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Bump the pip group across 51 directories with 6 updates - #585

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Bump the pip group across 51 directories with 6 updates#585
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dependabot/pip/dot-aitk/requirements/pip-58ab57a057

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Bumps the pip group with 6 updates in the /.aitk/requirements directory:

Package From To
aiohttp 3.14.0 3.14.3
cryptography 49.0.0 50.0.0
onnx 1.17.0 1.22.0
torch 2.6.0 2.13.0
transformers 4.51.3 5.5.0
gitpython 3.1.50 3.1.58

Bumps the pip group with 2 updates in the /meta-llama-Llama-3.1-8B-Instruct/OpenVINO directory: torch and transformers.
Bumps the pip group with 5 updates in the /microsoft-Phi-4-mini-instruct/QAIRT directory:

Package From To
aiohttp 3.13.3 3.14.3
cryptography 45.0.7 50.0.0
onnx 1.17.0 1.22.0
torch 2.1.2 2.13.0
transformers 4.46.0 5.5.0

Bumps the pip group with 2 updates in the /meta-llama-Llama-3.1-8B-Instruct/QAIRT directory: aiohttp and transformers.
Bumps the pip group with 1 update in the /Qwen-Qwen3-8B/QNN directory: transformers.
Bumps the pip group with 3 updates in the /Flux.2-Klein-4B/RyzenAI directory: onnx, torch and transformers.
Bumps the pip group with 3 updates in the /sd-legacy-stable-diffusion-v1-5/VitisAI directory: onnx, torch and transformers.
Bumps the pip group with 2 updates in the /microsoft-Phi-3.5-mini-instruct/QNN directory: onnx and transformers.
Bumps the pip group with 2 updates in the /meta-llama-Llama-3.1-8B-Instruct/QNN directory: onnx and transformers.
Bumps the pip group with 2 updates in the /microsoft-Phi-4-reasoning/QNN directory: onnx and transformers.
Bumps the pip group with 2 updates in the /mistralai-Mistral-7B-v0.3/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /mistralai-Mistral-7B-Instruct-v0.3/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /mistralai-Mistral-7B-Instruct-v0.2/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /mistralai-Mistral-7B-Instruct-v0.1/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /microsoft-Phi-4-mini-reasoning/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /microsoft-Phi-3.5-mini-instruct/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /microsoft-Phi-3-mini-4k-instruct/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /microsoft-Phi-3-mini-128k-instruct/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /microsoft-Phi-4-mini-instruct/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /meta-llama-Llama-3.2-3B-Instruct/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /meta-llama-Llama-3.2-3B/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /meta-llama-Meta-Llama-3-8B/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /meta-llama-Llama-3.1-8B/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /meta-llama-Llama-3.2-1B/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /meta-llama-Llama-3.2-1B-Instruct/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /meta-llama-Llama-2-7b-chat-hf/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /gpt-oss-20b/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /meta-llama-Llama-3.1-8B-Instruct/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /meta-llama-Llama-2-7b-hf/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /deepseek-ai-DeepSeek-R1-Distill-Qwen-7B/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /deepseek-ai-DeepSeek-R1-Distill-Llama-8B/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /codellama-CodeLlama-7b-Instruct-hf/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /amd-AMD-OLMo-1B-SFT-DPO/VitisAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /Qwen-Qwen2.5-Coder-7B-Instruct/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /Qwen-Qwen2.5-3B/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /Qwen-Qwen2.5-Coder-0.5B-Instruct/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /Qwen-Qwen2.5-7B-Instruct/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /Qwen-Qwen2.5-Coder-1.5B-Instruct/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /Qwen-Qwen2.5-3B-Instruct/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /Qwen-Qwen2-7B/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /Qwen-Qwen2.5-1.5B-Instruct/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /Qwen-Qwen2-7B-Instruct/VitisAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /Qwen-Qwen2-1.5B/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /Qwen-Qwen2.5-0.5B-Instruct/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /Qwen-Qwen1.5-7B-Chat/RyzenAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /mistralai-Mistral-7B-Instruct-v0.3/VitisAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /mistralai-Mistral-7B-Instruct-v0.2/VitisAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /microsoft-Phi-4-mini-instruct/VitisAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /microsoft-Phi-4-mini-reasoning/VitisAI directory: onnx and transformers.
Bumps the pip group with 2 updates in the /microsoft-Phi-3.5-mini-instruct/VitisAI directory: onnx and transformers.

