* 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
* 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.
* 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>
* 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
* 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>
