn}) @@ -885,30 +817,25 @@ def test_bind_reaches_the_dit_and_all_twenty_four_attention_layers(submodule): def test_the_submodule_owns_the_dit_and_is_not_the_dit(submodule): """The structural reason the test above can pass at all. - ``NodeSubmodule.bind_node_resources`` walks ``self.modules()`` but skips - ``self`` (``submodule_base.py``: ``if bind is not None and module is not - self``). A submodule that *is* the DiT -- by subclassing it, or by defining - ``bind_resources`` on itself -- would therefore never be visited, and - anything the root came to need would sit unbound. - - Nothing on the DiT root needs a resource today (``commit`` is threaded down - as an argument), so this is a structural guard rather than a live bug: it - keeps the walk able to reach the root if that ever changes. + ``bind_node_resources`` walks ``self.modules()`` but skips ``self``; a + submodule that *is* the DiT would never be visited, leaving the root + unbound. Nothing on the root needs a resource today, so this is a guard + against that changing, not a live bug. """ assert not isinstance(submodule, WaypointDiT) assert isinstance(submodule.dit, WaypointDiT) and submodule.dit is not submodule - # The DiT has to be a *child module*, not a plain attribute -- self.modules() + # The DiT must be a *child module*, not a plain attribute -- self.modules() # is the only thing the walk follows. assert any(m is submodule.dit for m in submodule.modules()) - # And the submodule must not answer bind_resources itself: the walk would - # skip it, so defining one is a method that never runs. + # The submodule itself must not define bind_resources: the walk skips + # self, so one would never run. assert getattr(type(submodule), "bind_resources", None) is None def test_binding_without_a_declared_resource_fails_at_bind(submodule): - """Not mid-forward. The layers resolve with ``.get`` -- correct for a layer, - which may sit on a node owning only some resources -- so the node is the - frame that still knows which keys it declared and can name the missing one. + """Not mid-forward: layers resolve with ``.get`` (correct, since a layer + may sit on a node owning only some resources), so the node is what still + knows which keys it declared and can name the missing one. """ for partial in ({ATTN_RESOURCE: object()}, {KV_RESOURCE: object()}, {}): with pytest.raises(KeyError): @@ -934,10 +861,9 @@ def test_max_batch_size_is_step_batch_size_for_both_walks(config): def test_dit_can_batch_is_true(submodule): - """The eager path must batch too. A captured lease replays batched - regardless, but with ``can_batch`` False a graphs-off (or uncaptured-shape) - multi-row step falls to one forward per request instead of a single batched - forward -- the one place the port used to diverge from the other models.""" + """The eager path must batch too: a captured lease replays batched + regardless, but with ``can_batch`` False a graphs-off (or + uncaptured-shape) multi-row step falls back to one forward per request.""" assert submodule.can_batch(batch=None, model_inputs=[]) is True @@ -950,9 +876,8 @@ def test_both_dit_walks_are_optional_captures(submodule, config): """Prime and rollout both capture; the declaration order is capture order.""" configs = submodule.get_cuda_graph_configs(torch.device("meta")) # Rollout first: the two share one graph pool and rollout's five forwards - # are a superset of prime's one, so rollout sizes the pool. The runner's - # largest-first sort is stable and both specs are (1, tokens_per_frame), - # so this list order is the order they are captured in. + # are a superset of prime's one, so rollout sizes the pool; the runner's + # largest-first sort is stable given both specs are (1, tokens_per_frame). assert [cfg.capture_graph_walk for cfg in configs] == [ROLLOUT_WALK, PRIME_WALK] for cfg in configs: assert cfg.compile is False @@ -965,8 +890,7 @@ def test_both_dit_walks_are_optional_captures(submodule, config): rollout_cfg, prime_cfg = configs # Both walks' captured buckets are a real ceiling on the eager batch size: - # prime now captures the same buckets rollout does, so a prime batch bigger - # than the largest captured bucket is refused, not run eager. + # a prime batch bigger than the largest captured bucket is refused, not run eager. assert rollout_cfg.caps_eager_batch_size is True assert prime_cfg.caps_eager_batch_size is True @@ -1133,11 +1057,11 @@ def encoder(taehv_weights, ae_config): class _FakeDit(torch.nn.Module): """Stands in for the DiT in the fused-decode tests below. - What is under test there is TAEHV history bookkeeping riding along inside - ``WaypointDitSubmodule`` -- isolation across requests, fixed addresses - across walks, cleanup -- not the denoiser, so this returns a deterministic - latent shaped like the real one instead of running 24 attention layers on - CPU. The real DiT has its own coverage above and in ``test_waypoint_dit.py``. + What's under test there is TAEHV history bookkeeping inside + ``WaypointDitSubmodule`` -- isolation, fixed addresses, cleanup -- not the + denoiser, so this returns a deterministic latent instead of running 24 + attention layers on CPU (real DiT coverage is above and in + ``test_waypoint_dit.py``). """ def __init__(self, config: WaypointConfig, dtype: torch.dtype = torch.bfloat16): @@ -1161,9 +1085,8 @@ def append_frame(self, latent, pos, *, mouse, button, scroll): @pytest.fixture def decoder(taehv_weights, ae_config): """A ``WaypointDitSubmodule`` over a fake dit: the fused node under test, - minus the denoiser. Named ``decoder`` still, because every test below - exercises the TAEHV history half of this node, the half the standalone - decoder node used to own.""" + minus the denoiser. Named ``decoder`` because every test below exercises + the TAEHV history half of this node.""" return WaypointDitSubmodule(_FakeDit(ae_config), taehv_weights, ae_config) @@ -1184,11 +1107,10 @@ def _decode( decoder, *, request_id="r0", graph_walk=ROLLOUT_WALK, seed_latent: torch.Tensor | None = None, num_steps: int = 8, ): - """Drive the fused submodule through one real step of its own - ``prepare_inputs`` / ``forward`` / ``postprocess`` cycle -- the same - sequence the engine runs -- rather than poking ``taehv`` directly, since - what several tests below check is that this cycle seeds, threads and - advances the nine histories correctly across requests and walks. + """Drive the fused submodule through one real + ``prepare_inputs``/``forward``/``postprocess`` cycle -- the same sequence + the engine runs -- rather than poking ``taehv`` directly, since several + tests below check that this cycle threads the nine histories correctly. """ info = _fwd_info(request_id, graph_walk=graph_walk, num_steps=num_steps) if graph_walk == PRIME_WALK: @@ -1279,10 +1201,9 @@ def test_forward_batched_hands_each_request_its_own_row(decoder, monkeypatch): def test_decode_latent_batches_rows_independently(taehv_weights, ae_config): """``decode_latent`` at B=2 equals two independent B=1 calls, row for row. - MemBlock and TGrow (``_FakeTaehv``'s decoder) are convs/reshapes that never - mix across the batch dim, so nothing here should make row 1 depend on row - 0's latent -- the property the batched engine path now relies on. - """ + MemBlock and TGrow (``_FakeTaehv``'s decoder) are convs/reshapes that + never mix across the batch dim, so row 1 must not depend on row 0's + latent -- the property the batched engine path relies on.""" latent_shape = (1, ae_config.channels, *ae_config.latent_shape[1:]) generator = torch.Generator().manual_seed(7) latents = [ @@ -1339,9 +1260,8 @@ def test_fused_decode_histories_are_isolated_interleaved_and_cleaned_up(decoder, before_b = { key: value.clone() for key, value in decoder.request_state("b").tensors.items() } - # A generous num_steps: what is under test is address/isolation stability - # across interleaved requests, not the overshoot veto (covered on its own - # above), so nothing here should be able to trip it. + # A generous num_steps: what's under test is address/isolation stability + # across interleaved requests, not the overshoot veto (covered above). for _ in range(20): _decode(decoder, request_id="a", graph_walk=ROLLOUT_WALK, num_steps=100) _decode(decoder, request_id="b", graph_walk=ROLLOUT_WALK, num_steps=100) @@ -1393,9 +1313,8 @@ def test_ae_graphs_are_compiled_for_capture_but_remain_optional(encoder, decoder # The fused dit+decode node compiles its own two reference-shaped regions # (WaypointDiT.compile_regions, gated by config.compile_dit); letting the - # engine also compile this wrapper would fuse across that boundary (see - # test_both_dit_walks_are_optional_captures), so its captures stay - # uncompiled regardless. + # engine also compile this wrapper would fuse across that boundary, so its + # captures stay uncompiled regardless. assert fused_configs assert all(cfg.compile is False for cfg in fused_configs) assert decoder.disable_torch_compile is True @@ -1527,11 +1446,11 @@ def test_process_prompt_rejects_invalid_action_values(model, action, message): def test_the_seed_clip_is_one_latent_frame_of_uint8_rgb(model, config): - """``image_inputs`` is what the vae_encoder node consumes, and the streaming - encoder emits one latent per ``temporal_compression`` frames: a short clip - would buffer and return nothing, a long one would encode twice and leave the - second latent unclaimed. Checked here, at the API boundary, so a malformed - request is a 400 rather than a rollout that dies on a worker.""" + """The streaming encoder emits one latent per ``temporal_compression`` + frames: a short clip would buffer and return nothing, a long one would + encode twice and leave the second latent unclaimed. Checked at the API + boundary, so a malformed request is a 400 rather than a rollout that dies + on a worker.""" n = config.temporal_compression frame = torch.zeros((720, 1280, 3), dtype=torch.uint8) diff --git a/test/modular/test_waypoint_weight_loader.py b/test/modular/test_waypoint_weight_loader.py index d14a8a9b3..47a09b7a4 100644 --- a/test/modular/test_waypoint_weight_loader.py +++ b/test/modular/test_waypoint_weight_loader.py @@ -8,8 +8,7 @@ Everything runs on CPU with no checkpoint and no GPU. Why that bar and not a looser one: almost every way this loader can be wrong is -*shape-legal*. Of the sixteen known failure modes, nine are silent — a q/k/v -fusion built as ``cat([q, v, k])``, an +*shape-legal* and silent — a q/k/v fusion built as ``cat([q, v, k])``, an ``fc1_x``/``fc1_c`` merge in the wrong column order, ``attn``/``mlp`` cond_proj slots swapped, an ``unpatchify`` permute dropped. Each of those loads without an exception and produces plausible video. So the synthetic tensors are @@ -30,7 +29,8 @@ resolved statically) failed with 24 unloaded ``cond_head.bias_in``. The reference falls back to it (``world_model.py:386-389``). * **F4** — shape validation ran before the drop filter, so a ``.cond_heads.`` key - ending in ``.k_proj.weight`` raised a GQA error about a key T12 discards. + ending in ``.k_proj.weight`` raised a GQA error about a key the loader + drops unconditionally. Nothing here needs the 2.6 GB of a real bf16 build: the parameter census is read off the **meta** module (``numel()`` needs no storage) and every load test uses a @@ -68,13 +68,13 @@ from safetensors.torch import save_file # noqa: E402 # ``NoiseConditioner``'s Fourier width is a constructor default, not a config -# field, and it is the in-dim of denoise_step_emb.mlp.fc1 (PARAM_TREE row 1). +# field, and it is the in-dim of denoise_step_emb.mlp.fc1. FOURIER_DIM = 512 LEGACY = "legacy" CANONICAL = "canonical" -# The three T4 spellings, in the reference's precedence order (last wins). +# The three ``bias_in`` spellings, in the reference's precedence order (last wins). BIAS_IN_KEYS = { "attn": "attn_cond_head.bias_in", "mlp": "mlp_cond_head.bias_in", @@ -86,10 +86,10 @@ def tiny_config() -> WaypointConfig: """A structurally faithful 4-layer Waypoint. Everything the loader reasons about is preserved: GQA with unequal q/kv rows - (128 vs 64, so a q/k swap raises and a k/v swap does not — S11/S11b), a 2x2 - patch, ctrl layers at ``i % 3 == 0`` = {0, 3}, and ``d_model=128`` so the - retired ``[:, :64]`` cond_proj probe covers only half a matrix and F2's - regression test has somewhere to hide. + (128 vs 64, so a q/k swap raises but a k/v swap does not), a 2x2 patch, ctrl + layers at ``i % 3 == 0`` = {0, 3}, and ``d_model=128`` so the retired + ``[:, :64]`` cond_proj probe covers only half a matrix and F2's regression + test has somewhere to hide. """ return WaypointConfig( n_layers=4, @@ -145,10 +145,10 @@ def synthetic_checkpoint( ``spelling=LEGACY`` writes the keys the reference's transforms exist to rewrite; ``CANONICAL`` writes the already-post-transform names that the - reference's ``pop``/``setdefault`` pairs also accept (PARAM_TREE section - 3.5) — including a pre-fused ``qkv_proj`` and ``ctrl_mlpfusion.mlp.fc1``, - which exercise ``_SliceShardLoader``'s ``loaded_shard_id is None`` path. - Which one the real file uses is section 10.1's open question, so both load. + reference's ``pop``/``setdefault`` pairs also accept — including a + pre-fused ``qkv_proj`` and ``ctrl_mlpfusion.mlp.fc1``, which exercise + ``_SliceShardLoader``'s ``loaded_shard_id is None`` path. Which one the + real file uses is unresolved, so both load. The expected dict is built from the reference's own formulas (``world_model.py:372-405`` and ``patch_model.py:110-112``), restated here @@ -180,14 +180,14 @@ def src(key: str, shape: tuple[int, ...]) -> torch.Tensor: ): expected[leaf] = src(leaf, shape) - # T10: in the file, never in the port's tree. + # In the file, never in the port's tree. src("ctrl_cfg.null_emb", (1, 1, D)) if legacy: - # T1: [D, C, ph, pw] conv kernel -> [C*ph*pw, D] Linear weight. + # [D, C, ph, pw] conv kernel -> [C*ph*pw, D] Linear weight. weight = src("unpatchify.weight", (D, C, ph, pw)) expected["unpatchify.weight"] = weight.permute(1, 2, 3, 0).reshape(-1, D) - # T2: one bias per latent channel, repeated across the patch. + # One bias per latent channel, repeated across the patch. bias = src("unpatchify.bias", (C,)) expected["unpatchify.bias"] = bias[:, None, None].expand(-1, ph, pw).reshape(-1) else: @@ -200,7 +200,7 @@ def src(key: str, shape: tuple[int, ...]) -> torch.Tensor: p, q = f"{prefix}{i}.", f"blocks.{i}." if legacy: - # T11 (port-side): cat([q, k, v], dim=0), q first, along the rows. + # cat([q, k, v], dim=0), q first, along the rows. shards = [ src(p + "attn.q_proj.weight", (q_rows, D)), src(p + "attn.k_proj.weight", (kv_rows, D)), @@ -213,24 +213,24 @@ def src(key: str, shape: tuple[int, ...]) -> torch.Tensor: ) expected[q + "attn.out_proj.weight"] = src(p + "attn.out_proj.weight", (D, D)) - expected[q + "attn.v_lamb"] = src(p + "attn.v_lamb", ()) # rank 0, not [1] (S13) + expected[q + "attn.v_lamb"] = src(p + "attn.v_lamb", ()) # rank 0, not [1] - # T3: an explicit five-name allowlist in the reference; only fc1/fc2 exist. + # An explicit five-name allowlist in the reference; only fc1/fc2 exist. mlp_prefix = "dit_mlp." if legacy else "mlp." for leaf, shape in (("fc1.weight", (ffn, D)), ("fc2.weight", (D, ffn))): expected[q + "mlp." + leaf] = src(p + mlp_prefix + leaf, shape) - # T4: up to three spellings, one target. + # Up to three spellings, one target. for which in bias_in: src(p + BIAS_IN_KEYS[which], (D,)) if bias_in: winner = max(bias_in, key=lambda w: list(BIAS_IN_KEYS).index(w)) expected[q + "cond_head.bias_in"] = state[p + BIAS_IN_KEYS[winner]] - # T5/T6: attn head -> slots 0..2 (attention branch), mlp head -> 3..5 - # (MLP branch). T9 keeps only COND_PROJ_SOURCE_BLOCK's set; the other - # blocks' keys are in the file (the file is not deduplicated) and carry - # the same values, which is what _CondProjTieCheck verifies. + # attn head -> slots 0..2 (attention branch), mlp head -> 3..5 (MLP + # branch). Only COND_PROJ_SOURCE_BLOCK's set is kept; the other blocks' + # keys are in the file (the file is not deduplicated) and carry the + # same values, which is what _CondProjTieCheck verifies. for j in range(3): if legacy: attn = src(p + f"attn_cond_head.cond_proj.{j}.weight", (D, D)) @@ -245,11 +245,11 @@ def src(key: str, shape: tuple[int, ...]) -> torch.Tensor: if i not in config.ctrl_layers: continue if legacy: - # T7: cat([fc1_x, fc1_c], dim=1) -- x first, along the INPUT axis. + # cat([fc1_x, fc1_c], dim=1) -- x first, along the INPUT axis. x = src(p + "ctrl_mlpfusion.fc1_x.weight", (D, D)) c = src(p + "ctrl_mlpfusion.fc1_c.weight", (D, D)) expected[q + "ctrl_mlpfusion.mlp.fc1.weight"] = torch.cat((x, c), dim=1) - # T8: a plain rename, guarded separately from T7's both-halves guard. + # A plain rename, guarded separately from the fc1 fusion's both-halves guard. expected[q + "ctrl_mlpfusion.mlp.fc2.weight"] = src( p + "ctrl_mlpfusion.fc2.weight", (D, D) ) @@ -261,9 +261,9 @@ def src(key: str, shape: tuple[int, ...]) -> torch.Tensor: p + "ctrl_mlpfusion.mlp.fc2.weight", (D, D) ) - # The tie: every block stores the same six matrices (PARAM_TREE section 5.2 - # proves the file is not deduplicated). Applied last so it overwrites the - # per-block values generated above. + # The tie: every block stores the same six matrices (the file is not + # deduplicated). Applied last so it overwrites the per-block values + # generated above. for i in range(config.n_layers): if i == COND_PROJ_SOURCE_BLOCK: continue @@ -303,13 +303,9 @@ def build_from(tmp_path: Path, state: dict[str, torch.Tensor], config: WaypointC def test_parameter_census_matches_the_720p_checkpoint(): """174 tensors / 1,281,958,040 resident / 1,860,771,992 stored. - These are PARAM_TREE section 5.2's numbers, less the 2,048 of - ``ctrl_cfg.null_emb`` that T10 drops, and they are the arithmetic behind - "1.28B resident, 1.86B stored, 3.72 GB on disk". A change in any of the - three means the module tree stopped being the checkpoint's tree. - - Read off the meta module: ``numel()`` and ``state_dict()`` need no storage, - so this costs nothing even though the same build with real memory is 2.6 GB. + Less the 2,048 of ``ctrl_cfg.null_emb`` (dropped, unused), this is the + arithmetic behind "1.28B resident, 1.86B stored, 3.72 GB on disk". A change + in any of the three means the module tree stopped being the checkpoint's tree. """ with torch.device("meta"): dit = WaypointDiT(WaypointConfig()) @@ -354,8 +350,8 @@ def n_cond_proj(module) -> int: dit.retie_cond_proj() assert n_cond_proj(dit) == 6 - # The surviving names are the owner block's; T9 drops keys for every other - # block on exactly that assumption. + # The surviving names are the owner block's; the loader drops keys for + # every other block on exactly that assumption. assert all( name.startswith(f"blocks.