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PR_kwDOCUB6oc6srINg
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Fix/perceptionlm auto alias
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### What does this PR do?
Removes references to a non-existent base model from the Auto mappings so enumeration and lazy imports work as expected.
- Remove `perception_encoder` entries from Auto mappings
- `src/transformers/models/auto/modeling_auto.py`
- `src/transformers/models/auto/configuration_auto.py`
- Keep existing `perception_lm` entries intact
- Regenerate modeling files (no runtime behavior change)
Fixes: [#41387](https://github.com/huggingface/transformers/issues/41387)
### Motivation
`MODEL_MAPPING.items()` failed because Auto mappings referenced `perception_encoder`, which doesn’t exist. Per maintainer guidance, we should remove non-existent models from auto-map rather than adding aliases.
### Dependencies
None.
### Before submitting
- [x] Minimal change; no behavior changes.
- [x] Linked the issue.
- [x] No docs changes required.
- [x] Local check with `PYTHONPATH=src`:
```python
from transformers.models.auto.modeling_auto import MODEL_MAPPING
list(MODEL_MAPPING.items()) # succeeds
```
### Who can review?
- Model loading / auto mappings: @CyrilVallez
- Multimodal: @zucchini-nlp
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PR_kwDOCUB6oc6srELY
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Enhance the handling of Union types in HfArgumentParser
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# What does this PR do?
This PR enhance the handling of Union types containing `str` by two rules:
1. If `str` in `Union` and `Union` has more than one other types pass `str` to `parser.add_argument`. The caller is responsible to convert the string argument to a more proper type.
2. Otherwise, `Union` must be `X | str` and pass `X` to `parser.add_argument`
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PR_kwDOCUB6oc6sqfC1
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Fix generate outputs and simplify cache tests
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# What does this PR do?
Improve the cache tests.`test_past_key_values_format` should really rely on `_check_past_key_values_for_generate` as they do the same, and we otherwise have to overwrite both for models with non-standard cache format, which is really annoying!
This made me realize that `generate` does not correctly return the cache, if it's not called `past_key_values`. This fixes it as well.
cc @gante
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PR_kwDOCUB6oc6sqZA9
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Patch MistralCommonTokenizer
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# What does this PR do?
This PR patches the `MistralCommonTokenizer`:
1. [BUG FIX] spm now is correctly supported as previous usage would result in an error for `_piece_to_id` and `_is_control_token`.
2. [BUG FIX] previous implementation of `_piece_to_id` was incorrect for Tekkenizer.
a) the special tokens were not supported
b) the normal tokens were not shifted by adding the number of special tokens
4. [MAYBE BUG FIX] Changed `get_vocab` to a function that should better mimic what happens in `Transformers`: now the mapping is based on the real ids but some ids are missing due to conversion loss of some tokens in Tekken.
5. [FEATURE] `add_generation_prompt` has been added. This is to match signature of Transformers. In practice this value is ignored except if:
a) `continue_final_message` and `add_generation_prompt` are `True` an error is raised.
b) if `add_generation_prompt` is `True` and the last message is `assistant` then an error is raised as the user should have passed `continue_final_message`.
6. ~[FEATURE] Now the tokenizer is set to fast because:~
a) ~it is true~(Edit: not so sure about that after discussion)
b) it removes annoying message when initializing the tokenizer.
Edit 2: this has been reverted as it doesn't bring value and is misleading.
7. [OPTIMIZATION] now image tensors are converted without slowness warnings from torch.
8. [DOCS] updated some docs to remove unused args.
Also added minimal tests to ensure SPM also works in the future.
## 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.
- [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?
@patrickvonplaten @ArthurZucker @itazap
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PR_kwDOCUB6oc6sqS2u
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Update philosophy
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# What does this PR do?
Update our philosophy page to reflect developments in the recent [blog post](https://huggingface.co/spaces/transformers-community/Transformers-tenets).
Explicitly mention tenets, include modular transformers, and link to the blog.
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PR_kwDOCUB6oc6sp-37
| 41,437
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perception_lm: remove perception_encoder from auto-maps
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### What does this PR do?
Removes references to a non-existent base model from the Auto mappings so enumeration and lazy imports work as expected.
- Remove `perception_encoder` entries from Auto mappings
- `src/transformers/models/auto/modeling_auto.py`
- `src/transformers/models/auto/configuration_auto.py`
- Keep existing `perception_lm` entries intact
- Regenerate modeling files (no runtime behavior change)
Fixes: [#41387](https://github.com/huggingface/transformers/issues/41387)
### Motivation
`MODEL_MAPPING.items()` failed because Auto mappings referenced `perception_encoder`, which doesn’t exist. Per maintainer guidance, we should remove non-existent models from auto-map rather than adding aliases.
### Dependencies
None.
### Before submitting
- [x] Minimal change; no behavior changes.
- [x] Linked the issue.
- [x] No docs changes required.
- [x] Local check with `PYTHONPATH=src`:
```python
from transformers.models.auto.modeling_auto import MODEL_MAPPING
list(MODEL_MAPPING.items()) # succeeds
```
### Who can review?
- Model loading / auto mappings: @CyrilVallez
- Multimodal: @zucchini-nlp
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PR_kwDOCUB6oc6sphYF
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v5 dev version
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PR_kwDOCUB6oc6spg1E
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|
Allow passing `tp_plan` in `from_pretrained` directly
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# What does this PR do?
As per the title. Also fixes the tp_plan internal for non-based models.
I checked with the following, and it works nicely:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from torch.distributed.tensor.debug import visualize_sharding
from torch.distributed.tensor import DTensor
import os
rank = int(os.environ["RANK"])
model_id = "meta-llama/Llama-3.1-8B-Instruct"
# only shard attentions, but not mlps
tp_plan = {
"model.layers.*.self_attn.q_proj": "colwise",
"model.layers.*.self_attn.k_proj": "colwise",
"model.layers.*.self_attn.v_proj": "colwise",
"model.layers.*.self_attn.o_proj": "rowwise",
}
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
dtype=torch.bfloat16,
tp_plan=tp_plan,
)
inputs = tokenizer(["How are you?"], return_tensors="pt").to(rank)
out = model.generate(**inputs, max_new_tokens=10, do_sample=False)
text = tokenizer.batch_decode(out[:,inputs.input_ids.shape[-1]:])
if rank == 0:
print(text)
print(model.tp_plan)
print("SHARDING OF Q_PROJ")
visualize_sharding(model.model.layers[0].self_attn.q_proj.weight)
print("SHARDING OF UP_PROJ")
if isinstance(model.model.layers[0].mlp.up_proj.weight, DTensor):
visualize_sharding(model.model.layers[0].mlp.up_proj.weight)
else:
print("NOT SHARDED")
torch.distributed.destroy_process_group()
```
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PR_kwDOCUB6oc6somGb
| 41,434
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Fix: Add Perceiver model registry test and correct auto mappings
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# Summary
This PR adds testing and registry validation for the **Perceiver** model within the Hugging Face Transformers codebase.
## Changes Made
- Tested a standalone `test_registry.py` to validate Perceiver model registration and tokenizer mapping.
- Corrected auto model and tokenizer mappings to ensure `perceiver` is properly recognized in:
- `modeling_auto.py`
- `tokenization_auto.py`
- Verified successful forward pass for `PerceiverModel` with output shape `torch.Size([1, 256, 1280])`.
- Fixed missing config attributes (e.g., `input_channels`) to enable correct model instantiation.
- Confirmed tokenizer functionality via `PerceiverTokenizer`.
## Verification
<img width="1311" height="350" alt="Screenshot 2025-10-08 122041" src="https://github.com/user-attachments/assets/ff1a512e-fdf6-4fa3-beae-ef1e704afb79" />
<img width="1311" height="248" alt="Screenshot 2025-10-08 121126" src="https://github.com/user-attachments/assets/38ba7461-82b0-4be8-8403-02d3c64c19aa" />
<img width="1316" height="228" alt="Screenshot 2025-10-08 122210" src="https://github.com/user-attachments/assets/bc0a6575-3ce3-4247-9119-10375adcef48" />
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PR_kwDOCUB6oc6sn4s4
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Fix: remove invalid PerceptionEncoder and ParakeetCTCTokenizer registry entries (#41387)
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This PR fixes issue #41387 by commenting out invalid auto registry entries:
- Removed non-existent `PerceptionEncoder` mapping in `modeling_auto.py`
- Removed non-existent `ParakeetCTCTokenizer` mapping in `tokenization_auto.py`
Local tests:
<img width="1381" height="334" alt="image" src="https://github.com/user-attachments/assets/5947e69e-9f84-41c2-bd7c-e40dddc21b8a" />
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PR_kwDOCUB6oc6sm2W8
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[WIP] Refactor check_auto_docstring using AST
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# What does this PR do?
Use AST instead of parsing the raw code to check for missing args in docstrings of class/functions using auto_docstring
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I_kwDOCUB6oc7QNfGt
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gradient scaling occurs even though total gradient remains < max_grad_norm in trainer.py
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[] | 2025-10-07T22:13:08
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Even though gradients remain < max_grad_norm throughout training, the gradient still goes through a scaling process. For instance, I set max_grad_norm = 1, and grad_norm consistently remains <= 0.33. Because the trainer takes you through the grad clip process if max_grad_norm > 0 or not None, this operation always gets executed within torch's clip function: `clip_coef = max_norm / (total_norm + 1e-6)`. Is there a way to prevent this? Thanks.
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PR_kwDOCUB6oc6sk61a
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Fix Marian ONNX export with incorrect output (resolves #40122)
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## Description
This PR fixes the ONNX export issues for Marian translation models described in issue #40122. The original problem was that ONNX exported Marian models produced incorrect translation outputs compared to their PyTorch counterparts.
## Problem Summary
The issue manifested when users exported OPUS (Marian) models to ONNX format:
- **Wrong ONNX outputs**: Translations were incomplete or contained incorrect tokens
- **Missing attention masks**: Decoder wasn't properly handling attention masks during export
- **Token positioning errors**: Incorrect handling of token positions during autoregressive generation
- **Generation logic bugs**: The inference code had issues in the token generation loop
### Example of the Issue
**Input**: "Using handheld GPS devices and programs like Google Earth, members of the Trio Tribe, who live in the rainforests of southern Suriname, map out their ancestral lands to help strengthen their territorial claims."
**Expected Output**: باستخدام أجهزة GPS المحمولة وبرامج مثل Google Earth ، يقوم أعضاء Trio Tribe ، الذين يعيشون في الغابات المطيرة في جنوب سورينام ، برسم خرائط لأراضي أجدادهم للمساعدة في تعزيز مطالبهم الإقليمية.
**Broken ONNX Output**: باستخدام أجهزة GPS المحمولة وبرامج مثل جوجل أعضاء Tri، الذين يعيشون في الغابات جنوب سورينام رسم أراضي الفوركس للمساعدة تعزيز الإقليمية..