Updates aiohttp from 3.14.0 to 3.14.3
Updates cryptography from 49.0.0 to 50.0.0

Changelog

Sourced from cryptography's changelog.

50.0.0 - 2026-07-31


* **SECURITY ISSUE**:
  :func:`~cryptography.hazmat.primitives.serialization.pkcs7.pkcs7_decrypt_der`
  and its PEM and S/MIME variants no longer expose distinguishable errors or
  timing when unwrapping a ``RecipientInfo``'s ``encryptedKey``, which could
  act as a Bleichenbacher oracle for callers that decrypt untrusted messages.
  A random key is now substituted on failure, as described in :rfc:`3218`.
  Credit to **@X1AOxiang** for reporting the issue. **CVE-2026-69247**
* Deprecated Diffie-Hellman key exchange over finite fields (FFDH).
  Everything FFDH is deprecated, including the types in
  ``cryptography.hazmat.primitives.asymmetric.dh`` and loading FFDH keys or
  parameters with the key loading APIs. Users should migrate to a more
  modern key exchange algorithm.
* Added ``xof()`` class methods to
  :class:`~cryptography.hazmat.primitives.hashes.SHAKE128` and
  :class:`~cryptography.hazmat.primitives.hashes.SHAKE256` for constructing
  algorithm instances configured for use with
  :class:`~cryptography.hazmat.primitives.hashes.XOFHash`.
* The :mod:`X.509 verification <cryptography.x509.verification>` APIs are now
  considered stable and are subject to our API stability policy.
* Added the :doc:`/cobblestone` recipe, an implementation of the
  Cobblestone-128 and Cobblestone-256 instantiations of the `C2SP
  chunked-encryption specification
  <https://c2sp.org/chunked-encryption>`_ for streaming authenticated
  encryption of large messages.
* Parsing a Signed Certificate Timestamp list now rejects encodings that
  carry trailing bytes after the list or after an individual SCT, instead of
  silently ignoring them.
* Added support for using :class:`~cryptography.x509.Name` as a field type in
  the :doc:`/hazmat/asn1/index` module.
* Loading a public key or an EC private key now rejects DER where the
  ``subjectPublicKey`` (or EC ``publicKey``) ``BIT STRING`` declares a non-zero
  number of unused bits, instead of silently ignoring it.
* Parsing a CRL entry's ``InvalidityDate`` extension now rejects a
  ``GeneralizedTime`` that carries fractional seconds or another non-DER form,
  matching the strict encoding already required for every other X.509 time
  field.
* :func:`~cryptography.x509.ocsp.load_der_ocsp_request` and
  :func:`~cryptography.x509.ocsp.load_der_ocsp_response` now reject a request
  or response whose ``version`` field is not ``v1``, the only version defined
  by RFC 6960, matching the version validation already performed when loading
  certificates, CSRs and CRLs.
* :class:`~cryptography.hazmat.primitives.hashes.XOFHash` is now supported
  when building against AWS-LC.
* HMAC (and therefore PBKDF2-HMAC) with SHA-3 hashes is now supported when
  building against AWS-LC.
* Diffie-Hellman (:doc:`/hazmat/primitives/asymmetric/dh`) is now supported
  when building against AWS-LC.
</tr></table>

... (truncated)

Commits

Updates onnx from 1.17.0 to 1.22.0

Release notes

Sourced from onnx's releases.

v1.22.0

ONNX v1.22.0 is now available with exciting new features! We would like to thank everyone who contributed to this release! Please visit onnx.ai to learn more about ONNX and associated projects.

What's Changed

Breaking Changes and Deprecations

Spec and Operator

Two new operators LinearAttention-27 and CausalConvWithState-27 were introduced.

