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I_kwDOCUB6oc7R2Dj7
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Qwen3VL input_ids = input_ids[attention_mask[i] == 1]
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[] | 2025-10-16T07:10:10
| 2025-10-22T09:46:46
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NONE
| null | null | null | null |
### System Info
`transformers==4.57.1`
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
```
[rank0]: File "/root/miniconda3/lib/python3.11/site-packages/transformers/generation/utils.py", line 2787, in _sample
[rank0]: outputs = model_forward(**model_inputs, return_dict=True)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/root/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
[rank0]: return self._call_impl(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/root/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1845, in _call_impl
[rank0]: return inner()
[rank0]: ^^^^^^^
[rank0]: File "/root/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1793, in inner
[rank0]: result = forward_call(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/root/miniconda3/lib/python3.11/site-packages/transformers/utils/generic.py", line 1064, in wrapper
[rank0]: outputs = func(self, *args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/root/miniconda3/lib/python3.11/site-packages/transformers/models/qwen3_vl/modeling_qwen3_vl.py", line 1344, in forward
[rank0]: outputs = self.model(
[rank0]: ^^^^^^^^^^^
[rank0]: File "/root/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
[rank0]: return self._call_impl(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/root/miniconda3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
[rank0]: return forward_call(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/root/miniconda3/lib/python3.11/site-packages/transformers/utils/generic.py", line 1064, in wrapper
[rank0]: outputs = func(self, *args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/root/miniconda3/lib/python3.11/site-packages/transformers/models/qwen3_vl/modeling_qwen3_vl.py", line 1201, in forward
[rank0]: position_ids, rope_deltas = self.get_rope_index(
[rank0]: ^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/root/miniconda3/lib/python3.11/site-packages/transformers/models/qwen3_vl/modeling_qwen3_vl.py", line 949, in get_rope_index
[rank0]: input_ids = input_ids[attention_mask[i] == 1]
[rank0]: ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: IndexError: The shape of the mask [719] at index 0 does not match the shape of the indexed tensor [1] at index 0
```
### Expected behavior
error
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| 3,520,588,066
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PR_kwDOCUB6oc6uBMWy
| 41,642
|
Fix confusing cls assignment
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# What does this PR do?
Don't overwrite `cls` incidentally in `__new__`.
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PR_kwDOCUB6oc6uBDs_
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Fix typos in documentation
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# What does this PR do?
As the title says.
## Before submitting
- [X] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
Please tag fewer than 3 people.
Models:
- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
Library:
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh
Integrations:
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
Devices/Backends:
- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
-->
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AttributeError: BartTokenizerFast has no attribute image_token. Did you mean: 'mask_token'?
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[] | 2025-10-16T06:34:02
| 2025-10-17T09:00:36
| 2025-10-17T09:00:36
|
NONE
| null | null | null | null |
### System Info
Ubuntu
### Who can help?
_No response_
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
```python
import torch
import requests
from PIL import Image
from transformers import AutoProcessor, Florence2ForConditionalGeneration
model = Florence2ForConditionalGeneration.from_pretrained(
"microsoft/Florence-2-large",
dtype=torch.bfloat16,
)
processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large")
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
task_prompt = "<OD>"
inputs = processor(text=task_prompt, images=image, return_tensors="pt").to(model.device, torch.bfloat16)
generated_ids = model.generate(
**inputs,
max_new_tokens=1024,
num_beams=3,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
image_size = image.size
parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=image_size)
print(parsed_answer)
```
### Expected behavior
```
raise AttributeError(f"{self.__class__.__name__} has no attribute {key}")
AttributeError: BartTokenizerFast has no attribute image_token. Did you mean: 'mask_token'?
```
|
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I_kwDOCUB6oc7R0DqW
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MarianMTModel performance regression due to Bidirectional masks
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[] | 2025-10-16T02:21:38
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### System Info
torch 2.10.0.dev20251008+cpu
### Who can help?
@vasqu
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
`numactl -C 0-3 --membind 0 python test.py`
```python
import time
import torch
from transformers import pipeline, AutoTokenizer
model_id = "Helsinki-NLP/opus-mt-mul-en"
tokenizer = AutoTokenizer.from_pretrained(model_id)
generator = pipeline("translation_fr_to_en", model=model_id, dtype=torch.float16, tokenizer=tokenizer)
generation_config = generator.model.generation_config
generation_config.max_new_tokens = 128
generation_config.min_new_tokens = 128
generation_config.do_sample = False
generation_config.temperature = 1.0
generation_config.num_beams = 1
generation_config.cache_implementation="static"
input_sentence = "C'est fait et soumis. Vous pouvez jouer à « Survival of the Tastiest » sur Android, et sur le web. Le jeu sur le web fonctionne, mais vous devez simuler plusieurs touches pour déplacer les tables, ce qui peut être un peu déroutant. Il y a beaucoup de choses dont j'aimerais parler. Je vais passer en revue chaque sujet, au lieu de faire la liste habituelle de ce qui s'est bien passé et de ce qui s'est mal passé. Concept Travailler sur le thème a probablement été l'une des tâches les plus difficiles auxquelles j'ai dû faire face. A l'origine, j'avais une idée du type de jeu que je voulais développer, du point de vue du gameplay - quelque chose avec beaucoup d'ennemis/acteurs"
for _ in range(5):
output = generator(input_sentence, generation_config=generation_config)
for _ in range(5):
start = time.time()
output = generator(input_sentence, generation_config=generation_config)
end = time.time()
print(f"pipeline latency: {(end-start)*1000} ms")
```
### Expected behavior
The performance has 40% regression after PR [41265](https://github.com/huggingface/transformers/pull/41265).
Same issue as #41566 . But the fix PR #41586 only fixes the Bert model regression, and didn't fix MarianMTModel performance regression.
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PR_kwDOCUB6oc6t_J2K
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Format MarkDown documentation and tiny fixes
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# What does this PR do?
This PR fixes MarkDown document issues detected by markdownlint-cli2. Some duplicated `autodoc` entries are also removed.
## Before submitting
- [X] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
Please tag fewer than 3 people.
Models:
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- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
Library:
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh
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- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
Devices/Backends:
- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
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PR_kwDOCUB6oc6t-keq
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Update benchmark_v2_a10_caller.yml
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optional benchmark_tag input
environment variables
# What does this PR do?
<!--
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Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.
Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.
Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
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<!-- Remove if not applicable -->
Fixes # (issue)
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
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If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
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Models:
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- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
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Adjust device logging level and add minor fixes
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This commit addresses a noisy warning and improves the robustness of the base pipeline implementation.
- The device placement message in the pipeline base class has been changed from a `warning` to a `debug` log. This reduces log noise for users who are aware of their device setup, while still providing the information for debugging purposes.
- Additionally, potential `UnboundLocalError` exceptions in the `_pad` and `check_model_type` functions have been prevented by initializing variables before their conditional assignment.
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PR_kwDOCUB6oc6t8Kyk
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Enforce check_auto_docstring
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# What does this PR do?
Fix some small issues with auto_docstring and raise an error instead of just warning if something is wrong when running check_auto_docstring
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PR_kwDOCUB6oc6t74dm
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[MODEL] Nanochat implementation
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This PR introduces Andrej Karpathy's `nanochat` to transformers.
The transformers compatible weights are currently available in two places:
- A [pr](https://huggingface.co/karpathy/nanochat-d32/discussions/1) on Karpathy's repo (large model)
- A [repo](https://huggingface.co/nanochat-students/nanochat-d20) on the community org (small model)
# ToDo
- CI is failing dues to tests for `SwiftFormerForQuestionAnswering` etc.
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PR_kwDOCUB6oc6t7xRI
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[v5] 🚨Refactor subprocessors handling in processors
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# What does this PR do?
Refactor the handling of subprocessors in processors.
- Main change is that we deduce the subprocessors from the init signature instead of having to manually add "subprocessor_class" attributes.
- This means we can remove all `attributes` attribute in processors, along with all `"subprocessor"_class` attributes
- We also now have one source of truth to determine which image processor will be loaded by default (the Auto sub processors classes)
This PR is a requirement for https://github.com/huggingface/transformers/pull/41388, as otherwise we'd have to manually check that all `image_processor_class` attributes are set to "AutoImageProcessor"
Cc @ArthurZucker @Cyrilvallez @zucchini-nlp @molbap (and also @ydshieh as this might break some parts of the CI 👀, although I checked that all processor tests are passing still, except kosmos2.5 but that's because of a PIL.UnidentifiedImageError ;)).
Update: I'm seeing some tests breaking in `test_processor_auto.py`, related to registering custom processors and subprocessors in transformers. How used is this and can we break it slightly for v5? 👀
Update 2: It looks like it's not really a problem. The only edge case that will break is if a custom processor was defined by inheriting from `ProcessorMixin`, without overriding `__init__`. ProcessorMixin used to have "feature_extractor" and "tokenizer" attributes by default, now it doesn't (which makes more sense imo)
Fixed the tests by modifying the custom processor on the hub to add an init.
🚨Breaking change:
- If a model was saved with a processor, and another processor is used to load the checkpoint, the subprocessors loaded used to be the ones hardcoded in the other processor class definition, but now they will be the ones that were saved originally.
For example:
```python
processor = OwlViTProcessor.from_pretrained("some_owlv2_checkpoint")
print(type(processor.image_processor)
# Used to be OwlViTImageProcessor, will now be Owlv2ImageProcessor, which makes more sense in my opinion
```
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PR_kwDOCUB6oc6t7mJ8
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[CI] Build translated docs
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Actually, this might be the reason why the translated docs aren't building when commenting `build-doc`. The `tr` and `te` language codes don't exist 😅
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Incorrect access of dataset field fixed
|
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In `test_tokenizer`:
`for text in dataset[i]["premise"].values():
test_string(slow, fast, text)
# hypothesis, all languages
for text in dataset[i]["hypothesis"]["translation"]:
test_string(slow, fast, text)`
The "premise" field in the XNLI dataset is a string, not a dictionary**. Calling .values() on a string will raise an error. Similarly, "hypothesis" is also a string (not a dictionary), so "hypothesis"]["translation" will fail.
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I_kwDOCUB6oc7RvLte
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Incorrect Access of Dataset Fields in check_tokenizers.py
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The `premise` field in the XNLI dataset is a string, not a dictionary**. Calling `.values()` on a string will raise an error.
Similarly, `hypothesis` is also a string (not a dictionary), so `"hypothesis"]["translation"` will fail.
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PR_kwDOCUB6oc6t7ROg
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Make Idefics exportable, assume no padding
|
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# What does this PR do?
Makes Idefics 3 exportable by assuming batch size of 1, unrolling the loop to a single iteration.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case. https://github.com/huggingface/optimum-executorch/pull/163#discussion_r2429871579
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@zucchini-nlp
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I_kwDOCUB6oc7RvFTE
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Cannot import name 'AutoImageProcessor' from 'transformers'
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NONE
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### System Info
Intel CPU
Nvidia 3090
ubuntu 22.04
python 3.10.12
transformers=5.0.0.dev0 (installed from the official git repo)
### PS:
It's also tested with transformers=4.57.1, which is installed using "pip install", the same error persisted while executing "from transformers import AutoImageProcessor, AutoModel".
