* Check for requires_grad when initing weights
* Add unit test
* Move sinusoidal positional encoding generation after post_init()
* Add modules to skip init list
* Move create_sinusoidal_embeddings to _init_weights
* add support for qwen2 MoE models
* update docs
* add support for qwen2 MoE models
* update docs
* update model name & test
* update readme
* update class names & readme & model_doc of Qwen2MoE.
* update architecture name
* fix qwen2_moe tests
* use Qwen2Tokenizer instead of Qwen2MoeTokenizer
* update modeling_qwen2_moe.py
* fix model architecture
* fix qwen2_moe tests
* use Qwen2Tokenizer instead of Qwen2MoeTokenizer
* update modeling_qwen2_moe.py
* fix model architecture
* fix style
* fix test when there are sparse and non sparse layers
* fixup
* Update README.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fixup
* fixup
* add archive back
* add support for qwen2 MoE models
* update docs
* update model name & test
* update readme
* update class names & readme & model_doc of Qwen2MoE.
* update architecture name
* fix qwen2_moe tests
* use Qwen2Tokenizer instead of Qwen2MoeTokenizer
* update modeling_qwen2_moe.py
* fix model architecture
* fixup
* fix qwen2_moe tests
* use Qwen2Tokenizer instead of Qwen2MoeTokenizer
* fix style
* fix test when there are sparse and non sparse layers
* fixup
* add archive back
* fix integration test
* fixup
---------
Co-authored-by: bozheng-hit <dsoul0621@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* attempt to fix
* the actual fix that works with compilation!
* this?
* temporary update
* nit?
* dispatcg to memory efficient?
* update both models that have static cache support
* fix copies fix compile
* make sure fix
* fix cohere and gemma
* fix beams?
* nit
* slipped through the cracks
* nit
* nits
* update
* fix-copies
* skip failing tests
* nits
* Added SuperPoint docs
* Added tests
* Removed commented part
* Commit to create and fix add_superpoint branch with a new branch
* Fixed dummy_pt_objects
* Committed missing files
* Fixed README.md
* Apply suggestions from code review
Fixed small changes
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Moved ImagePointDescriptionOutput from modeling_outputs.py to modeling_superpoint.py
* Removed AutoModelForKeypointDetection and related stuff
* Fixed inconsistencies in image_processing_superpoint.py
* Moved infer_on_model logic simply in test_inference
* Fixed bugs, added labels to forward method with checks whether it is properly a None value, also added tests about this logic in test_modeling_superpoint.py
* Added tests to SuperPointImageProcessor to ensure that images are properly converted to grayscale
* Removed remaining mentions of MODEL_FOR_KEYPOINT_DETECTION_MAPPING
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Fixed from (w, h) to (h, w) as input for tests
* Removed unnecessary condition
* Moved last_hidden_state to be the first returned
* Moved last_hidden_state to be the first returned (bis)
* Moved last_hidden_state to be the first returned (ter)
* Switched image_width and image_height in tests to match recent changes
* Added config as first SuperPointConvBlock init argument
* Reordered README's after merge
* Added missing first config argument to SuperPointConvBlock instantiations
* Removed formatting error
* Added SuperPoint to README's de, pt-br, ru, te and vi
* Checked out README_fr.md
* Fixed README_fr.md
* Test fix README_fr.md
* Test fix README_fr.md
* Last make fix-copies !
* Updated checkpoint path
* Removed unused SuperPoint doc
* Added missing image
* Update src/transformers/models/superpoint/modeling_superpoint.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Removed unnecessary import
* Update src/transformers/models/superpoint/modeling_superpoint.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Added SuperPoint to _toctree.yml
---------
Co-authored-by: steven <steven.bucaillle@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Steven Bucaille <steven.bucaille@buawei.com>
* use user_defined_symbols
* fixup
* nit
* add a very robust test
* make sure all models are tested with the `pretrained_tokenizer_to_test`
* should we make sure we test all of them?
* merge
* remove the id
* fix test
* update
* ousies
* oups
* fixup
* fix copies check
* remove `pretrained_tokenizer_to_test`
* Cohere Model Release (#1)
Cohere Model Release
* Remove unnecessary files and code (#2)
Some cleanup
* Delete cohere-model directory (#3)
* Make Fix (#5)
* Pr fixes (#6)
* fixes for pr
* pr fixes for the format
* pr fixes for the format
* src/transformers/models/auto/tokenization_auto.py
* Tokenizer test (#8)
* tokenizer test
* format fix
* Adding Docs and other minor changes (#7)
* Add modeling tests (#9)
* Smol Fix (#11)
* tokenization tests are fixed
* format fixes
* fix pr doc tests
* fix pr doc tests
* fix pr doc tests
* fix pr style check
* small changes in cohere.md
* FIX: Address final comments for transformers integration (#13)
* fix modeling final nits and add proper test file
* for now leave empty tests
* add integration test
* push new test
* fix modeling cohere (#14)
* Update chat templates to use the new API (#15)
---------
Co-authored-by: ahmetustun <ahmetustun89@gmail.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Added pytests for pvt-v2, all passed
* Added pvt_v2 to docs/source/end/model_doc
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Reverted batch eval changes for PR
* Expanded type support for Pvt-v2 config
* Fixed config docstring. Added channels property
* Fixed model names in tests
* Fixed config backbone compat. Added additional type support for image size in config
* Fixed config backbone compat
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* Set key and value layers to use separate linear modules. Fixed pruning function
* Set AvgPool to 7
* Fixed issue in init
* PvT-v2 now works in AutoModel
* Successful conversion of pretrained weights for PVT-v2
* Successful conversion of pretrained weights for PVT-v2 models
* Added pytests for pvt-v2, all passed
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* Set key and value layers to use separate linear modules. Fixed pruning function
* Set AvgPool to 7
* Fixed issue in init
* PvT-v2 now works in AutoModel
* Successful conversion of pretrained weights for PVT-v2
* Successful conversion of pretrained weights for PVT-v2 models
* Added pytests for pvt-v2, all passed
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* Reverted batch eval changes for PR
* Updated index.md
* Expanded type support for Pvt-v2 config
* Fixed config docstring. Added channels property
* Fixed model names in tests
* Fixed config backbone compat
* Ran fix-copies
* Fixed PvtV2Backbone tests
* Added TFRegNet to OBJECTS_TO_IGNORE in check_docstrings.py
* Fixed backbone stuff and fixed tests: all passing
* Ran make fixup
* Made modifications for code checks
* Remove ONNX config from configuration_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Use explicit image size dict in test_modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Make image_size optional in test_modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove _ntuple use in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove reference to fp16_enabled
* Model modules now take config as first argument even when not used
* Replaced abbreviations for "SR" and "AP" with explicit "spatialreduction" and "averagepooling"
* All LayerNorm now instantiates with config.layer_norm_eps
* Added docstring for depth-wise conv layer
* PvtV2Config now only takes Union[int, Tuple[int, int]] for image size
* Refactored PVTv2 in prep for gradient checkpointing
* Gradient checkpointing ready to test
* Removed override of _set_gradient_checkpointing
* Cleaned out old code
* Applied code fixup
* Applied code fixup
* Began debug of pvt_v2 tests
* Leave handling of num_labels to base pretrained config class
* Deactivated gradient checkpointing tests until it is fixed
* Removed PvtV2ImageProcessor which duped PvtImageProcessor
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* Set key and value layers to use separate linear modules. Fixed pruning function
* Set AvgPool to 7
* Fixed issue in init
* PvT-v2 now works in AutoModel
* Successful conversion of pretrained weights for PVT-v2
* Successful conversion of pretrained weights for PVT-v2 models
* Added pytests for pvt-v2, all passed
* Added pvt_v2 to docs/source/end/model_doc
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Reverted batch eval changes for PR
* Expanded type support for Pvt-v2 config
* Fixed config docstring. Added channels property
* Fixed model names in tests
* Fixed config backbone compat. Added additional type support for image size in config
* Fixed config backbone compat
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* Set key and value layers to use separate linear modules. Fixed pruning function
* Set AvgPool to 7
* Fixed issue in init
* PvT-v2 now works in AutoModel
* Successful conversion of pretrained weights for PVT-v2
* Successful conversion of pretrained weights for PVT-v2 models
* Added pytests for pvt-v2, all passed
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* Set key and value layers to use separate linear modules. Fixed pruning function
* Set AvgPool to 7
* Fixed issue in init
* PvT-v2 now works in AutoModel
* Successful conversion of pretrained weights for PVT-v2
* Successful conversion of pretrained weights for PVT-v2 models
* Added pytests for pvt-v2, all passed
* Ran fix-copies and fixup. All checks passed
* Added additional ReLU for linear attention mode
* pvt_v2_b2_linear converted and working
* Reverted batch eval changes for PR
* Expanded type support for Pvt-v2 config
* Fixed config docstring. Added channels property
* Fixed model names in tests
* Fixed config backbone compat
* Ran fix-copies
* Fixed PvtV2Backbone tests
* Added TFRegNet to OBJECTS_TO_IGNORE in check_docstrings.py
* Fixed backbone stuff and fixed tests: all passing
* Ran make fixup
* Made modifications for code checks
* Remove ONNX config from configuration_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Use explicit image size dict in test_modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Make image_size optional in test_modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove _ntuple use in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove reference to fp16_enabled
* Model modules now take config as first argument even when not used
* Replaced abbreviations for "SR" and "AP" with explicit "spatialreduction" and "averagepooling"
* All LayerNorm now instantiates with config.layer_norm_eps
* Added docstring for depth-wise conv layer
* PvtV2Config now only takes Union[int, Tuple[int, int]] for image size
* Refactored PVTv2 in prep for gradient checkpointing
* Gradient checkpointing ready to test
* Removed override of _set_gradient_checkpointing
* Cleaned out old code
* Applied code fixup
* Applied code fixup
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Ran fix-copies and fixup. All checks passed
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Reverted batch eval changes for PR
* Fixed config docstring. Added channels property
* Fixed config backbone compat
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Ran fix-copies and fixup. All checks passed
* Allowed for batching of eval metrics
* copied models/pvt to adapt to pvt_v2
* First commit of pvt_v2
* PvT-v2 now works in AutoModel
* Fixed config backbone compat
* Ran fix-copies
* Began debug of pvt_v2 tests
* Leave handling of num_labels to base pretrained config class
* Deactivated gradient checkpointing tests until it is fixed
* Removed PvtV2ImageProcessor which duped PvtImageProcessor
* Fixed issue from rebase
* Fixed issue from rebase
* Set tests for gradient checkpointing to skip those using reentrant since it isn't supported
* Fixed issue from rebase
* Fixed issue from rebase
* Changed model name in docs
* Removed duplicate PvtV2Backbone
* Work around type switching issue in tests
* Fix model name in config comments
* Update docs/source/en/model_doc/pvt_v2.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Changed name of variable from 'attn_reduce' to 'sr_type'
* Changed name of variable from 'attn_reduce' to 'sr_type'
* Changed from using 'sr_type' to 'linear_attention' for clarity
* Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
Removed old code
* Changed from using 'sr_type' to 'linear_attention' for clarity
* Fixed Class names to be more descriptive
* Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
Removed outdated code
* Moved paper abstract to single line in pvt_v2.md
* Added usage tips to pvt_v2.md
* Simplified module inits by passing layer_idx
* Fixed typing for hidden_act in PvtV2Config
* Removed unusued import
* Add pvt_v2 to docs/source/en/_toctree.yml
* Updated documentation in docs/source/en/model_doc/pvt_v2.md to be more comprehensive.
* Updated documentation in docs/source/en/model_doc/pvt_v2.md to be more comprehensive.
* Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
Move function parameters to single line
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
Update year of copyright to 2024
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
Make code more explicit
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Updated sr_ratio to be more explicit spatial_reduction_ratio
* Removed excess type hints in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Move params to single line in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Removed needless comment in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update copyright date in pvt_v2.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Moved params to single line in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Updated copyright date in configuration_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Cleaned comments in modeling_pvt_v2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Renamed spatial_reduction Conv2D operation
* Revert "Update src/transformers/models/pvt_v2/modeling_pvt_v2.py
"
This reverts commit c4a04416dd.
* Updated conversion script to reflect module name change
* Deprecated reshape_last_stage option in config
* Removed unused imports
* Code formatting
* Fixed outdated decorators on test_inference_fp16
* Added "Copied from" comments in test_modeling_pvt_v2.py
* Fixed import listing
* Updated model name
* Force empty commit for PR refresh
* Fixed linting issue
* Removed # Copied from comments
* Added PVTv2 to README_fr.md
* Ran make fix-copies
* Replace all FoamoftheSea hub references with OpenGVLab
* Fixed out_indices and out_features logic in configuration_pvt_v2.py
* Made ImageNet weight conversion verification optional in convert_pvt_v2_to_pytorch.py
* Ran code fixup
* Fixed order of parent classes in PvtV2Config to fix the to_dict method override
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* initial implementation of flash attention for gptj
* modify flash attention and overwrite test_flash_attn_2_generate_padding_right
* update flash attention support list
* remove the copy line in the `CodeGenBlock`
* address copy mechanism
* Update src/transformers/models/gptj/modeling_gptj.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add GPTJ attention classes
* add expected outputs in the gptj test
* Ensure repo consistency with 'make fix-copies'
---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add tests for batching support
* Update src/transformers/models/fastspeech2_conformer/modeling_fastspeech2_conformer.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/fastspeech2_conformer/modeling_fastspeech2_conformer.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update tests/test_modeling_common.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update tests/test_modeling_common.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update tests/test_modeling_common.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* fixes and comments
* use cosine distance for conv models
* skip mra model testing
* Update tests/models/vilt/test_modeling_vilt.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* finzalize and make style
* check model type by input names
* Update tests/models/vilt/test_modeling_vilt.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fixed batch size for all testers
* Revert "fixed batch size for all testers"
This reverts commit 525f3a0a05.
* add batch_size for all testers
* dict from model output
* do not skip layoutlm
* bring back some code from git revert
* Update tests/test_modeling_common.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/test_modeling_common.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* clean-up
* where did minus go in tolerance
* make whisper happy
* deal with consequences of losing minus
* deal with consequences of losing minus
* maskformer needs its own test for happiness
* fix more models
* tag flaky CV models from Amy's approval
* make codestyle
---------
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* left-padding test revisited
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* initial-commit
* start cleaning
* small nits
* small nits
* current updates
* add kernels
* small refactoring little step
* add comments
* styling
* nit
* nits
* Style
* Small changes
* Push dummy mambda simple slow
* nit
* Use original names
* Use original names and remove norm
* Updates for inference params
* Style nd updates
* nits
* Match logits
* Add a test
* Add expected generated text
* nits doc, imports and styling
* style
* oups
* dont install kernels, invite users to install the required kernels
* let use use the original packages
* styling
* nits
* fix some copieds
* update doc
* fix-copies
* styling done
* nits
* fix import check
* run but wrong cuda ress
* mamba CUDA works :)
* fix the fast path
* config naming nits
* conversion script is not required at this stage
* finish fixing the fast path: generation make sense now!
* nit
* Let's start working on the CIs
* style
* better style
* more nits
* test nit
* quick fix for now
* nits
* nit
* nit
* nit
* nits
* update test rest
* fixup
* update test
* nit
* some fixes
* nits
* update test values
* fix styling
* nit
* support peft
* integrations tests require torchg
* also add slow markers
* styling
* chose forward wisely
* nits
* update tests
* fix gradient checkpointing
* fixup
* nit
* fix doc
* check copies
* fix the docstring
* fix some more tests
* style
* fix beam search
* add init schene
* update
* nit
* fix
* fixup the doc
* fix the doc
* fixup
* tentative update but slow is no longer good
* nit
* should we always use float32?
* nits
* revert wrong changes
* res in float32
* cleanup
* skip fmt for now
* update generation values
* update test values running original model
* fixup
* update tests + rename inference_params to cache_params + make sure training does not use cache_params
* small nits
* more nits
* fix final CIs
* style
* nit doc
* I hope final doc nits
* nit
* 🫠
* final touch!
* fix torch import
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Apply suggestions from code review
* fix fix and fix
* fix base model prefix!
* nit
* Update src/transformers/models/mamba/__init__.py
* Update docs/source/en/model_doc/mamba.md
Co-authored-by: Lysandre Debut <hi@lysand.re>
* nit
---------
Co-authored-by: Lysandre Debut <hi@lysand.re>
* First draft
* More improvements
* More improvements
* More fixes
* Fix copies
* More improvements
* More fixes
* More improvements
* Convert checkpoint
* More improvements, set up tests
* Fix more tests
* Add UdopModel
* More improvements
* Fix equivalence test
* More fixes
* Redesign model
* Extend conversion script
* Use real inputs for conversion script
* Add image processor
* Improve conversion script
* Add UdopTokenizer
* Add fast tokenizer
* Add converter
* Update README's
* Add processor
* Add fully fledged tokenizer
* Add fast tokenizer
* Use processor in conversion script
* Add tokenizer tests
* Fix one more test
* Fix more tests
* Fix tokenizer tests
* Enable fast tokenizer tests
* Fix more tests
* Fix additional_special_tokens of fast tokenizer
* Fix tokenizer tests
* Fix more tests
* Fix equivalence test
* Rename image to pixel_values
* Rename seg_data to bbox
* More renamings
* Remove vis_special_token
* More improvements
* Add docs
* Fix copied from
* Update slow tokenizer
* Update fast tokenizer design
* Make text input optional
* Add first draft of processor tests
* Fix more processor tests
* Fix decoder_start_token_id
* Fix test_initialization
* Add integration test
* More improvements
* Improve processor, add test
* Add more copied from
* Add more copied from
* Add more copied from
* Add more copied from
* Remove print statement
* Update README and auto mapping
* Delete files
* Delete another file
* Remove code
* Fix test
* Fix docs
* Remove asserts
* Add doc tests
* Include UDOP in exotic model tests
* Add expected tesseract decodings
* Add sentencepiece
* Use same design as T5
* Add UdopEncoderModel
* Add UdopEncoderModel to tests
* More fixes
* Fix fast tokenizer
* Fix one more test
* Remove parallelisable attribute
* Fix copies
* Remove legacy file
* Copy from T5Tokenizer
* Fix rebase
* More fixes, copy from T5
* More fixes
* Fix init
* Use ArthurZ/udop for tests
* Make all model tests pass
* Remove UdopForConditionalGeneration from auto mapping
* Fix more tests
* fixups
* more fixups
* fix the tokenizers
* remove un-necessary changes
* nits
* nits
* replace truncate_sequences_boxes with truncate_sequences for fix-copies
* nit current path
* add a test for input ids
* ids that we should get taken from c9f7a32f57
* nits converting
* nits
* apply ruff
* nits
* nits
* style
* fix slow order of addition
* fix udop fast range as well
* fixup
* nits
* Add docstrings
* Fix gradient checkpointing
* Update code examples
* Skip tests
* Update integration test
* Address comment
* Make fixup
* Remove extra ids from tokenizer
* Skip test
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update year
* Address comment
* Address more comments
* Address comments
* Add copied from
* Update CI
* Rename script
* Update model id
* Add AddedToken, skip tests
* Update CI
* Fix doc tests
* Do not use Tesseract for the doc tests
* Remove kwargs
* Add original inputs
* Update casting
* Fix doc test
* Update question
* Update question
* Use LayoutLMv3ImageProcessor
* Update organization
* Improve docs
* Update forward signature
* Make images optional
* Remove deprecated device argument
* Add comment, add add_prefix_space
* More improvements
* Remove kwargs
---------
Co-authored-by: ArthurZucker <arthur.zucker@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* 🐛 Fix oneformer instance post processing when using panoptic task type
* ✅ Add unit test for oneformer instance post processing panoptic bug
---------
Co-authored-by: Nick DeGroot <1966472+nickthegroot@users.noreply.github.com>
* remove control flow
* update gptneox
* update ....