Reference Implementation

Utilities and Tools

Build, CI and Tests

... (truncated)

Commits

Updates torch from 2.6.0 to 2.13.0

Release notes

Sourced from torch's releases.

PyTorch 2.13.0 Release Notes

Highlights

For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release.

Tracked Regressions

ROCm wheels break torch.compile on CPU in environments without a GPU

Running a torch==2.13.0+rocm7.2 wheel in an environment where no GPU is available (torch.cuda.is_available() is False) breaks torch.compile on the CPU path: the first compile raises RuntimeError: Can't detect vectorized ISA for CPU (#189194). This is a regression from torch==2.12.1+rocm7.2, which compiles CPU code fine (detecting e.g. VecAVX2) in the same setup. The 2.13 ROCm wheel appears to rely on something present in the ROCm builder image to detect the CPU vectorized ISA, so it works when run on a ROCm image but fails on a plain CPU-only image.

Workaround: run the +rocm wheel on a ROCm image, or install a standard CPU/CUDA build for GPU-less environments.

Backwards Incompatible Changes

  • Stop building CPython 3.13t (free-threaded) binaries (#182951)

    Upstream pypa/manylinux removed CPython 3.13t (free-threaded) on 2026-05-07, because 3.13t was experimental and has been superseded by the now-non-experimental CPython 3.14t. As a result, PyTorch 2.13 no longer ships cp313t wheels (Linux, Triton, and related artifacts). Users on the free-threaded interpreter should move to Python 3.14t.

    PyTorch 2.12:

    # cp313t (free-threaded 3.13) wheels were available
python3.13t -m pip install torch

    PyTorch 2.13:

... (truncated)

Commits
  • cf30153 [release/2.13] Strip +PTX from CUDA arch list on release/RC builds (#188914) ...
  • 3e3e24b [release/2.13] Restrict cuda-bindings to Python < 3.15 for CUDA 12.9 builds (...
  • 7986b06 [release/2.13] Bump binary build timeout 280 -> 400 minutes (#188551)
  • 0bdbc26 [release/2.13] Add CUDA 12.9 to TORCH_CUDA_ARCH_LIST tables (#188443)
  • 9cabb45 [release/2.13] Update manywheel docker image pin to 78e737ad (#188409)
  • 78e737a [release/2.13] Revert "Tighten generalized scatter graph target (#184075)" (#...
  • 0bb9b5b [release/2.13] Revert "dynamo: round-trip torch.cuda.stream ctx mgr across gr...
  • aaac2bf [release/2.13] Revert "[Reland] Port D104346887/PR 182675 for index_add fast ...
  • 9330813 Fix build_with_debinfo.py broken by CONFIGURE_DEPENDS globbing (#188192)
  • 4e077a7 Remove setuptools upper bound (#188190)
  • Additional commits viewable in compare view

Updates transformers from 4.51.3 to 5.5.0

Release notes

Sourced from transformers's releases.

Release v5.5.0

New Model additions

Gemma4

Gemma 4 is a multimodal model with pretrained and instruction-tuned variants, available in 1B, 13B, and 27B parameters. The architecture is mostly the same as the previous Gemma versions. The key differences are a vision processor that can output images of fixed token budget and a spatial 2D RoPE to encode vision-specific information across height and width axis.

You can find all the original Gemma 4 checkpoints under the Gemma 4 release.

The key difference from previous Gemma releases is the new design to process images of different sizes using a fixed-budget number of tokens. Unlike many models that squash every image into a fixed square (like 224×224), Gemma 4 keeps the image's natural aspect ratio while making it the right size. There a a couple constraints to follow:

  • The total number of pixels must fit within a patch budget
  • Both height and width must be divisible by 48 (= patch size 16 × pooling kernel 3)

[!IMPORTANT] Gemma 4 does not apply the standard ImageNet mean/std normalization that many other vision models use. The model's own patch embedding layer handles the final scaling internally (shifting values to the [-1, 1] range).

The number of "soft tokens" (aka vision tokens) an image processor can produce is configurable. The supported options are outlined below and the default is 280 soft tokens per image.

