* 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 bbdde5fab02465f24b54b227390073082cb32093.
* Revert "Reverts to tokenizer or feature extractor when available"
This reverts commit 77659ff5d21b6cc0baf6f443017e35e056a525bb.
* 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
* 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>