* nits
* Actually let's just break. Otherwise we are silently failing which imo is not optimal
* version BC
* fix tests
* fix eager causal
* nit
* add a test
* style
* nits
* nits
* more nits for the test
* update and fix
* make sure cuda graphs are not skipped
* read token is needed for meta llama
* update!
* fiixup
* compile test should be slow
* fix thet fix copies
* stle 🫠
* stash commit
* stash commit
* It works!
* Remove unnecessary change
* We don't actually need the cache_dir!
* Update docstring
* Add test
* Add test with custom cache dir too
* Update model repo path
* Revert "Add tie_weights() to LM heads and set bias in set_output_embeddings() (#28948)"
This reverts commit 725f4ad1cc.
* Revert "Patch to skip failing `test_save_load_low_cpu_mem_usage` tests (#29043)"
This reverts commit 4156f517ce.
* add add_dummy_prefix_space option to slow
* checking kwargs might be better. Should be there for all spm tokenizer IMO
* nits
* fix copies
* more copied
* nits
* add prefix space
* nit
* nits
* Update src/transformers/convert_slow_tokenizer.py
* fix inti
* revert wrong styling
* fix
* nits
* style
* updates
* make sure we use slow tokenizer for conversion instead of looking for the decoder
* support llama ast well
* update llama tokenizer fast
* nits
* nits nits nits
* update the doc
* update
* update to fix tests
* skip unrelated tailing test
* Update src/transformers/convert_slow_tokenizer.py
* add proper testing
* test decode as well
* more testing
* format
* fix llama test
* Apply suggestions from code review
* pass through trust_remote_code for dynamically loading unregistered tokenizers specified by config
add test
* change directories back to previous directory after test
* fix ruff check
* Add a note to that block for future in case we want to remove it later
---------
Co-authored-by: Matt <rocketknight1@gmail.com>
* Add tie_weights() to LM heads and set bias in set_output_embeddings()
The bias were not tied correctly in some LM heads, and this change should fix that.
* Moving test_save_and_load_low_cpu_mem_usage to ModelTesterMixin
* Adding _tie_weights() to MPNet and Vilt
* Skip test for low cpu mem usage for Deta/DeformableDetr since they cannot init on meta device
* Rename to test name to save_load to match the convention
* Update the processing so bbox coords are adjusted for padding
* Just pad masks
* Tidy up, add tests
* Better tests
* Fix yolos and mark as slow for pycocotols
* Fix yolos - return_tensors
* Clarify padding and normalization behaviour
* add sudachi_projection option
* Upgrade sudachipy>=0.6.8
* add a test case for sudachi_projection
* Compatible with older versions of SudachiPy
* make fixup
* make style
* error message for unidic download
* revert jumanpp test cases
* format options for sudachi_projection
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* format options for sudachi_split_mode and sudachi_dict_type
* comment
* add tests for full_tokenizer kwargs
* pass projection arg directly
* require_sudachi_projection
* make style
* revert upgrade sudachipy
* check is_sudachi_projection_available()
* revert dependency_version_table and bugfix
* style format
* simply raise ImportError
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* simply raise ImportError
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* refactor with addedtokens decoder
* style
* get rid of lang code to id
* style
* keep some things for BC
* update tests
* add the mask token at the end of the vocab
* nits
* nits
* fix final tests
* style
* nits
* Update src/transformers/models/nllb/tokenization_nllb_fast.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* nits
* style?
* Update src/transformers/convert_slow_tokenizer.py
* make it a tad bit more custom
* ruff please stop
Co-Authored by avidale
<dale.david@mail.ru>
* Update
Co-authored-by: avidale
<dale.david@mail.ru>
* Update
Co-authored-by: avidale <dale.david@mail.ru>
* oupts
* ouft
* nites
* test
* fix the remaining failing tests
* style
* fix failing test
* ficx other test
* temp dir + test the raw init
* update test
* style
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* This is a test commit
* testing commit
* final commit with some changes
* Removed copy statement
* Fixed formatting issues
* Fixed error added past_key_values in the forward method
* Fixed a trailing whitespace. Damn the formatting rules are strict
* Added the copy statement
* Fix typos and grammar mistakes in docs and examples
* Fix typos in docstrings and comments
* Fix spelling of `tokenizer` in model tests
* Remove erroneous spaces in decorators
* Remove extra spaces in Markdown link texts
* Adding [T5/MT5/UMT5]ForTokenClassification
* Add auto mappings for T5ForTokenClassification and variants
* Adding ForTokenClassification to the list of models
* Adding attention_mask param to the T5ForTokenClassification test
* Remove outdated comment in test
* Adding EncoderOnly and Token Classification tests for MT5 and UMT5
* Fix typo in umt5 string
* Add tests for all the existing MT5 models
* Fix wrong comment in dependency_versions_table
* Reverting change to common test for _keys_to_ignore_on_load_missing
The test is correctly picking up redundant keys in _keys_to_ignore_on_load_missing.
* Removing _keys_to_ignore_on_missing from MT5 since the key is not used in the model
* Add fix-copies to MT5ModelTest
* up
* Fix more
* Correct more
* Fix more tests
* fix fast tests
* Fix more
* fix more
* push all files
* finish all
* make style
* Fix timestamp wrap
* make style
* make style
* up
* up
* up
* Fix lang detection behavior
* Fix lang detection behavior
* Add lang detection test
* Fix lang detection behavior
* make style
* Update src/transformers/models/whisper/generation_whisper.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* better error message
* make style tests
* add warning
---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Port core files + ESM (because ESM code is odd)
* Search-replace in modelling code
* Fix up transfo_xl as well
* Fix other core files + tests (still need to add correct import to tests)
* Fix cookiecutter
* make fixup, fix imports in some more core files
* Auto-add imports to tests
* Cleanup, add imports to sagemaker tests
* Use correct exception for importing tf_keras
* Fixes in modeling_tf_utils
* make fixup
* Correct version parsing code
* Ensure the pipeline tests correctly revert to float32 after each test
* Ensure the pipeline tests correctly revert to float32 after each test
* More tf.keras -> keras
* Add dtype cast
* Better imports of tf_keras
* Add a cast for tf.assign, just in case
* Fix callback imports
* Enable instantiating model with pretrained backbone weights
* Remove doc updates until changes made in modeling code
* Use load_backbone instead
* Add use_timm_backbone to the model configs
* Add missing imports and arguments
* Update docstrings
* Make sure test is properly configured
* Include recent DPT updates
* fix the function load_balancing_loss_func in Mixtral_Moe to include attention_mask
* format code using black and ruff
* skip computing mask if attention_mask=None
* add tests for load balancing loss Mixtral-Moe
* fix assert loss is different in mixtral_test
* fix pad_leng
* use assertNotAlmostEqual and print to debug
* remove print for debug
* minor updates
* reduce rtol and atol
* Enable instantiating model with pretrained backbone weights
* Update tests so backbone checkpoint isn't passed in
* Remove doc updates until changes made in modeling code
* Clarify pretrained import
* Update configs - docs and validation check
* Update src/transformers/utils/backbone_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Clarify exception message
* Update config init in tests
* Add test for when use_timm_backbone=True
* Small test updates
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* [DETA] fix freeze/unfreeze function
* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add freeze/unfreeze test case in DETA
* fix type
* fix typo 2
* fix : enable aux and enc loss in training pipeline
* Add unsynced variables from original DETA for training
* modification for passing CI test
* make style
* make fix
* manual make fix
* change deta_modeling_test of configuration 'two_stage' default to TRUE and minor change of dist checking
* remove print
* divide configuration in DetaModel and DetaForObjectDetection
* image smaller size than 224 will give topk error
* pred_boxes and logits should be equivalent to two_stage_num_proposals
* add missing part in DetaConfig
* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add docstring in configure and prettify TO DO part
* change distribute related code to accelerate
* Update src/transformers/models/deta/configuration_deta.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/deta/test_modeling_deta.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* protect importing accelerate
* change variable name to specific value
* wrong import
* fix aux_loss in conditional_detr
* add test aux_loss
* add aux_loss test in deta and table_transformer
* fix yolos since it doesn't have auxiliary function
* fix maskformer auxiliary_loss related code
* make style
* change param 'auxiliary_loss' to 'use_auxiliary_loss'
* change param 'auxiliary_loss' to 'use_auxiliary_loss' in tests
* make style & fix-copies, also revert yolos related parameter
* revert variable name 'use_auxiliary_loss' to 'auxiliary_loss' due to DetrConfig
* revert variable name in yolos
* revert maskformer
* add aux_loss test in maskformer
* make style
* Update src/transformers/models/yolos/configuration_yolos.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Allow non-special tokens to be added
* Add test, fix token adding code
* Revert changes to id_to_token and token_to_id
* Update the ESM tokenizer to be a bit more standardized
* Update src/transformers/models/esm/tokenization_esm.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* finalize
* make fix copies whisper
* [Tests] Make sure that we don't run tests mulitple times
* Update src/transformers/models/whisper/modeling_whisper.py
* [Tests] Make sure that we don't run tests mulitple times
* fix more
* improve
* improve
* improve further
* improve more
* improve
* fix more
* git commit and git push
* fix more
* fix more
* fix more
* New try
* Fix more whisper stuff
* Improve
* correct more
* correct more
* correct more
* Fix some tests
* Add more tests
* correct more
* correct more
* correct more
* push
* correct more
* Fix more
* Better
* without dec mask
* correct more
* clean
* save intermediate
* Fix more
* Fix VAD for large-v2
* Save new
* Correct more
* make cleaner
* correct tests
* correct src
* Finish
* Fix more
* Fix more
* finish
* Fix edge cases
* fix return_dict_in_generate
* fix all tests
* make style
* add docstrings
* add docstrings
* Fix logit processor
* make style
* fix pipeline test
* fix more style
* Apply suggestions from code review
* apply feedback Sanchit
* correct more
* Apply suggestions from code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* correct more
* correct more
* correct more
* Fix staticmethod
* correct more
* fix
* fix slow tests
* make style
* fix tokenizer test
* fix tokenizer test
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* finish
* finish
* revert kwargs change
---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* first commit
* correct default value non causal
* update config and modeling code
* update converting checkpoint
* clean modeling and fix tests
* make style
* add new config parameters to docstring
* fix copied from statements
* Apply suggestions from code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* make position_embeddings_type docstrings clearer
* clean converting script
* remove function not used
* clean modeling file
* apply suggestion for test file + add convert script to not_doctested
* modify tests according to review - cleaner logic and more tests
* Apply nit suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add checker of valid position embeddings type
* instantiate new layer norm layer with the right eps
* fix freeze_feature_encoder since it can be None in some cases
* add test same output in convert script
* restore wav2vec2conformer and add new model
* create processor and FE + clean
* add new model code
* fix convert script and set default config parameters
* correct model id paths
* make style
* make fix-copies and cleaning files
* fix copied from statements
* complete .md and fixe copies
* clean convert script argument defaults
* fix config parameters docstrings
* fix config docstring
* add copied from and enrich FE tests
* fix copied from and repo-consistency
* add autotokenizer
* make test input length shorter and change docstring code
* fix docstrings and copied from
* add add_adapter to ASR training example
* make testing of adapters more robust
* adapt to multi adapter layers
* refactor input_values->input_features and remove w2v2-bert feature extractor
* remove pretraining model
* remove depreciated features and useless lines
* add copied from and ignore statements to modeling tests
* remove pretraining model #2
* change import in convert script
* change default in convert script
* update readme and remove useless line
* Update tests/models/wav2vec2_bert/test_processor_wav2vec2_bert.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* refactor BERT to Bert for consistency
* remove useless ignore copy statement
* add persistent to buffer in rotary
* add eps in LayerNorm init and remove copied from
* add adapter activation parameters and add copied from statements
* Fix copied statements and add unitest.skip reasons
* add copied statement in test_processor
* refactor processor
* make style
* replace numpy random by torch rand
* remove expected output CTC
* improve converting script with processor class
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* remove gumbel class
* remove tests related to previously deleted class
* Update src/transformers/models/wav2vec2_bert/configuration_wav2vec2_bert.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* correct typos
* remove uused parameters
* update processor to takes both text and audio
* update checkpoints
* update expected output and add ctc expected output
* add label_attention_mask
* replace pt with np in processor tests
* fix typo
* revert to behaviour with labels_attention_mask
---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix
* last attempt
* current work
* fix forward compatibility
* save all special tokens
* current state
* revert additional changes
* updates
* remove tokenizer.model
* add a test and the fix
* nit
* revert one more break
* fix typefield issue
* quality
* more tests
* fix fields for FC
* more nits?
* new additional changes
* how
* some updates
* the fix
* where do we stand
* nits
* nits
* revert unrelated changes
* nits nits nits
* styling
* don't break llama just yet
* revert llama changes
* safe arg check
* fixup
* Add a test for T5
* Necessary changes
* Tests passing, added tokens need to not be normalized. If the added tokens are normalized, it will the stripping which seems to be unwanted for a normal functioning
* Add even more tests, when normalization is set to True (which does not work 😓 )
* Add even more tests, when normalization is set to True (which does not work 😓 )
* Update to main
* nits
* fmt
* more and more test
* comments
* revert change as tests are failing
* make the test more readble
* nits
* refactor the test
* nit
* updates
* simplify
* style
* style
* style convert slow
* Update src/transformers/convert_slow_tokenizer.py
* skip bf16 test if not supported by device
* fix
* fix bis
* use is_torch_bf16_available_on_device
* use is_torch_fp16_available_on_device
* fix & use public llama
* use 1b model
* fix flacky test
---------
Co-authored-by: Your Name <you@example.com>
* Fix bug in SpeechT5 speech decoder prenet's forward method
- Removed redundant `repeat` operation on speaker_embeddings in the forward method. This line was erroneously duplicating the embeddings, leading to incorrect input size for concatenation and performance issues.
- Maintained original functionality of the method, ensuring the integrity of the speech decoder prenet's forward pass remains intact.
- This change resolves a critical bug affecting the model's performance in handling speaker embeddings.
* Refactor SpeechT5 text to speech integration tests
- Updated SpeechT5ForTextToSpeechIntegrationTests to accommodate the variability in sequence lengths due to dropout in the speech decoder pre-net. This change ensures that our tests are robust against random variations in generated speech, enhancing the reliability of our test suite.
- Removed hardcoded dimensions in test assertions. Replaced with dynamic checks based on model configuration and seed settings, ensuring tests remain valid across different runs and configurations.
- Added new test cases to thoroughly validate the shapes of generated spectrograms and waveforms. These tests leverage seed settings to ensure consistent and predictable behavior in testing, addressing potential issues in speech generation and vocoder processing.
- Fixed existing test cases where incorrect assumptions about output shapes led to potential errors.
* Fix bug in SpeechT5 speech decoder prenet's forward method
- Removed redundant `repeat` operation on speaker_embeddings in the forward method. This line was erroneously duplicating the embeddings, leading to incorrect input size for concatenation and performance issues.
- Maintained original functionality of the method, ensuring the integrity of the speech decoder prenet's forward pass remains intact.
- This change resolves a critical bug affecting the model's performance in handling speaker embeddings.
* Refactor SpeechT5 text to speech integration tests
- Updated SpeechT5ForTextToSpeechIntegrationTests to accommodate the variability in sequence lengths due to dropout in the speech decoder pre-net. This change ensures that our tests are robust against random variations in generated speech, enhancing the reliability of our test suite.
- Removed hardcoded dimensions in test assertions. Replaced with dynamic checks based on model configuration and seed settings, ensuring tests remain valid across different runs and configurations.
- Added new test cases to thoroughly validate the shapes of generated spectrograms and waveforms. These tests leverage seed settings to ensure consistent and predictable behavior in testing, addressing potential issues in speech generation and vocoder processing.
- Fixed existing test cases where incorrect assumptions about output shapes led to potential errors.
* Enhance handling of speaker embeddings in SpeechT5
- Refined the generate and generate_speech functions in the SpeechT5 class to robustly handle two scenarios for speaker embeddings: matching the batch size (one embedding per sample) and one-to-many (a single embedding for all samples in the batch).
- The update includes logic to repeat the speaker embedding when a single embedding is provided for multiple samples, and a ValueError is raised for any mismatched dimensions.
- Also added corresponding test cases to validate both scenarios, ensuring complete coverage and functionality for diverse speaker embedding situations.
* Improve Test Robustness with Randomized Speaker Embeddings
* Correct the implementation of auxiliary loss of mixtrtal
* correct the implementation of auxiliary loss of mixtrtal
* Implement a simpler calculation method
---------
Co-authored-by: zhangliangxu3 <zhangliangxu3@jd.com>
* chore(phi): Updates configuration_phi with missing keys.
* chore(phi): Adds first draft of combined modeling_phi.
* fix(phi): Fixes according to latest review.
* fix(phi): Removes pad_vocab_size_multiple to prevent inconsistencies.
* fix(phi): Fixes unit and integration tests.
* fix(phi): Ensures that everything works with microsoft/phi-1 for first integration.
* fix(phi): Fixes output of docstring generation.
* fix(phi): Fixes according to latest review.
* fix(phi): Fixes according to latest review.
* fix(tests): Re-enables Phi-1.5 test.
* fix(phi): Fixes attention overflow on PhiAttention (for Phi-2).
* fix(phi): Improves how queries and keys are upcast.
* fix(phi): Small updates on latest changes.