### Who can help?
_No response_
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
1. in any python script: from transformers import AutoImageProcessor, AutoModel
### Expected behavior
it shouldn't raise the error:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ImportError: cannot import name 'AutoImageProcessor' from 'transformers' (/usr/local/lib/python3.10/dist-packages/transformers/__init__.py)
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[`Executorch`] Simplify for encoder models
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Now that we include a fast path using no vmapping, we can revert the extra treatment. Followup to #41586
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PR_kwDOCUB6oc6t6Fp6
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[v5] Return a BatchEncoding dict from apply_chat_template by default
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Tokenizers return a BatchEncoding dict by default, but `apply_chat_template` doesn't. This is just an accident of how I wrote it originally, which we were stuck with for backward compatibility reasons. Ideally, I think `apply_chat_template` should return exactly the same format as tokenizers, since it also performs tokenization most of the time. It's now `v5` time, so we can start making that happen :sweat_smile:
This PR also updates tests, and removes very old `test_tokenization_for_chat` tests. These model-specific tests don't do anything useful anymore, since the `apply_chat_template` functionality is unified across tokenizers; they're mostly a legacy leftover from when model classes used to need custom chat tokenization functions.
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PR_kwDOCUB6oc6t6FSV
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[`Masks`] Fix mask handling in eager for vision models
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As per title, some vision models use masks, let's sync with bert this time and reduce errors that could be introduced
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Fix serving continuous batching
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# What does this PR do?
Serving is broken with continuous batching to due recent PRs. This PR fixes it
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I_kwDOCUB6oc7RtmNl
| 41,623
|
Qwen Audio DisjunctiveConstraint missing
|
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[] | 2025-10-15T14:44:20
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CONTRIBUTOR
| null | null | null | null |
### System Info
- `transformers` version: 4.57.0.dev0
- Platform: Linux-6.14.0-33-generic-x86_64-with-glibc2.39
- Python version: 3.12.11
- Huggingface_hub version: 1.0.0.rc5
- Safetensors version: 0.6.2
- Accelerate version: 1.10.1
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (accelerator?): 2.8.0+cu128 (CUDA)
- Using distributed or parallel set-up in script?: no
- Using GPU in script?: yes
- GPU type: NVIDIA RTX PRO 2000 Blackwell Generation Laptop GPU
### Who can help?
@eustlb @ebezzam @vasqu
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
Take the example code from the website:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
import torch
torch.manual_seed(1234)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-Audio", trust_remote_code=True)
# use bf16
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-Audio", device_map="auto", trust_remote_code=True, bf16=True).eval()
# use fp16
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-Audio", device_map="auto", trust_remote_code=True, fp16=True).eval()
# use cpu only
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-Audio", device_map="cpu", trust_remote_code=True).eval()
# use cuda device
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-Audio", device_map="cuda", trust_remote_code=True).eval()
# Specify hyperparameters for generation (No need to do this if you are using transformers>4.32.0)
# model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-Audio", trust_remote_code=True)
audio_url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Audio/1272-128104-0000.flac"
sp_prompt = "<|startoftranscript|><|en|><|transcribe|><|en|><|notimestamps|><|wo_itn|>"
query = f"<audio>{audio_url}</audio>{sp_prompt}"
audio_info = tokenizer.process_audio(query)
inputs = tokenizer(query, return_tensors='pt', audio_info=audio_info)
inputs = inputs.to(model.device)
pred = model.generate(**inputs, audio_info=audio_info)
response = tokenizer.decode(pred.cpu()[0], skip_special_tokens=False,audio_info=audio_info)
print(response)
# <audio>https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Audio/1272-128104-0000.flac</audio><|startoftranscription|><|en|><|transcribe|><|en|><|notimestamps|><|wo_itn|>mister quilting is the apostle of the middle classes and we are glad to welcome his gospel<|endoftext|>
```
results in the error:
```
tokenizer_config.json: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 211/211 [00:00<00:00, 753kB/s]
tokenization_qwen.py: 22.8kB [00:00, 32.0MB/s]
audio.py: 15.0kB [00:00, 16.0MB/s]
A new version of the following files was downloaded from https://huggingface.co/Qwen/Qwen-Audio:
- audio.py
. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
A new version of the following files was downloaded from https://huggingface.co/Qwen/Qwen-Audio:
- tokenization_qwen.py
- audio.py
. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
qwen.tiktoken: 2.56MB [00:00, 52.5MB/s]
special_tokens_map.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.00/3.00 [00:00<00:00, 8.41kB/s]
audio_start_id: 155163, audio_end_id: 155164, audio_pad_id: 151851.
config.json: 1.26kB [00:00, 2.82MB/s]
configuration_qwen.py: 2.35kB [00:00, 5.27MB/s]
A new version of the following files was downloaded from https://huggingface.co/Qwen/Qwen-Audio:
- configuration_qwen.py
. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
modeling_qwen.py: 58.3kB [00:00, 59.1MB/s]
Encountered exception while importing transformers_stream_generator: cannot import name 'DisjunctiveConstraint' from 'transformers' (/home/ruzickal/Code/entity-analyzer-backend/transformers/src/transformers/__init__.py)
Traceback (most recent call last):
File "/home/ruzickal/Code/entity-analyzer-backend/audio-llm-api/test_qwen_audio.py", line 15, in <module>
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-Audio", device_map="cuda", trust_remote_code=True).eval()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ruzickal/Code/entity-analyzer-backend/transformers/src/transformers/models/auto/auto_factory.py", line 367, in from_pretrained
model_class = get_class_from_dynamic_module(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ruzickal/Code/entity-analyzer-backend/transformers/src/transformers/dynamic_module_utils.py", line 594, in get_class_from_dynamic_module
final_module = get_cached_module_file(
^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ruzickal/Code/entity-analyzer-backend/transformers/src/transformers/dynamic_module_utils.py", line 422, in get_cached_module_file
modules_needed = check_imports(resolved_module_file)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ruzickal/Code/entity-analyzer-backend/transformers/src/transformers/dynamic_module_utils.py", line 248, in check_imports
importlib.import_module(imp)
File "/home/ruzickal/.pyenv/versions/3.12.11/lib/python3.12/importlib/__init__.py", line 90, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen importlib._bootstrap>", line 1387, in _gcd_import
File "<frozen importlib._bootstrap>", line 1360, in _find_and_load
File "<frozen importlib._bootstrap>", line 1331, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 935, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 999, in exec_module
File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed
File "/home/ruzickal/.cache/pypoetry/virtualenvs/audio-llm-api-nPR59pGu-py3.12/lib/python3.12/site-packages/transformers_stream_generator/__init__.py", line 1, in <module>
from .main import init_stream_support
File "/home/ruzickal/.cache/pypoetry/virtualenvs/audio-llm-api-nPR59pGu-py3.12/lib/python3.12/site-packages/transformers_stream_generator/main.py", line 1, in <module>
from transformers import (
ImportError: cannot import name 'DisjunctiveConstraint' from 'transformers' (/home/ruzickal/Code/entity-analyzer-backend/transformers/src/transformers/__init__.py)
```
### Expected behavior
Run the example code without an error
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I_kwDOCUB6oc7RtgVO
| 41,622
|
Florence 2 _supports_sdpa missing in remote code
|
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[] | 2025-10-15T14:38:12
| 2025-10-16T09:09:29
| 2025-10-16T09:09:29
|
CONTRIBUTOR
| null | null | null | null |
### System Info
- `transformers` version: 4.57.0.dev0
- Platform: Linux-6.14.0-33-generic-x86_64-with-glibc2.39
- Python version: 3.12.11
- Huggingface_hub version: 1.0.0.rc5
- Safetensors version: 0.6.2
- Accelerate version: 1.10.1
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (accelerator?): 2.8.0+cu128 (CUDA)
- Using distributed or parallel set-up in script?: no
- Using GPU in script?: yes
- GPU type: NVIDIA RTX PRO 2000 Blackwell Generation Laptop GPU
### Who can help?
@yonigozlan @molbap
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
Running the example script:
```python
import requests
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large", torch_dtype=torch_dtype, trust_remote_code=True).to(device)
processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large", trust_remote_code=False)
prompt = "<OD>"
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype)
generated_ids = model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=4096,
num_beams=3,
do_sample=False
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = processor.post_process_generation(generated_text, task="<OD>", image_size=(image.width, image.height))
print(parsed_answer)
```
Leads to the following error:
```
`torch_dtype` is deprecated! Use `dtype` instead!
Traceback (most recent call last):
File "/home/ruzickal/Code/entity-analyzer-backend/audio-llm-api/test.py", line 11, in <module>
model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large", torch_dtype=torch_dtype, trust_remote_code=True).to(device)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ruzickal/Code/entity-analyzer-backend/transformers/src/transformers/models/auto/auto_factory.py", line 378, in from_pretrained
return model_class.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ruzickal/Code/entity-analyzer-backend/transformers/src/transformers/modeling_utils.py", line 270, in _wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/ruzickal/Code/entity-analyzer-backend/transformers/src/transformers/modeling_utils.py", line 4473, in from_pretrained
model = cls(config, *model_args, **model_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ruzickal/.cache/huggingface/modules/transformers_modules/microsoft/Florence_hyphen_2_hyphen_large/21a599d414c4d928c9032694c424fb94458e3594/modeling_florence2.py", line 2535, in __init__
super().__init__(config)
File "/home/ruzickal/Code/entity-analyzer-backend/transformers/src/transformers/modeling_utils.py", line 1822, in __init__
self.config._attn_implementation_internal = self._check_and_adjust_attn_implementation(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ruzickal/Code/entity-analyzer-backend/transformers/src/transformers/modeling_utils.py", line 2416, in _check_and_adjust_attn_implementation
applicable_attn_implementation = self.get_correct_attn_implementation(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ruzickal/Code/entity-analyzer-backend/transformers/src/transformers/modeling_utils.py", line 2450, in get_correct_attn_implementation
self._sdpa_can_dispatch(is_init_check)
File "/home/ruzickal/Code/entity-analyzer-backend/transformers/src/transformers/modeling_utils.py", line 2314, in _sdpa_can_dispatch
if not self._supports_sdpa:
^^^^^^^^^^^^^^^^^^^
File "/home/ruzickal/.cache/pypoetry/virtualenvs/audio-llm-api-nPR59pGu-py3.12/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1962, in __getattr__
raise AttributeError(
AttributeError: 'Florence2ForConditionalGeneration' object has no attribute '_supports_sdpa'
```
### Expected behavior
Output the detections
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PR_kwDOCUB6oc6t5lAk
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Add LightOnOCR model implementation
|
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[] | 2025-10-15T14:23:41
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# What does this PR do?
Implementation of the LightonOCR model following Modular Transformers architecture.
Our model is a 1B parameter OCR model using Pixtral as the vision encoder and Qwen3 as the LLM decoder.
I still have an issue with auto configuration, which I'm not familiar with:
`🚨 Config not found for lightonocr. You can manually add it to HARDCODED_CONFIG_FOR_MODELS in utils/auto_docstring.py`
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<!-- Remove if not applicable -->
Fixes # (issue)
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
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Models:
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- multimodal models: @zucchini-nlp
- graph models: @clefourrier
Library:
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh
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- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
Devices/Backends:
- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
-->
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PR_kwDOCUB6oc6t49nL
| 41,620
|
Remove the head masking block in some vision models
|
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# What does this PR do?
#36545 introduced a mistake regarding the headmask
before that PR, we had some
```
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
```
but after the PR, it became
```
# Mask heads if we want to
if attention_mask is not None:
attn_weights = attn_weights * attention_mask
```
which is apparently wrong.
Furthermore, we already remove the support of head masking.
This PRs simply remove the blocks.
|
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PR_kwDOCUB6oc6t4k-d
| 41,619
|
Fix FP-Quant quantization fallback CPU dispatch.
|
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# What does this PR do?
<!--
Congratulations! You've made it this far! You're not quite done yet though.
Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.
Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.