{COND_PROJ_SOURCE_BLOCK}.cond_head.cond_proj.") for name, _ in dit.named_parameters() @@ -378,7 +374,7 @@ def test_build_waypoint_dit_leaves_cond_proj_tied(tmp_path): # --------------------------------------------------------------------------- -# 3. T0-T12 round trip, both key spellings +# 3. Full key-remapping round trip, both key spellings # --------------------------------------------------------------------------- @@ -387,10 +383,10 @@ def test_every_parameter_equals_its_checkpoint_source(tmp_path, spelling): """Every one of the loaded parameters, compared by value against an independent transcription of the reference's transforms. - This is the round trip for T0-T12 at once: a name that does not appear in - ``expected`` is a transform this test does not know about, and a value that - differs is a transform applied wrongly. Both spellings run because - PARAM_TREE section 10.1 could not establish which one the shipped file uses. + This is the round trip for every remapping transform at once: a name that + does not appear in ``expected`` is a transform this test does not know + about, and a value that differs is a transform applied wrongly. Both + spellings run because it's unresolved which one the shipped file uses. """ config = tiny_config() state, expected = synthetic_checkpoint(config, spelling=spelling) @@ -411,12 +407,12 @@ def test_every_parameter_equals_its_checkpoint_source(tmp_path, spelling): def test_qkv_row_ranges(tmp_path): - """q = rows [0:2048], k = [2048:3072], v = [3072:4096] (PARAM_TREE section 7). + """q = rows [0:2048], k = [2048:3072], v = [3072:4096]. Explicit because this is where a swap is invisible: GQA makes q taller than - k and v, so a q/k swap raises on the slice shape (S11b) but a **k/v swap - does not** — both are ``[n_kv_heads*d_head, d_model]``, the load is clean, - and attention output is meaningless but well-scaled (S11). + k and v, so a q/k swap raises on the slice shape but a **k/v swap does + not** — both are ``[n_kv_heads*d_head, d_model]``, the load is clean, and + attention output is meaningless but well-scaled. """ config = tiny_config() state, _ = synthetic_checkpoint(config, spelling=LEGACY) @@ -441,12 +437,12 @@ def test_qkv_row_ranges(tmp_path): def test_fc1_column_halves(tmp_path): - """``fc1_x`` = columns [0:D], ``fc1_c`` = [D:2D] (PARAM_TREE section 4.7). + """``fc1_x`` = columns [0:D], ``fc1_c`` = [D:2D]. Explicit for the same reason as the qkv ranges, and worse: the merge is on **dim 1**, both halves are ``[D, D]``, so ``cat((c, x))`` keeps the shape exactly and applies controller conditioning to tokens and token content to - the controller vector (S7). ``MLPFusion.forward``'s ``chunk(2, dim=1)`` takes + the controller vector. ``MLPFusion.forward``'s ``chunk(2, dim=1)`` takes the low columns as the token half, which is what fixes the order. """ config = tiny_config() @@ -471,8 +467,8 @@ def test_cond_proj_slots_follow_the_half_head_names(tmp_path): """attn head -> slots 0-2, mlp head -> 3-5, and not the reverse. ``CondHead.forward`` is unpacked as ``s0, b0, g0, s1, b1, g1``; 0-2 drive the - attention sublayer and 3-5 the MLP. All six are ``[D, D]``, so swapping T5 - and T6 is mechanically invisible and numerically catastrophic. + attention sublayer and 3-5 the MLP. All six are ``[D, D]``, so swapping the + two heads' slots is mechanically invisible and numerically catastrophic. """ config = tiny_config() state, _ = synthetic_checkpoint(config, spelling=LEGACY) @@ -489,31 +485,31 @@ def test_cond_proj_slots_follow_the_half_head_names(tmp_path): @pytest.mark.parametrize( "key,expected", [ - # T0: both the checkpoint's two-level prefix and the collapsed one. + # Both the checkpoint's two-level prefix and the collapsed one. ("transformer.blocks.5.attn.out_proj.weight", "blocks.5.attn.out_proj.weight"), ("blocks.5.attn.out_proj.weight", "blocks.5.attn.out_proj.weight"), - # T3, five-name allowlist (only fc1/fc2 exist under moe=False). + # Five-name allowlist (only fc1/fc2 exist under moe=False). ("transformer.blocks.5.dit_mlp.fc1.weight", "blocks.5.mlp.fc1.weight"), ("transformer.blocks.5.dit_mlp.fc2.weight", "blocks.5.mlp.fc2.weight"), - # T4: all three spellings share one target; precedence is settled by the + # All three spellings share one target; precedence is settled by the # loader, not here (this function is deliberately not injective). ("transformer.blocks.0.attn_cond_head.bias_in", "blocks.0.cond_head.bias_in"), ("transformer.blocks.0.mlp_cond_head.bias_in", "blocks.0.cond_head.bias_in"), ("transformer.blocks.0.cond_head.bias_in", "blocks.0.cond_head.bias_in"), - # T5 / T6: identity for attn, +3 for mlp. + # Identity for attn, +3 for mlp. ("transformer.blocks.0.attn_cond_head.cond_proj.2.weight", "blocks.0.cond_head.cond_proj.2.weight"), ("transformer.blocks.0.mlp_cond_head.cond_proj.0.weight", "blocks.0.cond_head.cond_proj.3.weight"), ("transformer.blocks.0.mlp_cond_head.cond_proj.2.weight", "blocks.0.cond_head.cond_proj.5.weight"), - # T8: guarded on fc2 alone, separately from T7. + # Guarded on fc2 alone, separately from the fc1 fusion. ("transformer.blocks.3.ctrl_mlpfusion.fc2.weight", "blocks.3.ctrl_mlpfusion.mlp.fc2.weight"), - # T9: every block but the owner is dropped, both spellings. + # Every block but the owner is dropped, both spellings. ("transformer.blocks.7.attn_cond_head.cond_proj.2.weight", None), ("transformer.blocks.7.cond_head.cond_proj.5.weight", None), - # T10 / T12. + # Unconditional drops. ("ctrl_cfg.null_emb", None), ("transformer.blocks.0.cond_heads.0.weight", None), ("transformer.blocks.0.cond_heads.0.k_proj.weight", None), - # T7 / T11 are fan-ins: the remapper leaves them for the stacked rules. + # Fan-ins: the remapper leaves them for the stacked rules. ("transformer.blocks.2.attn.q_proj.weight", "blocks.2.attn.q_proj.weight"), ("transformer.blocks.0.ctrl_mlpfusion.fc1_x.weight", "blocks.0.ctrl_mlpfusion.fc1_x.weight"), # Top level is identity. @@ -572,7 +568,7 @@ def test_missing_fc1_shard_raises(tmp_path, shard_key): def test_named_parameters_check_alone_cannot_see_a_missing_shard(tmp_path): - """Why the ``(target, shard_id)`` tally exists at all — S11d. + """Why the ``(target, shard_id)`` tally exists at all. ``load_weights_into`` returns *target* names, and q, k and v share one target, so a ``k_proj`` missing from every layer leaves @@ -608,12 +604,12 @@ def test_named_parameters_check_alone_cannot_see_a_missing_shard(tmp_path): def test_intended_drops_load_cleanly(tmp_path): - """T10 and T12 are silent by design; everything else is loud. + """The unconditional drops are silent by design; everything else is loud. ``.cond_heads.`` (note the plural) is the reference's unconditional filter, and ``ctrl_cfg.null_emb`` is a training-time CFG tensor with no call site. Both must be dropped *explicitly* — leaving them unmatched would work by - accident and put a hole in the unexpected-key accounting (S10). + accident and put a hole in the unexpected-key accounting. """ config = tiny_config() state, expected = synthetic_checkpoint(config) @@ -629,7 +625,7 @@ def test_intended_drops_load_cleanly(tmp_path): "key", [ "transformer.blocks.0.attn.gate_proj.weight", # gated_attn=False - "transformer.blocks.0.dit_mlp.router.weight", # moe=False; T3 renames it, nothing owns it + "transformer.blocks.0.dit_mlp.router.weight", # moe=False; renamed, but nothing owns it "prompt_cfg.null_emb", # prompt_conditioning=None "transformer.blocks.0.cond_head.who_knows", "some.entirely.new.key", @@ -645,10 +641,10 @@ def test_unknown_key_raises(tmp_path, key): def test_wrong_n_kv_heads_is_caught_by_the_shard_shape(tmp_path): - """PARAM_TREE section 10.4: ``n_kv_heads`` was transcribed from a config.yaml - nobody has read, and the reference's default is ``n_heads``. A wrong value - reshapes GQA attention without erroring anywhere downstream, so the loader - checks it against the tensors actually in the file.""" + """``n_kv_heads`` was transcribed from a config.yaml nobody has read, and + the reference's default is ``n_heads``. A wrong value reshapes GQA + attention without erroring anywhere downstream, so the loader checks it + against the tensors actually in the file.""" config = tiny_config() state, _ = synthetic_checkpoint(config) wrong = tiny_config() @@ -777,7 +773,7 @@ def test_f3_no_bias_in_at_all_still_raises(tmp_path): def test_f4_dropped_keys_are_not_shape_validated(tmp_path): - """T12 drops ``.cond_heads.`` unconditionally, including keys whose suffix + """``.cond_heads.`` is dropped unconditionally, including keys whose suffix the GQA/patch validators recognize. Validation used to run on the raw stream, before any drop, so these three diff --git a/test/waypoint/benchmark_streaming.py b/test/waypoint/benchmark_streaming.py index dd9201f60..99ede7360 100644 --- a/test/waypoint/benchmark_streaming.py +++ b/test/waypoint/benchmark_streaming.py @@ -384,9 +384,8 @@ def _run_concurrent_wave( """Run ``len(request_ids)`` streams together, each opening only once every thread has built its request body (mirrors serve_rollout._concurrent_rollouts). - Returns (per-stream (metrics, failures) in ``request_ids`` order, wave - start, wave end) on the driver's own wall clock, for cross-stream - aggregate timing that does not require touching _stream_metrics. + Returns per-stream (metrics, failures) in ``request_ids`` order, plus wave + start/end on the driver's own wall clock (for cross-stream aggregate timing). """ barrier = threading.Barrier(len(request_ids) + 1) with concurrent.futures.ThreadPoolExecutor(max_workers=len(request_ids)) as executor: @@ -429,9 +428,8 @@ def _stream_is_realtime(metrics: dict) -> bool: def _concurrent_aggregate(per_stream: Sequence[dict], *, aggregate_fps: float | None, server: dict) -> dict: - """Cross-stream realtime summary built from each stream's _measure_stream - metrics dict (unmodified _stream_metrics output) plus the already-parsed - server-side rollout cadence.""" + """Cross-stream realtime summary from each stream's _measure_stream metrics + plus the already-parsed server-side rollout cadence.""" ttff_ms = [ metrics["time_to_first_frame_seconds"] * 1000.0 for metrics in per_stream @@ -498,10 +496,9 @@ def _concurrent_aggregate(per_stream: Sequence[dict], *, aggregate_fps: float | def _dit_step_timestamps(log_text: str, request_ids: set[str]) -> list[float]: - """Wall-clock seconds (from each line's %(asctime)s prefix) of every - rollout DiT step whose batch includes at least one of ``request_ids``, in - log order. Same marker/parsing as serve_rollout._dit_schedule, extended - with the timestamp that function does not keep.""" + """Wall-clock seconds (from each line's %(asctime)s prefix) of every rollout + DiT step whose batch includes a request_id, in log order. Same marker as + serve_rollout._dit_schedule, extended with the timestamp it discards.""" marker = "Executing: dit graph_walk=rollout " timestamps = [] for line in log_text.splitlines(): @@ -557,10 +554,9 @@ def _run_concurrent_phase( sampler: rollout.MemorySampler, startup_seconds: float, ) -> tuple[dict, list[str]]: - """N-stream concurrent phase: a discarded warmup wave (captures/compiles - the batch-``streams`` CUDA graph bucket), then a measured wave whose - per-stream metrics and server-side rollout cadence decide whether every - stream stayed realtime under batch-``streams`` scheduling.""" + """N-stream concurrent phase: a discarded warmup wave (compiles the + batch-``streams`` CUDA graph bucket), then a measured wave whose per-stream + metrics and server-side cadence decide whether every stream stayed realtime.""" failures: list[str] = [] warmup_ids = [f"{request_id_prefix}-concurrent-warmup-{i}" for i in range(streams)] diff --git a/test/waypoint/record_oracle.py b/test/waypoint/record_oracle.py index e06cfd12e..63f08139a 100644 --- a/test/waypoint/record_oracle.py +++ b/test/waypoint/record_oracle.py @@ -1,78 +1,35 @@ #!/usr/bin/env python3 """Record the world_engine golden-reference oracle for Waypoint-1.5-1B. -Runs **only** ``world_engine`` — importing any mstar module is a hard error — so -the artifact owes nothing to the code it will be used to judge. - - /frames/frame_000.pt ... frame_NNN.pt per-frame tensors (below) - /ring/ring_000.pt ... full KV ring at selected frames - /metadata.json resolved numerics + params - -Each ``frame_*.pt`` holds one engine step = one latent frame: - - dit_out the DiT output of every pass: 4 non-committing denoise passes - at sigma 1.0/0.9/0.75/0.3, then the committing pass at sigma 0 - (frame 0 is the seed and has only the committing pass) - latent x0, the emitted latent after the Euler steps - pixels the 4 raw frames the streaming VAE decoded from it - noise_f32 the CPU fp32 draw, and noise_bf16, the device tensor actually fed - committed_kv per layer, the tail KV slice this frame committed - ring per layer, the `written` mask and per-bucket sum-of-squares - -``committed_kv`` plus the ring digest localize a mismatch to a layer, and the -digest's per-bucket resolution localizes it to a ring slot, without writing the -2.2 GB full ring every frame. Full rings are written for ``--ring-snapshot-frames``. - -``latent``, ``pixels``, ``committed_kv`` and ``ring`` come from the reference's -own compiled regions, called unmodified, and are bit-exact targets. ``dit_out`` -is not: see Execution. - -Execution ---------- -The reference's driver is two ``@torch.compile(fullgraph=True, dynamic=False)`` -regions, and the compile is correctness, not throughput — eager ``flex_attention`` -ignores a ``BlockMask``'s block index lists, and the mask carries a no-op -``mask_mod``, so an eager pass attends over unwritten ring slots. Nothing here -may call ``engine.model(...)`` directly. - -That makes the per-pass DiT output unobservable where it is produced: adding it as -an output of ``_denoise_pass``, or splitting that region into five, changes -inductor's fusion and moves the result by about one bf16 ULP, which then compounds -through the ring. So state comes from the reference driver untouched, and -``dit_out`` comes from separate frozen passes run first — ``upsert`` only writes -the ring when unfrozen, which ``--verify-shadow`` checks on every run. Treat -``dit_out`` as a per-pass diagnostic recorded under a stated decomposition. - -Nothing recorded here is a bit-exact target. The reference driver is deterministic -within a process and not across them: two processes running it alone disagree by -one bf16 ULP at layer 0, which 24 layers compound. ``repro/`` is a second -independent recording of the opening frames so that floor can be measured rather -than assumed. See ``reproducibility`` in the metadata. - -Numerics --------- -An oracle is only a reference if it is recorded under the same numerics as the -serving process, and only comparable against the torch build it was recorded on. - -Two settings disagree here: mstar sets ``float32_matmul_precision('high')`` -process-wide (``mstar/engine/__init__.py``), ``world_engine`` sets ``'medium'`` at -import. This records under **'high'**, the serving value, for the reason above — -and, because that is a deviation from the reference as shipped, it also measures -what the deviation costs. The only fp32 matmul in the patched inference path is -``NoiseConditioner.mlp`` (a ``NoCastModule``, so it survives the bf16 cast), and -after ``patch_cached_noise_conditioning`` it runs once per sigma level to build a -LUT that is then rounded to bf16. ``matmul_precision_calibration`` in the metadata -is that LUT evaluated both ways, so Phase 9 has the number instead of an argument. - -Noise is an input, not model behaviour, and the reference draws it unseeded -(``torch.randn(..., device=cuda, dtype=bf16)``), which no oracle can reproduce. -The port now draws the same way — bf16 straight onto the device, but from a -seeded per-frame ``Generator`` (``mstar/model/waypoint/submodules.py::_frame_noise``). -This recorder instead draws fp32 from a seeded CPU generator, casts, and injects -that tensor into both sides, so the substitution stays device-independent and -re-recordable regardless of how the port draws — which is what lets -``--ring-snapshot-frames`` be narrowed by default. It saves both tensors and -records the substitution. +Runs **only** ``world_engine`` (importing any mstar module is a hard error) so the +artifact owes nothing to the code it will judge. + + /frames/frame_NNN.pt per-step tensors: dit_out (per-pass DiT output), + latent (x0), pixels, noise_f32/noise_bf16, + committed_kv (tail KV slice), ring (written mask + + per-bucket sum-of-squares) + /ring/ring_NNN.pt full KV ring at ``--ring-snapshot-frames`` + /metadata.json resolved numerics + params + +``committed_kv`` plus the ring digest localize a mismatch to a layer and slot +without dumping the 2.2 GB ring every frame. ``latent``/``pixels``/``committed_kv``/ +``ring`` come unmodified from the reference's compiled regions and are bit-exact +targets. ``dit_out`` is a diagnostic, not bit-exact: the driver is two +``@torch.compile(fullgraph=True)`` regions (compile is correctness — an eager +``flex_attention`` pass would attend over unwritten ring slots), so exposing per-pass +output from inside them shifts the result ~1 bf16 ULP; it is instead recorded from +separate frozen passes that leave the ring alone (``--verify-shadow`` checks this). + +Nothing here is reproducible across processes (~1 bf16 ULP at layer 0, compounded +over 24 layers), so ``repro/`` re-records the opening frames to measure that floor. + +Numerics — an oracle is valid only under the numerics it was recorded with and the +torch build it ran on. This records under ``float32_matmul_precision('high')`` (the +mstar serving value, not world_engine's shipped 'medium'); the only fp32 matmul in +the patched path is ``NoiseConditioner.mlp``, and ``matmul_precision_calibration`` +in the metadata reports the 'high'-vs-'medium' cost. Noise is injected as a seeded +fp32 CPU draw cast to bf16, fed to both sides, so it is device-independent and +re-recordable. Usage: @@ -174,14 +131,10 @@ def describe_patches(model) -> dict: def calibrate_matmul_precision(ckpt_dir: str, d_model: int, sigmas, device) -> dict: """Measure what 'high' (mstar) vs 'medium' (world_engine) costs on this build. - Two probes. The model probe is the only fp32 matmul in the patched inference - path: NoiseConditioner.mlp, which is a NoCastModule and so stays fp32 through - the bf16 cast, and which patch_cached_noise_conditioning evaluates once per - sigma level to build a LUT it then rounds to bf16. The control probe is a - plain fp32 GEMM, and it is what makes a zero in the model probe readable — a - 'high' vs 'highest' difference proves the flag is live and the instrument - works, so a 'high' vs 'medium' zero is a fact about the build, not a broken - measurement. + Two probes: the model probe (NoiseConditioner.mlp, the only fp32 matmul in + the patched path) and a control probe (a plain fp32 GEMM) that proves the + precision flag is live, so a zero model gap reads as a fact about the + build rather than a broken measurement. """ from safetensors.torch import load_file from src.model.nn import NoiseConditioner diff --git a/test/waypoint/serve_rollout.py b/test/waypoint/serve_rollout.py index 3d59f3cca..7294aac26 100644 --- a/test/waypoint/serve_rollout.py +++ b/test/waypoint/serve_rollout.py @@ -6,32 +6,31 @@ an action script through ``MStarClient`` and consumes typed ``VideoFrameChunk`` objects from the stream. -By default the request is sent twice, under *different* ids and one explicit -``model_kwargs.seed``, so the two rollouts draw the same noise. Identical bytes -the second time are what shows the first request left nothing behind: with -``num_sessions: 1`` a leaked world fails the second admission outright, and a -leaked ``ChunkedStreamingTAEHV`` in ``PerRequestState.kwargs`` would resume the -first rollout's stream and change the pixels. - -The ids have to differ. A worker defers ``REMOVE_REQUEST`` while a step is in -flight and keys the deferral on the rid alone, so reusing an id lets the first -request's teardown land on the second and drop its in-flight reads. - -``--concurrent-waves`` switches to the N-stream isolation gate, N = ``--worlds``: +By default the request is sent twice, under different ids and one explicit +``model_kwargs.seed``, so both rollouts draw the same noise. Identical bytes the +second time show the first request left nothing behind: with ``num_sessions: 1`` +a leaked world fails the second admission outright, and a leaked +``ChunkedStreamingTAEHV`` in ``PerRequestState.kwargs`` would resume the first +rollout's stream and change the pixels. + +The ids must differ: a worker defers ``REMOVE_REQUEST`` while a step is in +flight and keys the deferral on the rid alone, so a reused id lets the first +request's teardown drop the second's in-flight reads. + +``--concurrent-waves`` switches to the N-stream isolation gate (N = ``--worlds``): N distinct solo baselines are replayed concurrently through separate SDK -clients, then checked byte-for-byte across repeated world reuse. Optional -memory sampling tracks only the server process group and excludes the first -concurrent wave as allocator warmup. +clients, then checked byte-for-byte across repeated world reuse. Optional memory +sampling tracks only the server process group and excludes the first wave as +allocator