Soft Tokens Patches (before pooling) Approx. Image Area
70 630 ~161K pixels
140 1,260 ~323K pixels
280 2,520 ~645K pixels
560 5,040 ~1.3M pixels
1,120 10,080 ~2.6M pixels

To encode positional information for each patch in the image, Gemma 4 uses a learned 2D position embedding table. The position table stores up to 10,240 positions per axis, which allows the model to handle very large images. Each position is a learned vector of the same dimensions as the patch embedding. The 2D RoPE which Gemma 4 uses independently rotate half the attention head dimensions for the x-axis and the other half for the y-axis. This allows the model to understand spatial relationships like "above," "below," "left of," and "right of."

NomicBERT

NomicBERT is a BERT-inspired encoder model that applies Rotary Position Embeddings (RoPE) to create reproducible long context text embeddings. It is the first fully reproducible, open-source text embedding model with 8192 context length that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on short-context MTEB and long context LoCo benchmarks. The model generates dense vector embeddings for various tasks including search, clustering, and classification using specific instruction prefixes.

Links: Documentation | Paper

MusicFlamingo

Music Flamingo is a fully open large audio–language model designed for robust understanding and reasoning over music. It builds upon the Audio Flamingo 3 architecture by including Rotary Time Embeddings (RoTE), which injects temporal position information to enable the model to handle audio sequences up to 20 minutes. The model features a unified audio encoder across speech, sound, and music with special sound boundary tokens for improved audio sequence modeling.

Links: Documentation | Paper

... (truncated)

Commits
  • c1c3424 update
  • 20bff68 update release workflow
  • 8956441 v5.5.0
  • 5135e5e casually dropping the most capable open weights on the planet (#45192)
  • a594e09 Internalise the NomicBERT model (#43067)
  • 4932e97 Fix resized LM head weights being overwritten by post_init (#45079)
  • 57e8413 [Qwen3.5 MoE] Add _tp_plan to ForConditionalGeneration (#45124)
  • b10552e Fix TypeError: 'NoneType' object is not iterable in GenerationMixin.generate ...
  • 423f2a3 fix(models): Fix dtype mismatch in SwitchTransformers and TimmWrapperModel (#...
  • ade7a05 Generalize gemma vision mask to videos (#45185)
  • Additional commits viewable in compare view

Updates gitpython from 3.1.50 to 3.1.58

Release notes

Sourced from gitpython's releases.

3.1.58 - Security and Fixes

What's Changed

New Contributors

Full Changelog: gitpython-developers/GitPython@3.1.57...3.1.58

3.1.57 - Security and Fixes

What's Changed

New Contributors

Full Changelog: gitpython-developers/GitPython@3.1.56...3.1.57

3.1.56 - SECURITY

What's Changed

Full Changelog: gitpython-developers/GitPython@3.1.55...3.1.56

3.1.55 - Security

What's Changed

... (truncated)

Commits
  • 30be45d prepare changelog for upcoming release
  • fc2f02c Merge pull request #2197 from Cyrus580529/shared-symlink-guard
  • b10e250 test: use the shared guard instead of local copies
  • e3e5da8 test: skip tests that need symlink privileges
  • 30d05e3 test: add a shared symlink capability guard
  • 9a8f6fe Merge pull request #2204 from gitpython-developers/security-fixes
  • f2550b6 Guard pathspec file inputs in high-level commands
  • d9ddb55 Guard unsafe git init options
  • 9b5dcaf Guard read-tree index output paths
  • 96a888f Check joined short-option values before Git execution
  • Additional commits viewable in compare view

Updates torch from 2.11.0 to 2.13.0

Release notes

Sourced from torch's releases.

PyTorch 2.13.0 Release Notes

Highlights

For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release.

Tracked Regressions

ROCm wheels break torch.compile on CPU in environments without a GPU

Running a torch==2.13.0+rocm7.2 wheel in an environment where no GPU is available (torch.cuda.is_available() is False) breaks torch.compile on the CPU path: the first compile raises RuntimeError: Can't detect vectorized ISA for CPU (#189194). This is a regression from torch==2.12.1+rocm7.2, which compiles CPU code fine (detecting e.g. VecAVX2) in the same setup. The 2.13 ROCm wheel appears to rely on something present in the ROCm builder image to detect the CPU vectorized ISA, so it works when run on a ROCm image but fails on a plain CPU-only image.