* optionally preprocess segmentation maps for mobilevit
* changed pretrained model name to that of segmentation model
* removed voc-deeplabv3 from model archive list
* added preprocess_image and preprocess_mask methods for processing images and segmentation masks respectively
* added tests for segmentation masks based on segformer feature extractor
* use crop_size instead of size
* reverting to initial model
* Add first draft
* Use appropriate gelu function
* More improvements
* More improvements
* More improvements
* Convert checkpoint
* More improvements
* Improve docs, remove print statements
* More improvements
* Add link
* remove unused masking function
* begin tokenizer
* do_lower_case
* debug
* set split_special_tokens=True
* Remove script
* Fix style
* Fix rebase
* Use same design as CLIP
* Add fast tokenizer
* Add SiglipTokenizer to init, remove extra_ids
* Improve conversion script
* Use smaller inputs in conversion script
* Update conversion script
* More improvements
* Add processor to conversion script
* Add tests
* Remove print statements
* Add tokenizer tests
* Fix more tests
* More improvements related to weight initialization
* More improvements
* Make more tests pass
* More improvements
* More improvements
* Add copied from
* Add canonicalize_text
* Enable fast tokenizer tests
* More improvements
* Fix most slow tokenizer tests
* Address comments
* Fix style
* Remove script
* Address some comments
* Add copied from to tests
* Add more copied from
* Add more copied from
* Add more copied from
* Remove is_flax_available
* More updates
* Address comment
* Remove SiglipTokenizerFast for now
* Add caching
* Remove umt5 test
* Add canonicalize_text inside _tokenize, thanks Arthur
* Fix image processor tests
* Skip tests which are not applicable
* Skip test_initialization
* More improvements
* Compare pixel values
* Fix doc tests, add integration test
* Add do_normalize
* Remove causal mask and leverage ignore copy
* Fix attention_mask
* Fix remaining tests
* Fix dummies
* Rename temperature and bias
* Address comments
* Add copied from to tokenizer tests
* Add SiglipVisionModel to auto mapping
* Add copied from to image processor tests
* Improve doc
* Remove SiglipVisionModel from index
* Address comments
* Improve docs
* Simplify config
* Add first draft
* Make it like mistral
* More improvements
* Fix attention_mask
* Fix output_attentions
* Add note in docs
* Convert multilingual model
* Convert large checkpoint
* Convert more checkpoints
* Add pipeline support, correct image_mean and image_std
* Use padding=max_length by default
* Make processor like llava
* Add code snippet
* Convert more checkpoints
* Set keep_punctuation_string=None as in OpenCLIP
* Set normalized=False for special tokens
* Fix doc test
* Update integration test
* Add figure
* Update organization
* Happy new year
* Use AutoModel everywhere
---------
Co-authored-by: patil-suraj <surajp815@gmail.com>
* [DETA] fix freeze/unfreeze function
* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add freeze/unfreeze test case in DETA
* fix type
* fix typo 2
* fix : enable aux and enc loss in training pipeline
* Add unsynced variables from original DETA for training
* modification for passing CI test
* make style
* make fix
* manual make fix
* change deta_modeling_test of configuration 'two_stage' default to TRUE and minor change of dist checking
* remove print
* divide configuration in DetaModel and DetaForObjectDetection
* image smaller size than 224 will give topk error
* pred_boxes and logits should be equivalent to two_stage_num_proposals
* add missing part in DetaConfig
* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add docstring in configure and prettify TO DO part
* change distribute related code to accelerate
* Update src/transformers/models/deta/configuration_deta.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/deta/test_modeling_deta.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* protect importing accelerate
* change variable name to specific value
* wrong import
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* remove token_type_ids from model_input_names (like #24788)
* removed test that assumed token_type_ids should be present and updated a model reference so that it points to an available model)
* start - docs, SpeechT5 copy and rename
* add relevant code from FastSpeech2 draft, have tests pass
* make it an actual conformer, demo ex.
* matching inference with original repo, includes debug code
* refactor nn.Sequentials, start more desc. var names
* more renaming
* more renaming
* vocoder scratchwork
* matching vocoder outputs
* hifigan vocoder conversion script
* convert model script, rename some config vars
* replace postnet with speecht5's implementation
* passing common tests, file cleanup
* expand testing, add output hidden states and attention
* tokenizer + passing tokenizer tests
* variety of updates and tests
* g2p_en pckg setup
* import structure edits
* docstrings and cleanup
* repo consistency
* deps
* small cleanup
* forward signature param order
* address comments except for masks and labels
* address comments on attention_mask and labels
* address second round of comments
* remove old unneeded line
* address comments part 1
* address comments pt 2
* rename auto mapping
* fixes for failing tests
* address comments part 3 (bart-like, train loss)
* make style
* pass config where possible
* add forward method + tests to WithHifiGan model
* make style
* address arg passing and generate_speech comments
* address Arthur comments
* address Arthur comments pt2
* lint changes
* Sanchit comment
* add g2p-en to doctest deps
* move up self.encoder
* onnx compatible tensor method
* fix is symbolic
* fix paper url
* move models to espnet org
* make style
* make fix-copies
* update docstring
* Arthur comments
* update docstring w/ new updates
* add model architecture images
* header size
* md wording update
* make style
* First draft
* More improvements
* More improvements
* Make all tests pass
* Remove script
* Update image processor
* Address comments
* Use new gradient checkpointing method
* Convert checkpoints, add integration test
* Do not keep aspect ratio for now
* Set keep_aspect_ratio=False for beit, add integration test
* Remove print statement
* fixes: code fixes on is_batched condition to also check for batched audio data in torch.Tensor format instead of only just checking for batched audio data in np.ndarray format
* Update src/transformers/models/seamless_m4t/feature_extraction_seamless_m4t.py
Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>
* refactor: code refactoring to remove torch framework dependency
* docs: updated docstring to add torch tensor compatibility
* test: add test cases to incorporate torch tensor inputs
* test: ran make fix-copies for code conformity
* test: refactor test to separate the test_call into test_call_numpy and test_call_torch
---------
Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>
* Fix vision text dual encoder
* Small cleanup for wav2vec2 (not fixed yet)
* Small fix for vision_encoder_decoder
* Fix SAM builds
* Update TFBertTokenizer test with modern exporting + tokenizer
* Fix DeBERTa
* Fix DeBERTav2
* Try RAG fix but it's impossible to test locally
* Actually fix RAG now that I got FAISS working somehow
* Fix Wav2Vec2, add sermon
* Fix Hubert
* some nits
* update test
* add support d\sd[a
* remove some dummy inputs
* all good
* style
* nits
* fixes
* fix more copies
* nits
* styling
* fix
* Update src/transformers/models/mistral/modeling_mistral.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* add a slow test just to be sure
* fixup
---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Add a convenience method for building in your own name scope
* Second attempt at auto layer building
* Revert "Second attempt at auto layer building"
This reverts commit e03a3aaecf9ec41a805582b83cbdfe3290a631be.
* Attempt #3
* Revert "Attempt #3"
This reverts commit b9df7a0857560d29b5abbed6127d9e9eca77cf47.
* Add missing attributes that we're going to need later
* Add some attributes we're going to need later
* A fourth attempt! Feel the power flow through you!
* Revert "A fourth attempt! Feel the power flow through you!"
This reverts commit 6bf4aaf3875d6f28485f50187617a4c616c8aff7.
* Add more values we'll need later
* TF refactor that we'll need later
* Revert "TF refactor that we'll need later"
This reverts commit ca07202fb5b7b7436b893baa8d688b4f348ea7b9.
* Revert "Revert "TF refactor that we'll need later""
This reverts commit 1beb0f39f293ed9c27594575e1c849aadeb15c13.
* make fixup
* Attempt five!
* Revert "Attempt five!"
This reverts commit 3302207958dfd0374b0447a51c06eea51a506044.
* Attempt six - this time don't add empty methods
* Revert "Attempt six - this time don't add empty methods"
This reverts commit 67d60129be75416b6beb8f47c7d38d77b18d79bb.
* Attempt seven - better base model class detection!
* Revert "Attempt seven - better base model class detection!"
This reverts commit 5f14845e92ea0e87c598da933bfbfee10f553bc9.
* Another attribute we'll need later
* Try again with the missing attribute!
* Revert "Try again with the missing attribute!"
This reverts commit 760c6f30c5dffb3e04b0e73c34a77d1882a0fef7.
* This is the attempt that will pierce the heavens!
* Revert "This is the attempt that will pierce the heavens!"
This reverts commit c868bb657de057aca7a5260350a3f831fc4dfee6.
* Attempt seven - snag list is steadily decreasing
* Revert "Attempt seven - snag list is steadily decreasing"
This reverts commit 46fbd975deda64429bfb3e5fac4fc0370c00d316.
* Attempt eight - will an empty snag list do it?
* Revert "Attempt eight - will an empty snag list do it?"
This reverts commit 7c8a3c2b083253649569e9877e02054ae5cec67b.
* Fixes to Hubert issues that cause problems later
* Trying again with Conv1D/SeparableConv fixes
* Revert "Trying again with Conv1D/SeparableConv fixes"
This reverts commit 55092bca952bc0f750aa1ffe246a640bf1e2036e.
* Apply the build shape fixes to Wav2Vec2 as well
* One more attempt!
* Revert "One more attempt!"
This reverts commit 5ac3e4cb01b9458cc93312873725f9444ae7261c.
* Another attempt!
* Revert "Another attempt!"
This reverts commit ea16d890e019d7de8792a3b8e72f3b1c02adae50.
* Let's see how many failures we get without the internal build method
* Fix OpenAI
* Fix MobileBERT
* (Mostly) fix GroupVIT
* Fix BLIP
* One more BLIP fix
* One more BLIP fix!
* Fix Regnet
* Finally fully fix GroupViT
* Fix Data2Vec and add the new AdaptivePool
* Fix Segformer
* Fix Albert
* Fix Deberta/DebertaV2
* Fix XLM
* Actually fix XLM
* Fix Flaubert
* Fix lxmert
* Fix Resnet
* Fix ConvBERT
* Fix ESM
* Fix Convnext / ConvnextV2
* Fix SAM
* Fix Efficientformer
* Fix LayoutLMv3
* Fix speech_to_text
* Fix mpnet and mobilevit
* Fix Swin
* Fix CTRL
* Fix CVT
* Fix DPR
* Fix Wav2Vec2
* Fix T5
* Fix Hubert
* Fix GPT2
* Fix Whisper
* Fix DeiT
* Fix the encoder-decoder / dual-encoder classes
* make fix-copies
* build in name scope
* Fix summarization test
* Fix tied weight names for BART + Blenderbot
* Fix tied weight name building
* Fix to TFESM weight building
* Update TF SAM
* Expand all the shapes out into Big Boy Shapes
* fix a typo and add an illustrative test
* appease black
* reduce code duplication and add Annotion type back with a pending deprecation warning
* remove unused code
* change warning type
* black formatting fix
* change enum deprecation approach to support 3.8 and earlier
* add stacklevel
* fix black issue
* fix ruff issues
* fix ruff issues
* move tests to own mixin
* include yolos
* fix black formatting issue
* fix black formatting issue
* use logger instead of warnings and include target version for deprecation
* [DETA] fix freeze/unfreeze function
* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add freeze/unfreeze test case in DETA
* fix type
* fix typo 2
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add sdpa
* wip
* cleaning
* add ref
* yet more cleaning
* and more :)
* wip llama
* working llama
* add output_attentions=True support
* bigcode sdpa support
* fixes
* gpt-bigcode support, require torch>=2.1.1
* add falcon support
* fix conflicts falcon
* style
* fix attention_mask definition
* remove output_attentions from attnmaskconverter
* support whisper without removing any Copied from statement
* fix mbart default to eager renaming
* fix typo in falcon
* fix is_causal in SDPA
* check is_flash_attn_2_available in the models init as well in case the model is not initialized through from_pretrained
* add warnings when falling back on the manual implementation
* precise doc
* wip replace _flash_attn_enabled by config.attn_implementation
* fix typo
* add tests
* style
* add a copy.deepcopy on the config in from_pretrained, as we do not want to modify it inplace
* obey to config.attn_implementation if a config is passed in from_pretrained
* fix is_torch_sdpa_available when torch is not installed
* remove dead code
* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/bart/modeling_bart.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* remove duplicate pretraining_tp code
* add dropout in llama
* precise comment on attn_mask
* add fmt: off for _unmask_unattended docstring
* precise num_masks comment
* nuke pretraining_tp in LlamaSDPAAttention following Arthur's suggestion
* cleanup modeling_utils
* backward compatibility
* fix style as requested
* style
* improve documentation
* test pass
* style
* add _unmask_unattended tests
* skip meaningless tests for idefics
* hard_check SDPA requirements when specifically requested
* standardize the use if XXX_ATTENTION_CLASSES
* fix SDPA bug with mem-efficient backend on CUDA when using fp32
* fix test
* rely on SDPA is_causal parameter to handle the causal mask in some cases
* fix FALCON_ATTENTION_CLASSES
* remove _flash_attn_2_enabled occurences
* fix test
* add OPT to the list of supported flash models
* improve test
* properly test on different SDPA backends, on different dtypes & properly handle separately the pad tokens in the test
* remove remaining _flash_attn_2_enabled occurence
* Update src/transformers/modeling_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/modeling_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/modeling_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/modeling_attn_mask_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update docs/source/en/perf_infer_gpu_one.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* remove use_attn_implementation
* fix docstring & slight bug
* make attn_implementation internal (_attn_implementation)
* typos
* fix tests
* deprecate use_flash_attention_2=True
* fix test
* add back llama that was removed by mistake
* fix tests
* remove _flash_attn_2_enabled occurences bis
* add check & test that passed attn_implementation is valid
* fix falcon torchscript export
* fix device of mask in tests
* add tip about torch.jit.trace and move bt doc below sdpa
* fix parameterized.expand order
* move tests from test_modeling_attn_mask_utils to test_modeling_utils as a relevant test class is already there
* update sdpaattention class with the new cache
* Update src/transformers/configuration_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/bark/modeling_bark.py
* address review comments
* WIP torch.jit.trace fix. left: test both eager & sdpa
* add test for torch.jit.trace for both eager/sdpa
* fix falcon with torch==2.0 that needs to use sdpa
* fix doc
* hopefully last fix
* fix key_value_length that has no default now in mask converter
* is it flacky?
* fix speculative decoding bug
* tests do pass
* fix following #27907
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Fix issues in add and is_done for BeamHypotheses
* make newly added arguments optional for better compatibility
* Directly use cur_len as generated_len, add note for retrocompatibility
* update test expectation
* make cur_len represents the length of the entire sequence including the decoder prompt
* remove redundant if/else in testing
* Un-skip tests
* Add aliasing support to tf_to_pt_weight_rename
* Refactor tf-to-pt weight rename for simplicity
* Patch mobilebert
* Let us pray that the transfo-xl one works
* Add XGLM rename
* Expand the test to see if we can get more models to break
* Expand the test to see if we can get more models to break
* Fix MPNet (it was actually an unrelated bug)
* Fix MPNet (it was actually an unrelated bug)
* Add speech2text fix
* Update src/transformers/modeling_tf_pytorch_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/mobilebert/modeling_tf_mobilebert.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update to always return a tuple from tf_to_pt_weight_rename
* reformat
* Add a couple of missing tuples
* Remove the extra test for tie_word_embeddings since it didn't cause any unexpected failures anyway
* Revert changes to modeling_tf_mpnet.py
* Skip MPNet test and add explanation
* Add weight link for BART
* Add TODO to clean this up a bit
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add model like
* logits match
* minor fixes
* fixes
* up
* up
* add todo
* llava processor
* keep the processor simple
* add conversion script
* fixup
* fix copies
* up
* add to index
* fix config + logits
* fix
* refactor
* more refactor
* more refactor
* fix copies
* add authors
* v1 tests
* add `LlavaProcessor` in init
* remove unneeded import
* up
* up
* docs
* up
* fix CI
* fix CI
* add attention mask in test
* make fixup
* remove the vision model
* that' s the dirty way to do it
* nits
* nits
* updates
* add more tests
* add input tests
* fixup
* more styling
* nits
* updates amd cleanup
* fixup the generation expected results
* fix the testing script
* some cleanup and simplification which does not work yet but almost there!
* make correct dispatch operations
* vectorize works for batch of images and text
* last todos
* nits
* update test and modeling code
* remove useless function for now
* fix few issues
* fix generation
* some nits
* add bakllava
* nits
* remove duplicated code
* finis merge
* cleanup
* missed this line
* fill the todos
* add left padding offset
* add left and rignt padding logic
* bool to properly index
* make sure
* more cleanups
* batch is fixed 😉
* add correct device for tensor creation
* fix some dtype missmatch
* ruff
* update conversion script
* Update src/transformers/__init__.py
* fa 2 support + fix conversion script
* more
* correct reshaping
* fix test dict
* fix copies by ignoring
* fix nit
* skip clip vision model
* fixup
* fixup
* LlavaForVisionText2Text -> LlavaForCausalLM
* update
* fix
* raise correct errors
* fix
* docs
* nuke for now
* nits here and there
* fixup
* fix remaining tests
* update LlavaForConditionalGeneration instead of CausalLM
* fixups
* pipeline support
* slow and piepline tests
* supports batch
* nits
* cleanup
* fix first integration tests
* add pad token where needed
* correct etsts
* fixups
* update pipeline testr
* fix quality
* nits
* revert unneeded change
* nit
* use BatchFeature
* from ...feature_extraction_utils import BatchFeature
* nits
* nits
* properly update
* more f*** nits
* fix copies
* comment
* keep slow test slow
* Update src/transformers/models/llava/processing_llava.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add piepline example
* add pixel values in docstrign
* update pr doctest
* fix
* fix slow tests
* remove hack
* fixup
* small note
* forward contrib credits from PR25789
* forward contrib credits from original implementation and work
* add arthur
* Update src/transformers/models/llava/processing_llava.py
Co-authored-by: Lysandre Debut <hi@lysand.re>
* update docstring
* nit
* move to not doctested because of timeout issues
* fixup
* add description
* more
* fix-copies
* fix docs
* add beam search
* add more comments
* add typehints on processor
* add speedup plot
* update slow tests and docs
* push test
* push batched test
* fix batched generation with different number of images
* remove benchmark due to a bug
* fix test
* fix copies
* add gcolab demo
---------
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: shauray8 <shauray8@users.noreply.github.com>
Co-authored-by: haotian-liu <haotian-liu@users.noreply.github.com>
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Copies `modeling_flax_gpt_neo.py` to start
* MLP Block. WIP Attention and Block
* Adds Flax implementation of `LlamaMLP`
Validated with in-file test.
Some slight numeric differences, but assuming it isn't an issue
* Adds `FlaxLlamaRMSNorm` layer
`flax.linen` includes `RMSNorm` layer but not necessarily in all
versions. Hence, we add in-file.
* Adds FlaxLlamaAttention
Copied from GPT-J as it has efficient caching implementation as well as
rotary embeddings.
Notice numerically different, but not by a huge amount. Needs
investigating
* Adds `FlaxLlamaDecoderLayer`
numerically inaccurate, debugging..
* debugging rotary mismatch
gptj uses interleaved whilst llama uses contiguous
i think they match now but still final result is wrong.
maybe drop back to just debugging attention layer?
* fixes bug with decoder layer
still somewhat numerically inaccurate, but close enough for now
* adds markers for what to implement next
the structure here diverges a lot from the PT version.
not a big fan of it, but just get something working for now
* implements `FlaxLlamaBlockCollection`]
tolerance must be higher than expected, kinda disconcerting
* Adds `FlaxLlamaModule`
equivalent PyTorch model is `LlamaModel`
yay! a language model🤗
* adds `FlaxLlamaForCausalLMModule`
equivalent to `LlamaForCausalLM`
still missing returning dict or tuple, will add later
* start porting pretrained wrappers
realised it probably needs return dict as a prereq
* cleanup, quality, style
* readds `return_dict` and model output named tuples
* (tentatively) pretrained wrappers work 🔥
* fixes numerical mismatch in `FlaxLlamaRMSNorm`
seems `jax.lax.rsqrt` does not match `torch.sqrt`.
manually computing `1 / jax.numpy.sqrt` results in matching values.
* [WIP] debugging numerics
* numerical match
I think issue was accidental change of backend. forcing CPU fixes test.
We expect some mismatch on GPU.
* adds in model and integration tests for Flax Llama
summary of failing:
- mul invalid combination of dimensions
- one numerical mismatch
- bf16 conversion (maybe my local backend issue)
- params are not FrozenDict
* adds missing TYPE_CHECKING import and `make fixup`
* adds back missing docstrings
needs review on quality of docstrings, not sure what is required.