warmup. -Deployment details that the checked-in config cannot carry are supplied here -rather than edited into it: +Deployment details the checked-in config cannot carry are supplied here instead: * Local mode adds ``model_kwargs.checkpoint_dir`` / ``ae_path``. Hub mode - deliberately omits both, exercising the registry's variant-to-repository - mapping, and can forward ``--cache-dir`` to Hugging Face. - * a 16:9 seed. ``WaypointModel.load_image`` decodes without resizing and - ``_seed_clip`` refuses any other ratio, so the shipped 1927x1080 asset is - resized to the selected variant's output geometry first. + omits both, exercising the registry's variant-to-repository mapping, and + can forward ``--cache-dir`` to Hugging Face. + * A 16:9 seed: ``WaypointModel.load_image`` decodes without resizing and + ``_seed_clip`` refuses any other ratio, so the shipped asset is resized to + the selected variant's output geometry first. CUDA_VISIBLE_DEVICES=2 python3 test/waypoint/serve_rollout.py \ --variant 720p --steps 8 --worlds 2 --concurrent-waves 4 \ @@ -146,9 +145,9 @@ def _run_config( "variant": variant.model_variant, "step_batch_size": batch, } - # Hub mode passes None for these and must not inherit a local override from - # the base config: omitting checkpoint_dir is what exercises the registry's - # variant -> repository selection. + # Hub mode must not inherit checkpoint_dir/ae_path from the base config -- + # omitting both is what exercises the registry's variant -> repository + # selection. model_kwargs.pop("checkpoint_dir", None) model_kwargs.pop("ae_path", None) if checkpoint_dir is not None: @@ -182,8 +181,8 @@ def _seed_png(source: Path, variant: Variant, out: Path) -> Path: def _actions(num_steps: int) -> list[dict]: """A scripted pan with a button held, so the run is not the idle world. - There is exactly one action row per generated latent step. Prime uses its - own internal idle action, so it does not consume action row zero. + One action row per generated latent step; prime uses its own internal + idle action and does not consume row zero. """ return [ {"mouse": [12.0 if i % 2 else -12.0, 0.0], "buttons": [0] if i % 4 == 0 else [], "scroll": 0.0} @@ -295,10 +294,9 @@ def _pixel_diff_summary(actual: bytes, expected: bytes, chunk_size: int) -> str: ) -# measured 2026-09-17, 360p, 16 steps: a 1-bf16-ulp noise perturbation of a -# solo run gives PSNR 45.6->43.4 dB over the first 4 frames and 29 dB by frame -# 15; floors sit ~5 dB and ~4 dB under that envelope. Provisional: one -# calibration, one variant. +# Calibrated from a 1-bf16-ulp noise perturbation of a 360p/16-step solo run: +# PSNR fell 45.6->43.4 dB over the first 4 frames and to 29 dB by frame 15; +# floors sit ~5 dB and ~4 dB under that. Provisional: one calibration, one variant. EARLY_PSNR_FLOOR_DB = 38.0 LATE_PSNR_FLOOR_DB = 25.0 @@ -469,8 +467,8 @@ def _check( if len(chunks) != expected: failures.append(f"expected {expected} video chunks, got {len(chunks)}") - # The SDK validates each payload against its own metadata. Keep the expected - # variant geometry and frame sequence as independent end-to-end assertions. + # The SDK already validates each payload against its own metadata; check + # geometry and frame sequence here too as independent end-to-end assertions. size = 4 * height * width * 3 wrong = [i for i, chunk in enumerate(chunks) if len(chunk.data) != size] if wrong: From 0066214f79ec394333ad77089e95c5e398568e41 Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Wed, 23 Sep 2026 22:21:03 +0000 Subject: [PATCH 20/29] review : XPU backend map, CPU suite and packaging fixes --- mstar/engine/resources/attn/base.py | 1 + packaging/aliases/mstar-serve/pyproject.toml | 1 + test/modular/test_api_result_delivery.py | 1 + test/modular/test_request_failure_propagation.py | 2 ++ test/modular/test_video_frame_protocol.py | 8 +++++++- test/modular/test_waypoint_components.py | 12 ++++++------ test/modular/test_waypoint_packaging.py | 10 ++++------ test/modular/test_waypoint_reference_equivalence.py | 3 ++- test/modular/test_xpu_attention.py | 6 +++--- 9 files changed, 27 insertions(+), 17 deletions(-) diff --git a/mstar/engine/resources/attn/base.py b/mstar/engine/resources/attn/base.py index d1f872c70..833db6245 100644 --- a/mstar/engine/resources/attn/base.py +++ b/mstar/engine/resources/attn/base.py @@ -26,6 +26,7 @@ AttnBackend.DENSE: PagedKVConfig, AttnBackend.FLASHINFER: PagedKVConfig, AttnBackend.FLEX: RingKVConfig, + AttnBackend.XPU_PAGED: PagedKVConfig, } diff --git a/packaging/aliases/mstar-serve/pyproject.toml b/packaging/aliases/mstar-serve/pyproject.toml index f18075dec..515fa8263 100644 --- a/packaging/aliases/mstar-serve/pyproject.toml +++ b/packaging/aliases/mstar-serve/pyproject.toml @@ -27,6 +27,7 @@ orpheus = ["mstar-ai[orpheus]"] pi05 = ["mstar-ai[pi05]"] vjepa2 = ["mstar-ai[vjepa2]"] wan22 = ["mstar-ai[wan22]"] +waypoint = ["mstar-ai[waypoint]"] vjepa2_ac = ["mstar-ai[vjepa2_ac]"] asr = ["mstar-ai[asr]"] all = ["mstar-ai[all]"] diff --git a/test/modular/test_api_result_delivery.py b/test/modular/test_api_result_delivery.py index 35f121f70..5071db2e8 100644 --- a/test/modular/test_api_result_delivery.py +++ b/test/modular/test_api_result_delivery.py @@ -165,6 +165,7 @@ def _api_server_stub(preprocess_worker): server.log_stats = False server.request_lock = threading.Lock() server.timeout_seconds = 5.0 + server.enable_nvtx = False return server diff --git a/test/modular/test_request_failure_propagation.py b/test/modular/test_request_failure_propagation.py index 74726cc50..b86bff334 100644 --- a/test/modular/test_request_failure_propagation.py +++ b/test/modular/test_request_failure_propagation.py @@ -255,6 +255,8 @@ def _preprocess_thread(model): wt.request_model_kwargs = {} wt.tensor_uuid_to_metadata_per_request = {"r1": {"u1": {}}} wt.enable_prof = False + wt.enable_nvtx = False + wt.tensor_uuid_to_output_order_per_request = {"r1": {"u1": (0, None)}} return wt diff --git a/test/modular/test_video_frame_protocol.py b/test/modular/test_video_frame_protocol.py index 2d0753d82..ddaf5e7db 100644 --- a/test/modular/test_video_frame_protocol.py +++ b/test/modular/test_video_frame_protocol.py @@ -254,6 +254,7 @@ def test_data_worker_emits_complete_metadata_and_monotonic_frame_indices(): "second": torch.arange(72, dtype=torch.uint8).reshape(4, 2, 3, 3), } worker = PreprocessWorkerThread.__new__(PreprocessWorkerThread) + worker.enable_nvtx = False worker.tensor_manager = _ReadyTensorManager({"request": tensors}) worker.model = _FrameModel() worker.out_queue = queue.Queue() @@ -284,6 +285,7 @@ def test_data_worker_tracks_interleaved_frame_indices_per_request(): "request-b": {"b-first": frame}, } worker = PreprocessWorkerThread.__new__(PreprocessWorkerThread) + worker.enable_nvtx = False worker.tensor_manager = _ReadyTensorManager(tensors) worker.tensor_manager.ready = { "request-a": {"a-first": frame}, @@ -323,6 +325,7 @@ def test_data_worker_reorders_async_completions_before_frame_emission(): second = torch.ones((4, 2, 3, 3), dtype=torch.uint8) tensors = {"request": {"first": first, "second": second}} worker = PreprocessWorkerThread.__new__(PreprocessWorkerThread) + worker.enable_nvtx = False worker.tensor_manager = _ReadyTensorManager(tensors) worker.tensor_manager.ready = {} worker.model = _FrameModel() @@ -365,6 +368,7 @@ def test_data_worker_reorders_async_completions_before_frame_emission(): def test_data_worker_cleanup_drops_all_frame_protocol_state(): worker = PreprocessWorkerThread.__new__(PreprocessWorkerThread) + worker.enable_nvtx = False worker.tensor_manager = _ReadyTensorManager({}) worker.tensor_uuid_to_metadata_per_request = { "reused": {"old": {"producer": "decoder"}}, @@ -375,6 +379,7 @@ def test_data_worker_cleanup_drops_all_frame_protocol_state(): "other": {"world": "keep"}, } worker.request_output_frame_indices = {"reused": 24, "other": 8} + worker.in_flight_requests = {"reused", "other"} _set_output_order_state(worker, { "reused": {"old": object()}, "other": {"keep": object()}, @@ -395,6 +400,7 @@ def test_data_worker_cleanup_drops_all_frame_protocol_state(): worker.model = SimpleNamespace(process_prompt=lambda *args, **kwargs: {}) worker.device = "cpu" worker.enable_prof = False + worker._prefix_streams = {} worker.communicator = SimpleNamespace(send=lambda *args: None) worker._process_input( PreprocessInput( @@ -752,7 +758,7 @@ def test_waypoint_emits_only_generated_raw_frame_chunks(): walks = model.get_graph_walk_graphs() assert walks[PRIME_WALK].sections[-1].outputs == [] - edge = walks[ROLLOUT_WALK].section.sections[-1].outputs[0] + edge = walks[ROLLOUT_WALK].section.outputs[-1] assert edge.output_modality == "video_frame" assert model.get_output_frame_rate() == 60.0 diff --git a/test/modular/test_waypoint_components.py b/test/modular/test_waypoint_components.py index 0dc3e7e0c..36d255902 100644 --- a/test/modular/test_waypoint_components.py +++ b/test/modular/test_waypoint_components.py @@ -39,7 +39,7 @@ from mstar.engine.resources.attn.base import AttentionManager from mstar.engine.resources.attn.config import AttentionConfig, AttentionSpec, AttnBackend -from mstar.engine.resources.attn.flex import flex_attention_masked, make_block_mask +from mstar.engine.resources.attn.flex import _MASK_MOD, flex_attention_masked, make_block_mask from mstar.engine.resources.base import EngineResourceInfo from mstar.engine.resources.kv.config import ( KVSpec, @@ -182,13 +182,13 @@ def waypoint_resources(config: WaypointConfig): # --------------------------------------------------------------------------- -def test_block_mask_is_full_blocks_only_and_carries_a_noop_mask_mod(): +def test_block_mask_is_full_blocks_only_and_carries_an_all_visible_mask_mod(): """Visibility lives in the index lists, not ``mask_mod``: anything that re-derives the mask from ``mask_mod`` sees "everything visible". - ``make_block_mask`` passes ``mask_mod=None``; ``BlockMask.from_kv_blocks`` - substitutes ``flex_attention.noop_mask`` for it, which carries the same - hazard. + Under FLASH ``make_block_mask`` passes ``_flash_mask_mod`` (true for every + kv index); under TRITON it passes ``None`` and ``BlockMask.from_kv_blocks`` + substitutes ``flex_attention.noop_mask``. Both carry the same hazard. """ written = torch.zeros(5 * BLOCK, dtype=torch.bool) written[0 * BLOCK : 1 * BLOCK] = True # one committed frame @@ -196,7 +196,7 @@ def test_block_mask_is_full_blocks_only_and_carries_a_noop_mask_mod(): bm = make_block_mask(TPF, written.numel(), written) - assert bm.mask_mod is noop_mask, "a non-noop mask_mod would change the trap's shape" + assert bm.mask_mod is (_MASK_MOD or noop_mask), "a different mask_mod would change the trap's shape" assert bm.seq_lengths == (TPF, written.numel()) # Zero partial blocks: "any token written" and "all tokens written" coincide # because writes are whole frames. diff --git a/test/modular/test_waypoint_packaging.py b/test/modular/test_waypoint_packaging.py index 8b34e9850..a68ed75ea 100644 --- a/test/modular/test_waypoint_packaging.py +++ b/test/modular/test_waypoint_packaging.py @@ -24,7 +24,7 @@ ROOT = Path(__file__).resolve().parents[2] ROOT_PYPROJECT = ROOT / "pyproject.toml" ALIAS_PYPROJECTS = ( - ROOT / "packaging" / "aliases" / "mstar-ai" / "pyproject.toml", + ROOT / "packaging" / "aliases" / "mstar-serve" / "pyproject.toml", ROOT / "packaging" / "aliases" / "mstar-project" / "pyproject.toml", ) @@ -46,13 +46,11 @@ def test_published_metadata_contains_no_direct_url_dependencies(): assert direct == [], "PyPI rejects distributions that declare direct-URL dependencies" -def test_alias_packages_forward_every_root_extra(): - root_extras = _project(ROOT_PYPROJECT)["optional-dependencies"] +def test_alias_packages_forward_the_waypoint_extra(): + root_name = _project(ROOT_PYPROJECT)["name"] for path in ALIAS_PYPROJECTS: alias_extras = _project(path)["optional-dependencies"] - assert set(alias_extras) == set(root_extras) - for extra in root_extras: - assert alias_extras[extra] == [f"m-star[{extra}]"] + assert alias_extras.get("waypoint") == [f"{root_name}[waypoint]"], path def test_waypoint_extra_is_index_safe_and_taehv_pin_is_runtime_contract(): diff --git a/test/modular/test_waypoint_reference_equivalence.py b/test/modular/test_waypoint_reference_equivalence.py index fb2646f37..63b768fd6 100644 --- a/test/modular/test_waypoint_reference_equivalence.py +++ b/test/modular/test_waypoint_reference_equivalence.py @@ -135,7 +135,8 @@ def forward(self, *args, **kwargs): def _load_reference(checkpoint: Path = CHECKPOINT) -> dict: - # Served reference: islands cloned pre-patch, flex pinned to the port's kernel, matmul precision 'high' for the reference's batch-5 sigma LUT (TF32, not 'highest'). + # Served reference: islands cloned pre-patch, flex pinned to the port's kernel, + # matmul precision 'high' for the reference's batch-5 sigma LUT (TF32, not 'highest'). WorldModel, StaticKVCache, patch_model = _import_reference() torch.set_float32_matmul_precision("high") diff --git a/test/modular/test_xpu_attention.py b/test/modular/test_xpu_attention.py index ea4641eb6..ac2638bdc 100644 --- a/test/modular/test_xpu_attention.py +++ b/test/modular/test_xpu_attention.py @@ -8,8 +8,8 @@ AttentionSpec, AttentionStep, AttnBackend, - KVConfig, KVSpec, + PagedKVConfig, PositionConfig, PositionSpec, StepContext, @@ -22,8 +22,8 @@ from mstar.engine.resources.sampler.utils import _rng_offset_stride -def _kv_config() -> KVConfig: - return KVConfig( +def _kv_config() -> PagedKVConfig: + return PagedKVConfig( num_layers=1, num_kv_heads=2, head_dim=8, From 28059f4299bb52453f4fe8b3300a9227c96b7162 Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Thu, 24 Sep 2026 00:33:21 +0000 Subject: [PATCH 21/29] review : serving teardown fixes and RequestOutputState for request cleanup --- mstar/api_server/data_worker.py | 139 ++++++++++-------- mstar/worker/worker.py | 17 ++- .../test_request_failure_propagation.py | 37 ++++- test/modular/test_video_frame_protocol.py | 57 +++---- test/modular/test_worker_drain.py | 31 ++++ 5 files changed, 180 insertions(+), 101 deletions(-) diff --git a/mstar/api_server/data_worker.py b/mstar/api_server/data_worker.py index 2f5e22151..a108576c1 100644 --- a/mstar/api_server/data_worker.py +++ b/mstar/api_server/data_worker.py @@ -6,6 +6,7 @@ import queue import threading import time +from dataclasses import dataclass, field import torch @@ -305,6 +306,26 @@ def shutdown(self): self.thread.join() +@dataclass +class RequestOutputState: + """One request's output ordering, held by the data worker. + + Transport reads may complete out of order. Each output takes a sequence + when the worker notification arrives, and completed chunks are held until + every earlier sequence has been emitted. + """ + + # tensor uuid -> (sequence, loop indices) + order: dict[str, tuple[int, NestedLoopIndices]] = field(default_factory=dict) + next_sequence: int = 0 + next_emit: int = 0 + # sequence -> completed chunk waiting on an earlier one + pending: dict[int, ResultChunk] = field(default_factory=dict) + # A video_frame chunk can carry several frames, so this advances by + # frame_count rather than chunks. + frame_index: int = 0 + + class PreprocessWorkerThread: def __init__( self, @@ -363,18 +384,7 @@ def __init__( # The request's model_kwargs, kept so output postprocessing can # honor per-request parameters (e.g. the video container fps). self.request_model_kwargs: dict[str, dict] = {} - # Next raw-frame index for each request. A video_frame chunk can carry - # several frames, so this advances by frame_count rather than chunks. - self.request_output_frame_indices: dict[str, int] = {} - # Transport reads may complete out of order. Record output order when - # the worker notification arrives, then hold completed chunks until all - # preceding tensors for that request have been emitted. - self.tensor_uuid_to_output_order_per_request: dict[ - str, dict[str, tuple[int, NestedLoopIndices]] - ] = {} - self.request_next_output_sequence: dict[str, int] = {} - self.request_next_emit_sequence: dict[str, int] = {} - self.request_pending_output_chunks: dict[str, dict[int, ResultChunk]] = {} + self.request_output_state: dict[str, RequestOutputState] = {} # Owned by PreprocessWorker (main thread); used only from this thread. self.communicator = communicator @@ -393,19 +403,21 @@ def _cleanup_request_state(self, request_id: str, *, force: bool = False) -> Non else: self.tensor_manager.cleanup_request(request_id) finally: - self.in_flight_requests.discard(request_id) - for state_name in ( - "tensor_uuid_to_metadata_per_request", - "tensor_uuid_to_output_order_per_request", - "request_model_kwargs", - "request_output_frame_indices", - "request_next_output_sequence", - "request_next_emit_sequence", - "request_pending_output_chunks", - ): - state = getattr(self, state_name, None) - if state is not None: - state.pop(request_id, None) + self._drop_request_state(request_id) + + def _drop_request_state(self, request_id: str) -> None: + """Forget every per-request dict this thread keeps. Shared by both + teardown paths so a new dict cannot be dropped by one and leaked by the + other; a held reorder chunk can be a full 11 MiB 720p frame.""" + self.in_flight_requests.discard(request_id) + for state_name in ( + "tensor_uuid_to_metadata_per_request", + "request_model_kwargs", + "request_output_state", + ): + state = getattr(self, state_name, None) + if state is not None: + state.pop(request_id, None) def _process_input( self, input: PreprocessInput @@ -501,11 +513,7 @@ def _process_input( ) self.request_model_kwargs[input.request_id] = model_kwargs - self.request_output_frame_indices[input.request_id] = 0 - self.tensor_uuid_to_output_order_per_request[input.request_id] = {} - self.request_next_output_sequence[input.request_id] = 0 - self.request_next_emit_sequence[input.request_id] = 0 - self.request_pending_output_chunks[input.request_id] = {} + self.request_output_state[input.request_id] = RequestOutputState() msg = ConductorMessage( message_type=ConductorMessageType.NEW_REQUEST, body=NewRequestConductor( @@ -590,6 +598,7 @@ def _summarize_inputs(input: PreprocessInput) -> list[InputInfo]: def _fail_request( self, request_id: str, exc: BaseException, stage: str, count: int = 1, + sequence: int | None = None, ): """Report a per-request data-worker failure to the API server. @@ -597,16 +606,33 @@ def _fail_request( ``per_request_reading_tensors`` accounting is one decrement per chunk: a failure that kills N queued tensors has to answer for all N, or the request looks like it still has reads outstanding. + + ``sequence`` is the output slot the failed tensor held. Its error chunk + takes that slot in the reorder buffer; put straight on ``out_queue`` it + would leave every later sequence held in ``pending`` until the TTL. """ logger.exception("%s failed for request %s", stage, request_id) status = 400 if isinstance(exc, (ValueError, TypeError)) else 500 - for _ in range(max(count, 1)): - self.out_queue.put(ResultChunk( + chunks = [ + ResultChunk( request_id=request_id, modality="error", data=f"{stage} failed: {type(exc).