* Extremely small change to TF SAM dummies to reduce memory usage on build
* remove debug breakpoint
* Debug print statement to track array sizes
* More debug shape printing
* More debug shape printing
* Now remove the debug shape printing
* make fixup
* make fixup
* Rework TF type hints to use | None instead of Optional[] for tf.Tensor
* Rework TF type hints to use | None instead of Optional[] for tf.Tensor
* Don't forget the imports
* Add the imports to tests too
* make fixup
* Refactor tests that depended on get_type_hints
* Better test refactor
* Fix an old hidden bug in the test_keras_fit input creation code
* Fix for the Deit tests
* Use bool instead of uint8/byte in DebertaV2 to make it compatible with TensorRT
TensorRT cannot accept onnx graph with uint8/byte intermediate tensors. This PR uses bool tensors instead of unit8/byte tensors to make the exported onnx file can work with TensorRT.
* fix: use bool instead of uint8/byte in Deberta and SEW-D
---------
Co-authored-by: Yuxian Qiu <yuxianq@nvidia.com>
* doc refocused on using optimum, tflite
* minor updates to fix checks
* Apply suggestions from code review
Co-authored-by: regisss <15324346+regisss@users.noreply.github.com>
* TFLite to separate page, added links
* Removed the onnx list builder
* make style
* Update docs/source/en/serialization.mdx
Co-authored-by: regisss <15324346+regisss@users.noreply.github.com>
---------
Co-authored-by: regisss <15324346+regisss@users.noreply.github.com>
* Added lion and paged optimizers and made original tests pass.
* Added tests for paged and lion optimizers.
* Added and fixed optimizer tests.
* Style and quality checks.
---------
Co-authored-by: younesbelkada <younesbelkada@gmail.com>
* Added lion and paged optimizers and made original tests pass.
* Added tests for paged and lion optimizers.
* Added and fixed optimizer tests.
* Style and quality checks.
* Initial draft. Some tests fail.
* Fixed dtype bug.
* Fixed bug caused by torch_dtype='auto'.
* All test green for 8-bit and 4-bit layers.
* Added fix for fp32 layer norms and bf16 compute in LLaMA.
* Initial draft. Some tests fail.
* Fixed dtype bug.
* Fixed bug caused by torch_dtype='auto'.
* All test green for 8-bit and 4-bit layers.
* Added lion and paged optimizers and made original tests pass.
* Added tests for paged and lion optimizers.
* Added and fixed optimizer tests.
* Style and quality checks.
* Fixing issues for PR #23479.
* Added fix for fp32 layer norms and bf16 compute in LLaMA.
* Reverted variable name change.
* Initial draft. Some tests fail.
* Fixed dtype bug.
* Fixed bug caused by torch_dtype='auto'.
* All test green for 8-bit and 4-bit layers.
* Added lion and paged optimizers and made original tests pass.
* Added tests for paged and lion optimizers.
* Added and fixed optimizer tests.
* Style and quality checks.
* Added missing tests.
* Fixup changes.
* Added fixup changes.
* Missed some variables to rename.
* revert trainer tests
* revert test trainer
* another revert
* fix tests and safety checkers
* protect import
* simplify a bit
* Update src/transformers/trainer.py
* few fixes
* add warning
* replace with `load_in_kbit = load_in_4bit or load_in_8bit`
* fix test
* fix tests
* this time fix tests
* safety checker
* add docs
* revert torch_dtype
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* multiple fixes
* update docs
* version checks and multiple fixes
* replace `is_loaded_in_kbit`
* replace `load_in_kbit`
* change methods names
* better checks
* oops
* oops
* address final comments
---------
Co-authored-by: younesbelkada <younesbelkada@gmail.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* New TF version compatibility fixes
* Remove dummy print statement, move expand_1d
* Make a proper framework inference function
* Make a proper framework inference function
* ValueError -> TypeError
* Making `safetensors` a core dependency.
To be merged later, I'm creating the PR so we can try it out.
* Update setup.py
* Remove duplicates.
* Even more redundant.
* Update modeling_open_llama.py
Fix typo in `use_memorry_efficient_attention` parameter name
* Update configuration_open_llama.py
Fix typo in `use_memorry_efficient_attention` parameter name
* Update configuration_open_llama.py
Take care of backwards compatibility ensuring that the previous parameter name is taken into account if used
* Update configuration_open_llama.py
format to adjust the line length
* Update configuration_open_llama.py
proper code formatting using `make fixup`
* Update configuration_open_llama.py
pop the argument not to let it be set later down the line
* Fix: Change tensors to integers in torch.split() for torch.dynamo and torch.compile compatibility
* Applied the suggested fix to the utils/check_copies.py test
* Applied the suggested fix by changing the original function that gets copied
* First commit
* Add auto-translation with GPT-4
* make fixup
* Add a functional layernorm for TF
* Add all the auxiliary imports etc.
* Add the extra processor and tests
* rebase to main
* Add all the needed fixes to the GPT code
* make fixup
* Make convolutions channels-last so they run on CPU
* make fixup
* Fix final issues
* Fix other models affected by test change
* Clarify comment on the sparse_prompt_embeddings check
* Refactor functional_layernorm, use shape_list in place of .shape in some places
* Remove deprecated torch-alike code
* Update tests/models/sam/test_modeling_tf_sam.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/sam/test_modeling_tf_sam.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Refactor processor with common methods and separated private methods
* make fixup
* Quietly delete the file that didn't do anything (sorry Sylvain)
* Refactor the processor tests into one file
* make fixup
* Clean up some unnecessary indirection
* Fix TF mask postprocessing
* Add more processor equivalence tests
* Refactor generate_crop_boxes to use framework-neutral np code
* Make the serving output correctly conditional
* Fix error message line length
* Use dict keys rather than indices internally in both TF and PT SAM call/forward
* Return dicts internally in the call/forward methods
* Revert changes to common tests and just override check_pt_tf_outputs
* Revert changes to other model tests
* Clarify comments for functional layernorm
* Add missing transpose from PT code
* Removed unused copied from in PT code
* Remove overrides for tests that don't exist in TF
* Fix transpose and update tests for PT and TF to check pred_masks
* Add training flag
* Update tests to use TF checkpoints
* Update index.mdx
* Add missing cross-test decorator
* Remove optional extra asterisks
* Revert return_dict changes in PT code
* Update src/transformers/models/sam/modeling_tf_sam.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Remove None return annotations on init methods
* Update tests/models/sam/test_processor_sam.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Fix input_boxes shapes
* make fixup
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