Workaround: run the +rocm wheel on a ROCm image, or install a standard CPU/CUDA build for GPU-less environments.

Backwards Incompatible Changes

  • Stop building CPython 3.13t (free-threaded) binaries (#182951)

    Upstream pypa/manylinux removed CPython 3.13t (free-threaded) on 2026-05-07, because 3.13t was experimental and has been superseded by the now-non-experimental CPython 3.14t. As a result, PyTorch 2.13 no longer ships cp313t wheels (Linux, Triton, and related artifacts). Users on the free-threaded interpreter should move to Python 3.14t.

    PyTorch 2.12:

    # cp313t (free-threaded 3.13) wheels were available
python3.13t -m pip install torch

    PyTorch 2.13:

... (truncated)

Commits
  • cf30153 [release/2.13] Strip +PTX from CUDA arch list on release/RC builds (#188914) ...
  • 3e3e24b [release/2.13] Restrict cuda-bindings to Python < 3.15 for CUDA 12.9 builds (...
  • 7986b06 [release/2.13] Bump binary build timeout 280 -> 400 minutes (#188551)
  • 0bdbc26 [release/2.13] Add CUDA 12.9 to TORCH_CUDA_ARCH_LIST tables (#188443)
  • 9cabb45 [release/2.13] Update manywheel docker image pin to 78e737ad (#188409)
  • 78e737a [release/2.13] Revert "Tighten generalized scatter graph target (#184075)" (#...
  • 0bb9b5b [release/2.13] Revert "dynamo: round-trip torch.cuda.stream ctx mgr across gr...
  • aaac2bf [release/2.13] Revert "[Reland] Port D104346887/PR 182675 for index_add fast ...
  • 9330813 Fix build_with_debinfo.py broken by CONFIGURE_DEPENDS globbing (#188192)
  • 4e077a7 Remove setuptools upper bound (#188190)
  • Additional commits viewable in compare view

Updates transformers from 4.52.3 to 5.5.0

Release notes

Sourced from transformers's releases.

Release v5.5.0

New Model additions

Gemma4

Gemma 4 is a multimodal model with pretrained and instruction-tuned variants, available in 1B, 13B, and 27B parameters. The architecture is mostly the same as the previous Gemma versions. The key differences are a vision processor that can output images of fixed token budget and a spatial 2D RoPE to encode vision-specific information across height and width axis.

You can find all the original Gemma 4 checkpoints under the Gemma 4 release.

The key difference from previous Gemma releases is the new design to process images of different sizes using a fixed-budget number of tokens. Unlike many models that squash every image into a fixed square (like 224×224), Gemma 4 keeps the image's natural aspect ratio while making it the right size. There a a couple constraints to follow:

  • The total number of pixels must fit within a patch budget
  • Both height and width must be divisible by 48 (= patch size 16 × pooling kernel 3)

[!IMPORTANT] Gemma 4 does not apply the standard ImageNet mean/std normalization that many other vision models use. The model's own patch embedding layer handles the final scaling internally (shifting values to the [-1, 1] range).

The number of "soft tokens" (aka vision tokens) an image processor can produce is configurable. The supported options are outlined below and the default is 280 soft tokens per image.

Soft Tokens Patches (before pooling) Approx. Image Area
70 630 ~161K pixels
140 1,260 ~323K pixels
280 2,520 ~645K pixels
560 5,040 ~1.3M pixels
1,120 10,080 ~2.6M pixels

To encode positional information for each patch in the image, Gemma 4 uses a learned 2D position embedding table. The position table stores up to 10,240 positions per axis, which allows the model to handle very large images. Each position is a learned vector of the same dimensions as the patch embedding. The 2D RoPE which Gemma 4 uses independently rotate half the attention head dimensions for the x-axis and the other half for the y-axis. This allows the model to understand spatial relationships like "above," "below," "left of," and "right of."