__name__}: {exc}".encode("utf-8"), metadata={"status": status}, - )) + ) + for _ in range(max(count, 1)) + ] + if sequence is not None and self._sequence_unanswered(request_id, sequence): + self._queue_completed_output(request_id, sequence, chunks.pop()) + for chunk in chunks: + self.out_queue.put(chunk) + + def _sequence_unanswered(self, request_id: str, sequence: int) -> bool: + """True if no chunk has been queued for ``sequence`` yet.""" + state = self.request_output_state.get(request_id) + if state is None: + return True + return sequence >= state.next_emit and sequence not in state.pending def _read_result_tensor( self, result: ResultTensors @@ -618,16 +644,14 @@ def _read_result_tensor( ) if result.request_id not in self.tensor_uuid_to_metadata_per_request: self.tensor_uuid_to_metadata_per_request[result.request_id] = {} - output_order = self.tensor_uuid_to_output_order_per_request.setdefault( - result.request_id, {} + state = self.request_output_state.setdefault( + result.request_id, RequestOutputState() ) - sequence = self.request_next_output_sequence.setdefault(result.request_id, 0) for tensor_info in result.graph_edge.tensor_info: self.tensor_uuid_to_metadata_per_request[result.request_id][ tensor_info.uuid] = result.metadata - output_order[tensor_info.uuid] = (sequence, result.loop_indices) - sequence += 1 - self.request_next_output_sequence[result.request_id] = sequence + state.order[tensor_info.uuid] = (state.next_sequence, result.loop_indices) + state.next_sequence += 1 def _queue_completed_output( self, @@ -635,25 +659,20 @@ def _queue_completed_output( sequence: int, chunk: ResultChunk, ) -> None: - pending = self.request_pending_output_chunks.setdefault(request_id, {}) - if sequence in pending: + state = self.request_output_state.setdefault(request_id, RequestOutputState()) + if sequence in state.pending: raise RuntimeError( f"duplicate completed output sequence {sequence} for request {request_id}" ) - pending[sequence] = chunk + state.pending[sequence] = chunk - next_sequence = self.request_next_emit_sequence.setdefault(request_id, 0) - while next_sequence in pending: - ready = pending.pop(next_sequence) + while state.next_emit in state.pending: + ready = state.pending.pop(state.next_emit) if ready.modality == "video_frame": - frame_index = self.request_output_frame_indices[request_id] - ready.metadata["frame_index"] = frame_index - self.request_output_frame_indices[request_id] = ( - frame_index + ready.metadata["frame_count"] - ) + ready.metadata["frame_index"] = state.frame_index + state.frame_index += ready.metadata["frame_count"] self.out_queue.put(ready) - next_sequence += 1 - self.request_next_emit_sequence[request_id] = next_sequence + state.next_emit += 1 def _discard_result_tensor( self, result: ResultTensors @@ -679,11 +698,10 @@ def _process_read_tensors(self): # and keep draining everyone else's tensors. Letting it # escape to run()'s catch-all would abandon the rest of this # pass and leave the client waiting on the request timeout. + sequence = None try: sequence, loop_indices = ( - self.tensor_uuid_to_output_order_per_request[request_id][ - tensor_info.uuid - ] + self.request_output_state[request_id].order[tensor_info.uuid] ) logger.debug( "Postprocessing output sequence %d for request %s at %s", @@ -763,13 +781,14 @@ def _process_read_tensors(self): except Exception as exc: # noqa: BLE001 — must reach the client self._fail_request( request_id, exc, f"{modality} output postprocessing", + sequence=sequence, ) self.tensor_uuid_to_metadata_per_request.get( request_id, {} ).pop(tensor_info.uuid, None) - self.tensor_uuid_to_output_order_per_request.get( - request_id, {} - ).pop(tensor_info.uuid, None) + state = self.request_output_state.get(request_id) + if state is not None: + state.order.pop(tensor_info.uuid, None) self.tensor_manager.dereference( request_id=request_id, uuid=tensor_info.uuid @@ -850,9 +869,7 @@ def _hard_cleanup(self, request_id: str) -> None: self._draining_rids.discard(request_id) self._reads_done_sent.discard(request_id) self.tensor_manager.force_cleanup_request(request_id) - self.tensor_uuid_to_metadata_per_request.pop(request_id, None) - self.request_model_kwargs.pop(request_id, None) - self.in_flight_requests.discard(request_id) + self._drop_request_state(request_id) def run(self): while not self.stop_event.is_set(): diff --git a/mstar/worker/worker.py b/mstar/worker/worker.py index e4496691d..ade83af1e 100644 --- a/mstar/worker/worker.py +++ b/mstar/worker/worker.py @@ -1751,6 +1751,21 @@ def _assemble_speculation( tp_seq=tp_seq, ) + def _is_tearing_down(self, rid: str) -> bool: + """Removed, aborted or failed: no further speculative work for ``rid``. + + A deferred drain only fires once the rid leaves ``_in_flight_rids``, + and a same-node speculation chain keeps it there every step, so a + drain the chain does not see would never fire and the rollout would + run to ``max_iters`` for a client that has gone. + """ + return ( + rid in self._pending_removes + or rid in self._pending_drains + or rid in self._draining_rids + or rid in self.scheduler.failed_rids + ) + def _try_speculate_next( self, pending: PendingBatch @@ -1834,7 +1849,7 @@ def _try_speculate_next( loop = wgio.loops.get(spec_node_info.loop_name) # check conditions where the rid cannot be furtuer speculated - already_removed = rid in self._pending_removes + already_removed = self._is_tearing_down(rid) already_stopped = spec_node_info.is_new_loop_iter and PendingLoopStop( rid, graph_walk, spec_node_info.loop_name ) in self._pending_loop_stops diff --git a/test/modular/test_request_failure_propagation.py b/test/modular/test_request_failure_propagation.py index b86bff334..c0fb190ce 100644 --- a/test/modular/test_request_failure_propagation.py +++ b/test/modular/test_request_failure_propagation.py @@ -247,7 +247,7 @@ def test_engine_failure_does_not_clobber_an_earlier_error(): def _preprocess_thread(model): - from mstar.api_server.data_worker import PreprocessWorkerThread + from mstar.api_server.data_worker import PreprocessWorkerThread, RequestOutputState wt = PreprocessWorkerThread.__new__(PreprocessWorkerThread) wt.out_queue = queue.Queue() @@ -256,7 +256,9 @@ def _preprocess_thread(model): wt.tensor_uuid_to_metadata_per_request = {"r1": {"u1": {}}} wt.enable_prof = False wt.enable_nvtx = False - wt.tensor_uuid_to_output_order_per_request = {"r1": {"u1": (0, None)}} + wt.request_output_state = { + "r1": RequestOutputState(order={"u1": (0, None)}, next_sequence=1), + } return wt @@ -289,6 +291,37 @@ def postprocess(self, tensor, modality, request_kwargs=None): assert dereferenced == ["u1"] +def test_a_failed_output_releases_the_outputs_held_behind_it(): + """Sequence 1 finished first and waits in the reorder buffer for 0. When 0 + fails, its error chunk must take slot 0 so 1 is released in order; emitted + around the buffer, 1 would sit in ``pending`` until the API server's TTL.""" + + class _BadModel: + def postprocess(self, tensor, modality, request_kwargs=None): + raise RuntimeError("decode failed") + + wt = _preprocess_thread(_BadModel()) + held = ResultChunk(request_id="r1", modality="text", data=b"later", metadata={}) + wt.request_output_state["r1"].pending[1] = held + wt.request_output_state["r1"].next_sequence = 2 + edge = SimpleNamespace( + name="text_output", tensor_info=[SimpleNamespace(uuid="u1")], + ) + wt.tensor_manager = SimpleNamespace( + get_ready_tensors=lambda: {"r1": [edge]}, + get_tensor=lambda request_id, uuid: object(), + dereference=lambda request_id, uuid: None, + ) + + assert wt._process_read_tensors() is True + chunks = [wt.out_queue.get_nowait(), wt.out_queue.get_nowait()] + assert [c.modality for c in chunks] == ["error", "text"] + assert chunks[1] is held + assert wt.out_queue.empty() + assert wt.request_output_state["r1"].pending == {} + assert wt.request_output_state["r1"].next_emit == 2 + + def test_result_transfer_failure_answers_for_every_queued_tensor(): """The API server decrements its outstanding-read count once per chunk, so a read that never starts owes one error chunk per tensor it dropped.""" diff --git a/test/modular/test_video_frame_protocol.py b/test/modular/test_video_frame_protocol.py index ddaf5e7db..8bc10c46a 100644 --- a/test/modular/test_video_frame_protocol.py +++ b/test/modular/test_video_frame_protocol.py @@ -25,6 +25,7 @@ from mstar.api_server import entrypoint # noqa: E402 from mstar.api_server.data_worker import ( # noqa: E402 PreprocessWorkerThread, + RequestOutputState, _video_frame_metadata, ) from mstar.api_server.entrypoint import SUPPORTED_MODALITIES, APIServer # noqa: E402 @@ -223,29 +224,23 @@ def register_for_send(self, request_id, tensor_infos): pass -def _set_output_order_state(worker, tensors_by_request): +def _set_output_order_state(worker, tensors_by_request, frame_indices=None): loop_indices = NestedLoopIndices( loop_name_order=["rollout_loop"], loop_indices={"rollout_loop": 0}, wg_fwd_pass_idx=0, ) - worker.tensor_uuid_to_output_order_per_request = { - request_id: { - name: (sequence, loop_indices) - for sequence, name in enumerate(tensors) - } - for request_id, tensors in tensors_by_request.items() - } - worker.request_next_output_sequence = { - request_id: len(tensors) + worker.request_output_state = { + request_id: RequestOutputState( + order={ + name: (sequence, loop_indices) + for sequence, name in enumerate(tensors) + }, + next_sequence=len(tensors), + frame_index=(frame_indices or {}).get(request_id, 0), + ) for request_id, tensors in tensors_by_request.items() } - worker.request_next_emit_sequence = { - request_id: 0 for request_id in tensors_by_request - } - worker.request_pending_output_chunks = { - request_id: {} for request_id in tensors_by_request - } def test_data_worker_emits_complete_metadata_and_monotonic_frame_indices(): @@ -259,7 +254,6 @@ def test_data_worker_emits_complete_metadata_and_monotonic_frame_indices(): worker.model = _FrameModel() worker.out_queue = queue.Queue() worker.request_model_kwargs = {"request": {"world": "test"}} - worker.request_output_frame_indices = {"request": 0} worker.tensor_uuid_to_metadata_per_request = {"request": {name: {"producer": "decoder"} for name in tensors}} _set_output_order_state(worker, {"request": tensors}) @@ -271,7 +265,7 @@ def test_data_worker_emits_complete_metadata_and_monotonic_frame_indices(): assert all(chunk.metadata["frame_count"] == 4 for chunk in chunks) assert all(chunk.metadata["pixel_format"] == "rgb24" for chunk in chunks) assert all(chunk.metadata["producer"] == "decoder" for chunk in chunks) - assert worker.request_output_frame_indices["request"] == 8 + assert worker.request_output_state["request"].frame_index == 8 assert worker.tensor_manager.dereferenced == [ ("request", "first"), ("request", "second"), @@ -297,7 +291,6 @@ def test_data_worker_tracks_interleaved_frame_indices_per_request(): "request-a": {"world": "test"}, "request-b": {"world": "test"}, } - worker.request_output_frame_indices = {"request-a": 0, "request-b": 0} worker.tensor_uuid_to_metadata_per_request = { request_id: {name: {} for name in request_tensors} for request_id, request_tensors in tensors.items() } @@ -314,10 +307,9 @@ def test_data_worker_tracks_interleaved_frame_indices_per_request(): "request-a", ] assert [chunk.metadata["frame_index"] for chunk in chunks] == [0, 0, 4] - assert worker.request_output_frame_indices == { - "request-a": 8, - "request-b": 4, - } + assert { + rid: state.frame_index for rid, state in worker.request_output_state.items() + } == {"request-a": 8, "request-b": 4} def test_data_worker_reorders_async_completions_before_frame_emission(): @@ -331,12 +323,8 @@ def test_data_worker_reorders_async_completions_before_frame_emission(): worker.model = _FrameModel() worker.out_queue = queue.Queue() worker.request_model_kwargs = {"request": {"world": "test"}} - worker.request_output_frame_indices = {"request": 0} worker.tensor_uuid_to_metadata_per_request = {} - worker.tensor_uuid_to_output_order_per_request = {"request": {}} - worker.request_next_output_sequence = {"request": 0} - worker.request_next_emit_sequence = {"request": 0} - worker.request_pending_output_chunks = {"request": {}} + worker.request_output_state = {"request": RequestOutputState()} for iteration, name in enumerate(("first", "second")): worker._read_result_tensor(ResultTensors( @@ -378,24 +366,19 @@ def test_data_worker_cleanup_drops_all_frame_protocol_state(): "reused": {"world": "old"}, "other": {"world": "keep"}, } - worker.request_output_frame_indices = {"reused": 24, "other": 8} worker.in_flight_requests = {"reused", "other"} _set_output_order_state(worker, { "reused": {"old": object()}, "other": {"keep": object()}, - }) + }, frame_indices={"reused": 24, "other": 8}) worker._cleanup_request_state("reused") assert worker.tensor_manager.cleaned == ["reused"] assert "reused" not in worker.tensor_uuid_to_metadata_per_request assert "reused" not in worker.request_model_kwargs - assert "reused" not in worker.request_output_frame_indices - assert "reused" not in worker.tensor_uuid_to_output_order_per_request - assert "reused" not in worker.request_next_output_sequence - assert "reused" not in worker.request_next_emit_sequence - assert "reused" not in worker.request_pending_output_chunks - assert worker.request_output_frame_indices == {"other": 8} + assert "reused" not in worker.request_output_state + assert worker.request_output_state["other"].frame_index == 8 worker.model = SimpleNamespace(process_prompt=lambda *args, **kwargs: {}) worker.device = "cpu" @@ -413,7 +396,7 @@ def test_data_worker_cleanup_drops_all_frame_protocol_state(): ) ) - assert worker.request_output_frame_indices["reused"] == 0 + assert worker.request_output_state["reused"] == RequestOutputState() assert worker.request_model_kwargs["reused"] == {"world": "new"} diff --git a/test/modular/test_worker_drain.py b/test/modular/test_worker_drain.py index afac399d6..a90af9b34 100644 --- a/test/modular/test_worker_drain.py +++ b/test/modular/test_worker_drain.py @@ -40,6 +40,7 @@ def _worker( w.scheduler = SimpleNamespace( clear_rid=lambda rid: w.cleared.append(rid), # noqa: PLW0108 fail_rids=lambda rids: w.failed.update(rids), # noqa: PLW0108 + failed_rids=w.failed, pending_tp_follow_count=dict.fromkeys(tp_follow, 1), ) w.worker_graphs_manager = SimpleNamespace( @@ -116,6 +117,27 @@ def test_drain_deferred_behind_inflight_gpu_step(): assert "X" in w._draining_rids and len(_reads_done(w)) == 1 +def test_speculation_stops_for_a_rid_being_torn_down(): + """A same-node speculation chain keeps its rids in flight every step, so a + drain deferred behind it only fires once the chain lets the rid go.""" + w = _worker(known_rids=("X", "Y"), in_flight=("X", "Y")) + assert not w._is_tearing_down("X") + + Worker._drain_request(w, DrainRequest(request_id="X")) + assert "X" in w._pending_drains + assert w._is_tearing_down("X") + assert not w._is_tearing_down("Y") + + w._pending_drains.clear() + w._in_flight_rids.clear() + Worker._drain_request(w, DrainRequest(request_id="X")) + assert "X" in w._draining_rids and "X" in w.scheduler.failed_rids + assert w._is_tearing_down("X") + + w._pending_removes.add("Y") + assert w._is_tearing_down("Y") + + def test_follower_ignores_conductor_drain(): w = _worker(is_follower=True) Worker._drain_request(w, DrainRequest(request_id="X")) # source=CONDUCTOR @@ -180,6 +202,7 @@ def _preprocess(inflight_reads=False): wt.tensor_uuid_to_metadata_per_request = {} wt.request_model_kwargs = {} wt.in_flight_requests = set() + wt.request_output_state = {} return wt @@ -239,12 +262,19 @@ def test_preprocess_finished_reading_gates_ack_when_not_drained(): def test_preprocess_hard_cleanup_force_drops_and_clears(): + from mstar.api_server.data_worker import RequestOutputState + wt = _preprocess() wt._draining_rids.add("X") wt._reads_done_sent.add("X") wt.tensor_uuid_to_metadata_per_request["X"] = {"u": {}} wt.request_model_kwargs["X"] = {} wt.in_flight_requests.add("X") + # the completed-request path ends here, so the output-order state must go too + wt.request_output_state["X"] = RequestOutputState( + order={"u": (0, None)}, next_sequence=2, next_emit=1, + pending={1: object()}, frame_index=8, + ) wt._hard_cleanup("X") assert wt.forced == ["X"] @@ -253,3 +283,4 @@ def test_preprocess_hard_cleanup_force_drops_and_clears(): assert "X" not in wt.tensor_uuid_to_metadata_per_request assert "X" not in wt.request_model_kwargs assert "X" not in wt.in_flight_requests + assert "X" not in wt.request_output_state From d6e2bdb7b5781d4e1ab1e35a261cf2ce92c456e5 Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Thu, 24 Sep 2026 00:50:39 +0000 Subject: [PATCH 22/29] review : read1 header scan so small binary frames stream --- mstar/client/media.py | 7 ++- test/modular/test_binary_framing.py | 66 +++++++++++++++++++++++++++++ 2 files changed, 72 insertions(+), 1 deletion(-) diff --git a/mstar/client/media.py b/mstar/client/media.py index db9f6ecb9..eadb47954 100644 --- a/mstar/client/media.py +++ b/mstar/client/media.py @@ -72,11 +72,16 @@ def iter_binary_frames(raw, read_size: int = _FRAME_HEADER_READ_SIZE) -> Iterato production, a ``BytesIO`` in tests. ``read_size`` only bounds the header scan; payload reads ask for exactly what is outstanding. """ + # ``read(n)`` blocks until ``n`` bytes or EOF, which stalls the header scan + # behind a full 64 KiB on small frames. ``read1`` returns as soon as any + # bytes are available, like a single ``recv()``; fall back to ``read`` for + # objects that lack it. + read_header = raw.read1 if hasattr(raw, "read1") else raw.read buf = bytearray() while True: newline = buf.find(b"\n") while newline < 0: - block = raw.read(read_size) + block = read_header(read_size) if not block: if buf: raise RuntimeError( diff --git a/test/modular/test_binary_framing.py b/test/modular/test_binary_framing.py index f9f20f43d..1ddf9bec6 100644 --- a/test/modular/test_binary_framing.py +++ b/test/modular/test_binary_framing.py @@ -10,8 +10,12 @@ import base64 import io import json +import threading +import time +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer import pytest +import requests from fastapi.testclient import TestClient from mstar.api_server import entrypoint @@ -89,6 +93,68 @@ def test_binary_frames_survive_short_reads(): assert [f["bytes"] for f in frames] == [c.data for c in chunks] +def test_binary_frames_arrive_incrementally_over_a_real_stream(): + """PR-review regression: the header scan used ``raw.read(read_size)``, + which blocks until ``read_size`` bytes or EOF. Small frames sent slowly + over a real connection therefore all arrived at once when the server + closed the stream, instead of as each one landed. ``BytesIO`` and + ``_Dribble`` can't expose this (neither one blocks like a real socket), + so this drives ``iter_binary_frames`` over an actual local HTTP server + with a chunked body. + """ + n_frames = 6 + gap = 0.05 + all_sent = threading.Event() + + class _Handler(BaseHTTPRequestHandler): + protocol_version = "HTTP/1.1" + disable_nagle_algorithm = True + + def do_GET(self): + self.send_response(200) + self.send_header("Transfer-Encoding", "chunked") + self.end_headers() + for i in range(n_frames): + header = json.dumps( + {"modality": "audio", "nbytes": 4, "metadata": {"i": i}} + ).encode() + b"\n" + body = header + b"abcd" + self.wfile.write(b"%x\r\n%s\r\n" % (len(body), body)) + self.wfile.flush() + if i < n_frames - 1: + time.sleep(gap) + all_sent.set() + self.wfile.write(b"0\r\n\r\n") + self.wfile.flush() + + def log_message(self, *args): + pass + + class _Server(ThreadingHTTPServer): + daemon_threads = True + + server = _Server(("127.0.0.1", 0), _Handler) + thread = threading.Thread(target=server.serve_forever, daemon=True) + thread.start() + try: + resp = requests.get( + f"http://127.0.0.1:{server.server_address[1]}/", stream=True, timeout=5 + ) + try: + frames = iter_binary_frames(resp.raw) + first = next(frames) + # The bug buffered every frame until the connection closed; this + # must fire while the server is still mid-stream. + assert not all_sent.is_set() + assert first["metadata"] == {"i": 0} + assert [f["metadata"]["i"] for f in frames] == list(range(1, n_frames)) + finally: + resp.close() + finally: + server.shutdown() + thread.join(timeout=5) + + def test_binary_frames_reject_truncated_payload(): wire = _wire(_chunks()) with pytest.raises(RuntimeError, match="ended 10 bytes short"): From 877f5ebda4f981655d450ded2106517caf6465f9 Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Thu, 24 Sep 2026 18:44:23 +0000 Subject: [PATCH 23/29] review : declared cuda graph seq dims, encoder shape gate, order-safe capture tests --- mstar/engine/cuda_graph_config.py | 15 ++++++- mstar/engine/cuda_graph_runner.py | 44 ++++++++---------- mstar/model/waypoint/submodules.py | 45 ++++++++++++++----- test/modular/test_cuda_graph_capture.py | 60 ++++++++++++++++++------- test/modular/test_waypoint_shell.py | 49 +++++++++++++++++++- 5 files changed, 156 insertions(+), 57 deletions(-) diff --git a/mstar/engine/cuda_graph_config.py b/mstar/engine/cuda_graph_config.py index 38c2e3947..4ede7d04c 100644 --- a/mstar/engine/cuda_graph_config.py +++ b/mstar/engine/cuda_graph_config.py @@ -33,6 +33,12 @@ def __init__( # (eager) batch size for the walk. Default True keeps the conservative # behavior: never batch beyond a captured graph size. caps_eager_batch_size: bool = True, + # Maps a static-input key, as returned by the submodule's + # ``preprocess``, to the dim that varies with the bucket. Overrides + # the runner's size-matching guess (``CudaGraphRunner._seq_dim``), + # which can pick the wrong dim when an unrelated axis happens to + # match the bucket's token count. + input_seq_dims: dict[str, int] | None = None, ): self.capture_graph_walk = capture_graph_walk self.replay_graph_walks = replay_graph_walks or [capture_graph_walk] @@ -41,6 +47,7 @@ def __init__( self.capture_batch_sizes = capture_batch_sizes self.capture_forward_method = capture_forward_method self.caps_eager_batch_size = caps_eager_batch_size + self.input_seq_dims = input_seq_dims @abstractmethod def get_config_type(self) -> CudaGraphConfigType: @@ -67,6 +74,7 @@ def __init__( capture_forward_method: str = "forward_batched", caps_eager_batch_size: bool = True, total_tokens_multiplier: int = 1, + input_seq_dims: dict[str, int] | None = None, ): super().