## Solution
This PR provides a comprehensive fix through:
### 1. Enhanced Decoder Wrapper (`MarianDecoderONNX`)
- Proper handling of decoder attention masks during ONNX export
- Correct application of causal masking for autoregressive generation
- Maintains compatibility with the original PyTorch model behavior
### 2. Fixed ONNX Export Functions
- `export_marian_encoder_to_onnx()`: Proper encoder export with correct input handling
- `export_marian_decoder_to_onnx()`: Enhanced decoder export with attention mask fixes
- Proper dynamic axes configuration for flexible input sizes
### 3. Corrected Inference Logic
- Fixed token generation loop that matches PyTorch behavior
- Proper initialization of decoder inputs with BOS tokens
- Correct attention mask creation during sequential generation
- Fixed token position indexing in the generation loop
### 4. Comprehensive Testing
- Unit tests for encoder/decoder wrappers
- Integration tests comparing PyTorch vs ONNX outputs
- End-to-end translation validation
- Edge case testing for various input scenarios
## Files Changed
- **`examples/pytorch/seq2seq/marian_onnx_export.py`**: Main utility script with fixed export functions
- **`tests/models/marian/test_marian_onnx_export.py`**: Comprehensive test suite
- **`examples/pytorch/seq2seq/README_marian_onnx.md`**: Detailed documentation and usage guide
## Key Technical Fixes
### Attention Mask Handling
```python
# Apply causal masking to decoder attention mask
batch_size, seq_len = input_ids.shape
causal_mask = torch.tril(torch.ones(seq_len, seq_len, device=input_ids.device, dtype=torch.bool))
expanded_causal_mask = causal_mask.unsqueeze(0).expand(batch_size, -1, -1)
decoder_attention_mask = decoder_attention_mask.unsqueeze(1) & expanded_causal_mask
```
### Fixed Generation Loop
```python
for step in range(1, max_length):
# Create current decoder attention mask (attend only to generated tokens so far)
current_decoder_mask = np.zeros((batch_size, max_length), dtype=np.int64)
current_decoder_mask[:, :step] = 1
# Get next token logits at the correct position
next_token_logits = logits[0, step - 1, :]
next_token_id = np.argmax(next_token_logits)
```
## Supported Models
Tested with various OPUS models:
- `Helsinki-NLP/opus-mt-en-ar` (English to Arabic)
- `Helsinki-NLP/opus-mt-en-de` (English to German)
- `Helsinki-NLP/opus-mt-en-fr` (English to French)
- `Helsinki-NLP/opus-mt-en-es` (English to Spanish)
## Breaking Changes
None. This is a pure fix that maintains backward compatibility while correcting the ONNX export behavior.
## Usage Example
```python
from transformers import MarianMTModel, MarianTokenizer
from examples.pytorch.seq2seq.marian_onnx_export import export_marian_decoder_to_onnx
# Load model
model = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-ar")
tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-ar")
# Export with fix
export_marian_decoder_to_onnx(model, tokenizer, "decoder_fixed.onnx")
```
## Closes
Closes #40122
## Checklist
- [x] I have tested this change on the original problematic example
- [ ] I have added comprehensive tests
- [ ] I have added documentation
- [x] The fix maintains backward compatibility
- [x] ONNX outputs now match PyTorch outputs
- [x] All existing tests still pass
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PR_kwDOCUB6oc6sk3fr
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enhance patched_tearDown to support python 3.11+
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@ydshieh , I tried your `PATCH_TESTING_METHODS_TO_COLLECT_OUTPUTS=yes`, it's cool. And my env is Python 3.12, so the `_Outcome.errors` has changed to `_Outcome.result.errors`, so I do a minor enhancement to make it work in my env. @ydshieh Pls help review, thx very much.
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PR_kwDOCUB6oc6skm08
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enable some falcon-mamba uts on xpu
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enable some falcon-mamba uts on xpu.
@ydshieh @SunMarc pls help review, thx very much
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PR_kwDOCUB6oc6si9r6
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[`FA`] Cleanup loading logic
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We currently ignore anything passed through the interface and forced correct loading at attn setting time. This changes it to be more flexible where power users may customly switch it up by just setting it in the config.
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PR_kwDOCUB6oc6simON
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fix gemma3n case failure
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# Problem
`pytest -rA tests/models/gemma3n/test_modeling_gemma3n.py::Gemma3nIntegrationTest::test_generation_beyond_sliding_window_with_generation_config` generate different output even `do_sample=False` in `generation_config = GenerationConfig(max_new_tokens=20, do_sample=False)`, which makes UT fails
# Root cause
custom generation_config will be overode by model generation_config even it explicitly set, when the set value equals global default.
# Fix
Add a condition to make the overriding only happens when the custom_generation_config doesn't explicitly set the field.
@SunMarc , pls help review, thx very much.
|
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PR_kwDOCUB6oc6sifK3
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Remove deprecation warning
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# What does this PR do?
As per the title
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PR_kwDOCUB6oc6siADe
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Create pull_request_template.md
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# What does this PR do?
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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).
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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?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
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<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
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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: @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
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- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo
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- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
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-->
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PR_kwDOCUB6oc6shziC
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[`JetMoe`] Fix KV head repetition and padding free
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During the vllm refactor 2 small issues have been introduced
- Jetmoe doesn't repeat the kv heads with `repeat_interleave` but uses `repeat`, i.e. different patterns
- Position ids wasn't passed down leading to padding free not working
Should hopefully align the tests again in #41377 (at least checked locally on our dgx machines that the outputs are matching again) cc @ydshieh
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PR_kwDOCUB6oc6shyd4
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Fix tests fsdp
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# What does this PR do?
This PR fixes all fsdp tests as there were some regressions:
- fsdp typing changed so hf parser had issues
- `fused_adamw_torch` (new default for users with torch >=2.8) + fp16 + FSDP don't work cc @winglian @qgallouedec did you face this issue ?
- Fix cmd by using `fsdp_config`, this was broken for way too long
## Run tests with 2 gpus
`RUN_SLOW=True pytest tests/fsdp/test_fsdp.py`
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PR_kwDOCUB6oc6shvm_
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Restore cuda graphs to continuous batching
|
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This PR restores cuda graphs in continuous batching. The main changes associated with this are:
1. the logic of how to generate tokens have been moved to the CB processor, which also handles the cuda graphs
2. the generation step automatically slices the tensors to remove all padding unless cuda graphs are activated
3. cuda graphs are captured on padded shapes, which is 25%, 50%, 75% or 100% of the queries axis and 1/8, ... 8/8 of the keys values axis, to strike a balance between the amount of padding and the quantity of cuda graphs
Documentation is kind of lacking but will be added in next commits, I am opening the PR so @ArthurZucker can test stuff out
- [x] Add more documentation
- [x] Test it out and add performance numbers with / without on AMD / Nvidia with the three main attn implementations
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PR_kwDOCUB6oc6shu1i
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[Qwen3VLMoe] Fixed: Expected self.dtype to be equal to src.dtype - routing_weights casting
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CONTRIBUTOR
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## What does this PR do?
Fixed: Expected self.dtype to be equal to src.dtype - routing_weights casting
## Related issue
Fixes [#41418](https://github.com/huggingface/transformers/issues/41418)
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PR_kwDOCUB6oc6shlOf
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First QAT for Finegrained FP8
|
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# What does this PR do?
Adds QAT support for finegrained FP8, simply calling `prepare_model` should prepare the model for qat:
```
quantization_config = FineGrainedFP8Config(
weight_block_size=(128, 128),
)
model = AutoModelForMaskedLM.from_pretrained(model_name, device_map="auto", dtype=torch.bfloat16)
quantizer = FineGrainedFP8HfQuantizer(quantization_config)
model = quantizer.prepare_model(model)
```
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Qwen3 VL Moe: Expected self.dtype to be equal to src.dtype
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CONTRIBUTOR
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### System Info
- `transformers` version: 4.57.0
- Platform: Linux-5.14.0-452.el9.x86_64-x86_64-with-glibc2.34
- Python version: 3.12.11
- Huggingface_hub version: 0.35.3
- Safetensors version: 0.6.2
- Accelerate version: 1.10.1
- Accelerate config: not found
- DeepSpeed version: 0.17.6
- PyTorch version (accelerator?): 2.8.0+cu128 (CUDA)
- 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?: Yes
- Using GPU in script?: Yes
- GPU type: NVIDIA A100 80GB PCIe
### 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
```python
import os
import torch
from transformers import EarlyStoppingCallback, Qwen3VLMoeForConditionalGeneration, AutoProcessor
from trl import SFTTrainer, SFTConfig
from peft import LoraConfig
from datasets import load_dataset
# Prepare dataset
def formatting_prompts_func(examples):
images = [[image] for image in examples["image"]]
prompt_template =[
{
"role": "system",
"content": [
{
"type": "text",
"text": """You are an helpful assistant"""
},
]
},
{
"role": "user",
"content": [
{
"type": "image",
},
{
"type": "text",
"text": "Process the provided images."