NomicBERT

NomicBERT is a BERT-inspired encoder model that applies Rotary Position Embeddings (RoPE) to create reproducible long context text embeddings. It is the first fully reproducible, open-source text embedding model with 8192 context length that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on short-context MTEB and long context LoCo benchmarks. The model generates dense vector embeddings for various tasks including search, clustering, and classification using specific instruction prefixes.

Links: Documentation | Paper

MusicFlamingo

Music Flamingo is a fully open large audio–language model designed for robust understanding and reasoning over music. It builds upon the Audio Flamingo 3 architecture by including Rotary Time Embeddings (RoTE), which injects temporal position information to enable the model to handle audio sequences up to 20 minutes. The model features a unified audio encoder across speech, sound, and music with special sound boundary tokens for improved audio sequence modeling.

Links: Documentation | Paper

... (truncated)

Commits
  • c1c3424 update
  • 20bff68 update release workflow
  • 8956441 v5.5.0
  • 5135e5e casually dropping the most capable open weights on the planet (#45192)
  • a594e09 Internalise the NomicBERT model (#43067)
  • 4932e97 Fix resized LM head weights being overwritten by post_init (#45079)
  • 57e8413 [Qwen3.5 MoE] Add _tp_plan to ForConditionalGeneration (#45124)
  • b10552e Fix TypeError: 'NoneType' object is not iterable in GenerationMixin.generate ...
  • 423f2a3 fix(models): Fix dtype mismatch in SwitchTransformers and TimmWrapperModel (#...
  • ade7a05 Generalize gemma vision mask to videos (#45185)
  • Additional commits viewable in compare view
Description has been truncated

@dependabot
Bump the pip group across 51 directories with 6 updates
762f069 
---
updated-dependencies:
- dependency-name: aiohttp
  dependency-version: 3.14.3
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: cryptography
  dependency-version: 50.0.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: onnx
  dependency-version: 1.22.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: torch
  dependency-version: 2.13.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-version: 5.5.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: gitpython
  dependency-version: 3.1.58
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: torch
  dependency-version: 2.13.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-version: 5.5.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: aiohttp
  dependency-version: 3.14.3
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: cryptography
  dependency-version: 50.0.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: onnx
  dependency-version: 1.22.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: torch
  dependency-version: 2.13.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-version: 5.5.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: aiohttp
  dependency-version: 3.14.3
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-version: 5.5.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-version: 5.5.0
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...

Signed-off-by: dependabot[bot] <support@github.com>
Copilot AI lite review requested due to automatic review settings August 13, 2026 22:57
@dependabot dependabot Bot added dependencies Pull requests that update a dependency file python Pull requests that update python code labels Aug 13, 2026
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dependabot Bot requested review from a team as code owners August 13, 2026 22:57
@dependabot dependabot Bot added dependencies Pull requests that update a dependency file python Pull requests that update python code labels Aug 13, 2026
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Pull request overview

Note

Copilot could not run the full agentic suite for this review because it was automatically requested on a bot-authored pull request. Request a review from Copilot under Reviewers to retry with the full agentic suite. Improved support for bot-authored pull requests is coming soon.

This PR updates dependency pins across multiple model/accelerator “recipe” environments, primarily bumping ONNX and Transformers versions (and in some places PyTorch, aiohttp, and cryptography) to newer releases.

Changes:

  • Bump onnx to 1.22.0 broadly across VitisAI/RyzenAI/QNN/QAIRT requirement sets
  • Bump transformers to 5.5.0 broadly across recipes
  • Update several platform-specific pins (e.g., torch, aiohttp, cryptography, gitpython) in .aitk and QAIRT/QNN environments

Reviewed changes

Copilot reviewed 69 out of 69 changed files in this pull request and generated 4 comments.