__init__( capture_graph_walk=capture_graph_walk, @@ -76,6 +84,7 @@ def __init__( capture_batch_sizes=capture_batch_sizes, capture_forward_method=capture_forward_method, caps_eager_batch_size=caps_eager_batch_size, + input_seq_dims=input_seq_dims, ) self.single_request_inputs = single_request_inputs # ``single_request_inputs.input_seq_len`` is also read per-label by the @@ -122,7 +131,8 @@ def __init__( compile: bool = True, capture_batch_sizes: list[int] | None = None, capture_forward_method: str = "forward_batched", - caps_eager_batch_size: bool = True + caps_eager_batch_size: bool = True, + input_seq_dims: dict[str, int] | None = None, ): super().__init__( capture_graph_walk=capture_graph_walk, @@ -131,7 +141,8 @@ def __init__( compile=compile, capture_batch_sizes=capture_batch_sizes, capture_forward_method=capture_forward_method, - caps_eager_batch_size=caps_eager_batch_size + caps_eager_batch_size=caps_eager_batch_size, + input_seq_dims=input_seq_dims, ) self.make_node_input = make_node_input self.capture_token_lengths = capture_token_lengths diff --git a/mstar/engine/cuda_graph_runner.py b/mstar/engine/cuda_graph_runner.py index 18a3d802f..49a5453cb 100644 --- a/mstar/engine/cuda_graph_runner.py +++ b/mstar/engine/cuda_graph_runner.py @@ -522,9 +522,12 @@ def prepare() -> dict[str, Any]: # same GPU addresses; replay writes real values into them. for key, value in list(static_inputs.items()): if isinstance(value, torch.Tensor): + seq_dim_override = ( + config.input_seq_dims.get(key) if config.input_seq_dims else None + ) static_inputs[key] = self._intern_static_buffer( spec.config_idx, key, value, - seq_len=spec.num_tokens, batch_size=spec.bs, + seq_len=spec.num_tokens, seq_dim_override=seq_dim_override, ) static_input_keys = tuple( key for key, value in static_inputs.items() @@ -600,26 +603,12 @@ def _dummy_engine_inputs( ) @staticmethod - def _seq_dim(value: torch.Tensor, seq_len: int, batch_size: int | None = None) -> int: - """Index of the dim that varies with this bucket, else 0. - - Dim 0 is checked first against both accepted sizes — the flattened - token count (``seq_len``) and the row count (``batch_size``) — since - that is where every real per-bucket-varying Waypoint input already - lives (``preprocess`` concatenates rows on dim 0). Only a tensor with - something else on dim 0 falls through to the later-dim scan, which - exists for mrope-style ids that carry seq in a later dim (e.g. - ``[3, seq]`` → 1). - - Checking dim 0 first, rather than folding ``batch_size`` into the same - scan, matters: a fixed-width tensor unrelated to batching can collide - with ``seq_len`` on a later dim (Waypoint's ``button`` is ``[B, 1, - 256]``, and 360p's bs=2 bucket also has 256 tokens) — that used to get - hoisted before dim 0's real, smaller ``batch_size`` match was ever - checked. - """ - if value.shape and value.shape[0] in (seq_len, batch_size): - return 0 + def _seq_dim(value: torch.Tensor, seq_len: int) -> int: + """Index of the first dim whose size matches ``seq_len``, else 0. + + Used to bring the (bucket-varying) seq dim to the front for shared-buffer + interning: most inputs are seq-leading (returns 0), but mrope-style ids + carry seq in a later dim (e.g. ``[3, seq]`` → 1).""" for dim, size in enumerate(value.shape): if size == seq_len: return dim @@ -627,7 +616,7 @@ def _seq_dim(value: torch.Tensor, seq_len: int, batch_size: int | None = None) - def _intern_static_buffer( self, config_idx: int, key: str, value: torch.Tensor, - seq_len: int | None = None, batch_size: int | None = None, + seq_len: int | None = None, seq_dim_override: int | None = None, ) -> torch.Tensor: """Return a slice view into the shared buffer for (config_idx, key). @@ -635,8 +624,10 @@ def _intern_static_buffer( largest-first) bucket's shape; smaller buckets reslice its leading dim. If ``seq_len`` is given, the seq dim is moved to the front for storage and back on return, so the captured forward sees the original layout. - ``batch_size`` — the bucket's row count — disambiguates dim 0 from a - coincidental same-size match elsewhere; see `_seq_dim`. + ``seq_dim_override``, from the config's ``input_seq_dims``, is used + instead of `_seq_dim`'s size-based guess when a caller already knows + which dim varies with the bucket (`_seq_dim` can pick the wrong dim + when an unrelated axis happens to match ``seq_len``). """ buf_key = (config_idx, key) if seq_len is None: @@ -647,7 +638,10 @@ def _intern_static_buffer( # — and the buffer is shared, so its layout cannot vary anyway seq_dim = self._static_buffer_seq_dims[buf_key] else: - seq_dim = self._seq_dim(value, seq_len, batch_size) + seq_dim = ( + seq_dim_override if seq_dim_override is not None + else self._seq_dim(value, seq_len) + ) self._static_buffer_seq_dims[buf_key] = seq_dim stored = value.movedim(seq_dim, 0) if seq_dim != 0 else value shared = self._shared_static_buffers.get(buf_key) diff --git a/mstar/model/waypoint/submodules.py b/mstar/model/waypoint/submodules.py index 6bd87e315..c16d3ebbf 100644 --- a/mstar/model/waypoint/submodules.py +++ b/mstar/model/waypoint/submodules.py @@ -558,18 +558,25 @@ def template(latent_key: str) -> NodeInputs: # falling back to a runtime eager re-trace (see # ``RingKVManager.plan``). walks.append((PRIME_WALK, "latent")) - return [ - BatchedCudaGraphConfig( + configs = [] + for walk, latent_key in walks: + single_request_inputs = template(latent_key) + configs.append(BatchedCudaGraphConfig( capture_graph_walk=walk, - single_request_inputs=template(latent_key), + single_request_inputs=single_request_inputs, capture_batch_sizes=batch_sizes, capture_forward_method="forward_batched", # The DiT compiles its two reference-shaped fullgraph regions # itself. Compiling this wrapper would fuse across their boundary. compile=False, - ) - for walk, latent_key in walks - ] + # Every tensor here is a per-request row that ``preprocess`` + # concatenates on dim 0 -- including ``button``, whose + # ``n_buttons`` can coincidentally equal a bucket's token + # count (e.g. 360p at bs=2) and fool the runner's size-based + # guess. + input_seq_dims={key: 0 for key in single_request_inputs.tensor_inputs}, + )) + return configs # ------------------------------------------------------------------ # step tail @@ -677,8 +684,8 @@ def forward( **kwargs, ) -> NameToTensorList: """Encode the seed clip into the latent the dit primes the world on. - Request ids are safe to read here and not on the dit: this node is never - captured, so never handed a capture dummy's ids.""" + Request ids are never read here: forward deletes ``engine_inputs`` + outright, so it never needs a real request's ids, captured or not.""" del graph_walk, kwargs del engine_inputs latent = encode_seed_clip( @@ -688,17 +695,19 @@ def forward( # reference adds it (``WorldEngine.append_frame``). return {"latent": [latent.unsqueeze(1)]} + @property + def _captured_image_shape(self) -> tuple[int, int, int, int]: + """The one ``image`` shape ``get_cuda_graph_configs`` captures.""" + return (self.config.temporal_compression, *self.pixel_size, 3) + def get_cuda_graph_configs( self, device: torch.device, tp_world_size: int = 1 ) -> list[CudaGraphConfig]: del tp_world_size if not self.config.cuda_graph: return [] - height, width = self.pixel_size image = torch.zeros( - (self.config.temporal_compression, height, width, 3), - dtype=self.ae_dtype, - device=device, + self._captured_image_shape, dtype=self.ae_dtype, device=device, ) return [BatchedCudaGraphConfig( capture_graph_walk=PRIME_WALK, @@ -710,3 +719,15 @@ def get_cuda_graph_configs( capture_forward_method="forward_batched", compile=True, )] + + def can_use_cuda_graphs(self, batch, model_inputs) -> bool: + """``_seed_clip`` accepts any 16:9 size, but the graph's static + buffer is sized for exactly one H/W (``pixel_size``). A batch with + any other image shape must run eager, where ``encode_seed_clip`` + resizes it to ``encoded_size``.""" + if not super().can_use_cuda_graphs(batch, model_inputs): + return False + return all( + node_inputs.tensor_inputs["image"].shape == self._captured_image_shape + for node_inputs in model_inputs + ) diff --git a/test/modular/test_cuda_graph_capture.py b/test/modular/test_cuda_graph_capture.py index 2ecad6f99..08869cfcb 100644 --- a/test/modular/test_cuda_graph_capture.py +++ b/test/modular/test_cuda_graph_capture.py @@ -33,11 +33,15 @@ @pytest.fixture(autouse=True) -def fake_cuda_runtime(monkeypatch): +def fake_cuda_runtime(request, monkeypatch): """The only real CUDA calls on this path are the graph pool handle and the memory readings around it; `_FakeRunner` stands in for the capture itself. - Stubbing them keeps these policy tests running where there is no GPU.""" - if torch.cuda.is_available(): + Stubbing them keeps these policy tests running where there is no GPU. + + Stubbed on a GPU too: `_FakeRunner` is on the CPU device, and the real + `memory_allocated(cpu)` raises once any earlier test has initialised CUDA. + Only the `requires_cuda` tests, which capture for real, keep the runtime.""" + if requires_cuda.mark in request.node.iter_markers(): return monkeypatch.setattr(torch.cuda, "is_available", lambda: True) monkeypatch.setattr(torch.cuda, "memory_allocated", lambda device=None: 0) @@ -245,21 +249,47 @@ def __init__(self): self._capture_clone_bytes_naive = 0 -def test_button_shares_its_buffer_once_batch_size_disambiguates_the_axis(): - """Waypoint 360p at bs=2: the bucket's token count is 2*128 = 256, which - is also ``n_buttons``. Passing the real call site's ``batch_size`` lets - ``_seq_dim`` settle on dim 0 (bs=2 there) before it ever scans for a - ``seq_len`` match, so button reslices the shared buffer like every other - per-row tensor instead of falling back to a private allocation.""" +def test_seq_dim_picks_the_only_matching_dim_even_at_batch_size_one(): + """A [1, seq_len] tensor at bs=1: dim 0's size (1) never collides with + ``batch_size`` here because `_seq_dim` no longer takes one — it just + scans for ``seq_len``, so a coincidental dim-0 size never shadows the + real seq dim. This is the case from the PR review comment on + cuda_graph_runner.py:621.""" + value = torch.zeros(1, 512) + assert CudaGraphRunner._seq_dim(value, seq_len=512) == 1 + + +def test_seq_dim_guesses_the_wrong_dim_when_button_collides_with_seq_len(): + """Documents `_seq_dim`'s plain size scan on Waypoint's ``button`` + shape ``[bs, 1, n_buttons]``: at 360p bs=2 the bucket's token count + (2*128=256) equals ``n_buttons``, so the scan hoists dim 2 instead of the + real batch-varying dim 0. Config-declared ``input_seq_dims`` is how + Waypoint overrides this guess — see + `test_button_shares_its_buffer_via_input_seq_dims_override`.""" + value = torch.zeros(2, 1, 256) + assert CudaGraphRunner._seq_dim(value, seq_len=256) == 2 + + +def test_button_shares_its_buffer_via_input_seq_dims_override(): + """Waypoint's config declares ``input_seq_dims={"button": 0, ...}``, so + ``_intern_static_buffer`` uses that dim instead of `_seq_dim`'s guess + (which would hoist dim 2, see + `test_seq_dim_guesses_the_wrong_dim_when_button_collides_with_seq_len`). + button then reslices the shared buffer like every other per-row tensor + instead of falling back to a private allocation.""" runner = _InternRunner() big = torch.arange(2 * 256, dtype=torch.float32).reshape(2, 1, 256) small = -torch.arange(256, dtype=torch.float32).reshape(1, 1, 256) - shared_view = runner._intern_static_buffer(0, "button", big, seq_len=256, batch_size=2) + shared_view = runner._intern_static_buffer( + 0, "button", big, seq_len=256, seq_dim_override=0 + ) assert shared_view.shape == (2, 1, 256) assert torch.equal(shared_view, big) - resliced = runner._intern_static_buffer(0, "button", small, seq_len=128, batch_size=1) + resliced = runner._intern_static_buffer( + 0, "button", small, seq_len=128, seq_dim_override=0 + ) assert runner._static_buffer_seq_dims[(0, "button")] == 0 assert resliced.shape == (1, 1, 256) @@ -271,15 +301,13 @@ def test_button_shares_its_buffer_once_batch_size_disambiguates_the_axis(): def test_a_smaller_bucket_that_shrinks_off_the_hoisted_axis_raises(): - """Without a batch size to disambiguate — a caller that only knows the - flattened token count — a fixed-width tensor can still coincidentally + """Without an override, a fixed-width tensor can still coincidentally match ``seq_len`` on a non-batch dim (here: button's n_buttons=256 lines up with the bs=2 bucket's token count), hoisting the wrong axis. The bs=1 bucket then shrinks along dim 0, not the hoisted one, and can't reslice the shared buffer. This stays a hard failure rather than a silent private - allocation; the real fix is giving `_seq_dim` enough information (the - batch size) to never hoist the wrong axis in the first place — see - `test_button_shares_its_buffer_once_batch_size_disambiguates_the_axis`.""" + allocation; the real fix is a config-declared ``input_seq_dims`` override + — see `test_button_shares_its_buffer_via_input_seq_dims_override`.""" runner = _InternRunner() big = torch.arange(2 * 256, dtype=torch.float32).reshape(2, 1, 256) small = -torch.arange(256, dtype=torch.float32).reshape(1, 1, 256) diff --git a/test/modular/test_waypoint_shell.py b/test/modular/test_waypoint_shell.py index 9ae238f41..3ee7e6a4b 100644 --- a/test/modular/test_waypoint_shell.py +++ b/test/modular/test_waypoint_shell.py @@ -28,18 +28,20 @@ sys.path.insert(0, ".") from mstar.conductor.request_info import CurrentForwardPassInfo +from mstar.engine.engine import ExecutingBatch from mstar.engine.resources import ( AttentionSpec, AttnBackend, KVSpec, RingKVConfig, RingKVStep, + StepContext, ) from mstar.engine.resources.runner import topo_sort from mstar.graph.base import GraphEdge, Loop, Sequential, SpeculativeNodeInfo from mstar.graph.graph_io import WorkerGraphIO from mstar.graph.special_destinations import EMIT_TO_CLIENT -from mstar.model.submodule_base import ModelInputsFromEngine +from mstar.model.submodule_base import ModelInputsFromEngine, NodeInputs from mstar.model.waypoint.components.attention import WaypointAttention from mstar.model.waypoint.components.dit import WaypointDiT from mstar.model.waypoint.components.taehv import decode_latent, initial_decoder_histories @@ -420,7 +422,8 @@ def test_no_prepared_tensor_carries_tokens_per_frame_in_its_shape( At 720P nothing collides, but the margin is thin: a 256-token-per-frame variant would put ``button``'s ``n_buttons=256`` in the crosshairs. With ``step_batch_size > 1``, 360p at bs=2 does collide with ``n_buttons``; - that case is handled runner-side instead (see ``test_cuda_graph_capture.py``). + that case is handled via the config's ``input_seq_dims`` override instead + (see ``get_cuda_graph_configs`` and ``test_cuda_graph_capture.py``). """ inputs = _controller_stream(config, frames=4) inputs["latent"] = [torch.zeros((1, 1, *config.latent_shape))] @@ -461,6 +464,20 @@ def test_prepared_shapes_and_dtypes_are_the_capture_template_exactly(submodule, assert frame_pos.shape == (1,) and frame_pos.dtype == torch.int64 +def test_input_seq_dims_covers_every_static_input_on_dim_zero(submodule): + """Every tensor the DiT's config templates -- including ``button``, whose + ``n_buttons`` can collide with a bucket's token count -- is a per-request + row ``preprocess`` concatenates on dim 0. ``input_seq_dims`` must declare + exactly those keys, all on dim 0, or the runner falls back to its + size-based guess for whichever key is missing.""" + for cfg in submodule.get_cuda_graph_configs(torch.device("meta")): + assert cfg.input_seq_dims is not None, cfg.capture_graph_walk + assert set(cfg.input_seq_dims) == set(cfg.single_request_inputs.tensor_inputs), ( + cfg.capture_graph_walk + ) + assert set(cfg.input_seq_dims.values()) == {0}, cfg.capture_graph_walk + + # --------------------------------------------------------------------------- # Noise: stateless in (seed, frame_pos) # --------------------------------------------------------------------------- @@ -1155,6 +1172,34 @@ def test_the_encoder_emits_the_dit_s_priming_latent(encoder, ae_config): assert latent.dtype == torch.bfloat16 +def _batch(graph_walk: str) -> ExecutingBatch: + return ExecutingBatch( + node_name="Encoder", + step_context=StepContext( + request_ids=("r0",), graph_walk=graph_walk, slot=None, + capture=False, plan_results={}, + ), + per_request_info={}, + ) + + +def _image_inputs(shape: tuple[int, int, int, int]) -> list[NodeInputs]: + return [NodeInputs(tensor_inputs={"image": torch.zeros(shape)})] + + +def test_encoder_only_replays_the_captured_image_shape(encoder, ae_config): + """The graph's static buffer is sized for exactly one H/W; any other + 16:9 image, or the wrong graph walk, must fall back to eager.""" + captured = (ae_config.temporal_compression, 360, 640, 3) + bigger = (ae_config.temporal_compression, 720, 1280, 3) + smaller = (ae_config.temporal_compression, 180, 320, 3) + + assert encoder.can_use_cuda_graphs(_batch(PRIME_WALK), _image_inputs(captured)) + assert not encoder.can_use_cuda_graphs(_batch(PRIME_WALK), _image_inputs(bigger)) + assert not encoder.can_use_cuda_graphs(_batch(PRIME_WALK), _image_inputs(smaller)) + assert not encoder.can_use_cuda_graphs(_batch(ROLLOUT_WALK), _image_inputs(captured)) + + def test_the_fused_decode_turns_one_frame_into_one_raw_clip(decoder, ae_config): out = _decode(decoder, graph_walk=PRIME_WALK, seed_latent=torch.zeros( (1, 1, ae_config.channels, *ae_config.latent_shape[1:]), dtype=torch.bfloat16 From e18d2b76f54c40ad9117041949ab8c65fb021b1e Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Thu, 24 Sep 2026 19:39:15 +0000 Subject: [PATCH 24/29] review : pin flash-attn-4, fall back to TRITON when FLASH cannot run --- docs/installation.rst | 18 +++-- mstar/engine/resources/attn/flex.py | 75 ++++++++++++-------- test/modular/test_flex_attention_resource.py | 54 ++++++++++++++ 3 files changed, 113 insertions(+), 34 deletions(-) diff --git a/docs/installation.rst b/docs/installation.rst index 7427c4e27..b5996e9db 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -292,20 +292,30 @@ default. That backend needs the **flash-attn-4** package, which provides FA3-style sm90 kernel with TMA and warpgroup specialisation. It is **not on PyPI** as of 2026-09-17, and it is **not** pulled in by any extra. -Install it from the upstream repo's ``flash_attn/cute`` subdirectory: +FLASH needs **torch 2.11 or newer**: older torch does not pass flash-attn-4 the +block-sparse block size, and capture fails with ``Block sparse tensors ... +require explicit sparse_block_size[0]``. Note this rules out the flash-attn +prebuilt wheels above, which stop at ``torch2.10``. + +Install it from the upstream repo's ``flash_attn/cute`` subdirectory, pinned to +the revision this was tested against (with ``nvidia-cutlass-dsl`` 4.7.1 