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-->
<!-- Remove if not applicable -->
Added a workaround for a hard-coded missing key CPU dispatch that broke pre-quantized model loading for FP-Quant.
`weight` was assumed to be missing and auto-dispatched to CPU but it's a parameter that gets set to `None` in post-loading (in `pre_forward`).
Fixed FP-Quant test typos.
Blocked by #41613 too.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
Please tag fewer than 3 people.
Models:
- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
Library:
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh
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- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
Devices/Backends:
- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
-->
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PR_kwDOCUB6oc6t3-HV
| 41,618
|
Update a dataset reop link
|
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| 2025-10-15T12:47:31
| 2025-10-15T12:41:38
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# What does this PR do?
`https://huggingface.co/kirp/kosmos2_5/resolve/main/receipt_00008.png` is no longer available (likely the author deleted it).
That is used during the PR. Let's use the official repo. now
https://huggingface.co/microsoft/kosmos-2.5/resolve/main/receipt_00008.png
|
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PR_kwDOCUB6oc6t3tSo
| 41,617
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Reinstate early CUDA init fix
|
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MEMBER
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vLLM found that this import was causing CUDA to be initialised early
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PR_kwDOCUB6oc6t3r9-
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Remove deprecated code
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# What does this PR do?
As per title
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I_kwDOCUB6oc7Rq2Y_
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Issue Report: Abnormal GPU Utilization Pattern - DDP Training CLIP Model from Scratch
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[] | 2025-10-15T11:52:04
| 2025-10-17T00:50:03
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NONE
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## Problem Description
Encountering abnormal GPU utilization patterns when training a CLIP model from scratch using DDP (Distributed Data Parallel). The monitoring charts show:
- **GPU Memory Allocation**: All 8 GPUs maintain a stable ~10% memory allocation
- **GPU Utilization**: Highly irregular fluctuation pattern, oscillating between 0-100% with significant idle periods
This pattern suggests potential issues with:
1. GPU synchronization/waiting problems
2. Data loading bottleneck
3. Gradient accumulation/communication issues
4. Torch compile or Flash Attention compatibility problems
## Environment Information
```yaml
Hardware:
- GPU: 8x A100
- Node: Single node
Software:
- Framework: PyTorch + Transformers
- Distributed: DDP (NCCL backend)
- Mixed Precision: BF16
- Optimizer: AdamW Fused
- Special Features:
- torch_compile=True
- flash_attention_2
- gradient_checkpointing=False
```
## Training Configuration
```python
# Key Parameters
per_device_batch_size: 128
gradient_accumulation_steps: 1 (calculated)
target_global_batch_size: 1024
learning_rate: 1e-3
dataloader_num_workers: 8
dataloader_prefetch_factor: 4
dataloader_persistent_workers: True
```
## Reproduction Code
<details>
<summary>Complete Training Script (Click to expand)</summary>
```python
# -*- coding: utf-8 -*-
import os
import torch
from datasets import load_dataset
from transformers import (
CLIPProcessor,
CLIPModel,
CLIPConfig,
TrainingArguments,
Trainer
)
from utils import (
count_parameters,
create_cc_dataset,
parse_args,
create_laion_dataset
)
def is_main_process():
"""Check if current process is main process"""
return int(os.environ.get('LOCAL_RANK', 0)) == 0
def collate_fn(batch, processor):
if 'text' in batch[0]:
texts = [b['text'] for b in batch]
elif 'conversations' in batch[0]:
texts = [b['conversations'][1]['value'] for b in batch]
images = [b['image'] for b in batch]
return processor(
text=texts,
images=images,
return_tensors="pt",
padding=True,
truncation=True,
max_length=77
)
def collate_fn_laion(batch, processor):
texts = [b['txt'] for b in batch]
images = [b['jpg'] for b in batch]
out = processor(
text=texts,
images=images,
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=77
)
return out
def train(args):
world_size = int(os.environ.get('WORLD_SIZE', 1))
rank = int(os.environ.get('RANK', 0))
local_rank = int(os.environ.get('LOCAL_RANK', 0))
print(f"[Rank {rank}] World size: {world_size}, Local rank: {local_rank}")
print(f"[Rank {rank}] CUDA devices: {torch.cuda.device_count()}")
# Set random seed
torch.manual_seed(args.seed)
if is_main_process():
print("==== Config ====")
for k, v in vars(args).items():
print(f"{k}: {v}")
# Performance optimization: Enable TF32
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
# Model initialization
cfg = CLIPConfig.from_pretrained(
args.target_model,
_attn_implementation='flash_attention_2',
torch_dtype=torch.bfloat16
)
model = CLIPModel(config=cfg)
nparams = count_parameters(model)
if is_main_process():
print(f"Model Parameters: {nparams}")
# Initialize processor
processor = CLIPProcessor.from_pretrained(args.target_model, use_fast=False)
# Load dataset
if is_main_process():
print("[info] Loading dataset ...")
if "cc12m" in args.data_path:
train_dataset = create_cc_dataset(
"data/cc8m_metadata.jsonl",
os.path.join(args.data_path, 'cc8m'),
num_data=args.num_data
)
dname = 'cc12m'
elif 'mscoco' in args.data_path:
train_dataset = load_dataset(args.data_path)['train']
dname = 'mscoco'
elif 'laion' in args.data_path.lower():
train_dataset = create_laion_dataset(args.data_path, num_data=args.num_data)
dname = 'laion'
# Calculate parameters based on configuration
# Target: BS=1024, LR=1e-3, Warmup=10K, Samples=3B
total_gpus = world_size
target_global_bs = 1024
per_device_bs = 128
gradient_accumulation = target_global_bs // (per_device_bs * total_gpus)
learning_rate = 1e-3
warmup_steps = 10000
total_samples = 3_000_000_000
total_steps = total_samples // target_global_bs
if is_main_process():
print(f"[info] Target global batch size: {target_global_bs}")
print(f"[info] Per device batch size: {per_device_bs}")
print(f"[info] Gradient accumulation steps: {gradient_accumulation}")
print(f"[info] Total training steps: {total_steps}")
print(f"[info] Warmup steps: {warmup_steps}")
print(f"[info] Learning rate: {learning_rate}")
# Run name for WandB
run_name = f"{dname}-bs{target_global_bs}-lr{learning_rate}-{args.width}-{args.tower}-{args.backbone}"
# Training arguments
training_args = TrainingArguments(
output_dir=args.save_dir,
run_name=run_name,
# Training configuration
per_device_train_batch_size=per_device_bs,
gradient_accumulation_steps=gradient_accumulation,
max_steps=total_steps,
# Optimizer configuration
learning_rate=learning_rate,
weight_decay=0.01,
max_grad_norm=4.0,
# Learning rate scheduler
optim="adamw_torch_fused",
lr_scheduler_type="cosine",
warmup_steps=warmup_steps,
# Mixed precision - BF16 for A100
fp16=False,
bf16=True,
bf16_full_eval=True,
# Logging and saving
logging_steps=100,
logging_first_step=True,
save_strategy="steps",
save_steps=5000,
save_total_limit=3,
# Evaluation
eval_strategy="no",
load_best_model_at_end=False,
# Data loading - Performance optimization
dataloader_num_workers=8,
dataloader_pin_memory=True,
remove_unused_columns=False,
dataloader_drop_last=True,
dataloader_prefetch_factor=4,
dataloader_persistent_workers=True,
# Distributed training - Critical configuration
ddp_backend='nccl',
ddp_find_unused_parameters=False,
ddp_bucket_cap_mb=25,
local_rank=local_rank,
# Other performance optimizations
seed=args.seed,
report_to="wandb" if is_main_process() else "none",
gradient_checkpointing=False,
save_on_each_node=False,
torch_compile=True, # Suspicious point 1
tf32=True,
ddp_broadcast_buffers=False,
)
collate_fn_to_use = collate_fn_laion if dname == 'laion' else collate_fn
# Create Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
data_collator=lambda x: collate_fn_to_use(x, processor=processor)
)
model.train()
trainer.train(resume_from_checkpoint=args.resume if args.resume else None)
if rank == 0:
final_path = os.path.join(args.save_dir, args.save_name)
trainer.save_model(final_path)
print(f"[info] Training finished. Final model saved to: {final_path}")
def main():
args = parse_args()
train(args)
if __name__ == "__main__":
main()
```
</details>
<details>
<summary>Launch Script (Click to expand)</summary>
```bash
#!/bin/bash
# Find a free port safely
find_free_port() {
for i in {1..50}; do
port=$(( (RANDOM % 40000) + 20000 )) # 20000-59999
if ! ss -ltn | awk '{print $4}' | grep -q ":$port$"; then
echo $port
return 0
fi
done
echo "No free port found after 50 tries" >&2
return 1
}
# Environment variables
export OMP_NUM_THREADS=1
export PYTHONWARNINGS="ignore::FutureWarning"
export TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
echo "Training with learning rate: $lr"
MASTER_ADDR=$(hostname)
FREE_PORT=$(find_free_port)
if [ $? -ne 0 ]; then
echo "Error: Could not find a free port. Exiting."
exit 1
fi
echo "Using rendezvous: ${MASTER_ADDR}:${FREE_PORT}"
torchrun \
--nproc_per_node=8 \
--rdzv_backend=c10d \
--rdzv_endpoint=${MASTER_ADDR}:${FREE_PORT} \
clip_hf.py \
--num_workers 32 \
--save_name main_t-cc12m-mup-lr${lr}.pt \
--save_dir $SAVE_DIR \
--data_path $DATA_PATH
```
</details>
## Observed Symptoms
### 1. Abnormally Low GPU Memory Usage
- All 8 GPUs show only ~10% memory allocation
- This is unusually low for CLIP training with batch_size=128
- May indicate data is not being properly distributed to GPUs
### 2. Severe GPU Utilization Fluctuations
- Frequent oscillation between 0-100%
- Significant idle cycles present
- GPUs don't appear to be working synchronously
### 3. Potential Bottleneck Points
- **Data Loading**: Despite 8 workers + prefetch, may still have I/O bottleneck
- **DDP Synchronization**: Gradient synchronization between GPUs may have waiting issues
- **Torch Compile**: Potential compatibility issues with DDP or Flash Attention
- **Gradient Accumulation**: Although set to 1, may have abnormal interactions with other components
## Questions Needing Help
1. **What is the root cause of this GPU utilization pattern?**
- Is it a data loading bottleneck?
- Is it a DDP communication issue?
- Is it a torch.compile compatibility issue with DDP/FA2?
2. **How to diagnose the specific bottleneck?**
- What profiling code should be added?
- What metrics should be monitored?
3. **Suggested optimization directions?**
- Should `torch_compile` be disabled?
- Should we switch back to standard attention?
- Do dataloader parameters need adjustment?
## Expected Behavior
- GPU utilization should maintain a stable 80-95% level
- Memory usage should be higher (at least 30-50%)
- All GPUs should work synchronously without obvious idle cycles
## Additional Information
- Dataset: CC12M / LAION
- Model: CLIP (training from scratch)
- Goal: Reproduce OpenAI CLIP-like training configuration
---
**Monitoring Screenshots:**
<img width="479" height="653" alt="Image" src="https://github.com/user-attachments/assets/5c9caa60-1bb7-4eac-b321-73659c72e3a9" />
)
- Top chart: GPU Memory Allocation (~10% across all GPUs)
- Bottom chart: GPU Utilization (severe fluctuation 0-100%)
## Possible Related Issues
- Interaction between `torch.compile` and DDP
- Flash Attention 2 compatibility with distributed training
- Dataloader performance in multi-GPU setup
- NCCL communication overhead
Any insights or suggestions would be greatly appreciated!
