* 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>
* 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>
* 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>
* 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>
* 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 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
* [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>
* 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
* 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
* 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>
* 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>
* Add check-ops script
* Finish to implement check_tf_ops and start the test
* Make the test mandatory only for BERT
* Update tf_ops folder
* Remove useless classes
* Add the ONNX test for GPT2 and BART
* Add a onnxruntime slow test + better opset flexibility
* Fix test + apply style
* fix tests
* Switch min opset from 12 to 10
* Update src/transformers/file_utils.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Fix GPT2
* Remove extra shape_list usage
* Fix GPT2
* Address Morgan's comments
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Remove redundant test_head_masking = True flags
* Remove all redundant test_head_masking flags in PyTorch test_modeling_* files
* Make test_head_masking = True as a default choice in test_modeling_tf_commong.py
* Remove all redundant test_head_masking flags in TensorFlow
test_modeling_tf_* files
* Put back test_head_masking=False fot TFT5 models
* Add head_mask/decoder_head_mask for TF BART models
* Add head_mask and decoder_head_mask input arguments for TF BART-based
models as a TF counterpart to the PR #9569
* Add test_headmasking functionality to tests/test_modeling_tf_common.py
* TODO: Add a test to verify that we can get a gradient back for
importance score computation
* Remove redundant #TODO note
Remove redundant #TODO note from tests/test_modeling_tf_common.py
* Fix assertions
* Make style
* Fix ...Model input args and adjust one new test
* Add back head_mask and decoder_head_mask to BART-based ...Model
after the last commit
* Remove head_mask ande decoder_head_mask from input_dict
in TF test_train_pipeline_custom_model as these two have different
shape than other input args (Necessary for passing this test)
* Revert adding global_rng in test_modeling_tf_common.py
* Add decoder_head_mask for PyTorch T5 model
* Add decoder_head_mask args into T5Model and T5ForConditionalGeneration
* Slightly change the order of input args to be in accordance
with the convention from BART-based models introduced within the PR #9569.
* Make style for modeling_t5.py
* Add decoder_head_mask for TF T5 models
* Separate head_mask and decoder_head_mask args in TF T5 models
* Slightly change the order of input args to follow convention
of BART-based models updated in PR #9569
* Update test_forward_signature tests/test_modeling_tf_common.py
w.r.t. the changed order of input args
* Add FutureWarnings for T5 and TFT5 models
* Add FutureWarnings for T5 and TFT5 models warning a user that
input argument `head_mask` was split into two arguments -
`head_mask` and `decoder_head_mask`
* Add default behaviour - `decoder_head_mask` is set to copy
`head_mask`
* Fix T5 modeling and FutureWarning
* Make proper usage of head_mask and decoder_head_mask
in cross_attention
* Fix conditions for raising FutureWarning
* Reformat FutureWarning in T5 modeling
* Refactor the warning message
* Resize the biases in same time than the embeddings
* Trigger CI
* Biases are not reset anymore
* Remove get_output_embeddings + better LM model detection in generation utils
* Apply style
* First test on BERT
* Update docstring + new name
* Apply the new resizing logic to all the models
* fix tests
* Apply style
* Update the template
* Fix naming
* Fix naming
* Apply style
* Apply style
* Remove unused import
* Revert get_output_embeddings
* Trigger CI
* Update num parameters
* Restore get_output_embeddings in TFPretrainedModel and add comments
* Style
* Add decoder resizing
* Style
* Fix tests
* Separate bias and decoder resize
* Fix tests
* Fix tests
* Apply style
* Add bias resizing in MPNet
* Trigger CI
* Apply style
* remove make on the fly linear embedding
* start refactor
* big first refactor
* save intermediate
* save intermediat
* correct mask issue
* save tests
* refactor padding masks
* make all tests pass
* further refactor
* make pegasus test pass
* fix bool if
* fix leftover tests
* continue
* bart renaming
* delete torchscript test hack
* fix imports in tests
* correct shift
* fix docs and repo cons
* re-add fix for FSTM
* typo in test
* fix typo
* fix another typo
* continue
* hot fix 2 for tf
* small fixes
* refactor types linting
* continue
* finish refactor
* fix import in tests
* better bart names
* further refactor and add test
* delete hack
* apply sylvains and lysandres commens
* small perf improv
* further perf improv
* improv perf
* fix typo
* make style
* small perf improv
* Use the CI to identify failing tests
* Remove from all examples and tests
* More default switch
* Fixes
* More test fixes
* More fixes
* Last fixes hopefully
* Use the CI to identify failing tests
* Remove from all examples and tests
* More default switch
* Fixes
* More test fixes
* More fixes
* Last fixes hopefully
* Run on the real suite
* Fix slow tests
* Update some tests
* Small update
* Apply style
* Use max_position_embeddings
* Create a fake attribute
* Create a fake attribute
* Update wrong name
* Wrong TransfoXL model file
* Keep the common tests agnostic
* Add next sentence prediction loss computation
* Apply style
* Fix tests
* Add forgotten import
* Add forgotten import
* Use a new parameter
* Remove kwargs and use positional arguments
* Output global_attentions in Longformer models
* make style
* small refactoring
* fix tests
* make fix-copies
* add for tf as well
* remove comments in test
* make fix-copies
* make style
* add docs
* make docstring pretty
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
* Test TF GPU CI
* Change cache
* Fix missing torch requirement
* Fix some model tests
Style
* LXMERT
* MobileBERT
* Longformer skip test
* XLNet
* The rest of the tests
* RAG goes OOM in multi gpu setup
* YAML test files
* Last fixes
* Skip doctests
* Fill mask tests
* Yaml files
* Last test fix
* Style
* Update cache
* Change ONNX tests to slow + use tiny model
* WIP refactoring pipeline tests - switching to fast tokenizers
* fix dialog pipeline and fill-mask
* refactoring pipeline tests backbone
* make large tests slow
* fix tests (tf Bart inactive for now)
* fix doc...
* clean up for merge
* fixing tests - remove bart from summarization until there is TF
* fix quality and RAG
* Add new translation pipeline tests - fix JAX tests
* only slow for dialog
* Fixing the missing TF-BART imports in modeling_tf_auto
* spin out pipeline tests in separate CI job
* adding pipeline test to CI YAML
* add slow pipeline tests
* speed up tf and pt join test to avoid redoing all the standalone pt and tf tests
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
* Update src/transformers/pipelines.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/pipelines.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Update src/transformers/testing_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add require_torch and require_tf in is_pt_tf_cross_test
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Matt