* try fixing push-ci
* move to new runners
* move benchmark.yml to new runners
* move doctest_job.yml to new runners
* move doctests.yml to new runners
* move push-important-models.yml to new runners
* move self-pr-slow-ci.yml to new runners
* fix typo
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* fix working directory
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* fix working directory
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* improve code
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
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Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* Update an keyerror on _save_check_point prevent confusion of missing metric keys
* Update grammar error and case sensitive.
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* adding update KeyError on _evaluate function to align with _save_checkpoint function
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Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* When we set self.dt_proj.bias = None, it removes the bias parameter from the model. When we later tried to assign a tensor to self.dt_proj.bias, it caused a TypeError because PyTorch expects a Parameter object.
* When we set self.dt_proj.bias = None, it removes the bias parameter from the model. When we later tried to assign a tensor to self.dt_proj.bias, it caused a TypeError because PyTorch expects a Parameter object.
* When we set self.dt_proj.bias = None, it removes the bias parameter from the model. When we later tried to assign a tensor to self.dt_proj.bias, it caused a TypeError because PyTorch expects a Parameter object.
* Trainer - deprecate tokenizer for processing_class
* Extend chage across Seq2Seq trainer and docs
* Add tests
* Update to FutureWarning and add deprecation version
* add support for custom inputs and batched inputs in ProcessorTesterMixin
* Fix batch_size behavior ProcessorTesterMixin
* Change format prepare inputs batched
* Remove override test pixtral processor
* Remove unnecessary tests and cleanup after new prepare_inputs functions
* Fix instructBlipVideo image processor
* fix(copy): fixup copy
* fix(deformable_detr): move weight initialization to the right place
* fix(grounding_dino): move weight initialization to the right place
* fix(rt_detr): move weight initialization to the right place
* [run-slow] deformable_detr, grounding_dino, rt_detr
* Remove max_new_tokens arg
* Add ASR pipeline to testing
* make fixup
* Factor the output test out into a util
* Full error reporting
* Full error reporting
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Small comment
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Co-authored-by: Lysandre Debut <hi@lysand.re>
* Add include_loss_for_metrics
* Fix styling
* Initialize inputs and losses to avoid AttributeError
* Ruff styling
* Refactor compute_metrics and update EvalPrediction
* Change Naming
* Added include_for_metrics to group both args
* Fix style
* Change warnings to logger
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
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Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Validate the eval dataset in advance.
* format
* format
* format
* Update src/transformers/trainer.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* format
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Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* fix(m2m_100): skip dropout in eval for flash_attn
* fix(misc): skip dropout in eval for flash attn various models
* chore(m2m_100): copy flash attn from bart
* chore: run make fix-copies
* [run-slow] bart, m2m_100
* refactor image features selection
* break line
* remove whitespace
* add pr comments: include projection and rename function
* make fix-copies
* fix get_image_feature in vip llava
* Fix Mamba slow path bug with dtype mismatch.
* Update test_modeling_mamba.py
* Improve style.
* Fix issue with cache position of dtype mismatch test.
* Change test for slow path.
* Revert changes.
* Switch to buggy code and add test to catch it.
* Fix the dtype mismatch bug and add test code to verify it.
* Fix minor bug with test.
* Fix incorrect dtype of model output.
* Fix incorrect dtype of cache.
* Fix incorrect dtype of ssm cache.
* Fix incorrect dtype of conv state.
* Remove assertion for ssm state.
* Add assertion for conv state dtype.
* Fix all issues with dtype mismatch test.
* HQQ model serialization attempt
* fix hqq dispatch and unexpected keys
* style
* remove check_old_param
* revert to check HQQLinear in quantizer_hqq.py
* revert to check HQQLinear in quantizer_hqq.py
* update HqqConfig default params
* make ci happy
* make ci happy
* revert to HQQLinear check in quantizer_hqq.py
* check hqq_min version 0.2.0
* set axis=1 as default in quantization_config.py
* validate_env with hqq>=0.2.0 version message
* deprecated hqq kwargs message
* make ci happy
* remove run_expected_keys_check hack + bump to 0.2.1 min hqq version
* fix unexpected_keys hqq update
* add pre_quantized check
* add update_expected_keys to base quantizerr
* ci base.py fix?
* ci base.py fix?
* fix "quantization typo" src/transformers/utils/quantization_config.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix post merge
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Co-authored-by: Marc Sun <marc@huggingface.co>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Enable non-safetensor serialization and deserialization for TorchAoConfig quantized model
Summary:
After https://github.com/huggingface/huggingface_hub/pull/2440 we added non-safetensor serialization and deserialization
in huggingface, with this we can now add the support in transformers
Note that we don't plan to add safetensor serialization due to different goals of wrapper tensor subclass and safetensor
see README for more details
Test Plan:
tested locally
Reviewers:
Subscribers:
Tasks:
Tags:
* formatting
* formatting
* minor fix
* formatting
* address comments
* comments
* minor fix
* update doc
* refactor compressed tensor quantizer
* fix return type
* update to union
* fix gate_logits typing
* fix num_experts type
* fix typing
* run fix-copies
* add doc for top_k
* run fix-copies
* empty commit to trigger CI
