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Python: Bump transformers from 4.41.2 to 4.42.4 in /python (#7275)

Browse Source 
Bumps [transformers](https://github.com/huggingface/transformers) from
4.41.2 to 4.42.4.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/huggingface/transformers/releases">transformers's
releases</a>.</em></p>
<blockquote>
<h2>Patch release v4.42.4</h2>
<h1>Mostly gemma2 support FA2 softcapping!</h1>
<p>but also fix the sliding window for long context and other typos.</p>
<ul>
<li>[Gemma2] Support FA2 softcapping (<a
href="https://redirect.github.com/huggingface/transformers/issues/31887">#31887</a>)
by <a
href="https://github.com/ArthurZucker"><code>@​ArthurZucker</code></a></li>
<li>[ConvertSlow] make sure the order is preserved for addedtokens (<a
href="https://redirect.github.com/huggingface/transformers/issues/31902">#31902</a>)
by <a
href="https://github.com/ArthurZucker"><code>@​ArthurZucker</code></a></li>
<li>Fixes to alternating SWA layers in Gemma2 (<a
href="https://redirect.github.com/huggingface/transformers/issues/31775">#31775</a>)
by <a
href="https://github.com/turboderp"><code>@​turboderp</code></a></li>
<li>Requires for torch.tensor before casting (<a
href="https://redirect.github.com/huggingface/transformers/issues/31755">#31755</a>)
by <a
href="https://github.com/echarlaix"><code>@​echarlaix</code></a></li>
</ul>
<p>Was off last week could not get this out, thanks all for your
patience 🥳</p>
<h2>Patch release v4.42.3</h2>
<h1>Make sure we have attention softcapping for &quot;eager&quot; GEMMA2
model</h1>
<p>After experimenting, we noticed that for the 27b model mostly,
softcapping is a must. So adding it back (it should have been there, but
an error on my side made it disappear) sorry all! 😭</p>
<ul>
<li>Gemma capping is a must for big models (<a
href="https://redirect.github.com/huggingface/transformers/issues/31698">#31698</a>)</li>
</ul>
<h2>Patch release v4.42.2</h2>
<h1>Patch release</h1>
<p>Thanks to our 2 contributors for their prompt fixing mostly applies
for training and FA2!</p>
<ul>
<li>Fix Gemma2 4d attention mask (<a
href="https://redirect.github.com/huggingface/transformers/issues/31674">#31674</a>)
by <a href="https://github.com/hiyouga"><code>@​hiyouga</code></a></li>
<li>don't zero out the attention_mask when using sliding window with
flash attention (<a
href="https://redirect.github.com/huggingface/transformers/issues/31670">#31670</a>)
by <a
href="https://github.com/winglian"><code>@​winglian</code></a></li>
</ul>
<h2>v4.42.1: Patch release</h2>
<p>Patch release for commit:</p>
<ul>
<li>[HybridCache] Fix get_seq_length method (<a
href="https://redirect.github.com/huggingface/transformers/issues/31661">#31661</a>)</li>
</ul>
<h2>v4.42.0: Gemma 2, RTDETR, InstructBLIP, LLAVa Next, New Model
Adder</h2>
<h2>New model additions</h2>
<h3>Gemma-2</h3>
<p>The Gemma2 model was proposed in <a
href="https://blog.google/technology/developers/Gemma2-open-models/">Gemma2:
Open Models Based on Gemini Technology and Research</a> by Gemma2 Team,
Google.
Gemma2 models are trained on 6T tokens, and released with 2 versions, 2b
and 7b.</p>
<p>The abstract from the paper is the following:</p>
<p><em>This work introduces Gemma2, a new family of open language models
demonstrating strong performance across academic benchmarks for language
understanding, reasoning, and safety. We release two sizes of models (2
billion and 7 billion parameters), and provide both pretrained and
fine-tuned checkpoints. Gemma2 outperforms similarly sized open models
on 11 out of 18 text-based tasks, and we present comprehensive
evaluations of safety and responsibility aspects of the models,
alongside a detailed description of our model development. We believe
the responsible release of LLMs is critical for improving the safety of
frontier models, and for enabling the next wave of LLM
innovations</em></p>
<p><img
src="https://github.com/huggingface/transformers/assets/30755778/798b25f4-485a-4b60-abe5-af612def209b"
alt="image" /></p>
<ul>
<li>Add gemma 2 by <a
href="https://github.com/ArthurZucker"><code>@​ArthurZucker</code></a>
in <a
href="https://redirect.github.com/huggingface/transformers/issues/31659">#31659</a></li>
</ul>
<h3>RTDETR</h3>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/huggingface/transformers/commit/fc35907f95459d7a6c5281dfadd680b6f7b620e3"><code>fc35907</code></a>
v4.42.4</li>
<li><a
href="https://github.com/huggingface/transformers/commit/e002fcd8edc5ee24d9ac71c6790b129fdd94982b"><code>e002fcd</code></a>
[<code>Gemma2</code>] Support FA2 softcapping (<a
href="https://redirect.github.com/huggingface/transformers/issues/31887">#31887</a>)</li>
<li><a
href="https://github.com/huggingface/transformers/commit/2e434169e7b052420be4d5b2aa1d9c8da6ebe3e7"><code>2e43416</code></a>
[<code>ConvertSlow</code>] make sure the order is preserved for
addedtokens (<a
href="https://redirect.github.com/huggingface/transformers/issues/31902">#31902</a>)</li>
<li><a
href="https://github.com/huggingface/transformers/commit/c43fd9d159f7480e62d02f1966ce83385cebea59"><code>c43fd9d</code></a>
Fixes to alternating SWA layers in Gemma2 (<a
href="https://redirect.github.com/huggingface/transformers/issues/31775">#31775</a>)</li>
<li><a
href="https://github.com/huggingface/transformers/commit/0be998bc7f9393c1672a7ff93fbccd15d5c39f3c"><code>0be998b</code></a>
Requires for torch.tensor before casting (<a
href="https://redirect.github.com/huggingface/transformers/issues/31755">#31755</a>)</li>
<li><a
href="https://github.com/huggingface/transformers/commit/b7ee1e80b912c6cdd93b54dd77af061fde151d28"><code>b7ee1e8</code></a>
v4.42.3</li>
<li><a
href="https://github.com/huggingface/transformers/commit/da50b41a272724dba0821d643a44dfebb23e3786"><code>da50b41</code></a>
Gemma capping is a must for big models (<a
href="https://redirect.github.com/huggingface/transformers/issues/31698">#31698</a>)</li>
<li><a
href="https://github.com/huggingface/transformers/commit/086c74efdf98b4e64ac40863ce190144316873a5"><code>086c74e</code></a>
v4.42.2</li>
<li><a
href="https://github.com/huggingface/transformers/commit/869186743da66563d07ea8ea81ebfc88bbf5139c"><code>8691867</code></a>
Fix Gemma2 4d attention mask (<a
href="https://redirect.github.com/huggingface/transformers/issues/31674">#31674</a>)</li>
<li><a
href="https://github.com/huggingface/transformers/commit/7edc9931c52d9de22d2c569122dfc3d927fd280e"><code>7edc993</code></a>
don't zero out the attention_mask when using sliding window with flash
attent...</li>
<li>Additional commits viewable in <a
href="https://github.com/huggingface/transformers/compare/v4.41.2...v4.42.4">compare
view</a></li>
</ul>
</details>
<br />