Furthermore, need to check if `CHECKPOINT_FOR_DOC` is valid. See TODO
* commenting out equivalence test as can just use common
* debugging
* Fixes bug where mask and pos_ids were swapped in pretrained models
This results in all tests passing now 🔥
* cleanup of modeling file
* cleanup of test file
* Resolving simpler review comments
* addresses more minor review comments
* fixing introduced pytest errors from review
* wip additional slow tests
* wip tests
need to grab a GPU machine to get real logits for comparison
otherwise, slow tests should be okay
* `make quality`, `make style`
* adds slow integration tests
- checking logits
- checking hidden states
- checking generation outputs
* `make fix-copies`
* fix mangled function following `make fix-copies`
* adds missing type checking imports
* fixes missing parameter checkpoint warning
* more finegrained 'Copied from' tags
avoids issue of overwriting `LLAMA_INPUTS_DOCSTRING`
* swaps import guards
??? how did these get swapped initially?
* removing `inv_freq` again as pytorch version has now removed
* attempting to get CI to pass
* adds doc entries for llama flax models
* fixes typo in __init__.py imports
* adds back special equivalence tests
these come from the gpt neo flax tests. there is special behaviour for these models that needs to override the common version
* overrides tests with dummy to see if CI passes
need to fill in these tests later
* adds my contribution to docs
* `make style; make quality`
* replaces random masking with fixed to work with flax version
* `make quality; make style`
* Update src/transformers/models/llama/modeling_flax_llama.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Update src/transformers/models/llama/modeling_flax_llama.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Update src/transformers/models/llama/modeling_flax_llama.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Update src/transformers/models/llama/modeling_flax_llama.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Update src/transformers/models/llama/modeling_flax_llama.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Update src/transformers/models/llama/modeling_flax_llama.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* updates `x`->`tensor` in `rotate_half`
* addresses smaller review comments
* Update docs/source/en/model_doc/llama.md
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* adds integration test class
* adds `dtype` to rotary embedding to cast outputs
* adds type to flax llama rotary layer
* `make style`
* `make fix-copies`
* Apply suggestions from code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* applies suggestions from review
* Update modeling_flax_llama.py
* `make fix-copies`
* Update tests/models/llama/test_modeling_llama.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Update src/transformers/models/llama/modeling_flax_llama.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* fixes shape mismatch in FlaxLlamaMLP
* applies some suggestions from reviews
* casts attn output logits to f32 regardless of dtype
* adds attn bias using `LlamaConfig.attention_bias`
* adds Copied From comments to Flax Llama test
* mistral and persimmon test change -copy from llama
* updates docs index
* removes Copied from in tests
it was preventing `make fix-copies` from succeeding
* quality and style
* ignores FlaxLlama input docstring
* adds revision to `_CHECKPOINT_FOR_DOC`
* repo consistency and quality
* removes unused import
* removes copied from from Phi test
now diverges from llama tests following FlaxLlama changes
* adds `_REAL_CHECKPOINT_FOR_DOC`
* removes refs from pr tests
* reformat to make ruff happy
---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Added test cases for rembert refering to albert and reformer test_tokenization
* removed CURL_CA_BUNDLE='
* Added flag test_sentencepiece_ignore_case and space_between_special_tokens to True
* Overrided test_added_tokens_serialization
* As slow->fast token failed due to the different initialization for [MASK] for slow and fast, Therefore it required to make the initialization for [MASK] token uniform between fast and slow token
* Added few more test cases in test_encode_decode_round_trip and modefied the slow token (mask_token) to have AddedToken instance with lstrip=True
* Added few test cases in test_encoder_decoder round trip and also modified slow tokenizer of rembert to have mask_token as AddedToken with lstrip = True
* Cleaned the code and added fmt: skip to avoid line breaks after make style + added comments to indicate from the copied test cases
* Corrected few comments
* Fixed quality issue
* Ran fix-copies
* Fixed few minor issues as (make fix-copies) broke few test cases while stripping the text
* Reverted the changes made by repo-consistancy
---------
Co-authored-by: Kokane <kokanen@apac.corpdir.net>
* add working convertion script
* first non-working version of modeling code
* update modeling code (working)
* make style
* make fix-copies
* add config docstrings
* add config to ignore docstrings formatage due to unconventional markdown
* fix copies
* fix generation num_return_sequences
* enrich docs
* add and fix tests beside integration tests
* update integration tests
* update repo id
* add tie weights and make style
* correct naming in .md
* fix imports and so on
* correct docstrings
* fix fp16 speech forward
* fix speechencoder attention
* make style
* fix copied from
* rename SeamlessM4Tv2-v2 to SeamlessM4Tv2
* Apply suggestions on configuration
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* remove useless public models
* fix private models + better naming for T2U models
* clean speech encoder relative position embeddings
* refactor chunk attention
* add docstrings to chunk attention method
* improve naming and docstrings
* rename some attention variables + add temperature sampling in T2U model
* rename DOCSTRINGS variable names
* make style + remove 2 useless config parameters
* enrich model card
* remove any attention_head reference + fix temperature in T2U
* new fmt and make style
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* rename spkr_id->speaker_id and change docstrings of get_char_input_ids
* simplify v2attention
* make style
* Update seamless_m4t_v2.md
* update code and tests with last update
* update repo ids
* fill article name, abstract andauthors
* update not_doctested and slow_doc tests
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add distribution head to forecasting
* formatting
* Add generate function for forecasting
* Add generate function to prediction task
* formatting
* use argsort
* add past_observed_mask ordering
* fix arguments
* docs
* add back test_model_outputs_equivalence test
* formatting
* cleanup
* formatting
* use ACT2CLS
* formatting
* fix add_start_docstrings decorator
* add distribution head and generate function to regression task
add distribution head and generate function to regression task. Also made add PatchTSTForForecastingOutput, PatchTSTForRegressionOutput.
* add distribution head and generate function to regression task
add distribution head and generate function to regression task. Also made add PatchTSTForForecastingOutput, PatchTSTForRegressionOutput.
* fix typos
* add forecast_masking
* fixed tests
* use set_seed
* fix doc test
* formatting
* Update docs/source/en/model_doc/patchtst.md
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* better var names
* rename PatchTSTTranspose
* fix argument names and docs string
* remove compute_num_patches and unused class
* remove assert
* renamed to PatchTSTMasking
* use num_labels for classification
* use num_labels
* use default num_labels from super class
* move model_type after docstring
* renamed PatchTSTForMaskPretraining
* bs -> batch_size
* more review fixes
* use hidden_state
* rename encoder layer and block class
* remove commented seed_number
* edit docstring
* Add docstring
* formatting
* use past_observed_mask
* doc suggestion
* make fix-copies
* use Args:
* add docstring
* add docstring
* change some variable names and add PatchTST before some class names
* formatting
* fix argument types
* fix tests
* change x variable to patch_input
* format
* formatting
* fix-copies
* Update tests/models/patchtst/test_modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* move loss to forward
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* formatting
* fix a bug when pre_norm is set to True
* output_hidden_states is set to False as default
* set pre_norm=True as default
* format docstring
* format
* output_hidden_states is None by default
* add missing docs
* better var names
* docstring: remove default to False in output_hidden_states
* change labels name to target_values in regression task
* format
* fix tests
* change to forecast_mask_ratios and random_mask_ratio
* change mask names
* change future_values to target_values param in the prediction class
* remove nn.Sequential and make PatchTSTBatchNorm class
* black
* fix argument name for prediction
* add output_attentions option
* add output_attentions to PatchTSTEncoder
* formatting
* Add attention output option to all classes
* Remove PatchTSTEncoderBlock
* create PatchTSTEmbedding class
* use config in PatchTSTPatchify
* Use config in PatchTSTMasking class
* add channel_attn_weights
* Add PatchTSTScaler class
* add output_attentions arg to test function
* format
* Update doc with image patchtst.md
* fix-copies
* rename Forecast <-> Prediction
* change name of a few parameters to match with PatchTSMixer.
* Remove *ForForecasting class to match with other time series models.
* make style
* Remove PatchTSTForForecasting in the test
* remove PatchTSTForForecastingOutput class
* change test_forecast_head to test_prediction_head
* style
* fix docs
* fix tests
* change num_labels to num_targets
* Remove PatchTSTTranspose
* remove arguments in PatchTSTMeanScaler
* remove arguments in PatchTSTStdScaler
* add config as an argument to all the scaler classes
* reformat
* Add norm_eps for batchnorm and layernorm
* reformat.
* reformat
* edit docstring
* update docstring
* change variable name pooling to pooling_type
* fix output_hidden_states as tuple
* fix bug when calling PatchTSTBatchNorm
* change stride to patch_stride
* create PatchTSTPositionalEncoding class and restructure the PatchTSTEncoder
* formatting
* initialize scalers with configs
* edit output_hidden_states
* style
* fix forecast_mask_patches doc string
* doc improvements
* move summary to the start
* typo
* fix docstring
* turn off masking when using prediction, regression, classification
* return scaled output
* adjust output when using distribution head
* remove _num_patches function in the config
* get config.num_patches from patchifier init
* add output_attentions docstring, remove tuple in output_hidden_states
* change SamplePatchTSTPredictionOutput and SamplePatchTSTRegressionOutput to SamplePatchTSTOutput
* remove print("model_class: ", model_class)
* change encoder_attention_heads to num_attention_heads
* change norm to norm_layer
* change encoder_layers to num_hidden_layers
* change shared_embedding to share_embedding, shared_projection to share_projection
* add output_attentions
* more robust check of norm_type
* change dropout_path to path_dropout
* edit docstring
* remove positional_encoding function and add _init_pe in PatchTSTPositionalEncoding
* edit shape of cls_token and initialize it
* add a check on the num_input_channels.
* edit head_dim in the Prediction class to allow the use of cls_token
* remove some positional_encoding_type options, remove learn_pe arg, initalize pe
* change Exception to ValueError
* format
* norm_type is "batchnorm"
* make style
* change cls_token shape
* Change forecast_mask_patches to num_mask_patches. Remove forecast_mask_ratios.
* Bring PatchTSTClassificationHead on top of PatchTSTForClassification
* change encoder_ffn_dim to ffn_dim and edit the docstring.
* update variable names to match with the config
* add generation tests
* change num_mask_patches to num_forecast_mask_patches
* Add examples explaining the use of these models
* make style
* Revert "Revert "[time series] Add PatchTST (#25927)" (#27486)"
This reverts commit 78f6ed6c70.
* make style
* fix default std scaler's minimum_scale
* fix docstring
* close code blocks
* Update docs/source/en/model_doc/patchtst.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/patchtst/test_modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/configuration_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix tests
* add add_start_docstrings
* move examples to the forward's docstrings
* update prepare_batch
* update test
* fix test_prediction_head
* fix generation test
* use seed to create generator
* add output_hidden_states and config.num_patches
* add loc and scale args in PatchTSTForPredictionOutput
* edit outputs if if not return_dict
* use self.share_embedding to check instead checking type.
* remove seed
* make style
* seed is an optional int
* fix test
* generator device
* Fix assertTrue test
* swap order of items in outputs when return_dict=False.
* add mask_type and random_mask_ratio to unittest
* Update modeling_patchtst.py
* add add_start_docstrings for regression model
* make style
* update model path
* Edit the ValueError comment in forecast_masking
* update examples
* make style
* fix commented code
* update examples: remove config from from_pretrained call
* Edit example outputs
* Set default target_values to None
* remove config setting in regression example
* Update configuration_patchtst.py
* Update configuration_patchtst.py
* remove config from examples
* change default d_model and ffn_dim
* norm_eps default
* set has_attentions to Trye and define self.seq_length = self.num_patche
* update docstring
* change variable mask_input to do_mask_input
* fix blank space.
* change logger.debug to logger.warning.
* remove unused PATCHTST_INPUTS_DOCSTRING
* remove all_generative_model_classes
* set test_missing_keys=True
* remove undefined params in the docstring.
---------
Co-authored-by: nnguyen <nnguyen@us.ibm.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Nam Nguyen <namctin@gmail.com>
Co-authored-by: Wesley Gifford <79663411+wgifford@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Fix mistral generate for long prompt / response
* Add unit test
* fix linter
* fix linter
* fix test
* add assisted generation test for mistral and load the model in 4 bit + fa2
* initial commit
* Add inital testing files and modify __init__ files to add UnivNet imports.
* Fix some bugs
* Add checkpoint conversion script and add references to transformers pre-trained model.
* Add UnivNet entries for auto.
* Add initial docs for UnivNet.
* Handle input and output shapes in UnivNetGan.forward and add initial docstrings.
* Write tests and make them pass.
* Write docs.
* Add UnivNet doc to _toctree.yml and improve docs.
* fix typo
* make fixup
* make fix-copies
* Add upsample_rates parameter to config and improve config documentation.
* make fixup
* make fix-copies
* Remove unused upsample_rates config parameter.
* apply suggestions from review
* make style
* Verify and add reason for skipped tests inherited from ModelTesterMixin.
* Add initial UnivNetGan integration tests
* make style
* Remove noise_length input to UnivNetGan and improve integration tests.
* Fix bug and make style
* Make UnivNet integration tests pass
* Add initial code for UnivNetFeatureExtractor.
* make style
* Add initial tests for UnivNetFeatureExtractor.
* make style
* Properly initialize weights for UnivNetGan
* Get feature extractor fast tests passing
* make style
* Get feature extractor integration tests passing
* Get UnivNet integration tests passing
* make style
* Add UnivNetGan usage example
* make style and use feature extractor from hub in integration tests
* Update tips in docs
* apply suggestions from review
* make style
* Calculate padding directly instead of using get_padding methods.
* Update UnivNetFeatureExtractor.to_dict to be UnivNet-specific.
* Update feature extractor to support using model(**inputs) and add the ability to generate noise and pad the end of the spectrogram in __call__.
* Perform padding before generating noise to ensure the shapes are correct.
* Rename UnivNetGan.forward's noise_waveform argument to noise_sequence.
* make style
* Add tests to test generating noise and padding the end for UnivNetFeatureExtractor.__call__.
* Add tests for checking batched vs unbatched inputs for UnivNet feature extractor and model.
* Add expected mean and stddev checks to the integration tests and make them pass.
* make style
* Make it possible to use model(**inputs), where inputs is the output of the feature extractor.
* fix typo in UnivNetGanConfig example
* Calculate spectrogram_zero from other config values.
* apply suggestions from review
* make style
* Refactor UnivNet conversion script to use load_state_dict (following persimmon).
* Rename UnivNetFeatureExtractor to UnivNetGanFeatureExtractor.
* make style
* Switch to using torch.tensor and torch.testing.assert_close for testing expected values/slices.
* make style
* Use config in UnivNetGan modeling blocks.
* make style
* Rename the spectrogram argument of UnivNetGan.forward to input_features, following Whisper.
* make style
* Improving padding documentation.
* Add UnivNet usage example to the docs.
* apply suggestions from review
* Move dynamic_range_compression computation into the mel_spectrogram method of the feature extractor.
* Improve UnivNetGan.forward return docstring.
* Update table in docs/source/en/index.md.
* make fix-copies
* Rename UnivNet components to have pattern UnivNet*.
* make style
* make fix-copies
* Update docs
* make style
* Increase tolerance on flaky unbatched integration test.
* Remove torch.no_grad decorators from UnivNet integration tests to try to avoid flax/Tensorflow test errors.
* Add padding_mask argument to UnivNetModel.forward and add batch_decode feature extractor method to remove padding.
* Update documentation and clean up padding code.
* make style
* make style
* Remove torch dependency from UnivNetFeatureExtractor.
* make style
* Fix UnivNetModel usage example
* Clean up feature extractor code/docstrings.
* apply suggestions from review
* make style
* Add comments for tests skipped via ModelTesterMixin flags.
* Add comment for model parallel tests skipped via the test_model_parallel ModelTesterMixin flag.
* Add # Copied from statements to copied UnivNetFeatureExtractionTest tests.
* Simplify UnivNetFeatureExtractorTest.test_batch_decode.
* Add support for unbatched padding_masks in UnivNetModel.forward.
* Refactor unbatched padding_mask support.
* make style
* [Whisper] Add seq gen
* [Whisper] Add seq gen
* more debug
* Fix whisper logit processor
* Improve whisper code further
* Fix more
* more debug
* more debug
* Improve further
* Add tests
* Prep for batch size > 1
* Get batch_size>1 working
* Correct more
* Add extensive tests
* more debug
* more debug
* more debug
* add more tests
* more debug
* Apply suggestions from code review
* more debug
* add comments to explain the code better
* add comments to explain the code better
* add comments to explain the code better
* Add more examples
* add comments to explain the code better
* fix more
* add comments to explain the code better
* add comments to explain the code better
* correct
* correct
* finalize
* Apply suggestions from code review
* Apply suggestions from code review
* tvp model for video grounding
add tokenizer auto
fix param in TVPProcessor
add docs
clear comments and enable different torch dtype
add image processor test and model test and fix code style
* fix conflict
* fix model doc
* fix image processing tests
* fix tvp tests
* remove torch in processor
* fix grammar error
* add more details on tvp.md
* fix model arch for loss, grammar, and processor
* add docstring and do not regard TvpTransformer, TvpVisionModel as individual model
* use pad_image
* update copyright
* control first downsample stride
* reduce first only works for ResNetBottleNeckLayer
* fix param name
* fix style
* add testing
* fix style
* rm init_weight
* fix style
* add post init
* fix comments
* do not test TvpTransformer
* fix warning
* fix style
* fix example
* fix config map
* add link in config
* fix comments
* fix style
* rm useless param
* change attention
* change test
* add notes
* fix comments
* fix tvp
* import checkpointing
* fix gradient checkpointing
* Use a more accurate example in readme
* update
* fix copy
* fix style
* update readme
* delete print
* remove tvp test_forward_signature
* remove TvpTransformer
* fix test init model
* merge main and make style
* fix tests and others
* fix image processor
* fix style and model_input_names
* fix tests
* fix image_attention gate in idefics modeling
* update comment
* cleaner gating
* fix gate condition
* create attention gate once
* update comment
* update doc of cross-attention forward
* improve comment
* bring back no_images
* pass cross_attention_gate similarly to no_images gate
* add information on gate shape
* fix no_images placement
* make tests for gate
* take off no_images logic
* update test based on comments
* raise value error if cross_attention_gate is None
* send cross_attention_gate to device
* Revert "send cross_attention_gate to device"
This reverts commit 054f842284.
* send cross_attention_gate to device
* fix device in test + nit
* fill hidden_states with zeros instead of multiplying with the gate
* style
* Update src/transformers/models/idefics/modeling_idefics.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/idefics/modeling_idefics.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Load idx2sym from pretrained vocab file in Transformer XL
When loading vocab file from a pretrained tokenizer for Transformer XL,
although the pickled vocabulary file contains a idx2sym key, it isn't
loaded, because it is discarded as the empty list already exists as
an attribute.
Solution is to explicitly take it into account, just like for sym2idx.
* ran make style
* try to stylify using ruff
* might need to remove these changes?