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PR_kwDOCUB6oc6t3L39
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[`Docs`] Fix changed references
|
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[] | 2025-10-15T11:29:13
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CONTRIBUTOR
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#41445 moved `load_sharded_checkpoint` to the trainer utils instead of the modeling utils. This PR changes the remaining references and (hopefully) fixes the CI on main with the doc builder
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PR_kwDOCUB6oc6t3JLR
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Fix quantization base class
|
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MEMBER
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# What does this PR do?
This PR fixes issues that was created from this [PR](https://github.com/huggingface/transformers/pull/41445) regarding quantization.
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PR_kwDOCUB6oc6t2ojq
| 41,612
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Fix EncoderDecoder cache
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In #41569 we restored thr `__iter__` method to `DynamicCache` but I missed the fact that it was also removed from `EncoderDecoderCache`. This PR fixes that and modifies the `__init__` of `EncoderDecoderCache` in the case of DDP, so it is compatible with the new system.
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PR_kwDOCUB6oc6t2lQC
| 41,611
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Docs add custom loss example
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This PR adds a new section to the `Trainer` documentation explaining the best practice for implementing a custom loss function.
It provides a clear code example that shows how to subclass `Trainer` and override the `compute_loss` method. This addresses a common user question and makes the documentation more complete.
Fixes #41247
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PR_kwDOCUB6oc6t2i_J
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torch 2.9 don't ❤️ torchcodec 💔
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# What does this PR do?
Let's pin torch 2.8 for now.
See https://github.com/meta-pytorch/torchcodec/issues/912
It is causing problems on our CircleCI for now.
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PR_kwDOCUB6oc6t2bTU
| 41,609
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Fix gemma gguf tokenizer
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This PR fixes an issue where `AutoTokenizer.from_pretrained` would instantiate an incorrect `Unigram` tokenizer when loading from a Gemma GGUF file, instead of the correct `BPE`-based `GemmaTokenizer`.
The root cause was that the GGUF loading logic did not have an explicit check to handle the Gemma architecture specifically. This fix introduces a check that:
1. Detects if a `gguf_file` is being loaded.
2. Reads the GGUF metadata to confirm the architecture is `gemma` and the tokenizer model is `bpe`.
3. Overrides the `tokenizer_class` in the loaded configuration to `GemmaTokenizer`, forcing the rest of the function to instantiate the correct class.
This ensures that loading a tokenizer from a Gemma GGUF file produces the same tokenization as loading from the original Hugging Face repository.
**Fixes #41494**
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PR_kwDOCUB6oc6t2Nmn
| 41,608
|
Import `expand_device_map` instead of redefining it
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# What does this PR do?
This function has been moved to integrations but was not deleted properly
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PR_kwDOCUB6oc6t2CoO
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[v5] Delete `videos` from image processing classes
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# What does this PR do?
As per title, it was deprecated for v5
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PR_kwDOCUB6oc6t16MT
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fix(processing): Filter kwargs in ProcessorMixin call to prevent Type…
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This PR fixes an issue where multimodal processors could raise a `TypeError` when called with arguments specific to one modality. The `ProcessorMixin.__call__` method was passing all keyword arguments to each sub-processor (tokenizer, feature extractor, etc.), causing a crash if a processor received an argument it didn't recognize (e.g., `EncodecFeatureExtractor` receiving `pad_to_multiple_of` from the tokenizer).
This fix resolves the problem by filtering the keyword arguments before they are passed to each sub-processor. It uses Python's `inspect` module to get the valid parameters for each processor's `__call__` method and only passes the arguments that match. This prevents `TypeError`s and makes the processor logic more robust.
**Fixes #41598**
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PR_kwDOCUB6oc6t15-Q
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Fix fp32_ln for various models
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This PR fixes the test `test_flash_attn_2_fp32_ln` for several models:
- `bark` was failing the test because it call `_flash_attention_forward` directly without checking the `queries` dtype, and so the test could fail if the dtype was `torch.float32`. To fix this we re-factored out a code block into a function `get_target_dtype` that takes care of infering whether to cast the fp32 tesnor to fp16 or bf16, and added a called to it before the call to FA
- same for `stablelm`
- `mllama` was failing the test because `MllamaTextSelfAttention` lacks the `is_causal`attribute, which was added and set to True (it's a text attention so it's causal, as discussed in #39182)
- same for `kosmos2` but the test still fails for many many other reasons
The list of fixed test is here:
```
FAILED tests/models/bark/test_modeling_bark.py::BarkSemanticModelTest::test_flash_attn_2_fp32_ln - RuntimeError: FlashAttention only support fp16 and bf16 data type
FAILED tests/models/bark/test_modeling_bark.py::BarkCoarseModelTest::test_flash_attn_2_fp32_ln - RuntimeError: FlashAttention only support fp16 and bf16 data type
FAILED tests/models/mllama/test_modeling_mllama.py::MllamaForCausalLMModelTest::test_flash_attn_2_fp32_ln - AttributeError: 'MllamaTextSelfAttention' object has no attribute 'is_causal'
FAILED tests/models/mllama/test_modeling_mllama.py::MllamaForConditionalGenerationModelTest::test_flash_attn_2_fp32_ln - AttributeError: 'MllamaTextSelfAttention' object has no attribute 'is_causal'
FAILED tests/models/stablelm/test_modeling_stablelm.py::StableLmModelTest::test_flash_attn_2_fp32_ln - RuntimeError: FlashAttention only support fp16 and bf16 data type
```
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PR_kwDOCUB6oc6t12rw
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Fix TypeError: find_adapter_config_file() got an unexpected keyword argument '_adapter_model_path'
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Fix TypeError: find_adapter_config_file() got an unexpected keyword argument '_adapter_model_path'
- Pass original dict instead of copy to `maybe_load_adapters`
The `adapter_kwargs` is passed to `maybe_load_adapters` as unpacked keyword arguments: Python does not pass the original dictionary, but instead creates a new dictionary inside the function; so the `pop` inside the function does not have the intended behavior.
https://github.com/huggingface/transformers/blob/5db730786d87bde8397a207bd84ae0014054fc37/src/transformers/modeling_utils.py#L4383-L4387
```python
def maybe_load_adapters(
pretrained_model_name_or_path,
download_kwargs: DownloadKwargs,
**adapter_kwargs,
):
...
_adapter_model_path = adapter_kwargs.pop("_adapter_model_path", None)
```
This issue was introduced by PR:
- #41445
Related downstream issue:
- https://github.com/huggingface/trl/issues/4273
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PR_kwDOCUB6oc6t1c_F
| 41,603
|
Fix video processing channel format
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# What does this PR do?
FIxes failing VJEPA test. The test was failing because the video was not permuted to the correct format that torchvision expects `(T, C, H, W)`
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PR_kwDOCUB6oc6tz856
| 41,601
|
enable sdpa enable gqa logic for Ascend NPU
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| 2025-10-15T13:45:29
|
CONTRIBUTOR
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# What does this PR do?
As discussion [(link)](https://github.com/huggingface/transformers/pull/41143#issuecomment-3346569299) in #41143 , we set `enable_gqa=False` by force when running on Ascend NPU.
After that, we made a series of experiments to make sure `torch.nn.functional.scaled_dot_product_attention` api work fine on Ascend NPU when setting `enable_gqa` to `True` or `False`, so this limitation can be removed now :)
Note: Experiments based on `torch==2.5.1` and `torch_npu==2.5.1`
Fixes # (issue)
Not related.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
|
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I_kwDOCUB6oc7Rmxnr
| 41,600
|
torch can not be detected by transformers on aarch
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[] | 2025-10-15T06:49:29
| 2025-10-16T03:20:53
| 2025-10-16T03:20:53
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NONE
| null | null | null | null |
### System Info
transformers 4.55.0
python 3.10.12
plateform aarch
### Who can help?
@ivarflakstad
i know it's different between AMD and arm, unfortunately i can not image who can help me in this issue.
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
ngc torch container : `nvcr.io/nvidia/pytorch:24.01-py3`
`pip install transformers==4.55.0`
`python -c "from transformers import AutoModel"`
error :
<img width="1282" height="777" alt="Image" src="https://github.com/user-attachments/assets/ff70e166-afe3-49a9-b8b1-bf41123aa496" />
i had search for this error, someone said it's because you didn't install torch. However, i had checked the NGC container does contain torch v2.2.0 as follows.
<img width="457" height="157" alt="Image" src="https://github.com/user-attachments/assets/32f1b3e5-ae57-4af3-9c64-d4134148db88" />
### Expected behavior
successfully import AutoModel.
|
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PR_kwDOCUB6oc6tyPhY
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More markdown file fixes
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# What does this PR do?
This PR applies markdownlint-cli2 to format and fix markdown files.
<!--
Congratulations! You've made it this far! You're not quite done yet though.
Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.
Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.
Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->
<!-- Remove if not applicable -->
## Before submitting
- [X] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
Please tag fewer than 3 people.
Models:
- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
Library:
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @zach-huggingface @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker @S1ro1
- CIs: @ydshieh
Integrations:
- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
Devices/Backends:
- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
-->
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I_kwDOCUB6oc7Rk2oD
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EncodecFeatureExtractor.__call__() got an unexpected keyword argument 'pad_to_multiple_of'
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[] | 2025-10-15T02:23:50
| 2025-10-24T11:09:29
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NONE
| null | null | null | null |
### System Info
- `transformers` version: 4.57.0
- Platform: Windows-10-10.0.26200-SP0
- Python version: 3.10.18
- Huggingface_hub version: 0.35.3
- Safetensors version: 0.5.3
- Accelerate version: 1.10.1
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (accelerator?): 2.4.0+cpu (NA)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: no
### Who can help?
Hello @eustlb @ebezzam @vasqu
Just want to report a suspected bug. I keep getting an error when i try to inference the CSM-1B model using transformers with context. The error that i got is `Error with generated segments: EncodecFeatureExtractor.__call__() got an unexpected keyword argument 'pad_to_multiple_of'`. Perhaps the issue is particularly with how the audio is stored (doesn't give error when i apply_chat_template using one text input). I tried many things to store the audio such as storing it in Torch.tensor format and referencing it from a save wav file, but it still failed.
Here is how i saved the generated_segments:
```
audio_file = f"temp_vad_recording_{int(time.time())}.wav"
audio_tensor = torch.tensor(audio_data, dtype=torch.float32).unsqueeze(0)
torchaudio.save(audio_file, audio_tensor, sample_rate)
user_input = self.transcribe_audio(audio_file)
self.generated_segments.append(
{
"role": "1",
"content": [{"type": "text", "text": user_input}, {"type": "audio", "path": audio_file}],
}
)
```
Here is how i applied chat template:
```
inputs = self.tts_processor.apply_chat_template(
self.generated_segments,
tokenize=True,
return_dict=True,
).to(self.tts_generator.device)
```
### Information
- [ ] The official example scripts
- [x] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)
### Reproduction
1. run the code using a vad and whisper for transcribing into text:
```
audio_file = f"temp_vad_recording_{int(time.time())}.wav"
audio_tensor = torch.tensor(audio_data, dtype=torch.float32).unsqueeze(0)
torchaudio.save(audio_file, audio_tensor, sample_rate)
user_input = self.transcribe_audio(audio_file)
self.generated_segments.append(
{
"role": "1",
"content": [{"type": "text", "text": user_input}, {"type": "audio", "path": audio_file}],
}
)
```
### Expected behavior
apply_chat_template should work when referencing local audio files in the path, like mentioned in the the error `Incorrect format used for `audio`. Should be an url linking to an audio, a local path, or numpy array.`
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PR_kwDOCUB6oc6tyDWr
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|
Standardize RoBERTa model card following issue #36979
|
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**What does this PR do?**
This PR standardizes the RoBERTa model card following the format established in issue #36979, making it more accessible and user-friendly.
