* 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
* Change the way tracing happens, enabling dynamic axes out of the box
* Update the tests and modeling xlnet
* Add the non recoding of leaf modules to avoid recording more values for the methods to record than what will be seen at tracing time (which would otherwise desynchronize the recorded values and the values that need to be given to the proxies during tracing, causing errors).
* Comments and making tracing work for gpt-j and xlnet
* Refactore things related to num_choices (and batch_size, sequence_length)
* Update fx to work on PyTorch 1.10
* Postpone autowrap_function feature usage for later
* Add copyrights
* Remove unnecessary file
* Fix issue with add_new_model_like
* Apply suggestions
* 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>
* 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>
* 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 first draft
* Make forward pass work
* Improve conversion script
* Add notebook that checks if it works
* Add BeitForSemanticSegmentation to the tests
* More improvements
* Make BeitForSemanticSegmentation consistent with Segformer
* Small bug fix
* Add BeitForSemanticSegmentation to docs
* Make sure model doesn't output hidden states when the user doesn't want to
* Make it possible to convert the large model
* Fix issue
* Fix conversion script for large model
* Add auxiliary_head option to semantic segmentation model
* Apply suggestions from @sgugger's review
* Apply suggestions from code review
* Fix failing test
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
* Symbolic trace dynamic axes support for BERT like models (albert, bert, distilbert, mobilebert, electra, megatron-bert)
* Sanity checks before tracing that make sure the model to trace is supported
* Adapted to PyTorch 1.9
Co-authored-by: Michael Benayoun <michael@huggingface.co>
* Make gradient_checkpointing a training argument
* Update src/transformers/modeling_utils.py
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Update src/transformers/configuration_utils.py
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
* Fix tests
* Style
* document Gradient Checkpointing as a performance feature
* Small rename
* PoC for not using the config
* Adapt BC to new PoC
* Forgot to save
* Rollout changes to all other models
* Fix typo
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Stas Bekman <stas@stason.org>
* fix AutoModel.from_pretrained(..., torch_dtype=...)
* fix to_diff_dict
* add better test
* torch is not always available when a model has self.torch_dtype
* 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>
* 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>
* Squash all commits of modeling_detr_v7 branch into one
* Improve docs
* Fix tests
* Style
* Improve docs some more and fix most tests
* Fix slow tests of ViT, DeiT and DETR
* Improve replacement of batch norm
* Restructure timm backbone forward
* Make DetrForSegmentation support any timm backbone
* Fix name of output
* Address most comments by @LysandreJik
* Give better names for variables
* Conditional imports + timm in setup.py
* Address additional comments by @sgugger
* Make style, add require_timm and require_vision to testsé
* Remove train_backbone attribute of DetrConfig, add methods to freeze/unfreeze backbone
* Add png files to fixtures
* Fix type hint
* Add timm to workflows
* Add `BatchNorm2d` to the weight initialization
* Fix retain_grad test
* Replace model checkpoints by Facebook namespace
* Fix name of checkpoint in test
* Add user-friendly message when scipy is not available
* Address most comments by @patrickvonplaten
* Remove return_intermediate_layers attribute of DetrConfig and simplify Joiner
* Better initialization
* Scipy is necessary to get sklearn metrics
* Rename TimmBackbone to DetrTimmConvEncoder and rename DetrJoiner to DetrConvModel
* Make style
* Improve docs and add 2 community notebooks
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Cleaner and more scalable implementation of symbolic tracing with torch.fx, and provides support for new architectures:
- ALBERT
- DistilBERT
- MobileBERT
- MegatronBERT
- GPT2
- GPT Neo
Co-authored-by: Michael Benayoun <michael@huggingface.co>
* Fix cross-attention head mask for Torch BART models
* Fix head masking for cross-attention module for the following
models: BART, Blenderbot, Blenderbot_small, M2M_100, Marian, MBart,
Pegasus
* Enable test_headmasking for M2M_100 model
* Fix cross_head_mask for FSMT, LED and T5
* This commit fixes `head_mask` for cross-attention modules
in the following models: FSMT, LED, T5
* It also contains some smaller changes in doc so that
it is be perfectly clear the shape of `cross_head_mask`
is the same as of `decoder_head_mask`
* Update template
* Fix template for BartForCausalLM
* Fix cross_head_mask for Speech2Text models
* Fix cross_head_mask in templates
* Fix args order in BartForCausalLM template
* Fix doc in BART templates
* Make more explicit naming
* `cross_head_mask` -> `cross_attn_head_mask`
* `cross_layer_head_mask` -> `cross_attn_layer_head_mask`
* Fix doc
* make style quality
* Fix speech2text docstring
* 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>
* Squash all commits into one
* Update ViTFeatureExtractor to use image_utils instead of torchvision
* Remove torchvision and add Pillow
* Small docs improvement
* Address most comments by @sgugger
* Fix tests
* Clean up conversion script
* Pooler first draft
* Fix quality
* Improve conversion script
* Make style and quality
* Make fix-copies
* Minor docs improvements
* Should use fix-copies instead of manual handling
* Revert "Should use fix-copies instead of manual handling"
This reverts commit fd4e591bce.
* Place ViT in alphabetical order
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Added check to ensure model name passed to from_pretrained and model are the same
* Added test to check from_pretrained throws assert error when passed an incompatiable model name
* Modified assert in from_pretrained with f-strings. Modified test to ensure desired assert message is being generated
* Added check to ensure config and model has model_type
* Fix FlauBERT heads
Co-authored-by: vimarsh chaturvedi <vimarsh chaturvedi>
Co-authored-by: Stas Bekman <stas@stason.org>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