* use ruf format andruff check
* use isinstance instead of type comparision
* use # fmt: skip
* use # fmt: skip
* nits
* soem styling changes
* update ci job
* nits isinstance
* more files update
* nits
* more nits
* small nits
* check and format
* revert wrong changes
* actually use formatter instead of checker
* nits
* well docbuilder is overwriting this commit
* revert notebook changes
* try to nuke docbuilder
* style
* fix feature exrtaction test
* remve `indent-width = 4`
* fixup
* more nits
* update the ruff version that we use
* style
* nuke docbuilder styling
* leve the print for detected changes
* nits
* Remove file I/O
Co-authored-by: charliermarsh
<charlie.r.marsh@gmail.com>
* style
* nits
* revert notebook changes
* Add # fmt skip when possible
* Add # fmt skip when possible
* Fix
* More ` # fmt: skip` usage
* More ` # fmt: skip` usage
* More ` # fmt: skip` usage
* NIts
* more fixes
* fix tapas
* Another way to skip
* Recommended way
* Fix two more fiels
* Remove asynch
Remove asynch
---------
Co-authored-by: charliermarsh <charlie.r.marsh@gmail.com>
* fix speecht5 wrong attention mask when padding
* enable batch generation and add parameter attention_mask
* fix doc
* fix format
* batch postnet inputs, return batched lengths, and consistent to old api
* fix format
* fix format
* fix the format
* fix doc-builder error
* add test, cross attention and docstring
* optimize code based on reviews
* docbuild
* refine
* not skip slow test
* add consistent dropout for batching
* loose atol
* add another test regarding to the consistency of vocoder
* fix format
* refactor
* add return_concrete_lengths as parameter for consistency w/wo batching
* fix review issues
* fix cross_attention issue
* Initial commit of PatchTST model classes
Co-authored-by: Phanwadee Sinthong <phsinthong@gmail.com>
Co-authored-by: Nam Nguyen <namctin@gmail.com>
Co-authored-by: Vijay Ekambaram <vijaykr.e@gmail.com>
Co-authored-by: Ngoc Diep Do <55230119+diepi@users.noreply.github.com>
Co-authored-by: Wesley Gifford <79663411+wgifford@users.noreply.github.com>
* Add PatchTSTForPretraining
* update to include classification
Co-authored-by: Phanwadee Sinthong <phsinthong@gmail.com>
Co-authored-by: Nam Nguyen <namctin@gmail.com>
Co-authored-by: Vijay Ekambaram <vijaykr.e@gmail.com>
Co-authored-by: Ngoc Diep Do <55230119+diepi@users.noreply.github.com>
Co-authored-by: Wesley Gifford <79663411+wgifford@users.noreply.github.com>
* clean up auto files
* Add PatchTSTForPrediction
* Fix relative import
* Replace original PatchTSTEncoder with ChannelAttentionPatchTSTEncoder
* temporary adding absolute path + add PatchTSTForForecasting class
* Update base PatchTSTModel + Unittest
* Update ForecastHead to use the config class
* edit cv_random_masking, add mask to model output
* Update configuration_patchtst.py
* add masked_loss to the pretraining
* add PatchEmbeddings
* Update configuration_patchtst.py
* edit loss which considers mask in the pretraining
* remove patch_last option
* Add commits from internal repo
* Update ForecastHead
* Add model weight initilization + unittest
* Update PatchTST unittest to use local import
* PatchTST integration tests for pretraining and prediction
* Added PatchTSTForRegression + update unittest to include label generation
* Revert unrelated model test file
* Combine similar output classes
* update PredictionHead
* Update configuration_patchtst.py
* Add Revin
* small edit to PatchTSTModelOutputWithNoAttention
* Update modeling_patchtst.py
* Updating integration test for forecasting
* Fix unittest after class structure changed
* docstring updates
* change input_size to num_input_channels
* more formatting
* Remove some unused params
* Add a comment for pretrained models
* add channel_attention option
add channel_attention option and remove unused positional encoders.
* Update PatchTST models to use HF's MultiHeadAttention module
* Update paper + github urls
* Fix hidden_state return value
* Update integration test to use PatchTSTForForecasting
* Adding dataclass decorator for model output classes
* Run fixup script
* Rename model repos for integration test
* edit argument explanation
* change individual option to shared_projection
* style
* Rename integration test + import cleanup
* Fix outpu_hidden_states return value
* removed unused mode
* added std, mean and nops scaler
* add initial distributional loss for predition
* fix typo in docs
* add generate function
* formatting
* add num_parallel_samples
* Fix a typo
* copy weighted_average function, edit PredictionHead
* edit PredictionHead
* add distribution head to forecasting
* formatting
* Add generate function for forecasting
* Add generate function to prediction task
* formatting
* use argsort
* add past_observed_mask ordering
* fix arguments
* docs
* add back test_model_outputs_equivalence test
* formatting
* cleanup
* formatting
* use ACT2CLS
* formatting
* fix add_start_docstrings decorator
* add distribution head and generate function to regression task
add distribution head and generate function to regression task. Also made add PatchTSTForForecastingOutput, PatchTSTForRegressionOutput.
* add distribution head and generate function to regression task
add distribution head and generate function to regression task. Also made add PatchTSTForForecastingOutput, PatchTSTForRegressionOutput.
* fix typos
* add forecast_masking
* fixed tests
* use set_seed
* fix doc test
* formatting
* Update docs/source/en/model_doc/patchtst.md
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* better var names
* rename PatchTSTTranspose
* fix argument names and docs string
* remove compute_num_patches and unused class
* remove assert
* renamed to PatchTSTMasking
* use num_labels for classification
* use num_labels
* use default num_labels from super class
* move model_type after docstring
* renamed PatchTSTForMaskPretraining
* bs -> batch_size
* more review fixes
* use hidden_state
* rename encoder layer and block class
* remove commented seed_number
* edit docstring
* Add docstring
* formatting
* use past_observed_mask
* doc suggestion
* make fix-copies
* use Args:
* add docstring
* add docstring
* change some variable names and add PatchTST before some class names
* formatting
* fix argument types
* fix tests
* change x variable to patch_input
* format
* formatting
* fix-copies
* Update tests/models/patchtst/test_modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* move loss to forward
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/models/patchtst/modeling_patchtst.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* formatting
* fix a bug when pre_norm is set to True
* output_hidden_states is set to False as default
* set pre_norm=True as default
* format docstring
* format
* output_hidden_states is None by default
* add missing docs
* better var names
* docstring: remove default to False in output_hidden_states
* change labels name to target_values in regression task
* format
* fix tests
* change to forecast_mask_ratios and random_mask_ratio
* change mask names
* change future_values to target_values param in the prediction class
* remove nn.Sequential and make PatchTSTBatchNorm class
* black
* fix argument name for prediction
* add output_attentions option
* add output_attentions to PatchTSTEncoder
* formatting
* Add attention output option to all classes
* Remove PatchTSTEncoderBlock
* create PatchTSTEmbedding class
* use config in PatchTSTPatchify
* Use config in PatchTSTMasking class
* add channel_attn_weights
* Add PatchTSTScaler class
* add output_attentions arg to test function
* format
* Update doc with image patchtst.md
* fix-copies
* rename Forecast <-> Prediction
* change name of a few parameters to match with PatchTSMixer.
* Remove *ForForecasting class to match with other time series models.
* make style
* Remove PatchTSTForForecasting in the test
* remove PatchTSTForForecastingOutput class
* change test_forecast_head to test_prediction_head
* style
* fix docs
* fix tests
* change num_labels to num_targets
* Remove PatchTSTTranspose
* remove arguments in PatchTSTMeanScaler
* remove arguments in PatchTSTStdScaler
* add config as an argument to all the scaler classes
* reformat
* Add norm_eps for batchnorm and layernorm
* reformat.
* reformat
* edit docstring
* update docstring
* change variable name pooling to pooling_type
* fix output_hidden_states as tuple
* fix bug when calling PatchTSTBatchNorm
* change stride to patch_stride
* create PatchTSTPositionalEncoding class and restructure the PatchTSTEncoder
* formatting
* initialize scalers with configs
* edit output_hidden_states
* style
* fix forecast_mask_patches doc string
---------
Co-authored-by: Gift Sinthong <gift.sinthong@ibm.com>
Co-authored-by: Nam Nguyen <namctin@gmail.com>
Co-authored-by: Vijay Ekambaram <vijaykr.e@gmail.com>
Co-authored-by: Ngoc Diep Do <55230119+diepi@users.noreply.github.com>
Co-authored-by: Wesley Gifford <79663411+wgifford@users.noreply.github.com>
Co-authored-by: Wesley M. Gifford <wmgifford@us.ibm.com>
Co-authored-by: nnguyen <nnguyen@us.ibm.com>
Co-authored-by: Ngoc Diep Do <diiepy@gmail.com>
Co-authored-by: Kashif Rasul <kashif.rasul@gmail.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* only dir not even init
* init
* tokenizer removed and reference of codegen added
* modeling file updated a lot remaining app_rotary_emb
* conversion script done
* conversion script fixed, a lot of factoring done and most tests pass
* added token_clf and extractive_QA_head
* integration tests pass
* flash attn tests pass!
* config done
* more docs in modeling file
* some style fix
* style and others
* doc test error fix
* more doc fix
* some attention fixes
* most fixes
* style and other fixes
* docs fix and config
* doc fix
* some comments
* conversion script updated
* conversion script updated
* Revert "conversion script updated"
This reverts commit e92378c54084ec0747041b113083d1746ecb6c7f.
* final comments
* add Phi to language_modeling.md
* edit phi.md file
* rebase and fix
* removed phi-1.5 example
* changed model_type from 'phi'->'mixformer-sequential'
* small change
* small change
* revert \small change
* changed mixformer-sequential->phi
* small change
* added phi-1.5 example instead of phi-1
* doc test might pass now
* rebase and small change
* added the dropout layer
* more fixes
* modified .md file
* very very small doc change
* init commit
* attention arch done except rotary emb
* rotary emb done
* text encoder working
* outputs matching
* arch first pass done
* make commands done, tests and docs remaining
* all tests passed, only docs remaining
* docs done
* doc-builder fix
* convert script removed(not relevant)
* minor comments done
* added ckpt conversion script
* tokenizer done
* very minor fix of index.md 2
* mostly make fixup related
* all done except fe and rotary emb
* very small change
* removed unidecode dependency
* style changes
* tokenizer removed require_backends
* added require_inflect to tokenizer tests
* removed VOCAB_FILES in tokenizer test
* inflect dependency removed
* added rotary pos emb cache and simplified the apply method
* style
* little doc change
* more comments
* feature extractor added
* added processor
* auto-regressive config added
* added CLVPConditioningEncoder
* comments done except the test one
* weights added successfull(NOT tested)
* tokenizer fix with numbers
* generate outputs matching
* almost tests passing Integ tests not written
* Integ tests added
* major CUDA error fixed
* docs done
* rebase and multiple fixes
* fixed rebase overwrites
* generate code simplified and tests for AutoRegressive model added
* minor changes
* refectored gpt2 code in clvp file
* weights done and all code refactored
* mostly done except the fast_tokenizer
* doc test fix
* config file's doc fixes
* more config fix
* more comments
* tokenizer comments mostly done
* modeling file mostly refactored and can load modules
* ClvpEncoder tested
* ClvpDecoder, ClvpModel and ClvpForCausalLM tested
* integration and all tests passed
* more fixes
* docs almost done
* ckpt conversion refectored
* style and some failing tests fix
* comments
* temporary output fix but test_assisted_decoding_matches_greedy_search test fails
* majority changes done
* use_cache outputs same now! Along with the asisted_greedy_decoding test fix
* more comments
* more comments
* prepare_inputs_for_generation fixed and _prepare_model_inputs added
* style fix
* clvp.md change
* moved clvpconditionalencoder norms
* add model to new index
* added tokenizer input_ids_with_special_tokens
* small fix
* config mostly done
* added config-tester and changed conversion script
* more comments
* comments
* style fix
* some comments
* tokenizer changed back to prev state
* small commnets
* added output hidden states for the main model
* style fix
* comments
* small change
* revert small change
* .
* Update clvp.md
* Update test_modeling_clvp.py
* :)
* some minor change
* new fixes
* remove to_dict from FE
* add audio_utils usage in the FE of SpeechToText
* clean unecessary parameters of AudioSpectrogramTransformer FE
* add audio_utils usage in AST
* add serialization tests and function to FEs
* make style
* remove use_torchaudio and move to_dict to FE
* test audio_utils usage
* make style and fix import (remove torchaudio dependency import)
* fix torch dependency for jax and tensor tests
* fix typo
* clean tests with suggestions
* add lines to test if is_speech_availble is False
* Use Llama RoPE implementation for Falcon
+ Add copy functionalities
* Use standard cache format for Falcon
* Simplify apply_rotary_pos_emb, copy from Llama
* Remove unnecessary cache conversion test
We don't need to convert any caches anymore!
* Resolve copy complaint
* Fixing m4t.
* Trying to remove comparison ? Odd test failure.
* Adding shared. But why on earth does it hang ????
* Putting back the model weights checks the test is silently failing on
cuda.
* Fix style + unremoved comment.
* Fix Fuyu image scaling bug
It could produce negative padding and hence inference errors for certain
image sizes.
* initial rework commit
* add batching capabilities, refactor image processing
* add functional batching for a list of images and texts
* make args explicit
* Fuyu processing update (#27133)
* Add file headers
* Add file headers
* First pass - preprocess method with standard args
* First pass image processor rework
* Small tweaks
* More args and docstrings
* Tidying iterating over batch
* Tidying up
* Modify to have quick tests (for now)
* Fix up
* BatchFeature
* Passing tests
* Add tests for processor
* Sense check when patchifying
* Add some tests
* FuyuBatchFeature
* Post-process box coordinates
* Update to `size` in processor
* Remove unused and duplicate constants
* Store unpadded dims after resize
* Fix up
* Return FuyuBatchFeature
* Get unpadded sizes after resize
* Update exception
* Fix return
* Convert input `<box>` coordinates to model format.
* Post-process point coords, support multiple boxes/points in a single
sequence
* Replace constants
* Update src/transformers/models/fuyu/image_processing_fuyu.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Preprocess List[List[image]]
* Update src/transformers/models/fuyu/image_processing_fuyu.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update to Amy's latest state.
* post-processing returns a list of tensors
* Fix error when target_sizes is None
Co-authored-by: Pablo Montalvo <pablo.montalvo.leroux@gmail.com>
* Update src/transformers/models/fuyu/image_processing_fuyu.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update src/transformers/models/fuyu/image_processing_fuyu.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update src/transformers/models/fuyu/image_processing_fuyu.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update src/transformers/models/fuyu/image_processing_fuyu.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Review comments
* Update src/transformers/models/fuyu/image_processing_fuyu.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Fix up
* Fix up
---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-72-126.ec2.internal>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Pablo Montalvo <pablo.montalvo.leroux@gmail.com>
* Fix conflicts in fuyu_follow_up_image_processing (#27228)
fixing conflicts and updating on main
* Revert "Fix conflicts in fuyu_follow_up_image_processing" (#27232)
Revert "Fix conflicts in fuyu_follow_up_image_processing (#27228)"
This reverts commit acce10b6c6.
---------
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-72-126.ec2.internal>
* add whisper fa2
* correct
* change all
* correct
* correct
* fix more
* fix more
* fix more
* fix more
* fix more
* fix more
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix more
* fix more
* fix more
* fix more
* fix more
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add type annotations to TFConvNextDropPath
* Use tf.debugging.assert_equal for TFConvNextEmbeddings shape check
* Add TensorFlow implementation of ConvNeXTV2
* check_docstrings: add TFConvNextV2Model to exclusions
TFConvNextV2Model and TFConvNextV2ForImageClassification have docstrings
which are equivalent to their PyTorch cousins, but a parsing issue prevents them
from passing the test.
Adding exclusions for these two classes as discussed in #25558.
* Safetensors serialization by default
* First pass on the tests
* Second pass on the tests
* Third pass on the tests
* Fix TF weight loading from TF-format safetensors
* Specific encoder-decoder fixes for weight crossloading
* Add VisionEncoderDecoder fixes for TF too
* Change filename test for pt-to-tf
* One missing fix for TFVisionEncoderDecoder
* Fix the other crossload test
* Support for flax + updated tests
* Apply suggestions from code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Sanchit's comments
* Sanchit's comments 2
* Nico's comments
* Fix tests
* cleanup
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* stronger GC tests
* better tests and skip failing tests
* break down into 3 sub-tests
* break down into 3 sub-tests
* refactor a bit
* more refactor
* fix
* last nit
* credits contrib and suggestions
* credits contrib and suggestions
---------
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add early stopping logits processor
* black formmated
* indent
* follow method signature
* actual logic
* check for None
* address comments on docstrings and method signature
* add unit test under `LogitsProcessorTest` wip
* unit test passing
* black formatted
* condition per sample
* add to BarkModelIntegrationTests
* wip BarkSemanticModelTest
* rename and add to kwargs handling
* not add to BarkSemanticModelTest
* correct logic and assert last outputs tokens different in test
* doc-builder style
* read from kwargs as well
* assert len of with less than that of without
* ruff
* add back seed and test case
* add original impl default suggestion
* doc-builder
* rename and use softmax
* switch back to LogitsProcessor and update docs wording
* camelCase and spelling and saving compute
* assert strictly less than
* assert less than
* expand test_generate_semantic_early_stop instead
* Add a default decoder_attention_mask for EncoderDecoderModel during training
Since we are already creating the default decoder_input_ids from the labels, we should also
create a default decoder_attention_mask to go with it.
* Fix test constant that relied on manual_seed()
The test was changed to use a decoder_attention_mask that ignores padding instead (which is
the default one created by BERT when attention_mask is None).
