GitLab

* 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>

* 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

Co-authored-by: ydshieh <ydshieh@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

* TF Bert inference - support np.ndarray optional arguments

* apply np input tests to all TF architectures

* Start the work for TFCLIPModel

* Convert to TF code (TODO: loss + doc)

* Clean up

* Fix pooled_output for TFCLIPTextTransformer - using tf.gather_nd

* assert -> raise error

* Expose TFCLIPModel

* Deal with dummy_inputs

* Add tests

* Fix all tests. TODO: manual check weight loading + add more comments

* Fix pt tf equivalence test

* fixes

* update TFCLIPVisionEmbeddings's Conv2D

* Fix loss + overwrite test_pt_tf_model_equivalence from common

* Add a comment about the change about MainLayer in test_keras_save_load

* Set return_loss=True in TFCLIPModelTester + make tests pass

* overwrite test_pt_tf_model_equivalence from tf common

* fix base_model_prefix

* Fix examples

* remove unused

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* apply review suggestions

* change self.pre_layrnorm to self.pre_layernorm

* apply more review suggestions

* return attention probs before dropout (to align with PT)

* fix weight init

* fix

* build doc

* fix missing doc

* fix for test
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.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>

* 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

* 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>

* Experimenting with adding proper get_config() and from_config() methods

* Adding a test for get/from config

* Fix test for get/from config

* 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>

* Update Transformers to huggingface_hub >= 0.1.0

* Forgot to save...

* Style

* Fix test

* up

* up

* up

* up

* up

* up

* up

* add clip

* fix clip PyTorch

* fix clip PyTorch

* up

* up

* up

* up

* up

* up

* up

* Fix

* Style

* Name

* Fix tests

* Style

* Remove embed sizes checking

* Disable some tests

* Fix

* Apply suggestion

* 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

TF BART models - Add `cross_attentions` to model output and fix cross-attention head masking (#10699)

* 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>

* Add support for multiple models for one config in auto classes

* Use get_values everywhere

* Prettier doc

* 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>

* Pipeline tests should be slow

* Temporarily mark some tests as slow

* Temporarily mark Barthez tests as slow

* rework savedmodel slow test

* Improve savedmodel tests

* Remove useless content

* 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>

* Refacto BERT

* Restore all the concerned models

* Remove print

* Update template

* Apply Sylvain's and Morgan's comments

* Fix cast

* Put the cast inside call

* Remove cond in ebds

* Fix funnel

* Restore previous dot product (attention_scores) computation

* Add ConvBERT and BART

* Make all the S2S models ONNX compliant

* Fix test

* Fix check copies

* Fix Longformer and LED

* Add a test for graph execution with inputs_embeds

* Apply style

* 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

* Fix saved model tests + fix a graph issue in longformer

* Apply style