**Changes made:**
✅ **Enhanced Badge Support**
- Added TensorFlow support (orange badge)
- Added Flax support (yellow badge)
- Maintained PyTorch and SDPA badges
✅ **Conversational Description**
- Rewrote in beginner-friendly tone: "RoBERTa is like BERT's smarter cousin"
- Explained key differences in simple terms
- Highlighted practical benefits for sentiment analysis and text classification
✅ **Practical Usage Examples**
- Added sentiment analysis examples with `cardiffnlp/twitter-roberta-base-sentiment-latest`
- Complete AutoModel workflow with confidence scores
- CLI example for command-line usage
- All examples are functional and tested
✅ **Contributor Attribution**
- Added tip box crediting original contributor [Joao Gante](https://huggingface.co/joaogante)
✅ **Comprehensive Resources Section**
- Original paper link (1907.11692)
- Official Facebook AI implementation
- Hugging Face blog posts and guides
- Training documentation links
✅ **Enhanced Notes Section**
- RoBERTa-specific technical details
- Dynamic masking explanation
- Byte-level BPE tokenizer benefits
**Before submitting:**
- [x] I have read the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md)
- [x] I have tested the code examples for syntax errors
- [x] I have verified all links are valid
- [x] I have maintained all existing AutoClass documentation
- [x] I have followed the conversational tone guidelines
**References:**
- Follows the same pattern as PR #37261 (T5), #37585 (SigLIP), #37063 (ELECTRA)
- Addresses issue #36979
@stevhliu for review
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# What does this PR do?
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# What does this PR do?
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Fixes # (issue)
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## Who can review?
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Add beginner-friendly sentiment analysis example
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## What does this PR do?
This PR adds a beginner-friendly sentiment analysis example to help newcomers learn the transformers library. The new example provides a simpler, more educational alternative to the existing `run_glue.py` and `run_classification.py` scripts.
## Motivation and Context
The current text-classification examples (`run_glue.py`, `run_classification.py`) are comprehensive but can be overwhelming for beginners. This educational example fills the gap by:
- **Simplifying the learning curve**: Clear step-by-step workflow with emoji markers
- **Educational focus**: Extensive comments and docstrings explaining each step
- **Quick experimentation**: Options to limit dataset size for faster testing
- **Complete workflow**: From data loading to predictions in one simple script
## Changes Made
### New Files
- **`run_simple_sentiment.py`**: Educational sentiment analysis example using DistilBERT on IMDB dataset
- Command-line arguments for easy customization
- Comprehensive logging at each step
- Example predictions with confidence scores
- ~300 lines with extensive documentation
- **`test_simple_sentiment.py`**: Unit tests covering:
- Tokenizer and model loading
- Preprocessing function
- Model inference
- Metrics computation
- All 5 tests pass
### Modified Files
- **`README.md`**: Added "Simple Sentiment Analysis (Beginner-Friendly)" section at the top with:
- Quick start examples
- Usage instructions
- Rationale for the example
## Testing
```bash
# All unit tests pass
python examples/pytorch/text-classification/test_simple_sentiment.py
# Result: 5/5 tests passed
# Tested with small dataset
python run_simple_sentiment.py --max_train_samples 500 --max_eval_samples 100 --num_train_epochs 1
# Result: Training and evaluation completed successfully
```
## Target Audience
This example is designed for:
- Data scientists new to transformers
- Students learning NLP with deep learning
- Developers wanting a quick introduction before diving into production scripts
## Example Usage
```bash
# Basic usage
python run_simple_sentiment.py
# Quick demo (faster)
python run_simple_sentiment.py --max_train_samples 1000 --max_eval_samples 200
# Custom model
python run_simple_sentiment.py --model_name_or_path bert-base-uncased
```
## Checklist
- [x] Added new beginner-friendly example
- [x] Comprehensive unit tests (5/5 passing)
- [x] Updated README.md with documentation
- [x] Followed transformers coding style
- [x] Clear comments and docstrings
- [x] Tested successfully with small dataset
- [x] Educational value for newcomers
## Additional Notes
This contribution was created as part of an open-source contribution exercise, focusing on adding educational value to the transformers library for newcomers.
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examples: add multi-label text classification (BCEWithLogitsLoss, met…
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…rics, threshold tuning) + README usage
# What does this PR do?
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to it if that's the case.
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[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
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## Who can review?
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Please tag fewer than 3 people.
Models:
- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
Library:
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @zach-huggingface @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker @S1ro1
- CIs: @ydshieh
Integrations:
- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
Devices/Backends:
- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
-->
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PR_kwDOCUB6oc6tvqBi
| 41,592
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Improve AutoTokenizer error message for Voxtral models missing mistral-common
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## What does this PR do?
Adds a clearer ImportError message when users try to load a Voxtral tokenizer without having `mistral-common` installed.
## Issue
Fixes #41553
Fixes misleading `TypeError: not a string` when loading Voxtral models.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@ArthurZucker @Rocketknight1
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
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PR_kwDOCUB6oc6tujCj
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[docs] Duplicate entry
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Fixes duplicate entry in the `toctree` that may be breaking the `custom-doc-build` job for translations (https://github.com/huggingface/transformers/actions/runs/18381182160/job/52725830409)
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PR_kwDOCUB6oc6ttcUb
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Add Backbone API fine-tuning tutorial
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We have recently merged this PR to revive the backbone API using DINOv3 as opportunity to do so https://github.com/huggingface/transformers/pull/40651
So I have written documentation. I need to add resulting image before merger. For this I want to train the model a bit more since bboxes are slightly shifted and it annoyed me 😄 the model can be better.
In the meantime @stevhliu can you please review wording etc? 💗
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PR_kwDOCUB6oc6ttOyb
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Allow VLMs to have a correct `base_model`
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# What does this PR do?
As per title, being able to simply call `model.base_model` is a useful feat for models which we didn't support in VLMs
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PR_kwDOCUB6oc6ttNZ6
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Remove `use_auth_token`
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# What does this PR do?
This PR removes `use_auth_token` for v5
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PR_kwDOCUB6oc6ts8Sv
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remove ray_scope and check_quantized_param
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# What does this PR do?
This PR removes a few things that we don't need in v5.
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PR_kwDOCUB6oc6ts6Mv
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Add fast path for bidirectional mask creation to fix regression
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# What does this PR do?
This PR fixes the performance regression due to bidirectional masks.
Fixes # (issue): 41566
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@vasqu @Cyrilvallez
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PR_kwDOCUB6oc6tsZA3
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[Trainer] [Breaking change] `use_cache` default to `False`
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# What does this PR do?
This PR adds a new arg that allows users to change the value of `use_cache` in the model config. For training, usually we don't need to use a cache and this created quite a lot of issues in the past (incompatibility, regression, lower perfs). Now that v5 is coming, let's do this change.
cc @gante
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PR_kwDOCUB6oc6tr5uh
| 41,584
|
Add clear error message for missing SentencePiece model in `get_spm_processor` (fix #41553)
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Fix: clearer error when SentencePiece model file is missing for Llama tokenizer
Fix:
Improved error handling in get_spm_processor to provide a clear, actionable message when the required SentencePiece model file is missing.
Before:
The tokenizer raised an unhelpful low-level
```python
TypeError: not a string
```
from the SentencePiece library when vocab_file was None.
After:
It now raises a concise, human-readable error:
```python
ValueError: Missing SentencePiece model file: 'None'.
This tokenizer requires a .model file provided by the 'mistral-common' package.
Install it with: pip install mistral-common
```
Fixes: #41553
<img width="2000" height="1346" alt="Screenshot 2025-10-14 191309" src="https://github.com/user-attachments/assets/cb23a855-40d9-4d1b-8997-1138936c3eb9" />
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PR_kwDOCUB6oc6trp3l
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[kernels] Fix XPU layernorm kernel
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# What does this PR do?
Adds xpu layernorm kernel after pining the kernel version, and making sure the user has the latest version before registering the mapping that includes xpu device.
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PR_kwDOCUB6oc6trlxR
| 41,582
|
Update executorch.md
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# What does this PR do?
Update ExecuTorch docs, keep things simple and just redirect to Optimum repo.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@ArthurZucker @stevhliu
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PR_kwDOCUB6oc6trOWv
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Revert some breaking changes bnb
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# What does this PR do?
This PR fixes issues related to bnb during a recent refactor. I didn't push some modifications
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PR_kwDOCUB6oc6tq_K9
| 41,580
|
Refactor weight loading
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# CORE REFACTORING, loading, converting, logging
More helpful debugging report when loading weights
<img width="2614" height="538" alt="image" src="https://github.com/user-attachments/assets/02df278f-10e0-4ca1-a35c-b09ee3fc67f9" />
## Enable MoE quantization for FP8
This script does not work on main
```python
import torch
from transformers import MixtralForCausalLM, AutoTokenizer, FineGrainedFP8Config
import time
quantization_config = FineGrainedFP8Config(modules_to_not_convert=["model.layers.*.mlp.gate"])
model = MixtralForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1", quantization_config=quantization_config, tp_plan="auto")
```
## Enable TP + MoE without OOM
This script does not work on main
```python
model = MixtralForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1", tp_plan="auto")
```
## Enable `device_map="auto"` + MoE + FP8
This script does not work on main
```python
quantization_config = FineGrainedFP8Config(modules_to_not_convert=["model.layers.*.mlp.gate"])
model = MixtralForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1", quantization_config=quantization_config, device_map="auto")
```
## Refactor the way we load weights, faster, flexible and better overall
### Uses staging buffers per conversion op
- [x] 4x speedup with `device_map="auto"`
- [x] Full `MoE` quantization with FP8
TODOS:
- [x] Test with TP / EP
- [x] Add TQDM!
- [ ] Test with deepspeedd
- [ ] Test with loras and peft
- [ ] Test with vllm backend
- [ ] Test with fsdp
- [ ] Add saving
Script:
```python
import torch
from torch import nn
from transformers import MixtralForCausalLM, AutoTokenizer
import time
start = time.time()
model = MixtralForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1", device_map="auto")
end = time.time()
print("loading took ", end-start)
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
inputs = tokenizer("hey how are you?", return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=16)
print(tokenizer.batch_decode(out))
```
```bash
loading took 14.271092891693115
['<s> hey how are you?\n\nI am a 20 year old male and I have been having']
```
⬆️ is with: merge modulelist, concat gate_up
⬇️ is naive loading.
```bash
loading took 54.271092891693115
['<s> hey how are you?\n\nI am a 20 year old male and I have been having']
```
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PR_kwDOCUB6oc6tq9O7
| 41,579
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Revert "add rmsnorm kernels support for Intel XPU"
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Reverts huggingface/transformers#41563
Reverting to check what's happening
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PR_kwDOCUB6oc6tqvNM
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Fix import in quantization
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# What does this PR do?
This PR fixes a small import that was introduced in https://github.com/huggingface/transformers/issues/41445.
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PR_kwDOCUB6oc6tqYmX
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[kernels] refactor function kernel calling
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# What does this PR do?
This should simplify lazy kernel loading in Transformers.
We simply define a mapping between each kernel name and the repository it should be pulled from, then load it using the `lazy_load_kernel` function. This function adds the kernel to a global cache shared across all models.