* Create the decoder_attention_mask using decoder_input_ids instead of labels
* Fix formatting in test
* adds agnostic decorators and availability fns
* renaming decorators and fixing imports
* updating some representative example tests
bloom, opt, and reformer for now
* wip device agnostic functions
* lru cache to device checking functions
* adds `TRANSFORMERS_TEST_DEVICE_SPEC`
if present, imports the target file and updates device to function
mappings
* comments `TRANSFORMERS_TEST_DEVICE_SPEC` code
* extra checks on device name
* `make style; make quality`
* updates default functions for agnostic calls
* applies suggestions from review
* adds `is_torch_available` guard
* Add spec file to docs, rename function dispatch names to backend_*
* add backend import to docs example for spec file
* change instances of to
* Move register backend to before device check as per @statelesshz changes
* make style
* make opt test require fp16 to run
---------
Co-authored-by: arsalanu <arsalanu@graphcore.ai>
Co-authored-by: arsalanu <hzji210@gmail.com>
* first raw commit
* still POC
* tentative convert script
* almost working speech encoder conversion scripts
* intermediate code for encoder/decoders
* add modeling code
* first version of speech encoder
* make style
* add new adapter layer architecture
* add adapter block
* add first tentative config
* add working speech encoder conversion
* base model convert works now
* make style
* remove unnecessary classes
* remove unecessary functions
* add modeling code speech encoder
* rework logics
* forward pass of sub components work
* add modeling codes
* some config modifs and modeling code modifs
* save WIP
* new edits
* same output speech encoder
* correct attention mask
* correct attention mask
* fix generation
* new generation logics
* erase comments
* make style
* fix typo
* add some descriptions
* new state
* clean imports
* add tests
* make style
* make beam search and num_return_sequences>1 works
* correct edge case issue
* correct SeamlessM4TConformerSamePadLayer copied from
* replace ACT2FN relu by nn.relu
* remove unecessary return variable
* move back a class
* change name conformer_attention_mask ->conv_attention_mask
* better nit code
* add some Copied from statements
* small nits
* small nit in dict.get
* rename t2u model -> conditionalgeneration
* ongoing refactoring of structure
* update models architecture
* remove SeamlessM4TMultiModal classes
* add tests
* adapt tests
* some non-working code for vocoder
* add seamlessM4T vocoder
* remove buggy line
* fix some hifigan related bugs
* remove hifigan specifc config
* change
* add WIP tokenization
* add seamlessM4T working tokenzier
* update tokenization
* add tentative feature extractor
* Update converting script
* update working FE
* refactor input_values -> input_features
* update FE
* changes in generation, tokenizer and modeling
* make style and add t2u_decoder_input_ids
* add intermediate outputs for ToSpeech models
* add vocoder to speech models
* update valueerror
* update FE with languages
* add vocoder convert
* update config docstrings and names
* update generation code and configuration
* remove todos and update config.pad_token_id to generation_config.pad_token_id
* move block vocoder
* remove unecessary code and uniformize tospeech code
* add feature extractor import
* make style and fix some copies from
* correct consistency + make fix-copies
* add processor code
* remove comments
* add fast tokenizer support
* correct pad_token_id in M4TModel
* correct config
* update tests and codes + make style
* make some suggested correstion - correct comments and change naming
* rename some attributes
* rename some attributes
* remove unecessary sequential
* remove option to use dur predictor
* nit
* refactor hifigan
* replace normalize_mean and normalize_var with do_normalize + save lang ids to generation config
* add tests
* change tgt_lang logic
* update generation ToSpeech
* add support import SeamlessM4TProcessor
* fix generate
* make tests
* update integration tests, add option to only return text and update tokenizer fast
* fix wrong function call
* update import and convert script
* update integration tests + update repo id
* correct paths and add first test
* update how new attention masks are computed
* update tests
* take first care of batching in vocoder code
* add batching with the vocoder
* add waveform lengths to model outputs
* make style
* add generate kwargs + forward kwargs of M4TModel
* add docstrings forward methods
* reformate docstrings
* add docstrings t2u model
* add another round of modeling docstrings + reformate speaker_id -> spkr_id
* make style
* fix check_repo
* make style
* add seamlessm4t to toctree
* correct check_config_attributes
* write config docstrings + some modifs
* make style
* add docstrings tokenizer
* add docstrings to processor, fe and tokenizers
* make style
* write first version of model docs
* fix FE + correct FE test
* fix tokenizer + add correct integration tests
* fix most tokenization tests
* make style
* correct most processor test
* add generation tests and fix num_return_sequences > 1
* correct integration tests -still one left
* make style
* correct position embedding
* change numbeams to 1
* refactor some modeling code and correct one test
* make style
* correct typo
* refactor intermediate fnn
* refactor feedforward conformer
* make style
* remove comments
* make style
* fix tokenizer tests
* make style
* correct processor tests
* make style
* correct S2TT integration
* Apply suggestions from Sanchit code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* correct typo
* replace torch.nn->nn + make style
* change Output naming (waveforms -> waveform) and ordering
* nit renaming and formating
* remove return None when not necessary
* refactor SeamlessM4TConformerFeedForward
* nit typo
* remove almost copied from comments
* add a copied from comment and remove an unecessary dropout
* remove inputs_embeds from speechencoder
* remove backward compatibiliy function
* reformate class docstrings for a few components
* remove unecessary methods
* split over 2 lines smthg hard to read
* make style
* replace two steps offset by one step as suggested
* nice typo
* move warnings
* remove useless lines from processor
* make generation non-standard test more robusts
* remove torch.inference_mode from tests
* split integration tests
* enrich md
* rename control_symbol_vocoder_offset->vocoder_offset
* clean convert file
* remove tgt_lang and src_lang from FE
* change generate docstring of ToText models
* update generate docstring of tospeech models
* unify how to deal withtext_decoder_input_ids
* add default spkr_id
* unify tgt_lang for t2u_model
* simplify tgt_lang verification
* remove a todo
* change config docstring
* make style
* simplify t2u_tgt_lang_id
* make style
* enrich/correct comments
* enrich .md
* correct typo in docstrings
* add torchaudio dependency
* update tokenizer
* make style and fix copies
* modify SeamlessM4TConverter with new tokenizer behaviour
* make style
* correct small typo docs
* fix import
* update docs and add requirement to tests
* add convert_fairseq2_to_hf in utils/not_doctested.txt
* update FE
* fix imports and make style
* remove torchaudio in FE test
* add seamless_m4t.md to utils/not_doctested.txt
* nits and change the way docstring dataset is loaded
* move checkpoints from ylacombe/ to facebook/ orga
* refactor warning/error to be in the 119 line width limit
* round overly precised floats
* add stereo audio behaviour
* refactor .md and make style
* enrich docs with more precised architecture description
* readd undocumented models
* make fix-copies
* apply some suggestions
* Apply suggestions from code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* correct bug from previous commit
* refactor a parameter allowing to clean the code + some small nits
* clean tokenizer
* make style and fix
* make style
* clean tokenizers arguments
* add precisions for some tests
* move docs from not_tested to slow
* modify tokenizer according to last comments
* add copied from statements in tests
* correct convert script
* correct parameter docstring style
* correct tokenization
* correct multi gpus
* make style
* clean modeling code
* make style
* add copied from statements
* add copied statements
* add support with ASR pipeline
* remove file added inadvertently
* fix docstrings seamlessM4TModel
* add seamlessM4TConfig to OBJECTS_TO_IGNORE due of unconventional markdown
* add seamlessm4t to assisted generation ignored models
---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* initial commit
* add processor, add fuyu naming
* add draft processor
* fix processor
* remove dropout to fix loading of weights
* add image processing fixes from Pedro
* fix
* fix processor
* add basic processing fuyu test
* add documentation and TODO
* address comments, add tests, add doc
* replace assert with torch asserts
* add Mixins and fix tests
* clean imports
* add model tester, clean imports
* fix embedding test
* add updated tests from pre-release model
* Processor: return input_ids used for inference
* separate processing and model tests
* relax test tolerance for embeddings
* add test for logit comparison
* make sure fuyu image processor is imported in the init
* fix formattingh
* more formatting issues
* and more
* fixups
* remove some stuff
* nits
* update init
* remove the fuyu file
* Update integration test with release model
* Update conversion script.
The projection is not used, as confirmed by the authors.
* improve geenration
* Remove duplicate function
* Trickle down patches to model call
* processing fuyu updates
* remove things
* fix prepare_inputs_for_generation to fix generate()
* remove model_input
* update
* add generation tests
* nits
* draft leverage automodel and autoconfig
* nits
* fix dtype patch
* address comments, update READMEs and doc, include tests
* add working processing test, remove refs to subsequences
* add tests, remove Sequence classification
* processing
* update
* update the conversion script
* more processing cleanup
* safe import
* take out ModelTesterMixin for early release
* more cl;eanup
* more cleanup
* more cleanup
* and more
* register a buffer
* nits
* add postprocessing of generate output
* nits
* updates
* add one working test
* fix test
* make fixup works
* fixup
* Arthur's updates
* nits
* update
* update
* fix processor
* update tests
* passe more fixups
* fix
* nits
* don't import torch
* skip fuyu config for now
* fixup done
* fixup
* update
* oups
* nits
* Use input embeddings
* no buffer
* update
* styling processing fuyu
* fix test
* update licence
* protect torch import
* fixup and update not doctested
* kwargs should be passed
* udpates
* update the impofixuprts in the test
* protect import
* protecting imports
* protect imports in type checking
* add testing decorators
* protect top level import structure
* fix typo
* fix check init
* move requires_backend to functions
* Imports
* Protect types
---------
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: ArthurZucker <arthur.zucker@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Lysandre <lysandre@huggingface.co>
* fix
* last attempt
* current work
* fix forward compatibility
* save all special tokens
* current state
* revert additional changes
* updates
* remove tokenizer.model
* add a test and the fix
* nit
* revert one more break
* fix typefield issue
* quality
* more tests
* fix fields for FC
* more nits?
* new additional changes
* how
* some updates
* simplify all
* more nits
* revert some things to original
* nice
* nits
* a small hack
* more nits
* ahhaha
* fixup
* update
* make test run on ci
* use subtesting
* update
* Update .circleci/create_circleci_config.py
* updates
* fixup
* nits
* replace typo
* fix the test
* nits
* update
* None max dif pls
* a partial fix
* had to revert one thing
* test the fast
* updates
* fixup
* and more nits
* more fixes
* update
* Oupsy 👁️
* nits
* fix marian
* on our way to heaven
* Update src/transformers/models/t5/tokenization_t5.py
Co-authored-by: Lysandre Debut <hi@lysand.re>
* fixup
* Update src/transformers/tokenization_utils_fast.py
Co-authored-by: Leo Tronchon <leo.tronchon@gmail.com>
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Leo Tronchon <leo.tronchon@gmail.com>
* fix phobert
* skip some things, test more
* nits
* fixup
* fix deberta
* update
* update
* more updates
* skip one test
* more updates
* fix camembert
* can't test this one
* more good fixes
* kind of a major update
- seperate what is only done in fast in fast init and refactor
- add_token(AddedToken(..., speicla = True)) ignores it in fast
- better loading
* fixup
* more fixups
* fix pegasus and mpnet
* remove skipped tests
* fix phoneme tokenizer if self.verbose
* fix individual models
* update common tests
* update testing files
* all over again
* nits
* skip test for markup lm
* fixups
* fix order of addition in fast by sorting the added tokens decoder
* proper defaults for deberta
* correct default for fnet
* nits on add tokens, string initialized to special if special
* skip irrelevant herbert tests
* main fixes
* update test added_tokens_serialization
* the fix for bart like models and class instanciating
* update bart
* nit!
* update idefix test
* fix whisper!
* some fixup
* fixups
* revert some of the wrong chanegs
* fixup
* fixup
* skip marian
* skip the correct tests
* skip for tf and flax as well
---------
Co-authored-by: Lysandre Debut <hi@lysand.re>
Co-authored-by: Leo Tronchon <leo.tronchon@gmail.com>
* Adjust length limits and allow naked conversation list inputs
* Adjust length limits and allow naked conversation list inputs
* Maybe use a slightly more reasonable limit than 1024
* Skip tests for old models that never supported this anyway
* Cleanup input docstrings
* More docstring cleanup + skip failing TF test
* Make fixup
* add FA-2 support for mistral
* fixup
* add sliding windows
* fixing few nits
* v1 slicing cache - logits do not match
* add comment
* fix bugs
* more mem efficient
* add warning once
* add warning once
* oops
* fixup
* more comments
* copy
* add safety checker
* fixup
* Update src/transformers/models/mistral/modeling_mistral.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* copied from
* up
* raise when padding side is right
* fixup
* add doc + few minor changes
* fixup
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add tokenizer kwarg inputs
* Adding tokenizer_kwargs to _sanitize_parameters
* Add truncation=True example to tests
* Update test_pipelines_fill_mask.py
* Update test_pipelines_fill_mask.py
* make fix-copies and make style
* Update fill_mask.py
Replace single tick with double
* make fix-copies
* Style
---------
Co-authored-by: Lysandre <lysandre@huggingface.co>
* fix wav2vec2
* nit
* stash
* one more file to update
* fix byt5
* vocab size is 256, don't change that!
* use other revision
* test persimon in smaller size
* style
* tests
* nits
* update add tokens from pretrained
* test tokenization
* nits
* potential fnet fix?
* more nits
* nits
* correct test
* assert close
* udpate
* ouch
* fix it
* some more nits
* FINALLU
* use `adept` checkpoints
* more adept checkpoints
* that was invlved!
* fix test for bart. Order is correct now let's skip BPEs
* ouf
* styling
* fix bert....
* slow refactoring
* current updates
* massive refactoring
* update
* NICE!
* update to see where I am at
* updates
* update
* update
* revert
* updates
* updates
* start supporting legacy_save
* styling
* big update
* revert some changes
* nits
* nniiiiiice
* small fixes
* kinda fix t5 with new behaviour
* major update
* fixup
* fix copies
* today's updates
* fix byt5
* upfate
* update
* update
* updates
* update vocab size test
* Barthez does not use not need the fairseq offset ids
* super calll must be after
* calll super
* move all super init
* move other super init
* fixup
* nits
* more fixes
* nits
* more fixes
* nits
* more fix
* remove useless files
* ouch all of them are affected
* and more!
* small imporvements
* no more sanitize token
* more changes around unique no split tokens
* partially fix more things
* keep legacy save but add warning
* so... more fixes
* updates
* guess deberta tokenizer could be nuked
* fixup
* fixup did some bad things
* nuke it if it breaks
* remove prints and pretrain fast from slow with new format.
* fixups
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fiou
* nit
* by default specials should not be normalized?
* update
* remove brakpoint
* updates
* a lot of updates
* fixup
* fixes revert some changes to match fast
* small nits
* that makes it cleaner
* fix camembert accordingly
* update
* some lest breaking changes
* update
* fixup
* fix byt5 and whisper mostly
* some more fixes, canine's byte vocab
* fix gpt2
* fix most of the perceiver tests (4 left)
* fix layout lmv3
* fixup
* fix copies for gpt2 style
* make sure to only warn once
* fix perciever and gpt2 tests
* some more backward compatibility: also read special tokens map because some ppl use it........////.....
* fixup
* add else when reading
* nits
* fresh updates
* fix copies
* will this make everything faster?
* fixes
* more fixes
* update
* more fixes
* fixup
* is the source of truth right?
* sorry camembert for the troubles
* current updates
* fixup
* update led
* update
* fix regression
* fix single word
* more model specific fixes
* fix t5 tests
* fixup
* more comments
* update
* fix nllb
* rstrip removed
* small fixes
* better handle additional_special_tokens and vocab sizes
* fixing
* styling
* fix 4 / 21
* fixup
* fix nlbb's tests
* some fixes
* fix t5
* fixes
* style
* fix canine tests
* damn this is nice
* nits
* m2m100 nit
* fixups
* fixes!
* fixup
* stash
* fix merge
* revert bad change
* fixup
* correct order for code Llama
* fix speecht5 post merge
* styling
* revert source of 11 fails
* small nits
* all changes in one go
* fnet hack
* fix 2 more tests
* update based on main branch of tokenizers
* fixup
* fix VITS issues
* more fixes
* fix mgp test
* fix camembert issues
* oups camembert still has 2 failing tests
* mluke fixes
* decode fixes
* small nits
* nits
* fix llama and vits
* fix camembert
* smal nits
* more fixes when initialising a fast from a slow and etc
* fix one of the last test
* fix CPM tokenizer test
* fixups
* fix pop2piano
* fixup
* ⚠️ Change tokenizers required version ⚠️
* ⚠️ Change tokenizers required version ⚠️
* "tokenizers>=0.14,<0.15", don't forget smaller than
* fix musicgen tests and pretraiendtokenizerfast
* fix owlvit and all
* update t5
* fix 800 red
* fix tests
* fix the fix of the fix of t5
* styling
* documentation nits
* cache _added_tokens_encoder
* fixups
* Nit
* fix red tests
* one last nit!
* make eveything a lot simpler
* Now it's over 😉
* few small nits
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* updates that work for now
* tests that should no be skipped / changed and fixed next
* fixup
* i am ashamed
* pushe the fix
* update
* fixups
* nits
* fix added_tokens_encoder
* fix canine test
* fix pegasus vocab
* fix transfoXL
* fixup
* whisper needs to be fixed for train new
* pegasus nits
* more pegasus fixes
* minor update
* better error message in failed test
* fix whisper failing test
* fix whisper failing test
* fix pegasus
* fixup
* fix **** pegasus
* reset things
* remove another file
* attempts to fix the strange custome encoder and offset
* nits here and there
* update
* fixup
* nit
* fix the whisper test
* nits nits
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* updates based on review
* some small update to potentially remove
* nits
* import rlu cache
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Lysandre Debut <hi@lysand.re>
* move warning to `from_pretrained`
* update tests results now that the special tokens are always added
---------
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Lysandre Debut <hi@lysand.re>
* moved `ctrl` to `Salesforce/ctrl`
redirects should theoretically work, but still updating those repo references for clarity
* Fixup
* Slow doc tests
* Add modeling file
---------
Co-authored-by: Lysandre <lysandre@huggingface.co>
* add pos embed interpolation for vision encoder
* style
* update config with interpolate_pos_encoding arg
* fix imports formatting
* take off copied from on vision embeddings
* add test for image embeddings interpolation
* add credit for interpolation code
* Update src/transformers/models/idefics/configuration_idefics.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/idefics/vision.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix condition to check nbr image patches match shape of pos embeddings
* use kwargs in the forward methods for interpolation
* fix tests
* have interpolate_pos_encoding default to False instead of None
* Update tests/models/idefics/test_modeling_idefics.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/idefics/test_modeling_idefics.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/idefics/test_modeling_idefics.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/idefics/configuration_idefics.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* take off for loop meant to print k,v
* add interpolate_pos_encoding arg in prepare_inputs_for_generation
* add test for interpolated generation
* fix edge case num_patches == num_positions and height == width
* add test for edge case
* fix pos_embed in interpolate
* allow interpolation in bf16 with upcasting
* Update src/transformers/models/idefics/vision.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/idefics/vision.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add multiple images tests for interpolation and generation
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add Bros boilerplate
* copy and pasted modeling_bros.py from official Bros repo
* update copyright of bros files
* copy tokenization_bros.py from official repo and update import path
* copy tokenization_bros_fast.py from official repo and update import path
* copy configuration_bros.py from official repo and update import path
* remove trailing period in copyright line
* copy and paste bros/__init__.py from official repo
* save formatting
* remove unused unnecessary pe_type argument - using only crel type
* resolve import issue
* remove unused model classes
* remove unnecessary tests
* remove unused classes
* fix original code's bug - layer_module's argument order
* clean up modeling auto
* add bbox to prepare_config_and_inputs
* set temporary value to hidden_size (32 is too low because of the of the
Bros' positional embedding)
* remove decoder test, update create_and_check* input arguemnts
* add missing variable to model tests
* do make fixup
* update bros.mdx
* add boilerate plate for no_head inference test
* update BROS_PRETRAINED_MODEL_ARCHIVE_LIST (add naver-clova-ocr prefix)
* add prepare_bros_batch_inputs function
* update modeling_common to add bbox inputs in Bros Model Test
* remove unnecessary model inference
* add test case
* add model_doc
* add test case for token_classification
* apply fixup
* update modeling code
* update BrosForTokenClassification loss calculation logic
* revert logits preprocessing logic to make sure logits have original shape
* - update class name
* - add BrosSpadeOutput
- update BrosConfig arguments
* add boilerate plate for no_head inference test
* add prepare_bros_batch_inputs function
* add test case
* add test case for token_classification
* update modeling code
* update BrosForTokenClassification loss calculation logic
* revert logits preprocessing logic to make sure logits have original shape
* apply masking on the fly
* add BrosSpadeForTokenLinking
* update class name
put docstring to the beginning of the file
* separate the logits calculation logic and loss calculation logic
* update logic for loss calculation so that logits shape doesn't change
when return
* update typo
* update prepare_config_and_inputs
* update dummy node initialization
* update last_hidden_states getting logic to consider when return_dict is False
* update box first token mask param
* bugfix: remove random attention mask generation
* update keys to ignore on load missing
* run make style and quality
* apply make style and quality of other codes
* update box_first_token_mask to bool type
* update index.md
* apply make style and quality
* apply make fix-copies
* pass check_repo
* update bros model doc
* docstring bugfix fix
* add checkpoint for doc, tokenizer for doc
* Update README.md
* Update docs/source/en/model_doc/bros.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update bros.md
* Update src/transformers/__init__.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update docs/source/en/model_doc/bros.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* apply suggestions from code review
* apply suggestions from code review
* revert test_processor_markuplm.py
* Update test_processor_markuplm.py
* apply suggestions from code review
* apply suggestions from code review
* apply suggestions from code review
* update BrosSpadeELForTokenClassification head name to entity linker
* add doc string for config params
* update class, var names to more explicit and apply suggestions from code review
* remove unnecessary keys to ignore
* update relation extractor to be initialized with config
* add bros processor
* apply make style and quality
* update bros.md
* remove bros tokenizer, add bros processor that wraps bert tokenizer
* revert change
* apply make fix-copies
* update processor code, update itc -> initial token, stc -> subsequent token
* add type hint
* remove unnecessary condition branches in embedding forward
* fix auto tokenizer fail
* update docstring for each classes
* update bbox input dimension as standard 2 points and convert them to 4
points in forward pass
* update bros docs
* apply suggestions from code review : update Bros -> BROS in bros.md
* 1. box prefix var -> bbox
2. update variable names to be more explicit
* replace einsum with torch matmul
* apply style and quality
* remove unused argument
* remove unused arguments
* update docstrings
* apply suggestions from code review: add BrosBboxEmbeddings, replace
einsum with classical matrix operations
* revert einsum update
* update bros processor
* apply suggestions from code review
* add conversion script for bros
* Apply suggestions from code review
* fix readme
* apply fix-copies
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* First commit while I figure this out
* make fixup
* Remove unused method
* Store prompt attrib
* Fix prompt argument for tests
* Make same changes in fast tokenizer
* Remove global prompts from fast tokenizer too
* stash commit
* stash commit
* Migrate PromptConfig to its True Final Location
* Replace Conversation entirely with the new class
* Import/dependency fixes
* Import/dependency fixes
* Change format for lots of default prompts
* More default prompt fixups
* Revert llama old methods so we can compare
* Fix some default configs
* Fix some default configs
* Fix misspelled kwarg
* Fixes for Blenderbot
* make fixup
* little rebase cleanup
* Add basic documentation
* Quick doc fix
* Truncate docstring for now
* Add handling for the case when messages is a single string
* Quick llama merges
* Update conversational pipeline and tests
* Add a couple of legacy properties for backward compatibility
* More legacy handling
* Add docstring for build_conversation_input_ids
* Restructure PromptConfig
* Let's start T E M P L A T I N G
* Refactor all default configs to use templates instead
* Revert changes to the special token properties since we don't need them anymore
* More class templates
* Make the sandbox even sandier
* Everything replaced with pure templating
* Remove docs for PromptConfig
* Add testing and optional requirement boilerplate
* Fix imports and make fixup
* Fix LLaMA tests and add Conversation docstring
* Finally get LLaMA working with the template system
* Finally get LLaMA working with the template system
* make fixup
* make fixup
* fmt-off for the long lists of test tokens
* Rename method to apply_chat_template for now
* Start on documentation
* Make chat_template a property that reads through to the default if it's not set
* Expand docs
* Expand chat templating doc some more
* trim/lstrip blocks by default and update doc
* Few doc tweaks
* rebase cleanup
* Clarify docstring
* rebase cleanup
* rebase cleanup
* make fixup
* Quick doc edit
* Reformat the standard template to match ChatML
* Re-add PEFT check
* Update docs/source/en/chat_templating.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add apply_chat_template to the tokenizer doc
* make fixup
* Add doc links
* Fix chat links
* Fix chat links
* Explain system messages in the doc
* Add chat template test
* Proper save-loading for chat template attribute
* Add test skips for layout models
* Remove _build_conversation_input_ids, add default_chat_template to code_llama
* Make sure all LLaMA models are using the latest template
* Remove default_system_prompt block in code_llama because it has no default prompt
* Update ConversationPipeline preprocess
* Add correct #Copied from links to the default_chat_templates
* Remove unneeded type checking line
* Add a dummy mark_processsed method
* Reorganize Conversation to have **deprecated_kwargs
* Update chat_templating.md
* Quick fix to LLAMA tests
* Small doc tweaks
* Add proper docstrings and "copied from" statements to all default chat templates
* Merge use_default_system_prompt support for code_llama too
* Improve clarity around self.chat_template
* Docstring fix
* Fix blenderbot default template
* More doctest fix
* Break out some tokenizer kwargs
* Update doc to explain default templates
* Quick tweaks to tokenizer args
* Cleanups for tokenizer args
* Add note about cacheing
* Quick tweak to the chat-templating doc
* Update the LLaMA template with error checking and correct system message embedding
* make fixup
* make fixup
* add requires_jinja
* Cleanup to expected output formatting
* Add cacheing
* Fix typo in llama default template
* Update LLaMA tests
* Update documentation
* Improved legacy handling in the Conversation class
* Update Jinja template with proper error handling
* Quick bugfix
* Proper exception raising
* Change cacheing behaviour so it doesn't try to pickle an entire Jinja env
* make fixup
* rebase cleanup
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* [Whisper Tokenizer] Fix tests after adding timestamps
* fix s2t tokenizer tests
* fix vocab test
* backwards comp
* fix tests
* comment
* style
* fix last test
* fix fast
* make faster
* move logic to decode
* remove skip test
* fix decode with offsets
* fix special tokens
* empty commit to re-trigger ci
* use lru cache
* add: check to remove metaspace from marian tokenizer
* fix: metaspace character being removed from everywhere
* fix: remove redundant check at top
* add: test for marian tokenizer decode fix
* fix: simplified the test
* intiial commit
* updates
* nits
* update conversion script
* update conversion script
* use path to load
* add tips etc
* some modeling logic
* modeling update
* more nits
* nits
* normal layer norm
* update config and doc
* nits
* update doc remove unused
* update
* fix inits and stuff
* fixup
* revert wrong changes
* updates
* more nits
* add default config values to the configuration file
* fixup happy
* update
* 2 tests left
* update readmes
* more nits
* slow test and more documentation
* update readme
* fix licences
* styling
* use fast if possible when saving tokenizer
* remove todo
* remove tokenization tests
* small last nits
* Apply suggestions from code review
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* nits to skip the timout doctest
* fix integration test
* fix test
* update eos token
* update to allow fast tokenization
* styling
* fix codeLlama as well for the update post processor
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add more copied from statements
* update
* doc passes doctest
* remove `# final layer norm?`
* change docstring prompot
* update
* Update README.md
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* don't doctest the conversion script as it requires more packages
* don't init a model in the config
* oups
* fix doctest
---------
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Put Falcon back
* Update src/transformers/models/auto/configuration_auto.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update test
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* return when length is zero
* Add tests
Co-authored-by: Avnish Narayan <38871737avnishn@users.noreply.github.com>
* Co-authored-by: avnishn
<38871737+avnishn@users.noreply.github.com>
* codeLlama doc should not be on Main
* update test
---------
Co-authored-by: Avnish Narayan <38871737avnishn@users.noreply.github.com>
* fixing name position_embeddings to object_queries
* [fix] renaming variable and docstring do object queries
* [fix] comment position_embedding to object queries
* [feat] changes from make-fix-copies to keep consistency
* Revert "[feat] changes from make-fix-copies to keep consistency"
This reverts commit 56e3e9ede1.
* [tests] fix wrong expected score
* [fix] wrong assignment causing wrong tensor shapes
* [fix] fixing position_embeddings to object queries to keep consistency (make fix copies)
* [fix] make fix copies, renaming position_embeddings to object_queries
* [fix] positional_embeddingss to object queries, fixes from make fix copies
* [fix] comments frmo make fix copies
* [fix] adding args validation to keep version support
* [fix] adding args validation to keep version support -conditional detr
* [fix] adding args validation to keep version support - maskformer
* [style] make fixup style fixes
* [feat] adding args checking
* [feat] fixcopies and args checking
* make fixup
* make fixup
---------
Co-authored-by: Lorenzobattistela <lorenzobattistela@gmail.com>
* add all
* Revert "Delete .github directory"
This reverts commit 9b0ff7b052e2b20b629a26fb13606b78a42944d1.
* make conversion script backward compatible
* fixup
* more styling
* copy to llama changes
* fix repo consistency
* nits
* document correct classes
* updates
* more fixes
* nits
* update auto mappings
* add readmes
* smallupdates
* llama-code replace with llama_code
* make fixup
* updates to the testsing suite
* fix fast nits
* more small fixes
* fix decode
* fix template processing
* properly reset the normalizer
* nits processor
* tokenization tests pass
* styling
* last tests
* additional nits
* one test is left
* nits
Co-authored-by faabian <faabian@users.noreply.github.com>
* update failing test
* fixup
* remove decode infilling users should handle it on their onw after generation, padding can be a problem
* update
* make test slow and more meaningfull
* fixup
* doc update
* fixup
* Apply suggestions from code review
* add kwargs doc
* tokenizer requires `requires_backend`
* type requires_backends
* CodeLlama instead of LlamaCode
* more name cahnges
* nits
* make doctests happy
* small pipeline nits
* last nit
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* update
* add codellama to toctree
---------
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Correct attention mask dtype
* reformat code
* add a test for boolean mask
* convert test to fast test
* delete unwanted print
* use assertTrue for testing
* Add FlaxClipTextModelWithProjection
This is necessary to support the Flax port of Stable Diffusion XL: fb6d705fb5/text_encoder_2/config.json (L3)
Co-authored-by: Martin Müller <martin.muller.me@gmail.com>
Co-authored-by: Juan Acevedo <juancevedo@gmail.com>
* Use FlaxCLIPTextModelOutput
* make fix-copies again
* Apply suggestions from code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Use `return_dict` for consistency with other uses.
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Fix docstring example.
* Add new model to FlaxCLIPTextModelTest
* Add to IGNORE_NON_AUTO_CONFIGURED list
* Fix naming convention.
---------
Co-authored-by: Martin Müller <martin.muller.me@gmail.com>
Co-authored-by: Juan Acevedo <juancevedo@gmail.com>
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* properly support Sequence of pretokenizers
* actual fix
* make sure the fix works. Tests are not working for sure!
* hacky way
* add TODO
* update
* add a todo
* nits
* rename test
* nits
* rename test
* add: NumberNormalizer works for integers, floats, common currencies, negative numbers and percentages
* fix: renamed number normalizer class and added normalization to SpeechT5Processor
* fix: restyled with black and ruff, should pass code quality tests
* fix: moved normalization to tokenizer and other small changes to normalizer
* add: test for normalization and changed the existing full tokenizer test
* fix: tokenization tests now pass, made changes to existing tokenization where normalization is covered; added normalize arg to func signature
* fix: changed default normalize setting to False, modified the tests a bit
* fix: added support for comma separated numbers, tokenization on the fly with kwargs and normalizer getter setter funcs
* init commit
* config updated also some modeling
* Processor and Model config combined
* extraction pipeline(upto before spectogram & mel_conditioner) added but not properly tested
* model loading successful!
* feature extractor done!
* FE can now be called from HF
* postprocessing added in fe file
* same as prev commit
* Pop2PianoConfig doc done
* cfg docs slightly changed
* fe docs done
* batched
* batched working!
* temp
* v1
* checking
* trying to go with generate
* with generate and model tests passed
* before rebasing
* .
* tests done docs done remaining others & nits
* nits
* LogMelSpectogram shifted to FeatureExtractor
* is_tf rmeoved from pop2piano/init
* import solved
* tokenization tests added
* minor fixed regarding modeling_pop2piano
* tokenizer changed to only return midi_object and other changes
* Updated paper abstract(Camera-ready version) (#2)
* more comments and nits
* ruff changes
* code quality fix
* sg comments
* t5 change added and rebased
* comments except batching
* batching done
* comments
* small doc fix
* example removed from modeling
* ckpt
* forward it compatible with fe and generation done
* comments
* comments
* code-quality fix(maybe)
* ckpts changed
* doc file changed from mdx to md
* test fixes
* tokenizer test fix
* changes
* nits done main changes remaining
* code modified
* Pop2PianoProcessor added with tests
* other comments
* added Pop2PianoProcessor to dummy_objects
* added require_onnx to modeling file
* changes
* update .md file
* remove extra line in index.md
* back to the main index
* added pop2piano to index
* Added tokenizer.__call__ with valid args and batch_decode and aligned the processor part too
* changes
* added return types to 2 tokenizer methods
* the PR build test might work now
* added backends
* PR build fix
* vocab added
* comments
* refactored vocab into 1 file
* added conversion script
* comments
* essentia version changed in .md
* comments
* more tokenizer tests added
* minor fix
* tests extended for outputs acc check
* small fix
---------
Co-authored-by: Jongho Choi <sweetcocoa@snu.ac.kr>
* draft changes
* update and add tests
* styling for no
* move test
* path to usable model
* update test
* small update
* update bertbased tokenizers
* don'tuse kwargs for _tokenize
* don'tuse kwargs for _tokenize
* fix copies
* update
* update test for special tokenizers
* fixup
* skip two tests
* remove pdb breakpiont()
* wowo
* rewrite custom tests
* nits
* revert chang in target keys
* fix markup lm
* update documentation of the argument
* Replaces calls to `.cuda` with `.to(torch_device)` in tests
`torch.Tensor.cuda()` is a pre-0.4 solution to changing a tensor's device. It is recommended to prefer `.to(...)` for greater flexibility and error handling. Furthermore, this makes it more consistent with other tests (that tend to use `.to(torch_device)`) and ensures the correct device backend is used (if `torch_device` is neither `cpu` or `cuda`).
* addressing review comments
* more formatting changes in Bloom test
* `make style`
* Update tests/models/bloom/test_modeling_bloom.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fixes style failures
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix EVERYTHING
* more fixes
* ⚗️⚗️ Tokenizer magic ⚗️⚗️
* wrong value but test passes for the TODO
* update
* updat
* safe protobuf import?
* style
* non gated repo
* update
* fixup
* Update src/transformers/models/llama/tokenization_llama.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/llama/tokenization_llama.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/t5/test_tokenization_t5.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* nits
* fix t5 too
* use assert equal
* fix llama decoding
* nits on t5
* fixup
* only remove the prefix space, not other spaces
* more deconding tests and more todos
* fix CI as well
* fixup
* skip failing test on CI (its tf its ok)
* skip test_subword_regularization_tokenizer that is also crashing on the CI for TF
* update llama
* revert good fixes
* fixup
* empty
* explain why we need to encode with an additional token
* better warning?