}
]
}
]
completion_template = [
{
"role": "assistant",
"content": [
{
"type": "text",
"text": "example" ## just a text field to simulate
}
]
}
]
prompts = [prompt_template] * len(examples)
completions = [completion_template] * len(examples)
return {"images": images, "prompt": prompts, "completion": completions}
epochs = 15
learning_rate = 1e-4
per_device_batch_size = 1
gradient_accumulation_steps = 2
weight_decay = 0.01
warmup_steps = 0
lora_rank = 8
adapter_path = f"adapters/reproduce"
output_path = f"{adapter_path}/outputs/"
# Load tokenizer
model_path = "Qwen/Qwen3-VL-30B-A3B-Instruct"
processor = AutoProcessor.from_pretrained(model_path)
model = Qwen3VLMoeForConditionalGeneration.from_pretrained(
model_path,
dtype=torch.bfloat16,
)
training_args = SFTConfig(
max_length=None,
# padding="longest",
seed=3407,
output_dir=output_path,
num_train_epochs=epochs,
learning_rate=learning_rate,
lr_scheduler_type="cosine",
per_device_train_batch_size=per_device_batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
per_device_eval_batch_size=1,
eval_accumulation_steps=1,
warmup_steps=warmup_steps,
logging_steps=10,
logging_strategy="steps",
do_eval=True,
bf16_full_eval=True,
eval_strategy="epoch",
save_strategy="epoch",
packing=False,
fp16=not torch.cuda.is_bf16_supported(),
bf16=torch.cuda.is_bf16_supported(),
gradient_checkpointing=True,
weight_decay=weight_decay,
load_best_model_at_end=True,
metric_for_best_model="eval_loss",
greater_is_better=False,
completion_only_loss=True,
eos_token=processor.tokenizer.eos_token,
)
# LoRA configuration;
peft_config = LoraConfig(
r=lora_rank,
lora_alpha=lora_rank * 2,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=[
"self_attn.q_proj",
"self_attn.k_proj",
"self_attn.v_proj",
"self_attn.o_proj",
],
)
model.enable_input_require_grads()
# Load dataset
dataset = load_dataset("huggingface/documentation-images", split="train[:1]") ## just an image/text dataset to simulate
formatted_dataset = dataset.map(
formatting_prompts_func,
batched=True,
remove_columns=dataset.column_names,
)
# Initialize trainer
trainer = SFTTrainer(
model=model,
peft_config=peft_config,
processing_class=processor,
args=training_args,
train_dataset=formatted_dataset,
eval_dataset=formatted_dataset,
)
trainer.train()
trainer.save_model(output_dir=adapter_path)
```
```bash
$ accelerate launch --mixed_precision bf16 --use_fsdp --fsdp_sharding_strategy 1 --fsdp_backward_prefetch NO_PREFETCH --fsdp_offload_params true --fsdp_auto_wrap_policy TRANSFORMER_BASED_WRAP --fsdp_transformer_layer_cls_to_wrap Qwen3VLMoeVisionBlock,Qwen3VLMoeTextDecoderLayer --num_machines 1 --num_processes 4 main.py
```
```
[rank0]: Traceback (most recent call last):
[rank0]: File "/home/quintas/workspace/reproduce.py", line 142, in <module>
[rank0]: # accelerate launch --mixed_precision bf16 --use_fsdp --fsdp_sharding_strategy 1 --fsdp_backward_prefetch NO_PREFETCH --fsdp_offload_params true --fsdp_auto_wrap_policy TRANSFORMER_BASED_WRAP --fsdp_transformer_layer_cls_to_wrap Qwen3VLMoeVisionBlock,Qwen3VLMoeTextDecoderLayer --num_machines 1 --num_processes 4 Qwen3-VL-30B-A3B-Instruct/adapters/invoice_reading/train.py
[rank0]: ^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/trainer.py", line 2325, in train
[rank0]: return inner_training_loop(
[rank0]: ^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/trainer.py", line 2790, in _inner_training_loop
[rank0]: self._maybe_log_save_evaluate(
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/trainer.py", line 3221, in _maybe_log_save_evaluate
[rank0]: metrics = self._evaluate(trial, ignore_keys_for_eval)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/trainer.py", line 3170, in _evaluate
[rank0]: metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/trainer.py", line 4489, in evaluate
[rank0]: output = eval_loop(
[rank0]: ^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/trainer.py", line 4685, in evaluation_loop
[rank0]: losses, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/trainer.py", line 4902, in prediction_step
[rank0]: loss, outputs = self.compute_loss(
[rank0]: ^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/trl/trainer/sft_trainer.py", line 1096, in compute_loss
[rank0]: (loss, outputs) = super().compute_loss(
[rank0]: ^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/trainer.py", line 4110, in compute_loss
[rank0]: outputs = model(**inputs)
[rank0]: ^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
[rank0]: return self._call_impl(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
[rank0]: return forward_call(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/accelerate/utils/operations.py", line 818, in forward
[rank0]: return model_forward(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/accelerate/utils/operations.py", line 806, in __call__
[rank0]: return convert_to_fp32(self.model_forward(*args, **kwargs))
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/amp/autocast_mode.py", line 44, in decorate_autocast
[rank0]: return func(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py", line 854, in forward
[rank0]: output = self._fsdp_wrapped_module(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
[rank0]: return self._call_impl(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
[rank0]: return forward_call(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/accelerate/utils/operations.py", line 818, in forward
[rank0]: return model_forward(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/accelerate/utils/operations.py", line 806, in __call__
[rank0]: return convert_to_fp32(self.model_forward(*args, **kwargs))
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/amp/autocast_mode.py", line 44, in decorate_autocast
[rank0]: return func(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/peft/peft_model.py", line 1850, in forward
[rank0]: return self.base_model(
[rank0]: ^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
[rank0]: return self._call_impl(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
[rank0]: return forward_call(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/peft/tuners/tuners_utils.py", line 222, in forward
[rank0]: return self.model.forward(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/utils/generic.py", line 1064, in wrapper
[rank0]: outputs = func(self, *args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py", line 1601, in forward
[rank0]: outputs = self.model(
[rank0]: ^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
[rank0]: return self._call_impl(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
[rank0]: return forward_call(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/utils/generic.py", line 1064, in wrapper
[rank0]: outputs = func(self, *args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py", line 1389, in forward
[rank0]: outputs = self.language_model(
[rank0]: ^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
[rank0]: return self._call_impl(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
[rank0]: return forward_call(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/utils/generic.py", line 1064, in wrapper
[rank0]: outputs = func(self, *args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py", line 962, in forward
[rank0]: layer_outputs = decoder_layer(
[rank0]: ^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
[rank0]: return self._call_impl(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
[rank0]: return forward_call(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py", line 854, in forward
[rank0]: output = self._fsdp_wrapped_module(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/modeling_layers.py", line 94, in __call__
[rank0]: return super().__call__(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
[rank0]: return self._call_impl(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
[rank0]: return forward_call(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/utils/deprecation.py", line 172, in wrapped_func
[rank0]: return func(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py", line 391, in forward
[rank0]: hidden_states = self.mlp(hidden_states)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
[rank0]: return self._call_impl(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
[rank0]: return forward_call(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/quintas/miniconda3/envs/Comudel/lib/python3.12/site-packages/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py", line 149, in forward
[rank0]: router_weights = torch.zeros_like(router_logits).scatter_(1, router_indices, routing_weights)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: RuntimeError: scatter(): Expected self.dtype to be equal to src.dtype
```
### Expected behavior
Train successful
|
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PR_kwDOCUB6oc6shdMn
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🚨 [v5] Prune `prune_heads`
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# What does this PR do?
Spiritual successor to #41076 -- removes `prune_heads` and related code/tests/docs.
`prune_heads` was a feature that we stopped including in models a while ago. For instance, the `test_head_pruning_save_load_from_config_init` test passes in 28 model classes, but is skipped in 444.
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PR_kwDOCUB6oc6shZHm
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Stop collecting all model parameters to save models when using DeepSpeed and LoRA
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# What does this PR do?
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Fixes #41344
## 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?
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.
@SunMarc
(Let me tag you since you replied to the issue. Thanks for taking time!)
<!-- 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
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Documentation: @stevhliu
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PR_kwDOCUB6oc6shXA9
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Fix bnb fsdp loading for pre-quantized checkpoint
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# What does this PR do?
This PR fixes bnb loading when using FSDP for pre-quantized checkpoints. This happened because we changed how we load quantized checkpoints as we need to cache all the quantized stats before creating the quantized weight.
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PR_kwDOCUB6oc6sgzK_
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Fixing comments in __init__ file
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# What does this PR do?
Fixes comments in global __init__ file
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:facepalm: CB nit!
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# What does this PR do?
Fix a small typo
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PR_kwDOCUB6oc6sgO_3
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[Parakeet] unnecessary warning & auto mapping
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# What does this PR do?
- fixes #41387
- and fixes warning
```
🚨 Config not found for parakeet. You can manually add it to HARDCODED_CONFIG_FOR_MODELS in utils/auto_docstring.py
```
- and fixes auto tokenizer mapping that was not updated when moving from `ParakeetCTCTokenizer` to `ParakeetTokenizerFast`
---
What triggers the unexpected warning:
`@auto_doctring` on a class (not on a method) (here `ParakeetPreTrainedModel`, `ParakeetEncoder`, `ParakeetForCTC`) triggers `auto_method_docstring` call **with parent_class kwargs setted**, that will trigger looking for a config class with the model name `parakeet` (extracted from `modeling_parakeet.py`). Yet we don't have a config class for `parakeet` alone, we have `ParakeetEncoderConfig` and `ParakeetCTCConfig`.
To solve, let's add `"parakeet": "ParakeetCTCConfig"` to `HARDCODED_CONFIG_FOR_MODELS`.
>[!NOTE]
> When other Parakeet variants will be integrated `ParakeetForTDT` and `ParakeetForRNTT`, then we might want to add a global ParakeetConfig that encapsulate them all
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PR_kwDOCUB6oc6sgNED
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Small Fix for imports
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# What does this PR do?
When using `importlib.util.find_spec(pkg_name)`, this might return some packages that are not importable but still exist. In this pr we simply check if they can be loaded
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PR_kwDOCUB6oc6sfsoz
| 41,410
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Remove Python 3.9 classifier
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| 2025-10-08T13:39:26
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# What does this PR do?
Follows recent clean-up.
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PR_kwDOCUB6oc6sfrBb
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Fix early CUDA initialisation
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This import added in https://github.com/huggingface/transformers/pull/41370 was identified to be causing CUDA to initialise early.
It was identified in vLLM's Transformers nightly test https://buildkite.com/vllm/ci/builds/33777/steps/canvas?sid=0199bcd4-8cf7-442a-a94d-df2969d65bde
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PR_kwDOCUB6oc6sfh0j
| 41,408
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Benchmark overhaul
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| 2025-10-14T19:41:43
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COLLABORATOR
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This PR overhauls the benchmarking suite that is included in transformers.
The benchmarking suite is now based around three main components:
- `BenchmarkingConfig` is a dataclass-like object which contains everything needed to reproduce a benchmark on the same machine: input length, generation length, whether to use `kernels` or `compile`, attention implementation, etc. (subject to name change)
- `BenchmarkRunner` is the class that runs the benchmarks defined by the configs, with a given number of measurement iterations, warmup iterations, and a model-id. The runner takes care of setting up the runs in a way that ensures no run interacts with the downstream ones: the model is reloaded, the cache is emptied and the GPU memory is flushed. It also saves the results, the config, and any additional metadata needed to reproduce the benchmark, like hardware information and package versions.
- The created results files, which contain enough informations to induces (to my knowledge) most of the metrics used to evaluate a model: e2e_atency, tpot, ttft, even inter-token latency. Results also include a sample of what has been generated, which is useful to check if it was gibberish. The results files are in json format and are made to be easily created from the dataclass-like objects and vice versa.
For now, the new benchmarking suite replaces the `benchmark_v2` part of `transformers` but it could also overwrite the `benchmark` (v1) part. It would be good to make that decision in this PR. And update the CI workflows that rely on the current `benchmark_v2` (putting the PR in draft mode until then).
An example of how to use the new benchmarking suite can be found in `run_benchmarks.py`.
The format of the results file can (and may be bound to) change as we develop tools to analyze them.
If there is a metric you want to see measured in `transformers`, please leave a comment before this is merged :slightly_smiling_face:
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PR_kwDOCUB6oc6sfKM7
| 41,407
|
Fix local start time handling in trainer.py
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# What does this PR do?
Fixes #40560
This PR resolves an issue in `Trainer` where continuing training from a checkpoint (`--resume_from_checkpoint`) causes unrealistically high **tokens per second** values at the beginning of resumed runs.
### Root Cause
When resuming training, the `start_time` passed to `_maybe_log_save_evaluate()` originates from the *initial* training session rather than the resumed one. This leads to inflated throughput metrics since elapsed time is computed from the first session’s start instead of the current resume point.
### Fix Implemented
- Reset the local timer used for performance logging when resuming from a checkpoint.
- Ensured `_maybe_log_save_evaluate()` uses a localized `start_time` (`_local_start_time`) to compute logging metrics accurately for each resumed session.
- Prevented accumulation of prior global step timing data that caused misleading throughput spikes.
The change ensures that throughput metrics reflect only the current training run’s performance rather than cumulative global time.
### Example Reproduction
Run:
```bash
python examples/pytorch/language-modeling/run_clm.py \
--model_name_or_path gpt2 \
--dataset_name wikitext \
--do_train \
--output_dir ./clm_test --overwrite_output_dir
````
Then resume:
```bash
python examples/pytorch/language-modeling/run_clm.py \
--model_name_or_path ./clm_test \
--resume_from_checkpoint ./clm_test \
--output_dir ./clm_test_resume
```
Before the fix, tokens/sec skyrockets at the start.
After the fix, the metric remains consistent across resumed and fresh runs.
---
## Before submitting
* [x] This PR fixes an existing issue (`#40560`).
* [x] Discussed and reproduced via issue thread and local tests.
* [x] Code updated to prevent metric carryover across resumed runs.
