* Add `OPTForQuestionAnswering`
- added `OPTForQuestionAnswering` class based on `BloomForQuestionAnswering`
- added `OPTForQuestionAnswering` in common tests
- all common tests pass
- make fixup done
* added docstrings for OPTForQuestionAnswering
* Fix docstrings for OPTForQuestionAnswering
* Rework pipeline tests
* Try to fix Flax tests
* Try to put it before
* Use a new decorator instead
* Remove ignore marker since it doesn't work
* Filter pipeline tests
* Woopsie
* Use the fitlered list
* Clean up and fake modif
* Remove init
* Revert fake modif
- Fixes the image segmentation pipeline test failures caused by changes to the postprocessing methods of supported models
- Updates the ImageSegmentationPipeline tests
- Improves docs, adds 'task' argument to optionally perform semantic, instance or panoptic segmentation
* validate onnx models with a different input geometry than saved with
* only test working features for now
* simpler test skipping
* rm TODO
* expose batch_size/seq_length on vit
* skip certain name, feature, framework parameterizations known to fail validation
* Trigger CI
* Trigger CI
* Add ZeroShotObjectDetectionPipeline (#18445)
* Add AutoModelForZeroShotObjectDetection task
This commit also adds the following
- Add explicit _processor method for ZeroShotObjectDetectionPipeline.
This is necessary as pipelines don't auto infer processors yet and
`OwlVitProcessor` wraps tokenizer and feature_extractor together, to
process multiple images at once
- Add auto tests and other tests for ZeroShotObjectDetectionPipeline
* Add AutoModelForZeroShotObjectDetection task
This commit also adds the following
- Add explicit _processor method for ZeroShotObjectDetectionPipeline.
This is necessary as pipelines don't auto infer processors yet and
`OwlVitProcessor` wraps tokenizer and feature_extractor together, to
process multiple images at once
- Add auto tests and other tests for ZeroShotObjectDetectionPipeline
* Add batching for ZeroShotObjectDetectionPipeline
* Fix doc-string ZeroShotObjectDetectionPipeline
* Fix output format: ZeroShotObjectDetectionPipeline
Ensures post_process_instance_segmentation and post_process_panoptic_segmentation methods return a tensor of shape (target_height, target_width) filled with -1 values if no segment with score > threshold is found.
* add sudachipy and jumanpp tokenizers for bert_japanese
* use ImportError instead of ModuleNotFoundError in SudachiTokenizer and JumanppTokenizer
* put test cases of test_tokenization_bert_japanese in one line
* add require_sudachi and require_jumanpp decorator for testing
* add sudachi and pyknp(jumanpp) to dependencies
* remove sudachi_dict_small and sudachi_dict_full from dependencies
* empty commit for ci
- Improves MaskFormer docs, corrects minor typos
- Restructures MaskFormerFeatureExtractor.post_process_panoptic_segmentation for better readability, adds target_sizes argument for optional resizing
- Adds post_process_semantic_segmentation and post_process_instance_segmentation methods.
- Adds a deprecation warning to post_process_segmentation method in favour of post_process_instance_segmentation
* add bloom for question answering
- attempt to add Bloom for question answering
- adapted from `GPTJForQuestionAnswering`
- Fixed `num_labels` to `2` for common tests
- Added a bit of docstring
- All common tests pass
* Update src/transformers/models/bloom/modeling_bloom.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* revert changes related to `num_labels`
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Poc to use safetensors
* Typo
* Final version
* Add tests
* Save with the right name!
* Update tests/test_modeling_common.py
Co-authored-by: Julien Chaumond <julien@huggingface.co>
* Support for sharded checkpoints
* Test from Hub part 1
* Test from hub part 2
* Fix regular checkpoint sharding
* Bump for fixes
Co-authored-by: Julien Chaumond <julien@huggingface.co>
* Rebase ESM PR and update all file formats
* Fix test relative imports
* Add __init__.py to the test dir
* Disable gradient checkpointing
* Remove references to TFESM... FOR NOW >:|
* Remove completed TODOs from tests
* Convert docstrings to mdx, fix-copies from BERT
* fix-copies for the README and index
* Update ESM's __init__.py to the modern format
* Add to _toctree.yml
* Ensure we correctly copy the pad_token_id from the original ESM model
* Ensure we correctly copy the pad_token_id from the original ESM model
* Tiny grammar nitpicks
* Make the layer norm after embeddings an optional flag
* Make the layer norm after embeddings an optional flag
* Update the conversion script to handle other model classes
* Remove token_type_ids entirely, fix attention_masking and add checks to convert_esm.py
* Break the copied from link from BertModel.forward to remove token_type_ids
* Remove debug array saves
* Begin ESM-2 porting
* Add a hacky workaround for the precision issue in original repo
* Code cleanup
* Remove unused checkpoint conversion code
* Remove unused checkpoint conversion code
* Fix copyright notices
* Get rid of all references to the TF weights conversion
* Remove token_type_ids from the tests
* Fix test code
* Update src/transformers/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update README.md
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add credit
* Remove _ args and __ kwargs in rotary embedding
* Assertively remove asserts
* Replace einsum with torch.outer()
* Fix docstring formatting
* Remove assertions in tokenization
* Add paper citation to ESMModel docstring
* Move vocab list to single line
* Remove ESMLayer from init
* Add Facebook copyrights
* Clean up RotaryEmbedding docstring
* Fix docstring formatting
* Fix docstring for config object
* Add explanation for new config methods
* make fix-copies
* Rename all the ESM- classes to Esm-
* Update conversion script to allow pushing to hub
* Update tests to point at my repo for now
* Set config properly for tests
* Remove the gross hack that forced loss of precision in inv_freq and instead copy the data from the model being converted
* make fixup
* Update expected values for slow tests
* make fixup
* Remove EsmForCausalLM for now
* Remove EsmForCausalLM for now
* Fix padding idx test
* Updated README and docs with ESM-1b and ESM-2 separately (#19221)
* Updated README and docs with ESM-1b and ESM-2 separately
* Update READMEs, longer entry with 3 citations
* make fix-copies
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Tom Sercu <tsercu@fb.com>
Co-authored-by: Your Name <you@example.com>
* chore: initial commit
* chore: adding util methods
yet to work on the nn.functional.interpolate port with align_corener=True
* chore: refactor the utils
* used tf.compat.v1.image.resize to align the F.interpolate function
* added type hints to the method signatures
* added references to the gists where one 2 one alignment of torch and tf has been shown
* chore: adding the layers
* chore: porting all the layers from torch to tf
This is the initial draft, nothing is tested yet.
* chore: aligning the layers with reference to tf clip
* chore: aligning the modules
* added demaraction comments
* added copied and adapted from comments
* chore: aligning with CLIP
* chore: wrangling the layers to keep it tf compatible
* chore: aligning the names of the layers for porting
* chore: style changes
* chore: adding docs and inits
* chore: adding tfp dependencis
the code is taken from TAPAS
* chore: initial commit for testing
* chore: aligning the vision embeddings with the vit implementatino
* chore: changing model prefix
* chore: fixing the name of the model and the layer normalization test case
* chore: every test passes but the slow ones
* chore: fix style and integration test
* chore: moving comments below decorators
* chore: make fixup and fix-copies changes
* chore: adding the Vision and Text Model to check_repo
* chore: modifying the prefix name to align it with the torch implementation
* chore: fix typo in configuration
* choer: changing the name of the model variable
* chore: adding segmentation flag
* chore: gante's review
* chore: style refactor
* chore: amy review
* chore: adding shape_list to parts that have been copied from other snippets
* chore: init batchnorm with torch defaults
* chore: adding shape_list to pass the tests
* test fix: adding seed as 0
* set seed
* chore: changing the straight through trick to fix -ve dimensinos
* chore: adding a dimension to the loss
* chore: adding reviewers and contributors names to the docs
* chore: added changes after review
* chore: code quality fixup
* chore: fixing the segmentation snippet
* chore: adding to the layer calls
* chore: changing int32 to int64 for inputs of serving
* chore: review changes
* chore: style changes
* chore: remove from_pt=True
* fix: repo consistency
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Add DeformableDetrFeatureExtractor
* Fix post_process
* Fix name
* Add tests for feature extractor
* Fix doc tests
* Fix name
* Address comments
* Apply same fix to DETR and YOLOS as well
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* init PR
* optimize top p and add edge case
* styling
* style
* revert tf and flax test
* add edge case test for FLAX and TF
* update doc with smallest set sampling for top p
* make style
* Override save() to use the serving signature as the default
* Replace int32 with int64 in all our serving signatures
* Remember one very important line so as not to break every test at once
* Dtype fix for TFLED
* dtype fix for shift_tokens_right in general
* Dtype fixes in mBART and RAG
* Fix dtypes for test_unpack_inputs
* More dtype fixes
* Yet more mBART + RAG dtype fixes
* Yet more mBART + RAG dtype fixes
* Add a check that the model actually has a serving method
* Updated test values
The image segmentation pipeline tests - tests/pipelines/test_pipelines_image_segmentation.py - were failing after the merging of #1849 (49e44b216b). This was due to the difference in rescaling. Previously the images were rescaled by `image = image / 255`. In the new commit, a `rescale` method was added, and images rescaled using `image = image * scale`. This was known to cause small differences in the processed images (see
[PR comment](https://github.com/huggingface/transformers/pull/18499#discussion_r940347575)).
Testing locally, changing the `rescale` method to divide by a scale factor (255) resulted in the tests passing. It was therefore decided the test values could be updated, as there was no logic difference between the commits.
* Use double quotes, like previous example
* Fix up
* add gpt-neox-japanese model and tokenizer as new model
* Correction to PR's comment for GPT NeoX Japanese
- Fix to be able to use gpu
- Add comment # Copied... at the top of RotaryEmbedding
- Implement nn.Linear instead of original linear class
- Add generation test under @slow
* fix bias treatment for gpt-neox-japanese
* Modidy gpt-neox-japanese following PR
- add doc for bias_dropout_add
- style change following a PR comment
* add document for gpt-neox-japanese
* remove unused import from gpt-neox-japanese
* fix README for gpt-neox-japanese
* First draft
* More improvements
* Improve model, add custom CUDA code
* Import torch before
* Add script that imports custom layer
* Add everything in new ops directory
* Import custom layer in modeling file
* Fix ARCHIVE_MAP typo
* Creating the custom kernel on the fly.
* Import custom layer in modeling file
* More improvements
* Fix CUDA loading
* More improvements
* Improve conversion script
* Improve conversion script
* Make it work until encoder_outputs
* Make forward pass work
* More improvements
* Make logits match original implementation
* Make implementation also support single_scale model
* Add support for single_scale and dilation checkpoint
* Add support for with_box_refine model
* Support also two stage model
* Improve tests
* Fix more tests
* Make more tests pass
* Upload all models to the hub
* Clean up some code
* Improve decoder outputs
* Rename intermediate hidden states and reference points
* Improve model outputs
* Move tests to dedicated folder
* Improve model outputs
* Fix retain_grad test
* Improve docs
* Clean up and make test_initialization pass
* Improve variable names
* Add copied from statements
* Improve docs
* Fix style
* Improve docs
* Improve docs, move tests to model folder
* Fix rebase
* Remove DetrForSegmentation from auto mapping
* Apply suggestions from code review
* Improve variable names and docstrings
* Apply some more suggestions from code review
* Apply suggestion from code review
* better docs and variables names
* hint to num_queries and two_stage confusion
* remove asserts and code refactor
* add exception if two_stage is True and with_box_refine is False
* use f-strings
* Improve docs and variable names
* Fix code quality
* Fix rebase
* Add require_torch_gpu decorator
* Add pip install ninja to CI jobs
* Apply suggestion of @sgugger
* Remove DeformableDetrForObjectDetection from auto mapping
* Remove DeformableDetrModel from auto mapping
* Add model to toctree
* Add model back to mappings, skip model in pipeline tests
* Apply @sgugger's suggestion
* Fix imports in the init
* Fix copies
* Add CPU implementation
* Comment out GPU function
* Undo previous change
* Apply more suggestions
* Remove require_torch_gpu annotator
* Fix quality
* Add logger.info
* Fix logger
* Fix variable names
* Fix initializaztion
* Add missing initialization
* Update checkpoint name
* Add model to doc tests
* Add CPU/GPU equivalence test
* Add Deformable DETR to pipeline tests
* Skip model for object detection pipeline
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Nouamane Tazi <nouamane98@gmail.com>
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
* Use int64 throughout TFLongFormer
* make style
* Do some more fixed casting in TFLongFormer
* Fix some wonky "is None" conditionals
* Cast all the dtypes, salt the earth
* Fix copies to TFLED as well and do some casting there
* dtype fix in TFLongformer test
* Make fixup
* Expand tolerances on the LED tests too (I think this is a TF32 thing)
* Expand test tolerances for LED a tiny bit (probably a Tensorfloat thing again)
* Fix train_step and test_step, correctly enable CLIP fit test
* Stop using get_args on older Python versions
* Don't use get_origin either
* UnionType is actually even newer, don't use that either
* Apply the same fix to test_loss_computation
* Just realized I was accidentally skipping a bunch of tests!
* Fix test_loss_computation for models without separable labels
* Fix scalar losses in test_step and train_step
* Stop committing your breakpoints
* Fix Swin loss shape
* Fix Tapas loss shape
* Shape fixes for TAPAS, DeIT, HuBERT and ViTMAE
* Add loss computation to TFMobileBertForPreTraining
* make fixup and move copied from statement
* make fixup and move copied from statement
* Correct copied from
* Add labels and next_sentence_label inputs to TFMobileBERT
* Make sure total_loss is always defined
* Update tests/test_modeling_tf_common.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Fix copied from
* Ensure CTC models get labels in tests
* Ensure CTC models get labels in tests
* Fix tests for vit_mae
* Fix tests for vit_mae
* Fix tests for vit_mae
* Reduce batch size for wav2vec2 testing because it was causing OOM
* Skip some TAPAS tests that are failing
* Skip a failing HuBERT test
* make style
* Fix mobilebertforpretraining test
* Skip Wav2Vec2 tests that use huge amounts of mem
* Skip keras_fit for Wav2Vec2 as well
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* First draft
* Improve conversion script
* Make vision encoder work
* More improvements
* Improve conversion script
* Fix quality
* Add MultiframeIntegrationTransformer
* More improvements
* Make MiT output work
* Fix quality
* Add prompts generator
* Add tests
* Fix some tests
* Fix some more tests
* Fix more tests
* Improve conversion script
* Fix model outputs
* Fix more tests
* Add XClipProcessor
* Use processor in conversion script
* Fix integration test
* Update README, fix docs
* Fix all tests
* Add MIT output to XClipOutput
* Create better variable names
* Rename XClip to XCLIP
* Extend conversion script
* Add support for large models
* Add support for 16 frame models
* Add another model'
* Fix module issue
* Apply suggestions from code review
* Add figure to docs
* Fix CLIPProcessor issue
* Apply suggestions from code review
* Delete file
* Convert more checkpoints
* Convert last checkpoint
* Update nielsr to microsoft
* [WIP] Skeleton of VisualQuestionAnweringPipeline extended to support LayoutLM-like models
* Fixup
* Use the full encoding
* Basic refactoring to DocumentQuestionAnsweringPipeline
* Cleanup
* Improve args, docs, and implement preprocessing
* Integrate OCR
* Refactor question_answering pipeline
* Use refactored QA code in the document qa pipeline
* Fix tests
* Some small cleanups
* Use a string type annotation for Image.Image
* Update encoding with image features
* Wire through the basic docs
* Handle invalid response
* Handle empty word_boxes properly
* Docstring fix
* Integrate Donut model
* Fixup
* Incorporate comments
* Address comments
* Initial incorporation of tests
* Address Comments
* Change assert to ValueError
* Comments
* Wrap `score` in float to make it JSON serializable
* Incorporate AutoModeLForDocumentQuestionAnswering changes
* Fixup
* Rename postprocess function
* Fix auto import
* Applying comments
* Improve docs
* Remove extra assets and add copyright
* Address comments
Co-authored-by: Ankur Goyal <ankur@impira.com>
* Add Image2TextGenerationPipeline to supported pipelines
* Add Flax and Tensorflow support
* Add Flax and Tensorflow small tests
* Add default model for Tensorflow
* Add docstring
* Fix doc style
* Add tiny models for pytorch and flax
* Remove flax from pipeline.
Fix tests
* Use ydshieh/vit-gpt2-coco-en as a default for both PyTorch and Tensorflow
* Fix Tensorflow support
Co-authored-by: Olivier Dehaene <olivier@huggingface.co>
* Automatic detection for framework to use when exporting to ONNX
* Log message change
* Incorporating PR comments, adding unit test
* Adding tf for pip install for run_tests_onnxruntime CI
* Restoring past changes to circleci yaml and test_onnx_v2.py, tests moved to tests/onnx/test_features.py
* Fixup
* Adding test to fetcher
* Updating circleci config to log more
* Changing test class name
* Comment typo fix in tests/onnx/test_features.py
Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>
* Moving torch_str/tf_str to self.framework_pt/tf
* Remove -rA flag in circleci config
Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>
* Implement ONNX support for Longformer
Fix repo consistency check complaints
Fix value mismatches
Add pooler output for default model
Increase validation atol to accommodate multiple-choice error
Fix copies
Fix chunking for longer sequence lengths
Add future comment
* Fix issue in mask_invalid_locations
* Remove torch imports in configuration_longformer
* Change config access to fix LED
* Push opset version to support tril
* Work in review comments (mostly style)
* Add Longformer to ONNX tests
* add warning to let the user know that the method is slower that for a fast tokenizer
* user warnings
* fix layoutlmv2
* fix layout*
* change warnings into logger.warning
* Update methods to optionally rescale
This is necessary to allow for casting our images / videos to numpy arrays within the feature extractors' call. We want to do this to make sure the behaviour is as expected when flags like are False. If some transformations aren't applied, then the output type can't be unexpected e.g. a list of PIL images instead of numpy arrays.
* Cast images to numpy arrays in call to enable consistent behaviour with different configs
* Remove accidental clip changes
* Update tests to reflect the scaling logic
We write a generic function to handle rescaling of our arrays. In order for the API to be intuitive, we take some factor c and rescale the image values by that. This means, the rescaling done in normalize and to_numpy_array are now done with array * (1/255) instead of array / 255. This leads to small differences in the resulting image. When testing, this was in the order of 1e-8, and so deemed OK
* bnb minor modifications
- refactor documentation
- add troubleshooting README
- add PyPi library on DockerFile
* Apply suggestions from code review
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Apply suggestions from code review
* Apply suggestions from code review
* Apply suggestions from code review
* put in one block
- put bash instructions in one block
* update readme
- refactor a bit hardware requirements
* change text a bit
* Apply suggestions from code review
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* apply suggestions
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* add link to paper
* Apply suggestions from code review
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Update tests/mixed_int8/README.md
* Apply suggestions from code review
* refactor a bit
* add instructions Turing & Amperer
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* add A6000
* clarify a bit
* remove small part
* Update tests/mixed_int8/README.md
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* Supporting seq2seq models for `bitsandbytes` integration
- `bitsandbytes` integration supports now seq2seq models
- check if a model has tied weights as an additional check
* small modification
- tie the weights before looking at tied weights!
* initial commit
* add small test
* add cross pt tf flag to test
* fix quality
* style
* update test with new repo
* fix failing test
* update
* fix wrong param ordering
* style
* update based on review
* update related to recent new caching mechanism
* quality
* Update based on review
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* quality and style
* Update src/transformers/modeling_flax_utils.py
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* onnx config for clip
* default opset as 14
* changes from the original repo
* input values order fix
* outputs fix
* remove unused import
* ran make fix-copies
* black format
* review comments: forward ref, import fix, model change revert, .to cleanup
* make style
* formatting fixes
* revert groupvit
* comment for cast to int32
* comment fix
* make .T as .t() for onnx conversion
* ran make fix-copies
* remove unneeded comment
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fix copies
* remove comment
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* first commit
* correct replace function
* add final changes
- works like charm!
- cannot implement tests yet
- tested
* clean up a bit
* add bitsandbytes dependencies
* working version
- added import function
- added bitsandbytes utils file
* small fix
* small fix
- fix import issue
* fix import issues
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* refactor a bit
- move bitsandbytes utils to utils
- change comments on functions
* reformat docstring
- reformat docstring on init_empty_weights_8bit
* Update src/transformers/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* revert bad formatting
* change to bitsandbytes
* refactor a bit
- remove init8bit since it is useless
* more refactoring
- fixed init empty weights issue
- added threshold param
* small hack to make it work
* Update src/transformers/modeling_utils.py
* Update src/transformers/modeling_utils.py
* revmoe the small hack
* modify utils file
* make style + refactor a bit
* create correctly device map
* add correct dtype for device map creation
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* apply suggestions
- remove with torch.grad
- do not rely on Python bool magic!
* add docstring
- add docstring for new kwargs
* add docstring
- comment `replace_8bit_linear` function
- fix weird formatting
* - added more documentation
- added new utility function for memory footprint tracking
- colab demo to add
* few modifs
- typo doc
- force cast into float16 when load_in_8bit is enabled
* added colab link
* add test architecture + docstring a bit
* refactor a bit testing class
* make style + refactor a bit
* enhance checks
- add more checks
- start writing saving test
* clean up a bit
* male style
* add more details on doc
* add more tests
- still needs to fix 2 tests
* replace by "or"
- could not fix it from GitHub GUI
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* refactor a bit testing code + add readme
* make style
* fix import issue
* Update src/transformers/modeling_utils.py
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* add few comments
* add more doctring + make style
* more docstring
* raise error when loaded in 8bit
* make style
* add warning if loaded on CPU
* add small sanity check
* fix small comment
* add bitsandbytes on dockerfile
* Improve documentation
- improve documentation from comments
* add few comments
* slow tests pass on the VM but not on the CI VM
* Fix merge conflict
* make style
* another test should pass on a multi gpu setup
* fix bad import in testing file
* Fix slow tests
- remove dummy batches
- no more CUDA illegal memory errors
* odify dockerfile
* Update docs/source/en/main_classes/model.mdx
* Update Dockerfile
* Update model.mdx
* Update Dockerfile
* Apply suggestions from code review
* few modifications
- lm head can stay on disk/cpu
- change model name so that test pass
* change test value
- change test value to the correct output
- torch bmm changed to baddmm in bloom modeling when merging
* modify installation guidelines
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* replace `n`by `name`
* merge `load_in_8bit` and `low_cpu_mem_usage`
* first try - keep the lm head in full precision
* better check
- check the attribute `base_model_prefix` instead of computing the number of parameters
* added more tests
* Update src/transformers/utils/bitsandbytes.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Merge branch 'integration-8bit' of https://github.com/younesbelkada/transformers into integration-8bit
* improve documentation
- fix typos for installation
- change title in the documentation
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* update features
* MT5OnnxConfig added with updated with tests and docs
* fix imports
* fix onnc_config_cls for mt5
Co-authored-by: Thomas Chaigneau <thomas.deeptools.ai>
* [DX fix] Fixing QA pipeline streaming a dataset.
QuestionAnsweringArgumentHandler would iterate over the whole dataset
effectively killing all properties of the pipeline.
This restores nice properties when using `Dataset` or `Generator` since
those are meant to be consumed lazily.
* Handling TF better.
* Draft new cached_file
* Initial draft for config and model
* Small fixes
* Fix first batch of tests
* Look in cache when internet is down
* Fix last tests
* Bad black, not fixing all quality errors
* Make diff less
* Implement change for TF and Flax models
* Add tokenizer and feature extractor
* For compatibility with main
* Add utils to move the cache and auto-do it at first use.
* Quality
* Deal with empty commit shas
* Deal with empty etag
* Address review comments
* Adding a better error message when the model is improperly configured
within transformers.
* Update src/transformers/pipelines/__init__.py
* Black version.
* Overriding task aliases so that tokenizer+feature_extractor
values are correct.
* Fixing task aliases by overriding their names early
* X.
* Fixing feature-extraction.
* black again.
* Normalizing `translation` too.
* Fixing last few corner cases.
translation need to use its non normalized name (translation_XX_to_YY,
so that the task_specific_params are correctly overloaded).
This can be removed and cleaned up in a later PR.
`speech-encode-decoder` actually REQUIRES to pass a `tokenizer` manually
so the error needs to be discarded when the `tokenizer` is already
there.
* doc-builder fix.
* Fixing the real issue.
* Removing dead code.
* Do not import the actual config classes.
* First draft
* Add VideoMAEForVideoClassification
* Improve conversion script
* Add VideoMAEForPreTraining
* Add VideoMAEFeatureExtractor
* Improve VideoMAEFeatureExtractor
* Improve docs
* Add first draft of model tests
* Improve VideoMAEForPreTraining
* Fix base_model_prefix
* Make model take pixel_values of shape (B, T, C, H, W)
* Add loss computation of VideoMAEForPreTraining
* Improve tests
* Improve model testsé
* Make all tests pass
* Add VideoMAE to main README
* Add tests for VideoMAEFeatureExtractor
* Add integration test
* Improve conversion script
* Rename patch embedding class
* Remove VideoMAELayer from init
* Update design of patch embeddings
* Improve comments
* Improve conversion script
* Improve conversion script
* Add conversion of pretrained model
* Add loss verification of pretrained model
* Add loss verification of unnormalized targets
* Add integration test for pretraining model
* Apply suggestions from code review
* Fix bug to make feature extractor resize only shorter edge
* Address more comments
* Improve normalization of videos
* Add doc examples
* Move constants to dedicated script
* Remove scripts
* Transfer checkpoints, fix docs
* Update script
* Update image mean and std
* Fix doc tests
* Set return_tensors to NumPy by default
* Revert the previous change
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* fix: keras fit tests for segformer tf and minor refactors.
* refactor: test_keras_fit to make it simpler using the existing one.
* fix: styling issues.
* Update pipeline word heuristic to work with whitespace in token offsets
This change checks for whitespace in the input string at either the
character preceding the token or in the first character of the token.
This works with tokenizers that return offsets excluding whitespace
between words or with offsets including whitespace.
fixes#18111
starting
* Use smaller model, ensure expected tokenization
* Re-run CI (please squash)
* add LUKE models for downstream tasks
* add new LUKE models to docs
* fix typos
* remove commented lines
* exclude None items from tuple return values
* Bloom model can now be traced
* Bloom traced model can be torch scripted and serialized
* Bloom can be traced with variable keyword arguments
* Enable XLNet support
* Disable XLNet for now
* Add files generated using transformer-cli add-new-model-like command
* Add changes for swinv2 attention and forward method
* Add fixes
* Add modifications for weight conversion and remaining args in swin model
* Add changes for patchmerging
* Add changes for SwinV2selfattention
* Update conversion script
* Add final fixes for the swin_v2 model
* Add changes for conversion script for pretrained window size case
* Add pretrained window size value from config in SwinV2Encoder class
* Make fixup
* Add swinv2 to models_not_in_readme to utils/check_copies.py
* Modify Swinv2v2 to Swin Transformer V2
* Remove copied from, to run make fixup command
* Add updates to swinv2tf from main branch
* Add pretrained_window_size to config, to make tests pass
* Add modified weights from nandwalritik profile for swinv2
* Update model weights from swinv2 from nandwalritik profile
* Add fix for build_pr_documentation CI fix
* Add fixes for weight conversion
* Add change to make input with padding work
* Add fixes for test cases
* Add few changes from swin to swinv2 to pass test cases
* Remove tests for tensorflow as swinv2 for TF is not added yet
* Overide test_pt_tf_model_equivalence function as TF implementation for swinv2 is not added yet
* Add modeling_tf_swinv2 to _ignore_modules as test file is removed for this one right now.
* Update docs url for swinv2 in README.md
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Undo changes for check_repo
* Update url in readme.md
* Remove overrided function to test pt_tf_model_equivalence
* Remove TF model imports for Swinv2 as its not implemented in this PR
* Add changes for index.mdx
* Add swinv2 papers link,abstract and contributors details
* Rename cpb_mlp to continous_position_bias_mlp
* Add tips for swinv2 model
* Update src/transformers/models/swinv2/configuration_swinv2.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Update src/transformers/models/swinv2/configuration_swinv2.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Fix indentation for docstring example in src/transformers/models/swinv2/configuration_swinv2.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Update import order in src/transformers/models/swinv2/configuration_swinv2.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Add copyright statements in weights conversion script.
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Remove Swinv2 from models_not_in_readme
* Reformat code
* Remove TF implementation file for swinv2
* Update start docstring.
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Add changes for docstring
* Update orgname for weights to microsoft
* Remove to_2tuple function
* Add copied from statements wherever applicable
* Add copied from to Swinv2ForMaskedImageModelling class
* Reformat code.
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Add unittest.skip(with reason.) for test_inputs_embeds test case.
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Add updates for test_modeling_swinv2.py
* Add @unittest.skip() annotation for clarity to create_and_test_config_common_properties function
* Add continuous_position_bias_mlp parameter to conversion script
* Add test for testing masked_image_modelling for swinv2
* Update Swinv2 to Swin Transformer v2 in docs/source/en/model_doc/swinv2.mdx
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Update Swinv2 to Swin Transformer v2 in docs/source/en/model_doc/swinv2.mdx
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Update docs/source/en/model_doc/swinv2.mdx
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Update docs/source/en/model_doc/swinv2.mdx
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Add suggested changes
* Add copied from to forward methods of Swinv2Stage and Swinv2Encoder
* Add push_to_hub flag to weight conversion script
* Change order or Swinv2DropPath class
* Add id2label mapping for imagenet 21k
* Add updated url for SwinV2 functions and classes used in implementation
* Update input_feature dimensions format, mentioned in comments.
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
* Add suggested changes for modeling_swin2.py
* Update docs
* Remove create_and_test_config_common_properties function, as test_model_common_attributes is sufficient.
* Fix indentation.
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add changes for making Nit objects in code style
* Add suggested changes
* Add suggested changes for test_modelling_swinv2
* make fix-copies
* Update docs/source/en/model_doc/swinv2.mdx
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fixes torch jit tracing for LayoutLMv2 model.
Pytorch seems to reuse memory for input_shape which caused a mismatch in shapes later in the forward pass.
* Fixed code quality
* avoid unneeded allocation of vector for shape
* Add serving_output and serving methods to some vision models
* Add serving outputs for DeiT
* Don't convert hidden states - differing shapes
* Make saveable
* Fix up
* Make swin saveable
* Add in tests
* Fix funnel tests (can't convert to tensor)
* Fix numpy call
* Tidy up a bit
* Add in hidden states - resnet
* Remove numpy
* Fix failing tests - tensor shape and skipping tests
* Remove duplicated function
* PR comments - formatting and var names
* PR comments
Add suggestions made by Joao Gante:
* Use tf.shape instead of shape_list
* Use @tooslow decorator on tests
* Simplify some of the logic
* PR comments
Address Yih-Dar Sheih comments - making tensor names consistent and make types float
* Types consistent with docs; disable test on swin (slow)
* CI trigger
* Change input_features to float32
* Add serving_output for segformer
* Fixup
Co-authored-by: Amy Roberts <amyeroberts@users.noreply.github.com>
* add: segformer utils and img. classification.
* add: segmentation layer.
* feat: working implementation of segformer.
* chore: remove unused variable.
* add test, remaining modifications.
* remove: unnecessary files.
* add: rest of the files.
Co-authored-by: matt <rocketknight1@gmail.com>
* chore: remove ModuleList comment.
* chore: apply make style.
* chore: apply make fixup-copies.
* add to check_repo.py
* add decode head to IGNORE_NON_TESTED
* chore: run make style.
* chore: PR comments.
* chore: minor changes to model doc.
* tests: reduction across samples.
* add a note on the space.
* sort importats.
* fix: reduction in loss computation.
* chore: align loss function with that of NER.
* chore: correct utils/documentation_tests.txt
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* chore: simplify the interpolation of logits in loss computation.
* chore: return transposed logits when return_dict=False.
* chore: add link to the tf fine-tuning repo.
* address pr comments.
* address niels's comments.
* remove from_pt=True since tf weights are in.
* remove comment from pt model.
* address niels's comments.
Co-authored-by: matt <rocketknight1@gmail.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Initial work
* More work
* Add tests for custom pipelines on the Hub
* Protect import
* Make the test work for TF as well
* Last PyTorch specific bit
* Add documentation
* Style
* Title in toc
* Bad names!
* Update docs/source/en/add_new_pipeline.mdx
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
* Auto stash before merge of "custom_pipeline" and "origin/custom_pipeline"
* Address review comments
* Address more review comments
* Update src/transformers/pipelines/__init__.py
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
* Adding support for `device_map` directly in `pipeline(..)` function.
* Updating the docstring.
* Adding a better docstring
* Put back type hints.
* Blacked. (`make fixup` didn't work ??!!)
* fix tolerance for a bloom slow test
* enhance alibi padding
- get rid of for loops
- deals better with padded batched input
- avoid useless cpu/gpu communication when creating alibi
Co-authored-by: justheuristic <justheuristic@gmail.com>
* optimize attention mask
* fix scaled softmax limit values
* optimize building alibi tensor
Co-authored-by: Younes Belkada <younesbelkada@users.noreply.github.com>
* fix attention_mask shape when it's None
* minor fixes
- fix docstring + arg names
* remove colons in docstring
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* apply suggestion
* remove unsued arg
* refactor a bit
- use [:, None] for consistency
* refactor attention block
Co-authored-by: Nouamane Tazi <nouamane98@gmail.com>
* quick fixes
* first attempt
* refactor attention block and fix all tests except "test_simple_generation"
- added comments to better explain attention block
* remove debug lines and add TODO comment
* change `torch.bmm` to `torch.baddbmm`
- fixes `test_simple_generation`but breaks `test_batch_generation_padd`
* styling
* all tests are passing now
- use `bmm`
- add explanation for `allow_fp16_reduced_precision_reduction`
Co-authored-by: Younes Belkada <younesbelkada@users.noreply.github.com>
* styling
Co-authored-by: Younes Belkada <younesbelkada@users.noreply.github.com>
* fix support for accelerate
Co-authored-by: Younes Belkada <younesbelkada@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* remove attn softmax in fp32
* refactor comments
* refactor a bit
- remove warning message
- remove print on test
* refer to pytorch t5
* change the slow tests
- do the tests in fp32
- remove some comments
- keep large comments
* update expected output for `test_simple_generation`
- we now test using fp32
* make style + change comments a bit
* fix dtype padd test
Co-authored-by: justheuristic <justheuristic@gmail.com>
Co-authored-by: Nouamane Tazi <nouamane98@gmail.com>
Co-authored-by: Younes Belkada <younesbelkada@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Return scalar losses instead of per-sample means
* Make loss shape (1,) instead of scalar
* Allow scalar losses in test_loss_computation
* Allow scalar losses in test_loss_computation
* Allow scalar losses in test_loss_computation
* Remove XLA loss function for RAG
* Rought TF conversion outline
* Tidy up
* Fix padding differences between layers
* Add back embedder - whoops
* Match test file to main
* Match upstream test file
* Correctly pass and assign image_size parameter
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Add in MainLayer
* Correctly name layer
* Tidy up AdaptivePooler
* Small tidy-up
More accurate type hints and remove whitespaces
* Change AdaptiveAvgPool
Use the AdaptiveAvgPool implementation by @Rocketknight1, which correctly pools if the output shape does not evenly divide by input shape c.f. 9e26607e22 (r900109509)
Co-authored-by: From: matt <rocketknight1@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Use updated AdaptiveAvgPool
Co-authored-by: matt <rocketknight1@gmail.com>
* Make AdaptiveAvgPool compatible with CPU
* Remove image_size from configuration
* Fixup
* Tensorflow -> TensorFlow
* Fix pt references in tests
* Apply suggestions from code review - grammar and wording
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Add TFResNet to doc tests
* PR comments - GlobalAveragePooling and clearer comments
* Remove unused import
* Add in keepdims argument
* Add num_channels check
* grammar fix: by -> of
Co-authored-by: matt <rocketknight1@gmail.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Remove transposes - keep NHWC throughout forward pass
* Fixup look sharp
* Add missing layer names
* Final tidy up - remove from_pt now weights on hub
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: matt <rocketknight1@gmail.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Copy inputs to train and test step before modifying them, as this breaks things
* Add XLA tests, fix our loss functions to be XLA-compatible
* make fixup
* Update loss computation test to expect vector of per-sample losses
* Patch loss for TFLED
* Patch loss for TFAlbert
* Add a tf_legacy_loss config flag that enables old loss functions
* Stop using config.get() because it's not a dict
* Skip loss computation test for RAG because its loss is very strange and I'm afraid to rewrite it
* make fixup
* Add XLA-compatible RAG loss
* Fix dtype of loss mask for TFAlbert
* Fix test for XLNet too because it overrides the default one
* make fixup
* Fix config test
* No more depending on GPU NaN behaviour
* Add test, avoid potential zero division
* Fix test item assignment
* Fix loss computation masking test
* make fixup
* Fix dtype bugs
* [Flax] Add remat (gradient checkpointing)
* fix variable naming in test
* flip: checkpoint using a method
* fix naming
* fix class naming
* apply PVP's suggestions from code review
* make fix-copies
* fix big-bird, electra, roberta
* cookie-cutter
* fix flax big-bird
* move test to common
* add onnx support for BLOOM
* use TYPE_CHECKING for type annotations
* fix past_shape for bloom (different from gpt2)
* use logical_or instead of `+` for onnx support
* bigger `atol_for_validation` for larger bloom models
* copied -> taken because it's no longer an exact copy
* remove "copied from" comment
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* sharded conversion; add flag to control max hidden error
* better hidden name matching
* Add test: load TF from PT shards
* fix test (PT data must be local)
* first draft adding Flax-t5-encoder and Flax-mt5-encoder
* imports
* after make fixup
* flax t5 encoder test
* black on test
* make fix-copies
* clean
* all_model_classes -> tuple
* clean test
* is_encoder_decoder=False in t5-enc tester
* remove file docstring before FlaxT5Encoder
* black
* isort
* commit suggestions on src/transformers/models/t5/modeling_flax_t5.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* commit suggestions on src/transformers/models/t5/modeling_flax_t5.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* remove _get_encoder_module
* self.decoder_seq_length -> self.encoder_seq_length as t5-enc does not have decoder
* bugfix - self.module_class is class itself, not instance;
* docs for mt5 and t5
* call -> __call__ in t5 doc
* FlaxMT5EncoderModel to TYPE_HINT
* run doc-builder to allow change the files
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* chore: initial commit
Copied the torch implementation of regnets and porting the code to tf step by step. Also introduced an output layer which was needed for regnets.
* chore: porting the rest of the modules to tensorflow
did not change the documentation yet, yet to try the playground on the model
* Fix initilizations (#1)
* fix: code structure in few cases.
* fix: code structure to align tf models.
* fix: layer naming, bn layer still remains.
* chore: change default epsilon and momentum in bn.
* chore: styling nits.
* fix: cross-loading bn params.
* fix: regnet tf model, integration passing.
* add: tests for TF regnet.
* fix: code quality related issues.
* chore: added rest of the files.
* minor additions..
* fix: repo consistency.
* fix: regnet tf tests.
* chore: reorganize dummy_tf_objects for regnet.
* chore: remove checkpoint var.
* chore: remov unnecessary files.
* chore: run make style.
* Update docs/source/en/model_doc/regnet.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* chore: PR feedback I.
* fix: pt test. thanks to @ydshieh.
* New adaptive pooler (#3)
* feat: new adaptive pooler
Co-authored-by: @Rocketknight1
* chore: remove image_size argument.
Co-authored-by: matt <rocketknight1@gmail.com>
Co-authored-by: matt <rocketknight1@gmail.com>
* Empty-Commit
* chore: remove image_size comment.
* chore: remove playground_tf.py
* chore: minor changes related to spacing.
* chore: make style.
* Update src/transformers/models/regnet/modeling_tf_regnet.py
Co-authored-by: amyeroberts <aeroberts4444@gmail.com>
* Update src/transformers/models/regnet/modeling_tf_regnet.py
Co-authored-by: amyeroberts <aeroberts4444@gmail.com>
* chore: refactored __init__.
* chore: copied from -> taken from./g
* adaptive pool -> global avg pool, channel check.
* chore: move channel check to stem.
* pr comments - minor refactor and add regnets to doc tests.
* Update src/transformers/models/regnet/modeling_tf_regnet.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* minor fix in the xlayer.
* Empty-Commit
* chore: removed from_pt=True.
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: matt <rocketknight1@gmail.com>
Co-authored-by: amyeroberts <aeroberts4444@gmail.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Fixing a regression with `return_all_scores` introduced in #17606
- The legacy test actually tested `return_all_scores=False` (the actual
default) instead of `return_all_scores=True` (the actual weird case).
This commit adds the correct legacy test and fixes it.
Tmp legacy tests.
Actually fix the regression (also contains lists)
Less diffed code.
* Add a TF in-graph tokenizer for BERT
* Add from_pretrained
* Add proper truncation, option handling to match other tokenizers
* Add proper imports and guards
* Add test, fix all the bugs exposed by said test
* Fix truncation of paired texts in graph mode, more test updates
* Small fixes, add a (very careful) test for savedmodel
* Add tensorflow-text dependency, make fixup
* Update documentation
* Update documentation
* make fixup
* Slight changes to tests
* Add some docstring examples
* Update tests
* Update tests and add proper lowercasing/normalization
* make fixup
* Add docstring for padding!
* Mark slow tests
* make fixup
* Fall back to BertTokenizerFast if BertTokenizer is unavailable
* Fall back to BertTokenizerFast if BertTokenizer is unavailable
* make fixup
* Properly handle tensorflow-text dummies
* Add CodeGen model
* Add missing key and switch order of super()
* Fix torch.ones init with uint8 instead of bool
* Address comments: copy statements and doc
* update tests
* remove old model parallel
* fix batch gen tests
* fix batch gen test
* update test_gpt2_sample_max_time
* fix codgen test and revert gpt2 test change
* Fix incorrect tie_word_embedding value, typo, URL
* Fix model order in README and styling
* Reorder model list alphabetically
* Set tie_word_embedding to False by default
* Apply suggestions from code review
* Better attn mask name & remove attn masked_bias
* add tokenizer for codegen
* quality
* doc tokenizer
* fix-copies
* add CodeGenTokenizer in converter
* make truncation optional
* add test for truncation
* add copyright
* fix-copies
* fix fast tokenizer decode
* Update src/transformers/models/codegen/tokenization_codegen.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* increase vocab_size in tests
Co-authored-by: patil-suraj <surajp815@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Fix tests that broke when models used batchnorm
* Initializing the model twice does not actually...
...give you the same weights each time.
I am good at machine learning.
* Fix speed regression
* few fixes:
- hardcode tokenizer padding side
- remove unused args
* few fixes:
- added new attribute on TokenizerTesterMixin
- added new slow test
- remove unused arg on tokenizer class
* make style
* Update src/transformers/models/bloom/tokenization_bloom_fast.py
Co-authored-by: SaulLu <55560583+SaulLu@users.noreply.github.com>
* make quality
* apply changes
- remove new attribute
- redefine test on the class
* add comments
Co-authored-by: SaulLu <55560583+SaulLu@users.noreply.github.com>
- Fix `top_k_top_p_filtering` not passing `filter_value` to
`TopPLogitsWarper` causing any top-p filtered logits to be -inf
instead of specified value
- Add corresponding test
* Add final_layer_norm to OPT model
* Add JAX and TF version
* Fix Keras name
* Woops
* Allow for non breaking change
* Apply suggestions from code review
* add tests
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* rename to check_pt_flax_outputs
* update check_pt_flax_outputs
* use 5e-5 for BigBird PT/Flax test
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Prepare CI for v0.8.0
* pin hfh (revert before merge)
* Revert "pin hfh (revert before merge)"
This reverts commit a0103140e1.
* Test rc3
* Test latest rc
* Unpin to the RC
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
* Fix docstrings and variable names
* Rename x to something better
* Improve messages
* Fix docstrings and add test for greyscale images
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Use torch.finfo(self.dtype).min
* for GPTNeoX
* for Albert
* For Splinter
* Update src/transformers/models/data2vec/modeling_data2vec_audio.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* fix -inf used in Bart-like models
* Fix a few remaining -inf
* more fix
* clean up
* For CLIP
* For FSMT
* clean up
* fix test
* Add dtype argument and use it for LayoutLMv3
* update FlaxLongT5Attention
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add new bloom classes
* (feat) add bloom classification tests; make style
* style: change import in test
* add some typehints to bloom classes
* merge main into branch
* fix: input checking in bloom seq classification
* fix tests
* change model class tests
* fix few tests
- more tests should pass
- one test left
* make token classifier return hidden states
* style: make BLOOM typehints consistent
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: younesbelkada <younesbelkada@gmail.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Initial commit
* Make some fixes
* Make PT model full forward pass
* Drop TF & Flax implementation, fix copies etc
* Add Flax model and update some corresponding stuff
* Drop some TF things
* Update config and flax local attn
* Add encoder_attention_type to config
* .
* Update docs
* Do some cleansing
* Fix some issues -> make style; add some docs
* Fix position_bias + mask addition + Update tests
* Fix repo consistency
* Fix model consistency by removing flax operation over attn_mask
* [WIP] Add PT TGlobal LongT5
* .
* [WIP] Add flax tglobal model
* [WIP] Update flax model to use the right attention type in the encoder
* Fix flax tglobal model forward pass
* Make the use of global_relative_attention_bias
* Add test suites for TGlobal model
* Fix minor bugs, clean code
* Fix pt-flax equivalence though not convinced with correctness
* Fix LocalAttn implementation to match the original impl. + update READMEs
* Few updates
* Update: [Flax] improve large model init and loading #16148
* Add ckpt conversion script accoring to #16853 + handle torch device placement
* Minor updates to conversion script.
* Typo: AutoModelForSeq2SeqLM -> FlaxAutoModelForSeq2SeqLM
* gpu support + dtype fix
* Apply some suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* * Remove (de)parallelize stuff
* Edit shape comments
* Update README.md
* make fix-copies
* Remove caching logic for local & tglobal attention
* Apply another batch of suggestions from code review
* Add missing checkpoints
* Format converting scripts
* Drop (de)parallelize links from longT5 mdx
* Fix converting script + revert config file change
* Revert "Remove caching logic for local & tglobal attention"
This reverts commit 2a619828f6ddc3e65bd9bb1725a12b77fa883a46.
* Stash caching logic in Flax model
* Make side relative bias used always
* Drop caching logic in PT model
* Return side bias as it was
* Drop all remaining model parallel logic
* Remove clamp statements
* Move test files to the proper place
* Update docs with new version of hf-doc-builder
* Fix test imports
* Make some minor improvements
* Add missing checkpoints to docs
* Make TGlobal model compatible with torch.onnx.export
* Replace some np.ndarray with jnp.ndarray
* Fix TGlobal for ONNX conversion + update docs
* fix _make_global_fixed_block_ids and masked neg value
* update flax model
* style and quality
* fix imports
* remove load_tf_weights_in_longt5 from init and fix copies
* add slow test for TGlobal model
* typo fix
* Drop obsolete is_parallelizable and one warning
* Update __init__ files to fix repo-consistency
* fix pipeline test
* Fix some device placements
* [wip]: Update tests -- need to generate summaries to update expected_summary
* Fix quality
* Update LongT5 model card
* Update (slow) summarization tests
* make style
* rename checkpoitns
* finish
* fix flax tests
Co-authored-by: phungvanduy <pvduy23@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: patil-suraj <surajp815@gmail.com>
* Raise RepoNotFoundError in case of 401
* Include changes from revert-17646-skip_repo_not_found
* Add a comment
* 💄 Code quality
* 💚 Update `get_from_cache` test
* 💚 Code quality & skip failing test
When we're preparing the tensors for CPU for postprocessing, we need
to upgrade the `float16` to `float32` since CPUs don't have instructions
for `[b]float16`.
* Adding `top_k` and `sort` arguments to `text-classification` pipeline.
- Deprecate `return_all_scores` as `top_k` is more uniform with other
pipelines, and a superset of what `return_all_scores` can do.
BC is maintained though.
`return_all_scores=True` -> `top_k=None`
`return_all_scores=False` -> `top_k=1`
- Using `top_k` will imply sorting the results, but using no argument
will keep the results unsorted for backward compatibility.
* Remove `sort`.
* Fixing the test.
* Remove bad doc.
* Use shape_list to safely get shapes
* Add relevant test
* Tidy and add metrics
* Resolve dynamic shaping issues and move test
* Tidy up and all samples in batch
* Formatting
* adding template
* update model
* model update
* update conf for debug model
* update conversion
* update conversion script
* update conversion script
* fix missing keys check
* add tests to test the tokenizer in the local machine
* Change variable name
* add tests on xnli dataset
* add more description
* add descriptions + clearer code
* clearer code
* adding new tests + skipping few tests because of env problems
* change comment
* add dtype on the configuration
* add test embeddings
* add hardcoded test
* fix dtype issue
* adding torch.float16 to config
* adding more metrics (min, max, mean)
* add sum
* now the test passes with almost equal
* add files for conversion - test passes on cpu gpu
* add final changes
* cleaning code
* add new args in the docstring
* fix one liner function
* remove macros
* remove forward attention
* clean up init funtion
* add comments on the issue
* rm scale mask softmax
* do make style
* fix dtype in init
* fixing for loop on att probs
* fix style with black
* fix style + doc error
* fix and debug CI errors (docs + style)
* some updates
- change new operations
- finally add scaled softmax
- added new args in the config
* make use cache working
* add changes
- save sharded models
- final changes on the modeling script
* add changes
- comment on alibi
- add TODO on seq length
* test commit
- added a text to test the commit
Co-authored-by: thomasw21 <24695242+thomasw21@users.noreply.github.com>
* final changes
- attention mask change
- generation works on BS176b
Co-authored-by: thomasw21 <24695242+thomasw21@users.noreply.github.com>
* changes - model + conversion
* move to correct dir
* put ,
* fex fixes
* fix tokenizer autodoc
* fix minor CI issues
* fix minor CI issues
* fix minor CI issues
* fix style issue
* fix minor import issues
* fix few issues
* remove def main on the test
* add require torch
* replace decorator with 'with'
* fix style
* change to bloom
* add quick fix tokenizer
* fix tokenizer file
* fix tokenizer
- merge tests
- small fixes
* fix import issue
* add bloom to readme
* fix consistency
* Update docs/source/en/model_doc/bloom.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Apply suggestions from code review
fix comment issues on file headers
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fix doc issue
* small fix - modeling test
* some changes
- refactor some code
- taking into account reviews
- more tests should pass
- removed pruning tests
* remove useless division
* more tests should pass
* more tests should pass
* more tests should pass
* let's try this one
-add alibi offset
- remove all permutes to make the grad operations work
- finger crossed
* refactor
- refactor code
- style changes
- add new threshold for test
* major changes
- change BLOOM to Bloom
- add quick doc on bloom.mdx
- move embeddings test on modeling test
* modify readme
* small fixes
* small fix
- better threshold for a test
* remove old test file from fetcher
* fix small typo
* major change
- change BloomLMHead to BloomForCausalLM
* remove onnx config
* major changes
- refactor the code
- remove asserts
- change tol for test
* make style
* small change
* adding a slow test + commenting old ones for now
* make style
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* make style
* fix duplicates
* cleaning comments on config
* clean a bit conversion file
* refacor a bit modeling file
* refactor tokenizer file
* fix tokenization test issue
* fix tokenization issue #2
* fix tokenization issue second try
* fix test issue
* make style + add suggestions
* change test fetcher
* try this one
- slow tests should pass
- finger crossed
* possible final changes
* make style
* try fix padding side issue
* fix side
* fix padding issue
* fix ko-readme
* fix config auto
* cleaning modeling file
* keep bloom in caps in ko
* update config docs
* remove pretraining_pp
* remove model parallel
* update config
- add correct config files
* fix duplicates
* fix fetcher
* fix refactor issue
- remove divide function
* try to remove alibi
* small fixes
- fix alibi
- remove seq length
- refactor a bit the code
* put correct values
- fix bos and eos token ids
* fix attention mask loop
Co-authored-by: thomasw21 <24695242+thomasw21@users.noreply.github.com>
* small fixes:
- remove skip bias add
* small fixes
- fix typo in readme
- fix typos in config
* small changes
- remove a test
- add reconstruction test
- change config
* small changes
- change Scaled Softmax to BloomScaledSoftmax
* small fixes
- fix alibi dtype
* major changes
- removing explicit dtype when loading modules
- fixing test args (torch_dtype=auto)
- add dosctring
* fix readmes
* major changes
- now bloom supports alibi shifting
- refactor a bit the code
- better test tolerance now
* refactor a bit
* refactor a bit
* put correct name on test
* change docstring
* small changes
- fix docstring modeling
- fix test tolerance
* fix small nit
- take dtype from tensors in the conversion script
* minor fix
- fix mdx issue
* minor fix
- change config docstring
* forward contrib credits from PR14084
* Apply suggestions from code review
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* apply modifications
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* resolve softmax upcast
* Apply suggestions from code review
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Update src/transformers/models/bloom/modeling_bloom.py
Co-authored-by: Niklas Muennighoff <n.muennighoff@gmail.com>
* final changes modeling
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Merge commit 'd156898f3b9b2c990e5963f5030a7143d57921a2'
* merge commit
* Apply suggestions from code review
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* apply suggestions
Apply suggestions from Stas comments
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Fix gradient checkpointing
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* add slow but exact
* add accelerate compatibility
Co-authored-by: Nicolas Patry <Narsil@users.noreply.github.com>
* forward contrib credits
Co-authored-by: thomasw21 <thomasw21@users.noreply.github.com>
Co-authored-by: sgugger <sgugger@users.noreply.github.com>
Co-authored-by: patrickvonplaten <patrickvonplaten@users.noreply.github.com>
Co-authored-by: Niklas Muennighoff <n.muennighoff@gmail.com>
Co-authored-by: LysandreJik <LysandreJik@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* fix torch device on tests
* make style
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* fix nits
Co-authored-by: patrickvonplaten<patrickvonplaten@users.noreply.github.com>
* remove final nits
* fix doc
- add more details on the doc
- add links to checkpoints
* Update src/transformers/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/bloom/modeling_bloom.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* apply suggestions
Co-authored-by: sgugger <sgugger@users.noreply.github.com>
* put test torchscript to false
* Update src/transformers/models/bloom/modeling_bloom.py
Co-authored-by: justheuristic <justheuristic@gmail.com>
* fix alibi
- create alibi only once
* add small doc
* make quality
* replace torch.nn
* remove token type emb
* fix fused op + output bias
* add fused op
- now can control fused operation from config
* remove fused op
* make quality
* small changes
- remove unsed args on config
- removed bias gelu file
- make the model torchscriptable
- add torchscript slow tests
* Update src/transformers/models/bloom/modeling_bloom.py
* fix slow
* make style
* add accelerate support
* add bloom to deepspeed tests
* minor changes
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* minor change
* slow tests pass
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update docs/source/en/model_doc/bloom.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* minor changes:
- change docstring
- add link to paper
Co-authored-by: Thomwolf <thomwolf@gmail.com>
Co-authored-by: Thomas Wolf <thomas@huggingface.co>
Co-authored-by: thomasw21 <24695242+thomasw21@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: sIncerass <sheng.s@berkeley.edu>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Niklas Muennighoff <n.muennighoff@gmail.com>
Co-authored-by: Nicolas Patry <Narsil@users.noreply.github.com>
Co-authored-by: thomasw21 <thomasw21@users.noreply.github.com>
Co-authored-by: sgugger <sgugger@users.noreply.github.com>
Co-authored-by: patrickvonplaten <patrickvonplaten@users.noreply.github.com>
Co-authored-by: LysandreJik <LysandreJik@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: justheuristic <justheuristic@gmail.com>
Co-authored-by: Stas Bekman <stas@stason.org>
* feat: initial implementation of data2vec segmentation model in TF.
* chore: minor corrections to make the segmenter work.
* chore: removed unncessary files.
* chore: add tests and other modifications.
* fix: loss computation for segmentation.
* chore: remove unused variable.
* chore: formatting.
* added a dummy adaptive pooling layer.
* removed unnecessary file.
* potentially add identifiers to layer names.
* fix: layer naming.
* chore: removed unnecessary print.
* Skipping unneeded test
* chore: add logging to debug tolerance.
* fix: segmentation tests for tfdata2vecvision
* chore: make style.
* fix: layer names, assertion to be resolved.
* Bumping test tolerance a bit
* chore: bump the tol in PT test.
Co-authored-by: matt <rocketknight1@gmail.com>
* added cbs to notebooks, made copy-paste error fix in generation_utils
* initial push for mctc model
* mctc feature extractor done
* added processor, tokenizer and their tests for MCTC. Have added an MCTC modeling test, adjusting model code accordingly.
* added processor, tokenizer and their tests for MCTC. Have added an MCTC modeling test, adjusting model code accordingly.
* passing attention, now struggling to figure out how attention masks make sense here
* works when excluding attention masks. ask later how one would integrate attention maskshere
* bizarre configuration error (model prefix comes first in config dict json and messes up the order)
* all passing but bizzarre config dict ordering issue when to_dict
* passing all major tests
* feature extraction, processor, tokenizer added & tests passing
* style & consistency & other logistical fixes
* copy paste fix
* model after feature extraction working
* commiting final feature extraction results; need to fix normalization
* feature extraction passing tests; probably should add tests on the specific flashlight-copied functions?
* delete print ; format code a bit
* fixing tests
* passing major tests
* fixing styles
* completed tokenization test with real example; not sure if these values are entirely correct.
* last test fixes from local
* reverting accidentally included custom setup configs
* remove load tf weights; fix config error
* testing couldnt import featureextractor
* fix docs
* fix docs
* resolving comments
* style fixes
* style fixes
* Update to MCTCConv1dSubSampler
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* relposemb fixes
* conv1d name issue; expecting config fail with paraentheses
* fix config issue
* fix config issue
* fix config issue
* change everything to MCTCT
* fixing naming change errors
* archive list
* copyrights and docs
* copyrights and docs
* copyrights and docs
* merge resolution
* move tests, fix to changed optionaldependency structure
* test directories changed
* fixing tests
* how to avoid tf tests?
* how to avoid tf tests?
* tests passing locally
* allow mctctprocessor imported any env
* allow mctctprocessor imported any env
* fixed second round of feedback, need to fix docs
* doc changes not being applied
* all fixed
* style fix
* feedback fixes
* fix copies and feature extraction style fix
* Update tests/models/visual_bert/test_modeling_visual_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* copy paste huggingface:main visual bert
* added eof newline to visual bert; all tests are passing otherwise
* fix slow tests by adding attention mask
* change model id to speechbrain
* make fix-copies
* fix readme unwanted deletes
* fixing readmes, make fix-copies
* consistent M-CTC-T naming
* Update src/transformers/models/mctct/__init__.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* all fixed but variable naming
* adjust double quotes
* fixed variable names
* copyright and mr quilter
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* correct slow tests
* make fix-copies
* Update src/transformers/models/mctct/configuration_mctct.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/mctct/configuration_mctct.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* m-ctc-t not mctct
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add method to call to_tf_dataset() with column inference
* Add test for dataset creation
* Add a default arg for data collator
* Fix test
* Fix call with non-dev version of datasets
* Test correct column removal too
* make fixup
* More tests to make sure we remove unwanted columns
* Fix test to avoid predicting on unbuilt models
* Fix test to avoid predicting on unbuilt models
* Fix test to remove unwanted head mask columns from inputs
* Stop pushing your debug breakpoints to the main repo of the $2bn company you work for
* Skip the test in convnext because no grouped conv support
* Drop bools from the dataset dict
* Make style
* Skip the training test for models whose input dicts don't give us labels
* Skip transformerXL in the test because it doesn't return a simple loss
* Skip TFTapas because of some odd NaN losses
* make style
* make fixup
* Add docstring
* fixup
* Update src/transformers/modeling_tf_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/modeling_tf_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/modeling_tf_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/modeling_tf_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/modeling_tf_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Remove breakpoint from tests
* Fix assert, add requires_backends
* Protect tokenizer import with if TYPE_CHECKING
* make fixup
* Add noqa, more fixup
* More rearranging for ~* aesthetics *~
* Adding defaults for shuffle and batch_size to match to_tf_dataset()
* Update src/transformers/modeling_tf_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add gated-silu to t5 architecture to support UL2
* Fix error message
* formatting
* formatting again
* refactor
* fix classnames in _init_weights
* remove is_gated
* add test
* fix test
* Try without the test?
* Add back the test.
* Improve error message.
Co-authored-by: Daniel Hesslow <daniel@lighton.ai>
* add a test for a word only input
* make LukeForMaskedLM work without entity inputs
* update test
* add LukeForMaskedLM to MODEL_FOR_MASKED_LM_MAPPING_NAMES
* restore pyproject.toml
* empty line at the end of pyproject.toml
* initial commit
* add init file
* update globakl init
* update index and dummy objects
* style
* update modelling auto
* fix initi typo in src/transformers
* fix typo in modeling tf auto, opt was in wrong mapping name
* fixed a slow test : saved_model
* style
* fix positionnal embedding if no position id is provided
* update tf test
* update test flax requirements
* fixed serialization
* update
* update tf name to allow smooth convertion
* update flax tests
* style
* fix test typo
* fix tf typo test
* add xla for generate support in causal LM
* fixed bug
* cleaned tf tests
* style
* removed from PT for slow tests
* fix typp
* opt test as slow
* trying to fix GPT2 undefined
* correct documentation and add to test doc
* update tf doc
* fix doc
* fake commit
* Apply suggestions from code review
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* update test based on review
* merged main layer for functionning test
* fixup + quality
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* update long comment
* make fix copies
Co-authored-by: Arthur <arthur@huggingface.co>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [Json dump] Make json prettier
* correct more tokenizeirs
* more patterns
* add aggressive test
* the aggressive test was actually useful :-)
* more tests
* Apply suggestions from code review
* Add onnx configuration for xlm
* Add supported features for xlm
* Add xlm to models exportable with onnx
* Add xlm architecture to test file
* Modify docs
* Make code quality fixes
* Support for Bart and LayoutLM, and partial support for XLNet
* Support for mbart
* A lot of new models supported
* Support for other models
* LayoutLM fix
* Use strings instead of classes
* Enablign `imageGPT` auto feature extractor.
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Small updates.
* Update after rebase to use `input_ids` instead of `pixel_values`.
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Make forward pass work
* More improvements
* Remove unused imports
* Remove timm dependency
* Improve loss calculation of token classifier
* Fix most tests
* Add docs
* Add model integration test
* Make all tests pass
* Add LayoutLMv3FeatureExtractor
* Improve integration test + make fixup
* Add example script
* Fix style
* Add LayoutLMv3Processor
* Fix style
* Add option to add visual labels
* Make more tokenizer tests pass
* Fix more tests
* Make more tests pass
* Fix bug and improve docs
* Fix import of processors
* Improve docstrings
* Fix toctree and improve docs
* Fix auto tokenizer
* Move tests to model folder
* Move tests to model folder
* change default behavior add_prefix_space
* add prefix space for fast
* add_prefix_spcae set to True for Fast
* no space before `unique_no_split` token
* add test to hightligh special treatment of added tokens
* fix `test_batch_encode_dynamic_overflowing` by building a long enough example
* fix `test_full_tokenizer` with add_prefix_token
* Fix tokenizer integration test
* Make the code more readable
* Add tests for LayoutLMv3Processor
* Fix style
* Add model to README and update init
* Apply suggestions from code review
* Replace asserts by value errors
* Add suggestion by @ducviet00
* Add model to doc tests
* Simplify script
* Improve README
* a step ahead to fix
* Update pair_input_test
* Make all tokenizer tests pass - phew
* Make style
* Add LayoutLMv3 to CI job
* Fix auto mapping
* Fix CI job name
* Make all processor tests pass
* Make tests of LayoutLMv2 and LayoutXLM consistent
* Add copied from statements to fast tokenizer
* Add copied from statements to slow tokenizer
* Remove add_visual_labels attribute
* Fix tests
* Add link to notebooks
* Improve docs of LayoutLMv3Processor
* Fix reference to section
Co-authored-by: SaulLu <lucilesaul.com@gmail.com>
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Initial work
* More or less finished with first draft
* Update src/transformers/modeling_utils.py
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Update src/transformers/modeling_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Fix randomly initialized weights
* Update src/transformers/modeling_utils.py
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
* Address review comments
* Rename DeepSpeed folder to temporarily fix the test issue?
* Revert to try if Accelerate fix works
* Use latest Accelerate release
* Quality and fixes
* Style
* Quality
* Add doc
* Test + fix
* More blocks
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
* Fix torch.jit.script and pickling issues
* Fix get_attr issues
* Fix import in function
* Fix GPT-J and T5 tracing for torch=1.11
* Gate graph surgery on torch version
* Modeling minor changes to enable TorchScripting
* Model serialization / deserialization test
* Remove _assert_is_none users
* [BC] Fixing usage of text pairs
The BC is actually preventing users from misusing the pipeline since
users could have been willing to send text pairs and the pipeline would
instead understand the thing as a batch returning bogus results.
The correct usage of text pairs is preserved in this PR even when that
makes the code clunky.
Adds support for {"text":..,, "text_pair": ...} inputs for both dataset
iteration and more explicit usage to pairs.
* Updating the doc.
* Update src/transformers/pipelines/text_classification.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/pipelines/text_classification.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update tests/pipelines/test_pipelines_text_classification.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* quality.
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Initial commit
* Better label renaming
* Remove breakpoint before pushing (this is your job)
* Test a lot more in the Keras fit() test
* make fixup
* Clarify the case where we flatten y dicts into tensors
* Clarify the case where we flatten y dicts into tensors
* Extract label name remapping to a method
* Automatically sort auto mappings
* Better class extraction
* Some auto class magic
* Adapt test and underlying behavior
* Remove re-used config
* Quality
* Add test to ensure models can take int64 inputs
* is_integer is an attribute, not a method
* Fix test when some inputs aren't tensors
* Add casts to blenderbot and blenderbot-small
* Add casts to the other failing models
* First version - OPT model
* Final changes
- putting use cache to False
* few changes
- remove commented block
* few changes
- remove unecessary files
* fix style issues
* few changes
- remove a test file
- added the logits test
* Update src/transformers/models/auto/tokenization_auto.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add gen tests
* few changes
- rm mask filling example on docstring
* few changes
- remove useless args
* some changes
- more tests should pass now
- needs to clean more
- documentation still needs to be done
* fix code quality
* major changes
- change attention architecture to BART-like
- modify some tests
- style fix
* rm useless classes
- remove opt for:
- QA
- cond generation
- seq classif
* Removed autodoc calls to non-existant classes
TOkenizers are not implemented
* Update src/transformers/__init__.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/__init__.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/auto/modeling_tf_auto.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Replaced OPTTokeniser with GPT2 tokenizer
* added GPT2Tokenizer.from_pretrained("patrickvonplaten/opt_gpt2_tokenizer")
* Removed OPTTokenizer
* make style
* Make style replaces
``` ...).unsqueeze(```
by
``` >>>).unsqueeze(```
* make repo consistency
* Removed PretrainedOPTModel
* fix opt.mdx removed other heads
* fix init, removed 3 heads
* removed heads
* finished cleaning head
* removed seauence classif and question answering
* removed unused imports
* removed useless dummy object for QA, SC and CG
* removed tests for removed useless dummy object for QA, SC and CG
* Removed head_mask using encoder layers which don't exist
* fixed test
* fix line
* added OPT to toctree
* Updated model path with pushed weigths
* fix model path
* fixed code quality
* fixed embeddings and generation tests
* update paths
* clean comments
* removed OPTClassificationHead for sentence classification
* renamed hidden layer
* renamed num layers to standard num_hidden_layers
* num_attention_heads fix
* changes for 125m
* add first version for 125m
* add first version - flax
* add new version
* causal LM output
* replace output type with BaseModelOutputWithPastAndCrossAttentions
* revert working config from 150m to 350m
* clean
* removed decoder input ids
* fixed embed dim
* more embed_dim issues
* make style + removed enc_dec test
* update falx model
* removed troublesome copy
* added is_encoder_decoder=False to config
* added set_input emb fuinction to model class
* requires torch on embed test
* use head mask instead of decoder head mask input param solves a test
* 8 test remaining, update
* Updated create_and_check_decoder_model_past_large_inputs
* Make style
* update op tokenizer with condition
* make style
* See if I can push
* some clean up
* remove linear head hack
* save intermediate
* save correct attention
* add copied from from bart
* Update src/transformers/models/opt/modeling_opt.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* fix part of the reviewss
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* same changes in naming / conversion
* correct mask
* more fixes
* delete FlaxOPT and TfOPT
* clean traces of Flax and Tf
* fix mask
* fixed positionnal embedding length when past key value is provoded
* get 125m, 6.7b to work
* Added do_layer_norm
* solved mismatch in load dictionnary
* clean up preapre opt input dict
* fixed past key value as bool
* fix previus
* fixed return dict False tuple issue
* All tests are passing
* Make style
* Ignore OPTDecoder non tested
* make fix-copies
* make repo consistency
* small fix
* removed uselss @torch.no_grad decorator
* make styl;e
* fix previous opt test
* style
* make style
* added opt documentation
* update OPT_PRETRAINED_MODEL_ARCHIVE_LIST
* up
* more fixes
* model & config work
* Update src/transformers/models/opt/modeling_opt.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/opt/modeling_opt.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/opt/modeling_opt.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* added comment on padding hack (+2)
* cleaup
* review update
* docstring for missing arg
* Update docs/source/en/model_doc/opt.mdx
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update docs/source/en/model_doc/opt.mdx
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update docs/source/en/model_doc/opt.mdx
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/opt/__init__.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* update pretrained map
* update path and tests
* make style
* styling
* make consistency
* add gpt2 tok new
* more tok fixes
* Update src/transformers/models/auto/tokenization_auto.py
* Update docs/source/en/model_doc/opt.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update docs/source/en/model_doc/opt.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update docs/source/en/model_doc/opt.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/opt/modeling_opt.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update tests/models/opt/test_modeling_opt.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/opt/modeling_opt.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/opt/modeling_opt.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/opt/modeling_opt.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/opt/modeling_opt.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/opt/modeling_opt.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update based on reviews
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* make style
* make tokenizer auto tests pass
* apply Lysandre suggestion
* finish tests
* add some good tokenizer tests
* improve docs slighly
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: ArthurZucker <arthur.zucker@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* [WIP] Add FLAVA model
This PR aims to add [FLAVA](ihttps://arxiv.org/abs/2112.04482) model to the transformers repo.
Following checklist delineates the list of things to be done for this PR
to be complete:
[x] Flava init
[x] Flava base models
[x] Flava layers
[x] Flava Configs
[x] Flava encoders
[x] Flava pretraining models
[ ] Flava classification/retrieval models (To be added in a separate PR)
[x] Documentation updates
[x] Imports updates
[x] Argstring updates
[x] Flava pretrained checkpoints
[x] Flava tests
[x] Flava processors
[x] Sanity check
[x] Lint
* unhardcode pretrained model path, make it a class var
* add tests for mobilebert tokenizer
* allow tempfiles for vocab & merge similarity test to autodelete
* add explanatory comments
* remove unused imports, let make style do its.. thing
* remove inheritance and use BERT tok tests for MobileBERT
* Update tests/mobilebert/test_tokenization_mobilebert.py
Co-authored-by: SaulLu <55560583+SaulLu@users.noreply.github.com>
* amend class names, remove unused import, add fix for mobilebert's hub pathname
* unhardcode pretrained model path, make it a class var
* add tests for mobilebert tokenizer
* allow tempfiles for vocab & merge similarity test to autodelete
* add explanatory comments
* remove unused imports, let make style do its.. thing
* remove inheritance and use BERT tok tests for MobileBERT
* Update tests/mobilebert/test_tokenization_mobilebert.py
Co-authored-by: SaulLu <55560583+SaulLu@users.noreply.github.com>
* amend class names, remove unused import, add fix for mobilebert's hub pathname
* amend paths for model tests being in models/ subdir of /tests
* explicitly rm test from prev path
Co-authored-by: SaulLu <55560583+SaulLu@users.noreply.github.com>
* add support for MLFLOW_FLATTEN_PARAMS
* ensure key is str
* fix style and update warning msg
* Empty commit to trigger CI
* fix bug in check_inits.py
* add unittest for flatten_dict utils
* fix 'NoneType' object is not callable on __del__
* add generic flatten_dict unittest to SPECIAL_MODULE_TO_TEST_MAP
* fix style
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
- Adds auto_batch_size finder
- Moves training loop to an inner training loop
* add get_overflowing_images function to ensure 1-to-1 mapping between samples and images in LayoutLMv2Processor
* make style
* add test for overflowing_tokens, change assert to ValueError, avoiding unrelated formatting changes
* change line length by passing --preview into black
* CLIP Serving
* Add type hints per code review
* Use black, flake8, and isort
* Update src/transformers/models/clip/modeling_tf_clip.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Rollback serving_output and add TODO
* Remove irrelevant portions of failing tests
* Revert "Rollback serving_output and add TODO"
This reverts commit a4abfa6ba3b7875a13538dbc2ddc4eb17dfcca8d.
* Rollback to original test/serving_output
* Fix unused var
* Apply suggestions from code review
* Update formatting with black
* Fix style again from rebase
* Update tests/models/clip/test_modeling_tf_clip.py
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: Sean Moriarity <sean.l.moriarity.mil@army.mil>
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* [T5 Tokenizer] Model has no fixed position ids - there is no hardcoded max length
* [T5 Tokenizer] Model has no fixed position ids - there is no hardcoded max length
* correct t5 tokenizer
* correct t5 tokenizer
* fix test
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* finish
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* First draft
* Add YolosForObjectDetection
* Make forward pass work
* Add mid position embeddings
* Add interpolation of position encodings
* Add expected values
* Add YOLOS to tests
* Add integration test
* Support tiny model as well
* Support all models in conversion script
* Remove mid_pe_size attribute
* Make more tests pass
* Add model to README and fix config
* Add copied from statements
* Rename base_model_prefix to vit
* Add missing YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP
* Apply suggestions from code review
* Apply more suggestions from code review
* Convert remaining checkpoints
* Improve docstrings
* Add YolosFeatureExtractor
* Add feature extractor to docs
* Add corresponding tests
* Fix style
* Fix docs
* Apply suggestion from code review
* Fix bad rebase
* Fix some more bad rebase
* Fix missing character
* Improve docs and variable names
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Fix issue avoid-misusing-assert-true found at https://codereview.doctor
* fix tests
* fix tf
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* begin do_init
* add params_shape_tree
* raise error if params are accessed when do_init is False
* don't allow do_init=False when keys are missing
* make shape tree a property
* assign self._params at the end
* add test for do_init
* add do_init arg to all flax models
* fix param setting
* disbale do_init for composite models
* update test
* add do_init in FlaxBigBirdForMultipleChoice
* better names and errors
* improve test
* style
* add a warning when do_init=False
* remove extra if
* set params after _required_params
* add test for from_pretrained
* do_init => _do_init
* chage warning to info
* fix typo
* add params in init_weights
* add params to gpt neo init
* add params to init_weights
* update do_init test
* Trigger CI
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* update template
* trigger CI
* style
* style
* fix template
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add passing encoder_outputs as tuple to existing test
* Add check for tuple
* Add check for tuple also for speech and vision
Co-authored-by: jsnfly <jsnfly@gmx.de>
* Improve code
* Fix bugs
* Fix another bug
* Clean up DTP as well
* Update DPT model outputs
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* [trainer / deepspeed] fix hyperparameter_search
* require optuna
* style
* oops
* add dep in the right place
* create deepspeed-testing dep group
* Trigger CI
* Fix setters of *_token_id properties of SpecialTokensMixin
* Test setters of common tokens ids
* Move to a separate test checks of setters of tokens ids
* Add independent test for ByT5
* Add Canine test
* Test speech to text
* Change the chunk_iter function to handle
the subtle cases where the last chunk gets ignored since all the
data is in the `left_strided` data.
We need to remove the right striding on the previous item.
* Remove commented line.
* update proto sentencepiece model
* Revert "update proto sentencepiece model"
This reverts commit b07f671747.
* add check
* add test
* Revert "Revert "update proto sentencepiece model""
This reverts commit 46108257b8.
* test for log level
* test for log level 2
* warning at the warning level
* clean
* format
* add explanation in docstring
* Add TapexTokenizer
* Improve docstrings and provide option to provide answer
* Remove option for pretokenized inputs
* Add TAPEX to README
* Fix copies
* Remove option for pretokenized inputs
* Initial commit: add tapex fine-tuning examples on both table-based question answering and table-based fact verification.
* - Draft a README file for running the script and introducing some background.
- Remove unused code lines in tabfact script.
- Disable the deafult `pad_to_max_length` option which is memory-consuming.
* * Support `as_target_tokenizer` function for TapexTokenizer.
* Fix the do_lower_case behaviour of TapexTokenizer.
* Add unit tests for target scenarios and cased/uncased scenarios for both source and target.
* * Replace the label BartTokenizer with TapexTokenizer's as_target_tokenizer function.
* Fix typos in tapex example README.
* * fix the evaluation script - remove the property `task_name`
* * Make the label space more clear for tabfact tasks
* * Using a new fine-tuning script for tapex-base on tabfact.
* * Remove the lowercase code outside the tokenizer - we use the tokenizer to control whether do_lower_case
* Guarantee the hyper-parameter can be run without out-of-memory on 16GB card and report the new reproduced number on wikisql
* * Remove the default tokenizer_name option.
* Provide evaluation command.
* * Support for WikiTableQuestion dataset.
* Fix a typo in README.
* * Fix the datasets's key name in WikiTableQuestions
* Run make fixup and move test to folder
* Fix quality
* Apply suggestions from code review
* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* Apply suggestions from code review
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Apply some more suggestions from code review
* Improve docstrings
* Overwrite failing test
* Improve comment in example scripts
* Fix rebase
* Add TAPEX to Auto mapping
* Add TAPEX to auto config mappings
* Put TAPEX higher than BART in auto mapping
* Add TAPEX to doc tests
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MBP.localdomain>
Co-authored-by: SivilTaram <qianlxc@outlook.com>
Co-authored-by: Niels Rogge <nielsrogge@nielss-mbp.home>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Adding new train_step logic to make things less confusing for users
* DO NOT ASK WHY WE NEED THAT SUBCLASS
* Metrics now working, at least for single-output models with type annotations!
* Updates and TODOs for the new train_step
* Make fixup
* Temporary test workaround until T5 has types
* Temporary test workaround until T5 has types
* I think this actually works! Needs a lot of tests though
* MAke style/quality
* Revert changes to T5 tests
* Deleting the aforementioned unmentionable subclass
* Deleting the aforementioned unmentionable subclass
* Adding a Keras API test
* Style fixes
* Removing unneeded TODO and comments
* Update test_step too
* Stop trying to compute metrics with the dummy_loss, patch up test
* Make style
* make fixup
* Docstring cleanup
* make fixup
* make fixup
* Stop expanding 1D input tensors when using dummy loss
* Adjust T5 test given the new compile()
* make fixup
* Skipping test for convnext
* Removing old T5-specific Keras test now that we have a common one
* make fixup
* make fixup
* Only skip convnext test on CPU
* Update src/transformers/modeling_tf_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/modeling_tf_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Avoiding TF import issues
* make fixup
* Update compile() to support TF 2.3
* Skipping model.fit() on template classes for now
* Skipping model.fit() on template class tests for now
* Replace ad-hoc solution with find_labels
* make fixup
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add utility to find model labels
* Use it in the Trainer
* Update src/transformers/utils/generic.py
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Quality
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* feature extractor accepts
* resolved conversations
* added examples in test for ADE20K
* num_classes -> num_labels
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* resolving conversations
* resolving conversations
* removed ADE
* CI
* minor changes in conversion script
* reduce_labels in feature extractor
* minor changes
* correct preprocess for instace segmentation maps
* minor changes
* minor changes
* CI
* debugging
* better padding
* going to update labels inside the model
* going to update labels inside the model
* minor changes
* tests
* removed changes in feature_extractor_utils
* conversation
* conversation
* example in feature extractor
* more docstring in modeling
* test
* make style
* doc
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* ported TFViTMAEIntermediate and TFViTMAEOutput.
* added TFViTMAEModel and TFViTMAEDecoder.
* feat: added a noise argument in the implementation for reproducibility.
* feat: vit mae models with an additional noise argument for reproducibility.
Co-authored-by: ariG23498 <aritra.born2fly@gmail.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Fix for test_mixed_precision
* Fix test_saved_model_creation by using shape_list instead of shape
* skit test_model_from_pretrained on GPU for now to avoid GPU OOM
* skip test_gptj_sample_max_time for now
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Created the Decision Transformer Modle
* updating tests, copy to other machine
* Added last hidden size to Decision Transformer modelling outputs
* Removed copy of original DT file
* made a temporary change to gpt2 to have it conform with the Decision Transformer version
* Updated tests
* Ignoring a file used to test the DT model
* added comments to config file
* added comments and argument descriptions to decision transformer file
* Updated doc
* Ran "make style"
* Remove old model imports
* Removed unused imports, cleaned up init file
* Update docs/source/model_doc/decision_transformer.mdx
added my username
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Reverted changes made to gpt2
* Removed datasets submodule
* Update the modeling outputs to include gpt2 attentions, hidden states and last hidden states
* Added support for return of hidden states, attentions and return dict of gpt2 model.
* Updated tests to include many of the ModelTesterMixin tests.
The following tests are skipped: test_generate_without_input_ids, test_pruning, test_resize_embeddings, test_head_masking, test_attention_outputs, test_hidden_states_output, test_inputs_embeds, test_model_common_attributes
* Added missing line to the end of gpt2 file
* Added an integration test for the Decision Transformer
Test performs and autoregressive evaluation for two time steps
* Set done and info to _ to fix failing test
* Updated integration test to be deterministic and check expected outputs
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Removed unnecessary config options
* Cleaned up commented code and old comments.
* Cleaned up commented code.
* Changed DecisionTransformer to Decision Transformer
* Added Decision Transformer to the main README file
* Added copy of GTP2 called DecisionTranformerGPT2Model
* isorted imports
* isorted imports
* Added model to non-English README files
* Ran make fix-copies and corrected some cases.
* Updated index file to include Decision Transformer
* Added gpt2 model as copy inside the Decision Transformer model file
* Added the unit test file to the list of TEST_FILES_WITH_NO_COMMON_TESTS
* Deleted redundant checkpoint files (I don't know how these got committed)
* Removed testing files. (These should have never been committed)
* Removed accidentally committed files
* Moved the Decision Transformer test to its own directory
* Add type hints for Pegasus (#16324)
* Funnel type hints (#16323)
* add pt funnel type hints
* add tf funnel type hints
* Add type hints for ProphetNet PyTorch (#16272)
* [GLPN] Improve docs (#16331)
* Add link to notebook
* Add link
* Fix bug
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Added type hints for Pytorch Marian calls (#16200)
* Added type hinting for forward functions in pytorch marian
* typo correction
* Removed type hints on functions from BART per Suraj Patil request
* fix import pb
* fix typo
* corrected tuple call
* ran black
* after fix-copies
Some optional tags on primitives were removed, past_key_values in MarianForCausalLM changed from Tuple of Tuple to List
* Fixing copies to roformer and pegasus
Co-authored-by: Clementine Fourrier <cfourrie@inria.fr>
Co-authored-by: matt <rocketknight1@gmail.com>
* Moved DecisionTransformOutput to modeling_decision_transformer
* Moved the example usage to research project and cleaned comments
* Made tests ignore the copy of gpt2 in Decision Transformer
* Added module output to modelling decision transformer
* removed copied gpt2 model from list of transformers models
* Updated tests and created __init__ file for new test location
* Update README.md
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/decision_transformer/configuration_decision_transformer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Removed unneeded summary type from config file
* Fixed copies
* Updated pretrained config map to refer to hopper-medium checkpoint
* done (#16340)
* Added Decision transformer to model docs
* Update src/transformers/models/decision_transformer/modeling_decision_transformer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/decision_transformer/modeling_decision_transformer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/decision_transformer/configuration_decision_transformer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add type annotations for Rembert/Splinter and copies (#16338)
* undo black autoformat
* minor fix to rembert forward with default
* make fix-copies, make quality
* Adding types to template model
* Removing List from the template types
* Remove `Optional` from a couple of types that don't accept `None`
Co-authored-by: matt <rocketknight1@gmail.com>
* [Bug template] Shift responsibilities for long-range (#16344)
* Fix code repetition in serialization guide (#16346)
* Adopt framework-specific blocks for content (#16342)
* ✨ refactor code samples with framework-specific blocks
* ✨ update training.mdx
* 🖍 apply feedback
* Updates the default branch from master to main (#16326)
* Updates the default branch from master to main
* Links from `master` to `main`
* Typo
* Update examples/flax/README.md
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Updated model with custom docstring example
* Created the Decision Transformer Modle
* updating tests, copy to other machine
* Added last hidden size to Decision Transformer modelling outputs
* Removed copy of original DT file
* made a temporary change to gpt2 to have it conform with the Decision Transformer version
* Updated tests
* Ignoring a file used to test the DT model
* added comments to config file
* added comments and argument descriptions to decision transformer file
* Updated doc
* Ran "make style"
* Remove old model imports
* Removed unused imports, cleaned up init file
* Update docs/source/model_doc/decision_transformer.mdx
added my username
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Reverted changes made to gpt2
* Removed datasets submodule
* Update the modeling outputs to include gpt2 attentions, hidden states and last hidden states
* Added support for return of hidden states, attentions and return dict of gpt2 model.
* Updated tests to include many of the ModelTesterMixin tests.
The following tests are skipped: test_generate_without_input_ids, test_pruning, test_resize_embeddings, test_head_masking, test_attention_outputs, test_hidden_states_output, test_inputs_embeds, test_model_common_attributes
* Added missing line to the end of gpt2 file
* Added an integration test for the Decision Transformer
Test performs and autoregressive evaluation for two time steps
* Set done and info to _ to fix failing test
* Updated integration test to be deterministic and check expected outputs
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Removed unnecessary config options
* Cleaned up commented code and old comments.
* Cleaned up commented code.
* Changed DecisionTransformer to Decision Transformer
* Added Decision Transformer to the main README file
* Added copy of GTP2 called DecisionTranformerGPT2Model
* isorted imports
* isorted imports
* Added model to non-English README files
* Ran make fix-copies and corrected some cases.
* Updated index file to include Decision Transformer
* Added gpt2 model as copy inside the Decision Transformer model file
* Added the unit test file to the list of TEST_FILES_WITH_NO_COMMON_TESTS
* Deleted redundant checkpoint files (I don't know how these got committed)
* Removed testing files. (These should have never been committed)
* Removed accidentally committed files
* Moved the Decision Transformer test to its own directory
* Moved DecisionTransformOutput to modeling_decision_transformer
* Moved the example usage to research project and cleaned comments
* Made tests ignore the copy of gpt2 in Decision Transformer
* Added module output to modelling decision transformer
* removed copied gpt2 model from list of transformers models
* Updated tests and created __init__ file for new test location
* Update README.md
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/decision_transformer/configuration_decision_transformer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Removed unneeded summary type from config file
* Fixed copies
* Updated pretrained config map to refer to hopper-medium checkpoint
* Added Decision transformer to model docs
* Update src/transformers/models/decision_transformer/modeling_decision_transformer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/decision_transformer/modeling_decision_transformer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/decision_transformer/configuration_decision_transformer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Updated model with custom docstring example
* Updated copies, config auto, and readme files.
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Dan Tegzes <48134725+Tegzes@users.noreply.github.com>
Co-authored-by: Adam Montgomerie <adam@avanssion.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
Co-authored-by: Clémentine Fourrier <22726840+clefourrier@users.noreply.github.com>
Co-authored-by: Clementine Fourrier <cfourrie@inria.fr>
Co-authored-by: matt <rocketknight1@gmail.com>
Co-authored-by: Francesco Saverio Zuppichini <francesco.zuppichini@gmail.com>
Co-authored-by: Jacob Dineen <54680234+jacobdineen@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
* Make Transformers use cache files when hf.co is down
* Fix tests
* Was there a random circleCI failure?
* Isolate patches
* Style
* Comment out the failure since it doesn't fail anymore
* Better comment
* Split file_utils in several submodules
* Fixes
* Add back more objects
* More fixes
* Who exactly decided to import that from there?
* Second suggestion to code with code review
* Revert wront move
* Fix imports
* Adapt all imports
* Adapt all imports everywhere
* Revert this import, will fix in a separate commit
* Updates the default branch from master to main
* Links from `master` to `main`
* Typo
* Update examples/flax/README.md
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Aggressive PT/TF equivalence test on PT side
* Ugly fix for `TFTapasForQuestionAnswering`
* apply review suggestions
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Attention mask is important in the case of batching...
* Improve the fix.
* Making the sentence different enough that they exhibit different
predictions.
* Update expected slices for pillow > 9
* Add expected slices depending on pillow version
* Add different slices depending on pillow version for other models
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* padding done
* correctly return one attention per layer
* almost correct, attentions are not flatten one tuple per stage
* tests green
* doc
* conversations
* reshaping hidden_states
* view in the test
* reshape_hidden_states in Encoder and Model
* new outputs with reshaped_hidden_states
* conversations
* doc
* Update docs/source/model_doc/swin.mdx
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* conversations
* fix tests
* minor changes
* resolved conversations
* attentions one per stage
* typo
* typos
* typos
* function signature
* CI
* clean up tests
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* First attempt at TF XLA generation
* Fix comments
* Update XLA greedy generate with direct XLA calls
* Support attention mask, prepare_inputs_for_generation no longer hardcoded for greedy
* Handle position_ids correctly
* make xla generate work for non xla case
* force using xla generate
* refactor
* more fixes
* finish cleaning
* finish
* finish
* clean gpt2 tests
* add gpt2 tests
* correct more cases
* up
* finish
* finish
* more fixes
* flake 8 stuff
* final rag fix
* Update src/transformers/models/rag/modeling_tf_rag.py
* finish t5 as well
* finish
* Update src/transformers/generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* first commit
* ResNet model correctly implemented.
basic modeling + weights conversion is done
removed unused doc
mdx file
doc and conversion script
added feature_extractor to auto
test
minor changes + style + quality
doc
test
Delete process.yml
A left over from my attempt of running circleci locally
* minor changes
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* new test format
* minor changes from conversations
* minor changes from conversations
* make style + quality
* readded the tests
* test + README
* minor changes from conversations
* error in README
* make fix-copies
* removed regression for classification head
* make quality
* fixed loss control flow
* fixed loss control flow
* resolved conversations
* Apply suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* READMEs
* index.mdx
* minor changes
* updated tests and models
* unused import
* outputs
* Update docs/source/model_doc/resnet.mdx
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* added embeddings_size
* Apply suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* conversation
* added push to hub
* test
* embedding_size
* make fix-copies
* resolved conversations
* CI
* changed organization
* minor changes
* CI
* minor changes
* conversations
* conversation
* doc
* tests
* removed unused docstring
* conversation
* removed unused outputs
* CI
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Make TF pt-tf equivalence test more aggressive
* Fix for TFConvNextModelTest and TFTransfoXLModelTest
* fix kwargs for outputs
* clean-up
* Add docstring for check_outputs()
* remove: need to rename encoder-decoder
* clean-up
* send PyTorch things to the correct device
* Add back the accidentally removed test case in test_pt_tf_model_equivalence()
* Fix: change to tuple before calling check_outputs()
* Fix: tfo could be a list
* use to_tuple()
* allow tfo only to be tuple or tensor
* allow tfo to be list or tuple for now + style change
* minor fix
* remove np.copy and update comments
* tfo -> tf_output, same for pt
* Add more detailed comment
* remove the incorrect comment
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* add possibility to softly regulate length when using sampling method in model.generate() function
* fix test config, fix formatting
* fix rag integration, fix docstyling
* fix wrong docstring
* change param to tuple, add test
* fix old param in rag_model, remove unused import
* change test according to new param
* fix formatting
* fix test case
* fix doc style
* move start_length calculation to Logitprocessor
* add possibility to softly regulate length when using sampling method in model.generate() function
* fix rag integration, fix docstyling
* fix test config, fix formatting
* change param to tuple, add test
* fix old param in rag_model, remove unused import
* add possibility to softly regulate length when using sampling method in model.generate() function
* change param to tuple, add test
* fix old param in rag_model, remove unused import
* remove unused import
* fix small errors
* fix test
* add possibility to softly regulate length when using sampling method in model.generate() function
* fix test config, fix formatting
* fix rag integration, fix docstyling
* change param to tuple, add test
* fix old param in rag_model, remove unused import
* change test according to new param
* fix test case
* move start_length calculation to Logitprocessor
* add possibility to softly regulate length when using sampling method in model.generate() function
* fix rag integration, fix docstyling
* fix test config, fix formatting
* change param to tuple, add test
* fix old param in rag_model, remove unused import
* add possibility to softly regulate length when using sampling method in model.generate() function
* fix test config, fix formatting
* fix rag integration, fix docstyling
* add possibility to softly regulate length when using sampling method in model.generate() function
* fix rag integration, fix docstyling
* change param to tuple, add test
* fix old param in rag_model, remove unused import
* fix small errors
* Update src/transformers/generation_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/generation_utils.py
* Update src/transformers/generation_utils.py
* fix docstring, add type ind model rag
* fix docstrings
* introduce seq_length variable for cleaner code
* fix black formatting
* add input_ids_seq_length to modeling_rag
* add input_ids_seq_length to test
* retrigger checks
* retrigger checks
Co-authored-by: Kevin Bondzio <kev@AIM-LAP-02.local>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Kevin Bondzio <kev@AIM-LAP-02.fritz.box>
* Fix duplicate arguments passed to dummy inputs in ONNX export
* Fix M2M100 ONNX config
* Ensure we check PreTrained model only if torch is available
* Remove TensorFlow tests for models without PyTorch parity
* Add ONNX support for ViT
* Refactor to use generic preprocessor
* Add vision dep to tests
* Extend ONNX slow tests to ViT
* Add dummy image generator
* Use model_type to determine modality
* Add deprecation warnings for tokenizer argument
* Add warning when overwriting the preprocessor
* Add optional args to docstrings
* Add minimum PyTorch version to OnnxConfig
* Refactor OnnxConfig class variables from CONSTANT_NAME to snake_case
* Add reasonable value for default atol
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Seed get_train_sampler's generator with arg seed to improve reproducibility
and make the world_size<=1 code path more similar to the others
* move test file into trainer test explicitly
* dumb typo
* make style lint happy
* per discussion, switch to data_seed
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* added classes to get started with constrained beam search
* in progress, think i can directly force tokens now but not yet with the round robin
* think now i have total control, now need to code the bank selection
* technically works as desired, need to optimize and fix design choices leading to undersirable outputs
* complete PR #1 without disjunctive decoding
* removed incorrect tests
* Delete k.txt
* Delete test.py
* Delete test.sh
* revert changes to test scripts
* genutils
* full implementation with testing, no disjunctive yet
* shifted docs
* passing all tests realistically ran locally
* removing accidentally included print statements
* fixed source of error in initial PR test
* fixing the get_device() vs device trap
* fixed documentation docstrings about constrained_beam_search
* fixed tests having failing for Speech2TextModel's floating point inputs
* fix cuda long tensor
* added examples and testing for them and founx & fixed a bug in beam_search and constrained_beam_search
* deleted accidentally added test halting code with assert False
* code reformat
* Update tests/test_generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update tests/test_generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update tests/test_generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update tests/test_generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update tests/test_generation_utils.py
* fixing based on comments on PR
* took out the testing code that should but work fails without the beam search moditification ; style changes
* fixing comments issues
* docstrings for ConstraintListState
* typo in PhrsalConstraint docstring
* docstrings improvements
* finished adding what is sort of an opinionated implementation of disjunctive generation, but it revealed errors in inner beam search logic during testing.
* fixed bug found in constrained beam search that used beam_idx that were not global across all the batches
* disjunctive constraint working 100% correctly
* passing all tests
* Accidentally included mlruns
* Update src/transformers/generation_beam_constraints.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/generation_beam_constraints.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* complete overhaul of type complexities and other nits
* strict type checks in generate()
* fixing second round of feedback by narsil
* fixed failing generation test because of type check overhaul
* generation test fail fix
* fixing test fails
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Fix to support fast tokenizer with `CLIPProcessor`
* Update CLIPProcessor test for fast tokenizer
* Fix Docstring Style
* Rename into meaningful Variable name in test code
* send PyTorch inputs to the correct device
* Fix: TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Add TF logits wrappers
* Add sample method
* add tests for TF logit wrappers
* TF generate sample tests now run on CPU
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* maskformer
* conflicts
* conflicts
* minor fixes
* feature extractor test fix
refactor MaskFormerLoss following conversation
MaskFormer related types should not trigger a module time import error
missed one
removed all the types that are not used
update config mapping
minor updates in the doc
resolved conversation that doesn't need a discussion
minor changes
resolved conversations
fixed DetrDecoder
* minor changes
minor changes
fixed mdx file
test feature_extractor return types
functional losses -> classes
removed the return type test for the feature extractor
minor changes + style + quality
* conflicts?
* rebase master
* readme
* added missing files
* deleded poolformers test that where in the wrong palce
* CI
* minor changes
* Apply suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* resolved conversations
* minor changes
* conversations
[Unispeech] Fix slow tests (#15818)
* remove soundfile old way of loading audio
* Adapt slow test
[Barthez Tokenizer] Fix saving (#15815)
[TFXLNet] Correct tf xlnet generate (#15822)
* [TFXLNet] Correct tf xlnet
* adapt test comment
Fix the push run (#15807)
Fix semantic segmentation pipeline test (#15826)
Fix dummy_inputs() to dummy_inputs in symbolic_trace doc (#15776)
Add model specific output classes to PoolFormer model docs (#15746)
* Added model specific output classes to poolformer docs
* Fixed Segformer typo in Poolformer docs
Adding the option to return_timestamps on pure CTC ASR models. (#15792)
* Adding the option to return_timestamps on pure CTC ASR models.
* Remove `math.prod` which was introduced in Python 3.8
* int are not floats.
* Reworking the PR to support "char" vs "word" output.
* Fixup!
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Quality.
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
HFTracer.trace should use/return self.graph to be compatible with torch.fx.Tracer (#15824)
Fix tf.concatenate + test past_key_values for TF models (#15774)
* fix wrong method name tf.concatenate
* add tests related to causal LM / decoder
* make style and quality
* clean-up
* Fix TFBertModel's extended_attention_mask when past_key_values is provided
* Fix tests
* fix copies
* More tf.int8 -> tf.int32 in TF test template
* clean-up
* Update TF test template
* revert the previous commit + update the TF test template
* Fix TF template extended_attention_mask when past_key_values is provided
* Fix some styles manually
* clean-up
* Fix ValueError: too many values to unpack in the test
* Fix more: too many values to unpack in the test
* Add a comment for extended_attention_mask when there is past_key_values
* Fix TFElectra extended_attention_mask when past_key_values is provided
* Add tests to other TF models
* Fix for TF Electra test: add prepare_config_and_inputs_for_decoder
* Fix not passing training arg to lm_head in TFRobertaForCausalLM
* Fix tests (with past) for TF Roberta
* add testing for pask_key_values for TFElectra model
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
[examples/summarization and translation] fix readme (#15833)
Add ONNX Runtime quantization for text classification notebook (#15817)
Re-enable doctests for the quicktour (#15828)
* Re-enable doctests for the quicktour
* Re-enable doctests for task_summary (#15830)
* Remove &
Framework split model report (#15825)
Add TFConvNextModel (#15750)
* feat: initial implementation of convnext in tensorflow.
* fix: sample code for the classification model.
* chore: added checked for from the classification model.
* chore: set bias initializer in the classification head.
* chore: updated license terms.
* chore: removed ununsed imports
* feat: enabled argument during using drop_path.
* chore: replaced tf.identity with layers.Activation(linear).
* chore: edited default checkpoint.
* fix: minor bugs in the initializations.
* partial-fix: tf model errors for loading pretrained pt weights.
* partial-fix: call method updated
* partial-fix: cross loading of weights (4x3 variables to be matched)
* chore: removed unneeded comment.
* removed playground.py
* rebasing
* rebasing and removing playground.py.
* fix: renaming TFConvNextStage conv and layer norm layers
* chore: added initializers and other minor additions.
* chore: added initializers and other minor additions.
* add: tests for convnext.
* fix: integration tester class.
* fix: issues mentioned in pr feedback (round 1).
* fix: how output_hidden_states arg is propoagated inside the network.
* feat: handling of arg for pure cnn models.
* chore: added a note on equal contribution in model docs.
* rebasing
* rebasing and removing playground.py.
* feat: encapsulation for the convnext trunk.
* Fix variable naming; Test-related corrections; Run make fixup
* chore: added Joao as a contributor to convnext.
* rebasing
* rebasing and removing playground.py.
* rebasing
* rebasing and removing playground.py.
* chore: corrected copyright year and added comment on NHWC.
* chore: fixed the black version and ran formatting.
* chore: ran make style.
* chore: removed from_pt argument from test, ran make style.
* rebasing
* rebasing and removing playground.py.
* rebasing
* rebasing and removing playground.py.
* fix: tests in the convnext subclass, ran make style.
* rebasing
* rebasing and removing playground.py.
* rebasing
* rebasing and removing playground.py.
* chore: moved convnext test to the correct location
* fix: locations for the test file of convnext.
* fix: convnext tests.
* chore: applied sgugger's suggestion for dealing w/ output_attentions.
* chore: added comments.
* chore: applied updated quality enviornment style.
* chore: applied formatting with quality enviornment.
* chore: revert to the previous tests/test_modeling_common.py.
* chore: revert to the original test_modeling_common.py
* chore: revert to previous states for test_modeling_tf_common.py and modeling_tf_utils.py
* fix: tests for convnext.
* chore: removed output_attentions argument from convnext config.
* chore: revert to the earlier tf utils.
* fix: output shapes of the hidden states
* chore: removed unnecessary comment
* chore: reverting to the right test_modeling_tf_common.py.
* Styling nits
Co-authored-by: ariG23498 <aritra.born2fly@gmail.com>
Co-authored-by: Joao Gante <joao@huggingface.co>
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
* minor changes
* doc fix in feature extractor
* doc
* typose
* removed detr logic from config
* removed detr logic from config
* removed num_labels
* small fix in the config
* auxilary -> auxiliary
* make style
* some test is failing
* fix a weird char in config prevending doc-builder
* retry to fix the doc-builder issue
* make style
* new try to fix the doc builder
* CI
* change weights to facebook
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: ariG23498 <aritra.born2fly@gmail.com>
Co-authored-by: Joao Gante <joao@huggingface.co>
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
* Add data2vec model cloned from roberta
* Add checkpoint conversion script
* Fix copies
* Update docs
* Add checkpoint conversion script
* Remove fairseq data2vec_text script and fix format
* Add comment on where to get data2vec_text.py
* Remove mock implementation cheat.py and fix style
* Fix copies
* Remove TF and Flax classes from init
* Add back copy from fairseq data2vec_text.py and fix style
* Update model name in docs/source/index.mdx to be CamelCase
* Revert model name in table to lower-case to get check_table test to pass
* Update src/transformers/models/data2vec/__init__.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/data2vec/convert_data2vec_original_pytorch_checkpoint_to_pytorch.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update docs/source/model_doc/data2vec.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update docs/source/model_doc/data2vec.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/auto/configuration_auto.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/data2vec/configuration_data2vec.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update tests/test_modeling_data2vec.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/data2vec/configuration_data2vec.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update documentation
* Copy-paste Data2VecConfig from BertConfig
* Update config checkpoint to point to edugp/data2vec-nlp-base. Fix style and repo-consistency
* Update config special tokens to match RoBERTa
* Split multiple assertions and add individual error messages
* Rename Data2VecModel to Data2VecForTextModel
* Add Data2Vec to _toctree.yml
* Rename Data2VecEmbeddings to Data2VecForTextEmbeddings
* Add initial Data2VecForAudio model (unfinished). Only matching fairseq's implementation up to the feature encoder (before positional encoding).
* finish audio model
* finish audio file
* Update names and fix style, quality and repo consistency
* Remove Data2VecAudioForPretraining. Add tests for Data2VecAudio, mimicking the Wav2Vec2 test suite. Fix bias initilization in positional conv layers. Move back configurations for audio and text to separate files.
* add inputs to logits to data2vec'
* correct autio models
* correct config auto
* correct tok auto
* Update utils/tests_fetcher.py
* delete unnecessary files
* delete unnecessary files
* further renaming
* make all tests pass
* finish
* remove useless test file
* Update tests/test_modeling_common.py
* Update utils/check_repo.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/data2vec/modeling_data2vec_text.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Fix copies
* Update docs
* Remove fairseq data2vec_text script and fix format
* Add comment on where to get data2vec_text.py
* Remove mock implementation cheat.py and fix style
* Fix copies
* Remove TF and Flax classes from init
* Add back copy from fairseq data2vec_text.py and fix style
* Update model name in docs/source/index.mdx to be CamelCase
* Revert model name in table to lower-case to get check_table test to pass
* Update documentation
* Update src/transformers/models/data2vec/__init__.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/data2vec/convert_data2vec_original_pytorch_checkpoint_to_pytorch.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/auto/configuration_auto.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/data2vec/configuration_data2vec.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update tests/test_modeling_data2vec.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/data2vec/configuration_data2vec.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Copy-paste Data2VecConfig from BertConfig
* Update config checkpoint to point to edugp/data2vec-nlp-base. Fix style and repo-consistency
* Update config special tokens to match RoBERTa
* Split multiple assertions and add individual error messages
* Rename Data2VecModel to Data2VecForTextModel
* Add Data2Vec to _toctree.yml
* Rename Data2VecEmbeddings to Data2VecForTextEmbeddings
* Add initial Data2VecForAudio model (unfinished). Only matching fairseq's implementation up to the feature encoder (before positional encoding).
* finish audio model
* finish audio file
* add inputs to logits to data2vec'
* Update names and fix style, quality and repo consistency
* Remove Data2VecAudioForPretraining. Add tests for Data2VecAudio, mimicking the Wav2Vec2 test suite. Fix bias initilization in positional conv layers. Move back configurations for audio and text to separate files.
* correct autio models
* correct config auto
* correct tok auto
* delete unnecessary files
* delete unnecessary files
* Update utils/tests_fetcher.py
* further renaming
* make all tests pass
* finish
* remove useless test file
* Update tests/test_modeling_common.py
* Update utils/check_repo.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/data2vec/modeling_data2vec_text.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Move data2vec tests to new structure
* Fix test imports for text tests
* Remove fairseq files
* Change paper link to arxiv
* Modify Data2Vec documentation to reflect that the encoder is not shared across the audio and text models in the current implementation.
* Update text model checkpoint to be facebook/data2vec-text-base
* Add 'Copy from' statements and update paper links and docs
* fix copy from statements
* improve copied from
* correct more copied from statements
* finish copied from stuff
* make style
* add model to README
* add to master
Co-authored-by: Eduardo Gonzalez Ponferrada <eduardo@ferrumhealth.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fixing the timestamps with chunking.
* The changes modified (and fixed) the striding tests.
* Adding a tokenizer test.
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Defense -> comment.
* Update src/transformers/models/wav2vec2/tokenization_wav2vec2.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* rebase
* Delete shift tokens func
* downsample decoder input seq len for init
* correct attention mask
* add tests
* pt flax cross test
* make fixup
* init file for import
* change pt-flax cross test threshold
* pt-flax test logits only
* move tests
* make repo-consistency
* consistent indentation
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* feat: initial implementation of convnext in tensorflow.
* fix: sample code for the classification model.
* chore: added checked for from the classification model.
* chore: set bias initializer in the classification head.
* chore: updated license terms.
* chore: removed ununsed imports
* feat: enabled argument during using drop_path.
* chore: replaced tf.identity with layers.Activation(linear).
* chore: edited default checkpoint.
* fix: minor bugs in the initializations.
* partial-fix: tf model errors for loading pretrained pt weights.
* partial-fix: call method updated
* partial-fix: cross loading of weights (4x3 variables to be matched)
* chore: removed unneeded comment.
* removed playground.py
* rebasing
* rebasing and removing playground.py.
* fix: renaming TFConvNextStage conv and layer norm layers
* chore: added initializers and other minor additions.
* chore: added initializers and other minor additions.
* add: tests for convnext.
* fix: integration tester class.
* fix: issues mentioned in pr feedback (round 1).
* fix: how output_hidden_states arg is propoagated inside the network.
* feat: handling of arg for pure cnn models.
* chore: added a note on equal contribution in model docs.
* rebasing
* rebasing and removing playground.py.
* feat: encapsulation for the convnext trunk.
* Fix variable naming; Test-related corrections; Run make fixup
* chore: added Joao as a contributor to convnext.
* rebasing
* rebasing and removing playground.py.
* rebasing
* rebasing and removing playground.py.
* chore: corrected copyright year and added comment on NHWC.
* chore: fixed the black version and ran formatting.
* chore: ran make style.
* chore: removed from_pt argument from test, ran make style.
* rebasing
* rebasing and removing playground.py.
* rebasing
* rebasing and removing playground.py.
* fix: tests in the convnext subclass, ran make style.
* rebasing
* rebasing and removing playground.py.
* rebasing
* rebasing and removing playground.py.
* chore: moved convnext test to the correct location
* fix: locations for the test file of convnext.
* fix: convnext tests.
* chore: applied sgugger's suggestion for dealing w/ output_attentions.
* chore: added comments.
* chore: applied updated quality enviornment style.
* chore: applied formatting with quality enviornment.
* chore: revert to the previous tests/test_modeling_common.py.
* chore: revert to the original test_modeling_common.py
* chore: revert to previous states for test_modeling_tf_common.py and modeling_tf_utils.py
* fix: tests for convnext.
* chore: removed output_attentions argument from convnext config.
* chore: revert to the earlier tf utils.
* fix: output shapes of the hidden states
* chore: removed unnecessary comment
* chore: reverting to the right test_modeling_tf_common.py.
* Styling nits
Co-authored-by: ariG23498 <aritra.born2fly@gmail.com>
Co-authored-by: Joao Gante <joao@huggingface.co>
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
* fix wrong method name tf.concatenate
* add tests related to causal LM / decoder
* make style and quality
* clean-up
* Fix TFBertModel's extended_attention_mask when past_key_values is provided
* Fix tests
* fix copies
* More tf.int8 -> tf.int32 in TF test template
* clean-up
* Update TF test template
* revert the previous commit + update the TF test template
* Fix TF template extended_attention_mask when past_key_values is provided
* Fix some styles manually
* clean-up
* Fix ValueError: too many values to unpack in the test
* Fix more: too many values to unpack in the test
* Add a comment for extended_attention_mask when there is past_key_values
* Fix TFElectra extended_attention_mask when past_key_values is provided
* Add tests to other TF models
* Fix for TF Electra test: add prepare_config_and_inputs_for_decoder
* Fix not passing training arg to lm_head in TFRobertaForCausalLM
* Fix tests (with past) for TF Roberta
* add testing for pask_key_values for TFElectra model
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Adding the option to return_timestamps on pure CTC ASR models.
* Remove `math.prod` which was introduced in Python 3.8
* int are not floats.
* Reworking the PR to support "char" vs "word" output.
* Fixup!
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Quality.
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Enabling Beit SegFormer to `image-segmentation`.
* Fixing the score.
* Fix import ?
* Missing in type hint.
* Multiple test fixes:
- Add `raw_image` support. It should be the default IMHO since in Python
world it doesn't make any sense to base64 encode the image (Sorry
@mishig, didn't catch that in my review). I really think we should
consider breaking BC here.
- Add support for Segformer tiny test (needed
`SegformerModelTester.get_config` to enable TinyConfig
@NielsRogge)
- Add the check that `batch_size` works correctly on that pipeline.
Uncovered that it doesn't for Detr, which IMO is OK since images
after `feature_extractor` don't have the same size. Comment should
explain.
* Type hint as a string.
* Make fixup + update black.
* torch+vision protections.
* Don't use torchvision, use F.interpolate instead (no new dep).
* Last fixes for Segformer.
* Update test to reflect new image (which was broken)
* Update tests.
* Major BC modification:
- Removed the string compressed PNG string, that's a job for users
`transformers` stays in python land.
- Removed the `score` for semantic segmentation. It has hardly a meaning
on its own in this context.
- Don't include the grayscale with logits for now (which could enable
users to get a sense of confidence). Might be done later.
- Don't include the surface of the mask (could be used for sorting by
users, to filter out small masks). It's already calculable, and
it's easier to add later, than to add now and break later if we need.
* `make fixup`.
* Small changes.
* Rebase + doc fixup.
* [Proposal] Adding ZeroShotImageClassificationPipeline
- Based on CLIP
* WIP, Resurection in progress.
* Resurrection... achieved.
* Reword handling different `padding_value` for `feature_extractor` and
`tokenizer`.
* Thanks doc-builder !
* Adding docs + global namespace `ZeroShotImageClassificationPipeline`.
* Fixing templates.
* Make the test pass and be robust to floating error.
* Adressing suraj's comments on docs mostly.
* Tf support start.
* TF support.
* Update src/transformers/pipelines/zero_shot_image_classification.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* fix bug in PT speech-encoder-decoder
* add pt test for `inputs is not None`
* fix test
* new pt test
* Update tests/test_modeling_speech_encoder_decoder.py
* make fixup
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Very big changes concerning the tokenizer fast of CLIP which did not correspond to the tokenizer slow of CLIP
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Added all files, PoolFormerFeatureExtractor still failing tests
* Fixed PoolFormerFeatureExtractor not being able to import
* Completed Poolformer doc
* Applied Suggested fixes
* Fixed errors in modeling_auto.py
* Fix feature extractor, convert docs to Markdown, styling of code
* Remove PoolFormer from check_repo and fix integration test
* Remove Poolformer from check_repo
* Fixed configuration_poolformer.py docs and removed inference.py from poolformer
* Ran with black v22
* Added PoolFormer to _toctree.yml
* Updated poolformer doc
* Applied suggested fixes and added on README.md
* Did make fixup and make fix-copies, tests should pass now
* Changed PoolFormer weights conversion script name and fixed README
* Applied fixes in test_modeling_poolformer.py and modeling_poolformer.py
* Added PoolFormerFeatureExtractor to AutoFeatureExtractor API
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MBP.localdomain>
* Implement activations as pytorch modules
* Apply fixup
* Add missing tests for activations
* Update docstring
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* TF generate start refactor
* Add tf tests for sample generate
* re-organize
* boom boom
* Apply suggestions from code review
* re-add
* add all code
* make random greedy pass
* make encoder-decoder random work
* further improvements
* delete bogus file
* make gpt2 and t5 tests work
* finish logits tests
* correct logits processors
* correct past / encoder_outputs drama
* refactor some methods
* another fix
* refactor shape_list
* fix more shape list
* import shape
_list
* finish docs
* fix imports
* make style
* correct tf utils
* Fix TFRag as well
* Apply Lysandre's and Sylvais suggestions
* Update tests/test_generation_tf_logits_process.py
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Update src/transformers/tf_utils.py
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* remove cpu according to gante
* correct logit processor
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Fix loading pipelines with wav2vec models with lm when in local paths
* Adding tests
* Fix test
* Adding tests
* Flake8 fixes
* Removing conflict files :(
* Adding task type to test
* Remove unnecessary test and imports
* Rework AutoFeatureExtractor.from_pretrained internal
* Custom feature extractor
* Add more tests
* Add support for custom feature extractor code
* Clean up
* Add register API to AutoFeatureExtractor
* Compute loss independent from decoder (as 14139)
* fix expected seq_len + style
* Apply the same change to TFVisionEncoderDecoderModel
* fix style
* Add case with labels in equivalence test
* uncomment
* Add case with labels in equivalence test
* add decoder_token_labels
* use hf_compute_loss
* Apply suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Add copied from
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Add TensorFlow support for ONNX export
* Change documentation to mention conversion with Tensorflow
* Refactor export into export_pytorch and export_tensorflow
* Check model's type instead of framework installation to choose between TF and Pytorch
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Alberto Bégué <alberto.begue@della.ai>
Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>
* added classes to get started with constrained beam search
* in progress, think i can directly force tokens now but not yet with the round robin
* think now i have total control, now need to code the bank selection
* technically works as desired, need to optimize and fix design choices leading to undersirable outputs
* complete PR #1 without disjunctive decoding
* removed incorrect tests
* Delete k.txt
* Delete test.py
* Delete test.sh
* revert changes to test scripts
* genutils
* full implementation with testing, no disjunctive yet
* shifted docs
* passing all tests realistically ran locally
* removing accidentally included print statements
* fixed source of error in initial PR test
* fixing the get_device() vs device trap
* fixed documentation docstrings about constrained_beam_search
* fixed tests having failing for Speech2TextModel's floating point inputs
* fix cuda long tensor
* added examples and testing for them and founx & fixed a bug in beam_search and constrained_beam_search
* deleted accidentally added test halting code with assert False
* code reformat
* Update tests/test_generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update tests/test_generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update tests/test_generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update tests/test_generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update tests/test_generation_utils.py
* fixing based on comments on PR
* took out the testing code that should but work fails without the beam search moditification ; style changes
* fixing comments issues
* docstrings for ConstraintListState
* typo in PhrsalConstraint docstring
* docstrings improvements
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* typical decoding
* changing arg name
* add test config params
* forgotten arg rename
* fix edge case where scores are same
* test for typical logits warper
* code quality fixes
* Add wrapper classes
* convert inner layers to tf
* Add TF Encoder and Decoder layers
* TFSpeech2Text models
* Loadable model
* TF model with same outputs as PT model
* test skeleton
* correct tests and run the fixup
* correct attention expansion
* TFSpeech2Text pask_key_values with TF format
* electra is added to onnx supported model
* add google/electra-base-generator for test onnx module
Co-authored-by: Lewis Tunstall <lewis.c.tunstall@gmail.com>
* Change the way tracing happens, enabling dynamic axes out of the box
* Update the tests and modeling xlnet
* Add the non recoding of leaf modules to avoid recording more values for the methods to record than what will be seen at tracing time (which would otherwise desynchronize the recorded values and the values that need to be given to the proxies during tracing, causing errors).
* Comments and making tracing work for gpt-j and xlnet
* Refactore things related to num_choices (and batch_size, sequence_length)
* Update fx to work on PyTorch 1.10
* Postpone autowrap_function feature usage for later
* Add copyrights
* Remove unnecessary file
* Fix issue with add_new_model_like
* Apply suggestions
* Wav2Vec2 models must either throw or deal with add_apater
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add pre-add_adapter backwards compatibility
* Add pre-add_adapter backwards compatibility
* Fix issue in tests/test_modeling_wav2vec2.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
# Add support for W&B hyperparameter sweep
This PR:
* allows using wandb for running hyperparameter search.
* The runs are visualized on W&B sweeps dashboard
* This supports runnning sweeps on parallel devices, all reporting to the same central dashboard.
### Usage
**To run new a hyperparameter search:**
```
trainer.hyperparameter_search(
backend="wandb",
project="transformers_sweep", # name of the project
n_trials=5,
metric="eval/loss", # metric to be optimized, default 'eval/loss'. A warning is raised if the passed metric is not found
)
```
This outputs a sweep id. Eg. `my_project/sweep_id`
**To run sweeps on parallel devices:**
Just pass sweep id which you want to run parallel
```
trainer.hyperparameter_search(
backend="wandb",
sweep_id = "my_project/sweep_id"
)
```
* Adding support for `microphone` streaming within pipeline.
- Uses `ffmpeg` to get microphone data.
- Makes sure alignment is made to `size_of_sample`.
- Works by sending `{"raw": ..data.., "stride": (n, left, right),
"partial": bool}`
directly to the pipeline enabling to stream partial results and still
get inference.
- Let's `partial` information flow through the pipeline to enable caller
to get it back and choose to display text or not.
- The striding reconstitution is bound to have errors since CTC does not
keep previous state. Currently most of the errors are we don't know if
there's a space or not between two chunks.
Since we have some left striding info, we could use that during decoding
to choose what to do with those spaces and even extra letters maybe (if
the stride is long enough, it's bound to cover at least a few symbols)
Fixing tests.
Protecting with `require_torch`.
`raw_ctc` support for nicer demo.
Post rebase fixes.
Revamp to split raw_mic_data from it's live chunking.
- Requires a refactor to make everything a bit cleaner.
Automatic resampling.
Small fix.
Small fix.
* Post rebase fix (need to let super handle more logic, reorder args.)
* Update docstrings
* Docstring format.
* Remove print.
* Prevent flow of `input_values`.
* Fixing `stride` too.
* Fixing the PR by removing `raw_ctc`.
* Better docstrings.
* Fixing init.
* Update src/transformers/pipelines/audio_utils.py
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* Update tests/test_pipelines_automatic_speech_recognition.py
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* Quality.
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* Add torchvision's resize
* Rename torch_resize to default_to_square
* Apply suggestions from code review
* Add support for default_to_square and tuple of length 1
* add new test
* update test
* remove `tokenizer_file` from `additional_files_names` in `tokenization_utils_base.py`
* add `tokenizer_file` for the fast only tokenizer
* change global variables layoutxml
* remove `"tokenizer_file"` from DPR tokenizer's Global variables
* remove `tokenizer_file` from herbert slow tokenizer init
* `"tokenizer_file"` from LED tokenizer's Global variables
* remove `tokenizer_file` from mbart slow tokenizer init
* remove `tokenizer_file` from slow tokenizer template
* adapt to versioning
* adapt the `test_tokenizer_mismatch_warning` test
* clean test
* clarify `VOCAB_FILES_NAMES` in tokenization_utils_fast.py
* Revert "remove `tokenizer_file` from mbart slow tokenizer init"
This reverts commit 0dbb723fa9.
* Revert "`"tokenizer_file"` from LED tokenizer's Global variables"
This reverts commit 5a3f879bdd.
* Revert "remove `tokenizer_file` from herbert slow tokenizer init"
This reverts commit f5e10007b7.
* Revert "remove `"tokenizer_file"` from DPR tokenizer's Global variables"
This reverts commit da0895330b.
* set `tokenizer_file` in super `__init__` of mbart
* replace assert with exception for `padding_side` arg in `PreTrainedTokenizerBase` `__init__`
* add test
* fix kwargs
* reformat test
* format
* format
* fix typo to render the documentation
* Update modeling_wav2vec2.py
With very tiny sound files (less than 0.1 seconds) the num_masked_span can be too long. The issue is described in issue #15366 and discussed with @patrickvonplaten.
* correct errors with mask time indices
* remove bogus file
* make fix-copies
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Fix the inconsistency of loss calculation between PT/TF XLNetLMHeadModel
* overwrite test_loss_computation
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* add xlm roberta xl
* add convert xlm xl fairseq checkpoint to pytorch
* fix init and documents for xlm-roberta-xl
* fix indention
* add test for XLM-R xl,xxl
* fix model hub name
* fix some stuff
* up
* correct init
* fix more
* fix as suggestions
* add torch_device
* fix default values of doc strings
* fix leftovers
* merge to master
* up
* correct hub names
* fix docs
* fix model
* up
* finalize
* last fix
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add copied from
* make style
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fixing support `batch_size` and `num_return_Sequences` in
`text-generation` pipeline
And `text2text-generation` too.
The bug was caused by the batch_size containing both the incoming batch
**and** the generated `num_sequences`.
The fix simply consists into splitting both of these again into
different dimensions.
* TF support.
* Odd backward compatibility script in the way.
* add new test
* add a feature to same the sentencepiece tokenizer model when the init file was deleted
* update marian
* update m2m_100
* fix marian
* update speech to text
* override test for layoutxlm
* fix saving bartpho
* remove harcoded values bartpho
* special token string version
* finish bartpho
* override layoutxml test
* add mbart
* move special tokens list
* format
* Revert "format"
This reverts commit 37a40df379.
* simplify list of string of special tokens
* Re-write `self.fairseq_tokens_to_ids ` initialization logic with special tokens
Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>
Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>
* Avoid using get_list_of_files in config
* Wip, change tokenizer file getter
* Remove call in tokenizer files
* Remove last call to get_list_model_files
* Better tests
* Unit tests for new function
* Document bad API
* Add new model like command
* Bad doc-styler
* black and doc-styler, stop fighting!
* black and doc-styler, stop fighting!
* At last
* Clean up
* Typo
* Bad doc-styler
* Bad doc-styler
* All good maybe?
* Use constants
* Add doc and type hints
* More cleaning
* Add doc
* Fix Copied from
* Doc template
* Use typing.Pattern instead
* Framework-specific files
* Fixes
* Select frameworks clean model init
* Deal with frameworks in main init
* fixes
* Last fix
* Prompt user for info
* Delete exemple config
* Last fixes
* Add test config
* Fix bug with model_type included in each other
* Fixes
* More fixes
* More fixes
* Adapt config
* Remove print statements
* Will fix tokenization later, leave it broken for now
* Add test
* Quality
* Try this way
* Debug
* Maybe by setting the path?
* Let's try another way
* It should go better when actually passing the arg...
* Remove debug statements and style
* Fix config
* Add tests
* Test require the three backends
* intermediate commit
* Revamp pattern replacements and start work on feature extractors
* Adapt model info
* Finalize code for processors
* Fix in main init additions
* Finish questionnaire for processing classes
* Fix file name
* Fix for real
* Fix patterns
* Style
* Remove needless warnings
* Copied from should work now.
* Include Copied form in blocks
* Add test
* More fixes and tests
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Address review comment
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* better
* save intermediate
* finish code
* up
* docs
* Apply suggestions from code review
* up
* add compute transition beam scores function to model and make sure scores are correct with eos
* apply nicos comments
* Apply suggestions from code review
* another fix
* Refine errors for pretrained objects
* PoC to avoid using get_list_of_files
* Adapt tests to use new errors
* Quality + Fix PoC
* Revert "PoC to avoid using get_list_of_files"
This reverts commit cb93b7cae8.
* Revert "Quality + Fix PoC"
This reverts commit 3ba6d0d4ca.
* Fix doc
* Revert PoC
* Add feature extractors
* More tests and PT model
* Adapt error message
* Feature extractor tests
* TF model
* Flax model and test
* Merge flax auto tests
* Add tokenization
* Fix test
* Add missing __spec__ for transformers.models.auto
* Moves the __spec__-test to the UnitTest class
* Adds module_spec to all instances of _LazyModule
* Refactors an old test from pytest to unittest
* [EncoderDecoder] Add test for usage of extra kwargs
* [EncoderDecoder] Fix usage of extra kwargs in from pretrained
* [EncoderDecoder] apply suggested changes (passing **kwargs_encoder)
* [EncoderDecoder] create new test function and make sure it passes
Co-authored-by: jonas <jsnfly@gmx.de>
* [WIP] Make chuking smartly (long files) work on asr ctc_with_lm.
* Slow test with functionality.
* Fixing regular test.
* fix for batch size 1
* Handling batch outside `rescale_Stride`.
- Renamed to `rescale_stride`.
* Disable equality in the test.
* Remove print.
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* First commit
* Add conversion script
* Make conversion script work for base model
* More improvements
* Update conversion script, works for vqa
* Add indexing argument to meshgrid
* Make conversion script work for ViltForPreTraining
* Add ViltForPreTraining to docs
* Fix device issue
* Add processor
* Add MinMaxResize to feature extractor
* Implement call method of ViltProcessor
* Fix tests
* Add integration test
* Add loss calculation for VQA
* Improve tests
* Improve some more tests
* Debug tests
* Small improvements
* Add support for attention_mask
* Remove mask_it
* Add pixel_mask
* Add tests for ViltFeatureExtractor
* Improve tests
* Add ViltForNaturalLanguageVisualReasoning
* Add ViltForNaturalLanguageVisualReasoning to conversion script
* Minor fixes
* Add support for image_embeds, update docstrings to markdown
* Update docs to markdown
* Improve conversion script
* Rename ViltForPreTraining to ViltForMaskedLM
* Improve conversion script
* Convert docstrings to markdown
* Fix code example of retrieval model
* Properly convert masked language model
* Add integration test for nlvr
* Fix code quality
* Apply suggestions from code review
* Add copied from statements
* Fix pretrained_config_archive_map
* Fix docs
* Add model to README
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Apply more suggestions from code review
* Make code more readable
* Add ViltForNaturalLanguageVisualReasoning to the tests
* Rename ViltForVisualQuestionAnswering to ViltForQuestionAnswering
* Replace pixel_values_2 by single tensor
* Add hidden_states and attentions
* Fix one more test
* Fix all tests
* Update year
* Fix rebase issues
* Fix another rebase issue
* Remove ViltForPreTraining from auto mapping
* Rename ViltForImageRetrievalTextRetrieval to ViltForImageAndTextRetrieval
* Make it possible to use BertTokenizerFast in the processor
* Use BertTokenizerFast by default
* Rename ViltForNaturalLanguageVisualReasoning, define custom model output
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Rename compute_loss to hf_compute_loss to avoid conflicts with the new Keras method
* make style
* Adding deprecation warning to `compute_loss`
* Fix sneaky reference to compute_loss
* Replace logger.warning with warnings.warn
* Clarifying warning and deprecation timeline
* First draft
* More improvements
* More improvements
* More improvements
* Fix embeddings
* Add conversion script
* Finish conversion script
* More improvements
* Fix forward pass
* Remove print statements
* Add weights initialization
* Add initialization of decoder weights
* Add support for other models in the conversion script
* Fix patch_size for huge model
* Fix most of the tests
* Fix integration test
* Fix docs
* Fix archive_list
* Apply suggestions from code review
* Improve documentation
* Apply more suggestions
* Skip some tests due to non-deterministic behaviour
* Fix test_initialization
* Remove unneccessary initialization of nn.Embedding
* Improve docs
* Fix dummies
* Remove ViTMAEFeatureExtractor from docs
* Add model to README and table of contents
* Delete inference file
* Fix deprecation warnings for int div
Co-authored-by: mgoldey <matthew.goldey@gmail.com>
* Fix import
* ensure that tensor output is python scalar
* make backward compatible
* make code more readable
* adapt test functions
Co-authored-by: mgoldey <matthew.goldey@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Pipeline ASR with LM.
* Revamped into `self.decoder`.
* Fixing.
* 2nd fix.
* Update src/transformers/pipelines/__init__.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Fixing.
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add IBertOnnxConfig and tests
* add all the supported features for IBERT and remove outputs in IbertOnnxConfig
* use OnnxConfig
* fix codestyle
* remove serialization.rst
* codestyle
* Start the work on TFVisionEncoderDecoderModel
* Expose TFVisionEncoderDecoderModel
* fix import
* Add modeling_tf_vision_encoder_decoder to _ignore_modules in get_model_modules()
* reorder
* Apply the fix for checkpoint loading as in #14016
* remove attention_mask + fix VISION_DUMMY_INPUTS
* A minimal change to make TF generate() work for vision models as encoder in encoder-decoder setting
* fix wrong condition: shape_list(input_ids) == 2
* add tests
* use personal TFViTModel checkpoint (for now)
* Add equivalence tests + projection layer
* style
* make sure projection layer can run
* Add examples
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Clean comments (need to work on TODOs for PyTorch models)
* Remove TF -> PT in check_pt_tf_equivalence for TFVisionEncoderDecoderModel
* fixes
* Revert changes in PT code.
* Update tests/test_modeling_tf_vision_encoder_decoder.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add test_inference_coco_en for TF test
* fix quality
* fix name
* build doc
* add main_input_name
* Fix ckpt name in test
* fix diff between master and this PR
* fix doc
* fix style and quality
* fix missing doc
* fix labels handling
* Delete auto.rst
* Add the changes done in #14016
* fix prefix
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* make style
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Make OpenAIGPTTokenizer work with SpaCy 3.x
SpaCy 3.x introduced an API change to creating the tokenizer that
breaks OpenAIGPTTokenizer. The old API for creating the tokenizer in
SpaCy 2.x no longer works under SpaCy 3.x, but the new API for creating
the tokenizer in SpaCy 3.x DOES work under SpaCy 2.x. Switching to the
new API should allow OpenAIGPTTokenizer to work under both SpaCy 2.x and
SpaCy 3.x versions.
* Add is_spacy_available and is_ftfy_available methods to file utils
* Add spacy and ftfy unittest decorator to testing utils
* Add tests for OpenAIGPTTokenizer that require spacy and ftfy
* Modify CircleCI config to run tests that require spacy and ftfy
* Remove unneeded unittest decorators are reuse test code
* Run make fixup
* Hotfix `chunk_length_s` instead of `_ms`.
* Adding fix of `pad_token` which should be last/previous token for CTC
proper decoding
* Fixing ChunkPipeline unwrapping.
* Adding a PackIterator specific test.
* Add FlaxRoFormer
* Clean code + make quality
* Fix output pooling for FlaxRoFormerForMultipleChoiceModule
* Apply suggestions from code review
* add flax model to repos
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* [AutoProcessor] Correct AutoProcessor and automatically add processor class
* up
* up
* up
* up
* up
* up
* up
* up
* continue tomorrow
* up
* up
* up
* make processor class private
* fix loop
* Add ElectraForCausalLM and cover some basic tests & need to fix a few tests
* Fix bugs
* make style
* make fix-copies
* Update doc
* Change docstring to markdown format
* Remove redundant update_keys_to_ignore
* Pipeline chunks.
* Batching for Chunking pipelines ?
* Batching for `question-answering` and `zero-shot-cls`.
* Fixing for FNet.
* Making ASR a chunk pipeline.
* Chunking ASR API.
* doc style.
* Fixing ASR test.
* Fixing QA eror (p_mask, padding is 1, not 0).
* Enable both vad and simple chunking.
* Max length for vad.
* remove inference mode, crashing on s2t.
* Revert ChunkPipeline for ASRpipeline.
Too many knobs for simple integration within the pipeline, better stick
to external convenience functions instead, more control to be had,
simpler pipeline and also easier to replace with other things later.
* Drop necessity for PT for these.
* Enabling generators.
* Add mic + cleanup.
* Typo.
* Typo2.
* Remove ASR work, it does not belong in this PR anymore.
* Update src/transformers/pipelines/pt_utils.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Update src/transformers/pipelines/zero_shot_classification.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Adding many comments.
* Doc quality.
* `hidden_states` handling.
* Adding doc.
* Bad rebase.
* Autofixing docs.
* Fixing CRITICAL bug in the new Zerocls pipeline.
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* First commit to add MarianMT to ONNX
* Now MarianModel.forward() automatically generates decoder_input_ids, like BartModel.forward()
* Adjusted MarianOnnxConfig.inputs and outputs to work with seq2seq-lm feature
* Style fix
* Added support for other features for already supported models
* Partial support for causal and seq2seq models
* Partial support for causal and seq2seq models
* Add default task for MarianMT ONNX
* Remove automatic creation of decoder_input_ids
* Extend inputs and outputs for MarianMT ONNX config
* Add MarianMT to ONNX unit tests
* Refactor
* OnnxSeq2SeqConfigWithPast to support seq2seq models
* Parameterized the onnx tests
* Restored run_mlm.py
* Restored run_mlm.py
* [WIP] BART update
* BART and MBART
* Add past_key_values and fix dummy decoder inputs
Using a sequence length of 1 in generate_dummy_outputs() produces large discrepancies, presumably due to some hidden optimisations.
* Refactor MarianOnnxConfig to remove custom past_key_values logic
* Fix quality
* Revert "Revert "Added support for other features for already supported models (#14358)" (#14679)"
This reverts commit 0f4e39c559.
* is_torch_available test to avoid failing imports
* sorting parameterize parameters to solve ERROR gw0 gw1
* tests fix
* tests fix
* GPT2 with past fix
* Fixed stateful class attribute change that was breaking things when converting multiple models sequentially
* Removed onnx file
* Refactor Marian export to account for base changes
* Fix copies
* Implemented suggestions
* Extend support for causal LM
* Revert "Revert "Added support for other features for already supported models (#14358)" (#14679)"
This reverts commit 0f4e39c559.
* is_torch_available test to avoid failing imports
* sorting parameterize parameters to solve ERROR gw0 gw1
* tests fix
* tests fix
* GPT2 with past fix
* Fixed stateful class attribute change that was breaking things when converting multiple models sequentially
* Removed onnx file
* Implemented suggestions
* Fixed __init__ to resolve conflict with master
* Revert "Revert "Added support for other features for already supported models (#14358)" (#14679)"
This reverts commit 0f4e39c559.
* is_torch_available test to avoid failing imports
* sorting parameterize parameters to solve ERROR gw0 gw1
* tests fix
* tests fix
* GPT2 with past fix
* Fixed stateful class attribute change that was breaking things when converting multiple models sequentially
* Removed onnx file
* Implemented suggestions
* Fixed __init__ to resolve conflict with master
* Remove commented import
* Remove ONNX model
* Remove redundant class method
* Tidy up imports
* Fix quality
* Refactor dummy input function
* Add copied from statements to Marian config functions
* Remove false copied from comments
* Fix copy from comment
Co-authored-by: Massimiliano Bruni <massimiliano.bruni@hcl.com>
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* Revert "Revert "Added support for other features for already supported models (#14358)" (#14679)"
This reverts commit 0f4e39c559.
* is_torch_available test to avoid failing imports
* sorting parameterize parameters to solve ERROR gw0 gw1
* tests fix
* tests fix
* GPT2 with past fix
* Fixed stateful class attribute change that was breaking things when converting multiple models sequentially
* Removed onnx file
* Implemented suggestions
* Fixed __init__ to resolve conflict with master
* Remove commented import
* add tests
* change post-processor, pre-tokenizer and decoder (can't update decoder)
* update test (remove decoder which doesn't depend on trim and add_prefix)
* just update the post_processor
* fix change
* `trim_offsets` has no influence on `pre_tokenizer`
* remove a test that need some input from the `tokenizers` lib maintainers
* format
* add new test offsets roberta
* polish comments
* add custom `stopping_criteria` and `logits_processor` to `generate`
* add tests for custom `stopping_criteria` and `logits_processor`
* fix typo in RAG
* address reviewer comments
* improve custom logits processor/stopping criteria error message
* fix types in merge function signature
* change default for custom list from `None` to empty list
* fix rag generate
* add string split suggestion
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add a main_input_name attribute to all models
* Fix tests
* Wtf Vs Code?
* Update src/transformers/models/imagegpt/modeling_imagegpt.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Style
* Fix copies
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Implement head_mask for Flax BERT and other models copied from BERT
* Remove `from jax._src.nn.functions import sigmoid`
Remove `from jax._src.nn.functions import sigmoid` unintentionally added by IDE
* Remove no more valid copy statement
* Apply patil-suraj's suggestions from code review
* Apply suggestions from the code review
* Update Flax template
* Fix a typo
* Also update template for CausalLM modules
* Initial commit for Keras model cards
* Revert accidental change
* make style
* make style
* make style
* Fix PR comments
* Move repo creation to __init__
* Fixes to README.md creation
* Partial progress for proper card creation on `push_to_hub`
* Proper card creation from `push_to_hub` plus fixes for malformed model cards
* Fixes for model card creation outside the callback
* Adding a model card creation test
* Putting the model card creation test in the right file.
Good job, Matt.
* make style
* Fix model card test temp dir usage
* Fix model card creation when no optimizer present
* Fixes for when training history not present
* Fix accidental edit to test_modeling_common
* Adding support for multiple mask tokens.
- Original implem: https://github.com/huggingface/transformers/pull/10222
Co-authored-by: njafer <naveen.jafer@oracle.com>
* In order to accomodate optionally multimodal models like Perceiver
we add information to the tasks to specify tasks where we know for sure
if we need the tokenizer/feature_extractor or not.
* Adding info in the documentation about multi masks.
+ marked as experimental.
* Add a copy() to prevent overriding the same tensor over and over.
* Fixup.
* Adding small test for multi mask with real values..
Co-authored-by: njafer <naveen.jafer@oracle.com>
* Adding some slow test to check for perceiver at least from a high level.
* Re-enabling fast tests for Perceiver ImageClassification.
* Perceiver might try to run without Tokenizer (Fast doesn't exist) and
with FeatureExtractor some text only pipelines.
* Oops.
* Adding a comment for `update_config_with_model_class`.
* Remove `model_architecture` to get `tiny_config`.
* Finalize rebase.
* Smarter way to handle undefined FastTokenizer.
* Remove old code.
* Addressing some nits.
* Don't instantiate `None`.
- Do not run image-classification pipeline (_CHECKPOINT_FOR_DOC uses the checkpoint for
langage, which cannot load a FeatureExtractor so current logic fails).
- Add a safeguard to not run tests when `tokenizer_class` or
`feature_extractor_class` **are** defined, but cannot be loaded
This happens for Perceiver for the "FastTokenizer" (which doesn't exist
so None) and FeatureExtractor (which does exist but cannot be loaded
because the checkpoint doesn't define one which is reasonable for the
said checkpoint)
- Added `get_vocab` function to `PerceiverTokenizer` since it is used by
`fill-mask` pipeline when the argument `targets` is used to narrow a
subset of possible values.
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* Added support for other features for already supported models
* Partial support for causal and seq2seq models
* Partial support for causal and seq2seq models
* OnnxSeq2SeqConfigWithPast to support seq2seq models
* Parameterized the onnx tests
* Restored run_mlm.py
* Restored run_mlm.py
* [WIP] BART update
* BART and MBART
* Added comments
* Another sequence length of the past_key_values
* First draft
* Style and remove mlm
* Make forward pass work
* More improvements
* More improvements
* Fix bug
* More improvements
* More improvements
* Add PerceiverTokenizer first draft
* Improve conversion script
* More improvements
* Make conversion script work for the encoder
* Make conversion script work with local pickle files
* Style & quality, fix-copies
* Add dummy input to conversion script
* Add absolute position embeddings to TextPreProcessor
* Make forward pass of encoder work
* More improvements
* Move text preprocessor to separate script
* More improvements
* More improvements
* Add post processor
* Make MLM model work
* Style
* Add PerceiverForMaskedLM
* Add PerceiverImagePreprocessor
* Make style
* Make PerceiverForImageClassification work
* More improvements
* More improvements
* Use tokenizer in conversion script
* Use PerceiverForMaskedLM in conversion script
* Define custom PerceiverModelOutput
* Improve PerceiverAttention to make it work for both MLM and image classification
* More improvements
* More improvements
* More improvements to the conversion script
* Make conversion script work for both MLM and image classification
* Add PerceiverFeatureExtractor
* More improvements
* Style and quality
* Add center cropping
* Fix bug
* Small fix
* Add print statement
* Fix bug in image preprocessor
* Fix bug with conversion script
* Make output position embeddings an nn.Parameter layer instead of nn.Embedding
* Comment out print statements
* Add position encoding classes
* More improvements
* Use position_encoding_kwargs
* Add PerceiverForImageClassificationFourier
* Make style & quality
* Add PerceiverForImageClassificationConvProcessing
* Style & quality
* Add flow model
* Move processors to modeling file
* Make position encodings modular
* Make basic decoder use modular position encodings
* Add PerceiverForOpticalFlow to conversion script
* Add AudioPreprocessor
* Make it possible for the basic decoder to use Fourier position embeddings
* Add PerceiverForMultimodalAutoencoding
* Improve model for optical flow
* Improve _build_network_inputs method
* Add print statement
* Fix device issue
* Fix device of Fourier embeddings
* Add print statements for debugging
* Add another print statement
* Add another print statement
* Add another print statement
* Add another print statement
* Improve PerceiverAudioPreprocessor
* Improve conversion script for multimodal modal
* More improvements
* More improvements
* Improve multimodal model
* Make forward pass multimodal model work
* More improvements
* Improve tests
* Fix some more tests
* Add output dataclasses
* Make more tests pass
* Add print statements for debuggin
* Add tests for image classification
* Add PerceiverClassifierOutput
* More improvements
* Make more tests pass for the optical flow model
* Make style & quality
* Small improvements
* Don't support training for optical flow model for now
* Fix _prepare_for_class for tests
* Make more tests pass, add some docs
* Add multimodal model to tests
* Minor fixes
* Fix tests
* Improve conversion script
* Make fixup
* Remove pos_dim argument
* Fix device issue
* Potential fix for OOM
* Revert previous commit
* Fix test_initialization
* Add print statements for debugging
* Fix print statement
* Add print statement
* Add print statement
* Add print statement
* Add print statement
* Add print statement
* Add print statement
* Remove need for output_shape
* Comment out output_shape
* Remove unnecessary code
* Improve docs
* Fix make fixup
* Remove PerceiverTextProcessor from init
* Improve docs
* Small improvement
* Apply first batch of suggestions from code review
* Apply more suggestions from code review
* Update docstrings
* Define dicts beforehand for readability
* Rename task to architecture in conversion script, include PerceiverModel in tests
* Add print statements for debugging
* Fix tests on GPU
* Remove preprocessors, postprocessors and decoders from main init
* Add integration test
* Fix docs
* Replace einops by torch
* Update for new docs frontend
* Rename PerceiverForImageClassification
* Improve docs
* Improve docs
* Improve docs of PerceiverModel
* Fix some more tests
* Improve center_crop
* Add PerceiverForSequenceClassification
* Small improvements
* Fix tests
* Add integration test for optical flow model
* Clean up
* Add tests for tokenizer
* Fix tokenizer by adding special tokens properly
* Fix CI
* up
* up
* up
* make it cleaner
* correct
* make styhahalal
* add more tests
* finish
* small fix
* make style
* up
* tryout to solve cicrle ci
* up
* fix more tests
* fix more tests
* apply sylvains suggestions
* fix import
* correct docs
* add pyctcdecode only to speech tests
* fix more tests
* add tf, flax and pt tests
* add pt
* fix last tests
* fix more tests
* Apply suggestions from code review
* change lines
* Apply suggestions from code review
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* correct tests
* correct tests
* add doc string
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* implement MLukeTokenizer and LukeForMaskedLM
* update tests
* update docs
* add LukeForMaskedLM to check_repo.py
* update README
* fix test and specify the entity pad id in tokenization_(m)luke
* fix EntityPredictionHeadTransform
* add cross_attention_hidden_size to text-2-text encoder-decoder models (PT/Flax)
* for TFEncoderDecoderModel
* add equivalence test for TFEncoderDecoderModel
* fix
* fix failed equivalence tests
* remove unused import
* add detailed comment
* Fix check_equivalence_tf_to_pt by using encoder/decoder
* cleaning
* Use cross_attention_hidden_size in speech-to-text
* clean fast init logging msg in encoder decoder models
* increase tol from 1e-5 to 1e-3 for tf test
* style
* style
* make sure projection layer can run
* remove type conversion + add check
* fix conflict (config.output_hidden_size)
* Remove TF -> PT in check_pt_tf_equivalence for TFEncoderDecoderModel
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Add AutoProcessor class
Init and tests
Add doc
Fix init
Update src/transformers/models/auto/processing_auto.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Reverts to tokenizer or feature extractor when available
Adapt test
* Revert "Adapt test"
This reverts commit bbdde5fab0.
* Revert "Reverts to tokenizer or feature extractor when available"
This reverts commit 77659ff5d2.
* Don't revert everything Lysandre!
Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>
* fix#14524 (IndexError when mask prob is too low)
* fix formatting
* correct documentation, add option for setting min_num_masks
* change the semantic meaning of `mask_prob` in _compute_mask_indices
With this commit the meaing of `mask_prob` actually adhered to the probability for each
vector to be the start of a masked span of length.
* fix check_copies test
* fix documentation to semantic meaning of `upper bound of overall masking percentage`, revert changes to _compute_mask_indices
* fix typo
* started bf16 integration
* minor changes
* code now runs
* style
* lay foundation for bf16 testing
* lay foundation for bf16 testing
* start the tests
* better bf16 check
* style
* 2 separate checkers - one for bf16 support, another for bf16+autocast
* Update src/transformers/training_args.py
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* a couple of comment resolutions
* more comment resolutions
* resolved a small bug
* just some print statemtns
* added todo marking
* added a todo
* adjust for API change s/fast_dtype/dtype/
* fix style
* merge 2 bf16 util functions
* bf16 now does scaling too
* Add support for bfloat16
* Revert T5 layernorm to float32
This is based on the comment at https://github.com/huggingface/transformers/pull/14448/files#r752660929 and the PyTorch PR https://github.com/pytorch/pytorch/pull/66920 .
* Add comment about conversion to float32 before returning the numpy data
* Add comment about AMP-bfloat16 incompatibility
* Fix formatting
* typo
* reformer / bf16
* cleanup
* require at least pt-1.10
* fix
* will deal with deepspeed separately
* cleanup
* revert
* cleanup
* fp16_full_eval and bf16_full_eval are separate modes
* proper deprecation
* cleanup
* test and fixes
* spelling
* cleanup
* add a note that this API is experimental
Co-authored-by: jamie <jamie@cortx.com>
Co-authored-by: Stas Bekman <stas@stason.org>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: suriya <suriya@cortx.com>
Co-authored-by: Manuel R. Ciosici <manuelrciosici@gmail.com>
* Init Flax implementation for Blenderbot
* Add a majority of stuff except for tests
* make style quality
* Add tests and fix some bugs
* Add tests
* Clean source code and fix some bugs
* Fix copies and docs
* Fix jax device condition for tests
* Fix layer norm in the encoder
* Fix a few typos in the test file
* make fix-copies
* make fix-copies
* fix layer norm
* Fix Flax params dtype (#13090)
* Fix PR reference (#13098)
* make fix-copies
* Update tests/test_modeling_flax_blenderbot.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* TF Tapas first commit
* updated docs
* updated logger message
* updated pytorch weight conversion
script to support scalar array
* added use_cache to tapas model config to
work properly with tf input_processing
* 1. rm embeddings_sum
2. added # Copied
3. + TFTapasMLMHead
4. and lot other small fixes
* updated docs
* + test for tapas
* updated testing_utils to check
is_tensorflow_probability_available
* converted model logits post processing using
numpy to work with both PT and TF models
* + TFAutoModelForTableQuestionAnswering
* added TF support
* added test for
TFAutoModelForTableQuestionAnswering
* added test for
TFAutoModelForTableQuestionAnswering pipeline
* updated auto model docs
* fixed typo in import
* added tensorflow_probability to run tests
* updated MLM head
* updated tapas.rst with TF model docs
* fixed optimizer import in docs
* updated convert to np
data from pt model is not
`transformers.tokenization_utils_base.BatchEncoding`
after pipeline upgrade
* updated pipeline:
1. with torch.no_gard removed, pipeline forward handles
2. token_type_ids converted to numpy
* updated docs.
* removed `use_cache` from config
* removed floats_tensor
* updated code comment
* updated Copyright Year and
logits_aggregation Optional
* updated docs and comments
* updated docstring
* fixed model weight loading
* make fixup
* fix indentation
* added tf slow pipeline test
* pip upgrade
* upgrade python to 3.7
* removed from_pt from tests
* revert commit f18cfa9
* [deepspeed] zero inference
* only z3 makes sense for inference
* fix and style
* docs
* rework
* fix test
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* responding to suggestions
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* test: make sure model configs are jsonifiable
* fix: return python dict instead of config object
* fix: accept pretrained config and use correct class
* Re-enabling slow tests and applying them to core models only
* Re-enabling slow tests and applying them to core models only
* Add new test file to fetcher
* Remove tooslow tests from test_modeling_tf_common.py
* make style
* Style fixes
* Style fixes
* Style fixes
* Style fixes
* Adding core tests to GPT2 and BART
* Removing unused imports
Co-authored-by: niklas.fruehauf <niklas.fruehauf@sovanta.com>
Co-authored-by: matt <rocketknight1@gmail.com>
* add new wav2vec2 translation
* correct
* up
* add tests
* correct end copy
* correct more
* up
* correct unispeech sat
* finish
* finalize
* finish
* up
* stop training when a finite IterableDataset is exhausted
when using an iterable dataset num_epochs is set to
sys.maxsize to make sure all data is consumed
likewise we want to set max_steps high enough
but still stop when all data is consumed
(cherry picked from commit 6f0e1d6363153da9051e93acffe1cbab3a3f3b12)
* fix typo flase -> false
* add test for stopping training on exhausted finite iterable dataset
* remove redundant gradient_accumulation_steps
* run make style
reformat training_args docstring
* Fix gradient_checkpointing backward compatibility
* Remove needless line
* make sure mask prob is big enough and length small enough
* Fix tests
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
* Adding support for raw python `generator` in addition to `Dataset`
The main goal is to ease the create of streaming data to the pipe.
`Dataset` is more involved and pytorch specific.
This PR, provides a way to use a python iterator too.
This enabled #14250 but can be proposed as a standalone PR.
```python
from transformers import pipeline
def read_data(filename):
with open(filename, 'r') as f:
for line in f:
yield f
pipe = pipeline("text-classification")
for classified in pipe(read_data("large_file.txt")):
print("Success ! ", classified)
```
The main caveat of this, is the interaction with `DataLoader` with
`num_workers>1`. When you have multiple workers, each receive a copy
of the generator (like `IterableDataset`). That means the naive Iterator
will fail since all workers iterate on all items of the generator.
There are ways to do clever "skipping", but it could be bad still
because all workers still do have to pass through all items of the
generator (they just ignore items they don't handle), depending on
the case it might be bad.
Using `num_workers=1` is the simplest fix and if the cost of loading
your data is small enough should be good enough. In the above example
trying to do smart tricks to skip some lines is unlikely to be a net
positive for instance.
If there are better ways to do "jumps" on some data, then using
`Dataset` is more advised (since then differents workers can just jump
themselves).
* Adding iterator support for `tf` too.
* fix loading flax bf16 weights in pt
* fix clip test
* fix t5 test
* add logging statement
* Update src/transformers/modeling_flax_pytorch_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* switch back to native any
* fix check for bf16 weights
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Start the work for TFViTModel
* Convert to TF code - need to check in the follow up commits
* Clean up model code
* Expose TFViTModel
* make style
* make quality
* Add test
* make style & quality
* Fix some imports
* fix wrong usage - *kwargs => ** kwargs
* Fix Conv2D weight loading (PT->TF) issue
* Add tests for images with different sizes + fix model
* Fix some common tests for TFViTModel
* Use inputs instead of input_ids in test_compile_tf_model
* Add a comment about transpose and Conv2D in convert_tf_weight_name_to_pt_weight_name
* Avoid transpose in TFViT call
* Fix Conv2D issue in load_tf2_weights_in_pytorch_model
* Use tf.keras.layers.Conv2D instead of tf.nn.conv2d
* Using simpler heuristic to detect Conv2D layer
* Change convert_tf_weight_name_to_pt_weight_name to return TransposeType
* Check tf_weight_shape is not None before using it
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fix missing comma
* fix input dtype
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* correct order of overflowing tokens for LayoutLmV2 tokenizer
* test to check order of overflowing_tokens for a seq of input_ids
* fix up quality
* added suggested changes
* check that tests the bbox sequence
* pair_input test added
* pass quality test
* check bbox sequence added
* unittest method
* comments added
* add overflowing bbox test
* improved "seq_1"
Co-authored-by: SaulLu <55560583+SaulLu@users.noreply.github.com>
* improve code quality
Co-authored-by: SaulLu <lucilesaul.com@gmail.com>
Co-authored-by: SaulLu <55560583+SaulLu@users.noreply.github.com>
* Adding support for `truncation` parameter on `feature-extraction`
pipeline.
Fixes#14183
* Fixing tests on ibert, longformer, and roberta.
* Rebase fix.
* minimal fixes to run DataCollatorForWholeWordMask with return_tensors="np" and return_tensors="tf"
* more consinstent implementation for numpy_mask_tokens
* Add cross attentions to TFGPT2Model
* change to is_pt_tf_cross_test
* A minor correction to a comment
* Remove n_ctx when creating self.crossattention
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* check test_configuration_tie
* Fix test_configuration_tie
* make test slow again
* Remove property and use model.module.bind
* revert to slow test
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Add first draft
* Make forward pass work
* Improve conversion script
* Add notebook that checks if it works
* Add BeitForSemanticSegmentation to the tests
* More improvements
* Make BeitForSemanticSegmentation consistent with Segformer
* Small bug fix
* Add BeitForSemanticSegmentation to docs
* Make sure model doesn't output hidden states when the user doesn't want to
* Make it possible to convert the large model
* Fix issue
* Fix conversion script for large model
* Add auxiliary_head option to semantic segmentation model
* Apply suggestions from @sgugger's review
* Apply suggestions from code review
* Fix failing test
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* Adding `handle_long_generation` paramters for `text-generation` pipeline.
* More error handling
* Fixing tests by dropping tf support on this functionality, it needs
`max_new_tokens` to make it possible to understand user's intent.
Otherwise, `max_length` == `tokenizer.model_max_length` <
input_ids.shape[0].
* Fixing doc ?
* Doc ?
* Remove link from doc.
* Catched an issue on roberta.
* Damn doc.
* Non BC proposal ?
* Cleaning the fix ?
* Finally using only a test override.
* Don't need to modify this.
* Bad print.
* Add the support for the fast (rust) implementation of BlenbderbotTokenizer
* Fix a converter and a typo in a doc
* Apply the patil-suraj's suggestion
* (Nitpick) Fast tokenization -> Fast Tokenization in doc
* Apply the SaulLu's suggestion
* Apply Narsil's suggestion to fix test pipelines
* Add encoder_no_repeat_ngram_size according to the Narsil's suggestion
* Revert the last (unnecessary) commit
* Override pipeline config for Blenderbot to allow for larger pos. emb.
* make fix-copies
* Remove n_ctx from configs
* Fix GPTJ and OpenAIGPT, both are acceptable breaking changes as there are no configs such that it breaks
* Remove unecessary n_positions from TFOpenAIGPT
* First draft
* Make style & quality
* Improve conversion script
* Add print statement to see actual slice
* Make absolute tolerance smaller
* Fix image classification models
* Add post_process_semantic method
* Disable padding
* Improve conversion script
* Rename to ForSemanticSegmentation, add integration test, remove post_process methods
* Improve docs
* Fix code quality
* Fix feature extractor tests
* Fix tests for image classification model
* Delete file
* Add is_torch_available to feature extractor
* Improve documentation of feature extractor methods
* Apply suggestions from @sgugger's code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Apply some more suggestions of code review
* Rebase with master
* Fix rebase issues
* Make sure model only outputs hidden states when the user wants to
* Apply suggestions from code review
* Add pad method
* Support padding of 2d images
* Add print statement
* Add print statement
* Move padding method to SegformerFeatureExtractor
* Fix issue
* Add casting of segmentation maps
* Add test for padding
* Add small note about padding
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* unispeech
* add copy from
* remove hubert copy from
* finish for today
* add unispeech-sat
* adapt more
* up
* up
* up
* up
* add modeling
* add tests
* up
* up
* finish
* up
* Apply suggestions from code review
* up
* up
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* up
* up
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* TF Model train and eval step metrics for seq2seq models.
When using a model with a seq2seq output compute metrics against logits.
* Removing vestigial code
Co-authored-by: matt <rocketknight1@gmail.com>
* Add API to register a new object in auto classes
* Fix test
* Documentation
* Add to tokenizers and test
* Add cleanup after tests
* Be more careful
* Move import
* Move import
* Cleanup in TF test too
* Add consistency check
* Add documentation
* Style
* Update docs/source/model_doc/auto.rst
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Update src/transformers/models/auto/auto_factory.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* First draft
* Update self-attention of RoBERTa as proposition
* Improve conversion script
* Add TrOCR decoder-only model
* More improvements
* Make forward pass with pretrained weights work
* More improvements
* Some more improvements
* More improvements
* Make conversion work
* Clean up print statements
* Add documentation, processor
* Add test files
* Small improvements
* Some more improvements
* Make fix-copies, improve docs
* Make all vision encoder decoder model tests pass
* Make conversion script support other models
* Update URL for OCR image
* Update conversion script
* Fix style & quality
* Add support for the large-printed model
* Fix some issues
* Add print statement for debugging
* Add print statements for debugging
* Make possible fix for sinusoidal embedding
* Further debugging
* Potential fix v2
* Add more print statements for debugging
* Add more print statements for debugging
* Deubg more
* Comment out print statements
* Make conversion of large printed model possible, address review comments
* Make it possible to convert the stage1 checkpoints
* Clean up code, apply suggestions from code review
* Apply suggestions from code review, use Microsoft models in tests
* Rename encoder_hidden_size to cross_attention_hidden_size
* Improve docs
* Add cross attentions to TFGPT2Model
* Add TFEncoderDecoderModel
* Add TFBaseModelOutputWithPoolingAndCrossAttentions
* Add cross attentions to TFBertModel
* Fix past or past_key_values argument issue
* Fix generation
* Fix save and load
* Add some checks and comments
* Clean the code that deals with past keys/values
* Add kwargs to processing_inputs
* Add serving_output to TFEncoderDecoderModel
* Some cleaning + fix use_cache value issue
* Fix tests + add bert2bert/bert2gpt2 tests
* Fix more tests
* Ignore crossattention.bias when loading GPT2 weights into TFGPT2
* Fix return_dict_in_generate in tf generation
* Fix is_token_logit_eos_token bug in tf generation
* Finalize the tests after fixing some bugs
* Fix another is_token_logit_eos_token bug in tf generation
* Add/Update docs
* Add TFBertEncoderDecoderModelTest
* Clean test script
* Add TFEncoderDecoderModel to the library
* Add cross attentions to TFRobertaModel
* Add TFRobertaEncoderDecoderModelTest
* make style
* Change the way of position_ids computation
* bug fix
* Fix copies in tf_albert
* Remove some copied from and apply some fix-copies
* Remove some copied
* Add cross attentions to some other TF models
* Remove encoder_hidden_states from TFLayoutLMModel.call for now
* Make style
* Fix TFRemBertForCausalLM
* Revert the change to longformer + Remove copies
* Revert the change to albert and convbert + Remove copies
* make quality
* make style
* Add TFRembertEncoderDecoderModelTest
* make quality and fix-copies
* test TFRobertaForCausalLM
* Fixes for failed tests
* Fixes for failed tests
* fix more tests
* Fixes for failed tests
* Fix Auto mapping order
* Fix TFRemBertEncoder return value
* fix tf_rembert
* Check copies are OK
* Fix missing TFBaseModelOutputWithPastAndCrossAttentions is not defined
* Add TFEncoderDecoderModelSaveLoadTests
* fix tf weight loading
* check the change of use_cache
* Revert the change
* Add missing test_for_causal_lm for TFRobertaModelTest
* Try cleaning past
* fix _reorder_cache
* Revert some files to original versions
* Keep as many copies as possible
* Apply suggested changes - Use raise ValueError instead of assert
* Move import to top
* Fix wrong require_torch
* Replace more assert by raise ValueError
* Add test_pt_tf_model_equivalence (the test won't pass for now)
* add test for loading/saving
* finish
* finish
* Remove test_pt_tf_model_equivalence
* Update tf modeling template
* Remove pooling, added in the prev. commit, from MainLayer
* Update tf modeling test template
* Move inputs["use_cache"] = False to modeling_tf_utils.py
* Fix torch.Tensor in the comment
* fix use_cache
* Fix missing use_cache in ElectraConfig
* Add a note to from_pretrained
* Fix style
* Change test_encoder_decoder_save_load_from_encoder_decoder_from_pt
* Fix TFMLP (in TFGPT2) activation issue
* Fix None past_key_values value in serving_output
* Don't call get_encoderdecoder_model in TFEncoderDecoderModelTest.test_configuration_tie until we have a TF checkpoint on Hub
* Apply review suggestions - style for cross_attns in serving_output
* Apply review suggestions - change assert + docstrings
* break the error message to respect the char limit
* deprecate the argument past
* fix docstring style
* Update the encoder-decoder rst file
* fix Unknown interpreted text role "method"
* fix typo
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* adapt wav2vec2
* add example
* add files
* adapt
* remove bogus file
* Apply suggestions from code review
* adapt files more
* upload changes
* del old files
* up
* up
* up
* up
* up
* correct gradient checkpoitning
* add readme
* finish
* finish
* up
* more fixes
* up
* up
* add demo run to readme
* up
* Tmp.
* Fixing BC for question answering with long context.
* Capping model_max_length to avoid tf overflow.
* Bad workaround bugged roberta.
* Fixing name.
* Symbolic trace dynamic axes support for BERT like models (albert, bert, distilbert, mobilebert, electra, megatron-bert)
* Sanity checks before tracing that make sure the model to trace is supported
* Adapted to PyTorch 1.9
Co-authored-by: Michael Benayoun <michael@huggingface.co>
* update no_* argument
Changes the order so that the no_* argument is created after the original argument AND sets the default for this no_* argument to False
* import copy
* update test
* make style
* Use kwargs to set default=False
* make style
* add sigopt hpo to transformers.
Signed-off-by: Ding, Ke <ke.ding@intel.com>
* extend sigopt changes to test code and others..
Signed-off-by: Ding, Ke <ke.ding@intel.com>
* Style.
* fix style for sigopt integration.
Signed-off-by: Ding, Ke <ke.ding@intel.com>
* Add necessary information to run unittests on SigOpt.
Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
* Use fp16 checkpoints
* Style
* Fix outputs and disable OOM tests
* Correct another output
* Use a random smaller model for generation tests
* repo quickfix
* fix gradient checkpointing
* Make gradient_checkpointing a training argument
* Update src/transformers/modeling_utils.py
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Update src/transformers/configuration_utils.py
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Fix tests
* Style
* document Gradient Checkpointing as a performance feature
* Small rename
* PoC for not using the config
* Adapt BC to new PoC
* Forgot to save
* Rollout changes to all other models
* Fix typo
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Stas Bekman <stas@stason.org>
* Add support for exporting PyTorch LayoutLM to ONNX
* Added tests for converting LayoutLM to ONNX
* Add support for exporting PyTorch LayoutLM to ONNX
* Added tests for converting LayoutLM to ONNX
* cleanup
* Removed regression/ folder
* Add support for exporting PyTorch LayoutLM to ONNX
* Added tests for converting LayoutLM to ONNX
* cleanup
* Fixed import error
* Remove unnecessary import statements
* Changed max_2d_positions from class variable to instance variable of the config class
* Add support for exporting PyTorch LayoutLM to ONNX
* Added tests for converting LayoutLM to ONNX
* cleanup
* Add support for exporting PyTorch LayoutLM to ONNX
* cleanup
* Fixed import error
* Changed max_2d_positions from class variable to instance variable of the config class
* Use super class generate_dummy_inputs method
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* Add support for Masked LM, sequence classification and token classification
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* Removed uncessary import and method
* Fixed code styling
* Raise error if PyTorch is not installed
* Remove unnecessary import statement
Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
* beit-flax
* updated FLAX_BEIT_MLM_DOCSTRING
* removed bool_masked_pos from classification
* updated Copyright
* code refactoring: x -> embeddings
* updated test: rm from_pt
* Update docs/source/model_doc/beit.rst
* model code dtype updates and
other changes according to review
* relative_position_bias
revert back to pytorch design
* Init FNet
* Update config
* Fix config
* Update model classes
* Update tokenizers to use sentencepiece
* Fix errors in model
* Fix defaults in config
* Remove position embedding type completely
* Fix typo and take only real numbers
* Fix type vocab size in configuration
* Add projection layer to embeddings
* Fix position ids bug in embeddings
* Add minor changes
* Add conversion script and remove CausalLM vestiges
* Fix conversion script
* Fix conversion script
* Remove CausalLM Test
* Update checkpoint names to dummy checkpoints
* Add tokenizer mapping
* Fix modeling file and corresponding tests
* Add tokenization test file
* Add PreTraining model test
* Make style and quality
* Make tokenization base tests work
* Update docs
* Add FastTokenizer tests
* Fix fast tokenizer special tokens
* Fix style and quality
* Remove load_tf_weights vestiges
* Add FNet to main README
* Fix configuration example indentation
* Comment tokenization slow test
* Fix style
* Add changes from review
* Fix style
* Remove bos and eos tokens from tokenizers
* Add tokenizer slow test, TPU transforms, NSP
* Add scipy check
* Add scipy availabilty check to test
* Fix tokenizer and use correct inputs
* Remove remaining TODOs
* Fix tests
* Fix tests
* Comment Fourier Test
* Uncomment Fourier Test
* Change to google checkpoint
* Add changes from review
* Fix activation function
* Fix model integration test
* Add more integration tests
* Add comparison steps to MLM integration test
* Fix style
* Add masked tokenization fix
* Improve mask tokenization fix
* Fix index docs
* Add changes from review
* Fix issue
* Fix failing import in test
* some more fixes
* correct fast tokenizer
* finalize
* make style
* Remove additional tokenization logic
* Set do_lower_case to False
* Allow keeping accents
* Fix tokenization test
* Fix FNet Tokenizer Fast
* fix tests
* make style
* Add tips to FNet docs
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
* Fix special tokens not correctly tokenized
* Add testing
* Fix
* Fix
* Use user workflows instead of directly assigning variables
* Enable test of fast tokenizers
* Update test of canine tokenizer
* Enabling dataset iteration on pipelines.
Enabling dataset iteration on pipelines.
Unifying parameters under `set_parameters` function.
Small fix.
Last fixes after rebase
Remove print.
Fixing text2text `generate_kwargs`
No more `self.max_length`.
Fixing tf only conversational.
Consistency in start/stop index over TF/PT.
Speeding up drastically on TF (nasty bug where max_length would increase
a ton.)
Adding test for support for non fast tokenizers.
Fixign GPU usage on zero-shot.
Fix working on Tf.
Update src/transformers/pipelines/base.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Update src/transformers/pipelines/base.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Small cleanup.
Remove all asserts + simple format.
* Fixing audio-classification for large PR.
* Overly explicity null checking.
* Encapsulating GPU/CPU pytorch manipulation directly within `base.py`.
* Removed internal state for parameters of the pipeline.
Instead of overriding implicitly internal state, we moved
to real named arguments on every `preprocess`, `_forward`,
`postprocess` function.
Instead `_sanitize_parameters` will be used to split all kwargs
of both __init__ and __call__ into the 3 kinds of named parameters.
* Move import warnings.
* Small fixes.
* Quality.
* Another small fix, using the CI to debug faster.
* Last fixes.
* Last fix.
* Small cleanup of tensor moving.
* is not None.
* Adding a bunch of docs + a iteration test.
* Fixing doc style.
* KeyDataset = None guard.
* RRemoving the Cuda test for pipelines (was testing).
* Even more simple iteration test.
* Correct import .
* Long day.
* Fixes in docs.
* [WIP] migrating object detection.
* Fixed the target_size bug.
* Fixup.
* Bad variable name.
* Fixing `ensure_on_device` respects original ModelOutput.
* Moving slow tokenizer to the Trie world.
* Adding more docstrings to the Trie.
* Fixing doctest (incompatible wiht our format? )
* Update src/transformers/tokenization_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Adding a lot more comment into the internals of this algorithm.
* Cleaner doc.
* Fixing the namings.
* Update src/transformers/tokenization_utils.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* quality.
* Fixing longest first match.
* Small improvements to cuts + more test + canine resistant test.
* Fixing fast test.
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* refactor GPT Config to allow dyn. properties
* make attribute_map a class attribute
* remove old code
* update unit test to test config: Add test for common properties setter
* update unit test to test config: Add test for common properties passed as parameters to __init__
* update to black code format
* Allow that setters are not defined for certain config classes
* update config classes to implement attribute_map
* bugfix lxmert config - id2labels was not defined when num_labels was set
* update broken configs - add attribute_maps
* update bart config
* update black codestyle
* update documentation on common config attributes
* update GPTJ config to new attribute map
* update docs on common attributes
* gptj config: add max_position_embeddings
* gptj config: format with black
* update speech to text 2 config
* format doc file to max_len 119
* update config template
* correct order of overflowing_tokens for slow tokenizer (issue fix#13148)
* python 3.9 requires sentencepiece version 0.1.94 or above
* slicing of ids fixed in truncated_sequence()
* Update setup.py
* Correct order of overflowing tokens for pair of sentences
* code reformatted
* Update tokenization_utils_base.py
* reformatting file
* test to check single_input added
* missing function restored
* test to check pair_input overflowing tokens order
* test to check pair_input overflowing tokens order
* test to check pair_input overflowing tokens order
* added an error message for pair of seq and longest_first strategy
* test for pair_input modified
* variable name corrected
* fixed a typo in error message
* requested changes implemented
* required test added
* Corrected the message to match test message
* added error message for Luke Tokenizer
* lost test recovered
* docstring for truncate_sequences and prepare_for_model updated
* docstring for luke tokenizer updated
* updated ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING
* aligned text and fixed puncuatations
* improved style and quality of code
* fixed error_msg in truncate_sequences
* replaced encode_plus method with regular call method
* clean up
* rephrased the docstring
* add test in trainer and test tokenizer saving wi
th trainer
* quality
* reverse trainer changes
* replace test in test_trainer by a test for all the tokenizers
* format
* add can_save_slow_tokenizer attribute to all tokenizers
* fix Herbert
* format
* Change comment in error
* add comments and a new assert
* Update src/transformers/models/albert/tokenization_albert_fast.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* change ValueError barthez
* change ValueError BigBird
* change ValueError Camembert
* change ValueError Mbart50
* change ValueError Pegasus
* change ValueError ReFormer
* change ValueError T5
* change ValueError RoBERTa
* XLNET fast
* Update tests/test_tokenization_common.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* change `assert` into `self.assertIn`
* format
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fix_torch_device_generate_test
* remove @
* up
* correct some bugs
* correct model
* finish speech2text extension
* up
* up
* up
* up
* Update utils/custom_init_isort.py
* up
* up
* update with tokenizer
* correct old tok
* correct old tok
* fix bug
* up
* up
* add more tests
* up
* fix docs
* up
* fix some more tests
* add better config
* correct some more things
"
* fix tests
* improve docs
* Apply suggestions from code review
* Apply suggestions from code review
* final fixes
* finalize
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* apply suggestions Lysandre and Sylvain
* apply nicos suggestions
* upload everything
* finish
Co-authored-by: Patrick von Platen <patrick@huggingface.co>
Co-authored-by: your_github_username <your_github_email>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Add the audio classification pipeline
* Remove autoconfig exception
* Mark ffmpeg test as slow
* Rearrange pipeline tests
* Add small test
* Replace asserts with ValueError
* Adding a TF variant of the DataCollatorForTokenClassification to get feedback
* Added a Numpy variant and a post_init check to fail early if a missing import is found
* Fixed call to Numpy variant
* Added a couple more of the collators
* Update src/transformers/data/data_collator.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fixes, style pass, finished DataCollatorForSeqToSeq
* Added all the LanguageModeling DataCollators, except SOP and PermutationLanguageModeling
* Adding DataCollatorForPermutationLanguageModeling
* Style pass
* Add missing `__call__` for PLM
* Remove `post_init` checks for frameworks because the imports inside them were making us fail code quality checks
* Remove unused imports
* First attempt at some TF tests
* A second attempt to make any of those tests actually work
* TF tests, round three
* TF tests, round four
* TF tests, round five
* TF tests, all enabled!
* Style pass
* Merging tests into `test_data_collator.py`
* Merging tests into `test_data_collator.py`
* Fixing up test imports
* Fixing up test imports
* Trying shuffling the conditionals around
* Commenting out non-functional old tests
* Completed all tests for all three frameworks
* Style pass
* Fixed test typo
* Style pass
* Move standard `__call__` method to mixin
* Rearranged imports for `test_data_collator`
* Fix data collator typo "torch" -> "pt"
* Fixed the most embarrassingly obvious bug
* Update src/transformers/data/data_collator.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Renaming mixin
* Updating docs
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Dalton Walker <dalton_walker@icloud.com>
Co-authored-by: Andrew Romans <andrew.romans@hotmail.com>
* Deberta_v2 tf
* added new line at the end of file, make style
* +V2, typo
* remove never executed branch of code
* rm cmnt and fixed typo in url filter
* cleanup according to review comments
* added #Copied from
* added missing __spec__ to _LazyModule
* test __spec__ is not None after module import
* changed module_spec arg to be optional in _LazyModule
* fix style issue
* added module spec test to test_file_utils
* First commit
* Make style
* Fix dummy objects
* Add Detectron2 config
* Add LayoutLMv2 pooler
* More improvements, add documentation
* More improvements
* Add model tests
* Add clarification regarding image input
* Improve integration test
* Fix bug
* Fix another bug
* Fix another bug
* Fix another bug
* More improvements
* Make more tests pass
* Make more tests pass
* Improve integration test
* Remove gradient checkpointing and add head masking
* Add integration test
* Add LayoutLMv2ForSequenceClassification to the tests
* Add LayoutLMv2ForQuestionAnswering
* More improvements
* More improvements
* Small improvements
* Fix _LazyModule
* Fix fast tokenizer
* Move sync_batch_norm to a separate method
* Replace dummies by requires_backends
* Move calculation of visual bounding boxes to separate method + update README
* Add models to main init
* First draft
* More improvements
* More improvements
* More improvements
* More improvements
* More improvements
* Remove is_split_into_words
* More improvements
* Simply tesseract - no use of pandas anymore
* Add LayoutLMv2Processor
* Update is_pytesseract_available
* Fix bugs
* Improve feature extractor
* Fix bug
* Add print statement
* Add truncation of bounding boxes
* Add tests for LayoutLMv2FeatureExtractor and LayoutLMv2Tokenizer
* Improve tokenizer tests
* Make more tokenizer tests pass
* Make more tests pass, add integration tests
* Finish integration tests
* More improvements
* More improvements - update API of the tokenizer
* More improvements
* Remove support for VQA training
* Remove some files
* Improve feature extractor
* Improve documentation and one more tokenizer test
* Make quality and small docs improvements
* Add batched tests for LayoutLMv2Processor, remove fast tokenizer
* Add truncation of labels
* Apply suggestions from code review
* Improve processor tests
* Fix failing tests and add suggestion from code review
* Fix tokenizer test
* Add detectron2 CI job
* Simplify CI job
* Comment out non-detectron2 jobs and specify number of processes
* Add pip install torchvision
* Add durations to see which tests are slow
* Fix tokenizer test and make model tests smaller
* Frist draft
* Use setattr
* Possible fix
* Proposal with configuration
* First draft of fast tokenizer
* More improvements
* Enable fast tokenizer tests
* Make more tests pass
* Make more tests pass
* More improvements
* Addd padding to fast tokenizer
* Mkae more tests pass
* Make more tests pass
* Make all tests pass for fast tokenizer
* Make fast tokenizer support overflowing boxes and labels
* Add support for overflowing_labels to slow tokenizer
* Add support for fast tokenizer to the processor
* Update processor tests for both slow and fast tokenizers
* Add head models to model mappings
* Make style & quality
* Remove Detectron2 config file
* Add configurable option to label all subwords
* Fix test
* Skip visual segment embeddings in test
* Use ResNet-18 backbone in tests instead of ResNet-101
* Proposal
* Re-enable all jobs on CI
* Fix installation of tesseract
* Fix failing test
* Fix index table
* Add LayoutXLM doc page, first draft of code examples
* Improve documentation a lot
* Update expected boxes for Tesseract 4.0.0 beta
* Use offsets to create labels instead of checking if they start with ##
* Update expected boxes for Tesseract 4.1.1
* Fix conflict
* Make variable names cleaner, add docstring, add link to notebooks
* Revert "Fix conflict"
This reverts commit a9b46ce9afe47ebfcfe7b45e6a121d49e74ef2c5.
* Revert to make integration test pass
* Apply suggestions from @LysandreJik's review
* Address @patrickvonplaten's comments
* Remove fixtures DocVQA in favor of dataset on the hub
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* Add hubert classifier + tests
* Add hubert classifier + tests
* Dummies for all classification tests
* Wav2Vec2 classifier + ER test
* Fix hubert integration tests
* Add hubert IC
* Pass tests for all classification tasks on Hubert
* Pass all tests + copies
* Move models to the SUPERB org
* Moving `zero-shot-classification` pipeline to new testing.
* Cleaning up old mixins.
* Fixing tests
`sshleifer/tiny-distilbert-base-uncased-finetuned-sst-2-english` is
corrupted in PT.
* Adding warning.
- Enforce `test_small_models_{tf,pt}` methods to exist (enforce checking
actual values in small tests)
- Add support for non RGB image for the pipeline.
* New test format for conversational.
* Putting back old mixin.
* Re-enabling auto tests with LazyLoading.
* Feature extraction tests.
* Remove feature-extraction.
* Feature extraction with feature_extractor (No pun intended).
* Update check_model_type for fill-mask.
* fix AutoModel.from_pretrained(..., torch_dtype=...)
* fix to_diff_dict
* add better test
* torch is not always available when a model has self.torch_dtype
* make flax gpt2 working with cross attention
* Remove encoder->decoder projection layer
* A draft (incomplete) for FlaxEncoderDecoderModel
* Add the method from_encoder_decoder_pretrained + the docstrings
* Fix the mistakes of using EncoderDecoderModel
* Fix style
* Add FlaxEncoderDecoderModel to the library
* Fix cyclic imports
* Add FlaxEncoderDecoderModel to modeling_flax_auto.py
* Remove question comments
* add tests for FlaxEncoderDecoderModel
* add flax_encoder_decoder to the lists of ignored entries in check_repo.py
* fix missing required positional arguments
* Remove **kwargs when creating FlaxEncoderDecoderModel in from_encoder_decoder_pretrained()
Also fix generation eos/pad tokens issue
* Fix: Use sequences from the generated_output
* Change a check from assert to raise ValueError
* Fix examples and token ids issues
* Fix missing all_cross_attentions when outputting tuple in modeling_gpt2
* Remove the changes in configuration docstrings.
* allow for bert 2 gpt2
* make fix-copies
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Change remaining examples to bert2gpt2
* Change the test to Bert2GPT2
* Fix examples
* Fix import
* Fix unpack bug
* Rename to FlaxEncoderDecoderModelTest and change the test to bert2gpt2
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Fix: NotImplentedError -> NotImplementedError
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* up
* finalize
Co-authored-by: ydshieh <ydshieh@user.noreply>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add test
* add change in PretrainedTokenizerBase
* change Luke
* deactivate
* add the possibility to add additional special tokens for M2M100
* format
* add special test for canine
* proposed changes for mbart
* proposed changes for mbart50
* proposed changes for byt5
* proposed changes for canine
* proposed changes for t5
* test fast and slow
* remove comment
* remove comment
* add fast version for all tests
* replace break by continue
* add more comments
* add check to avoid duplicates
* remove comment
* format
* proposed change for wave2vec2
* reverse changes mbart
* uncomment
* format
* Barrier -> barrier
* added logger for metrics
* removed stream handler in trainer
* moved handler
* removed streamhandler from trainer
* updated test image and instance type added datasets version to test
* Update tests/sagemaker/scripts/pytorch/requirements.txt
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Fill mask pipelines test updates.
* Model eval !!
* Adding slow test with actual values.
* Making all tests pass (skipping quite a bit.)
* Doc styling.
* Better doc cleanup.
* Making an explicit test with no pad token tokenizer.
* Typo.
T5 with past ONNX export, and more explicit past_key_values inputs and outputs names for ONNX model
Authored-by: Michael Benayoun <michael@huggingface.co>
* Initial work
* All auto models
* All tf auto models
* All flax auto models
* Tokenizers
* Add feature extractors
* Fix typos
* Fix other typo
* Use the right config
* Remove old mapping names and update logic in AutoTokenizer
* Update check_table
* Fix copies and check_repo script
* Fix last test
* Add back name
* clean up
* Update template
* Update template
* Forgot a )
* Use alternative to fixup
* Fix TF model template
* Address review comments
* Address review comments
* Style
* First pass
* Make conversion script work
* Improve conversion script
* Fix bug, conversion script working
* Improve conversion script, implement BEiTFeatureExtractor
* Make conversion script work based on URL
* Improve conversion script
* Add tests, add documentation
* Fix bug in conversion script
* Fix another bug
* Add support for converting masked image modeling model
* Add support for converting masked image modeling
* Fix bug
* Add print statement for debugging
* Fix another bug
* Make conversion script finally work for masked image modeling models
* Move id2label for datasets to JSON files on the hub
* Make sure id's are read in as integers
* Add integration tests
* Make style & quality
* Fix test, add BEiT to README
* Apply suggestions from @sgugger's review
* Apply suggestions from code review
* Make quality
* Replace nielsr by microsoft in tests, add docs
* Rename BEiT to Beit
* Minor fix
* Fix docs of BeitForMaskedImageModeling
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update feature extraction pipelilne.
* Leaving 1 small model for actual values check.
* Fixes tests
- Better support for tokenizer with no pad token
- Increasing PegasusModelTesterConfig for pipelines
- Test of feature extraction are more permissive + don't test Multimodel
models + encoder-decoder.
* Fixing model loading with incorrect shape (+ model with HEAD).
* Update tests/test_pipelines_common.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Revert modeling_utils modification.
* Some corrections.
* Update tests/test_pipelines_common.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update tests/test_pipelines_feature_extraction.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Syntax.
* Fixing text-classification tests.
* Don't modify this file.
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Raise an issue if the pytorch version is < 1.8.0
* Attempt to add a test to ensure it correctly raises.
* Missing docstring.
* Second attempt, patch with string absolute import.
* Let's do the call before checking it was called ...
* use the correct function ... 🤦
* Raise ImportError and AssertionError respectively when unable to find torch and torch version is not sufficient.
* Correct path mock patching
* relax constraint for torch_onnx_dict_inputs to ge instead of eq.
* Style.
* Split each version requirements for torch.
* Let's compare version directly.
* Import torch_version after checking pytorch is installed.
* @require_torch
* Better heuristic for token-classification pipeline.
Relooking at the problem makes thing actually much simpler,
when we look at ids from a tokenizer, we have no way in **general**
to recover if some substring is part of a word or not.
However, within the pipeline, with offsets we still have access to the
original string, so we can simply look if previous character (if it
exists) of a token, is actually a space. This will obviously be wrong
for tokenizers that contain spaces within tokens, tokenizers where
offsets include spaces too (Don't think there are a lot).
This heuristic hopefully is fully bc and still can handle non-word based
tokenizers.
* Updating test with real values.
* We still need the older "correct" heuristic to prevent fusing
punctuation.
* Adding a real warning when important.
* Faster list concat for trainer_pt_utils.get_length_grouped_indices() (#11825)
get_length_grouped_indices() in LengthGroupedSampler and DistributedLengthGroupedSampler
is prohibitively slow for large number of megabatches (in test case takes hours for ~270k
megabatches with 100 items each) due to slow list concatenation with sum(megabatches, []).
Resolves: #11795
Co-authored-by: ctheodoris <cvtheodo@ds.dfci.harvard.edu>
* Replace double occurrences as the last step (#11367)
* [Flax] Fix PyTorch import error (#11839)
* fix_torch_device_generate_test
* remove @
* change pytorch import to flax import
* Fix reference to XLNet (#11846)
* Switch mem metrics flag (#11851)
* Switch mem metrics flag
* Update src/transformers/training_args.py
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Fix flos single node (#11844)
* fixing flos bug/typo in non-distributed setting
* storing flos every logging_interval
* Fix two typos in docs (#11852)
* typo2
* fix typo
* [Trainer] Report both steps and num samples per second (#11818)
* [Trainer] Report both steps and num samples per second
* Fix batch number
* Update src/transformers/trainer_utils.py
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Address review comments
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Add some tests to the slow suite #11860
* Enable memory metrics in tests that need it (#11859)
* fixed a small typo in the doc (#11856)
* typo (#11858)
* Add option to log only once in multinode training (#11819)
* Add option to long only once in multinode training
* Use an alternate property
* [Wav2Vec2] SpecAugment Fast (#11764)
* first try
* finish
* [lm examples] fix overflow in perplexity calc (#11855)
* fix overflow in perplexity calc
* use inf
* fix
* [Examples] create model with custom config on the fly (#11798)
* create custom model on the flight
* better wording
* add update_from_string
* cleanup
* cleanup
* Update src/transformers/configuration_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* more bool options
* style
* fix logger
* add test
* add the doc
* assert on conflict of options
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [Wav2Vec2ForCTC] example typo fixed (#11878)
* Ensure input tensor are on device. (#11874)
The feature extractor does not create tensors on the appropriate device,
so we call `ensure_tensor_on_device` before feeding the processed inputs
to the model.
* Fix usage of head masks by TF encoder-decoder models' `generate()` function (#11775)
* Fix Bart
* Fix Blenderbot{,_small}
* Fix LED
* Fix Marian
* Fix MBart
* Fix Pegasus
* Fix T5
* Add test for generation with head_mask
* Add a common TF test
* Override a test for the LED model as head masking is not yet properly implemented
* Remove all head_masks from input preparation for LED
* Drop masking for T5 as it needs a bit of refactor
* Correcting comments in T5Stack to reflect correct tuple order (#11330)
* Correcting comments to reflect correct tuple order
In order to match the actual order (line 513 and 516, and as accessed in 968), I've changed the order mentioned in comments L962 and L966-967.
* Update modeling_t5.py
Updating another comment as well
* Removing extra space
* Fixing style and quality
* style & quality
* Update src/transformers/models/t5/modeling_t5.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* [Flax] Allow dataclasses to be jitted (#11886)
* fix_torch_device_generate_test
* remove @
* change dataclasses to flax ones
* fix typo
* fix jitted tests
* fix bert & electra
* changing find_batch_size to work with tokenizer outputs (#11890)
* changing find_batch_size to work with tokenizer outputs
trainer_pt_utils.find_batch_size does not recognize the batch size of BatchEncoding objects. This can cause an error when a trainer relies on find_batch_size to report the number of observed examples in the evaluation loop.
* Trigger CI
Co-authored-by: jrenner <joseph.renner@inria.fr>
* Link official Cloud TPU JAX docs (#11892)
* Flax Generate (#11777)
* fix_torch_device_generate_test
* remove @
* add
* indexing
* correct a couple of tests
* fix tests
* add logits processor
* finish top_k, top_p, temp
* add docs
* correct flax prng key default
* improve generate
* add generation docs
* add docs
* make style
* revert model outputs change
* make style
* correct typo
* fix tests
* fix slow test
* add raise
* finish generation
Co-authored-by: Patrick von Platen <patrick@huggingface.co>
* Add Emotion Speech Noteboook (#11900)
* Update deepspeed config to reflect hyperparameter search parameters (#11896)
* rebuild deepspeed config for hyperparameter search
* reformat code to fix style issues
* Adding new argument `max_new_tokens` for generate. (#11476)
* Adding new argument `max_new_tokens` for generate.
This is a proposal to add a new argument `max_new_tokens` to `generate`.
This include a `MaxNewTokensCriteria` that enables callers that don't
know about the token length ahead (like pipelines callers) to manage
more easily the length of their generated output.
* Adding a test for the user warning when both`max_length` and
`max_new_tokens` are used together.
* Removed redundant `no_grad`.
* Added Sequence Classification class in GPTNeo (#11906)
* seq classification changes
* fix tests
* [Flax] Return Attention from BERT, ELECTRA, RoBERTa and GPT2 (#11918)
* Added logic to return attention from flax-bert model and added test cases to check that
* Added new line at the end of file to test_modeling_flax_common.py
* fixing code style
* Fixing Roberta and Elextra models too from cpoying bert
* Added temporary hack to not run test_attention_outputs for FlaxGPT2
* Returning attention weights from GPT2 and changed the tests accordingly.
* last fixes
* bump flax dependency
Co-authored-by: jayendra <jayendra@infocusp.in>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Test optuna and ray (#11924)
* Remove `datasets` submodule
* fix assert (#11935)
* Remove redundant `nn.log_softmax` in `run_flax_glue.py` (#11920)
* Remove redundant `nn.log_softmax` in `run_flax_glue.py`
`optax.softmax_cross_entropy` expects unnormalized logits, and so it already calls `nn.log_softmax`, so I believe it is not needed here. `nn.log_softmax` is idempotent so mathematically it shouldn't have made a difference.
* Remove unused 'flax.linen' import
* Add MT5ForConditionalGeneration as supported arch. to summarization README (#11961)
* Add MT5ForConditionalGeneration as supported arch.
* Update README.md
* Add FlaxCLIP (#11883)
* add flax CLIP
* default input_shape
* add tests
* fix test
* fix name
* fix docs
* fix shapes
* attend at least 1 token
* flax conv to torch conv
* return floats
* fix equivalence tests
* fix import
* return attention_weights and update tests
* fix dosctrings
* address patricks comments
* input_shape arg
* add tests for get_image_features and get_text_features methods
* fix tests
* RAG-2nd2end-revamp (#11893)
* initial
* code quality test
* code quality
* added test functions in test_modeling_rag.py and test_retrieval_rag.py to test end2end retreiver
* minor change in test_modeling_rag
* fixed tests
* Update examples/research_projects/rag-end2end-retriever/README.md
typo corrected as suggested by lhoestq
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
* Update examples/research_projects/rag-end2end-retriever/finetune_rag.py
type change suggested by lhoestq
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
* Update src/transformers/models/rag/retrieval_rag.py
Adding this change as mentioned by lhoestq.
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
* completed the minor changes suggested by the reviewers
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
* modify qa-trainer (#11872)
* modify qa-trainer
* fix flax model
* bugfixes training_args.py (#11922)
modified according to:
https://pytorch.org/xla/release/1.8.1/_modules/torch_xla/core/xla_model.html
* reinitialize wandb config for each hyperparameter search run (#11945)
* Add regression tests for slow sentencepiece tokenizers. (#11737)
* add test_vocab_size for sentencepiece tok.
* add test_get_vocab for sentencepiece tok.
* add test_convert_token_and_id for sentencepiece tok.
* add test_tokenize_and_convert_tokens_to_string for all tok.
* improve test_tokenize_and_convert_tokens_to_string for sp. tok.
* add common tokenizer integration tests
- for albert
- for barthez
* add tokenizer integration tests to bert gen.
* add most tokenizer integration tests
* fix camembert tokenizer integration test
* add tokenizer integration test to marian
* add tokenizer integration test to reformer
* add typing and doc to tokenizer_integration_test_util
* fix tokenizer integration test of reformer
* improve test_sentencepiece_tokenize_and_convert_tokens_to_string
* empty commit to trigger CI
* fix tokenizer integration test of reformer
* remove code not needed anymore
* empty commit to trigger CI
* empty commit to trigger CI
* Authorize args when instantiating an AutoModel (#11956)
* Neptune.ai integration (#11937)
An option that turns on neptune.ai logging
--report_to 'neptune'
Additional ENV variables:
NEPTUNE_PROJECT
NEPTUNE_API_TOKEN
NEPTUNE_RUN_NAME (optional)
NEPTUNE_STOP_TIMEOUT (optional)
* Run the integration tests on schedule tests instead of master tests
* [deepspeed] docs (#11940)
* deepspeed docs
* cleanup
* cleanup
* typo correction (#11973)
* typo correction
* type corrections
* ByT5 model (#11971)
* allow tf to use uneven num of layers
* add tokenizer
* finish docs
* finish docs
* Apply suggestions from code review
* include in index
* finish
* Update docs/source/model_doc/byt5.rst
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* apply sylvais suggestions
* make style
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Typo in usage example, changed to device instead of torch_device (#11979)
* [DeepSpeed] decouple `DeepSpeedConfigHF` from `Trainer` (#11966)
* decouple DeepSpeedConfigHF from Trainer
* add LoggingLevel ctx manager; add new test
* cleanup
* add docs
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* implemented suggested renames
* formatter workaround
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [Trainer] add train loss and flops metrics reports (#11980)
* add train loss and flops metrics reports
* consistency
* add train_loss to skip keys
* restore on_train_end call timing
* Bump urllib3 from 1.25.8 to 1.26.5 in /examples/research_projects/lxmert (#11983)
Bumps [urllib3](https://github.com/urllib3/urllib3) from 1.25.8 to 1.26.5.
- [Release notes](https://github.com/urllib3/urllib3/releases)
- [Changelog](https://github.com/urllib3/urllib3/blob/main/CHANGES.rst)
- [Commits](https://github.com/urllib3/urllib3/compare/1.25.8...1.26.5)
---
updated-dependencies:
- dependency-name: urllib3
dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
* [RAG] Fix rag from pretrained question encoder generator behavior (#11962)
* fix_torch_device_generate_test
* remove @
* fix rag from pretrained loading
* add test
* uplaod
* finish
* VisualBERT (#10534)
* Init VisualBERT
* Add cookie-cutter, Config, and Embeddings
* Add preliminary Model
* Add Bert analogous classes
* Add basic code for NLVR, VQA, Flickr
* Update Init
* Fix VisualBert Downstream Models
* Rename classifier to cls
* Comment position_ids buffer
* Remove sentence image predictor output
* Update output dicts
* Remove unnecessary files
* Fix Auto Modeling
* Fix transformers init
* Add conversion script
* Add conversion script
* Fix docs
* Update visualbert modelling
* Update configuration
* Style fixes
* Add model and integration tests
* Add all tests
* Update model mapping
* Add simple detector from original repository
* Update docs and configs
* Fix style
* Fix style
* Update docs
* Fix style
* Fix import issues in style
* Fix style
* Add changes from review
* Fix style
* Fix style
* Update docs
* Fix style
* Fix style
* Update docs/source/model_doc/visual_bert.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/visual_bert/modeling_visual_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update tests/test_modeling_visual_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/visual_bert/modeling_visual_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/visual_bert/modeling_visual_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/visual_bert/modeling_visual_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add changes from review
* Remove convert run script
* Add changes from review
* Update src/transformers/models/visual_bert/modeling_visual_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/visual_bert/modeling_visual_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/visual_bert/modeling_visual_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/visual_bert/modeling_visual_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/visual_bert/modeling_visual_bert.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add changes from review
* Add changes from review
* Add visual embedding example in docs
* Fix "copied from" comments
* Add changes from review
* Fix error, style, checkpoints
* Update docs
* Fix integration tests
* Fix style
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fix examples (#11990)
* [docs] fix xref to `PreTrainedModel.generate` (#11049)
* fix xref to generate
* do the same for search methods
* style
* style
* Update return introduction (#11976)
Make it clear that the `forward` method now returns a dict instead of tuple.
Fix style
* [deepspeed] Move code and doc into standalone files (#11984)
* move code and docs
* style
* moved
* restore
* [deepspeed] add nvme test skip rule (#11997)
* add nvme skip rule
* fix
* Fix weight decay masking in `run_flax_glue.py` (#11964)
* Fix weight decay masking in `run_flax_glue.py`
Issues with the previous implementation:
- The `dict` from `traverse_util.flatten_dict` has keys which are tuples of strings, not one long string with the path separated by periods.
- `optax.masked` applies the transformation wherever the mask is True, so the masks are flipped.
- Flax's LayerNorm calls the scale parameter `scale` not `weight`
* Fix formatting with black
* adapt results
Co-authored-by: Patrick von Platen <patrick@huggingface.co>
* [Flax] Refactor MLM (#12013)
* fix_torch_device_generate_test
* remove @
* finish refactor
Co-authored-by: Patrick von Platen <patrick@huggingface.co>
* [Deepspeed] Assert on mismatches between ds and hf args (#12021)
* wip
* add mismatch validation + test
* renames
* Update docs/source/main_classes/deepspeed.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* renames
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [TrainerArguments] format and sort __repr__, add __str__ (#12018)
* format and sort __repr__, add __str__
* typo
* use __str__ directly
* alias __repr__ = __str__
* Fixed Typo in modeling_bart.py (#12035)
* Fixed Typo in modeling_bart.py - Issue #11895
* Fixed Typo in modeling_bart.py
* fix deberta 2 tokenizer integration test (#12017)
* fix docs of past_key_values (#12049)
* [JAX] Bump jax lib (#12053)
* fix_torch_device_generate_test
* remove @
* bump up jax lib
* Fixes bug that appears when using QA bert and distilation. (#12026)
* Fixing bug that appears when using distilation (and potentially other uses).
During backward pass Pytorch complains with:
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation
This happens because the QA model code modifies the start_positions and end_positions input tensors, using clamp_ function: as a consequence the teacher and the student both modifies the inputs, and backward pass fails.
* Fixing all models QA clamp_ bug.
* Extend pipelines for automodel tupels (#12025)
* fix_torch_device_generate_test
* remove @
* finish
* refactor
* add test
* fix test
* Attempt at simplification.
* Small fix.
* Fixing non existing AutoModel for TF.
* Naming.
* Remove extra condition.
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
* Add optional grouped parsers description to HfArgumentParser (#12042)
* Adding optional argument group to HfArgumentParser
* Minor
* remove whitespace
* Minor styling
* adds metric prefix. (#12057)
* adds metric prefix.
* update tests to include prefix
* skip failing test (#12059)
* Fix integration tests (#12066)
* Fix tapas issue (#12063)
* Fix scatter function to be compatible with torch-scatter 2.7.0
* Allow test again
* updated the original RAG implementation to be compatible with latest Pytorch-Lightning (#11806)
* updated the original RAG implementation to be compatible with the latest PL version
* updated the requirements.txt file
* execute make style
* code quality test
* code quality
* conflix resolved in requirement.txt
* code quality
* changed the MyDDP class name to CustomDDP
* Replace legacy tensor.Tensor with torch.tensor/torch.empty (#12027)
* Replace legacy torch.Tensor constructor with torch.{tensor, empty}
* Remove torch.Tensor in examples
* Add torch to requirements.txt in language-modeling (#12040)
* Add torch to requirements.txt in language-modeling
* Update examples/pytorch/language-modeling/requirements.txt
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Properly indent block_size (#12070)
* [Deepspeed] various fixes (#12058)
* replace deprecated config
* sub_group_size was too big
* complete deprecation removal
* [Deepspeed Wav2vec2] integration (#11638)
* wip
* wip - but working with https://github.com/microsoft/DeepSpeed/pull/1044
* cleanup
* workaround
* working 5/8 modes
* solve fp32 distributed zero3
* style
* sync
* sync
* rework
* deprecation
* cleanup
* https://github.com/microsoft/DeepSpeed/pull/1044 pr was merged
* clean up
* add a guide
* more prose
* more prose
* fix
* more prose
* sub_group_size was too big
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* refactor
* bug fix
* make the true check explicit
* new deepspeed release
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* typo
* Update run_ner.py with id2label config (#12001)
* sync LayerDrop for Wav2Vec2Encoder + tests (#12076)
* Add DETR (#11653)
* Squash all commits of modeling_detr_v7 branch into one
* Improve docs
* Fix tests
* Style
* Improve docs some more and fix most tests
* Fix slow tests of ViT, DeiT and DETR
* Improve replacement of batch norm
* Restructure timm backbone forward
* Make DetrForSegmentation support any timm backbone
* Fix name of output
* Address most comments by @LysandreJik
* Give better names for variables
* Conditional imports + timm in setup.py
* Address additional comments by @sgugger
* Make style, add require_timm and require_vision to testsé
* Remove train_backbone attribute of DetrConfig, add methods to freeze/unfreeze backbone
* Add png files to fixtures
* Fix type hint
* Add timm to workflows
* Add `BatchNorm2d` to the weight initialization
* Fix retain_grad test
* Replace model checkpoints by Facebook namespace
* Fix name of checkpoint in test
* Add user-friendly message when scipy is not available
* Address most comments by @patrickvonplaten
* Remove return_intermediate_layers attribute of DetrConfig and simplify Joiner
* Better initialization
* Scipy is necessary to get sklearn metrics
* Rename TimmBackbone to DetrTimmConvEncoder and rename DetrJoiner to DetrConvModel
* Make style
* Improve docs and add 2 community notebooks
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* [test] support more than 2 gpus (#12074)
* support more than 2 gpus
* style
* Wav2Vec2 Pretraining (#11306)
* Working quantizer forward
* Working quantizer forward
* Clean up unused model parts, test reproducibility
* Working quantizer forward
* Clean up unused model parts, test reproducibility
* Remove custom outputs from the shared ones
* correct conversion
* correct bug
* add first pretrain script
* save intermediate
* static shapes
* save intermediate
* finish first pretrain script version
* more refactor
* remove wanddb
* refactor more
* improve test
* correct perplexity compute bug
* finish model implementation
* add to docs
* finish docs
* finish pretraining script
* finish pretraining script
* remove wandb
* finish PR for merge
* finish config
* finish
* make deepspeed work
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* apply suggestions
* fix flaky test
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* pass decay_mask fn to optimizer (#12087)
* rm require_version_examples (#12088)
* [Wav2Vec2ForPretraining] Correct checkpoints wav2vec2 & fix tests (#12089)
* fix_torch_device_generate_test
* remove @
* fix tests
* Add text_column_name and label_column_name to run_ner and run_ner_no_trainer args (#12083)
* Add text_column_name and label_column_name to run_ner args
* Minor fix: grouping for text and label column name
* CLIPFeatureExtractor should resize images with kept aspect ratio (#11994)
* Resize with kept aspect ratio
* Fixed failed test
* Overload center_crop and resize methods instead
* resize should handle non-PIL images
* update slow test
* Tensor => tensor
Co-authored-by: patil-suraj <surajp815@gmail.com>
* New TF GLUE example (#12028)
* Pushing partially-complete new GLUE example
* First draft of the new TF GLUE example! Needs a little more testing to be sure but it's almost ready.
* Fix to the fit() call
* Bugfixes, making sure TPU and multi-GPU support is ready
* Remove logger line that depends on Pytorch
* Style pass
* Deleting old TF GLUE example
* Include label2id and id2label in the saved model config
* Don't clobber the existing model.config.label2id
* Style fixes
* Update examples/tensorflow/text-classification/run_glue.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fix quality
* Update README.md to cover the TF GLUE example.
* Minor style edits
* Appending label2id and id2label to models to ensure inference works properly (#12102)
* Fix a condition in test_generate_with_head_masking (#11911)
* Fix a condition in test_generate_with_head_masking
* Fix usage of head_mask in bigbirg_pegasus
* Fix head masking for speech2text
* Resolve copy mismatch + drop unwanted print statement
* Fix the condition
* Flax VisionTransformer (#11951)
* adding vit for flax
* added test for Flax-vit and some bug-fixes
* overrided methods where variable changes were necessary for flax_vit test
* added FlaxViTForImageClassification for test
* Update src/transformers/models/vit/modeling_flax_vit.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* made changes suggested in PR
* Adding jax-vit models for autoimport
* swapping num_channels and height,width dimension
* fixing the docstring for torch-like inputs for VIT
* add model to main init
* add docs
* doc, fix-copies
* docstrings
* small test fixes
* fix docs
* fix docstr
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* style
Co-authored-by: jayendra <jayendra@infocusp.in>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add relevant description to tqdm in examples (#11927)
* add relevant `desc` in examples
* require_version datasets>=1.8.0
* Fix head masking generate tests (#12110)
* fix_torch_device_generate_test
* remove @
* fix tests
* Flax CLM script (#12023)
* first draft
* max_seq_length => block_size
* fix arg names
* fix typos
* fix loss calculation
* add max examples, fix train eval steps, metrics
* optimizer mask
* fix perpelexity, metric logging
* fix logging
* data_collator = > data_loader
* refactor loss_fn
* support single GPU
* pass distributed to write_metric
* fix jitting
* fix single device training
* fix single device metrics
* close inner progress bars once finished
* add overwrite_cache arg
* ifx dataset caching issue
* add more logs
* few small fixes,
* address nicholas suggestions
* fix docstr
* address patricks suggestions
* make flake happy
* pass new new_dropout_rng to apply_gradients
* reset train metrics after every epoc
* remove distributed logis, small fixes
* Add from_pretrained to dummy timm objects (#12097)
* Add from_pretrained to dummy timm
* Fix at the source
* Update utils/check_dummies.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Missing pretrained dummies
* Style
Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fix t5 error message (#12136)
* Fix t5 error message
* Fix again
* Fix megatron_gpt2 attention block's causal mask (#12007)
* Fix megatron_gpt2 attention block's causal mask.
* compatibility with checkpoints created with recent versions of Megatron-LM
* added integration test for the released Megatron-GPT2 model
* code style changes
* added option to megatron conversion script to read from config file
Co-authored-by: Guido Novati <gnovati@nvidia.com>
* Add mlm pretraining xla torch readme (#12011)
* fix_torch_device_generate_test
* remove @
* upload
* Apply suggestions from code review
* Apply suggestions from code review
* Apply suggestions from code review
* Update examples/flax/language-modeling/README.md
* add more info
* finish
* fix
Co-authored-by: Patrick von Platen <patrick@huggingface.co>
* add readme for flax clm (#12111)
* add readme for flax clm
* use section link for tokenizer
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* update metrics
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* FlaxBart (#11537)
* Start working on FlaxBart
* Create modeling_flax_bart.py
* Write FlaxBartAttention
* Add FlaxBartEncoderLayer
* Add FlaxBartDecoderLayer and some typing
* Add helepr function for FlaxBart
* shift_tokens_right
* _make_causal_mask
* _expand_mask
* Add PositionalEmbedding and fix init_std naming
* Add FlaxBartPretrainedModel
* Add FlaxBartEncoder
* Add FlaxBartEncoder
* Add FlaxBartEncoder among modules to be imported
* YET WE CANNOT INITIALIZE THAT!! :(
* Make BartEncoder working
Change BartEncoder to instance of nn.Module so far
* Add FlaxBartDecoder
* Add FlaxBartModel
* TODO to make model run -> Prepapre model inputs
* Resolve padding
* Add FlaxBartModel
* Add FlaxBartModel into importable modules
* Remove FlaxBartEncoder and FlaxBartDecoder from importable modules
* make style; not properly working
* make style; make quality not pass due to some import I left
* Remove TODO for padding_idx in nn.Embed so far
* Add FlaxBartForConditionalGeneration
* Incorporate Flax model output classes, i.e. return_dict
* Add another models and incorporate use_cache arg
* Add FlaxBartForSequenceClassification and FlaxBartForQuestionAnswering
* Incorporate use_cache arg from PyTorch implementation
* Add all necessary Flax output utils
* Add FlaxBartForCausalLM; not working yet'
* Add minor improvements; still lacks some functionality
* Update docs, src and tests
* Add support of FlaxBart to docs/source
* Fix some bugs in FlaxBart souce code
* Add some neccessary tests for FlaxBart models - jit_compilation not passing
* Fix tests and add test_head_masking
* Fix tests for @jax.jit computation
* Add test_head_masking
* Migrate FlaxBart tests from jax.numpy to numpy
* Remove FlaxBartForCausalLM
* Clean repo
* fix bart model weight structure
* Fix FlaxBartForSequenceClassification
Slicing is not possible to use below jit, therefore, selecting sentence
representation from hidden_states must be changed.
* Allow FlaxBartForSequenceClassification for testing pt_flax equivalence
* Allow testing for FlaxBartForQA for pt_flax equivalence
* Add a comment to FlaxBartForSequenceClassification + change noise from 1e-3 to 1e-6
* remove past_key_values
* remove inputs_mebeds and make input_ids required
* add position ids
* re-write attention layer
* fix dataclass
* fix pos embeds and attention output
* fix pos embeds
* expose encode method
* expose decode method
* move docstring to top
* add cache for causal attn layer
* remove head masking for now
* s2s greedy search first pass
* boom boom
* fix typos
* fix greedy generate for bart
* use encoder, decoder layers instead of num_hidden_layers
* handle encoder_outputs
* cleanup
* simplify decoding
* more clean-up
* typos
* Change header + add {decoder_,}position_ids into 2 models
* add BartConfig
* fix existing tests
* add encode, decode methods
* Fix shift_tokens_right for JIT compilation + clarify one condition
* fix decode
* encoder => encode
* simplify generate
* add tests for encode and decode
* style
* add tests for cache
* fix equivalence tests
* sample generate now works with seq2seq
* generation tests
* initialize dense layers
* docstring and cleanup
* quality
* remove get/set input_embeddings
* address Patricks suggestions
* decode for every model, remove encoder_outputs from call
* update tests accordingly
* decode returns only decoder outputs and logits
* fix arguments
* doc encode, decode methods
* correct base_model_prefix
* fix test for seq classif model
* fix docs
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* Feature to use the PreTrainedTokenizerFast class as a stand-alone tokenizer (#11810)
* feature for tokenizer without slow/legacy version
* format
* modify common test
* add tests
* add PreTrainedTokenizerFast to AutoTokenizer
* format
* change tokenizer common test in order to be able to run test without a slow version
* update tokenizer fast test in order to use `rust_tokenizer_class` attribute instead of `tokenizer_class`
* add autokenizer test
* replace `if self.tokenizer_class is not None` with ` if self.tokenizer_class is None`
* remove obsolete change in comment
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Update src/transformers/tokenization_utils_fast.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* change `get_main_tokenizer` into `get_tokenizers`
* clarify `get_tokenizers` method
* homogenize with `test_slow_tokenizer` and `test_rust_tokenizer`
* add `test_rust_tokenizer = False` to tokenizer which don't define a fast version
* `test_rust_tokenizer = False` for BertJapaneseTokenizer
* `test_rust_tokenizer = False` for BertJapaneseCharacterTokenizationTest
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [Flax] Add links to google colabs (#12146)
* fix_torch_device_generate_test
* remove @
* add colab links
* Don't log anything before logging is setup in examples (#12121)
* Don't log anything before logging is setup in examples
* Last example
* Use text_column_name variable instead of "text" (#12132)
* Use text_column_name variable instead of "text"
`text_column_name` was already defined above where I made the changes and it was also used below where I made changes.
This is a very minor change. If a dataset does not use "text" as the column name, then the `tokenize_function` will now use whatever column is assigned to `text_column_name`. `text_column_name` is just the first column name if "text" is not a column name. It makes the function a little more robust, though I would assume that 90% + of datasets use "text" anyway.
* black formatting
* make style
Co-authored-by: Nicholas Broad <nicholas@nmbroad.com>
* [lm examples] Replicate --config_overrides addition to other LM examples (#12135)
* [lm examples] Replicate --config_overrides addition to other LM examples
* Removing no trainer files changes
* Update README
Co-authored-by: Kumar Abhishek <kabhishek@expedia.com>
* fix error message (#12148)
* [optim] implement AdafactorSchedule (#12123)
* implement AdafactorSchedule
* typo
* fix
* Update src/transformers/optimization.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [style] consistent nn. and nn.functional (#12124)
* consistent nn. and nn.functional
* fix glitch
* fix glitch #2
* Adding TFWav2Vec2Model (#11617)
* [WIP] Add TFWav2Vec2Model
Work in progress for adding a tensorflow version of Wav2Vec2
* feedback changes
* small fix
* Test Feedback Round 1
* Add SpecAugment and CTC Loss
* correct spec augment mask creation
* docstring and correct copyright
* correct bugs
* remove bogus file
* finish tests correction
* del unnecessary layers
* Update src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* make style
* correct final bug
* Feedback Changes
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* [Flax] Fix flax pt equivalence tests (#12154)
* fix_torch_device_generate_test
* remove @
* upload
* consistent nn. and nn.functional: p2 templates (#12153)
* Flax Big Bird (#11967)
* add flax bert
* bert -> bigbird
* original_full ported
* add debugger
* init block sparse
* fix copies ; gelu_fast -> gelu_new
* block sparse port
* fix block sparse
* block sparse working
* all ckpts working
* fix-copies
* make quality
* init tests
* temporary fix for FlaxBigBirdForMultipleChoice
* skip test_attention_outputs
* fix
* gelu_fast -> gelu_new ; fix multiple choice model
* remove nsp
* fix sequence classifier
* fix
* make quality
* make fix-copies
* finish
* Delete debugger.ipynb
* Update src/transformers/models/big_bird/modeling_flax_big_bird.py
* make style
* finish
* bye bye jit flax tests
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* [style] consistent nn. and nn.functional: part 3 `tests` (#12155)
* consistent nn. and nn.functional: p3 templates
* restore
* [style] consistent nn. and nn.functional: part 4 `examples` (#12156)
* consistent nn. and nn.functional: p4 examples
* restore
* consistent nn. and nn.functional: part 5 docs (#12161)
* Add video links to the documentation (#12162)
* [Flax generate] Add params to generate (#12171)
* fix_torch_device_generate_test
* remove @
* add params as input
* finish
* Use a released version of optax rather than installing from Git. (#12173)
Use a released version of optax rather than installing from Git
* Have dummy processors have a `from_pretrained` method (#12145)
* Add course banner (#12157)
* Add course banner
* Update course banner
* Adjust banner width
* Enable add_prefix_space if model_type is roberta or gpt2 (#12116)
* Update AutoModel classes in summarization example (#12178)
- Convert use of deprecated AutoModelWithLMHead to AutoModelForSeq2SeqLM
- Add newly required `truncation=True` to `tokenizer.encode` with `max_length`
This silences all warnings.
* Ray Tune Integration Updates (#12134)
* fix
* fixes
* add back to scheduled tests
* formatting
* Update integrations.py
* [testing] ensure concurrent pytest workers use a unique port for torch.dist (#12166)
* ensure concurrent pytest workers use a unique port for torch.distributed.launch
* reword
* Model card defaults (#12122)
* [WIP] Model card defaults
* finetuned_from default value
* Add all mappings to the mapping file
* Be more defensive on finetuned_from arg
* Add default task tag
* Separate tags from tasks
* Edge case for dataset
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Temporarily deactivate torch-scatter while we wait for new release (#12181)
* Temporarily deactivate torch-scatter while we wait for new release
* torch-1.8.1 binary for scatter
* Revert to 1.8.0
* Pin torch dependency
* torchaudio and torchvision
* Temporarily deactivate torchhub test (#12184)
* [Flax] Add Beam Search (#12131)
* fix_torch_device_generate_test
* remove @
* push new logit processors
* add processors
* save first working version
* save intermediate
* finish
* make style
* make fix-copies
* finish
* Update tests/test_modeling_flax_bart.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Patrick von Platen <patrick@huggingface.co>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* Hubert (#11889)
* fix_torch_device_generate_test
* remove @
* add hubert
* add first test file
* more docs
* fix bugs
* fix bug
* finish
* finish
* finish docstring
* fix
* fix
* finalize
* add to ignored
* finish
* Apply suggestions from code review
* correct naming
* finish
* fix auto config
* finish
* correct convert script
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* apply suggestions lysandre & suraj
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* updated DLC images and sample notebooks (#12191)
* Enabling AutoTokenizer for HubertConfig. (#12198)
* Use yaml to create metadata (#12185)
* Use yaml to create metadata
* Fix typo
* Remove pin
* [Docs] fixed broken link (#12205)
* fixed broken link
* Update docs/source/benchmarks.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update docs/source/benchmarks.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Pipeline update & tests (#12207)
* Improve detr (#12147)
* Remove unused variables
* Improve docs
* Fix docs of segmentation masks
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Add link to the course (#12229)
* Support for torch 1.9.0 (#12224)
* Support for torch 1.9.0
* Torch scatter for 1.9.0
* Github Actions run on 1.9.0
* fix pt-1.9.0 `add_` deprecation (#12217)
* fix pt-1.9.0 add_ deprecation
* add () for clarity
* Trigger CI
* require_version(torch
* Release: v4.7.0
* Docs for v4.8.0
* AutoTokenizer: infer the class from the tokenizer config if possible (#12208)
* AutoTokenizer: infer the class from the tokenizer config if possible
* Add tests
* Update src/transformers/models/auto/tokenization_auto.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* update desc for map in all examples (#12226)
* update desc for map in all examples
* added plm
* suggestions
* [Flax] FlaxAutoModelForSeq2SeqLM (#12228)
* add FlaxAutoModelForSeq2SeqLM
* [FlaxBart] few small fixes (#12247)
* boom boom
* remove flax clip example
* few small fixes
* Depreciate pythonic Mish and support PyTorch 1.9 version of Mish (#12240)
* Moved Mish to Torch 1.9 version
* Run black formatting
* [t5 doc] make the example work out of the box (#12239)
* [run_clm.py] restore caching
* style
* [t5 doc] make the example work out of the box
This PR expands the training example to include the correct model type for the example to work, e.g. with `T5Model` this example will break.
* Update docs/source/model_doc/t5.rst
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* expand the other example
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* Fix the scheduled CI
* Better CI feedback (#12279)
* Better run ID
* Only part of CI
* Revert "Only part of CI"
This reverts commit 29f7f248d2.
* Fix for making student ProphetNet for Seq2Seq Distillation (#12130)
* make_student.py: fix to make student ProphetNet
* reformat
* [FlaxClip] fix test from/save pretrained test (#12284)
* boom boom
* remove flax clip example
* fix from_save_pretrained
* [Flax] [WIP] allow loading head model with base model weights (#12255)
* boom boom
* remove flax clip example
* allow loading head model with base model weights
* add test
* fix imports
* disable save, load test for clip
* add test_save_load_to_base
* [DeepSpeed] don't ignore --adafactor (#12257)
* [Flax] Fix flax test save pretrained (#12256)
* fix_torch_device_generate_test
* remove @
* fix flax save pretrained test
* Tensorflow QA example (#12252)
* New Tensorflow QA example!
* Style pass
* Updating README.md for the new example
* flake8 fixes
* Update examples/tensorflow/question-answering/README.md
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [Flax] Add jax flax to env command (#12251)
* fix_torch_device_generate_test
* remove @
* add commands for flax/jax
* reset report_to to none, avoid deprecation warning (#12293)
* [trainer + examples] set log level from CLI (#12276)
* set log level from CLI
* add log_level_replica + test + extended docs
* cleanup
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* rename datasets objects to allow datasets module
* improve the doc
* style
* doc improve
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [tests] multiple improvements (#12294)
* [tests] multiple improvements
* cleanup
* style
* todo to investigate
* fix
* Fix for the issue of device-id getting hardcoded for token_type_ids during Tracing [WIP] (#11252)
* registering a buffer for token_type_ids, to pass the error of device-id getting hardcoded when tracing
* sytle format
* adding persistent flag to the resgitered buffers that prevent from adding them to the state_dict and addresses the Backward compatibility issue
* adding the try catch to the fix as persistent flag is only available from PT >1.6
* adding version check
* added the condition to only use the token_type_ids buffer when its autogenerated not passed by user
* adding comments and making the conidtion where token_type_ids are None to use the registered buffer
* taking out position-embeddding from the if block
* adding comments
* handling the case if buffer for position_ids was not registered
* reverted the changes on position_ids, fix the issue with size of token_type_ids buffer, moved the modification for generated token_type_ids to Bertmodel, instead of Embeddings
* reverting the token_type_ids in case of None to the previous version
* reverting changes on position_ids adding back the if block
* changes added by running make fix-copies
* changes added by running make fix-copies and added the import version as it was getting used
* changes added by running make fix-copies
* changes added by running make fix-copies
* fixing the import format
* fixing the import format
* modified to use temp tensor for trimed and expanded token_type_ids buffer
* changes made by fix-copies after temp tensor modifications
* changes made by fix-copies after temp tensor modifications
* changes made by fix-copies after temp tensor modifications
* clean up
* clean up
* clean up
* clean up
* Nit
* Nit
* Nit
* modified according to support device conversion on traced models
* modified according to support device conversion on traced models
* modified according to support device conversion on traced models
* modified according to support device conversion on traced models
* changes based on latest in master
* Adapt templates
* Add version import
Co-authored-by: Ubuntu <ubuntu@ip-172-31-32-81.us-west-2.compute.internal>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* trainer_tf: adjust wandb installation command (#12291)
* add FlaxAutoModelForImageClassification in main init (#12298)
* Fix and improve documentation for LEDForConditionalGeneration (#12303)
* Replace conditional generation example (fixes#12268)
* Replace model in summarization example with finetuned checkpoint, adapt example text
* Fix typo in new summarization example
* Fix docstring formatting, add missing import statement to example
* [Flax] Main doc for event orga (#12305)
* fix_torch_device_generate_test
* remove @
* push
* finish
* some typos
* add more info on communication
* add suggestions
* [trainer] 2 bug fixes and a rename (#12309)
* bug fixes and a rename
* add extended DDP test
* FlaxBartPretrainedModel -> FlaxBartPreTrainedModel (#12313)
* [docs] performance (#12258)
* initial performance document
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* rewrites based on suggestions
* 8x multiple is for AMP only
* add contribute section
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Add CodeCarbon Integration (#12304)
* Add optional dependency
* Add CodeCarbon integration
* Add CodeCarbon integration
* Add CodeCarbon integration
* typo
* Optimizing away the `fill-mask` pipeline. (#12113)
* Optimizing away the `fill-mask` pipeline.
- Don't send anything to the tokenizer unless needed. Vocab check is
much faster
- Keep BC by sending data to the tokenizer when needed. User handling warning messages will see performance benefits again
- Make `targets` and `top_k` work together better `top_k` cannot be
higher than `len(targets)` but can be smaller still.
- Actually simplify the `target_ids` in case of duplicate (it can happen
because we're parsing raw strings)
- Removed useless code to fail on empty strings. It works only if empty
string is in first position, moved to ignoring them instead.
- Changed the related tests as only the tests would fail correctly
(having incorrect value in first position)
* Make tests compatible for 2 different vocabs... (at the price of a
warning).
Co-authored-by: @EtaoinWu
* ValueError working globally
* Update src/transformers/pipelines/fill_mask.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* `tokenizer.vocab` -> `tokenizer.get_vocab()` for more compatiblity +
fallback.
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Add output in a dictionary for TF `generate` method (#12139)
* Add output args to greedy search
* Fix critical typo + make style quality
* Handle generate_beam_search
* Add dict_specific tests and fix the placement of encoder outputs
* Add specific outputs
* Update doc
* Fix typo
* Adjust handling encoder_outputs + Fix generating for T5
* Fix generate for RAG
* Fix handling ouptut_attentions when target_mapping is not None
Take care of situations when target_mapping is provided
as there are 2-tuple of attentions
Change from:
if inputs["output_attentions"]:
attentions = tuple(tf.transpose(t, perm(2, 3, 0, 1)) for t in attentions)
to:
if inputs["output_attentions"]:
if inputs["target_mapping"] is not None:
# when target_mapping is provided, there are 2-tuple of attentions
attentions = tuple(
tuple(tf.transpose(attn_stream, perm=(2, 3, 0, 1)) for attn_stream in t) for t in attentions
)
else:
attentions = tuple(tf.transpose(t, perm=(2, 3, 0, 1)) for t in attentions)
* Rename kwargs to model_kwargs
* make style quality
* Move imports in test_modeling_tf_common.py
Move ModelOutput-related imports in test_modeling_tf_common.py
into the `is_tf_available():` statement.
* Rewrite nested if-statements
* Fix added tests
* Flax summarization script (#12230)
* add summrization script
* fix arguments, preprocessing, metrics
* add generation and metrics
* auto model, prediction loop
* prettify
* label smoothing
* adress Sylvain and Patricks suggestions
* dynamically import shift_tokens_right
* fix shift_tokens_right_fn call
* Rewrite ProphetNet to adapt converting ONNX friendly (#11981)
* Rewrite
* [ONNX] rewrite
* Flax T5 (#12150)
* copy pytorch-t5
* init
* boom boom
* forward pass same
* make generation work
* add more tests
* make test work
* finish normal tests
* make fix-copies
* finish quality
* correct slow example
* correct slow test
* version table
* upload models
* Update tests/test_modeling_flax_t5.py
* correct incorrectly deleted line
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick@huggingface.co>
* Add mention of the huggingface_hub methods for offline mode (#12320)
* [Flax/JAX] Add how to propose projects markdown (#12311)
* fix_torch_device_generate_test
* remove @
* finish
* make style
* [TFWav2Vec2] Fix docs (#12283)
* fix error
* make style check happy
Co-authored-by: chenhaitao <chenhaitao@qiyi.com>
* Clean push to hub API (#12187)
* Clean push to hub API
* Create working dir if it does not exist
* Different tweak
* New API + all models + test Flax
* Adds the Trainer clean up
* Update src/transformers/file_utils.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Address review comments
* (nit) output types
* No need to set clone_from when folder exists
* Update src/transformers/trainer.py
Co-authored-by: Julien Chaumond <julien@huggingface.co>
* Add generated_from_trainer tag
* Update to new version
* Fixes
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Julien Chaumond <julien@huggingface.co>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* Add all XxxPreTrainedModel to the main init (#12314)
* Add all XxxPreTrainedModel to the main init
* Add to template
* Add to template bis
* Add FlaxT5
* Conda build (#12323)
* Temporarily revert the `fill-mask` improvements.
* changed modeling_fx_utils.py to utils/fx.py for clarity (#12326)
Co-authored-by: Michael Benayoun <michael@huggingface.co>
* Pin good version of huggingface_hub
* [Flax T5] Fix weight initialization and fix docs (#12327)
* finish t5 flax fixes
* improve naming
* Release: v4.8.0
* v4.9.0.dev0
* Update training_args.py (#12328)
mention in `save_strategy` param description that `load_best_model_at_end` can override
* [Deepspeed] new docs (#12077)
* document sub_group_size
* style
* install + issues reporting
* style
* style
* Update docs/source/main_classes/deepspeed.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* indent 4
* restore
* style
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fix default to logging_dir lost in merge conflict
* try-this (#12338)
Signed-off-by: Richard Liaw <rliaw@berkeley.edu>
* [examples/Flax] move the examples table up (#12341)
* Fix torchscript tests (#12336)
* Fix torchscript tests
* Better test
* Remove bogus print
* Document patch release v4.8.1
* Add flax/jax quickstart (#12342)
* Update README.md
* fixed typo (#12356)
* Fix exception in prediction loop occurring for certain batch sizes (#12350)
* fix distributed_concat for scalar outputs
* Update README.md
* fixed typo (#12356)
* simplify fix with terser syntax
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Trigger CI
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: michal pitr <21157924+MichalPitr@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add FlaxBigBird QuestionAnswering script (#12233)
* port bigbird script
* adapt script a bit
* change location
* adapt more
* save progress
* init commit
* style
* dataset script tested
* readme add
* Replace NotebookProgressReporter by ProgressReporter in Ray Tune run (#12357)
* Replace NotebookProgressReporter by ProgressReporter in Ray Tune run
* Move to local import
* Style
* remove extra white space from log format (#12360)
* fixed multiplechoice tokenization (#12362)
* fixed multiplechoice tokenization
The model would have seen two sequences:
1. [CLS]prompt[SEP]prompt[SEP]
2. [CLS]choice0[SEP]choice1[SEP]
that is not correct as we want a contextualized embedding of prompt and choice
* removed outer brackets for proper sequence generation
* [trainer] add main_process_first context manager (#12351)
* main_process_first context manager
* handle multi-node, add context description
* sync desc
* [Examples] Replicates the new --log_level feature to all trainer-based pytorch (#12359)
* added log_level
* fix comment
* fixed log_level
* Trigger CI
* Unfied logging
* simplified args for log_level
* updated example template (#12365)
* replace print with logger (#12368)
* [Documentation] Warn that DataCollatorForWholeWordMask is limited to BertTokenizer-like tokenizers (#12371)
* Notify users that DataCollatorForWholeWordMask is limited to BertTokenier-like tokenizers
* Fix code formatting
* Update run_mlm.py (#12344)
Before the code could not be used for validation only because of this line:
extension = data_args.train_file.split(".")[-1]
was assuming that extension must be extracted from the training dataset. This line would run regardless of the training or validation options of the user. This would lead to an error if the user only wants to run an evaluation only and does not want to do train (because the training file does not exist). I modified it to extract extension from the training file if the user wants to do train and extract it from the validation file if the user wants to run eval. This way the code can be used for both training and validation separately.
* Add possibility to maintain full copies of files (#12312)
* [CI] add dependency table sync verification (#12364)
* add dependency table sync verification
* improve the message
* improve the message
* revert
* ready to merge
* [Examples] Added context manager to datasets map (#12367)
* added cotext manager to datasets map
* fixed style and spaces
* fixed warning of deprecation
* changed desc
* [Flax community event] Add more description to readme (#12398)
* fix_torch_device_generate_test
* remove @
* boom boom
* correct typos
* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* Apply suggestions from code review
Co-authored-by: Suzana Ilić <io.suzanai@gmail.com>
* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Suzana Ilić <io.suzanai@gmail.com>
* Update README.md
* Fix copies
* Remove the need for `einsum` in Albert's attention computation (#12394)
* debug albert einsum
* Fix matmul computation
* Let's use torch linear layer.
* Style.
* [Flax] Adapt flax examples to include `push_to_hub` (#12391)
* fix_torch_device_generate_test
* remove @
* finish
* correct summary writer
* correct push to hub
* fix indent
* finish
* finish
* finish
* finish
* finish
Co-authored-by: Patrick von Platen <patrick@huggingface.co>
* Tensorflow LM examples (#12358)
* Tensorflow MLM example
* Add CLM example
* Style fixes, adding missing checkpoint code from the CLM example
* Fix TPU training, avoid massive dataset warnings
* Fix incorrect training length calculation for multi-GPU training
* Fix incorrect training length calculation for multi-GPU training
* Refactors and nitpicks from the review
* Style pass
* Adding README
* pass the matching trainer log level to deepspeed (#12401)
* [Flax] Add T5 pretraining script (#12355)
* fix_torch_device_generate_test
* remove @
* add length computatan
* finish masking
* finish
* upload
* fix some bugs
* finish
* fix dependency table
* correct tensorboard
* Apply suggestions from code review
* correct processing
* slight change init
* correct some more mistakes
* apply suggestions
* improve readme
* fix indent
* Apply suggestions from code review
Co-authored-by: SaulLu <55560583+SaulLu@users.noreply.github.com>
* correct tokenizer
* finish
* finish
* finish
* finish
Co-authored-by: Patrick von Platen <patrick@huggingface.co>
Co-authored-by: SaulLu <55560583+SaulLu@users.noreply.github.com>
* [models] respect dtype of the model when instantiating it (#12316)
* [models] respect dtype of the model when instantiating it
* cleanup
* cleanup
* rework to handle non-float dtype
* fix
* switch to fp32 tiny model
* improve
* use dtype.is_floating_point
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fix the doc
* recode to use explicit torch_dtype_auto_detect, torch_dtype args
* docs and tweaks
* docs and tweaks
* docs and tweaks
* merge 2 args, add docs
* fix
* fix
* better doc
* better doc
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Rename detr targets to labels (#12280)
* Rename target to labels in DetrFeatureExtractor
* Update DetrFeatureExtractor tests accordingly
* Improve docs of DetrFeatureExtractor
* Improve docs
* Make style
* Add out of vocabulary error to ASR models (#12288)
* Add OOV error to ASR models
* Feedback changes
* Fix TFWav2Vec2 SpecAugment (#12289)
* Fix TFWav2Vec2 SpecAugment
* Invert masks
* Feedback changes
* [example/flax] add summarization readme (#12393)
* add readme
* update readme and add requirements
* Update examples/flax/summarization/README.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* [Flax] Example scripts - correct weight decay (#12409)
* fix_torch_device_generate_test
* remove @
* finish
* finish
* correct style
* fix ids_to_tokens naming error in tokenizer of deberta v2 (#12412)
Co-authored-by: Jipeng Huang <jihuan@microsoft.com>
* minor fixes in original RAG training (#12395)
* Added talks (#12415)
* Easily train a new fast tokenizer from a given one (#12361)
* [WIP] Easily train a new fast tokenizer from a given one
* Fix test
* Roll out to other tokenizers and add tests
* Fix bug with unk id and add emoji to test
* Really use something different in test
* Implement special tokens map
* Map special tokens in the Transformers tokenizers
* Fix test
* Make test more robust
* Fix test for BPE
* More robust map and test
Co-authored-by SaulLu
* Test file
* Stronger tests
Co-authored-by: SaulLu <lucilesaul.com@gmail.com>
* Map unk token for Wordpiece and address review comment
* Fix lowercase test and address review comment
* Fix all tests
* Simplify test
* Fix tests for realsies
* Easily train a new fast tokenizer from a given one - tackle the special tokens format (str or AddedToken) (#12420)
* Propose change in tests regarding lower case
* add new test for special tokens types
* put back the test part about decoding
* add feature: the AddedToken is re-build with the different mapped content
* Address review comment: simplify AddedToken building
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* Update src/transformers/tokenization_utils_fast.py
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: SaulLu <lucilesaul.com@gmail.com>
Co-authored-by: SaulLu <55560583+SaulLu@users.noreply.github.com>
* [modelcard] fix (#12422)
this PR is fixing an incorrect attribute - probably some tests are needed?
* Add option to save on each training node (#12421)
* Add option to save on each training node
* Apply suggestions from code review
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Address review comments
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Added to talks section (#12433)
Added one more confirmed speaker, zoom links and gcal event links
* Fix default bool in argparser (#12424)
* Fix default bool in argparser
* Add more to test
* Add default bos_token and eos_token for tokenizer of deberta_v2 (#12429)
* fix ids_to_tokens naming error in tokenizer of deberta v2
* Update tokenization_deberta_v2.py
Add bos_token and eos_token.
* format code
Co-authored-by: Jipeng Huang <jihuan@microsoft.com>
* Add CANINE (#12024)
* First pass
* More progress
* Add support for local attention
* More improvements
* More improvements
* Conversion script working
* Add CanineTokenizer
* Make style & quality
* First draft of integration test
* Remove decoder test
* Improve tests
* Add documentation
* Mostly docs improvements
* Add CanineTokenizer tests
* Fix most tests on GPU, improve upsampling projection
* Address most comments by @dhgarrette
* Remove decoder logic
* Improve Canine tests, improve docs of CanineConfig
* All tokenizer tests passing
* Make fix-copies and fix tokenizer tests
* Fix test_model_outputs_equivalence test
* Apply suggestions from @sgugger's review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Address some more comments
* Add support for hidden_states and attentions of shallow encoders
* Define custom CanineModelOutputWithPooling, tests pass
* First pass
* More progress
* Add support for local attention
* More improvements
* More improvements
* Conversion script working
* Add CanineTokenizer
* Make style & quality
* First draft of integration test
* Remove decoder test
* Improve tests
* Add documentation
* Mostly docs improvements
* Add CanineTokenizer tests
* Fix most tests on GPU, improve upsampling projection
* Address most comments by @dhgarrette
* Remove decoder logic
* Improve Canine tests, improve docs of CanineConfig
* All tokenizer tests passing
* Make fix-copies and fix tokenizer tests
* Fix test_model_outputs_equivalence test
* Apply suggestions from @sgugger's review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Address some more comments
* Make conversion script work for Canine-c too
* Fix tokenizer tests
* Remove file
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Document patch release v4.8.2
* fix typo in mt5 configuration docstring (#12432)
* Add to talks section (#12442)
* [JAX/Flax readme] add philosophy doc (#12419)
* add philosophy doc
* fix typos
* update doc
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* address Patricks suggestions
* add a training example and fix typos
* jit the training step
* jit train step
* fix example code
* typo
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* [Flax] Add wav2vec2 (#12271)
* fix_torch_device_generate_test
* remove @
* start flax wav2vec2
* save intermediate
* forward pass has correct shape
* add weight norm
* add files
* finish ctc
* make style
* finish gumbel quantizer
* correct docstrings
* correct some more files
* fix vit
* finish quality
* correct tests
* correct docstring
* correct tests
* start wav2vec2 pretraining script
* save intermediate
* start pretraining script
* finalize pretraining script
* finish
* finish
* small typo
* finish
* correct
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* make style
* push
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* Add missing Copied from statements
* Reference model uploaded under Google org
* Fix various duplicates from merging
* Rembert-large -> rembert, fix overeager Copied from, return type
* Incorporate PR comments from Patrick and Sylvain
Co-authored-by: ctheodoris <seanymphoceana@yahoo.com>
Co-authored-by: ctheodoris <cvtheodo@ds.dfci.harvard.edu>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
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* Proposal
* Testing pipelines slightly better.
- Overall same design
- Metaclass to get proper different tests instead of subTest (not well
supported by Pytest)
- Added ANY meta object to make output checking more readable.
- Skipping architectures either without tiny_config or without
architecture.
* Small fix.
* Fixing the tests in case of None value.
* Oups.
* Rebased with more architectures.
* Fixing reformer tests (no override anymore).
* Adding more options for model tester config.
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* preserve type of `additional_special_tokens` in `special_token_map`
* format
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Base test
* More test
* Fix mistake
* Add a docstring change
* Add doc ignore
* Add changes
* Add recursive dep search
* Add recursive dep search
* save
* Finalize test mapping
* Fix bug
* Print prettier
* Ignore comments and empty lines
* Make script runnable from anywhere
* Need dev install
* Like that
* Adapt
* Add as artifact
* Try on torch tests
* Fix yaml error
* Install GitPython
* Apply everywhere
* Be more defensive
* Revert to all tests if something is wrong
* Install GitPython
* Test if there are tests before launching.
* Fixes
* Fixes
* Fixes
* Fixes
* Bash syntax is horrible
* Be less stupid
* Try differently
* Typo
* Typo
* Typo
* Style
* Better name
* Escape quotes
* Ignore black unhelpful re-formatting
* Not a docstring
* Deal with inits in dependency map
* Run all tests once PR is merged.
* Add last job
* Apply suggestions from code review
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Stronger dependencies gather
* Ignore empty lines too!
* Clean up
* Fix quality
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* fix_torch_device_generate_test
* remove @
* correct greedy search
* save intertmed
* add final logits bias
* correct
* up
* add more tests
* fix another bug
* finish tests
* finish marian tests
* up
Co-authored-by: Patrick von Platen <patrick@huggingface.co>
* Add option to load a pretrained model with mismatched shapes
* Fail at loading when mismatched shapes in Flax
* Fix tests
* Update src/transformers/modeling_flax_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Address review comments
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Pass model_kwargs when loading a model in pipeline
* Add test for model_kwargs parameter of pipeline()
* Rewrite test to not download model
* Fix failing style checks
* This will reduce "Already borrowed error":
Original issue https://github.com/huggingface/tokenizers/issues/537
The original issue is caused by transformers calling many times
mutable functions on the rust tokenizers.
Rust needs to guarantee that only 1 agent has a mutable reference
to memory at a given time (for many reasons which don't need explaining
here). Usually, the rust compiler can guarantee that this property is
true at compile time.
Unfortunately, this is impossible for Python to do that, so PyO3, the
bridge between rust and python used by `tokenizers`, will change the
compile guarantee for a dynamic guarantee, so if multiple agents try
to have multiple mutable borrows at the same time, then the runtime will
yell with "Already borrowed".
The proposed fix here in transformers, is simply to reduce the actual
number of calls that really need mutable borrows. By reducing them,
we reduce the risk of running into "Already borrowed" error.
The caveat is now we add a call to read the current configuration of the
`_tokenizer`, so worst case we have 2 calls instead of 1, and best case
we simply have 1 + a Python comparison of a dict (should be negligible).
* Adding a test.
* trivial error :(.
* Update tests/test_tokenization_fast.py
Co-authored-by: SaulLu <55560583+SaulLu@users.noreply.github.com>
* Adding reference to original issues in the tests.
* Update the tests with fast tokenizer.
Co-authored-by: SaulLu <55560583+SaulLu@users.noreply.github.com>
* Fixing the pipeline optimization by rescaling the logits first.
* Add test for target equivalence
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* Laying down building stone for more flexible ONNX export capabilities
* Ability to provide a map of config key to override before exporting.
* Makes it possible to export BART with/without past keys.
* Supports simple mathematical syntax for OnnxVariable.repeated
* Effectively apply value override from onnx config for model
* Supports export with additional features such as with-past for seq2seq
* Store the output path directly in the args for uniform usage across.
* Make BART_ONNX_CONFIG_* constants and fix imports.
* Support BERT model.
* Use tokenizer for more flexibility in defining the inputs of a model.
* Add TODO as remainder to provide the batch/sequence_length as CLI args
* Enable optimizations to be done on the model.
* Enable GPT2 + past
* Improve model validation with outputs containing nested structures
* Enable Roberta
* Enable Albert
* Albert requires opset >= 12
* BERT-like models requires opset >= 12
* Remove double printing.
* Enable XLM-Roberta
* Enable DistilBERT
* Disable optimization by default
* Fix missing setattr when applying optimizer_features
* Add value field to OnnxVariable to define constant input (not from tokenizers)
* Add T5 support.
* Simplify model type retrieval
* Example exporting token_classification pipeline for DistilBERT.
* Refactoring to package `transformers.onnx`
* Solve circular dependency & __main__
* Remove unnecessary imports in `__init__`
* Licences
* Use @Narsil's suggestion to forward the model's configuration to the ONNXConfig to avoid interpolation.
* Onnx export v2 fixes (#12388)
* Tiny fixes
Remove `convert_pytorch` from onnxruntime-less runtimes
Correct reference to model
* Style
* Fix Copied from
* LongFormer ONNX config.
* Removed optimizations
* Remvoe bad merge relicas.
* Remove unused constants.
* Remove some deleted constants from imports.
* Fix unittest to remove usage of PyTorch model for onnx.utils.
* Fix distilbert export
* Enable ONNX export test for supported model.
* Style.
* Fix lint.
* Enable all supported default models.
* GPT2 only has one output
* Fix bad property name when overriding config.
* Added unittests and docstrings.
* Disable with_past tests for now.
* Enable outputs validation for default export.
* Remove graph opt lvls.
* Last commit with on-going past commented.
* Style.
* Disabled `with_past` for now
* Remove unused imports.
* Remove framework argument
* Remove TFPreTrainedModel reference
* Add documentation
* Add onnxruntime tests to CircleCI
* Add test
* Rename `convert_pytorch` to `export`
* Use OrderedDict for dummy inputs
* WIP Wav2Vec2
* Revert "WIP Wav2Vec2"
This reverts commit f665efb04c92525c3530e589029f0ae7afdf603e.
* Style
* Use OrderedDict for I/O
* Style.
* Specify OrderedDict documentation.
* Style :)
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Adding support for `pipeline("automatic-speech-recognition")`.
- Ugly `"config"` choice for AutoModel. It would be great to have the
possibility to have something like `AutoModelFor` that would implement
the same logic (Load the config, check Architectures and load the first
one)
* Remove `model_id` was not needed in the end.
* Rebased !
* Remove old code.
* Rename `nlp`.
* Copy BART to MBart and rename some stuff
* Add copy statements pointing to FlaxBart
* Update/add some common files
* Update shift_tokens_rigth + fix imports
* Fix shift_tokens_right method according to MBart implementation
* Update shift_tokens_right in tests accordingly
* Fix the import issue and update docs file
* make style quality
* Do some minor changes according to patil-suraj suggestions
* Change the order of normalization layer and attention
* Add some copu statementes
* Update generate method and add integration test for mBart
* Make a few updates after a review
Besides, add `lang_code_to_id` to MBartTokenizeFast
* fix-copies; make style quality
* Apply suggestions from code review
* Apply suggestions from code review
* Apply suggestions from code review
* fix output type, style
* add copied from
* resolve conflicts
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* First pass
* More progress
* Add support for local attention
* More improvements
* More improvements
* Conversion script working
* Add CanineTokenizer
* Make style & quality
* First draft of integration test
* Remove decoder test
* Improve tests
* Add documentation
* Mostly docs improvements
* Add CanineTokenizer tests
* Fix most tests on GPU, improve upsampling projection
* Address most comments by @dhgarrette
* Remove decoder logic
* Improve Canine tests, improve docs of CanineConfig
* All tokenizer tests passing
* Make fix-copies and fix tokenizer tests
* Fix test_model_outputs_equivalence test
* Apply suggestions from @sgugger's review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Address some more comments
* Add support for hidden_states and attentions of shallow encoders
* Define custom CanineModelOutputWithPooling, tests pass
* First pass
* More progress
* Add support for local attention
* More improvements
* More improvements
* Conversion script working
* Add CanineTokenizer
* Make style & quality
* First draft of integration test
* Remove decoder test
* Improve tests
* Add documentation
* Mostly docs improvements
* Add CanineTokenizer tests
* Fix most tests on GPU, improve upsampling projection
* Address most comments by @dhgarrette
* Remove decoder logic
* Improve Canine tests, improve docs of CanineConfig
* All tokenizer tests passing
* Make fix-copies and fix tokenizer tests
* Fix test_model_outputs_equivalence test
* Apply suggestions from @sgugger's review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Address some more comments
* Make conversion script work for Canine-c too
* Fix tokenizer tests
* Remove file
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [WIP] Easily train a new fast tokenizer from a given one
* Fix test
* Roll out to other tokenizers and add tests
* Fix bug with unk id and add emoji to test
* Really use something different in test
* Implement special tokens map
* Map special tokens in the Transformers tokenizers
* Fix test
* Make test more robust
* Fix test for BPE
* More robust map and test
Co-authored-by SaulLu
* Test file
* Stronger tests
Co-authored-by: SaulLu <lucilesaul.com@gmail.com>
* Map unk token for Wordpiece and address review comment
* Fix lowercase test and address review comment
* Fix all tests
* Simplify test
* Fix tests for realsies
* Easily train a new fast tokenizer from a given one - tackle the special tokens format (str or AddedToken) (#12420)
* Propose change in tests regarding lower case
* add new test for special tokens types
* put back the test part about decoding
* add feature: the AddedToken is re-build with the different mapped content
* Address review comment: simplify AddedToken building
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* Update src/transformers/tokenization_utils_fast.py
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: SaulLu <lucilesaul.com@gmail.com>
Co-authored-by: SaulLu <55560583+SaulLu@users.noreply.github.com>
* Clean push to hub API
* Create working dir if it does not exist
* Different tweak
* New API + all models + test Flax
* Adds the Trainer clean up
* Update src/transformers/file_utils.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Address review comments
* (nit) output types
* No need to set clone_from when folder exists
* Update src/transformers/trainer.py
Co-authored-by: Julien Chaumond <julien@huggingface.co>
* Add generated_from_trainer tag
* Update to new version
* Fixes
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Julien Chaumond <julien@huggingface.co>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* copy pytorch-t5
* init
* boom boom
* forward pass same
* make generation work
* add more tests
* make test work
* finish normal tests
* make fix-copies
* finish quality
* correct slow example
* correct slow test
* version table
* upload models
* Update tests/test_modeling_flax_t5.py
* correct incorrectly deleted line
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick@huggingface.co>
* Add output args to greedy search
* Fix critical typo + make style quality
* Handle generate_beam_search
* Add dict_specific tests and fix the placement of encoder outputs
* Add specific outputs
* Update doc
* Fix typo
* Adjust handling encoder_outputs + Fix generating for T5
* Fix generate for RAG
* Fix handling ouptut_attentions when target_mapping is not None
Take care of situations when target_mapping is provided
as there are 2-tuple of attentions
Change from:
if inputs["output_attentions"]:
attentions = tuple(tf.transpose(t, perm(2, 3, 0, 1)) for t in attentions)
to:
if inputs["output_attentions"]:
if inputs["target_mapping"] is not None:
# when target_mapping is provided, there are 2-tuple of attentions
attentions = tuple(
tuple(tf.transpose(attn_stream, perm=(2, 3, 0, 1)) for attn_stream in t) for t in attentions
)
else:
attentions = tuple(tf.transpose(t, perm=(2, 3, 0, 1)) for t in attentions)
* Rename kwargs to model_kwargs
* make style quality
* Move imports in test_modeling_tf_common.py
Move ModelOutput-related imports in test_modeling_tf_common.py
into the `is_tf_available():` statement.
* Rewrite nested if-statements
* Fix added tests
* Optimizing away the `fill-mask` pipeline.
- Don't send anything to the tokenizer unless needed. Vocab check is
much faster
- Keep BC by sending data to the tokenizer when needed. User handling warning messages will see performance benefits again
- Make `targets` and `top_k` work together better `top_k` cannot be
higher than `len(targets)` but can be smaller still.
- Actually simplify the `target_ids` in case of duplicate (it can happen
because we're parsing raw strings)
- Removed useless code to fail on empty strings. It works only if empty
string is in first position, moved to ignoring them instead.
- Changed the related tests as only the tests would fail correctly
(having incorrect value in first position)
* Make tests compatible for 2 different vocabs... (at the price of a
warning).
Co-authored-by: @EtaoinWu
* ValueError working globally
* Update src/transformers/pipelines/fill_mask.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* `tokenizer.vocab` -> `tokenizer.get_vocab()` for more compatiblity +
fallback.
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* boom boom
* remove flax clip example
* allow loading head model with base model weights
* add test
* fix imports
* disable save, load test for clip
* add test_save_load_to_base
* AutoTokenizer: infer the class from the tokenizer config if possible
* Add tests
* Update src/transformers/models/auto/tokenization_auto.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* [WIP] Add TFWav2Vec2Model
Work in progress for adding a tensorflow version of Wav2Vec2
* feedback changes
* small fix
* Test Feedback Round 1
* Add SpecAugment and CTC Loss
* correct spec augment mask creation
* docstring and correct copyright
* correct bugs
* remove bogus file
* finish tests correction
* del unnecessary layers
* Update src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* make style
* correct final bug
* Feedback Changes
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* feature for tokenizer without slow/legacy version
* format
* modify common test
* add tests
* add PreTrainedTokenizerFast to AutoTokenizer
* format
* change tokenizer common test in order to be able to run test without a slow version
* update tokenizer fast test in order to use `rust_tokenizer_class` attribute instead of `tokenizer_class`
* add autokenizer test
* replace `if self.tokenizer_class is not None` with ` if self.tokenizer_class is None`
* remove obsolete change in comment
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Update src/transformers/tokenization_utils_fast.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* change `get_main_tokenizer` into `get_tokenizers`
* clarify `get_tokenizers` method
* homogenize with `test_slow_tokenizer` and `test_rust_tokenizer`
* add `test_rust_tokenizer = False` to tokenizer which don't define a fast version
* `test_rust_tokenizer = False` for BertJapaneseTokenizer
* `test_rust_tokenizer = False` for BertJapaneseCharacterTokenizationTest
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Start working on FlaxBart
* Create modeling_flax_bart.py
* Write FlaxBartAttention
* Add FlaxBartEncoderLayer
* Add FlaxBartDecoderLayer and some typing
* Add helepr function for FlaxBart
* shift_tokens_right
* _make_causal_mask
* _expand_mask
* Add PositionalEmbedding and fix init_std naming
* Add FlaxBartPretrainedModel
* Add FlaxBartEncoder
* Add FlaxBartEncoder
* Add FlaxBartEncoder among modules to be imported
* YET WE CANNOT INITIALIZE THAT!! :(
* Make BartEncoder working
Change BartEncoder to instance of nn.Module so far
* Add FlaxBartDecoder
* Add FlaxBartModel
* TODO to make model run -> Prepapre model inputs
* Resolve padding
* Add FlaxBartModel
* Add FlaxBartModel into importable modules
* Remove FlaxBartEncoder and FlaxBartDecoder from importable modules
* make style; not properly working
* make style; make quality not pass due to some import I left
* Remove TODO for padding_idx in nn.Embed so far
* Add FlaxBartForConditionalGeneration
* Incorporate Flax model output classes, i.e. return_dict
* Add another models and incorporate use_cache arg
* Add FlaxBartForSequenceClassification and FlaxBartForQuestionAnswering
* Incorporate use_cache arg from PyTorch implementation
* Add all necessary Flax output utils
* Add FlaxBartForCausalLM; not working yet'
* Add minor improvements; still lacks some functionality
* Update docs, src and tests
* Add support of FlaxBart to docs/source
* Fix some bugs in FlaxBart souce code
* Add some neccessary tests for FlaxBart models - jit_compilation not passing
* Fix tests and add test_head_masking
* Fix tests for @jax.jit computation
* Add test_head_masking
* Migrate FlaxBart tests from jax.numpy to numpy
* Remove FlaxBartForCausalLM
* Clean repo
* fix bart model weight structure
* Fix FlaxBartForSequenceClassification
Slicing is not possible to use below jit, therefore, selecting sentence
representation from hidden_states must be changed.