and +torch 2.12.1): .. code-block:: bash git clone https://github.com/Dao-AILab/flash-attention + git -C flash-attention checkout 1bda8f9290cd48d030f1516f0e680cd464ef3554 uv pip install --torch-backend=auto ./flash-attention/flash_attn/cute This is pure Python plus ``nvidia-cutlass-dsl`` — there is no CUDA extension to build. Its kernels are JIT-compiled on first use, which adds roughly a minute to the first server startup. -If ``flash-attn-4`` isn't installed, set ``MSTAR_FLEX_BACKEND=TRITON`` to fall -back to the previous Triton flex kernel. It is correct but slower — about 1.8x -per attention call at 720p. +If ``flash-attn-4`` isn't importable or torch is older than 2.11, the server +logs a warning and falls back to the previous Triton flex kernel. It is correct +but slower — about 1.8x per attention call at 720p. Set +``MSTAR_FLEX_BACKEND=TRITON`` to choose it explicitly, or +``MSTAR_FLEX_BACKEND=FLASH`` to fail at startup instead of falling back. Matching your CUDA toolkit -------------------------- diff --git a/mstar/engine/resources/attn/flex.py b/mstar/engine/resources/attn/flex.py index 2392927be..cf6ee6194 100644 --- a/mstar/engine/resources/attn/flex.py +++ b/mstar/engine/resources/attn/flex.py @@ -11,6 +11,7 @@ 128" a real constraint here rather than a convenience. """ +import logging import os import torch @@ -30,39 +31,53 @@ __all__ = ["FlexAttentionManager", "flex_attention_masked", "make_block_mask"] -# Backend switch, read once at import. "FLASH" is the default. Setting -# MSTAR_FLEX_BACKEND=TRITON is the rollback switch: it restores the previous -# Triton flex kernel bit-for-bit; see the comment block below for why FLASH -# needs a non-trivial mask_mod. FLASH requires the flash-attn-4 package -# (flash_attn.cute) -- see docs/installation.rst. Torch itself raises "CUTE -# flash attention library is not available" if it's missing, so there's no -# extra check here. +logger = logging.getLogger(__name__) + +# Torch passes FLASH the sparse block size from 2.11; flash-attn-4 cannot infer +# it for every shape and raises during capture on older torch. +_FLASH_MIN_TORCH = "2.11" _ALLOWED_FLEX_BACKENDS = ("TRITON", "FLASH") -_FLEX_BACKEND = os.environ.get("MSTAR_FLEX_BACKEND", "FLASH") -if _FLEX_BACKEND not in _ALLOWED_FLEX_BACKENDS: - raise ValueError( - f"MSTAR_FLEX_BACKEND must be one of {_ALLOWED_FLEX_BACKENDS}, got {_FLEX_BACKEND!r}" - ) -# CORRECTNESS, not speed. Our BlockMask carries a NO-OP `mask_mod`: we pass -# `mask_mod=None` to `from_kv_blocks` and it substitutes `noop_mask`, so -# `bm.mask_mod` is a function returning True everywhere, not `None`. Visibility -# is therefore encoded *entirely* in the block index lists (`full_kv_indices` -# truncated to `full_kv_num_blocks`), which is what makes a ring expressible at -# all. The compiled kernel iterates exactly those blocks. The eager path does -# not -- it rebuilds the mask by evaluating `mask_mod` over the grid, and a noop -# mask_mod means "everything is visible", so eager attention silently reads -# every unwritten slot in the ring as a zero K/V and blends it in. -# -# Measured on the ported cache: eager output diverges from a masked-dense -# reference by 2.7e-01, while the compiled path matches it to 1.2e-07. Nothing -# raises. This is why the reference wraps both of its regions in -# @torch.compile(fullgraph=True) -- compilation is load-bearing for the *result* -# there too, not just the throughput. -# -# So the compile is pinned here (below, after the backend selection) rather than -# left to the caller: correctness must not depend on whether someone set +def _flash_unavailable_reason() -> str | None: + """Why FLASH cannot run in this environment, or None if it can.""" + if torch.__version__ < _FLASH_MIN_TORCH: # TorchVersion compares as a version + return f"torch {torch.__version__} < {_FLASH_MIN_TORCH}" + try: + import flash_attn.cute # noqa: F401 + except Exception as exc: # a broken install raises more than ImportError + return f"flash-attn-4 (flash_attn.cute) is not importable: {exc!r}" + return None + + +def _resolve_flex_backend() -> str: + """Read once at import. Unset defaults to FLASH and falls back to TRITON, + bit-for-bit the previous kernel, when FLASH cannot run; an explicit FLASH + fails instead. See docs/installation.rst for installing flash-attn-4.""" + requested = os.environ.get("MSTAR_FLEX_BACKEND") + if requested is not None and requested not in _ALLOWED_FLEX_BACKENDS: + raise ValueError( + f"MSTAR_FLEX_BACKEND must be one of {_ALLOWED_FLEX_BACKENDS}, got {requested!r}" + ) + if requested == "TRITON": + return "TRITON" + reason = _flash_unavailable_reason() + if reason is None: + return "FLASH" + if requested == "FLASH": + raise RuntimeError(f"MSTAR_FLEX_BACKEND=FLASH but {reason}") + logger.warning("flex attention: falling back to TRITON (%s)", reason) + return "TRITON" + + +_FLEX_BACKEND = _resolve_flex_backend() + + +# CORRECTNESS, not speed. Our BlockMask's `mask_mod` is a no-op, so visibility +# lives entirely in the block index lists. The compiled kernel iterates those; +# eager flex evaluates `mask_mod` instead, so it reads every unwritten ring slot +# as zero K/V (measured: eager 2.7e-01 vs compiled 1.2e-07 from a masked-dense +# reference, and nothing raises). So the compile is pinned here, not left to # `WaypointConfig.compile_dit`. diff --git a/test/modular/test_flex_attention_resource.py b/test/modular/test_flex_attention_resource.py index fa6366c77..3b1565aa2 100644 --- a/test/modular/test_flex_attention_resource.py +++ b/test/modular/test_flex_attention_resource.py @@ -54,6 +54,7 @@ KVSpec, StepContext, ) +from mstar.engine.resources.attn import flex as flex_module from mstar.engine.resources.attn.base import AttentionManager from mstar.engine.resources.attn.flex import ( _FLEX_BACKEND, @@ -204,6 +205,59 @@ def test_requires_kv_write_is_false_for_flex_and_true_for_the_paged_backend(): assert paged_manager.requires_kv_write is True +@pytest.fixture +def flash_env(monkeypatch): + """Drive ``_resolve_flex_backend`` with a chosen env var and FLASH verdict.""" + def set_env(requested, reason): + if requested is None: + monkeypatch.delenv("MSTAR_FLEX_BACKEND", raising=False) + else: + monkeypatch.setenv("MSTAR_FLEX_BACKEND", requested) + monkeypatch.setattr(flex_module, "_flash_unavailable_reason", lambda: reason) + return set_env + + +def test_the_default_backend_is_flash_when_it_can_run(flash_env): + flash_env(None, None) + assert flex_module._resolve_flex_backend() == "FLASH" + + +def test_the_default_backend_falls_back_to_triton_when_flash_cannot_run(flash_env, caplog): + flash_env(None, "torch 2.10.0 < 2.11") + assert flex_module._resolve_flex_backend() == "TRITON" + assert "torch 2.10.0 < 2.11" in caplog.text + + +def test_an_explicit_flash_request_fails_instead_of_falling_back(flash_env): + flash_env("FLASH", "flash-attn-4 (flash_attn.cute) is not importable") + with pytest.raises(RuntimeError, match="not importable"): + flex_module._resolve_flex_backend() + + +def test_an_explicit_triton_request_skips_the_flash_probe(flash_env, monkeypatch): + flash_env("TRITON", None) + monkeypatch.setattr(flex_module, "_flash_unavailable_reason", None) # raises if called + assert flex_module._resolve_flex_backend() == "TRITON" + + +def test_an_unknown_backend_is_rejected(flash_env): + flash_env("CUDNN", None) + with pytest.raises(ValueError, match="MSTAR_FLEX_BACKEND"): + flex_module._resolve_flex_backend() + + +@pytest.mark.parametrize( + ("version", "too_old"), + [("2.10.0+cu128", True), ("2.11.0", False), ("2.12.1+cu130", False)], +) +def test_flash_needs_a_torch_that_passes_the_sparse_block_size(monkeypatch, version, too_old): + from torch.torch_version import TorchVersion + + monkeypatch.setattr(torch, "__version__", TorchVersion(version)) + reason = flex_module._flash_unavailable_reason() + assert (reason is not None and reason.startswith("torch")) == too_old + + def test_plan_clears_the_inherited_cursors(): """``AttentionResource``'s contract: a step that never binds the label or layer cursor must not inherit the previous step's.""" From 7b775b994fde6e4010055d89785c5e8f9df46a15 Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Thu, 24 Sep 2026 20:00:35 +0000 Subject: [PATCH 25/29] review : validate_config_yaml model hook, key-list config checks over asdict --- mstar/conductor/conductor.py | 1 + mstar/model/base.py | 10 ++ mstar/model/waypoint/checkpoint.py | 43 +++----- mstar/model/waypoint/config.py | 127 ++++++++++++----------- mstar/model/waypoint/waypoint_model.py | 17 +-- test/modular/test_waypoint_checkpoint.py | 1 + test/modular/test_waypoint_shell.py | 34 ++++-- 7 files changed, 120 insertions(+), 113 deletions(-) diff --git a/mstar/conductor/conductor.py b/mstar/conductor/conductor.py index d7608cf62..71425040f 100644 --- a/mstar/conductor/conductor.py +++ b/mstar/conductor/conductor.py @@ -317,6 +317,7 @@ def __init__( ) assert "max_seq_len" in self.model_config assert "node_groups" in self.model_config + model.validate_config_yaml(self.model_config, model_config_file) self.default_sharding_config = model.get_sharding_config(model_config_file) self.worker_graphs = { diff --git a/mstar/model/base.py b/mstar/model/base.py index 574d3f120..3daeca236 100644 --- a/mstar/model/base.py +++ b/mstar/model/base.py @@ -298,6 +298,16 @@ def _get_worker_graphs_for_graph_walk( input_streams=input_streams, ) + def validate_config_yaml(self, config: dict, config_path: str) -> None: + """Reject a deployment YAML this model cannot serve. + + Called by the Conductor at startup with the parsed YAML. The model + sees only ``model_kwargs`` in ``__init__``, so checks on other keys + (``max_concurrent_requests``, ``resources``, ...) belong here. Raise + ``ValueError`` naming the key; ``config_path`` is for the message. + """ + return + def get_worker_graphs(self, config_path: str) -> list[WorkerGraph]: with open(config_path, "r") as f: config = yaml.safe_load(f) diff --git a/mstar/model/waypoint/checkpoint.py b/mstar/model/waypoint/checkpoint.py index df8b95bd9..56cd626f5 100644 --- a/mstar/model/waypoint/checkpoint.py +++ b/mstar/model/waypoint/checkpoint.py @@ -10,6 +10,7 @@ import json import re from collections.abc import Mapping +from dataclasses import asdict from importlib import import_module from pathlib import Path from typing import Any @@ -107,39 +108,25 @@ def _checkpoint_weight_files(checkpoint_dir: Path) -> tuple[Path, ...]: return shards +# Manifest keys that carry a WaypointConfig field of the same name. +_MANIFEST_CONFIG_FIELDS = ( + "inference_fps", "temporal_compression", "taehv_ae", "ae_uri", + "prompt_conditioning", "channels", "n_layers", "n_heads", "n_kv_heads", "d_model", + "mlp_ratio", "moe", "n_buttons", "patch", "base_fps", "local_window", + "global_window", "global_pinned_dilation", "global_attn_period", + "global_attn_offset", "rope_impl", "value_residual", "gated_attn", + "noise_conditioning", "ctrl_conditioning", "ctrl_cond_dropout", + "ctrl_conditioning_period", "scheduler_sigmas", +) + + def _expected_manifest(config: WaypointConfig) -> dict[str, Any]: + values = asdict(config) return { "model_type": "waypoint-1.5", - "inference_fps": config.inference_fps, - "temporal_compression": config.temporal_compression, - "taehv_ae": config.taehv_ae, - "ae_uri": config.ae_uri, - "prompt_conditioning": config.prompt_conditioning, - "channels": config.channels, - "n_layers": config.n_layers, - "n_heads": config.n_heads, - "n_kv_heads": config.n_kv_heads, - "d_model": config.d_model, - "mlp_ratio": config.mlp_ratio, "causal": True, - "moe": config.moe, - "n_buttons": config.n_buttons, - "patch": config.patch, - "base_fps": config.base_fps, - "local_window": config.local_window, - "global_window": config.global_window, - "global_pinned_dilation": config.global_pinned_dilation, - "global_attn_period": config.global_attn_period, - "global_attn_offset": config.global_attn_offset, "n_frames": config.max_frames, - "rope_impl": config.rope_impl, - "value_residual": config.value_residual, - "gated_attn": config.gated_attn, - "noise_conditioning": config.noise_conditioning, - "ctrl_conditioning": config.ctrl_conditioning, - "ctrl_cond_dropout": config.ctrl_cond_dropout, - "ctrl_conditioning_period": config.ctrl_conditioning_period, - "scheduler_sigmas": config.scheduler_sigmas, + **{key: values[key] for key in _MANIFEST_CONFIG_FIELDS}, } diff --git a/mstar/model/waypoint/config.py b/mstar/model/waypoint/config.py index 81d9f72da..e8b06b321 100644 --- a/mstar/model/waypoint/config.py +++ b/mstar/model/waypoint/config.py @@ -8,7 +8,7 @@ """ import math -from dataclasses import dataclass, field +from dataclasses import asdict, dataclass, field WAYPOINT_VARIANT_720P = "waypoint-1.5-1b-720p" WAYPOINT_VARIANT_360P = "waypoint-1.5-1b-360p" @@ -28,6 +28,58 @@ } +_POSITIVE_INT_FIELDS = ( + "n_layers", "n_heads", "n_kv_heads", "d_model", "mlp_ratio", "channels", + "tokens_per_frame", "height", "width", "local_window", "global_window", + "global_pinned_dilation", "global_attn_period", "ctrl_conditioning_period", + "n_buttons", "base_fps", "inference_fps", "temporal_compression", + "max_frames", "step_batch_size", +) + + +# What the released checkpoints were trained with; validate_supported_deployment +# rejects a deployment config that disagrees. +_RELEASED_CHECKPOINT_FIELDS: dict[str, object] = { + "n_layers": 24, + "n_heads": 32, + "n_kv_heads": 16, + "d_model": 2048, + "mlp_ratio": 4, + "channels": 32, + "patch": (2, 2), + "local_window": 16, + "global_window": 128, + "global_pinned_dilation": 8, + "global_attn_period": 4, + "global_attn_offset": -1, + "rope_impl": "ortho", + "rope_nyquist_frac": 0.8, + "rope_theta": 10_000.0, + "noise_conditioning": "wan", + "value_residual": True, + "gated_attn": False, + "moe": False, + "prompt_conditioning": None, + "ctrl_conditioning": True, + "ctrl_cond_dropout": 0.0, + "ctrl_conditioning_period": 3, + "n_buttons": 256, + "scheduler_sigmas": WAYPOINT_SCHEDULER_SIGMAS, + "base_fps": 15, + "inference_fps": 60, + "temporal_compression": 4, + "max_frames": 512, + "taehv_ae": True, + "ae_uri": "Overworld-Models/taehv1_5", + "auto_aspect_ratio": True, +} + + +def _as_tuple(value: object) -> object: + # A YAML override arrives as a list; the released values are tuples. + return tuple(value) if isinstance(value, list) else value + + @dataclass class WaypointConfig: """Waypoint-1.5-1B model configuration. @@ -158,32 +210,14 @@ def __post_init__(self) -> None: "WaypointDiT does not implement prompt cross-attention; this " f"checkpoint declares prompt_conditioning={self.prompt_conditioning!r}." ) - positive_ints = { - "n_layers": self.n_layers, - "n_heads": self.n_heads, - "n_kv_heads": self.n_kv_heads, - "d_model": self.d_model, - "mlp_ratio": self.mlp_ratio, - "channels": self.channels, - "tokens_per_frame": self.tokens_per_frame, - "height": self.height, - "width": self.width, - "local_window": self.local_window, - "global_window": self.global_window, - "global_pinned_dilation": self.global_pinned_dilation, - "global_attn_period": self.global_attn_period, - "ctrl_conditioning_period": self.ctrl_conditioning_period, - "n_buttons": self.n_buttons, - "base_fps": self.base_fps, - "inference_fps": self.inference_fps, - "temporal_compression": self.temporal_compression, - "max_frames": self.max_frames, - "step_batch_size": self.step_batch_size, - } - invalid = [name for name, value in positive_ints.items() if type(value) is not int or value <= 0] + values = asdict(self) + invalid = [ + name for name in _POSITIVE_INT_FIELDS + if type(values[name]) is not int or values[name] <= 0 + ] if invalid: - values = ", ".join(f"{name}={positive_ints[name]!r}" for name in invalid) - raise ValueError(f"Waypoint positive integer fields are invalid: {values}.") + listed = ", ".join(f"{name}={values[name]!r}" for name in invalid) + raise ValueError(f"Waypoint positive integer fields are invalid: {listed}.") if len(self.patch) != 2 or any(type(size) is not int or size <= 0 for size in self.patch): raise ValueError(f"patch must contain two positive integers; got {self.patch!r}.") if self.tokens_per_frame != self.height * self.width: @@ -250,44 +284,11 @@ def validate_supported_deployment(self) -> None: f"Waypoint variant {self.variant!r} requires " f"(tokens_per_frame, height, width)={expected_geometry}; got {actual_geometry}." ) - checkpoint_facts = { - "n_layers": (self.n_layers, 24), - "n_heads": (self.n_heads, 32), - "n_kv_heads": (self.n_kv_heads, 16), - "d_model": (self.d_model, 2048), - "mlp_ratio": (self.mlp_ratio, 4), - "channels": (self.channels, 32), - "patch": (self.patch, (2, 2)), - "local_window": (self.local_window, 16), - "global_window": (self.global_window, 128), - "global_pinned_dilation": (self.global_pinned_dilation, 8), - "global_attn_period": (self.global_attn_period, 4), - "global_attn_offset": (self.global_attn_offset, -1), - "rope_impl": (self.rope_impl, "ortho"), - "rope_nyquist_frac": (self.rope_nyquist_frac, 0.8), - "rope_theta": (self.rope_theta, 10_000.0), - "noise_conditioning": (self.noise_conditioning, "wan"), - "value_residual": (self.value_residual, True), - "gated_attn": (self.gated_attn, False), - "moe": (self.moe, False), - "prompt_conditioning": (self.prompt_conditioning, None), - "ctrl_conditioning": (self.ctrl_conditioning, True), - "ctrl_cond_dropout": (self.ctrl_cond_dropout, 0.0), - "ctrl_conditioning_period": (self.ctrl_conditioning_period, 3), - "n_buttons": (self.n_buttons, 256), - "scheduler_sigmas": (tuple(self.scheduler_sigmas), WAYPOINT_SCHEDULER_SIGMAS), - "base_fps": (self.base_fps, 15), - "inference_fps": (self.inference_fps, 60), - "temporal_compression": (self.temporal_compression, 4), - "max_frames": (self.max_frames, 512), - "taehv_ae": (self.taehv_ae, True), - "ae_uri": (self.ae_uri, "Overworld-Models/taehv1_5"), - "auto_aspect_ratio": (self.auto_aspect_ratio, True), - } + values = asdict(self) mismatches = [ - f"{name}={actual!r} (expected {expected!r})" - for name, (actual, expected) in checkpoint_facts.items() - if actual != expected + f"{name}={values[name]!r} (expected {expected!r})" + for name, expected in _RELEASED_CHECKPOINT_FIELDS.items() + if _as_tuple(values[name]) != expected ] if mismatches: raise ValueError( diff --git a/mstar/model/waypoint/waypoint_model.py b/mstar/model/waypoint/waypoint_model.py index 24b75949f..963569c77 100644 --- a/mstar/model/waypoint/waypoint_model.py +++ b/mstar/model/waypoint/waypoint_model.py @@ -35,7 +35,6 @@ from dataclasses import replace import torch -import yaml from mstar.communication.tensors import NameToTensorList from mstar.conductor.request_info import ( @@ -60,7 +59,7 @@ TensorPointerInfo, ) from mstar.graph.special_destinations import EMIT_TO_CLIENT -from mstar.model.base import ForwardPassArgs, Model, TensorAndMetadata, WorkerGraph +from mstar.model.base import ForwardPassArgs, Model, TensorAndMetadata from mstar.model.submodule_base import NodeSubmodule from mstar.model.waypoint.config import ( WAYPOINT_VARIANT_360P, @@ -200,7 +199,7 @@ def get_node_resources(self) -> list[NodeResourceSpec]: # Resident session count, not the step batch (that's # ``max_batch_size``/``step_batch_size``). Default 1; a deployment # raises it via ``resources: {kv: {num_sessions: N}}`` along with - # ``max_concurrent_requests`` (see ``get_worker_graphs``). + # ``max_concurrent_requests`` (see ``validate_config_yaml``). num_sessions=1, ) # Logged since nothing downstream prints this ~816 MiB/world cost. @@ -275,8 +274,8 @@ def get_graph_walk_graphs(self) -> dict[str, GraphSection]: return {PRIME_WALK: prime, ROLLOUT_WALK: rollout} - def get_worker_graphs(self, config_path: str) -> list[WorkerGraph]: - """Refuse to build unless the deployment caps concurrency at the number + def validate_config_yaml(self, config: dict, config_path: str) -> None: + """Refuse to serve unless the deployment caps concurrency at the number of worlds the ring was sized for. The primary gate on the world pool: a world is claimed at ``admit``, @@ -285,14 +284,7 @@ def get_worker_graphs(self, config_path: str) -> list[WorkerGraph]: pool. ``max_batch_size``/``step_batch_size`` caps a step's row count, not how many worlds may exist, so it does not substitute for this check. - - Checked here because this hook is the only place a model sees the - key: the Conductor reads it from the YAML and - ``api_server/entrypoint.py`` forwards only ``model_kwargs`` to - ``Model.