Repository: huggingface/transformers
Pull request: Add unified logits_to_keep support to LLMClass

# What does this PR do?

Add `logits_to_keep` arguments to `Glm4vForConditionalGeneration` and `Qwen2_5_VLForConditionalGeneration` class.


Fixes # (issue)
The `trl` repository now assumes that the Transformers library supports the `logits_to_keep` parameter starting from v4.49.0, since the required slicing step was removed in `grpo_trainer`. See the changes in [`grpo_trainer.py`](https://github.com/huggingface/trl/commit/b9572737b4796c6a72bacfd9ed78c686763725ea).

## 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.

<!-- 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
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier

Library:

- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface, @SunMarc and @qgallouedec
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber

Documentation: @stevhliu

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->


Changed files:
filename: src/transformers/models/glm4v/modeling_glm4v.py
patch: @@ -1455,6 +1455,7 @@ def forward(
         video_grid_thw: Optional[torch.LongTensor] = None,
         rope_deltas: Optional[torch.LongTensor] = None,
         cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs: Unpack[TransformersKwargs],
     ) -> Union[tuple, Glm4vCausalLMOutputWithPast]:
         r"""
@@ -1523,7 +1524,10 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
+
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
 
         loss = None
         if labels is not None:

filename: src/transformers/models/glm4v/modular_glm4v.py
patch: @@ -1381,6 +1381,7 @@ def forward(
         video_grid_thw: Optional[torch.LongTensor] = None,
         rope_deltas: Optional[torch.LongTensor] = None,
         cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs: Unpack[TransformersKwargs],
     ) -> Union[tuple, Glm4vCausalLMOutputWithPast]:
         r"""
@@ -1449,7 +1450,10 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
+
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
 
         loss = None
         if labels is not None:

filename: src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py
patch: @@ -1448,6 +1448,7 @@ def forward(
         rope_deltas: Optional[torch.LongTensor] = None,
         cache_position: Optional[torch.LongTensor] = None,
         second_per_grid_ts: Optional[torch.Tensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs: Unpack[TransformersKwargs],
     ) -> Union[tuple, Qwen2_5_VLCausalLMOutputWithPast]:
         r"""
@@ -1520,7 +1521,10 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
+
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
 
         loss = None
         if labels is not None:

filename: src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py
patch: @@ -723,6 +723,7 @@ def forward(
         rope_deltas: Optional[torch.LongTensor] = None,
         cache_position: Optional[torch.LongTensor] = None,
         second_per_grid_ts: Optional[torch.Tensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs: Unpack[TransformersKwargs],
     ) -> Union[tuple, Qwen2_5_VLCausalLMOutputWithPast]:
         r"""
@@ -795,7 +796,10 @@ def forward(
         )
 
         hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
+
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
 
         loss = None
         if labels is not None: