* Update CometCallback to allow reusing of the running experiment
* Fixups
* Remove useless TODO
* Add checks for minimum version of the Comet SDK
* Fix documentation and links.
Also simplify how the Comet Experiment name is passed
* Pass datasets trust_remote_code
* Pass trust_remote_code in more tests
* Add trust_remote_dataset_code arg to some tests
* Revert "Temporarily pin datasets upper version to fix CI"
This reverts commit b7672826ca.
* Pass trust_remote_code in librispeech_asr_dummy docstrings
* Revert "Pin datasets<2.20.0 for examples"
This reverts commit 833fc17a3e.
* Pass trust_remote_code to all examples
* Revert "Add trust_remote_dataset_code arg to some tests" to research_projects
* Pass trust_remote_code to tests
* Pass trust_remote_code to docstrings
* Fix flax examples tests requirements
* Pass trust_remote_dataset_code arg to tests
* Replace trust_remote_dataset_code with trust_remote_code in one example
* Fix duplicate trust_remote_code
* Replace args.trust_remote_dataset_code with args.trust_remote_code
* Replace trust_remote_dataset_code with trust_remote_code in parser
* Replace trust_remote_dataset_code with trust_remote_code in dataclasses
* Replace trust_remote_dataset_code with trust_remote_code arg
* Draft fast image processors
* Draft working fast version
* py3.8 compatible cache
* Enable loading fast image processors through auto
* Tidy up; rescale behaviour based on input type
* Enable tests for fast image processors
* Smarter rescaling
* Don't default to Fast
* Safer imports
* Add necessary Pillow requirement
* Woops
* Add AutoImageProcessor test
* Fix up
* Fix test for imagegpt
* Fix test
* Review comments
* Add warning for TF and JAX input types
* Rearrange
* Return transforms
* NumpyToTensor transformation
* Rebase - include changes from upstream in ImageProcessingMixin
* Safe typing
* Fix up
* convert mean/std to tesnor to rescale
* Don't store transforms in state
* Fix up
* Update src/transformers/image_processing_utils_fast.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/auto/image_processing_auto.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/auto/image_processing_auto.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/auto/image_processing_auto.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Warn if fast image processor available
* Update src/transformers/models/vit/image_processing_vit_fast.py
* Transpose incoming numpy images to be in CHW format
* Update mapping names based on packages, auto set fast to None
* Fix up
* Fix
* Add AutoImageProcessor.from_pretrained(checkpoint, use_fast=True) test
* Update src/transformers/models/vit/image_processing_vit_fast.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Add equivalence and speed tests
* Fix up
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Remove deprecated logic and warnings
* Add back some code that seems to be important...
* Let's just add all he nllb stuff back; removing it is a bit more involved
* Remove kwargs
* Remove more kwargs
* fix seq2seq data collator to respect the given padding strategy
further added tests for the seq2seq data collator in the style of the `data_collator_for_token_classification` (pt, tf, np)
* formatting and change bool equals "==" to "is"
* add missed return types in tests
* update numpy test as it can handle unequal shapes, not like pt or tf
* Update legacy Repository usage in `examples/pytorch/text-classification/run_glue_no_trainer.py`
Marked for deprecation here https://huggingface.co/docs/huggingface_hub/guides/upload#legacy-upload-files-with-git-lfs
* Fix import order
* Replace all example usage of deprecated Repository
* Fix remaining repo call and rename args variable
* Revert removing creation of gitignore files and don't change research examples
* add: initial script to train clm fim
* fix: if training model from scratch, new tokens will be added and embeddings resized
* fix: fixed attention_mask errors when generating FIM data
* fix: file formatted using black
* add: run_fim_no_trainer.py and fixed some comments in run_fim.py
* add: added fim examples to the README.md and ran code fixup
* fix: little bug in both fim training scripts
* fix: remove comment from notebook and added a note on fim related params
* fix: minor typo in README
* add: suggested minor changes to README and run_fim.py
* add: gradient_accumulation_steps and gradient_checkpointing args
* add: improved model embedding resizing
* add: pad_to_multiple_of and attn_implementation params
* add: requested minor changes
* add: deepspeed zero compatibility
* add: resize embeddings layer with zero3 support for fim model initialization
* change version
* nuke
* this doesn't make sense
* update some requirements.py
* revert + no main
* nits
* change cache number
* more pin
* revert
---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Fix typos and grammar mistakes in docs and examples
* Fix typos in docstrings and comments
* Fix spelling of `tokenizer` in model tests
* Remove erroneous spaces in decorators
* Remove extra spaces in Markdown link texts
* Pin torch to <2.2.0
* Pin torchvision and torchaudio as well
* Playing around with versions to see if this helps
* twiddle something to restart the CI
* twiddle it back
* Try changing the natten version
* make fixup
* Revert "Try changing the natten version"
This reverts commit de0d6592c3.