* [x] Verified correct tokens/sec logging after resume.
* [x] No docs changes needed (affects internal logging only).
* [ ] New test could be added under `tests/trainer/test_trainer_utils.py`.
---
## Who can review?
Tagging maintainers for `Trainer`:
* @zach-huggingface
* @SunMarc
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PR_kwDOCUB6oc6sd0EW
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🚨 [v5] Remove deprecated cache classes
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# What does this PR do?
🚨🚨🚨 DO NOT MERGE 🚨🚨🚨
Merging this PR will break `from transformers import Trainer` on the latest PEFT release (`0.17.1`), which in turn breaks our `make fixup` when PEFT is installed. PEFT `main` already sorted this issue ([here](https://github.com/huggingface/peft/commit/41c07f0445879da0e73ca3c682a0c359cd027f50)), but it is yet unreleased as of 7-oct-2025.
cc @BenjaminBossan (chatted on slack -- when `transformers` has a concrete timeline for v5, we can best evaluate an adequate timing for a PEFT release)
cc @Cyrilvallez FYI
_______________________________
This PR removes the deprecated cache classes in `cache_utils.py`.
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🚨 [v5] remove deprecated cache tuple input support
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# What does this PR do?
This PR:
- Removes the last remnants (🤞) of tuple cache input support, scheduled to be removed in v4.58
- Removes other minor items scheduled to be removed in v4.58, caught in the search
- Updates a few incorrect `past_key_values` type hints
- ⚠️ EDIT: add cache initialization in `forward` on models that were missing it
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PR_kwDOCUB6oc6sdlr6
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Remove deprecated args in Trainer for v5
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# What does this PR do?
This PR removes `half_precision_backend` and `include_tokens_per_second` as we move to v5.
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[Trainer] deprecate ray scope
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# What does this PR do?
This PR removes `ray_scope` from `TrainingArguments`. Instead, users will have to set the env var RAY_SCOPE if they want to change its value.
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PR_kwDOCUB6oc6sdPWr
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[v5] rm `utils/tf_ops/`
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# What does this PR do?
TF-related folder that flew under the radar :) (seems unused in the lib, latest commit was 4 years ago)
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[Model] Lfm2Moe
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# What does this PR do?
This PR implements [LFM2-8B-A1B](https://huggingface.co/LiquidAI/LFM2-8B-A1B), a hybrid Mixture-of-Experts architecture variant of [LFM2](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38). The LFM2 family is optimized for on-device inference by combining short‑range, input‑aware gated convolutions with grouped‑query attention (GQA). LFM2‑MoE keeps this fast backbone and introduces sparse MoE feed‑forward networks to add representational capacity without significantly increasing the active compute path.
## Before submitting
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [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.
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Library:
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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_kwDOCUB6oc6sc4JL
| 41,400
|
Automatically infer attention_mask when pad_token_id == eos_token_id
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[] | 2025-10-07T09:18:00
| 2025-10-08T16:27:25
| 2025-10-08T16:27:25
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CONTRIBUTOR
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# What does this PR do?
This PR updates the _prepare_special_tokens in the generation utilities to automatically infer a correct attention mask when none is provided and pad_token_id == eos_token_id.
<!--
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<!-- Remove if not applicable -->
Fixes ##41392
## 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?
@ArthurZucker @vasqu
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
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- 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
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- CIs: @ydshieh
Integrations:
- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
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- 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_kwDOCUB6oc7QDuxH
| 41,399
|
Filter models by Original Provider
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[] | 2025-10-07T09:13:49
| 2025-10-08T09:24:22
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NONE
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### Feature request
Add a filter that only displays official model releases.
### Motivation
When looking to compare various models I would like to start from the original model provider (Qwen, Kimi, DeepSeek, etc...) and then check out quantizations, finetunes, etc... With the volume of models being pushed on HuggingFace it feels impossible to collect the best 5-6 latest models so i can test them.
### Your contribution
I can only contribute the though process: A dictionary with model providers (keys) and the names of their model releases (values). When enabling the filter for original releases it should only display the models that are the values of that dictionary.
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I_kwDOCUB6oc7QC6-K
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OwlViT Process has error 'numpy.ndarray' object has no attribute 'to'
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[] | 2025-10-07T08:10:31
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NONE
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### System Info
- `transformers` version: 4.57.0.dev0
- Platform: macOS-15.7.1-x86_64-i386-64bit
- Python version: 3.12.10
- Huggingface_hub version: 1.0.0.rc2
- Safetensors version: 0.6.2
- Accelerate version: not installed
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (accelerator?): 2.2.2 (NA)
### Who can help?
@zucchini-nlp
### Information
- [x] The official example scripts
- [x] 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
I try to do zero-shot learning by using
model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32")
processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")
I also follow the tutorial
image = Image.open('1.png').convert("RGB")
text_queries = ["product"]
inputs = processor(text=text_queries, images=image, return_tensors="pt").to(device)
But I get error when I forward
# Get predictions
with torch.no_grad():
outputs = model(**inputs)
<img width="1270" height="179" alt="Image" src="https://github.com/user-attachments/assets/35c22297-74da-4f0f-95f8-9b386d978803" />
I get this error both from my local macbook or colab (both cpu/gpu)
### Expected behavior
it should able to forward model
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ViTForMaskedImageModeling (vit-base-patch16-224-in21k) distributed training warning
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[] | 2025-10-07T03:13:35
| 2025-10-13T23:54:47
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### System Info
I am having some strange errors/warnings when running the vit model. I am on version `4.56.0`. 2 A6000 NVIDIA GPUS. Ubuntu 20.04.6 LTS. Python 3.10.18. torch version 2.6.0+cu124.
### Who can help?
@molbap @yonigozlan
1. When I use the `AutoModel.from_pretrained` class for the `vit-base-patch16-224-in21k` checkpoint and wrap the model with `DDP` and run with `torchrun`, I get this error
```
[rank1]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/transformers/utils/generic.py", line 1064, in wrapper
[rank1]: outputs = func(self, *args, **kwargs)
[rank1]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/transformers/models/vit/modeling_vit.py", line 482, in forward
[rank1]: embedding_output = self.embeddings(
[rank1]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
[rank1]: return self._call_impl(*args, **kwargs)
[rank1]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
[rank1]: return forward_call(*args, **kwargs)
[rank1]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/transformers/models/vit/modeling_vit.py", line 112, in forward
[rank1]: mask_tokens = self.mask_token.expand(batch_size, seq_length, -1)
[rank1]: AttributeError: 'NoneType' object has no attribute 'expand'
```
However, when I use the `ViTForMaskedImageModeling.from_pretrained` class I get this warning
```
/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/autograd/graph.py:823: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.
grad.sizes() = [768, 768, 1, 1], strides() = [768, 1, 768, 768]
bucket_view.sizes() = [768, 768, 1, 1], strides() = [768, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:327.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/autograd/graph.py:823: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.
grad.sizes() = [768, 768, 1, 1], strides() = [768, 1, 768, 768]
bucket_view.sizes() = [768, 768, 1, 1], strides() = [768, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:327.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
```
Why does this happen? I found maybe one related issue [here](https://github.com/huggingface/transformers/issues/32614). I found [this too](https://github.com/huggingface/transformers/issues/23832) but I don't think this one is related.
### 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
run `CUDA_VISIBLE_DEVICES=1,2 torchrun --nproc_per_node=2 --master_port=12355 ecg_bench/test.py`
```
import os
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from transformers import ViTForMaskedImageModeling, AutoModel, AutoImageProcessor
from ecg_bench.configs.constants import HF_CACHE_DIR
def setup_ddp():
if not dist.is_initialized():
dist.init_process_group(backend="nccl")
local_rank = int(os.environ.get("LOCAL_RANK", 0))
torch.cuda.set_device(local_rank)
return local_rank
def test_model(model_class):
model = model_class.from_pretrained("google/vit-base-patch16-224-in21k", cache_dir=HF_CACHE_DIR)
processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k", cache_dir=HF_CACHE_DIR)
local_rank = setup_ddp()
model = DDP(model.to(f"cuda:{local_rank}"), device_ids=[local_rank])
images = torch.rand(2, 3, 224, 224)
inputs = processor(images=images, return_tensors="pt")
pixel_values = inputs["pixel_values"].to(f"cuda:{local_rank}")
bool_masked_pos = torch.zeros(2, 196, dtype=torch.bool, device=f"cuda:{local_rank}")
for i in range(2):
bool_masked_pos[i, torch.randperm(196)[:29]] = True
outputs = model(pixel_values=pixel_values, bool_masked_pos=bool_masked_pos)
loss = outputs.loss
loss.backward()
print(f"Loss: {loss.item()}")
if __name__ == "__main__":
test_model(ViTForMaskedImageModeling)
test_model(AutoModel)
```
### Expected behavior
```
(ecgv2) william@gpu2:~/ecg_dev/ECG-Bench-v2$ bash scripts/test_ddp_reproduction.sh
Running minimal reproduction test with DDP...
W1007 03:11:37.695000 3870938 site-packages/torch/distributed/run.py:792]
W1007 03:11:37.695000 3870938 site-packages/torch/distributed/run.py:792] *****************************************
W1007 03:11:37.695000 3870938 site-packages/torch/distributed/run.py:792] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W1007 03:11:37.695000 3870938 site-packages/torch/distributed/run.py:792] *****************************************
Some weights of ViTForMaskedImageModeling were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['decoder.0.bias', 'decoder.0.weight', 'vit.embeddings.mask_token']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of ViTForMaskedImageModeling were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['decoder.0.bias', 'decoder.0.weight', 'vit.embeddings.mask_token']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Fetching 1 files: 100%|██████████████████████████████████████████| 1/1 [00:00<00:00, 16070.13it/s]
Fetching 1 files: 100%|██████████████████████████████████████████| 1/1 [00:00<00:00, 19239.93it/s]
Fast image processor class <class 'transformers.models.vit.image_processing_vit_fast.ViTImageProcessorFast'> is available for this model. Using slow image processor class. To use the fast image processor class set `use_fast=True`.
Fast image processor class <class 'transformers.models.vit.image_processing_vit_fast.ViTImageProcessorFast'> is available for this model. Using slow image processor class. To use the fast image processor class set `use_fast=True`.
Fetching 1 files: 100%|██████████████████████████████████████████| 1/1 [00:00<00:00, 13443.28it/s]
Fetching 1 files: 100%|███████████████████████████████████████████| 1/1 [00:00<00:00, 8473.34it/s]
It looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.
It looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.
/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/autograd/graph.py:823: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.
grad.sizes() = [768, 768, 1, 1], strides() = [768, 1, 768, 768]
bucket_view.sizes() = [768, 768, 1, 1], strides() = [768, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:327.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/autograd/graph.py:823: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.
grad.sizes() = [768, 768, 1, 1], strides() = [768, 1, 768, 768]
bucket_view.sizes() = [768, 768, 1, 1], strides() = [768, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:327.)
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
Loss: 1.00250244140625Loss: 1.002082109451294
Fetching 1 files: 100%|███████████████████████████████████████████| 1/1 [00:00<00:00, 9664.29it/s]
Fetching 1 files: 100%|███████████████████████████████████████████| 1/1 [00:00<00:00, 6364.65it/s]
Fast image processor class <class 'transformers.models.vit.image_processing_vit_fast.ViTImageProcessorFast'> is available for this model. Using slow image processor class. To use the fast image processor class set `use_fast=True`.