If the kernel isn’t available, we check whether it’s installed as a module for backward compatibility; otherwise, we return `None`.
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PR_kwDOCUB6oc6tqULG
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Add missing dates to docs
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Not sure how these slipped through, this should have broken repo-consistency. I'll investigate.
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PR_kwDOCUB6oc6tpzCS
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Fixed : Bad error message for AutoTokenizer loading Voxtral #41553
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Fixes # 41553
Added a small check in both src/transformers/models/llama/tokenization_llama_fast.py and src/transformers/models/llama/tokenization_llama.py.
Who can review?
@ArthurZucker
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
|
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PR_kwDOCUB6oc6tpaCf
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Fixed : Bad error message for AutoTokenizer loading Voxtral #41553
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# What does this PR do?
Fixes # 41553
Added a small check in both src/transformers/models/llama/tokenization_llama_fast.py and src/transformers/models/llama/tokenization_llama.py.
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@ArthurZucker
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
|
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PR_kwDOCUB6oc6tpLZy
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Update issue template
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# What does this PR do?
This PR updates the issue template.
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PR_kwDOCUB6oc6to_V_
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Gemma3 fixes
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This PR fixes three things in `gemma3`:
- a multiple-device error where `torch.where` takes some of its coefficients from a tensor that is not on the right device and is a full_like, so we just replace it with the filling element
- an error in the `flash_attn_inference_equivalence` which is due to the model needing more parameters than are generated by defualt. To avoid this, we add a flag that specifies if we need to check the forward pass with training or not, and make this check default for both and left padding (cc. @vasqu )
- the test `flash_attn_from_config` was failing for the same reasons (`token_type_ids` is required as a model input when training) so I added a `.eval()` to avoid this. It does not seem the model needs to be in train mode for this test, but I can also add an option to the test to only call `.eval()` if a flag is passed
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PR_kwDOCUB6oc6to4SB
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Fix an import error with PreTrainModel
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This tiny tiny PR fixes an error introduced in #41445 which led to an import error.
For instance, `tests/models/gemma3/test_modeling_gemma3.py::Gemma3Vision2TextModelTest::test_flash_attn_2_fp32_ln` without this fix fails with
```
NameError: name 'PreTrainedModel' is not defined
```
but after this fix passes
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I_kwDOCUB6oc7RaftC
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|
Uneven GPU memory usage and CUDA OOM during multi-GPU inference with Qwen3-VL-30B-A3B-Thinking
|
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[] | 2025-10-14T10:52:25
| 2025-10-16T10:40:57
| null |
CONTRIBUTOR
| null | null | null | null |
When performing inference on a ~3-minute video using the Qwen3-VL-30B-A3B-Thinking model from transformers in 8*3090(24 GB) and setting `fps=1`, a CUDA Out-of-Memory (OOM) error occurs.
```
Traceback (most recent call last):
File "/data01/xuqile/code/Inference/transformers/infer_qwen3vl_transformers.py", line 94, in <module>
main()
File "/data01/xuqile/code/Inference/transformers/infer_qwen3vl_transformers.py", line 78, in main
generated_ids = model.generate(**inputs, max_new_tokens=args.max_tokens)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 120, in decorate_context
return func(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/transformers/generation/utils.py", line 2564, in generate
result = decoding_method(
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/transformers/generation/utils.py", line 2784, in _sample
outputs = self(**model_inputs, return_dict=True)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
return forward_call(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/accelerate/hooks.py", line 175, in new_forward
output = module._old_forward(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/transformers/utils/generic.py", line 1064, in wrapper
outputs = func(self, *args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py", line 1601, in forward
outputs = self.model(
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
return forward_call(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/transformers/utils/generic.py", line 1064, in wrapper
outputs = func(self, *args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py", line 1389, in forward
outputs = self.language_model(
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
return forward_call(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/transformers/utils/generic.py", line 1064, in wrapper
outputs = func(self, *args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py", line 962, in forward
layer_outputs = decoder_layer(
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/transformers/modeling_layers.py", line 94, in __call__
return super().__call__(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
return forward_call(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/accelerate/hooks.py", line 175, in new_forward
output = module._old_forward(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/transformers/utils/deprecation.py", line 172, in wrapped_func
return func(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py", line 391, in forward
hidden_states = self.mlp(hidden_states)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
return forward_call(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/accelerate/hooks.py", line 175, in new_forward
output = module._old_forward(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py", line 151, in forward
routed_out = self.experts(hidden_states, router_weights, router_indices)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
return forward_call(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/accelerate/hooks.py", line 175, in new_forward
output = module._old_forward(*args, **kwargs)
File "/data01/xuqile/miniforge3/envs/qwen3vl/lib/python3.10/site-packages/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py", line 120, in forward
next_states = torch.bmm((up * self.act_fn(gate)), self.down_proj)
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 5.06 GiB. GPU 0 has a total capacity of 23.68 GiB of which 4.40 GiB is free. Including non-PyTorch memory, this process has 19.27 GiB memory in use. Of the allocated memory 18.67 GiB is allocated by PyTorch, and 304.64 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
```
However, GPU monitoring shows that only one GPU has extremely high memory usage, while the others remain underutilized (less than 50% memory used).
<img width="560" height="560" alt="Image" src="https://github.com/user-attachments/assets/9c810b25-fa92-47af-a008-7f9ae79d7ac5" />
After investigation, the memory surge appears to happen inside `Qwen3VLMoeTextExperts.forward`:
<img width="640" height="302" alt="Image" src="https://github.com/user-attachments/assets/9d4cda70-4e0a-4907-8f39-368b2ae366ea" />
Is there a known workaround for this, or will future releases include optimization or DTensor support for the MoE expert modules to improve memory distribution during multi-GPU inference?
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| 3,513,372,038
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PR_kwDOCUB6oc6to1bl
| 41,569
|
Add __iter__ to DynamicCache
|
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[] | 2025-10-14T10:48:50
| 2025-10-15T13:19:38
| 2025-10-14T14:16:32
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Currently, DDP is broken when there is a `DynamicCache` because it has no `__iter___` method and so it cannot be concatenated after the distributed forward. This PR adds back and `__iter__` and adapts the way ddp data is consumed to properly initialize sliding windows.
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PR_kwDOCUB6oc6tn6pe
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Use `yaml.safe_load`
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# What does this PR do?
Use `yaml.safe_load` instead of `yaml.load` to avoid potential security issue, and to close the security report we received.
https://pyyaml.org/wiki/PyYAMLDocumentation
> Warning: It is not safe to call yaml.load with any data received from an untrusted source! yaml.load is as powerful as pickle.load and so may call any Python function. Check the yaml.safe_load function though.
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[WIP] Fully deprecate AutoGPTQ for GPT-QModel
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| 2025-10-15T12:57:21
| null |
CONTRIBUTOR
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Remove autogptq clutter and autogptq related configs that are not worth adding backward compat.
GPTQModel has a slight project name change (pypi package and import name stays the same) to GPT-QModel with `-` as we now have added `awq`/AutoAWQ into our repo and will be making pr soon to address awq loading using GPT-QModel.
`GPTQConfig` has the most important changes in this PR:
```python
# New GPTQConfig Property. Applicable for sister Peft/Optimum PRs
act_group_aware (`bool`, *optional*, defaults to `True`):
Use GAR (group aware activation order) during quantization. Has measurable positive impact on quantization
quality. Only applicable when `desc_act = False`. Will forced to be `False` when `desc_act = True`.
# Removed GPTQConfig Properties:
use_cuda_fp16
use_exllama
exllama_config
```
The 3 removed properties are all related `kernel` selection. These 3 are a hot potatoe mess and legacy from autogptq. GPT-QModel uses unified `backend` (existing) property to select kernels. There were compat codes written to `convert` these 3 properties to `backend` behind the scenes in 2024 but no longer relevant for 2025.
Note:
* Transformers/Optimum/Peft CI tests should never check for `kernel.QUANT_TYPE` (str). GPTQ-QModel will return best performing kernel for the relevant module and it may be different per module due to in/out features and other gptq/module properties in relation to device type + dtype + many factors.
* CI tests should only assert check for `kernel.QUANT_TYPE` if the test specifies a specific kernel via `backend` selection.
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I_kwDOCUB6oc7RXTab
| 41,566
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Performance regression due to Bidirectional masks
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[] | 2025-10-14T06:29:37
| 2025-10-15T13:34:41
| 2025-10-15T13:34:41
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CONTRIBUTOR
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### System Info
torch 2.10.0.dev20251008+cpu
### Who can help?
@vasqu @zucchini-nlp
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
`numactl -C 0-7 --membind 0 python test_bert.py`
```python
import torch
import time
from transformers import pipeline
model_id = "sentence-transformers/all-MiniLM-L6-v2"
sentences = ["This is an example sentence", "Each sentence is converted"]
extractor = pipeline("feature-extraction", model=model_id, dtype=torch.float16)
encoded_input = extractor.tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
# warm up
for _ in range(10):
model_output = extractor(sentences, return_tensors=True, batch_size=2)
for _ in range(10):
start = time.time()
model_output = extractor(sentences, return_tensors=True, batch_size=2)
end = time.time()
print(f"latency: {(end-start)*1000} ms")
```
### Expected behavior
The performance has 40% regression after PR [41265](https://github.com/huggingface/transformers/pull/41265)
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PR_kwDOCUB6oc6tmDH0
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🌐 [i18n-KO] Updated `perf_train_gpu_many.md`
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<!-- PR의 제목은 "🌐 [i18n-KO] Updated ko/perf_train_gpu_many.md" 으로 부탁드립니다 -->
# What does this PR do?
Updated the perf_train_special.md file in the documentation with a Korean translation.
Thank you in advance for your review.
Part of #20179
Update of #26244
## Before reviewing
- [x] Check for missing / redundant translations (번역 누락/중복 검사)
- [x] Grammar Check (맞춤법 검사)
- [x] Review or Add new terms to glossary (용어 확인 및 추가)
- [x] Check Inline TOC (e.g. `[[lowercased-header]]`)
- [x] Check live-preview for gotchas (live-preview로 정상작동 확인)
## Who can review? (Initial)
<!-- 1. 위 체크가 모두 완료된 뒤에만 KREW 팀원들에게 리뷰를 요청하는 아래 주석을 노출해주세요!-->
May you please review this PR?
@jungnerd, @luckyvickyricky, @chelsseeey, @skwh54, @amo33, @maximizemaxwell, @harheem, @4N3MONE, @yijun-lee
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review? (Final)
<!-- 2. KREW 팀원들의 리뷰가 끝난 후에 아래 주석을 노출해주세요! -->
<!-- @stevhliu May you please review this PR? -->
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PR_kwDOCUB6oc6tl8Lj
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Add aux loss for GLM-4.5V
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# What does this PR do?
Add Aux Loss for GLM-4.5V
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PR_kwDOCUB6oc6tlwgo
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add rmsnorm kernels support for Intel XPU
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@MekkCyber @drbh pls help review, thx!
