* 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>