Show a summary per file
File Description
sd-legacy-stable-diffusion-v1-5/VitisAI/requirements_vitisai_sd.txt Updates ONNX/torch/transformers pins for the VitisAI Stable Diffusion environment
mistralai-Mistral-7B-v0.3/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
mistralai-Mistral-7B-Instruct-v0.3/VitisAI/requirements_vitisai_llm.txt Updates ONNX and Transformers pins for VitisAI LLM environment
mistralai-Mistral-7B-Instruct-v0.3/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
mistralai-Mistral-7B-Instruct-v0.2/VitisAI/requirements_vitisai_llm.txt Updates ONNX and Transformers pins for VitisAI LLM environment
mistralai-Mistral-7B-Instruct-v0.2/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
mistralai-Mistral-7B-Instruct-v0.1/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
microsoft-Phi-4-reasoning/QNN/requirements.txt Updates ONNX and Transformers pins for QNN environment
microsoft-Phi-4-mini-reasoning/VitisAI/requirements_vitisai_llm.txt Updates ONNX and Transformers pins for VitisAI LLM environment
microsoft-Phi-4-mini-reasoning/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
microsoft-Phi-4-mini-instruct/VitisAI/requirements_vitisai_llm.txt Updates ONNX and Transformers pins for VitisAI LLM environment
microsoft-Phi-4-mini-instruct/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
microsoft-Phi-4-mini-instruct/QAIRT/requirements.txt Updates multiple pins including aiohttp/cryptography/onnx/torch/transformers for QAIRT
microsoft-Phi-3.5-mini-instruct/VitisAI/requirements_vitisai_llm.txt Updates ONNX and Transformers pins for VitisAI LLM environment
microsoft-Phi-3.5-mini-instruct/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
microsoft-Phi-3.5-mini-instruct/QNN/requirements.txt Updates ONNX and Transformers pins for QNN environment
microsoft-Phi-3-mini-4k-instruct/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
microsoft-Phi-3-mini-128k-instruct/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
meta-llama-Meta-Llama-3-8B/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
meta-llama-Llama-3.2-3B/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
meta-llama-Llama-3.2-3B-Instruct/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
meta-llama-Llama-3.2-1B/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
meta-llama-Llama-3.2-1B-Instruct/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
meta-llama-Llama-3.1-8B/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
meta-llama-Llama-3.1-8B-Instruct/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
meta-llama-Llama-3.1-8B-Instruct/QNN/requirements.txt Updates ONNX and Transformers pins for QNN environment
meta-llama-Llama-3.1-8B-Instruct/QAIRT/requirements.txt Updates aiohttp/onnx/torch constraints and Transformers pin for QAIRT
meta-llama-Llama-3.1-8B-Instruct/OpenVINO/requirements.txt Updates torch/transformers pins for OpenVINO environment
meta-llama-Llama-2-7b-hf/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
meta-llama-Llama-2-7b-chat-hf/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
gpt-oss-20b/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
deepseek-ai-DeepSeek-R1-Distill-Qwen-7B/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
deepseek-ai-DeepSeek-R1-Distill-Llama-8B/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
codellama-CodeLlama-7b-Instruct-hf/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
amd-AMD-OLMo-1B-SFT-DPO/VitisAI/requirements_vitisai_llm.txt Updates ONNX and Transformers pins for VitisAI LLM environment
Qwen-Qwen3-8B/QNN/requirements.txt Updates Transformers pin for QNN environment
Qwen-Qwen2.5-Coder-7B-Instruct/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
Qwen-Qwen2.5-Coder-1.5B-Instruct/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
Qwen-Qwen2.5-Coder-0.5B-Instruct/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
Qwen-Qwen2.5-7B-Instruct/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
Qwen-Qwen2.5-3B/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
Qwen-Qwen2.5-3B-Instruct/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
Qwen-Qwen2.5-1.5B-Instruct/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
Qwen-Qwen2.5-0.5B-Instruct/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
Qwen-Qwen2-7B/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
Qwen-Qwen2-7B-Instruct/VitisAI/requirements_vitisai_llm.txt Updates ONNX and Transformers pins for VitisAI LLM environment
Qwen-Qwen2-1.5B/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
Qwen-Qwen1.5-7B-Chat/RyzenAI/requirements_ryzenai_llm.txt Updates ONNX and Transformers pins for RyzenAI LLM environment
Flux.2-Klein-4B/RyzenAI/requirements_ryzenai_sd.txt Updates ONNX/torch/transformers pins for RyzenAI Stable Diffusion environment
.aitk/requirements/requirements-WinMLCLI.txt Updates aiohttp/cryptography/onnx/torch/transformers pins for WinMLCLI environment
.aitk/requirements/requirements-WCR.txt Updates aiohttp/onnx/torch/transformers pins for WCR environment
.aitk/requirements/requirements-WCR-SAM.txt Updates Transformers pin for WCR-SAM environment
.aitk/requirements/requirements-WCR-QAI.txt Updates gitpython pin for WCR-QAI environment
.aitk/requirements/requirements-QNN.txt Updates aiohttp/onnx/torch/transformers pins for QNN environment
.aitk/requirements/requirements-Profiling.txt Updates ONNX pin for profiling environment
.aitk/requirements/requirements-NvidiaGPU.txt Updates aiohttp/onnx/torch/transformers pins for NvidiaGPU environment
.aitk/requirements/requirements-NvidiaGPU-Qwen3.txt Updates Transformers pin for NvidiaGPU Qwen3 environment
.aitk/requirements/requirements-NvidiaGPU-GptqModel.txt Updates Transformers pin for NvidiaGPU GPTQ model environment
.aitk/requirements/requirements-IntelNPU.txt Updates aiohttp/onnx/torch/transformers pins for IntelNPU environment
.aitk/requirements/requirements-IntelNPU-WP.txt Updates ONNX and Transformers pins for IntelNPU-WP environment
.aitk/requirements/Intel/Test_py3.12.9.txt Updates aiohttp/onnx/torch/transformers pins for Intel test environment
.aitk/requirements/Intel/Test_py3.12.9-Transformers4.49.txt Updates Transformers pin in an Intel test file
.aitk/requirements/General/CUDA_py3.12.9.txt Updates aiohttp/onnx/torch/transformers pins for CUDA general environment
.aitk/requirements/General/CUDA_py3.12.9-NVModelOptQuantization.txt Updates ONNX pin for NV model optimization/quantization environment
.aitk/requirements/General/CPU_py3.12.9.txt Updates aiohttp/onnx/torch/transformers pins for CPU general environment
.aitk/requirements/General/CPU_py3.12.9-SAM.txt Updates Transformers pin for CPU SAM environment
.aitk/requirements/General/CPU_py3.12.9-QAI.txt Updates gitpython pin for CPU QAI environment
.aitk/requirements/AMD/Quark_py3.12.13.txt Updates aiohttp/onnx/torch/transformers pins for AMD Quark environment
Suppressed comments (2)