Fast image processor class <class 'transformers.models.vit.image_processing_vit_fast.ViTImageProcessorFast'> is available for this model. Using slow image processor class. To use the fast image processor class set `use_fast=True`.
Fetching 1 files: 100%|██████████████████████████████████████████| 1/1 [00:00<00:00, 18001.30it/s]
Fetching 1 files: 100%|██████████████████████████████████████████| 1/1 [00:00<00:00, 22192.08it/s]
[rank1]: Traceback (most recent call last):
[rank1]: File "/home/william/ecg_dev/ECG-Bench-v2/ecg_bench/test.py", line 37, in <module>
[rank1]: test_model(AutoModel)
[rank1]: File "/home/william/ecg_dev/ECG-Bench-v2/ecg_bench/test.py", line 29, in test_model
[rank1]: outputs = model(pixel_values=pixel_values, bool_masked_pos=bool_masked_pos)
[rank1]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
[rank1]: return self._call_impl(*args, **kwargs)
[rank1]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
[rank1]: return forward_call(*args, **kwargs)
[rank1]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/parallel/distributed.py", line 1643, in forward
[rank1]: else self._run_ddp_forward(*inputs, **kwargs)
[rank1]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/parallel/distributed.py", line 1459, in _run_ddp_forward
[rank1]: return self.module(*inputs, **kwargs) # type: ignore[index]
[rank1]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
[rank1]: return self._call_impl(*args, **kwargs)
[rank1]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
[rank1]: return forward_call(*args, **kwargs)
[rank1]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/transformers/utils/generic.py", line 1064, in wrapper
[rank1]: outputs = func(self, *args, **kwargs)
[rank1]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/transformers/models/vit/modeling_vit.py", line 482, in forward
[rank1]: embedding_output = self.embeddings(
[rank1]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
[rank1]: return self._call_impl(*args, **kwargs)
[rank1]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
[rank1]: return forward_call(*args, **kwargs)
[rank1]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/transformers/models/vit/modeling_vit.py", line 112, in forward
[rank1]: mask_tokens = self.mask_token.expand(batch_size, seq_length, -1)
[rank1]: AttributeError: 'NoneType' object has no attribute 'expand'
[rank0]: Traceback (most recent call last):
[rank0]: File "/home/william/ecg_dev/ECG-Bench-v2/ecg_bench/test.py", line 37, in <module>
[rank0]: test_model(AutoModel)
[rank0]: File "/home/william/ecg_dev/ECG-Bench-v2/ecg_bench/test.py", line 29, in test_model
[rank0]: outputs = model(pixel_values=pixel_values, bool_masked_pos=bool_masked_pos)
[rank0]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
[rank0]: return self._call_impl(*args, **kwargs)
[rank0]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
[rank0]: return forward_call(*args, **kwargs)
[rank0]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/parallel/distributed.py", line 1643, in forward
[rank0]: else self._run_ddp_forward(*inputs, **kwargs)
[rank0]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/parallel/distributed.py", line 1459, in _run_ddp_forward
[rank0]: return self.module(*inputs, **kwargs) # type: ignore[index]
[rank0]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
[rank0]: return self._call_impl(*args, **kwargs)
[rank0]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
[rank0]: return forward_call(*args, **kwargs)
[rank0]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/transformers/utils/generic.py", line 1064, in wrapper
[rank0]: outputs = func(self, *args, **kwargs)
[rank0]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/transformers/models/vit/modeling_vit.py", line 482, in forward
[rank0]: embedding_output = self.embeddings(
[rank0]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
[rank0]: return self._call_impl(*args, **kwargs)
[rank0]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
[rank0]: return forward_call(*args, **kwargs)
[rank0]: File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/transformers/models/vit/modeling_vit.py", line 112, in forward
[rank0]: mask_tokens = self.mask_token.expand(batch_size, seq_length, -1)
[rank0]: AttributeError: 'NoneType' object has no attribute 'expand'
[rank0]:[W1007 03:11:47.275383972 ProcessGroupNCCL.cpp:1496] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
W1007 03:11:48.425000 3870938 site-packages/torch/distributed/elastic/multiprocessing/api.py:897] Sending process 3871025 closing signal SIGTERM
E1007 03:11:48.589000 3870938 site-packages/torch/distributed/elastic/multiprocessing/api.py:869] failed (exitcode: 1) local_rank: 1 (pid: 3871026) of binary: /home/william/miniconda3/envs/ecgv2/bin/python3
Traceback (most recent call last):
File "/home/william/miniconda3/envs/ecgv2/bin/torchrun", line 10, in <module>
sys.exit(main())
File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 355, in wrapper
return f(*args, **kwargs)
File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/distributed/run.py", line 918, in main
run(args)
File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/distributed/run.py", line 909, in run
elastic_launch(
File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 138, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/home/william/miniconda3/envs/ecgv2/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 269, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
ecg_bench/test.py FAILED
------------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2025-10-07_03:11:48
host : gpu2
rank : 1 (local_rank: 1)
exitcode : 1 (pid: 3871026)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
Test completed!
```
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[Cache] lfm2 cache: allocate empty kv layers during init
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# What does this PR do?
The current Lfm2Cache adds empty kv-cache tensors when it detects a `layer_idx` that corresponds to an attention layer, but this ends up making the length of Lfm2Cache < `num_hidden_layers` if the last layer is not an attention layer. This change makes Lfm2Cache follow convention to the default cache by pre-allocating empty tensors during init, so we can keep the total length fixed to the number of hidden layers.
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Fixes # (issue)
## Before submitting
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PR_kwDOCUB6oc6sZNbY
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Enable FURB rules in ruff
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# What does this PR do?
ruff contains the below `FURB` rules:
```
FURB101 open and read should be replaced by Path({filename}).{suggestion}
FURB103 open and write should be replaced by Path({filename}).{suggestion}
FURB105 Unnecessary empty string passed to print
FURB110 Replace ternary if expression with or operator
FURB113 Use {suggestion} instead of repeatedly calling {name}.append()
FURB116 Replace {function_name} call with {display}
FURB118 Use operator.{operator} instead of defining a {target}
FURB122 Use of {}.write in a for loop
FURB129 Instead of calling readlines(), iterate over file object directly
FURB131 Prefer clear over deleting a full slice
FURB132 Use {suggestion} instead of check and remove
FURB136 Replace if expression with {min_max} call
FURB140 Use itertools.starmap instead of the generator
FURB142 Use of set.{}() in a for loop
FURB145 Prefer copy method over slicing
FURB148 enumerate value is unused, use for x in range(len(y)) instead
FURB152 Replace {literal} with math.{constant}
FURB154 Use of repeated consecutive {}
FURB156 Use of hardcoded string charset
FURB157 Verbose expression in Decimal constructor
FURB161 Use of bin({existing}).count('1')
FURB162 Unnecessary timezone replacement with zero offset
FURB163 Prefer math.{log_function}({arg}) over math.log with a redundant base
FURB164 Verbose method {method_name} in {constructor} construction
FURB166 Use of int with explicit base={base} after removing prefix
FURB167 Use of regular expression alias re.{}
FURB168 Prefer is operator over isinstance to check if an object is None
FURB169 When checking against None, use {} instead of comparison with type(None)
FURB171 Membership test against single-item container
FURB177 Prefer Path.cwd() over Path().resolve() for current-directory lookups
FURB180 Use of metaclass=abc.ABCMeta to define abstract base class
FURB181 Use of hashlib's .digest().hex()
FURB187 Use of assignment of reversed on list {name}
FURB188 Prefer str.removeprefix() over conditionally replacing with slice.
FURB189 Subclassing {subclass} can be error prone, use collections.{replacement} instead
FURB192 Prefer min over sorted() to compute the minimum value in a sequence
```
In my opinion these rules are useful and many of their fixed have been applied ion previous PRs.
|
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PR_kwDOCUB6oc6sYf8r
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Adding superglue fast image processing
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# What does this PR do?
TLDR :
- Implement fast processor for SuperGlue
- About 3 times faster
This PR aims to translate the features of the class `SuperGlueImageProcessor` in the fast equivalent class `SuperGlueImageProcessorFast`.
The implementation heavily follows the standard implementation but reduces memory consumption and about 3 times the execution speed on my hardware.
The implementation mostly refactor the image formatting in the `preprocessing` step, notably by using torch tensors instead of PIL or Numpy.
## Test Performed
RUN_SLOW=1 python -m pytest tests/models/superglue/test_image_processing_superglue.py
With an additional test based on the default processor tester (this test has not to be included in the repo) :
```python
@require_vision
@require_torch
def test_fast_is_faster_than_slow(self):
if not self.test_slow_image_processor or not self.test_fast_image_processor:
self.skipTest(reason="Skipping speed test")
if self.image_processing_class is None or self.fast_image_processing_class is None:
self.skipTest(reason="Skipping speed test as one of the image processors is not defined")
def measure_time(image_processor, image):
# Warmup
for _ in range(5):
_ = image_processor(image, return_tensors="pt")
all_times = []
for _ in range(10):
start = time.time()
_ = image_processor(image, return_tensors="pt")
all_times.append(time.time() - start)
# Take the average of the fastest 3 runs
avg_time = sum(sorted(all_times[:3])) / 3.0
return avg_time
dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
image_processor_slow = self.image_processing_class(**self.image_processor_dict)
image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
fast_time = measure_time(image_processor_fast, dummy_images)
slow_time = measure_time(image_processor_slow, dummy_images)
self.assertLessEqual(fast_time, slow_time)
```
By reviewing the flame graph, I noticed the improvement in every `__calls__` made to the fast version.
Callers of the old processor, and the full execution time of the method:
<img width="1239" height="391" alt="image" src="https://github.com/user-attachments/assets/0fedbb2f-991c-481e-9425-b041b4f31767" />
The equivalent but with the fast processor:
<img width="1255" height="385" alt="image" src="https://github.com/user-attachments/assets/ff658450-0c8c-459d-b58c-6da727555558" />
Some calls made during the test passes directly to the preprocess function, without passing by the `__call__` one, I am including them as well:
Slow
<img width="1150" height="209" alt="image" src="https://github.com/user-attachments/assets/b763e4c8-0336-42fe-a1de-3611dc2b0f66" />
Fast
<img width="1230" height="176" alt="image" src="https://github.com/user-attachments/assets/9979f600-e03b-4732-85eb-91a9ded018ee" />
## 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.
link : [Contributions Welcome] Add Fast Image Processors https://github.com/huggingface/transformers/issues/36978#issue-2947632853
- [ 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).
- [ ] Did you write any new necessary tests?
## Who can review?
Thank you for reviewing my PR @yonigozlan (or anyone else :) )
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PR_kwDOCUB6oc6sYRpY
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Fix MaskFormer/Mask2Former fast image processors
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# What does this PR do?
Depends on https://github.com/huggingface/transformers/pull/41391.
These two fast image processors had issues and were not properly tested:
- There was an issue where the processors would crash if `do_resize-False`
- After conversion to binary masks, the grouped masks cannot be stacked anymore, as their channels dimensions are not the same. This fix uses the method introduced in https://github.com/huggingface/transformers/pull/41391 to group the masks according to the shapes of the corresponding images.
