Repository: huggingface/transformers
Pull request: fix chunked prefill implementation issue-43082

# What does this PR do?

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Fixes #43082

## 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? Yes
- [ ] 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/43082
- [ ] 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). N/A
- [ ] Did you write any new necessary tests? N/A


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Models:

- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier

Library:

- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh

Integrations:

- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo

Devices/Backends:

- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad 

Documentation: @stevhliu

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Changed files:
filename: src/transformers/generation/utils.py
patch: @@ -3860,7 +3860,6 @@ def _prefill(self, input_ids: torch.LongTensor, generation_config: GenerationCon
                 model_kwargs["cache_position"] = torch.arange(
                     past_length, current_length, dtype=torch.long, device=input_chunk.device
                 )
-                model_kwargs["position_ids"] = model_kwargs["cache_position"].unsqueeze(0)
                 model_inputs = self.prepare_inputs_for_generation(input_chunk, **model_kwargs)
 
                 outputs = model_forward(**model_inputs, return_dict=True)