microsoft-Phi-4-mini-instruct/QAIRT/requirements.txt:1

  • These PyTorch ecosystem pins look internally inconsistent (torch=2.13.0 with torchaudio=2.1.2 and torchvision=0.16.2). This is likely to cause pip resolution failures or runtime/ABI incompatibilities. Recommended: align torchaudio/torchvision versions to the corresponding torch release (or keep torch at the version matching the currently pinned torchaudio/torchvision).
    microsoft-Phi-4-reasoning/QNN/requirements.txt:1
  • The comment warns that newer Transformers may be incompatible with GPTQ passes, but the requirement now pins a newer major version. Either update/remove the comment (if validated) or keep the pin consistent with the stated constraint so readers don’t assume the environment is known-broken.

💡 Add a code-review agent skill or configure MCP servers for context-aware, tailored reviews. Learn more in the docs.

Comment thread .aitk/requirements/requirements-NvidiaGPU.txt
Comment on lines +72 to +73
# torch==2.13.0
torch==2.13.0
Comment thread .aitk/requirements/Intel/Test_py3.12.9-Transformers4.49.txt
@@ -1 +1 @@
transformers==5.0.0rc3
transformers==5.5.0
Comment thread .aitk/requirements/AMD/Quark_py3.12.13.txt
@@ -3,7 +3,7 @@
--extra-index-url=https://pypi.amd.com/simple
Comment thread .aitk/requirements/AMD/Quark_py3.12.13.txt
tabulate==0.10.0
tokenizers==0.22.2
torch==2.7.1+cu128
torch==2.13.0
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