This PR fixes the issues and ensure that the integration tests are also ran with the fast image processors
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I_kwDOCUB6oc7P-U5j
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|
automaticlly setting attention mask when pas token is equal to the eos token
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[] | 2025-10-06T21:41:37
| 2025-10-08T16:26:44
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NONE
| null | null | null | null |
### Feature request
In AutoModelForCausalLM If the model eos token and pad token is the same, automaticly set the attention mask by treating the first eos token as a normal eos token and the rest as padding tokens.
### Motivation
hi, I am using qwen3-0.6b (infrence only, no training) and got the message:
'The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results'
I solved it for my specific case by treating the first eos of each token as an eos token and the rest as padding tokens.
It can be solved like that for all cases of AutoModelForCausalLM where the tokenizer padding size if left.
### Your contribution
Unfortunently I work full time and probably won't have the time for the overhead of contributing to a new project in the near future.
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PR_kwDOCUB6oc6sXgVA
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Add MLlama fast image processor
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# What does this PR do?
Finishes https://github.com/huggingface/transformers/pull/37539
It also adds support to group other inputs that mirrors the shape of images in `group_images_by_shape`, it could also be useful for some other future video processors @zucchini-nlp ;).
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PR_kwDOCUB6oc6sXROs
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docs: replace twitter.com with x.com
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# What does this PR do?
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Modified Whisper to have the FFN referenced as mlp instead of fc1 and fc2
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# What does this PR do?
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Fixes # (issue)
## Before submitting
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PR_kwDOCUB6oc6sWPew
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Default to fast image processors for all models
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# What does this PR do?
Following the trial testing with Qwen_VL image processors, this extends defaulting to fast image processors even for checkpoints saved with a slow one to all models.
Also made sure that all processors use AutoImageProcessor to instantiate their image_processor_class.
On that point, defining default subclass in processors feels a bit redundant, as we basically already have that in auto classes. It would be nice to get rid of this for v5, wdyt @molbap @zucchini-nlp @ArthurZucker ?
I'll open a PR for that too.
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I_kwDOCUB6oc7P8G3b
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Parakeet and PerceptionLM are not registered correctly for auto
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[] | 2025-10-06T18:58:51
| 2025-10-09T12:08:04
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CONTRIBUTOR
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### System Info
- `transformers` version: 4.57.0.dev0
- Platform: Linux-5.19.0-50-generic-x86_64-with-glibc2.35
- Python version: 3.13.7
- Huggingface_hub version: 1.0.0.rc2
- Safetensors version: 0.6.2
- Accelerate version: not installed
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (accelerator?): 2.8.0+cpu (NA)
### Who can help?
@Cyrilvallez
### 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
When trying to iterate over the auto-registered mappings as below, the PerceptionLM models throw errors because it is incorrectly registered.
```py
from transformers import MODEL_MAPPING
MODEL_MAPPING.items()
```
```
Traceback (most recent call last):
File "/home/fschlatt/Code/uni/transformers/tmp.py", line 4, in <module>
MODEL_MAPPING.items()
~~~~~~~~~~~~~~~~~~~^^
File "/home/fschlatt/Code/uni/transformers/src/transformers/models/auto/auto_factory.py", line 628, in items
self._load_attr_from_module(key, self._model_mapping[key]),
~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/fschlatt/Code/uni/transformers/src/transformers/models/auto/auto_factory.py", line 597, in _load_attr_from_module
return getattribute_from_module(self._modules[module_name], attr)
File "/home/fschlatt/Code/uni/transformers/src/transformers/models/auto/auto_factory.py", line 519, in getattribute_from_module
raise ValueError(f"Could not find {attr} neither in {module} nor in {transformers_module}!")
ValueError: Could not find PerceptionEncoder neither in <module 'transformers.models.perception_lm' from '/home/fschlatt/Code/uni/transformers/src/transformers/models/perception_lm/__init__.py'> nor in <module 'transformers' from '/home/fschlatt/Code/uni/transformers/src/transformers/__init__.py'>!
```
### Expected behavior
It should be possible to iterate over the mappings
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PR_kwDOCUB6oc6sVjDu
| 41,386
|
enable new model uts to xpu and fix some failures on xpu
|
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covered modeling:
- cohere2_vesion
- ernie4_5
- internvl
- llava
- llava_next
- phi
- qwen2.5_omni
- whisper
- xglm
@SunMarc @ydshieh , pls help review, thx very much.
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PR_kwDOCUB6oc6sVWi3
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[`Ernie 4.5 Moe`] Fix Moe and offloading
|
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| 2025-10-16T11:59:12
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|
The model is broken
- Moe was initialized with wrong dims (loading the models already failed)
- Gate needs the autocast decorator as well (we need to guarantee fp32)
- Not sure why but using the forward with the moe statics works with offloading while retrieving the parameter etc does not
- The router seems like an artefact
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PR_kwDOCUB6oc6sUhsG
| 41,384
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Remove `past_index`
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# What does this PR do?
This PR removes `past_index`. This was supposed to allow training a few old + specific arch models like transformersxl but I don't think anyone actually did it as there is no documentation at all. I think it is fine to remove it without any deprecation msg but happy to add it if needed. Maybe some users just copy paste Trainer code and try to maintain that.
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PR_kwDOCUB6oc6sUazX
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remove `tpu_num_cores`
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[] | 2025-10-06T16:43:31
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# What does this PR do?
This PR removes `tpu_num_cores` arguments in `TrainingArguments`. This arg is somewhat confusing because the value is either `1` or `None` which means that we are using all devices. Instead, we remove the arg to make it clearer. Users who wants to control the number of devices can use `TPU_NUM_DEVICES` as specified in xla docs. Note that this was only used in example scripts
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PR_kwDOCUB6oc6sUA7h
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Remove `local_rank` arg from `TrainingArguments`
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| 2025-10-10T09:34:14
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# What does this PR do?
This PR removes `local_rank` from `TrainingArguments`. This arg can be safely replaced by `local_process_index`. This arg was never meant to be set and was supposed to be there to be overwritten by torch distributed.
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PR_kwDOCUB6oc6sT0XT
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Fix overriding common_kwargs defaults in processor calls
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# What does this PR do?
After https://github.com/huggingface/transformers/pull/40931, the `common_kwargs` (`return_tensors` really) defaults were set after updating them with call kwargs, which mean it's impossible to override the defaults.
This fixes it by setting the common kwargs defaults before updating them with call kwargs, and fixes a test in `test_image_processing_owlv2.py` that was failing because of it (as Owlv2 sets return_tensors to `np` by default for some reasons)
Cc @zucchini-nlp to make sure I'm not missing anything!
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PR_kwDOCUB6oc6sTqcM
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RT-Detr correct 2d positional embeddings for non-square images
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# What does this PR do?
This PR solves a subtle bug in how 2d positional embeddings are generated in the RT-Detr architecture. After flattening the meshgrid that generated the 2d positional embeddings, and for non-square images, the positional embeddings no longer encode the correct 2d positional information.
For an image with width=4 and height=3 we get:
```python
import torch
# Create 2d grid
x = torch.tensor([0, 1, 2, 3])
y = torch.tensor([0, 1, 2])
grid_x, grid_y = torch.meshgrid(x, y, indexing='ij')
print(grid_x)
#tensor([[0, 0, 0],
# [1, 1, 1],
# [2, 2, 2],
# [3, 3, 3]])
print(grid_y)
#tensor([[0, 1, 2],
# [0, 1, 2],
# [0, 1, 2],
# [0, 1, 2]])
# Create image
image = torch.range(0,11).reshape((3,4)).int()
print(image)
#tensor([[ 0, 1, 2, 3],
# [ 4, 5, 6, 7],
# [ 8, 9, 10, 11]], dtype=torch.int32)
#Print flattened grid and image
print(grid_x.flatten())
# tensor([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3])
print(grid_y.flatten())
# tensor([0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2])
print(image.flatten())
# tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
```
Note how the first four pixels 0,1,2,3 get x positions 0,0,0,1 instead of the intended 0,0,0,0.
# Solution
The proposed solution is consistent with the current way that positional embeddings are generated, while generalizing correctly to non-square shapes.
Fixes # (issue)
#41379
## 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.
Models:
- vision models: @yonigozlan @molbap @qubvel
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I_kwDOCUB6oc7P5VXa
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RT-Detr 2d positional embedding bug
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[] | 2025-10-06T15:45:07
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CONTRIBUTOR
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I believe that there is a subtle bug in how 2d positional embeddings are generated for the rt_detr model. Specifically, when generating a meshgrid and then flattening, the positional embeddings no longer match the intended image pixels. We can see this in the script below
```python
import torch
# Create 2d grid
x = torch.tensor([0, 1, 2, 3])
y = torch.tensor([0, 1, 2])
grid_x, grid_y = torch.meshgrid(x, y, indexing='ij')
print(grid_x)
#tensor([[0, 0, 0],
# [1, 1, 1],
# [2, 2, 2],
# [3, 3, 3]])
print(grid_y)
#tensor([[0, 1, 2],
# [0, 1, 2],
# [0, 1, 2],
# [0, 1, 2]])
# Create image
image = torch.range(0,11).reshape((3,4)).int()
print(image)
#tensor([[ 0, 1, 2, 3],
# [ 4, 5, 6, 7],
# [ 8, 9, 10, 11]], dtype=torch.int32)
#Print flattened grid and image
print(grid_x.flatten())
# tensor([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3])
print(grid_y.flatten())
# tensor([0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2])
print(image.flatten())
# tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
```
This means that for images that are not square the embeddings don't encode the intended spatial information.
This issue comes from the following line. I believe setting indexing='xy' would have been the correct choice.
https://github.com/huggingface/transformers/blob/fa36c973fc5f2f473f58013097e21ed74025ea42/src/transformers/models/rt_detr/modeling_rt_detr.py#L1154
While RT-Detr was intended by the authors to work on square images, generalizing correctly to non-square images could be important for future research.
I will make a PR with a simple fix, that is consistent with the current positional embeddings but generalizes correctly to variable image sizes.
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PR_kwDOCUB6oc6sTjsP
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🚨🚨 Remove all traces of legacy cache format
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# What does this PR do?
As per the title. Remove all legacy format and deprecated classes/methods.
|
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[testing] Fix `JetMoeIntegrationTest`
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# What does this PR do?
> tests/models/jetmoe/test_modeling_jetmoe.py::JetMoeIntegrationTest::test_model_8b_generation
cause the pytest process being killed (and sometimes hangs for hours), see the log below.
It loads the model on `cpu`. Let's use "auto".
The outputs changes after #41324 (which is a fix of #40132), so the tests still fail but not causing the pytest process being killed and job hanging.
There is a new warning showing
> The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
cc @ArthurZucker for this output change issue.
Log when running before this PR:
> 2025-09-30T16:33:22.8891725Z tests/models/jetmoe/test_modeling_jetmoe.py::JetMoeIntegrationTest::test_model_8b_batched_generation
2025-09-30T16:33:22.8892381Z -------------------------------- live log call ---------------------------------
2025-09-30T16:33:22.8893314Z WARNING transformers.generation.configuration_utils:logging.py:328 The following generation flags are not valid and may be ignored: ['temperature']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
2025-09-30T16:33:23.3206200Z FAILED [ 99%]
2025-09-30T16:39:21.4805460Z Killed
2025-09-30T16:39:21.4847256Z tests/models/jetmoe/test_modeling_jetmoe.py::JetMoeIntegrationTest::test_model_8b_generation
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PR_kwDOCUB6oc6sTTZ4
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deprecate `jit_mode_eval`
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# What does this PR do?