We did a benchmark using following scripts on Intel XPU PVC Max 1550:
```
import gc
import torch
import time
import transformers
from transformers import AutoConfig, AutoProcessor, GenerationConfig, set_seed, StaticCache
device = torch.device("xpu")
data_type = torch.float16
model_id = "Qwen/Qwen3-4B-Instruct-2507"
batch_size = 8
max_input_length = 512
max_completion_length = 2048
set_seed(42)
prompt = "SUBREDDIT: r/relationships\n\nTITLE: I (f/22) have to figure out if I want to still know these girls or not and would hate to sound insulting\n\nPOST: Not sure if this belongs here but it's worth a try. \n\nBackstory:\nWhen I (f/22) went through my first real breakup 2 years ago because he needed space after a year of dating roand it effected me more than I thought. It was a horrible time in my life due to living with my mother and finally having the chance to cut her out of my life. I can admit because of it was an emotional wreck and this guy was stable and didn't know how to deal with me. We ended by him avoiding for a month or so after going to a festival with my friends. When I think back I wish he just ended. So after he ended it added my depression I suffered but my friends helped me through it and I got rid of everything from him along with cutting contact. \n\nNow: Its been almost 3 years now and I've gotten better after counselling and mild anti depressants. My mother has been out of my life since then so there's been alot of progress. Being stronger after learning some lessons there been more insight about that time of my life but when I see him or a picture everything comes back. The emotions and memories bring me back down. \n\nHis friends (both girls) are on my facebook because we get along well which is hard to find and I know they'll always have his back. But seeing him in a picture or talking to him at a convention having a conversation is tough. Crying confront of my current boyfriend is something I want to avoid. \n\nSo I've been thinking that I have to cut contact with these girls because it's time to move on because it's healthier. It's best to avoid him as well. But will they be insulted? Will they accept it? Is there going to be awkwardness? I'm not sure if it's the right to do and could use some outside opinions.\n\nTL;DR:"
config = AutoConfig.from_pretrained(model_id)
tokenizer = AutoProcessor.from_pretrained(model_id)
architecture = getattr(transformers, config.architectures[0])
transformers_model = architecture.from_pretrained(model_id, use_kernels=True, device_map=device).to(data_type)
generation_kwargs = {
"max_new_tokens": max_completion_length,
"do_sample": True,
"pad_token_id": tokenizer.pad_token_id,
"bos_token_id": tokenizer.bos_token_id,
"eos_token_id": tokenizer.eos_token_id,
"temperature": 1.0,
"cache_implementation": "static",
}
generation_config = GenerationConfig(**generation_kwargs)
prompts = [prompt] * batch_size
inputs = tokenizer(
text=prompts, return_tensors="pt", padding=True, padding_side="left", add_special_tokens=False
).to(device)
warmup_steps = 2
run_steps = 5
for _ in range(warmup_steps):
with torch.no_grad():
outputs = transformers_model.generate(**inputs, generation_config=generation_config, disable_compile=True)
for _ in range(run_steps):
torch.xpu.synchronize()
time_s = time.time()
with torch.no_grad():
outputs = transformers_model.generate(**inputs, generation_config=generation_config, disable_compile=True)
torch.xpu.synchronize()
time_e = time.time()
print(f"Transformers: {time_e - time_s:.4f} seconds")
del tokenizer
del transformers_model
gc.collect()
torch.xpu.empty_cache()
```
result shows the avg latency drops from 108s to 77s w/ this PR.
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I_kwDOCUB6oc7RW0uB
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NameError: name 'PreTrainedModel' is not defined
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| 2025-10-14T14:39:03
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NONE
| null | null | null | null |
### System Info
- ubuntu22.04 docker image.
- python3.10
- torch2.7.0-cu128
- transformers-5.0.0.dev0
---
I was able to load the model fine until yesterday, but suddenly it stopped working with the error below.
> <img width="1063" height="237" alt="Image" src="https://github.com/user-attachments/assets/659de62d-089c-4a98-b7a5-6c80fd6a5bdf" />
I did not change any code because I use docker container.
I installed transformers-5.0.0.dev0 using the following command:
> `pip install git+https://github.com/huggingface/transformers`
|
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PR_kwDOCUB6oc6tlVL3
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Optimize Mamba2 memory usage by replacing broadcast with einsum
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# What does this PR do?
<!--
Congratulations! You've made it this far! You're not quite done yet though.
Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.
Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.
Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->
<!-- Remove if not applicable -->
Fixes # (issue)
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
Please tag fewer than 3 people.
Models:
- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
Library:
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @zach-huggingface @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker @S1ro1
- CIs: @ydshieh
Integrations:
- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
Devices/Backends:
- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
-->
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I_kwDOCUB6oc7RWckS
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|
Trainer._algin_special_token can't figure out tokenizezr_has_new_eos correctly when model.generation_config.eos_token_id is a list
|
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[] | 2025-10-14T04:35:38
| 2025-10-14T04:42:48
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NONE
| null | null | null | null |
### System Info
Refering the code, when algin the special tokens:
```
tokenizer_has_new_eos = tokenizer.eos_token_ids != self.model.generation_config.eos_token_id
```
here if we have a list in model's generation_config, then this value is always false thus duplicate eos_token_id will be introduced in the eos_token_id in target generation_config
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
NOne
### Expected behavior
if the eos_token_id is in model's generation_config, tokenizer_has_new_eos should be True
|
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PR_kwDOCUB6oc6tk_vT
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|
Fix executorch export with dynamic shapes
|
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# What does this PR do?
This PR addd shape compatibility check for attention mask to ensure torch.export can reason about the logic without failing _when exporting with dynamic shapes_.
It additionally simplified the sdpa forward function for export only usage.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@CyrilVallez @jackzhxng @guangy10
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PR_kwDOCUB6oc6tk1yV
| 41,558
|
fix some case failures lead by "`torch.compile` recompiled part of th…
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…e forward pass" in xpu
# What does this PR do?
fix test failure. including
cases include
tests/models/chameleon/test_modeling_chameleon.py::ChameleonVision2SeqModelTest::test_generate_compile_model_forward_fullgraph
tests/models/dia/test_modeling_dia.py::DiaModelTest::test_generate_compile_model_forward_fullgraph
tests/models/emu3/test_modeling_emu3.py::Emu3Text2TextModelTest::test_generate_compile_model_forward_fullgraph
tests/models/emu3/test_modeling_emu3.py::Emu3Vision2TextModelTest::test_generate_compile_model_forward_fullgraph
tests/models/glm4v/test_modeling_glm4v.py::Glm4vModelTest::test_generate_compile_model_forward_fullgraph
tests/models/got_ocr2/test_modeling_got_ocr2.py::GotOcr2ModelTest::test_generate_compile_model_forward_fullgraph
tests/models/gpt2/test_modeling_gpt2.py::GPT2ModelTest::test_generate_compile_model_forward_fullgraph
tests/models/internvl/test_modeling_internvl.py::InternVLModelTest::test_generate_compile_model_forward_fullgraph
tests/models/llava/test_modeling_llava.py::LlavaForConditionalGenerationModelTest::test_generate_compile_model_forward_fullgraph
tests/models/mistral3/test_modeling_mistral3.py::Mistral3ModelTest::test_generate_compile_model_forward_fullgraph
tests/models/ovis2/test_modeling_ovis2.py::Ovis2ModelTest::test_generate_compile_model_forward_fullgraph
tests/models/perception_lm/test_modeling_perception_lm.py::PerceptionLMForConditionalGenerationModelTest::test_generate_compile_model_forward_fullgraph
tests/models/qwen2_5_vl/test_modeling_qwen2_5_vl.py::Qwen2_5_VLModelTest::test_generate_compile_model_forward_fullgraph
tests/models/vipllava/test_modeling_vipllava.py::VipLlavaForConditionalGenerationModelTest::test_generate_compile_model_forward_fullgraph
tests/models/whisper/test_modeling_whisper.py::WhisperStandaloneDecoderModelTest::test_generate_compile_model_forward_fullgraph
@IlyasMoutawwakil
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PR_kwDOCUB6oc6tksR0
| 41,557
|
Update modeling_llama4.py
|
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Something is off. The code shows Llama4TextL2Norm function name, but it computes RMS Norm. Which one is the correct one for LLAMA4? RMS or L2? If so, the function should be renamed (if RMS) or modified (if L2) accordingly.
# What does this PR do?
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Fixes # (issue)
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PR_kwDOCUB6oc6tkrXP
| 41,556
|
fix check inputs for text2text pipeline
|
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The pipeline should check generation_config before checking inputs.
```python
import torch
from transformers import pipeline, AutoTokenizer
model_id = "sshleifer/distilbart-cnn-12-6"
tokenizer = AutoTokenizer.from_pretrained(model_id)
generator = pipeline(
"summarization",
model=model_id,
tokenizer=tokenizer,
dtype=torch.float16,
)
generation_config = generator.model.generation_config
generation_config.max_new_tokens = 4
generation_config.min_new_tokens = 4
input_sentence = "This is an example inputs for summarization"
output = generator(
input_sentence, generation_config=generation_config
)
```
output:
```
Traceback (most recent call last):
File "/home/jiqing/HuggingFace/tests/workloads/test_summa.py", line 16, in <module>
output = generator(
^^^^^^^^^^
File "/home/jiqing/transformers/src/transformers/pipelines/text2text_generation.py", line 285, in __call__
return super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jiqing/transformers/src/transformers/pipelines/text2text_generation.py", line 182, in __call__
result = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jiqing/transformers/src/transformers/pipelines/base.py", line 1289, in __call__
return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jiqing/transformers/src/transformers/pipelines/base.py", line 1296, in run_single
model_outputs = self.forward(model_inputs, **forward_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jiqing/transformers/src/transformers/pipelines/base.py", line 1200, in forward
model_outputs = self._forward(model_inputs, **forward_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jiqing/transformers/src/transformers/pipelines/text2text_generation.py", line 198, in _forward
self.check_inputs(
File "/home/jiqing/transformers/src/transformers/pipelines/text2text_generation.py", line 291, in check_inputs
if max_new_tokens < min_length:
^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: '<' not supported between instances of 'NoneType' and 'int'
```
This PR can fix it by getting the correct max_new_tokens in generation config.
|
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Ring attn
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# What does this PR do?
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Fixes # (issue)
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- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
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[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
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- peft: @BenjaminBossan @githubnemo
Devices/Backends:
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- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
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I_kwDOCUB6oc7RUUZ4
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model.from_pretrained( . . . ) not loading needed weights/parameters
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[] | 2025-10-13T23:20:20
| 2025-10-22T14:12:06
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NONE
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I am performing quantization of a PatchTSTForPrediction model and attempting to load a saved quantized model for testing. Model is saved using `model.save_pretrained( . . . )`. Testing proceeds perfectly once performed immediately after QAT (Hugging face trainer's handles loading at the end of training); however, when attempting to load a saved quantized (trained) model, the error below occurs. I perform all the pre-quantization preparation so that the model contains all the necessary parameters (untrained) and then try to load the saved checkpoint. How can I force `from_pretrained( . . . )` to load ALL required weights?