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</details>

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Evan Mattson <[email protected]>
This commit is contained in:
dependabot[bot]
2024-07-18 20:51:31 +00:00
committed by GitHub
co-authored by Evan Mattson
parent 5c8f4fd622
commit d3e1105871
1 changed files with 22 additions and 18 deletions
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@@ -1651,13 +1651,13 @@ socks = ["socksio (==1.*)"]
[[package]]
name = "huggingface-hub"
version = "0.23.1"
version = "0.23.5"
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
optional = false
python-versions = ">=3.8.0"
files = [
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[package.dependencies]
@@ -2372,6 +2372,7 @@ python-versions = ">=3.7"
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@@ -3110,6 +3111,7 @@ description = "Nvidia JIT LTO Library"
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python-versions = ">=3"
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@@ -6008,20 +6010,20 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
[[package]]
name = "transformers"
version = "4.41.2"
version = "4.42.4"
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
optional = false
python-versions = ">=3.8.0"
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@@ -6034,14 +6036,15 @@ tqdm = ">=4.27"
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agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
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dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"]
dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
ftfy = ["ftfy"]
@@ -6052,25 +6055,26 @@ natten = ["natten (>=0.14.6,<0.15.0)"]
onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
optuna = ["optuna"]
quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"]
quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.4.4)", "urllib3 (<2.0.0)"]
ray = ["ray[tune] (>=2.7.0)"]
retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
ruff = ["ruff (==0.4.4)"]
sagemaker = ["sagemaker (>=2.31.0)"]
sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
sigopt = ["sigopt"]
sklearn = ["scikit-learn"]
speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
timm = ["timm"]
timm = ["timm (<=0.9.16)"]
tokenizers = ["tokenizers (>=0.19,<0.20)"]
torch = ["accelerate (>=0.21.0)", "torch"]
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
torchhub = ["filelock", "huggingface-hub (>=0.23.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17,<2.0)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
video = ["av (==9.2.0)", "decord (==0.6.0)"]
vision = ["Pillow (>=10.0.1,<=15.0)"]