This PR deprecates `jit_mode_eval` arguments. Going on, it is better to fully rely on torch compile.
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PR_kwDOCUB6oc6sS6f_
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`report_to` default changed to "none" + cleaning deprecated env var
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# What does this PR do?
This PR changes the default for reporting to "none" as we move to v5. Moreover, we clean some deprecations messages around some env env + we deprecate `logging_dir` that was solely used for tensorboard. Instead the user needs to pass a global variable.
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PR_kwDOCUB6oc6sSRqY
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Correct numerical regression in vision embeddings
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# What does this PR do?
This PR fixes a numerical regression bug in the vision positional embedding calculation that was introduced between `transformers` versions v4.54.1 and v4.55.1. The original change was made to improve exportability but resulted in a slight floating point difference.
The fix was applied to the base modular files (`modular_idefics2.py` and `modular_idefics3.py`) and then propagated to all dependent models via `make fix-copies`.
Fixes #41190
## 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/transformers/issues/41190
- [ ] Did you make sure to update the documentation with your changes?
- [ ] Did you write any new necessary tests?
## Who can review
@zucchini-nlp
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PR_kwDOCUB6oc6sSHm1
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[testing] reduce runtime of `HunYuanMoEV1IntegrationTest:test_model_generation`
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# What does this PR do?
This test takes 26 minutes to run, way too much to be helpful.
Let's reduce `max_new_tokens` to `10` and runs in 7 minutes.
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PR_kwDOCUB6oc6sSB-C
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Enable modular files from other libraries
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# 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.
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<!-- Remove if not applicable -->
This PR extends the modular model converter so that one can use a modular file in an external library. The generated files (modeling, configuration, etc) will located in the same folder as the modular file. This requires users to use the new `--source-library` flag.
Another PR will come so that one can use model classes that are outside of the Transformers library in their modular file.
## Before submitting
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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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PR_kwDOCUB6oc6sRbzv
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Correcting numerical regression in vision embeddings
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# What does this PR do?
This PR fixes a numerical regression bug in the vision positional embedding calculation that was introduced between `transformers` versions v4.54.1 and v4.55.1. The original change was made to improve exportability but resulted in a slight floating point difference, causing models like `Idefics2`, `Idefics3`, and `SmolVLM` to produce different outputs than the previous version.
The fix was applied to the base modular files (`modular_idefics2.py` and `modular_idefics3.py`) and then propagated to all dependent models via `make fix-copies`.
Fixes #41190
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PR_kwDOCUB6oc6sRTLF
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[`CB`] Refactors the way we access paged
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# What does this PR do?
The user decides on the function, but CB handles itself which "interface" wrapper to use. This should make stuff easier
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PR_kwDOCUB6oc6sRH6g
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Add logits_to_keep parameter to BertForSequenceClassification
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## Summary
This PR adds support for the `logits_to_keep` parameter to `BertForSequenceClassification`
## Motivation
The `logits_to_keep` parameter enables memory-efficient inference by computing logits only for the last N token positions. This optimization is particularly useful for:
- Generation tasks where only recent tokens are needed
- Memory-constrained environments
- Processing long sequences efficiently
## Implementation Details
- **Added** `logits_to_keep` parameter to the `forward()` method signature
- **Implemented** token slicing logic that activates when parameter is specified
- **Updated** docstring with comprehensive parameter documentation
- **Maintains** full backward compatibility (parameter defaults to `None`)
## Code Changes
The implementation adds approximately 10 lines of code:
1. New optional parameter in function signature
2. Conditional slicing logic for sequence outputs
3. Documentation in docstring
## Testing
Comprehensive local testing performed with `bert-base-uncased`:
- Single input and batch processing verified
- Multiple `logits_to_keep` values tested (1, 2, 3, 5, 10, None)
- Edge cases handled (0, negative values, values > sequence length)
- Training compatibility confirmed (gradient flow works)
- Output structure validation passed
- Small performance improvement observed (~0.6% faster)
## Backward Compatibility
- Parameter is optional and defaults to `None`
- When `None`, behavior is identical to current implementation
- No breaking changes to existing code
## Related Issues
Inspired by #40984
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[testing] update `test_longcat_generation_cpu`
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# What does this PR do?
Use "auto" instead of "cpu" to avoid the pytest process being killed.
(#40132 has issue with using "auto" for this model, and we still need an fix for that)
Also using `max_new_tokens=3` to run this test within 8 minutes (on a single A10 CI runner)
The expected output is not updated yet: will do it after the above mentioned issue is fixed.
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PR_kwDOCUB6oc6sQyBL
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🚨 Remove BetterTransformer
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# What does this PR do?
This API is completely out-of-date and superseded by everything we did on attention computation
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PR_kwDOCUB6oc6sQt9b
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Add in-out modalities as class attribute per model
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# What does this PR do?
Branches out from https://github.com/huggingface/transformers/pull/40884#discussion_r2400964015 to make review and merge faster
Adds a class attr for each model to indicate the supported input and output modalities. Out modalities will be `None` in case the model is not generative and "text" in most other cases. We have only a few models that can generate audio and image in the output. Note that for encoder decoder models that whisper the input modalities will contain both encoder ("audio") and decoder ("text") modalities
This will be used firstly for the pipeline and we can extend usage later to better testing suite and in preparing inputs better in generation with multimodal LLMs (e.g. if we move multimodal encoding to `GenrationMixin._prepare_multimodal_encodings`). No test added at this point, because there is nothing to test
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PR_kwDOCUB6oc6sP59o
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Update CONTRIBUTING.md
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This commit is to let know first timers that reviews are normal
# What does this PR do?
This PR adds an acknowledgment/feedback loop message to guide first-time contributors. It explicitly tells them:
“Your PR may get small suggestions; don’t worry — it’s normal."
This helps reduce discouragement for beginners and encourages them to continue contributing confidently.
It is important because many first-time contributors abandon their PRs when they receive minor review comments, not realizing that this is a standard part of the process. By adding this guidance, we make the contribution experience more welcoming and supportive.
## 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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PR_kwDOCUB6oc6sPfDn
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Change RT-Detr docs to reflect fixed 640x640 input size
|
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# What does this PR do?
The authors of RT-Detr mention that the model was trained on 640x640 images and was meant to be used for inference on 640x640 images. Also, the current implementation has certain quirks that make training/inferring on images of different sizes problematic. For example, the pixel masks used for batching images of varying sizes are discarded. I've added a few lines in the docs to notify the user about these issues.
``
proj_feats = [self.encoder_input_proj[level](source) for level, (source, mask) in enumerate(features)]
``
Fixes # (issue)
#41363
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
## 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.
Models:
- vision models: @yonigozlan @molbap
- documentation: @stevhliu
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I_kwDOCUB6oc7P009w
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RT-Detr docs should reflect fixed 640x640 input size
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[] | 2025-10-06T11:04:37
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CONTRIBUTOR
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The authors of RT-Detr mention that the model was trained on 640x640 images and was meant to be used for inference on 640x640 images. Also, the current implementation has certain quirks that make training/inferring on images of different sizes problematic. For example, the pixel masks used for batching images of varying sizes are discarded.
https://github.com/huggingface/transformers/blob/0452f28544f3626273d25f07f83c0e5f7da2d47a/src/transformers/models/rt_detr/modeling_rt_detr.py#L1645
The above are not clear in the current docs. I'll open a PR which adds a few lines in the docs to notify users about these issues.
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Added Hacktoberfest banner image to README.md
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Added the Hacktoberfest 2025 banner image to the README.md file.
This showcases the project’s participation in Hacktoberfest and encourages contributors to join.
Fixes #41244
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PR_kwDOCUB6oc6sOtfd
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[`ModularChecker`] QOL for the modular checker
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# What does this PR do?
- adds pretty print
- adds simple model-name as file
- adds release date parsing
- adds highest overall match
- optional Jaccard
- snapshot download
<img width="1724" height="1348" alt="image" src="https://github.com/user-attachments/assets/be01c840-cd27-47a7-ae41-d5f4bbb375c3" />
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PR_kwDOCUB6oc6sOeKl
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Add granit speech doc
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# What does this PR do?
Add usage example for the Granite Speech models
@eustlb
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PR_kwDOCUB6oc6sONBG
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Fix trainer for py3.9
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# What does this PR do?
This PR fixes trainer compatibility with py3.9. To be added in a patch for the latest release. Asked by optimum folks cc @IlyasMoutawwakil
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PR_kwDOCUB6oc6sMSVp
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Remove unused function patameters
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# What does this PR do?
Remove unused arguments detected by pylint.
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chore(docs): small README note (contrib test)
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# What does this PR do?
Tiny docs improvement / test contribution by Shankar. This PR is a small test to verify contribution workflow.
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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
- [ ] 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:
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- generate: @zucchini-nlp (visual-language models) or @gante (all others)
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PR_kwDOCUB6oc6sKWHJ
| 41,356
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Add DEIMv2 model, image processor, and basic tests
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# What does this PR do?
This PR introduces the DEIMv2 model along with its configuration, image processor, and unit tests. The changes include:
Model Implementation: Added Deimv2ForObjectDetection supporting object detection tasks.
Configuration: Added Deimv2Config for model hyperparameters.
Image Processing: Added Deimv2ImageProcessor to handle preprocessing and postprocessing of images.
Unit Tests: Added tests for configuration, image processing, and model forward pass (tests/models/deimv2/).
This PR builds upon and makes changes to PR by @kekwlboy12469, PR #41291.
Fixes #41211
Fixes missing or incomplete DEIMv2 model integration.
Fixes absence of unit tests for DEIMv2 model and image processor.
Fixes compatibility issues with Transformers pipelines for object detection.
## 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.
- [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.
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PR_kwDOCUB6oc6sJ8aV
| 41,355
|
Better typehints for `apply_chat_template`
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CONTRIBUTOR
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# What does this PR do?
Make better typehints for `apply_chat_template`. Now IDEs and type checkers will correctly infer types for this code.
```py
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
# Returns raw string when tokenize=False
raw_prompt: str = tokenizer.apply_chat_template(
[{"role": "user", "content": "Hello!"}],
tokenize=False,
)
# Returns token ids when tokenize=True (default) and return_dict=False
token_ids: list[int] = tokenizer.apply_chat_template(
[{"role": "user", "content": "Hello!"}],
)
# Returns BatchEncoding when return_dict=True
batch_encoding: BatchEncoding = tokenizer.apply_chat_template(
[{"role": "user", "content": "Hello!"}],
return_dict=True,
)
```
Similarly, can be done for [`ProcessorMixin.apply_chat_template`](https://github.com/huggingface/transformers/blob/e11a00a16f925b7d3b52f5007bdce3464edb361f/src/transformers/processing_utils.py#L1559), but I'm not sure how to add to it, because it's using kwargs instead of arguments
## 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?
@ArthurZucker and @itazap
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PR_kwDOCUB6oc6sJkB6
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Fixed tiny incorrect import in `gemma3`
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CONTRIBUTOR
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# What does this PR do?