* make fixup
* fix fix fix
* fix fix fix
---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* first commit
* correct default value non causal
* update config and modeling code
* update converting checkpoint
* clean modeling and fix tests
* make style
* add new config parameters to docstring
* fix copied from statements
* Apply suggestions from code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* make position_embeddings_type docstrings clearer
* clean converting script
* remove function not used
* clean modeling file
* apply suggestion for test file + add convert script to not_doctested
* modify tests according to review - cleaner logic and more tests
* Apply nit suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add checker of valid position embeddings type
* instantiate new layer norm layer with the right eps
* fix freeze_feature_encoder since it can be None in some cases
* add test same output in convert script
* restore wav2vec2conformer and add new model
* create processor and FE + clean
* add new model code
* fix convert script and set default config parameters
* correct model id paths
* make style
* make fix-copies and cleaning files
* fix copied from statements
* complete .md and fixe copies
* clean convert script argument defaults
* fix config parameters docstrings
* fix config docstring
* add copied from and enrich FE tests
* fix copied from and repo-consistency
* add autotokenizer
* make test input length shorter and change docstring code
* fix docstrings and copied from
* add add_adapter to ASR training example
* make testing of adapters more robust
* adapt to multi adapter layers
* refactor input_values->input_features and remove w2v2-bert feature extractor
* remove pretraining model
* remove depreciated features and useless lines
* add copied from and ignore statements to modeling tests
* remove pretraining model #2
* change import in convert script
* change default in convert script
* update readme and remove useless line
* Update tests/models/wav2vec2_bert/test_processor_wav2vec2_bert.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* refactor BERT to Bert for consistency
* remove useless ignore copy statement
* add persistent to buffer in rotary
* add eps in LayerNorm init and remove copied from
* add adapter activation parameters and add copied from statements
* Fix copied statements and add unitest.skip reasons
* add copied statement in test_processor
* refactor processor
* make style
* replace numpy random by torch rand
* remove expected output CTC
* improve converting script with processor class
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* remove gumbel class
* remove tests related to previously deleted class
* Update src/transformers/models/wav2vec2_bert/configuration_wav2vec2_bert.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* correct typos
* remove uused parameters
* update processor to takes both text and audio
* update checkpoints
* update expected output and add ctc expected output
* add label_attention_mask
* replace pt with np in processor tests
* fix typo
* revert to behaviour with labels_attention_mask
---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove `task` arg in `load_dataset` in image-classification example
* Manage case where "train" is not in dataset
* Add new args to manage image and label column names
* Similar to audio-classification example
* Fix README
* Update tests
While using `run_clm.py`,[^1] I noticed that some files were being added
to my global cache, not the local cache. I set the `cache_dir` parameter
for the one call to `evaluate.load()`, which partially solved the
problem. I figured that while I was fixing the one script upstream, I
might as well fix the problem in all other example scripts that I could.
There are still some files being added to my global cache, but this
appears to be a bug in `evaluate` itself. This commit at least moves
some of the files into the local cache, which is better than before.
To create this PR, I made the following regex-based transformation:
`evaluate\.load\((.*?)\)` -> `evaluate\.load\($1,
cache_dir=model_args.cache_dir\)`. After using that, I manually fixed
all modified files with `ruff` serving as useful guidance. During the
process, I removed one existing usage of the `cache_dir` parameter in a
script that did not have a corresponding `--cache-dir` argument
declared.
[^1]: I specifically used `pytorch/language-modeling/run_clm.py` from
v4.34.1 of the library. For the original code, see the following URL:
acc394c4f5/examples/pytorch/language-modeling/run_clm.py.
* docs: replace torch.distributed.run by torchrun
`transformers` now officially support pytorch >= 1.10.
The entrypoint `torchrun`` is present from 1.10 onwards.
Signed-off-by: Peter Pan <Peter.Pan@daocloud.io>
* Update src/transformers/trainer.py
with @ArthurZucker's suggestion
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
---------
Signed-off-by: Peter Pan <Peter.Pan@daocloud.io>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Renamed variable extension to builder_name
* If builder name is jsonl change to json to align with load_datasets
* Apply suggestions from code review
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
---------
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
* Remove the torch main_process_first context manager from TF examples
* Correctly set num_beams=1 in our examples, and add a guard in GenerationConfig.validate()
* Update src/transformers/generation/configuration_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Normalize only if needed
* Update examples/pytorch/image-classification/run_image_classification.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* if else in one line
* within block
* one more place, sorry for mess
* import order
* Update examples/pytorch/image-classification/run_image_classification.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update examples/pytorch/image-classification/run_image_classification_no_trainer.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* refactor: change default block_size
* fix: return tf to origin
* fix: change files to origin
* rebase
* rebase
* rebase
* rebase
* rebase
* rebase
* rebase
* rebase
* refactor: add min block_size to files
* reformat: add min block_size for run_clm tf
* Make training args fully immutable
* Working tests, PyTorch
* In test_trainer
* during testing
* Use proper dataclass way
* Fix test
* Another one
* Fix tf
* Lingering slow
* Exception
* Clean