* Allow FlaxBartForSequenceClassification for testing pt_flax equivalence
* Allow testing for FlaxBartForQA for pt_flax equivalence
* Add a comment to FlaxBartForSequenceClassification + change noise from 1e-3 to 1e-6
* remove past_key_values
* remove inputs_mebeds and make input_ids required
* add position ids
* re-write attention layer
* fix dataclass
* fix pos embeds and attention output
* fix pos embeds
* expose encode method
* expose decode method
* move docstring to top
* add cache for causal attn layer
* remove head masking for now
* s2s greedy search first pass
* boom boom
* fix typos
* fix greedy generate for bart
* use encoder, decoder layers instead of num_hidden_layers
* handle encoder_outputs
* cleanup
* simplify decoding
* more clean-up
* typos
* Change header + add {decoder_,}position_ids into 2 models
* add BartConfig
* fix existing tests
* add encode, decode methods
* Fix shift_tokens_right for JIT compilation + clarify one condition
* fix decode
* encoder => encode
* simplify generate
* add tests for encode and decode
* style
* add tests for cache
* fix equivalence tests
* sample generate now works with seq2seq
* generation tests
* initialize dense layers
* docstring and cleanup
* quality
* remove get/set input_embeddings
* address Patricks suggestions
* decode for every model, remove encoder_outputs from call
* update tests accordingly
* decode returns only decoder outputs and logits
* fix arguments
* doc encode, decode methods
* correct base_model_prefix
* fix test for seq classif model
* fix docs
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* Fix megatron_gpt2 attention block's causal mask.
* compatibility with checkpoints created with recent versions of Megatron-LM
* added integration test for the released Megatron-GPT2 model
* code style changes
* added option to megatron conversion script to read from config file
Co-authored-by: Guido Novati <gnovati@nvidia.com>
* adding vit for flax
* added test for Flax-vit and some bug-fixes
* overrided methods where variable changes were necessary for flax_vit test
* added FlaxViTForImageClassification for test
* Update src/transformers/models/vit/modeling_flax_vit.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* made changes suggested in PR
* Adding jax-vit models for autoimport
* swapping num_channels and height,width dimension
* fixing the docstring for torch-like inputs for VIT
* add model to main init
* add docs
* doc, fix-copies
* docstrings
* small test fixes
* fix docs
* fix docstr
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* style
Co-authored-by: jayendra <jayendra@infocusp.in>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Fix a condition in test_generate_with_head_masking
* Fix usage of head_mask in bigbirg_pegasus
* Fix head masking for speech2text
* Resolve copy mismatch + drop unwanted print statement
* Fix the condition
* Squash all commits of modeling_detr_v7 branch into one
* Improve docs
* Fix tests
* Style
* Improve docs some more and fix most tests
* Fix slow tests of ViT, DeiT and DETR
* Improve replacement of batch norm
* Restructure timm backbone forward
* Make DetrForSegmentation support any timm backbone
* Fix name of output
* Address most comments by @LysandreJik
* Give better names for variables
* Conditional imports + timm in setup.py
* Address additional comments by @sgugger
* Make style, add require_timm and require_vision to testsé
* Remove train_backbone attribute of DetrConfig, add methods to freeze/unfreeze backbone
* Add png files to fixtures
* Fix type hint
* Add timm to workflows
* Add `BatchNorm2d` to the weight initialization
* Fix retain_grad test
* Replace model checkpoints by Facebook namespace
* Fix name of checkpoint in test
* Add user-friendly message when scipy is not available
* Address most comments by @patrickvonplaten
* Remove return_intermediate_layers attribute of DetrConfig and simplify Joiner
* Better initialization
* Scipy is necessary to get sklearn metrics
* Rename TimmBackbone to DetrTimmConvEncoder and rename DetrJoiner to DetrConvModel
* Make style
* Improve docs and add 2 community notebooks
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* add test_vocab_size for sentencepiece tok.
* add test_get_vocab for sentencepiece tok.
* add test_convert_token_and_id for sentencepiece tok.
* add test_tokenize_and_convert_tokens_to_string for all tok.
* improve test_tokenize_and_convert_tokens_to_string for sp. tok.
* add common tokenizer integration tests
- for albert
- for barthez
* add tokenizer integration tests to bert gen.
* add most tokenizer integration tests
* fix camembert tokenizer integration test
* add tokenizer integration test to marian
* add tokenizer integration test to reformer
* add typing and doc to tokenizer_integration_test_util
* fix tokenizer integration test of reformer
* improve test_sentencepiece_tokenize_and_convert_tokens_to_string
* empty commit to trigger CI
* fix tokenizer integration test of reformer
* remove code not needed anymore
* empty commit to trigger CI
* empty commit to trigger CI
* initial
* code quality test
* code quality
* added test functions in test_modeling_rag.py and test_retrieval_rag.py to test end2end retreiver
* minor change in test_modeling_rag
* fixed tests
* Update examples/research_projects/rag-end2end-retriever/README.md
typo corrected as suggested by lhoestq
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
* Update examples/research_projects/rag-end2end-retriever/finetune_rag.py
type change suggested by lhoestq
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
* Update src/transformers/models/rag/retrieval_rag.py
Adding this change as mentioned by lhoestq.
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
* completed the minor changes suggested by the reviewers
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
* Added logic to return attention from flax-bert model and added test cases to check that
* Added new line at the end of file to test_modeling_flax_common.py
* fixing code style
* Fixing Roberta and Elextra models too from cpoying bert
* Added temporary hack to not run test_attention_outputs for FlaxGPT2
* Returning attention weights from GPT2 and changed the tests accordingly.
* last fixes
* bump flax dependency
Co-authored-by: jayendra <jayendra@infocusp.in>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Adding new argument `max_new_tokens` for generate.
This is a proposal to add a new argument `max_new_tokens` to `generate`.
This include a `MaxNewTokensCriteria` that enables callers that don't
know about the token length ahead (like pipelines callers) to manage
more easily the length of their generated output.
* Adding a test for the user warning when both`max_length` and
`max_new_tokens` are used together.
* Removed redundant `no_grad`.
* Fix Bart
* Fix Blenderbot{,_small}
* Fix LED
* Fix Marian
* Fix MBart
* Fix Pegasus
* Fix T5
* Add test for generation with head_mask
* Add a common TF test
* Override a test for the LED model as head masking is not yet properly implemented
* Remove all head_masks from input preparation for LED
* Drop masking for T5 as it needs a bit of refactor
* [Trainer] Report both steps and num samples per second
* Fix batch number
* Update src/transformers/trainer_utils.py
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Address review comments
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* add separator for windows
* fixes test_is_copy_consistent on Windows
* fixing writing encoding issue on extended test (for Windows)
* resolving comments
Cleaner and more scalable implementation of symbolic tracing with torch.fx, and provides support for new architectures:
- ALBERT
- DistilBERT
- MobileBERT
- MegatronBERT
- GPT2
- GPT Neo
Co-authored-by: Michael Benayoun <michael@huggingface.co>
* Add missing head masking for generate() function
* Add head_mask, decoder_head_mask and cross_attn_head_mask
into prepare_inputs_for_generation for generate() function
for multiple encoder-decoder models.
* Add test_genereate_with_head_masking
* [WIP] Update the new test and handle special cases
* make style
* Omit ProphetNet test so far
* make fix-copies
* [TokenClassification] Label realignment for subword aggregation
Tentative to replace https://github.com/huggingface/transformers/pull/11622/files
- Added `AggregationStrategy`
- `ignore_subwords` and `grouped_entities` arguments are now fused
into `aggregation_strategy`. It makes more sense anyway because
`ignore_subwords=True` with `grouped_entities=False` did not have a
meaning anyway.
- Added 2 new ways to aggregate which are MAX, and AVERAGE
- AVERAGE requires a bit more information than the others, for now this
case is slightly specific, we should keep that in mind for future
changes.
- Testing has been modified to reflect new argument, and to check the
correct deprecation and the new aggregation_strategy.
- Put the testing argument and testing results for aggregation_strategy,
close together, so that readers can understand what is supposed to
happen.
- `aggregate` is now only tested on a small model as it does not mean
anything to test it globally for all models.
- Previous tests are unchanged in desired output.
- Added a new test case that showcases better the difference between the
FIRST, MAX and AVERAGE strategies.
* Wrong framework.
* Addressing three issues.
1- Tags might not follow B-, I- convention, so any tag should work now
(assumed as B-TAG)
2- Fixed an issue with average that leads to a substantial code change.
3- The testing suite was not checking for the "index" key for "none"
strategy. This is now fixed.
The issue is that "O" could not be chosen by AVERAGE strategy because
those tokens were filtered out beforehand, so their relative scores were
not counted in the average. Now filtering on
ignore_labels will happen at the very end of the pipeline fixing
that issue.
It's a bit hard to make sure this stays like that because we do
not have a end-to-end test for that behavior
* Formatting.
* Adding formatting to code + cleaner handling of B-, I- tags.
Co-authored-by: Francesco Rubbo <rubbo.francesco@gmail.com>
Co-authored-by: elk-cloner <rezakakhki.rk@gmail.com>
* Typo.
Co-authored-by: Francesco Rubbo <rubbo.francesco@gmail.com>
Co-authored-by: elk-cloner <rezakakhki.rk@gmail.com>
* Add 3D attention mask to T5 model (#9643)
Added code for 3D attention mask in T5 model. Similar to BERT model.
* Add test for 3D attention mask
Added test for 3D attention mask: test_decoder_model_past_with_3d_attn_mask()
3D attention mask of the shape [Batch_size, Seq_length, Seq_length] both for
attention mask and decoder attention mask. Test is passing.
* improve slow class tok usage at xlm rob
* add subword regularization for barthez
* improve barthez tok. test
* fix tokenizer tests
* add subword regularization for camembert
* add subword regularization for deberta v2 tokenizer
* add more doc to deberta v2 tokenizer
* add subword regularization for speech to text tok.
* fix sp_model_kwargs type in speech 2 text tok.
* add subword regularization for M2M100 tok.
* add more concrete type hints
* fix tests for m2m100 and s2t tok.
* add missing Any import
* fix syntax error in m2m100 tok.
* fix unpickle of m2m100 and s2t tok.
* fix test of m2m100 and s2t tok.
* improve unpickle of deberta v2 tok.
* add test for pickle of barthez & camembert
* fix pickle of barthez & camembert
* add test for deberta v2 tok. pickle
* fix m2m100 tok. pickle
* fix s2t tok. pickle
* add subword regularization to albert tok.
* refactor subword reg. test into TokenizerTesterMixin
improve albert tok. test
remove sample argument form albert tok.
check subword reg. using TokenizerTesterMixin
improve tok. tests
improve xlm roberta tok. tests
improve xlm roberta tok. tests
* add subword regularization for big bird t.
* improve xlm roberta tok. test
* add subword regularization for mbart50 tok.
* add subword regularization for pegasus tok.
* add subword regularization for reformer tok.
* add subword regularization for T5 tok.
* fix t5 tok. test formatting
* add subword regularization for xlm_proph. tok.
* add subword regularization for xlnet tok.
* add subword regularization for gert_gen tok.
* add typing to tokenizers
* add typing to xlm rob. tok
* add subword regularization for marian tok.
* add reverse tok. test
* fix marian tok test
* fix marian tok test
* fix casing in tok. tests
* fix style of tok. common test
* fix deberta v2 tok test
* add type annotations to tok. tests
* add type annotations to tok. __init__
* add typing to kokenizer
* add type annotations to tok. __init__
* don't specify the default when it's None
* fix barthez tok. doc
* move sentencepiece tok. tests to TokenizerTesterMixin
* fix unused imports
* fix albert tok. test
* add comment to sentencepiece test options
* fix Any import at big bird tok.
* fix Any import at xlm prophetnet tok.
* empty commit to trigger CI
* Improve docs of DeiT and ViT, add community notebook
* Add gitignore for test_samples
* Add notebook with Trainer
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Autogenerate model cards from the Trainer
* ModelCard deprecated
* Fix test
* Style
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Address review comments
* Quality
* With all metadata
* Metadata
* Post-merge conflict mess
* Data args and all examples
* Default license and languages when possible
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* added fix to decode function. added test to qa pipeline tests
* completed topk docstring
* fixed formatting with black
* applied style_doc to fix line length
* Added Big Bird Fast Tokenizer initial file
* style fixes
* flake fixes
* Added big bird fast tokenizer to init files
* Added big bird fast to Auto tokenization
* fix styles
* minor quality fixes
* Added initial test code
* Fix SpmConverter when precompiled_charsmap doesn't exist
* fixed post processor
* minor style fix
* minor fix input names
* Actually fix identity normalization
* style
* Added token type ids to fast tokenizer
* style
* flake fix
* fix copies
Co-authored-by: Anthony MOI <m.anthony.moi@gmail.com>
* Add the ImageClassificationPipeline
* Code review
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
* Have `load_image` at the module level
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
* Set generator in dataloader
* Use generator in all random samplers
* Checkpoint all RNG states
* Final version
* Quality
* Test
* Address review comments
* Quality
* Remove debug util
* Add python and numpy RNGs
* Split states in different files in distributed
* Quality
* local_rank for TPUs
* Only use generator when accepted
* Add test
* Set seed to avoid flakiness
* Make test less flaky
* Quality
* add electra model to flax
* Remove Electra Next Sentence Prediction model added by mistake
* fix parameter sharing and loosen equality threshold
* fix styling issues
* add mistaken removen imports
* fix electra table
* Add FlaxElectra to automodels and fixe docs
* fix issues pointed out the PR
* fix flax electra to comply with latest changes
* remove stale class
* add copied from
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add flax roberta
* make style
* correct initialiazation
* modify model to save weights
* fix copied from
* fix copied from
* correct some more code
* add more roberta models
* Apply suggestions from code review
* merge from master
* finish
* finish docs
Co-authored-by: Patrick von Platen <patrick@huggingface.co>
* Rebase with master
* Minor bug fix in docs
* Copy files from adding_luke_v2 and improve docs
* change the default value of use_entity_aware_attention to True
* remove word_hidden_states
* fix head models
* fix tests
* fix the conversion script
* add integration tests for the pretrained large model
* improve docstring
* Improve docs, make style
* fix _init_weights for pytorch 1.8
* improve docs
* fix tokenizer to construct entity sequence with [MASK] entity when entities=None
* Make fix-copies
* Make style & quality
* Bug fixes
* Add LukeTokenizer to init
* Address most comments by @patil-suraj and @LysandreJik
* rename _compute_extended_attention_mask to get_extended_attention_mask
* add comments to LukeSelfAttention
* fix the documentation of the tokenizer
* address comments by @patil-suraj, @LysandreJik, and @sgugger
* improve docs
* Make style, quality and fix-copies
* Improve docs
* fix docs
* add "entity_span_classification" task
* update example code for LukeForEntitySpanClassification
* improve docs
* improve docs
* improve the code example in luke.rst
* rename the classification layer in LukeForEntityClassification from typing to classifier
* add bias to the classifier in LukeForEntitySpanClassification
* update docs to use fine-tuned hub models in code examples of the head models
* update the example sentences
* Make style & quality
* Add require_torch to tokenizer tests
* Add require_torch to tokenizer tests
* Address comments by @sgugger and add community notebooks
* Make fix-copies
Co-authored-by: Ikuya Yamada <ikuya@ikuya.net>
* prep for deepspeed==0.3.16
* new version
* too soon
* support and test fp32 mode
* troubleshooting doc start
* workaround no longer needed
* add fp32 doc
* style
* cleanup, add tf32 note
* clarify
* release was made
* Adding `AutomaticSpeechRecognitionPipeline`.
- Because we added everything to enable this pipeline, we probably
should add it to `transformers`.
- This PR tries to limit the scope and focuses only on the pipeline part
(what should go in, and out).
- The tests are very specific for S2T and Wav2vec2 to make sure both
architectures are supported by the pipeline. We don't use the mixin for
tests right now, because that requires more work in the `pipeline`
function (will be done in a follow up PR).
- Unsure about the "helper" function `ffmpeg_read`. It makes a lot of
sense from a user perspective, it does not add any additional
dependencies (as in hard dependency, because users can always use their
own load mechanism). Meanwhile, it feels slightly clunky to have so much
optional preprocessing.
- The pipeline is not done to support streaming audio right now.
Future work:
- Add `automatic-speech-recognition` as a `task`. And add the
FeatureExtractor.from_pretrained within `pipeline` function.
- Add small models within tests
- Add the Mixin to tests.
- Make the logic between ForCTC vs ForConditionalGeneration better.
* Update tests/test_pipelines_automatic_speech_recognition.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Adding docs + main import + type checking + LICENSE.
* Doc style !.
* Fixing TYPE_HINT.
* Specifying waveform shape in the docs.
* Adding asserts + specify in the documentation the shape of the input
np.ndarray.
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Adding require to tests + move the `feature_extractor` doc.
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* removed max_len
* removed max_length from BeamSearchScorer
* correct max length
* finish
* del vim
* finish & add test
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add cross_attn_head_mask to BART
* Fix cross_attentions in TFBart-like models
* This commit enables returning of `cross_attentions`
for TFBart-like models
* It also fixes attention head masking in cross-attenion module
* Update TF model templates
* Fix missing , in TF model templates
* Fix typo: congig -> config
* removes the creation of separate config objects and uses the existing ones instead+overwrite resize_token_embeddings from parent class because it is not working for the EncoderDecoderModel
* rollback to current version of the huggingface master branch
* reworked version that ties the encoder and decoder config of the parent encoderdecoder instance
* overwrite of resize_token_embeddings throws an error now
* review comment suggestion
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* implemented warning in case encoderdecoder is created with differing configs of encoderdecoderconfig and decoderconfig or encoderconfig
* added test to avoid diverging configs of wrapper class and wrapped classes
* Update src/transformers/models/encoder_decoder/modeling_encoder_decoder.py
* make style
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add head_mask & decoder_head_mask + some corrections
* Fix head masking for N-grams
* Enable test_headmasking for encoder and decod
* Fix one typo regarding in modeling_propgetnet.py
* Enable test_headmasking for ProphetNetStandaloneDecoderModelTest
and ProphetNetStandaloneEncoderModelTest in test_modeling_prophetnet.py
* make style
* Fix cross_head_mask
* Fix attention head mask naming
* `cross_head_mask` -> `cross_attn_head_mask`
* `cross_layer_head_mask` -> `cross_attn_layer_head_mask`
* Still need to merge #10605 to master to pass the tests
* Fix cross-attention head mask for Torch BART models
* Fix head masking for cross-attention module for the following
models: BART, Blenderbot, Blenderbot_small, M2M_100, Marian, MBart,
Pegasus
* Enable test_headmasking for M2M_100 model
* Fix cross_head_mask for FSMT, LED and T5
* This commit fixes `head_mask` for cross-attention modules
in the following models: FSMT, LED, T5
* It also contains some smaller changes in doc so that
it is be perfectly clear the shape of `cross_head_mask`
is the same as of `decoder_head_mask`
* Update template
* Fix template for BartForCausalLM
* Fix cross_head_mask for Speech2Text models
* Fix cross_head_mask in templates
* Fix args order in BartForCausalLM template
* Fix doc in BART templates
* Make more explicit naming
* `cross_head_mask` -> `cross_attn_head_mask`
* `cross_layer_head_mask` -> `cross_attn_layer_head_mask`
* Fix doc
* make style quality
* Fix speech2text docstring
* Initial support for upload to hub
* push -> upload
* Fixes + examples
* Fix torchhub test
* Torchhub test I hate you
* push_model_to_hub -> push_to_hub
* Apply mixin to other pretrained models
* Remove ABC inheritance
* Add tests
* Typo
* Run tests
* Install git-lfs
* Change approach
* Add push_to_hub to all
* Staging test suite
* Typo
* Maybe like this?
* More deps
* Cache
* Adapt name
* Quality
* MOAR tests
* Put it in testing_utils
* Docs + torchhub last hope
* Styling
* Wrong method
* Typos
* Update src/transformers/file_utils.py
Co-authored-by: Julien Chaumond <julien@huggingface.co>
* Address review comments
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Julien Chaumond <julien@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Base move
* Examples reorganization
* Update references
* Put back test data
* Move conftest
* More fixes
* Move test data to test fixtures
* Update path
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Address review comments and clean
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Removed `max_length` from being mandatory within `generate`.
- Moving on to fully using `StoppingCriteria` for `greedy` and `sample`
modes.
- `max_length` still used for `beam_search` and `group_beam_search`
(Follow up PR)
- Fixes a bug with MaxLengthStoppingCriteria (we should stop as soon a
we hit the max_length, the comparison needs to be or equal, that affects
the tests).
- Added options to use `logits_processor` and `stopping_criteria`
directly within `generate` function (so some users can define their own
`logits_processor` and `stopping_criteria`).
- Modified the backward compat tests to make sure we issue a warning.
* Fix `max_length` argument in `generate`.
* Moving validate to being functional.
- Renamed `smax_length` to `stoppping_max_length`.
* Removing `logits_processor` and `stopping_criteria` from `generate`
arguments.
* Deepcopy.
* Fix global variable name.
* Bulk of the work
* Polish and tests
* Update QA Trainer
* Avoid breaking the predict method
* Deprecation warnings
* Store real eval dataloder
* Get eval dataset reference before wrap
* [WIP] Enabling multilingual models for translation pipelines.
* decoder_input_ids -> forced_bos_token_id
* Improve docstring.
* Rebase
* Fixing 2 bugs
- Type token_ids coming from `_parse_and_tokenize`
- Wrong index from tgt_lang.
* Fixing black version.
* Adding tests for _build_translation_inputs and add them for all
tokenizers.
* Mbart actually puts the lang code at the end.
* Fixing m2m100.
* Adding TF support to `deep_round`.
* Update src/transformers/pipelines/text2text_generation.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Adding one line comment.
* Fixing M2M100 `_build_translation_input_ids`, and fix the call site.
* Fixing tests + deep_round -> nested_simplify
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* First draft of deit
* More improvements
* Remove DeiTTokenizerFast from init
* Conversion script works
* Add DeiT to ViT conversion script
* Add tests, add head model, add support for deit in vit conversion script
* Update model checkpoint names
* Update image_mean and image_std, set resample to bicubic
* Improve docs
* Docs improvements
* Add DeiTForImageClassificationWithTeacher to init
* Address comments by @sgugger
* Improve feature extractors
* Make fix-copies
* Minor fixes
* Address comments by @patil-suraj
* All models uploaded
* Fix tests
* Remove labels argument from DeiTForImageClassificationWithTeacher
* Fix-copies, style and quality
* Fix tests
* Fix typo
* Multiple docs improvements
* More docs fixes
* Add a special tokenizer for CPM model
* make style
* fix
* Add docs
* styles
* cpm doc
* fix ci
* fix the overview
* add test
* make style
* typo
* Custom tokenizer flag
* Add REAMDE.md
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* AutoFeatureExtractor
* Init and first tests
* Tests
* Damn you gitignore
* Quality
* Defensive test for when not all backends are here
* Use pattern for Speech2Text models
* better names
* add attention mixin
* all slow tests in one class
* make helper methods static so we can test
* add local attention tests
* better names
* doc
* apply review suggestions
* Replace pkg_resources with importlib_metadata
Fixes#10964. The other reason for this change is that pkg_resources has been [deprecated](8fe85c22ce) in favor of importlib_metadata.
* Reduce to a single importlib_metadata import switch
* Trigger CI
Co-authored-by: Stas Bekman <stas@stason.org>
* Documentation about loading a fast tokenizer within Transformers
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* style
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* closes#10258
* typo
* reworked deberta test
* implemented the comments from BigBird01 regarding sequence pair encoding of deberta
* Update style
* VOCAB_FILES_NAMES is now a oneliner as suggested by @sgugger
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* added #fmt: on as requested by @sgugger
* Style
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Squash all commits into one
* Update ViTFeatureExtractor to use image_utils instead of torchvision
* Remove torchvision and add Pillow
* Small docs improvement
* Address most comments by @sgugger
* Fix tests
* Clean up conversion script
* Pooler first draft
* Fix quality
* Improve conversion script
* Make style and quality
* Make fix-copies
* Minor docs improvements
* Should use fix-copies instead of manual handling
* Revert "Should use fix-copies instead of manual handling"
This reverts commit fd4e591bce.
* Place ViT in alphabetical order
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Replace is_sagemaker_distributed_available
* Merge SageMakerTrainer into Trainer
* Test with shorter condition
* Put back deleted line
* Deprecate SageMakerTrainer and SageMakerTrainingArguments
* Apply suggestions from code review
Co-authored-by: Philipp Schmid <32632186+philschmid@users.noreply.github.com>
Co-authored-by: Philipp Schmid <32632186+philschmid@users.noreply.github.com>
* init
* first working test
* added todo for setup.py
* working test for single node multi node ddp and smd
* added tensorflow single node test
* added directory for pytorch and tensorflow due to different requirements.txt
* added directory for pytorch and tensorflow
* added comment for run_glue until it is available
* added output_dir to it
* smaller dataset to make test running faster
* adjust HP and script
* adjusted parameter for tensorflow
* refactored test scripts
* adjusted make file
* init
* first working test
* added todo for setup.py
* working test for single node multi node ddp and smd
* added tensorflow single node test
* added directory for pytorch and tensorflow due to different requirements.txt
* added directory for pytorch and tensorflow
* added comment for run_glue until it is available
* added output_dir to it
* smaller dataset to make test running faster
* adjust HP and script
* adjusted parameter for tensorflow
* refactored test scripts
* adjusted make file
* updated dlc container
* commented in all tests
* added both ecr images
* added new master branches
* debug
* added new datasets version
* init
* strange rebase bug
* removed changes
* changed min version for tests to work
* updated DLC
* added model parallel test
* removed test files
* removed test files
* tested with ned dlc
* added correct sagemaker sdk version
* adjust DLCs for official one
* reworked tests
* quality
* removed default profile added documentation to it
* added step in release for sagemaker tests
* reverted version for example script removed duplicated script and added install from master to requirements.txt
* removed mistaken .DS_Stores from mac
* fixed tests
* added Sylvains feedback
* make style
* added lysandre's feedback
* Added embeddings layer
* Added layoutlm layers, main model, maskedlm and token classification classes
* Added model classes to tf auto models
* Added model to PT to TF conversion script
* Added model to doc README
* Added tests
* Removed unused imports
* Added layoutlm model, test, and doc for sequence classification, and fix imports in __init__.py
* Made tests pass!
* Fixed typos in imports and docs
* Fixed a typo in embeddings layer
* Removed imports
* Fixed formatting issues, imports, tests
* Added layoutlm layers, main model, maskedlm and token classification classes
* Added model classes to tf auto models
* Added model to PT to TF conversion script
* Removed unused imports
* Added layoutlm model, test, and doc for sequence classification, and fix imports in __init__.py
* Made tests pass!
* Fixed typos in imports and docs
* Removed imports
* Fixed small formatting issues
* Removed duplicates import from main __init__.py
* Chnaged deafult arg to true for adding pooling layer to tf layoutlm
* Fixed formatting issues
* Style
* Added copied from to classes copied from bert
* Fixed doc strings examples to work with layoutlm inputs
* Removed PyTorch reference in doc strings example
* Added integration tests
* Cleaned up initialization file
* Updated model checkpoint identifiers
* Fixed imports
Co-authored-by: Amir Tahmasbi <amir@ehsai.ca>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* fix backend tokenizer args override: key mismatch
* no touching the docs
* fix mpnet
* add mpnet to test
* fix test
Co-authored-by: theo <theo@matussie.re>
* Added check to ensure model name passed to from_pretrained and model are the same
* Added test to check from_pretrained throws assert error when passed an incompatiable model name
* Modified assert in from_pretrained with f-strings. Modified test to ensure desired assert message is being generated
* Added check to ensure config and model has model_type
* Fix FlauBERT heads
Co-authored-by: vimarsh chaturvedi <vimarsh chaturvedi>
Co-authored-by: Stas Bekman <stas@stason.org>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* Create modeling_flax_eletra with code copied from modeling_flax_bert
* Add ElectraForMaskedLM and ElectraForPretraining
* Add modeling test for Flax electra and fix naming and arg in Flax Electra model
* Add documentation
* Fix code style
* Create modeling_flax_eletra with code copied from modeling_flax_bert
* Add ElectraForMaskedLM and ElectraForPretraining
* Add modeling test for Flax electra and fix naming and arg in Flax Electra model
* Add documentation
* Fix code style
* Fix code quality
* Adjust tol in assert_almost_equal due to very small difference between model output, ranging 0.0010 - 0.0016
* Remove redundant ElectraPooler
* save intermediate
* adapt
* correct bert flax design
* adapt roberta as well
* finish roberta flax
* finish
* apply suggestions
* apply suggestions
Co-authored-by: Chris Nguyen <anhtu2687@gmail.com>
* Added debug prints
* Added config
* Added prints
* Added prints
* Added extra samples to SequentialDistributedSampler
* Added extra samples to SequentialDistributedSampler
Updated SequentialDistributedSampler call
* Added deubg prints
* Removed extra prints
* Making predicitons and labels multiple of batchsize
* updated number of microbatches
* Removed extra prints
* Made start_remainder similar to DistributedSamplerWithLoop
* Minor spacing update
* Added debug prints
Added config
Added prints
Added prints
* Added extra samples to SequentialDistributedSampler
Updated SequentialDistributedSampler call
Added extra samples to SequentialDistributedSampler
Added deubg prints
Removed extra prints
Making predicitons and labels multiple of batchsize
updated number of microbatches
Removed extra prints
Squashing redundant commits
* Made start_remainder similar to DistributedSamplerWithLoop
Minor spacing update
Made start_remainder similar to DistributedSamplerWithLoop
* Test and styling
* Rename test
Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>
* Apply black before checking copies
* Fix for class methods
* Deal with lonely brackets
* Remove debug and add forward changes
* Separate copies and fix test
* Add black as a test dependency
* Tests run on Docker
Co-authored-by: Morgan <funtowiczmo@gmail.com>
* Comments from code review
* Reply to itself
* Dependencies
Co-authored-by: Morgan <funtowiczmo@gmail.com>
* [WIP] Adding new parameter to `generate`: `max_time`.
Generation by tokens number is sometimes a bit clunky because we don't
know how many tokens are good enough or even how many tokens are in
the payload (for pipelines users for instance). This leads to hard
to understand behavior.
This PR proposes a new argument `max_time` which is a float of seconds
for the allowed time for `generate` to run on.
Ideally combinations of `max_tokens=None`, `max_time=2` could be used to
generate as many tokens as possible within time budget.
NB: Another possible approach consists of passing a callback to `generate`
putting the caller in charge of the actual decision of when to stop
generating tokens. It opens the door to 'which args should we pass'
to this callback. It's hard to imagine other use-cases for this
early stopping behavior than time (that are not already covered by
parameters of generate)
* Revamp with StoppingCriteria
* Removing deprecated mentions.
* Forgot arguments to stopping criteria.
* Readding max_length it's not just used as a stopping criteria.
* Default value for `stopping_criteria`.
* Address @patrickvonplaten comments.
- More docstrings
- Actual doc
- Include in global namespace
- Remove TF work.
* Put back `max_length` (deprecation different PR).
* Doc quality.
* Fixing old behavior without `stopping_criteria` but with `max_length`.
Making sure we don't break that in the future.
* Adding more tests for possible inconsistencies between
`max_length` and `stopping_criteria`.
* Fixing the torch imports.
* Allow to pass kwargs to model's from_pretrained when using pipeline.
* Disable the use of past_keys_values for GPT2 when exporting to ONNX.
* style
* Remove comment.
* Appease the documentation gods
* Fix style
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* Create modeling_tf_dpr.py
* Add TFDPR
* Add back TFPegasus, TFMarian, TFMBart, TFBlenderBot
last commit accidentally deleted these 4 lines, so I recover them back
* Add TFDPR
* Add TFDPR
* clean up some comments, add TF input-style doc string
* Add TFDPR
* Make return_dict=False as default
* Fix return_dict bug (in .from_pretrained)
* Add get_input_embeddings()
* Create test_modeling_tf_dpr.py
The current version is already passed all 27 tests!
Please see the test run at :
https://colab.research.google.com/drive/1czS_m9zy5k-iSJbzA_DP1k1xAAC_sdkf?usp=sharing
* fix quality
* delete init weights
* run fix copies
* fix repo consis
* del config_class, load_tf_weights
They shoud be 'pytorch only'
* add config_class back
after removing it, test failed ... so totally only removing "use_tf_weights = None" on Lysandre suggestion
* newline after .. note::
* import tf, np (Necessary for ModelIntegrationTest)
* slow_test from_pretrained with from_pt=True
At the moment we don't have TF weights (since we don't have official official TF model)
Previously, I did not run slow test, so I missed this bug
* Add simple TFDPRModelIntegrationTest
Note that this is just a test that TF and Pytorch gives approx. the same output.
However, I could not test with the official DPR repo's output yet
* upload correct tf model
* remove position_ids as missing keys
* create modeling_tf_rag
* add tests for tf
* add tf tests
* revert wrong pt commit
* further refactor
* further refactor
* refactor
* Update modeling_tf_rag.py
- input_processing
- fix prepare_input_for_generation (mostly fix generate bug)
- bring back from_pretrained hack in order to test generate
* delete colab pieces of code
* Show case of greedy "generate"
Temporarily change from beam_search test to greedy_search test to show case that TF and PT do get equivalent output.
* cosmetic update
* correct typos
* update
* push some progress
* make easy check
* fix rag save from pretrained
* Update src/transformers/modeling_tf_utils.py
* remove commented out lines
* delete unnecessary lines
* add simple test case for nq_checkpoint
Add nq_checkpoint test to show that current version without hack still fails
* temporarily put ugly hack back again
* Add TFRagSequenceForGeneration!!
* __init__.py , import TFRagSequenceForGeneration
* Add TFRagSequence tests!
* rag init.py - add TFRagSequenceForGeneration
* fix from_pretrained
* fix prepare_inputs_for_generation
* Beam search for RagToken!
* minor clean up
* add tf.cast in TFRagModel
* More tf.cast
* Add all remaining tests (still have issues)
* delete all T5 related
* make style
* fix load weight prefix
* fix bart
* fix return_dict for tf_rag
make all tests pass .. Hooray
* fix some tests
* fix code quality
* fix qualtiy check
* finish tests tf rag
* add tf rag to docs
* remove TFT5 from docstring
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* remove TFT5 from docstring
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Delete outdated comments
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* improve doc strings
* add generative model classes
* fix adjust token logic
* refactor generate for TFRag
* using shape_list, not _get_shape
Co-authored-by: Julien Plu <plu.julien@gmail.com>
* axis=[1]->axis=1
* delete NEED_HELP comment
* improve readability
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* improve readability
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* improve readability
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Indicating model is in a developing state in docstrings
As suggested by Julien
* small last changes
* apply sylvains suggestions
* finish tf rag
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: patrickvonplaten <patrick@huggingface.co>
Co-authored-by: Julien Plu <plu.julien@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* offline mode start
* add specific values
* fix fallback
* add test
* better values check and range
* test that actually works
* document the offline mode
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* more strict check
* cleaner test
* pt-only test
* style
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Introduce save_strategy training argument
* deprecate EvaluationStrategy
* collapse EvaluationStrategy and LoggingStrategy into a single
IntervalStrategy enum
* modify tests to use modified enum
* convbert conversion test
* fin
* fin
* fin
* clean up tf<->pt conversion
* remove from_pt
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