__init__``. """ - with open(config_path, "r") as f: - config = yaml.safe_load(f) or {} # Same block ``EngineManager.build`` feeds to # ``apply_yaml_overrides``, so the gate and the allocation agree. overrides = (config.get("resources") or {}).get(KV_RESOURCE) or {} @@ -339,7 +331,6 @@ def get_worker_graphs(self, config_path: str) -> list[WorkerGraph]: f"{config_path}. A step cannot batch more rows than there are " "resident worlds to supply them." ) - return super().get_worker_graphs(config_path) # ------------------------------------------------------------------ # Model ABC: I/O diff --git a/test/modular/test_waypoint_checkpoint.py b/test/modular/test_waypoint_checkpoint.py index 3fe5a428e..9633c0d8e 100644 --- a/test/modular/test_waypoint_checkpoint.py +++ b/test/modular/test_waypoint_checkpoint.py @@ -422,6 +422,7 @@ def test_shipped_config_builds_through_registry_and_engine_manager_without_netwo skip_weight_loading=True, ) + model.validate_config_yaml(model_config, str(config_path)) model.get_worker_graphs(str(config_path)) manager = EngineManager.build( node_names={DIT_NODE, VAE_ENCODER_NODE}, diff --git a/test/modular/test_waypoint_shell.py b/test/modular/test_waypoint_shell.py index 3ee7e6a4b..a7c0aa940 100644 --- a/test/modular/test_waypoint_shell.py +++ b/test/modular/test_waypoint_shell.py @@ -686,6 +686,11 @@ def _write_config(tmp_path, name: str, **extra) -> str: return str(path) +def _validate(model, path: str) -> None: + """What the Conductor does at startup with the deployment YAML.""" + model.validate_config_yaml(yaml.safe_load(pathlib.Path(path).read_text()), path) + + def _sessions(n: int) -> dict: """The ``resources:`` block a deployment writes to size the ring — the same one ``EngineManager.build`` feeds to ``apply_yaml_overrides``, which is why @@ -694,7 +699,7 @@ def _sessions(n: int) -> dict: @pytest.mark.parametrize("limit", [None, 0, -1, True, 1.0, "2"]) -def test_get_worker_graphs_refuses_a_deployment_with_no_admit_queue( +def test_validate_config_yaml_refuses_a_deployment_with_no_admit_queue( model, tmp_path, limit ): """The pool is finite, and this is the primary gate on it. @@ -714,11 +719,11 @@ def test_get_worker_graphs_refuses_a_deployment_with_no_admit_queue( extra = {} if limit is None else {"max_concurrent_requests": limit} path = _write_config(tmp_path, f"reject_{limit}.yaml", **extra) with pytest.raises(ValueError, match="max_concurrent_requests"): - model.get_worker_graphs(path) + _validate(model, path) @pytest.mark.parametrize("worlds", [0, -1, True, 1.0, 1.9, "2"]) -def test_get_worker_graphs_refuses_invalid_world_pool_size( +def test_validate_config_yaml_refuses_invalid_world_pool_size( model, tmp_path, worlds ): path = _write_config( @@ -728,11 +733,11 @@ def test_get_worker_graphs_refuses_invalid_world_pool_size( **_sessions(worlds), ) with pytest.raises(ValueError, match=r"resources\.kv\.num_sessions"): - model.get_worker_graphs(path) + _validate(model, path) @pytest.mark.parametrize(("limit", "worlds"), [(2, 1), (8, 4), (2, None)]) -def test_get_worker_graphs_refuses_more_arrivals_than_worlds( +def test_validate_config_yaml_refuses_more_arrivals_than_worlds( model, tmp_path, limit, worlds ): """A queue longer than the pool is a delayed failure: the conductor admits @@ -750,11 +755,11 @@ def test_get_worker_graphs_refuses_more_arrivals_than_worlds( path = _write_config(tmp_path, f"over_{limit}_{worlds}.yaml", **extra) with pytest.raises(ValueError, match="exceeds the"): - model.get_worker_graphs(path) + _validate(model, path) @pytest.mark.parametrize(("limit", "worlds"), [(1, None), (1, 1), (4, 4), (8, 8)]) -def test_get_worker_graphs_accepts_a_deployment_inside_its_pool( +def test_validate_config_yaml_accepts_a_deployment_inside_its_pool( model, tmp_path, limit, worlds ): """``limit == num_sessions`` is the shape that should be written, at any size. @@ -765,6 +770,7 @@ def test_get_worker_graphs_accepts_a_deployment_inside_its_pool( extra |= _sessions(worlds) path = _write_config(tmp_path, f"ok_{limit}_{worlds}.yaml", **extra) + _validate(model, path) graphs = model.get_worker_graphs(path) assert graphs @@ -784,6 +790,7 @@ def test_the_shipped_config_serializes_both_nodes_onto_one_rank(model): """ path = pathlib.Path(__file__).resolve().parents[2] / "configs" / "waypoint.yaml" + _validate(model, str(path)) graphs = model.get_worker_graphs(str(path)) assert {walk for g in graphs for walk in g.graph_walks} == {PRIME_WALK, ROLLOUT_WALK} @@ -793,7 +800,16 @@ def test_the_shipped_config_serializes_both_nodes_onto_one_rank(model): assert set(by_walk[ROLLOUT_WALK].section.get_nodes()) == {DIT_NODE} -def test_get_worker_graphs_warns_about_worlds_no_request_can_reach( +def test_validate_config_yaml_refuses_a_step_wider_than_the_world_pool(tmp_path): + model = WaypointModel(skip_weight_loading=True, step_batch_size=4) + path = _write_config( + tmp_path, "wide_step.yaml", max_concurrent_requests=2, **_sessions(2) + ) + with pytest.raises(ValueError, match="step_batch_size: 4"): + _validate(model, path) + + +def test_validate_config_yaml_warns_about_worlds_no_request_can_reach( model, tmp_path, caplog ): """Legal, only wasteful -- a warning, not a refusal. Each unreachable @@ -804,7 +820,7 @@ def test_get_worker_graphs_warns_about_worlds_no_request_can_reach( ) with caplog.at_level(logging.WARNING): - assert model.get_worker_graphs(path) + _validate(model, path) assert any("can never be filled" in r.getMessage() for r in caplog.records) From ac7abd1c4128b9eca81db81e91536f2a8a9c5685 Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Thu, 24 Sep 2026 22:13:39 +0000 Subject: [PATCH 26/29] review : parity suite per-session ring, reference mask_mod under FLASH, batched and cond-head cache tests --- .../test_waypoint_reference_equivalence.py | 250 +++++++++++++++++- 1 file changed, 236 insertions(+), 14 deletions(-) diff --git a/test/modular/test_waypoint_reference_equivalence.py b/test/modular/test_waypoint_reference_equivalence.py index 63b768fd6..fb2869a9c 100644 --- a/test/modular/test_waypoint_reference_equivalence.py +++ b/test/modular/test_waypoint_reference_equivalence.py @@ -12,7 +12,10 @@ Both sides here run everything eager except one shared attention kernel: ``src.patch_model.flex_attention`` is rebound to the port's own -``flex_attention_masked``, so attention cannot be the variable under test. +``flex_attention_masked``, so attention cannot be the variable under test. The +reference's ``BlockMask`` is rebuilt with the port's ``mask_mod`` on the way in: its +``mask_mod=None`` reads as "no mask" to the FLASH backend, which then attends over +the whole ring. **Every test is run on two ports**, built from the same checkpoint and differing only in ``WaypointConfig.reference_compat``: @@ -44,6 +47,7 @@ import pytest import torch +from torch.nn.attention.flex_attention import BlockMask sys.path.insert(0, ".") @@ -54,7 +58,7 @@ AttentionStep, AttnBackend, ) -from mstar.engine.resources.attn.flex import flex_attention_masked +from mstar.engine.resources.attn.flex import _MASK_MOD, flex_attention_masked from mstar.engine.resources.base import EngineResourceInfo from mstar.engine.resources.kv.config import KVSpec, RingKVConfig, RingKVLayerConfig, RingKVStep from mstar.engine.resources.kv.ring import RingKVManager @@ -134,6 +138,22 @@ def forward(self, *args, **kwargs): raise NotImplementedError("binding stand-in") +def _reference_flex_attention(q, k, v, *, block_mask, enable_gqa): + # Same blocks, the port's mask_mod: FLASH treats the reference's mask_mod=None + # as trivial and attends densely (flex.py, _flash_mask_mod). None under TRITON. + block_mask = BlockMask.from_kv_blocks( + block_mask.kv_num_blocks, + block_mask.kv_indices, + block_mask.full_kv_num_blocks, + block_mask.full_kv_indices, + BLOCK_SIZE=block_mask.BLOCK_SIZE, + mask_mod=_MASK_MOD, + seq_lengths=block_mask.seq_lengths, + compute_q_blocks=False, + ) + return flex_attention_masked(q, k, v, block_mask=block_mask, enable_gqa=enable_gqa) + + def _load_reference(checkpoint: Path = CHECKPOINT) -> dict: # Served reference: islands cloned pre-patch, flex pinned to the port's kernel, # matmul precision 'high' for the reference's batch-5 sigma LUT (TF32, not 'highest'). @@ -151,7 +171,7 @@ def _load_reference(checkpoint: Path = CHECKPOINT) -> dict: bare_cond_head = model.transformer.blocks[0].cond_head patch_model.apply_inference_patches(model) - patch_model.flex_attention = flex_attention_masked + patch_model.flex_attention = _reference_flex_attention cache = StaticKVCache(cfg, batch_size=1, dtype=DTYPE).to(device=DEVICE) return { "cfg": cfg, @@ -184,11 +204,13 @@ def reference(): yield _load_reference() -def _build_port(config, checkpoint: Path = CHECKPOINT): +def _build_port(config, checkpoint: Path = CHECKPOINT, *, num_sessions: int = 1): """Build a port from the checkpoint belonging to ``config``. The default preserves the original 720p harness. The explicit path is used by the 360p live-reference gate, whose weights are a distinct publication. + ``num_sessions`` is 1 for every test but the batching one, which needs a + second resident session to run two rows in one DiT call. """ dit = build_waypoint_dit(config, str(checkpoint), device=DEVICE) spec = KVSpec( @@ -200,7 +222,7 @@ def _build_port(config, checkpoint: Path = CHECKPOINT): head_dim=config.d_head, num_qo_heads=config.n_heads, tokens_per_frame=config.tokens_per_frame, - num_sessions=1, + num_sessions=num_sessions, layers=tuple( RingKVLayerConfig( ring_frames=config.ring_frames(i), @@ -283,17 +305,31 @@ def _reference_forward(reference, x, sigma_value: float, ctx, *, commit: bool): return reference["model"](x, sigma, **ctx, kv_cache=reference["kv"]).clone() +def _cond_idx(port, sigma_value: float, *, commit: bool) -> int: + """The ``scheduler_sigmas`` slot the cond_head cache (``d881b43e``) expects + for one ``_port_forward`` call: the trailing slot for the committing pass, + exactly as ``WaypointDiT._cache_pass`` picks it, or the slot whose + scheduled sigma equals ``sigma_value``, exactly as ``_denoise_pass``'s + ``enumerate`` does. + """ + sigmas = port["config"].scheduler_sigmas + if commit: + return len(sigmas) - 1 + return list(sigmas[:-1]).index(sigma_value) + + def _port_forward(port, x, sigma_value: float, ctx, frame_pos: int, *, commit: bool): with torch.inference_mode(): sigma = x.new_full((x.size(0), x.size(1)), sigma_value) return port["dit"]( x, sigma, - torch.tensor(frame_pos, dtype=torch.int64, device=DEVICE), + torch.full((x.size(0),), frame_pos, dtype=torch.int64, device=DEVICE), mouse=ctx["mouse"], button=ctx["button"], scroll=ctx["scroll"], commit=commit, + cond_idx=_cond_idx(port, sigma_value, commit=commit), ).clone() @@ -331,7 +367,7 @@ def _port_frame(port, noise, ctx, frame_pos: int): with torch.inference_mode(): x0 = dit.generate_frame( noise, - torch.tensor(frame_pos, dtype=torch.int64, device=DEVICE), + torch.full((noise.size(0),), frame_pos, dtype=torch.int64, device=DEVICE), mouse=ctx["mouse"], button=ctx["button"], scroll=ctx["scroll"], @@ -377,6 +413,30 @@ def _commit(port, frame: int) -> None: port["kv"].commit(*_step_and_context(port, frame)) +def _batch_step_and_context(frames: list[tuple[str, int]]): + """``_step_and_context``'s multi-row form: one step naming every ``(rid, + frame)`` pair, in the row order a batched forward's inputs use. Only the + batching test below drives more than one row per step.""" + rids = tuple(rid for rid, _ in frames) + return ( + RingKVStep(frames=tuple(frames)), + StepContext(request_ids=rids, graph_walk="rollout", slot=0, capture=False), + ) + + +def _admit_batch(port, frames: list[tuple[str, int]]) -> None: + step, context = _batch_step_and_context(frames) + outcome = port["kv"].admit(step, context) + assert outcome.ok, f"batch {frames} refused: {outcome.reason}" + context.plan_results["kv"] = port["kv"].plan(step, context) + port["attn"].plan(AttentionStep(), context) + assert not port["attn"].needs_token_visibility + + +def _commit_batch(port, frames: list[tuple[str, int]]) -> None: + port["kv"].commit(*_batch_step_and_context(frames)) + + # --------------------------------------------------------------------------- # Measuring # --------------------------------------------------------------------------- @@ -428,13 +488,18 @@ def _divergent_stages(reference_stages, port_stages, names): ] -def _comparable_ring(reference_layer, port_layer): +def _comparable_ring(reference_layer, port_layer, session_idx: int): """One world of ring state, in a shape the two sides share. The reference allocates 128 frame slots for a global layer but addresses only its 16 buckets, so its live region is ``[0, port ring_len)`` and its scratch is the tail ``[L, capacity)``. The port compacts the gap away. Callers assert the gap stays clear rather than trusting it. + + Since ``20965ec1`` the port's ring also holds one shared scratch session past + the resident pool (``total_sessions == num_sessions + 1``), so ``port_layer.kv``/ + ``written`` span every session, not just the one request under test. + ``session_idx`` slices out that one span. """ ring_len = port_layer.ring_len reference_kv = torch.cat( @@ -442,14 +507,37 @@ def _comparable_ring(reference_layer, port_layer): dim=3, ) reference_written = torch.cat((reference_layer.written[:ring_len], reference_layer.written[reference_layer.L :])) - return (reference_kv, reference_written), (port_layer.kv, port_layer.written) + lo, hi = port_layer.session_span(session_idx) + return (reference_kv, reference_written), (port_layer.kv[:, :, :, lo:hi], port_layer.written[lo:hi]) + + +def _assert_padding_session_clean(port) -> None: + """The one session past the resident pool (``RingKVManager.num_sessions``) + is never claimed by ``admit`` -- it only exists for a padded replay's dummy + rows -- and nothing here drives a padded step, so it must stay exactly at + the zero/scratch-tail-only state ``LayerRingCache.__init__`` leaves it in. + Cheap: a couple of tensor comparisons per layer. + """ + padding = port["kv"].num_sessions + for layer in port["kv"].layers: + lo, hi = layer.session_span(padding) + assert layer.kv[:, :, :, lo:hi].eq(0).all(), "padding session KV was written" + expected_written = torch.zeros(layer.capacity, dtype=torch.bool, device=layer.written.device) + expected_written[layer.ring_len :] = True + assert torch.equal(layer.written[lo:hi], expected_written), "padding session written mask moved" def _ring_deviation(reference, port) -> list[tuple[int, float, float, bool]]: - """Per layer: ``(index, max abs, relative, written masks equal)``.""" + """Per layer: ``(index, max abs, relative, written masks equal)``, for the + session the port's active request occupies. Also pins the padding session + untouched, since that is exactly the span a session-slicing bug would stop + catching. + """ + _assert_padding_session_clean(port) + session_idx = port["kv"].session_of(port["rid"]) rows = [] for i, (reference_layer, port_layer) in enumerate(zip(reference["kv"].layers, port["kv"].layers, strict=True)): - (ref_kv, ref_written), (port_kv, port_written) = _comparable_ring(reference_layer, port_layer) + (ref_kv, ref_written), (port_kv, port_written) = _comparable_ring(reference_layer, port_layer, session_idx) gap, relative = _deviation(ref_kv, port_kv) rows.append((i, gap, relative, torch.equal(ref_written, port_written))) return rows @@ -589,6 +677,40 @@ def test_the_compat_conditioner_reproduces_the_reference_sigma_lut(ports, refere ) +def test_the_cond_head_cache_matches_the_live_projection(ports, oracle): + """``d881b43e`` caches every block's cond_head modulation per sigma, and + ``build_waypoint_dit`` already runs ``materialize_runtime_tables`` before + handing a port back -- so every forward test in this file, compat or + exact, already runs the cached path (``CondHead.forward`` with + ``self._cache is not None``), never the ``_project`` GEMM it replaced. + This pins the mechanism directly against this suite's own checkpoint: + forcing every block back onto the live projection and re-running the + identical forward must reproduce the cached output bit-for-bit, the way + ``CondHead.build_cache``'s docstring promises. + """ + port = ports[True] + dit = port["dit"] + frame = _frame(oracle, 0) + ctx = _ctx(frame) + x = frame["latent"].to(DEVICE) + + _new_request(port) + _admit(port, 0) + cached_out = _port_forward(port, x, 1.0, ctx, 0, commit=False) + + caches = [block.cond_head._cache for block in dit.blocks] + assert all(cache is not None for cache in caches), "port was not built with the cond_head cache live" + for block in dit.blocks: + block.cond_head._cache = None + try: + live_out = _port_forward(port, x, 1.0, ctx, 0, commit=False) + finally: + for block, cache in zip(dit.blocks, caches, strict=True): + block.cond_head._cache = cache + + assert torch.equal(cached_out, live_out), "cond_head cache diverged from the live projection" + + @COMPAT_MODES def test_one_forward_matches_the_reference_on_an_empty_ring(ports, reference, oracle, compat): """The committing pass of the oracle's seed frame: weight loading, RoPE, adaLN @@ -756,10 +878,14 @@ def _load_snapshot(port, reference, oracle, frame_index: int) -> None: for layer, saved in zip(reference["kv"].layers, snapshot, strict=True): layer.kv.copy_(saved["kv"].to(DEVICE)) layer.written.copy_(saved["written"].to(DEVICE)) + # ``_new_request`` above only registers the rid; ``admit`` (called later, by + # the caller's own ``_admit``/``_port_frame``) is what actually claims a + # session. Slot 0 is the only one it can claim -- these ports are all + # ``num_sessions=1`` -- so this hardcodes it exactly as ``_reset`` does. for reference_layer, port_layer in zip(reference["kv"].layers, port["kv"].layers, strict=True): - (ref_kv, ref_written), _ = _comparable_ring(reference_layer, port_layer) - port_layer.kv.copy_(ref_kv) - port_layer.written.copy_(ref_written) + (ref_kv, ref_written), (port_kv, port_written) = _comparable_ring(reference_layer, port_layer, 0) + port_kv.copy_(ref_kv) + port_written.copy_(ref_written) def _rollout(port, reference, oracle, frames: int): @@ -857,3 +983,99 @@ def test_the_port_compacts_only_slots_the_reference_never_addresses(ports, oracl ) checked += 1 assert checked, "no global-layer ring snapshots found; the compaction claim is untested" + + +# --------------------------------------------------------------------------- +# L4 -- batched sessions +# --------------------------------------------------------------------------- + +def _roll(port, rows: list[tuple[str, list[dict]]], frames: int) -> dict[str, list[torch.Tensor]]: + """Step every row in ``rows`` together for local frames ``j = 0..frames-1``. + ``rows`` pairs a request id with its per-frame inputs (``kind``, + ``latent``/``noise_bf16``, ``ctx``), already on device. A batch of one row + runs the exact same admit/forward/commit sequence a batch of two does, so + the solo and batched arms below share this one driver. Returns each row's + per-frame output, cloned. + """ + outputs: dict[str, list[torch.Tensor]] = {rid: [] for rid, _ in rows} + with torch.inference_mode(): + for j in range(frames): + step = [(rid, j) for rid, _ in rows] + _admit_batch(port, step) + frames_j = [per_frame[j] for _, per_frame in rows] + ctx_cat = {key: torch.cat([f["ctx"][key] for f in frames_j], dim=0) for key in frames_j[0]["ctx"]} + if j == 0: + latent_cat = torch.cat([f["latent"] for f in frames_j], dim=0) + out = _port_forward(port, latent_cat, 0.0, ctx_cat, j, commit=True) + else: + noise_cat = torch.cat([f["noise_bf16"] for f in frames_j], dim=0) + out = port["dit"].generate_frame( + noise_cat, + torch.full((len(rows),), j, dtype=torch.int64, device=DEVICE), + mouse=ctx_cat["mouse"], + button=ctx_cat["button"], + scroll=ctx_cat["scroll"], + ) + _commit_batch(port, step) + for