Fixes tiny incorrect import in `gemma3`. This is the correct class name https://github.com/huggingface/transformers/blob/e11a00a16f925b7d3b52f5007bdce3464edb361f/src/transformers/models/paligemma/modeling_paligemma.py#L67
Fixes # (issue)
## 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?
@gante @zucchini-nlp
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PR_kwDOCUB6oc6sJjBK
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serve: add non-streaming mode to /v1/responses; stream event parity; remove placeholder logprobs
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- Add `stream=false` support in `POST /v1/responses` returning a single OpenAI `Response`.
- Emit streaming deltas consistently; avoid placeholder logprobs by using empty lists.
- Stabilize tests: robust event checks; deterministic full request.
**Files:**
- src/transformers/commands/serving.py
- tests/commands/test_serving.py
_**Notes**_: Minimal diff, aligns with AGENTS.md. Improves OpenAI parity.
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PR_kwDOCUB6oc6sJh7W
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Fix typo in model proposal template
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# What does this PR do?
This PR fixes a grammatical error.
## 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.
- [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. @stevhiku
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PR_kwDOCUB6oc6sJfDx
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serve: add non-streaming mode to /v1/responses; stream event parity; remove placeholder logprobs
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- Add `stream=false` support in `POST /v1/responses` returning a single OpenAI `Response`.
- Emit streaming deltas consistently; avoid placeholder logprobs by using empty lists.
- Stabilize tests: robust event checks; deterministic full request.
**Files:**
- src/transformers/commands/serving.py
- tests/commands/test_serving.py
_**Notes**_: Minimal diff, aligns with AGENTS.md. Improves OpenAI parity.
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PR_kwDOCUB6oc6sJFlW
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Resolve issue 41296
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# Issues solved 41296.
What does this PR do?
<!--
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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).
- [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).
- [ ] 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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PR_kwDOCUB6oc6sI-5P
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Create (3d_parrallel_v2.py) - Add 3D parallelism training example script
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This PR adds a new training example under examples/3D_parallel.py that demonstrates a simple training loop with distributed (DDP/TP/PP) support.
Changes included:
-Fixed invalid docstring (""" instead of """:).
-Added fallback for local_rank in non-distributed setups.
-Wrapped dist.get_rank() and dist.destroy_process_group() with if dist.is_initialized().
-Lowered num_proc in datasets.map to avoid crashes on small machines.
-Integrated optional Weights & Biases tracking (--with_tracking).
-This script is intended to help users quickly test 3D parallelism setups with Hugging Face models.
# 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.
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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 @
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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PR_kwDOCUB6oc6sIuf4
| 41,348
|
fix python39 trainer compatibility
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# What does this PR do?
Fix typehint in trainer for python3.9 compatibility
Fixes https://github.com/huggingface/transformers/issues/41339
## 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?
@zach-huggingface @SunMarc
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PR_kwDOCUB6oc6sHapv
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Fix flash_attention.py: wrong argument passing for attn_implementation
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# What does this PR do?
The name of the attn type argument for `_flash_attention_forward()` should be `implementation`, instead of `attn_implementation` which currently uses in the function call. This would result in wrong type specification.
<!--
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.
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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).
- [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?
## CC
@vasqu @ArthurZucker @CyrilVallez
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PR_kwDOCUB6oc6sGrm4
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|
Prefer raising `TypeError` exception for invalid type
|
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# What does this PR do?
Fixes raising of `TypeError` exception instead of `ValueError` for invalid type
## 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?
@Rocketknight1 @ArthurZucker
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I_kwDOCUB6oc7PpwEA
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Add standardized multimodal capability metadata
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| 2025-10-06T11:03:42
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NONE
| null | null | null | null |
### Feature request
Downstream systems like SGLang/vLLM rely on registry/plugin systems or model-specific integration and metadata to decide whether a model is multimodal (i.e. `has_processor` in MultiModalRegistry).
There is no consistent, library-level field in PretrainedConfig that captures modality support.
Having a standardized config field would help. rely on a standardized flag rather than custom heuristics or registry hacks.
anyway we can pass tags like the ones on the huggingface model tags to model configs without breaking/maintaining backstream support?
```
multimodal_tags = {
"multimodal",
"image-to-text",
"image-text-to-text",
"video-to-text",
"video-text-to-text",
"audio-to-text",
"audio-text-to-text",
"any-to-any",
}
```
### Motivation
```
supported_modalities: Optional[List[str]] = None
is_multimodal: bool = False
modality_token_limits: Optional[Dict[str, int]] = None
pipeline_tag ...
```
fields like these would be helpful
### Your contribution
would like to discuss if this idea makes sense and is possible to implement.
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I_kwDOCUB6oc7PppFH
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No need to gather all parameters to save model when training with DeepSpeed Zero3 and LoRA
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[] | 2025-10-04T14:31:55
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NONE
| null | null | null | null |
### Feature request
Gather only adapter weights to save model weights when training with LoRA and DeepSpeed Zero3.
### Motivation
When we use `Trainer` and train models with LoRA, trainer only saves adapter weights. However, when we use DeepSpeed Zero3, which distributes model weights on multi GPUs, and set `stage3_gather_16bit_weights_on_model_save: true`, current implementation tries to gather the whole model weights to rank0 in `self.accelerator.get_state_dict(self.deepspeed)`.
It is not optimal and also it can lead to OOM when training large models even if trained with LoRA.
### Your contribution
I think I can fix it by adding a few lines to `Trainer.save_model` as below:
before
```
elif self.is_deepspeed_enabled:
try:
state_dict = self.accelerator.get_state_dict(self.deepspeed)
```
after
```
elif self.is_deepspeed_enabled:
try:
accept_exclude_frozen_parameters = "exclude_frozen_parameters" in set(
inspect.signature(self.model_wrapped.save_checkpoint).parameters.keys()
)
zero3_sharding = self.deepspeed.config.get("zero_optimization", {}).get("stage", None) == 3
if accept_exclude_frozen_parameters and _is_peft_model(self.model) and zero3_sharding:
# When using PEFT with DeepSpeed ZeRO Stage 3,
# we do not need to load the frozen parameters
state_dict = self.deepspeed._zero3_consolidated_16bit_state_dict(exclude_frozen_parameters=True)
else:
state_dict = self.accelerator.get_state_dict(self.deepspeed)
```
If it is okay, I can take responsibility to file PR.
| null |
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PR_kwDOCUB6oc6sF7ye
| 41,343
|
Fix test for model with dotted name and relative imports
|
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[] | 2025-10-04T13:36:49
| 2025-10-07T12:55:54
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# What does this PR do?
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The test `test_model_with_dotted_name_and_relative_imports` added in #40745 is not working as expected because `model_id = "hf-internal-testing/remote_code_model_with_dots"` does not include `.` in the name.
If this PR change looks good, could you create this new remote model? (I didn't create it myself because relying on an external user's resource is risky, as you mentioned in https://github.com/huggingface/transformers/pull/40745#pullrequestreview-3206353873).
Below is a detailed explanation of why `test_model_with_dotted_name_and_relative_imports` is not working. The explanation is a bit long, so feel free to skip it if you understand the problem from the above explanation.
---
If we remove the `_sanitize_module_name` process simply by changing the method like below:
```py
def _sanitize_module_name(name: str) -> str:
r"""
Tries to sanitize a module name so that it can be used as a Python module.
The following transformations are applied:
1. Replace `.` in module names with `_dot_`.
2. Replace `-` in module names with `_hyphen_`.
3. If the module name starts with a digit, prepend it with `_`.
4. Warn if the sanitized name is a Python reserved keyword or not a valid identifier.
If the input name is already a valid identifier, it is returned unchanged.
"""
# Not replacing `\W` characters with `_` to avoid collisions, because `_` is very common
return name
```
The test should raise an error, but it actually passes:
```sh
pytest tests/models/auto/test_modeling_auto.py::AutoModelTest::test_model_with_dotted_name_and_relative_imports
================================================================================== test session starts ===================================================================================
platform linux -- Python 3.12.11, pytest-8.4.1, pluggy-1.6.0
rootdir: /home/shutotakahashi/projects/transformers-uv/transformers
configfile: pyproject.toml
plugins: anyio-4.11.0, xdist-3.8.0
collected 1 item
tests/models/auto/test_modeling_auto.py::AutoModelTest::test_model_with_dotted_name_and_relative_imports PASSED [100%]
```
The reason the test passes is that the current `model_id = "hf-internal-testing/remote_code_model_with_dots"` does not include `.` in the name, as reported in the original issue https://github.com/huggingface/transformers/issues/40496.
If we replace `model_id` with[ `August4293/test-model_v1.0`](https://huggingface.co/August4293/test-model_v1.0/tree/main), which includes `.` in the name (this model was uploaded by the original PR contributor in https://github.com/huggingface/transformers/pull/40745#issuecomment-3270735455), the test raises the expected error:
```py
def test_model_with_dotted_name_and_relative_imports(self):
"""
Test for issue #40496: AutoModel.from_pretrained() doesn't work for models with '.' in their name
when there's a relative import.
Without the fix, this raises: ModuleNotFoundError:
No module named 'transformers_modules.August4293.test-model_v1'
"""
model_id = "August4293/test-model_v1.0"
model = AutoModel.from_pretrained(model_id, trust_remote_code=True)
self.assertIsNotNone(model)
```
```sh
pytest tests/models/auto/test_modeling_auto.py::AutoModelTest::test_model_with_dotted_name_and_relative_imports
================================================================================== test session starts ===================================================================================
platform linux -- Python 3.12.11, pytest-8.4.1, pluggy-1.6.0
rootdir: /home/shutotakahashi/projects/transformers-uv/transformers
configfile: pyproject.toml
plugins: anyio-4.11.0, xdist-3.8.0
collected 1 item
tests/models/auto/test_modeling_auto.py::AutoModelTest::test_model_with_dotted_name_and_relative_imports FAILED [100%]
======================================================================================== FAILURES ========================================================================================
_____________________________________________________________ AutoModelTest.test_model_with_dotted_name_and_relative_imports _____________________________________________________________
self = <tests.models.auto.test_modeling_auto.AutoModelTest testMethod=test_model_with_dotted_name_and_relative_imports>
def test_model_with_dotted_name_and_relative_imports(self):
"""
Test for issue #40496: AutoModel.from_pretrained() doesn't work for models with '.' in their name
when there's a relative import.
Without the fix, this raises: ModuleNotFoundError:
No module named 'transformers_modules.August4293.test-model_v1'
"""
model_id = "August4293/test-model_v1.0"
> model = AutoModel.from_pretrained(model_id, trust_remote_code=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
tests/models/auto/test_modeling_auto.py:587:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
src/transformers/models/auto/auto_factory.py:367: in from_pretrained
model_class = get_class_from_dynamic_module(
src/transformers/dynamic_module_utils.py:606: in get_class_from_dynamic_module
return get_class_in_module(class_name, final_module, force_reload=force_download)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/transformers/dynamic_module_utils.py:312: in get_class_in_module
module_spec.loader.exec_module(module)
<frozen importlib._bootstrap_external>:999: in exec_module
???
<frozen importlib._bootstrap>:488: in _call_with_frames_removed
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
"""
Custom model with relative import to demonstrate the bug.
"""
from transformers import PreTrainedModel
from transformers.modeling_outputs import BaseModelOutput
import torch
import torch.nn as nn
# This relative import should cause the bug when the folder has a dot in the name
> from .another_module import custom_function
E ModuleNotFoundError: No module named 'transformers_modules.August4293.test-model_v1'
```
Fixes #40496
## 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?
@Rocketknight1
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