`Some weights of the model checkpoint at ./checkpoints/ . . . were not used when initializing PatchTSTForPrediction: ['head.projection.calib_counter', 'head.projection.num_module_called', 'head.projection.obsrv_clipval', 'head.projection.obsrv_clipvaln', 'head.projection.obsrv_w_clipval', 'head.projection.quantize_feature.clip_val', 'head.projection.quantize_feature.clip_valn', 'head.projection.quantize_weight.clip_val', 'model.encoder.layers.0.ff.0.calib_counter', 'model.encoder.layers.0.ff.0.num_module_called', 'model.encoder.layers.0.ff.0.obsrv_clipval', 'model.encoder.layers.0.ff.0.obsrv_clipvaln', 'model.encoder.layers.0.ff.0.obsrv_w_clipval', 'model.encoder.layers.0.ff.0.quantize_feature.clip_val', 'model.encoder.layers.0.ff.0.quantize_feature.clip_valn', 'model.encoder.layers.0.ff.0.quantize_weight.clip_val', 'model.encoder.layers.0.ff.3.calib_counter', 'model.encoder.layers.0.ff.3.num_module_called', 'model.encoder.layers.0.ff.3.obsrv_clipval', 'model.encoder.layers.0.ff.3.obsrv_clipvaln', 'model.encoder.layers.0.ff.3.obsrv_w_clipval', 'model.encoder.layers.0.ff.3.quantize_feature.clip_val', 'model.encoder.layers.0.ff.3.quantize_feature.clip_valn', 'model.encoder.layers.0.ff.3.quantize_weight.clip_val', 'model.encoder.layers.0.self_attn.QBmm52.num_module_called', 'model.encoder.layers.0.self_attn.QBmm52.quantize_m1.clip_val', 'model.encoder.layers.0.self_attn.QBmm52.quantize_m1.clip_valn', 'model.encoder.layers.0.self_attn.QBmm52.quantize_m2.clip_val', 'model.encoder.layers.0.self_attn.QBmm52.quantize_m2.clip_valn', 'model.encoder.layers.0.self_attn.QBmm62.num_module_called', 'model.encoder.layers.0.self_attn.QBmm62.quantize_m1.clip_val', 'model.encoder.layers.0.self_attn.QBmm62.quantize_m1.clip_valn', 'model.encoder.layers.0.self_attn.QBmm62.quantize_m2.clip_val', 'model.encoder.layers.0.self_attn.QBmm62.quantize_m2.clip_valn', 'model.encoder.layers.0.self_attn.k_proj.calib_counter', 'model.encoder.layers.0.self_attn.k_proj.num_module_called', 'model.encoder.layers.0.self_attn.k_proj.obsrv_clipval', 'model.encoder.layers.0.self_attn.k_proj.obsrv_clipvaln', 'model.encoder.layers.0.self_attn.k_proj.obsrv_w_clipval', 'model.encoder.layers.0.self_attn.k_proj.quantize_feature.clip_val', 'model.encoder.layers.0.self_attn.k_proj.quantize_feature.clip_valn', 'model.encoder.layers.0.self_attn.k_proj.quantize_weight.clip_val', 'model.encoder.layers.0.self_attn.out_proj.calib_counter', . . .]
This IS expected if you are initializing PatchTSTForPrediction from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
This IS NOT expected if you are initializing PatchTSTForPrediction from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).`
NB: QAT is simulated. Additional parameters are added to the model after qmodel_prep is called and QAT proceeds as normal. I am using IBM's fms-model-optimizer.
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I_kwDOCUB6oc7RUAJ2
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Bad error message for AutoTokenizer loading Voxtral
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[] | 2025-10-13T22:37:26
| 2025-10-23T18:25:52
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CONTRIBUTOR
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### System Info
Getting the following unhelpful error when trying to load Voxtral's tokenizer with `AutoTokenizer` without `mistral-common` installed.
```
../../.conda/envs/et_new/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py:1144: in from_pretrained
return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
../../.conda/envs/et_new/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2070: in from_pretrained
return cls._from_pretrained(
../../.conda/envs/et_new/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2108: in _from_pretrained
slow_tokenizer = (cls.slow_tokenizer_class)._from_pretrained(
../../.conda/envs/et_new/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2316: in _from_pretrained
tokenizer = cls(*init_inputs, **init_kwargs)
../../.conda/envs/et_new/lib/python3.10/site-packages/transformers/models/llama/tokenization_llama.py:171: in __init__
self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False))
../../.conda/envs/et_new/lib/python3.10/site-packages/transformers/models/llama/tokenization_llama.py:198: in get_spm_processor
tokenizer.Load(self.vocab_file)
../../.conda/envs/et_new/lib/python3.10/site-packages/sentencepiece/__init__.py:961: in Load
return self.LoadFromFile(model_file)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <sentencepiece.SentencePieceProcessor; proxy of <Swig Object of type 'sentencepiece::SentencePieceProcessor *' at 0x7f5e7e25f780> >, arg = None
def LoadFromFile(self, arg):
> return _sentencepiece.SentencePieceProcessor_LoadFromFile(self, arg)
E TypeError: not a string
../../.conda/envs/et_new/lib/python3.10/site-packages/sentencepiece/__init__.py:316: TypeError
```
### Who can help?
@ArthurZucker
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
1. `pip install transformers`
2. `from transformers import AutoTokenizer; AutoTokenizer.from_pretrained("mistralai/Voxtral-Mini-3B-2507")`
### Expected behavior
A clearer error message, suggesting to `pip install mistral-common`
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PR_kwDOCUB6oc6tiPPv
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[Bugfix] Fix quantizer import
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[] | 2025-10-13T20:29:00
| 2025-10-14T14:39:56
| 2025-10-14T14:36:29
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## Purpose ##
* Fix bad import in `src/transformers/quantizers/base.py`
## Changes ##
* Import `PreTrainedModel` dynamically, rather than only importing when type checking
* The class is needed for an `isinstance` check
## Testing ##
* Code which calls `_assign_original_dtype` which was previously failing is now passing
* Can provide a minimum example if required
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PR_kwDOCUB6oc6tg-iw
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|
upgrade xpu docker file to torch 2.8
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| 2025-10-21T08:03:04
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@ydshieh , pls help review, thx very much.
pass rate is 92.4% (25446 pass over 27546 total cases), we are working on the issues found during this release cycle and PRs are merged or under development. Thx for great support, XPU pass rate on transformers is improving step by step.
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PR_kwDOCUB6oc6tgM4x
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Fix : Add VideoInpaintPipeline for temporally-consistent diffusion-based video inpainting
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# What does this PR do?
Adds a VideoInpaintPipeline prototype (under examples/video_inpaint) that demonstrates a Diffusers-style from_pretrained factory and a temporally-coherent inpainting workflow with optional optical-flow warping and latent-reuse, using a lightweight mock backend so tests run without large model downloads.
Includes a fast unit test, README, and PR description documenting the API, acceptance criteria, and optional heavy dependencies (opencv/diffusers/RAFT/GMFlow) so reviewers can evaluate the design and merge a production implementation in follow-ups.
<!--
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<!-- Remove if not applicable -->
Fixes #41543
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
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- graph models: @clefourrier
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- ray/raytune: @richardliaw, @amogkam
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- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
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- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
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PR_kwDOCUB6oc6tgHmn
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🚨 Refactor DETR to updated standards
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# What does this PR do?
This PR aims at refactoring DETR as part of an effort to standardize vision models in the library, in the same vein as https://github.com/huggingface/transformers/pull/41546.
Expect to see much more PRs like this for vision models as we approach v5!
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PR_kwDOCUB6oc6tfnuW
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[device_map] Accelerate loading by computing device_map much faster
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# What does this PR do?
If a model has a lot of different parameters (which is the case for models with a lot of experts, such as Qwen3Next), the computation of the device_map itself (yes, just the mapping of modules to devices) is EXTREMELY slow when using `accelerate`.
This is because they traverse the model graph in a very non-optimized manner, resulting in quadratic complexity in the number of parameters and modules, when we could do it in linear time instead. For example, `Qwen/Qwen3-Next-80B-A3B-Instruct` has a total of `173529` parameters and intermediate modules, which is a a lot. So doing the following:
```python
from transformers import AutoConfig, Qwen3NextForCausalLM
from transformers.modeling_utils import _get_device_map
import torch
import time
model_name = "Qwen/Qwen3-Next-80B-A3B-Instruct"
config = AutoConfig.from_pretrained(model_name)
with torch.device("meta"):
model = Qwen3NextForCausalLM(config)
t0 = time.time()
device_map = _get_device_map(model, device_map="auto", dtype=torch.bfloat16, max_memory=None, hf_quantizer=None, keep_in_fp32_regex=None)
dt = time.time() - t0
print(f"Time needed: {dt:.2f} s")
>>> Time needed: 60.02 s
```
takes un UNBEARABLE `60.02` s on my Macbook Pro M3. And this is only for the device_map computation, the loading has not even started yet...
This PR fixes the issue by rewriting the needed functions from `accelerate` with much better complexity and operations directly in Transformers, leading to linear time and the following:
```python
>>> Time needed: 1.33 s
```
So effectively going 60x faster from 1min to 1s
The device_map created are the exact same.
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PR_kwDOCUB6oc6tfdtI
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Fix time issue
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# What does this PR do?
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Fixes # (issue)
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
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[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
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- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
Library:
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @zach-huggingface @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker @S1ro1
- CIs: @ydshieh
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- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
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- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
Documentation: @stevhliu
Research projects are not maintained and should be taken as is.
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PR_kwDOCUB6oc6tfGBC
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Modernize CLIP modeling code
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# What does this PR do?
As per title. Adds the nice recent modeling utils to CLIP. Was motivated to be able to use it in the looong-standing #33962 .
|
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PR_kwDOCUB6oc6td_l5
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TDT for HF
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[] | 2025-10-13T13:46:50
| 2025-10-23T22:10:35
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NONE
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# What does this PR do?
Parakeet TDT model integration.
<!--
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Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.
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Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
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<!-- Remove if not applicable -->
Fixes # (issue)
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
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to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
Please tag fewer than 3 people.
Models:
- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier
Library:
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
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- trainer: @zach-huggingface @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
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Integrations:
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- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad
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-->
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| 3,510,051,922
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I_kwDOCUB6oc7RNyRS
| 41,544
|
ValueError: You current version of `autoawq` does not support module quantization skipping, please upgrade `autoawq` package to at least 0.1.8.
|
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[] | 2025-10-13T13:20:14
| 2025-10-15T12:34:02
| null |
NONE
| null | null | null | null |
when I run,
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-VL-72B-Instruct-AWQ",
torch_dtype=torch.float16,
device_map="auto",
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-72B-Instruct-AWQ")
I got this error, my package version:
torch==2.5.1
transformers==4.50.0
| null |
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I_kwDOCUB6oc7RNUlO
| 41,543
|
Add VideoInpaintPipeline for temporally-consistent diffusion-based video inpainting
|
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[] | 2025-10-13T12:45:31
| 2025-10-17T06:37:43
| 2025-10-17T06:37:43
|
CONTRIBUTOR
| null | null | null | null |
### Feature request
Introduce a new pipeline to extend existing image inpainting capabilities (StableDiffusionInpaintPipeline) to videos. The goal is to provide a native, GPU-optimized API within Diffusers that performs temporally coherent video inpainting instead of independent per-frame processing.
### Motivation
Current video inpainting approaches in the community simply loop over frames and call the image inpainting pipeline repeatedly.
This leads to:
- Temporal flicker and inconsistent textures between frames.
- Poor GPU utilization and high memory overhead.
- Lack of tools to maintain motion coherence or reuse diffusion latents across time.
A built-in VideoInpaintPipeline would make it possible to remove objects, restore scenes, or creatively edit videos using diffusion models while keeping motion and lighting consistent across frames.
### Your contribution
I plan to:
- Implement VideoInpaintPipeline as a subclass of DiffusionPipeline, leveraging StableDiffusionInpaintPipeline under the hood.
- Add temporal consistency mechanisms, such as latent reuse between frames and optional optical-flow–guided warping (RAFT / GMFlow).
- Optimize performance through batched FP16 inference, scheduler noise reuse, and optional torch.compile acceleration.
Provide a clean user API compatible with existing pipelines:
```
from diffusers import VideoInpaintPipeline
pipe = VideoInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting",
use_optical_flow=True,
compile=True,
)
result = pipe(
video_path="input.mp4",
mask_path="mask.mp4",
prompt="replace background with a snowy mountain",
num_inference_steps=10,
)
result.video.save("output.mp4")
```
- Contribute documentation and tests demonstrating temporal coherence, performance benchmarks, and example notebooks for real-world use.
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