Remove deprecated logic and warnings #30743
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| @@ -395,11 +394,6 @@ def forward( | |||
| output_attentions: bool = False, | |||
| **kwargs, | |||
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| **kwargs, |
| @@ -551,14 +545,6 @@ def forward( | |||
| output_attentions: bool = False, | |||
| **kwargs, | |||
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| **kwargs, |
| @@ -789,11 +775,6 @@ def forward( | |||
| output_attentions: bool = False, | |||
| **kwargs, | |||
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| **kwargs, |
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same here kwargs needs to be removed
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| @property | ||
| def sin_cached(self): | ||
| logger.warning_once( | ||
| "The sin_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use " | ||
| "the forward method of RoPE from now on instead. It is not used in the `LlamaAttention` class" | ||
| ) | ||
| return self._sin_cached | ||
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| @property | ||
| def cos_cached(self): | ||
| logger.warning_once( | ||
| "The cos_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use " | ||
| "the forward method of RoPE from now on instead. It is not used in the `LlamaAttention` class" | ||
| ) | ||
| return self._cos_cached | ||
|
|
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the buffers that are registered need to be deleted as well
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same comments as for llama!
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same comment as llama
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@ArthurZucker Thanks for the review! I've removed all of the kwargs being passed now, and the |
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miguelm-almeida
added a commit
to mig-mfreitas/transformers-pic
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Jul 10, 2024
- Comply with the the tensor building logic introduced in huggingface#30743 - Add referencing to the optimized Attention Factor equation - Remove Dynamic YaRN for a more agile deployment Co-authored-by: mig-mfreitas <mig-mfreitas@users.noreply.github.com>
gante
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to gante/transformers
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Jul 23, 2024
- Comply with the the tensor building logic introduced in huggingface#30743 - Add referencing to the optimized Attention Factor equation - Remove Dynamic YaRN for a more agile deployment Co-authored-by: mig-mfreitas <mig-mfreitas@users.noreply.github.com>
gante
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that referenced
this pull request
Jul 23, 2024
* Add YaRN and Dynamic-YaRN RoPE Scaling Methods YaRN (Yet another RoPE extension method) combines the NTK-By-Parts Interpolation and Attention Scaling methods, improving upon existing RoPE interpolation methods for longer context window sizes. Fine-tuned models maintain their original performance across benchmarks while enabling efficient extrapolation and transfer learning for quicker convergence, especially in compute-limited environments. We implement YaRN and Dynamic-YaRN for the following list of models: - LLaMA - Falcon - GPT-NeoX - Olmo - Persimmon - Phi - StableLM - OpenLLaMA New unit tests are added to assert YaRN's correct behavior on both short and long sequence inputs. For more details, please refer to https://arxiv.org/abs/2309.00071. Co-authored-by: Miguel Almeida <miguel.pessanha.almeida@tecnico.ulisboa.pt> * Refactor YaRN implementation for LLaMA Iterate on YaRN implementation for LLaMA and remove diff from remaining models for increased PR modularity. This commit includes the following changes: - Merge 'yarn_rope_scaling' and 'rope_scaling' dictionaries - Remove unnecessary attributes ('extrapolation_factor' and 'finetuned') from YaRN classes - Inherit 'forward' method in YaRN classes from superclass - Rename 'yarn' method to 'compute_yarn_scaling' - Extend YaRN tests with further assertions - Fix style inconsistencies Co-authored-by: Miguel Monte e Freitas <miguelmontefreitas@tecnico.ulisboa.pt> * Refactor Tensor Building Logic for YaRN - Comply with the the tensor building logic introduced in #30743 - Add referencing to the optimized Attention Factor equation - Remove Dynamic YaRN for a more agile deployment Co-authored-by: mig-mfreitas <mig-mfreitas@users.noreply.github.com> * remove unwanted file --------- Co-authored-by: Miguel Almeida <miguel.pessanha.almeida@tecnico.ulisboa.pt> Co-authored-by: mig-mfreitas <mig-mfreitas@users.noreply.github.com> Co-authored-by: Joao Gante <joao@huggingface.co>
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What does this PR do?
Kills a bunch of deprecated code