i, (rid, _) in enumerate(rows): + outputs[rid].append(out[i : i + 1].clone()) + return outputs + + +def test_batched_generate_frame_matches_the_same_sessions_run_solo(ports, oracle): + """``20965ec1`` batches concurrent sessions into one DiT call. The ``ports`` + fixture is fixed at ``num_sessions=1``, so this builds its own two-session + port from the same checkpoint and config, then rolls two sessions for 20 + frames each on the oracle's real noise and controls -- local frame + ``j = 0..19``, past the local ring's 16-frame capacity, so the ring wraps + once. ``w0`` runs oracle frames 0..19; ``w1`` shares only frame 0's seed + latent, paired with oracle frame 20's controls at ``j=0`` and oracle + frames 21..39 at ``j=1..19``, so the two sessions diverge from frame 0 on. + Each row is run once as a solo B=1 rollout and once as a batched B=2 + rollout, and compared row by row, frame by frame. + + Frame 0 on an empty ring measured 0.00 ulp deviation between B=1 and B=2 on + both flex backends (jobs 8141/8142), so the bar here is bit-exact at every + frame, not a tolerance. + """ + config = ports[True]["config"] + port = _build_port(config, CHECKPOINT, num_sessions=2) + kv = port["kv"] + + seed = _frame(oracle, 0) + seed_latent = seed["latent"].to(DEVICE) + w0 = [{"kind": "seed", "latent": seed_latent, "ctx": _ctx(seed)}] + for j in range(1, 20): + frame = _frame(oracle, j) + w0.append({"kind": "gen", "noise_bf16": frame["noise_bf16"].to(DEVICE), "ctx": _ctx(frame)}) + + frame20 = _frame(oracle, 20) + w1 = [{"kind": "seed", "latent": seed_latent, "ctx": _ctx(frame20)}] + for j in range(1, 20): + frame = _frame(oracle, 20 + j) + assert frame["kind"] != "seed", f"oracle frame {20 + j} unexpectedly a seed frame" + w1.append({"kind": "gen", "noise_bf16": frame["noise_bf16"].to(DEVICE), "ctx": _ctx(frame)}) + + # ---- B=1: each session rolled alone. + kv.ingest_request("w0") + solo_w0 = _roll(port, [("w0", w0)], 20)["w0"] + kv.reset_request("w0", free=True) + kv.remove_request("w0") + + kv.ingest_request("w1") + solo_w1 = _roll(port, [("w1", w1)], 20)["w1"] + kv.reset_request("w1", free=True) + kv.remove_request("w1") + + # ---- B=2: both sessions rolled together. + kv.ingest_request("w0") + kv.ingest_request("w1") + batched = _roll(port, [("w0", w0), ("w1", w1)], 20) + _assert_padding_session_clean(port) + + solo = {"w0": solo_w0, "w1": solo_w1} + for rid in ("w0", "w1"): + gaps = [_deviation(solo[rid][j], batched[rid][j])[0] for j in range(20)] + print(f"{rid}: batched vs solo maxabs over 20 frames={max(gaps):.4e}") + first = next((j for j in range(20) if not torch.equal(solo[rid][j], batched[rid][j])), None) + assert first is None, f"{rid}: batching diverged from its solo run at frame {first}, maxabs={gaps[first]:.4e}" From bca99ddf095683b62a660046010a1dc305b4793a Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Thu, 24 Sep 2026 22:20:57 +0000 Subject: [PATCH 27/29] review : prefer_binary opt-in, waypoint clients request binary frames --- mstar/client/client.py | 12 ++++++------ test/modular/test_client_sdk.py | 8 ++++---- test/waypoint/benchmark_streaming.py | 4 +++- test/waypoint/serve_rollout.py | 4 ++-- 4 files changed, 15 insertions(+), 13 deletions(-) diff --git a/mstar/client/client.py b/mstar/client/client.py index a91ad9f8d..38ddd72d1 100644 --- a/mstar/client/client.py +++ b/mstar/client/client.py @@ -67,16 +67,16 @@ def __init__( timeout: float = 600.0, session: requests.Session | None = None, enable_nvtx: bool = False, - prefer_binary: bool = True, + prefer_binary: bool = False, ): self.base_url = base_url.rstrip("/") self.timeout = timeout self._session = session or requests.Session() - # Ask for length-framed binary payloads, but decide how to parse from - # the response's Content-Type. A server that does not implement the - # framing — an older build, or the Rust frontend, neither of which reads - # ``Accept`` — answers NDJSON and the historical path handles it. Set - # False to force NDJSON, which the streaming benchmark uses to A/B. + # Opt in to ask for length-framed binary payloads (e.g. waypoint's + # large video frames), but decide how to parse from the response's + # Content-Type. A server that does not implement the framing — an + # older build, or the Rust frontend, neither of which reads + # ``Accept`` — answers NDJSON and the historical path handles it. self._prefer_binary = prefer_binary # Splits the client's share of the streaming gap into the blocking # socket read and the decode of the line it returns. Without this the diff --git a/test/modular/test_client_sdk.py b/test/modular/test_client_sdk.py index e96abc056..7207df630 100644 --- a/test/modular/test_client_sdk.py +++ b/test/modular/test_client_sdk.py @@ -162,7 +162,7 @@ def test_stream_takes_the_binary_path_when_the_server_says_so(): ) resp = _fake_response(BINARY_STREAM_MEDIA_TYPE, header + body) - events, post = _stream_with(MStarClient("http://x"), resp) + events, post = _stream_with(MStarClient("http://x", prefer_binary=True), resp) assert [e.text for e in events] == [payload.decode("utf-8", "replace")] headers = post.call_args.kwargs["headers"] @@ -177,7 +177,7 @@ def test_stream_falls_back_to_ndjson_when_the_server_ignores_accept(): resp = _fake_response("application/x-ndjson", (line + "\n").encode()) with mock.patch.object(resp, "iter_lines", wraps=resp.iter_lines) as iter_lines: - events, _ = _stream_with(MStarClient("http://x"), resp) + events, _ = _stream_with(MStarClient("http://x", prefer_binary=True), resp) assert [e.text for e in events] == ["hi"] iter_lines.assert_called_once_with(chunk_size=1024 * 1024, decode_unicode=True) @@ -187,7 +187,7 @@ def test_stream_sends_no_negotiation_headers_when_binary_is_disabled(): line = json.dumps({"modality": "text", "data": base64.b64encode(b"hi").decode(), "metadata": {}}) resp = _fake_response("application/x-ndjson", (line + "\n").encode()) - events, post = _stream_with(MStarClient("http://x", prefer_binary=False), resp) + events, post = _stream_with(MStarClient("http://x"), resp) assert [e.text for e in events] == ["hi"] assert post.call_args.kwargs["headers"] == {} @@ -202,4 +202,4 @@ def test_stream_names_content_encoding_as_the_cause_on_a_compressed_binary_body( BINARY_STREAM_MEDIA_TYPE, b"\x1f\x8b garbage", {"Content-Encoding": "gzip"} ) with pytest.raises(RuntimeError, match="Content-Encoding 'gzip'"): - _stream_with(MStarClient("http://x"), resp) + _stream_with(MStarClient("http://x", prefer_binary=True), resp) diff --git a/test/waypoint/benchmark_streaming.py b/test/waypoint/benchmark_streaming.py index 99ede7360..b27105f89 100644 --- a/test/waypoint/benchmark_streaming.py +++ b/test/waypoint/benchmark_streaming.py @@ -995,7 +995,9 @@ def _run_benchmark(args: argparse.Namespace) -> dict: f"(worlds={args.worlds} batch={args.batch})" ) concurrent_result, concurrent_failures = _run_concurrent_phase( - client_factory=lambda: MStarClient(url, timeout=args.request_timeout), + client_factory=lambda: MStarClient( + url, timeout=args.request_timeout, prefer_binary=args.protocol == "binary" + ), seed_image=seed_image, variant=variant, streams=args.streams, diff --git a/test/waypoint/serve_rollout.py b/test/waypoint/serve_rollout.py index 7294aac26..a0592a1c5 100644 --- a/test/waypoint/serve_rollout.py +++ b/test/waypoint/serve_rollout.py @@ -932,7 +932,7 @@ def main() -> int: failures: list[str] = [] sampler: MemorySampler | None = None try: - client = MStarClient(url, timeout=args.request_timeout) + client = MStarClient(url, timeout=args.request_timeout, prefer_binary=True) started = time.time() _wait_for_health(client, proc, args.startup_timeout) print(f"server ready after {time.time() - started:.1f}s") @@ -1014,7 +1014,7 @@ def main() -> int: print(f"--- {phase}: {' + '.join(spec.request_id for spec in specs)} ---") log_offset = args.log.stat().st_size chunks_by_label = _concurrent_rollouts( - lambda: MStarClient(url, timeout=args.request_timeout), + lambda: MStarClient(url, timeout=args.request_timeout, prefer_binary=True), seed, args.steps, specs, From 08600fcbbf3baf4a603f574a67616ebd3914eda5 Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Fri, 25 Sep 2026 01:28:47 +0000 Subject: [PATCH 28/29] review : benchmark_streaming --save-videos writes each measured stream as mp4 --- pyproject.toml | 2 + .../modular/test_waypoint_benchmark_videos.py | 52 +++++++++++ test/waypoint/benchmark_streaming.py | 88 ++++++++++++++++++- 3 files changed, 138 insertions(+), 4 deletions(-) create mode 100644 test/modular/test_waypoint_benchmark_videos.py diff --git a/pyproject.toml b/pyproject.toml index d66406901..e968fb780 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -199,6 +199,8 @@ wan22 = [ ] waypoint = [ + # mp4 encode for benchmark_streaming.py --save-videos. + "av", "huggingface-hub", "safetensors", # TAEHV is installed separately at a pinned revision. PyPI rejects project diff --git a/test/modular/test_waypoint_benchmark_videos.py b/test/modular/test_waypoint_benchmark_videos.py new file mode 100644 index 000000000..2b631fc6a --- /dev/null +++ b/test/modular/test_waypoint_benchmark_videos.py @@ -0,0 +1,52 @@ +from __future__ import annotations + +import runpy +from pathlib import Path + +import pytest + +from mstar.client import VideoFrameChunk + + +@pytest.fixture(scope="module") +def benchmark(): + return runpy.run_path( + str(Path(__file__).parents[1] / "waypoint" / "benchmark_streaming.py") + ) + + +def _chunk(fill: int, frame_index: int, width: int, height: int, frame_count: int = 4) -> VideoFrameChunk: + data = bytes([fill]) * (frame_count * height * width * 3) + return VideoFrameChunk( + data, + { + "width": width, + "height": height, + "fps": 60.0, + "pixel_format": "rgb24", + "frame_index": frame_index, + "frame_count": frame_count, + }, + ) + + +def test_encode_mp4_writes_every_frame_at_the_chunk_geometry(benchmark, tmp_path): + av = pytest.importorskip("av") + encode_mp4 = benchmark["_encode_mp4"] + + width, height = 4, 2 + chunks = [_chunk(1, 0, width, height), _chunk(2, 4, width, height)] + + out = tmp_path / "clip.mp4" + frame_count, fps = encode_mp4(chunks, out) + + assert frame_count == 8 + assert fps == 60.0 + container = av.open(str(out)) + try: + stream = container.streams.video[0] + assert stream.width == width + assert stream.height == height + assert sum(1 for _ in container.decode(stream)) == 8 + finally: + container.close() diff --git a/test/waypoint/benchmark_streaming.py b/test/waypoint/benchmark_streaming.py index b27105f89..c29aba288 100644 --- a/test/waypoint/benchmark_streaming.py +++ b/test/waypoint/benchmark_streaming.py @@ -14,7 +14,8 @@ CUDA_VISIBLE_DEVICES=2 PYTHONPATH=. python3 test/waypoint/benchmark_streaming.py \ --variant 360p --physical-gpu 2 --steps 16 \ - --artifact /tmp/waypoint-streaming-360p.json + --artifact /tmp/waypoint-streaming-360p.json \ + --save-videos /tmp/waypoint-streaming-360p-videos """ from __future__ import annotations @@ -26,6 +27,7 @@ import json import os import re +import shutil import statistics import subprocess import sys @@ -198,6 +200,39 @@ def _startup_latency_metrics(samples: Sequence[float]) -> dict | None: } +def _encode_mp4(chunks: Sequence[VideoFrameChunk], out: Path, crf: int = 18) -> tuple[int, float]: + """Encode already-consumed chunks to an H.264 mp4 with PyAV. Called after a + stream's timed loop has finished, never inside it, so encoding cost is never + counted as stream latency.""" + try: + import av + except ImportError as exc: + raise RuntimeError( + "--save-videos needs PyAV to encode mp4s; install with `uv pip install av`" + ) from exc + import numpy as np + + width, height, fps = chunks[0].width, chunks[0].height, chunks[0].fps + container = av.open(str(out), mode="w") + stream = container.add_stream("libx264", rate=int(round(fps))) + stream.width, stream.height, stream.pix_fmt = width, height, "yuv420p" + stream.options = {"crf": str(crf), "preset": "medium"} + frame_bytes = width * height * 3 + n = 0 + for chunk in chunks: + buf = np.frombuffer(chunk.data, dtype=np.uint8) + assert buf.size % frame_bytes == 0 + for frame in buf.reshape(-1, height, width, 3): + vf = av.VideoFrame.from_ndarray(np.ascontiguousarray(frame), format="rgb24") + for packet in stream.encode(vf): + container.mux(packet) + n += 1 + for packet in stream.encode(): + container.mux(packet) + container.close() + return n, fps + + def _measure_stream( client: MStarClient, seed_image: Path, @@ -212,8 +247,14 @@ def _measure_stream( sleep: Callable[[float], None] = time.sleep, enable_nvtx: bool = False, start_barrier: threading.Barrier | None = None, + chunks_out: list[VideoFrameChunk] | None = None, ) -> tuple[dict, list[str]]: - """Consume one stream while retaining only timings and an incremental hash.""" + """Consume one stream while retaining only timings and an incremental hash. + + When ``chunks_out`` is given, each chunk's reference (not a copy) is also + appended to it for later encoding; callers must do that encoding outside + this function so it never counts toward the timings above. + """ stream = client.stream( images=seed_image, input_modalities=("image",), @@ -265,6 +306,8 @@ def _measure_stream( chunk_index = len(observations) failures.extend(_validate_chunk(event, chunk_index, variant)) digest.update(event.data) + if chunks_out is not None: + chunks_out.append(event) observations.append( ChunkObservation( arrival_seconds=arrived - started, @@ -380,12 +423,15 @@ def _run_concurrent_wave( seeds: Sequence[int], stall_threshold_seconds: float, enable_nvtx: bool, + chunk_lists: Sequence[list[VideoFrameChunk]] | None = None, ) -> tuple[list[tuple[dict, list[str]]], float, float]: """Run ``len(request_ids)`` streams together, each opening only once every thread has built its request body (mirrors serve_rollout._concurrent_rollouts). Returns per-stream (metrics, failures) in ``request_ids`` order, plus wave start/end on the driver's own wall clock (for cross-stream aggregate timing). + ``chunk_lists``, if given, is one list per request id that each stream's + chunks are appended to (see ``_measure_stream``'s ``chunks_out``). """ barrier = threading.Barrier(len(request_ids) + 1) with concurrent.futures.ThreadPoolExecutor(max_workers=len(request_ids)) as executor: @@ -402,8 +448,9 @@ def _run_concurrent_wave( stall_threshold_seconds=stall_threshold_seconds, enable_nvtx=enable_nvtx, start_barrier=barrier, + chunks_out=chunk_lists[index] if chunk_lists is not None else None, ) - for request_id, seed in zip(request_ids, seeds, strict=True) + for index, (request_id, seed) in enumerate(zip(request_ids, seeds, strict=True)) ] barrier.wait(timeout=30) wave_start = time.perf_counter() @@ -553,10 +600,14 @@ def _run_concurrent_phase( request_timeout: float, sampler: rollout.MemorySampler, startup_seconds: float, + save_videos_dir: Path | None = None, ) -> tuple[dict, list[str]]: """N-stream concurrent phase: a discarded warmup wave (compiles the batch-``streams`` CUDA graph bucket), then a measured wave whose per-stream - metrics and server-side cadence decide whether every stream stayed realtime.""" + metrics and server-side cadence decide whether every stream stayed realtime. + + ``save_videos_dir``, if given, saves only the measured wave's streams (the + warmup wave is throwaway CUDA graph compilation).""" failures: list[str] = [] warmup_ids = [f"{request_id_prefix}-concurrent-warmup-{i}" for i in range(streams)] @@ -582,6 +633,7 @@ def _run_concurrent_phase( print(f"concurrent measured: {streams} streams, {num_steps} step(s) each") _wait_for_phase_sample(sampler, proc, "concurrent-measured") log_offset = log_path.stat().st_size + chunk_lists = [[] for _ in measured_ids] if save_videos_dir is not None else None measured_results, wave_start, wave_end = _run_concurrent_wave( client_factory, seed_image, @@ -591,6 +643,7 @@ def _run_concurrent_phase( seeds=measured_seeds, stall_threshold_seconds=stall_threshold_seconds, enable_nvtx=enable_nvtx, + chunk_lists=chunk_lists, ) per_stream = [] for request_id, (metrics, stream_failures) in zip(measured_ids, measured_results, strict=True): @@ -598,6 +651,12 @@ def _run_concurrent_phase( per_stream.append(metrics) rollout._wait_for_cleanup(log_path, tuple(measured_ids), proc, request_timeout, offset=log_offset) + if save_videos_dir is not None: + for request_id, chunks in zip(measured_ids, chunk_lists, strict=True): + out = save_videos_dir / f"{request_id}.mp4" + n, _fps = _encode_mp4(chunks, out) + print(f"saved video: {out} ({n} frames)") + total_frames = sum(metrics["frame_count"] for metrics in per_stream) aggregate_fps = total_frames / (wave_end - wave_start) if wave_end > wave_start else None @@ -754,6 +813,14 @@ def _build_parser() -> argparse.ArgumentParser: parser.add_argument( "--artifact", type=Path, default=Path("/tmp/waypoint_streaming_benchmark.json") ) + parser.add_argument( + "--save-videos", + type=Path, + help=( + "write every measured stream (baseline, slow-consumer, and each " + "concurrent stream) as DIR/.mp4, plus a copy of --artifact" + ), + ) parser.add_argument( "--log", type=Path, default=Path("/tmp/waypoint_streaming_benchmark_server.log") ) @@ -830,6 +897,8 @@ def _run_benchmark(args: argparse.Namespace) -> dict: weight_source = f"registry Hub mapping for {variant.model_variant}" stall_threshold = _resolve_stall_threshold(args) + if args.save_videos is not None: + args.save_videos.mkdir(parents=True, exist_ok=True) port = args.port or rollout._free_port() url = f"http://127.0.0.1:{port}" @@ -961,6 +1030,9 @@ def _run_benchmark(args: argparse.Namespace) -> dict: ) _wait_for_phase_sample(sampler, proc, phase) request_id = f"{args.request_id}-{phase}" + saved_chunks: list[VideoFrameChunk] | None = ( + [] if args.save_videos is not None else None + ) metrics, stream_failures = _measure_stream( client, seed_image, @@ -971,6 +1043,7 @@ def _run_benchmark(args: argparse.Namespace) -> dict: consumer_pause_seconds=pause, stall_threshold_seconds=stall_threshold, enable_nvtx=args.enable_nvtx, + chunks_out=saved_chunks, ) failures.extend(f"{name}: {failure}" for failure in stream_failures) rollout._wait_for_cleanup( @@ -983,6 +1056,10 @@ def _run_benchmark(args: argparse.Namespace) -> dict: f" received {metrics['chunk_count']} chunks in " f"{metrics['request_wall_seconds']:.3f}s" ) + if saved_chunks: + out = args.save_videos / f"{request_id}.mp4" + n, _fps = _encode_mp4(saved_chunks, out) + print(f"saved video: {out} ({n} frames)") if runs["baseline"]["payload_sha256"] != runs["slow_consumer"]["payload_sha256"]: failures.append( @@ -1014,6 +1091,7 @@ def _run_benchmark(args: argparse.Namespace) -> dict: request_timeout=args.request_timeout, sampler=sampler, startup_seconds=startup_seconds, + save_videos_dir=args.save_videos, ) failures.extend(concurrent_failures) finally: @@ -1144,6 +1222,8 @@ def main(argv: Sequence[str] | None = None) -> int: return 2 _write_artifact(args.artifact, result) + if args.save_videos is not None: + shutil.copy2(args.artifact, args.save_videos / args.artifact.name) print(_human_summary(result, args.artifact)) return 0 if result["correctness"]["passed"] else 1 From 82630d1defac55ff2cd1ebd983ffc6cc2a48974f Mon Sep 17 00:00:00 2001 From: Garv Ghai <43917046+garv901@users.noreply.github.com> Date: Fri, 25 Sep 2026 01:30:14 +0000 Subject: [PATCH 29/29] review : note --save-videos memory cost in help text --- test/waypoint/benchmark_streaming.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/test/waypoint/benchmark_streaming.py b/test/waypoint/benchmark_streaming.py index c29aba288..1b5b3919d 100644 --- a/test/waypoint/benchmark_streaming.py +++ b/test/waypoint/benchmark_streaming.py @@ -818,7 +818,9 @@ def _build_parser() -> argparse.ArgumentParser: type=Path, help=( "write every measured stream (baseline, slow-consumer, and each " - "concurrent stream) as DIR/.mp4, plus a copy of --artifact" + "concurrent stream) as DIR/.mp4, plus a copy of --artifact. " + "Raw frames are held in memory until each stream ends (~0.7 MB/frame " + "at 360p, ~2.8 MB at 720p, 4 frames per step, per stream)" ), ) parser.add_argument(