* nits
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add copied from statements for image processors
* Move out rescale and normalize to base image processor
* Remove rescale and normalize from vit (post rebase)
* Update docstrings and tidy up
* PR comments
* Add input_data_format as preprocess argument
* Resolve tests and tidy up
* Remove num_channels argument
* Update doc strings -> default ints not in code formatting
* Refactor image processor test mixin
- Move test_call_numpy, test_call_pytorch, test_call_pil to mixin
- Rename mixin to reflect handling of logic more than saving
- Add prepare_image_inputs, expected_image_outputs for tests
* Fix for oneformer
* Update InstructBLIP values
Note: the tests are not independent. Running the test independentely produces different logits compared to running all the integration tests
* Update test values after rescale update
* Remove left over commented out code
* Revert to previous rescaling logic
* Update rescale tests
* Fix rescaling bug
* Add tests
* Update integration tests
* Fix up
* Update src/transformers/image_transforms.py
* Update test - new possible order in list
* Initial addition of t5forsequenceclassification
* Adding imports and adding tests
* Formatting
* Running make fix-copies
* Adding mt5forseq
* Formatting
* run make fix-copies
* Adding to docs
* Add model_parallel
* Fix bug
* Fix
* Remove TODO
* Fixing tests for T5ForSequenceClassification
* Undo changes to dependency_versions_table.py
* Change classification head to work with T5Config directly
* Change seq length to let tests pass
* PR comments for formatting
* Formatting
* Initial addition of UMT5ForSequenceClassification
* Adding to inits and formatting
* run make fix-copies
* Add doc for UMT5ForSeqClass
* Update UMT5 config
* Fix docs
* Skip torch fx test for SequenceClassification
* Formatting
* Add skip to UMT5 tests as well
* Fix umt5 tests
* Running make fix-copies
* PR comments
* Fix for change to sentence_representation
* Rename seq_len to hidden_size since that's what it is
* Use base_model to follow format of the rest of the library
* Update docs
* Extract the decoder_input_ids changes and make one liner
* Make one-liner
* pull and push updates
* add docs
* fix modeling
* Add and run test
* make copies
* add task
* fix tests and fix small issues
* Checks on a Pull Request
* fix docs
* add desc pvt.md
* Resolve typo in check_repo.py
* Specify encoding when opening modeling files
* Deprecate the OpenLlama architecture
* Add disclaimer pointing to Llama
I'm open to different wordings here
* Match the capitalisation of LLaMA
* add llama
* add other readmes
* update padding id in readme
* add link to paper
* fix paths and tokenizer
* more nits
* styling
* fit operation in 2 lines when possible
* nits
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add form
* update reademe
* update readme, we don't have a default pad token
* update test and tokenization
* LLaMA instead of Llama
* nits
* add expected text
* add greeedy output
* styling
* Update src/transformers/models/llama/modeling_llama.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* sequential device map
* skip relevant changes
---------
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* first raw version of the bark integration
* working code on small models with single run
* add converting script from suno weights 2 hf
* many changes
* correct past_kv output
* working implementation for inference
* update the converting script according to the architecture changes
* add a working end-to-end inference code
* remove some comments and make small changes
* remove unecessary comment
* add docstrings and ensure no unecessary intermediary output during audio generation
* remove done TODOs
* make style + add config docstrings
* modification for batch inference support on the whole model
* add details to .generation_audio method
* add copyright
* convert EncodecModel from original library to transformers implementation
* add two class in order to facilitate model and sub-models loading from the hub
* add support of loading the whole model
* add BarkProcessor
* correct modeling according to processor output
* Add proper __init__ and auto support
* Add up-to-date copyright/license message
* add relative import instead of absolute
* cleaner head_dim computation
* small comment removal or changes
* more verbose LayerNorm init method
* specify eps for clearer comprehension
* more verbose variable naming in the MLP module
* remove unecessary BarkBlock parameter
* clearer code in the forward pass of the BarkBlock
* remove _initialize_modules method for cleaner code
* Remove unnecessary methods from sub-models
* move code to remove unnecessary function
* rename a variable for clarity and change an assert
* move code and change variable name for clarity
* remove unnecessary asserts
* correct small bug
* correct a comment
* change variable names for clarity
* remove asserts
* change import from absolute to relative
* correct small error due to comma missing + correct import
* Add attribute Bark config
* add first version of tests
* update attention_map
* add tie_weights and resize_token_embeddings for fineModel
* correct getting attention_mask in generate_text_semantic
* remove Bark inference trick
* leave more choices in barkProcessor
* remove _no_split_modules
* fixe error in forward of block and introduce clearer notations
* correct converting script with last changes
* make style + add draft bark.mdx
* correct BarkModelTest::test_generate_text_semantic
* add Bark in main README
* add dummy_pt_objects for Bark
* add missing models in the main init
* correct test_decoder_model_past_with_large_inputs
* disable torchscript test
* change docstring of BarkProcessor
* Add test_processor_bark
* make style
* correct copyrights
* add bark.mdx + make style, quality and consistency
* Apply suggestions from code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Remove unnecessary test method
* simply logic of a test
* Only check first ids for slow audio generation
* split full end-to-end generation tests
* remove unneccessary comment
* change submodel names for clearer naming
* remove ModuleDict from modeling_bark
* combine two if statements
* ensure that an edge misued won't happen
* modify variable name
* move code snippet to the right place (coarse instead of semantic)
* change BarkSemanticModule -> BarkSemanticModel
* align BarkProcessor with transformers paradigm
* correct BarkProcessor tests with last commit changes
* change _validate_voice_preset to an instance method instead of a class method
* tie_weights already called with post_init
* add codec_model config to configuration
* update bark modeling tests with recent BarkProcessor changes
* remove SubModelPretrainedModel + change speakers embeddings prompt type in BarkModel
* change absolute imports to relative
* remove TODO
* change docstrings
* add examples to docs and docstrings
* make style
* uses BatchFeature in BarkProcessor insteads of dict
* continue improving docstrings and docs + make style
* correct docstrings examples
* more comprehensible speaker_embeddings load/Save
* rename speaker_embeddings_dict -> speaker_embeddings
* correct bark.mdx + add bark to documentation_tests
* correct docstrings configuration_bark
* integrate last nit suggestions
* integrate BarkGeneration configs
* make style
* remove bark tests from documentation_tests.txt because timeout - tested manually
* add proper generation config initialization
* small bark.mdx documentation changes
* rename bark.mdx -> bark.md
* add torch.no_grad behind BarkModel.generate_audio()
* replace assert by ValueError in convert_suno_to_hf.py
* integrate a series of short comments from reviewer
* move SemanticLogitsProcessors and remove .detach() from Bark docs and docstrings
* actually remove SemanticLogitsProcessor from modeling_bark.oy
* BarkProcessor returns a single output instead of tuple + correct docstrings
* make style + correct bug
* add initializer_range to BarkConfig + correct slow modeling tests
* add .clone() to history_prompt.coarse_prompt to avoid modifying input array
* Making sure no extra "`" are present
* remove extra characters in modeling_bark.py
* Correct output if history_prompt is None
* remove TODOs
* remove ravel comment
* completing generation_configuration_bark.py docstrings
* change docstrings - number of audio codebooks instead of Encodec codebooks
* change 'bias' docstrings in configuration_bark.py
* format code
* rename BarkModel.generate_audio -> BarkModel.generate_speech
* modify AutoConfig instead of EncodecConfig in BarkConfig
* correct AutoConfig wrong init
* refactor BarkModel and sub-models generate_coarse, generate_fine, generate_text_semantic
* remove SemanticLogitsProcessor and replace it with SuppressTokensLogitsProcessor
* move nb_codebook related config arguments to BarkFineConfig
* rename bark.mdx -> bark.md
* correcting BarkModelConfig from_pretrained + remove keys_to_ignore
* correct bark.md with correct hub path
* correct code bug in bark.md
* correct list tokens_to_suppress
* modify Processor to load nested speaker embeddings in a safer way
* correct batch sampling in BarkFineModel.generate_fine
* Apply suggestions from code review
Small docstrings correction and code improvements
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* give more details about num_layers in docstrings
* correct indentation mistake
* correct submodelconfig order of docstring variables
* put audio models in alphabetical order in utils/check_repo.my
* remove useless line from test_modeling_bark.py
* makes BarkCoarseModelTest inherits from (ModelTesterMixin, GenerationTesterMixin, unittest.TestCase) instead of BarkSemanticModelTest
* make a Tester class for each sub-model instead of inheriting
* add test_resize_embeddings=True for Bark sub-models
* add Copied from transformers.models.gpt_neo.modeling_gpt_neo.GPTNeoSelfAttention._split_heads
* remove 'Copied fom Bark' comment
* remove unneccessary comment
* change np.min -> min in modeling_bark.py
* refactored all custom layers to have Bark prefix
* add attention_mask as an argument of generate_text_semantic
* refactor sub-models start docstrings to have more precise config class definition
* move _tied_weights_keys overriding
* add docstrings to generate_xxx in modeling_bark.py
* add loading whole BarkModel to convert_suno_to_hf
* refactor attribute and variable names
* make style convert_suno
* update bark checkpoints
* remove never entered if statement
* move bark_modeling docstrings after BarkPretrainedModel class definition
* refactor modeling_bark.py: kv -> key_values
* small nits - code refactoring and removing unecessary lines from _init_weights
* nits - replace inplace method by variable assigning
* remove *optional* when necessary
* remove some lines in generate_speech
* add default value for optional parameter
* Refactor preprocess_histories_before_coarse -> preprocess_histories
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* correct usage after refactoring
* refactor Bark's generate_xxx -> generate and modify docstrings and tests accordingly
* update docstrings python in configuration_bark.py
* add bark files in utils/documentation_test.txt
* correct docstrings python snippet
* add the ability to use parameters in the form of e.g coarse_temperature
* add semantic_max_new_tokens in python snippet in docstrings for quicker generation
* Reformate sub-models kwargs in BakModel.generate
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* correct kwargs in BarkModel.generate
* correct attention_mask kwarg in BarkModel.generate
* add tests for sub-models args in BarkModel.generate and correct BarkFineModel.test_generate_fp16
* enrich BarkModel.generate docstrings with a description of how to use the kwargs
---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fix: Apostraphe splitting in the BasicTokenizer for CLIPTokenizer
* account for apostrophe at start of new word
* remove _run_split_on_punc, use re.findall instead
* remove debugging, make style and quality
* use pattern and punc splitting, repo-consistency will fail
* remove commented out debugging
* adds bool args to BasicTokenizer, remove pattern
* do_split_on_punc default True
* clean stray comments and line breaks
* rebase, repo-consistency
* update to just do punctuation split
* add unicode normalizing back
* remove redundant line
* Initial commit
* Update src/transformers/models/falcon/configuration_falcon.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/falcon/configuration_falcon.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Cleanup config docstring
* Update src/transformers/models/falcon/configuration_falcon.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Convert to relative imports
* Remove torch < 1.8 warning
* Restructure cos_sin header
* qkv -> query, key, value
* Refactor attention calculation
* Add a couple of config variables to account for the different checkpoints
* Successful merging of the code paths!
* Fix misplaced line in the non-parallel attention path
* Update config and tests
* Add a pad_token_id when testing
* Support output_attentions when alibi is None
* make fixup
* Skip KV cache shape test
* No more _keys_to_ignore_on_load_missing
* Simplify self attention a bit
* Simplify self attention a bit
* make fixup
* stash commit
* Some more attention mask updates
* Should pass all tests except assisted generation!
* Add big model generation test
* make fixup
* Add temporary workaround for test
* Test overrides for assisted generation
* Update src/transformers/models/falcon/modeling_falcon.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/falcon/modeling_falcon.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/falcon/modeling_falcon.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/models/falcon/test_modeling_falcon.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Test overrides for assisted generation
* Add generation demo
* Update copyright
* Make the docstring model actually small
* Add module-level docstring
* Remove all assertions
* Add copied from bloom
* Reformat the QKV layer
* Add copied from bloom
* Update src/transformers/models/falcon/modeling_falcon.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Remove unused line and reformat
* No single letter variables
* Cleanup return names
* Add copied from line
* Remove the deprecated arguments blocks
* Change the embeddings test to an alibi on/off test
* Remove position_ids from FalconForQA
* Remove old check for token type IDs
* Fix the alibi path when multi_query is False
* Update src/transformers/models/falcon/modeling_falcon.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/falcon/modeling_falcon.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/falcon/test_modeling_falcon.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update config naming
* Fix typo for new_decoder_architecture
* Add some comments
* Fix docstring
* Fix docstring
* Create range in the right dtype from the start
* Review comment cleanup
* n_head_kv -> num_kv_heads
* self.alibi -> self.use_alibi
* self.num_kv -> self.num_kv_heads
* Reorder config args
* Made alibi arguments Optional
* Add all model docstrings
* Add extra checkpoints
* Add author info for Falcon
* Stop removing token_type_ids because our checkpoints shouldn't return it anymore
* Add one hopeful comment for the future
* Fix typo
* Update tests, fix cache issue for generation
* Use -1e9 instead of -inf to avoid float overflow
* Recompute the rotary embeddings much less often
* Re-enable disabled tests
* One final fix to attention mask calculation, and update tests
* Cleanup targeting falcon-40b equivalency
* Post-rebase docs update
* Update docstrings, especially in the config
* More descriptive variable names, and comments where we can't rename them
---------
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* hidden layers, huh, what are they good for (absolutely nothing)
* Some tests break with 1 hidden layer, use 2
* Use 1 hidden layer in a few slow models
* Use num_hidden_layers=2 everywhere
* Slightly higher tol for groupvit
* Slightly higher tol for groupvit
* Adding warning messages to BERT for missing attention masks
These warning messages when there are pad tokens within the input ids and
no attention masks are given. The warning message should only show up once.
* Adding warning messages to BERT for missing attention masks
These warning messages are shown when the pad_token_id is not None
and no attention masks are given. The warning message should only
show up once.
* Ran fix copies to copy over the changes to some of the other models
* Add logger.warning_once.cache_clear() to the test
* Shows warning when there are no attention masks and input_ids start/end with pad tokens
* Using warning_once() instead and fix indexing in input_ids check
---------
Co-authored-by: JB Lau <hckyn@voyager2.local>
* don't add space before single letter chars that don't have a merge
* fix the fix
* fixup
* add a test
* more testing
* fixup
* hack to make sure fast is also fixed
* update switch transformers test
* revert convert slow
* Update src/transformers/models/t5/tokenization_t5.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add typechecking
* quality
---------
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Preliminary work on some models
* Fix test load missing and make sure nonpersistent buffers are tested
* Always ignore nonpersistent buffers if in state_dict
* Treat models
* More models
* Treat remaining models
* Fix quality
* Fix tests
* Remove draft
* This test is not needed anymore
* Fix copies
* Fix last test
* Newly added models
* Fix last tests
* Address review comments
* Fix TypeError: Object of type int64 is not JSON serializable
* Convert numpy.float64 and numpy.int64 to float and int for json serialization
* Black reformatted examples/pytorch/token-classification/run_ner_no_trainer.py
* * make style
* Squash 88 commits
* Use markdown
* Remove mdx files due to bad rebase
* Fix modeling files due to bad rebase
* Fix style
* Update comment
* fix
---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* let's go!
* initial implementation of token-level timestamps
* only return a single timestamp per token
* remove token probabilities
* fix return type
* fix doc comment
* strip special tokens
* rename
* revert to not stripping special tokens
* only support models that have alignment_heads
* add integration test
* consistently name it token-level timestamps
* small DTW tweak
* initial support for ASR pipeline
* fix pipeline doc comments
* resolve token timestamps in pipeline with chunking
* change warning when no final timestamp is found
* return word-level timestamps
* fixup
* fix bug that skipped final word in each chunk
* fix failing unit tests
* merge punctuations into the words
* also return word tokens
* also return token indices
* add (failing) unit test for combine_tokens_into_words
* make combine_tokens_into_words private
* restore OpenAI's punctuation rules
* add pipeline tests
* make requested changes
* PR review changes
* fix failing pipeline test
* small stuff from PR
* only return words and their timestamps, not segments
* move alignment_heads into generation config
* forgot to set alignment_heads in pipeline tests
* tiny comment fix
* grr
* Fix saved_model_creation_extended
* Skip the BLIP model creation test for now
* Fix TF SAM test
* Fix longformer tests
* Fix Wav2Vec2
* Add a skip for XLNet
* make fixup
* make fix-copies
* Add comments
* Fix one BLIP arg not being optional, remove misspelled arg
* Remove the lxmert test overrides and just use the base test_saved_model_creation
* saved_model_creation fixes and re-enabling tests across the board
* Remove unnecessary skip
* Stop caching sinusoidal embeddings in speech_to_text
* Fix transfo_xl compilation
* Fix transfo_xl compilation
* Fix the conditionals in xglm
* Set the save spec only when building
* Clarify comment
* Move comment correctly
* Correct embeddings generation for speech2text
* Mark RAG generation tests as @slow
* Remove redundant else:
* Add comment to clarify the save_spec line in build()
* Fix size tests for XGLM at last!
* make fixup
* Remove one band_part operation
* Mark test_keras_fit as @slow
* Revert whisper change and modify the test_compile_tf_model test
* make fixup
* Tweak test slightly
* Add functional model saving to test
* Ensure TF can infer shapes for data2vec
* Add override for efficientformer
* Mark test as slow
* Stop storing references to bound methods in tf.functions
* Remove the gc.collect calls now that we resolved the underlying problem
* Remove the default signature from model.serving entirely, big cleanup
* Remove _prune_signature as self.input_signature can prune itself
* Restore serving docstring
* Update int support test to check the input signature
* Make sure other tests also use model.input_signature and not serving.input_signature
* Restore _prune_signature
* Remove the doctest GC now it's no longer needed
* Correct core tests to use the pruned sig
* order lines correctly in core tests
* Add eager_serving back with a deprecation warning
* Fix model load when it has both code on the Hub and locally
* Add input check with timeout
* Add tests
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
* Some non-saved stuff
* Add feature extractors
* Add image processor
* Add model
* Add processor and tokenizer
* Reduce timeout
---------
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
* A fun new PR where I break the entire codebase again
* A fun new PR where I break the entire codebase again
* Handle cross-attention
* Move calls to model(model.dummy_inputs) to the new build() method
* Seeing what fails with the build context thing
* make fix-copies
* Let's see what fails with new build methods
* Fix the pytorch crossload build calls
* Fix the overridden build methods in vision_text_dual_encoder
* Make sure all our build methods set self.built or call super().build(), which also sets it
* make fix-copies
* Remove finished TODO
* Tentatively remove unneeded (?) line
* Transpose b in deberta correctly and remove unused threading local
* Get rid of build_with_dummies and all it stands for
* Rollback some changes to TF-PT crossloading
* Correctly call super().build()
* Add test_backbone for convnext
* Add TimmBackbone model
* Add check for backbone type
* Tidying up - config checks
* Update convnextv2
* Tidy up
* Fix indices & clearer comment
* Exceptions for config checks
* Correclty update config for tests
* Safer imports
* Safer safer imports
* Fix where decorators go
* Update import logic and backbone tests
* More import fixes
* Fixup
* Only import all_models if torch available
* Fix kwarg updates in from_pretrained & main rebase
* Tidy up
* Add tests for AutoBackbone
* Tidy up
* Fix import error
* Fix up
* Install nattan in doc_test_job
* Revert back to setting self._out_xxx directly
* Bug fix - out_indices mapping from out_features
* Fix tests
* Dont accept output_loading_info for Timm models
* Set out_xxx and don't remap
* Use smaller checkpoint for test
* Don't remap timm indices - check out_indices based on stage names
* Skip test as it's n/a
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Cleaner imports / spelling is hard
---------
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fix for ragged list
* unpin numba
* make style
* np.object -> object
* propagate changes to tokenizer as well
* np.long -> "long"
* revert tokenization changes
* check with tokenization changes
* list/tuple logic
* catch numpy
* catch else case
* clean up
* up
* better check
* trigger ci
* Empty commit to trigger CI
* Add tf code for efficientformer
* Fix return dict bug - return last hidden state after last stage
* Fix corresponding return dict bug
* Override test tol
* Change default values of training to False
* Set training to default False X3
* Rm axis from ln
* Set init in dense projection
* Rm debug stuff
* Make style; all tests pass.
* Modify year to 2023
* Fix attention biases codes
* Update the shape list logic
* Add a batch norm eps config
* Remove extract comments in test files
* Add conditional attn and hidden states return for serving output
* Change channel dim checking logic
* Add exception for withteacher model in training mode
* Revert layer count for now
* Add layer count for conditional layer naming
* Transpose for conv happens only in main layer
* Make tests smaller
* Make style
* Update doc
* Rm from_pt
* Change to actual expect image class label
* Remove stray print in tests
* Update image processor test
* Remove the old serving output logic
* Make style
* Make style
* Complete test
* Rework TF type hints to use | None instead of Optional[] for tf.Tensor
* Rework TF type hints to use | None instead of Optional[] for tf.Tensor
* Don't forget the imports
* Add the imports to tests too
* make fixup
* Refactor tests that depended on get_type_hints
* Better test refactor
* Fix an old hidden bug in the test_keras_fit input creation code
* Fix for the Deit tests
* First commit
* Add auto-translation with GPT-4
* make fixup
* Add a functional layernorm for TF
* Add all the auxiliary imports etc.
* Add the extra processor and tests
* rebase to main
* Add all the needed fixes to the GPT code
* make fixup
* Make convolutions channels-last so they run on CPU
* make fixup
* Fix final issues
* Fix other models affected by test change
* Clarify comment on the sparse_prompt_embeddings check
* Refactor functional_layernorm, use shape_list in place of .shape in some places
* Remove deprecated torch-alike code
* Update tests/models/sam/test_modeling_tf_sam.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/sam/test_modeling_tf_sam.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Refactor processor with common methods and separated private methods
* make fixup
* Quietly delete the file that didn't do anything (sorry Sylvain)
* Refactor the processor tests into one file
* make fixup
* Clean up some unnecessary indirection
* Fix TF mask postprocessing
* Add more processor equivalence tests
* Refactor generate_crop_boxes to use framework-neutral np code
* Make the serving output correctly conditional
* Fix error message line length
* Use dict keys rather than indices internally in both TF and PT SAM call/forward
* Return dicts internally in the call/forward methods
* Revert changes to common tests and just override check_pt_tf_outputs
* Revert changes to other model tests
* Clarify comments for functional layernorm
* Add missing transpose from PT code
* Removed unused copied from in PT code
* Remove overrides for tests that don't exist in TF
* Fix transpose and update tests for PT and TF to check pred_masks
* Add training flag
* Update tests to use TF checkpoints
* Update index.mdx
* Add missing cross-test decorator
* Remove optional extra asterisks
* Revert return_dict changes in PT code
* Update src/transformers/models/sam/modeling_tf_sam.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Remove None return annotations on init methods
* Update tests/models/sam/test_processor_sam.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Fix input_boxes shapes
* make fixup
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* initial working additions
* clean and rename, add cond stripping initial prompt to decode
* cleanup, edit create_initial_prompt_ids, add tests
* repo consistency, flip order of conditional
* fix error, move the processor fn to the tokenizer
* repo consistency, update test ids to corresponding tokenizer
* use convert_tokens_to_ids not get_vocab...
* use actual conditional in generate
* make sytle
* initial address comments
* initial working add new params to pipeline
* first draft of sequential generation for condition_on_previous_text
* add/update tests, make compatible with timestamps
* make compatible with diff. input kwargs and max length
* add None check
* add temperature check
* flip temp check operand
* refocusing to prev pr scope
* remove the params too
* make style
* edits, move max length incorporating prompt to whisper
* address comments
* remove asr pipeline prompt decoding, fix indexing
* address comments (more tests, validate prompt)
* un-comment out tests (from debug)
* remove old comment
* address comments
* fix typo
* remove timestamp token from test
* make style
* cleanup
* copy method to fast tokenizer, set max_new_tokens for test
* prompt_ids type just pt
* address Amy's comments
* make style
* First draft of RWKV-4
* Add support for generate
* Style post-rebase
* Properly use state
* Write doc
* Fix doc
* More math
* Add model to README, dummies and clean config
* Fix init
* multiple fixes:
- fix common tests
- fix configuraion default values
- add CI test for checking state computation
- fix some CI tests
* correct tokenizer
* some tweaks
- fix config docstring
- fix failing tests
* fix CI tests
- add output_attention / output_hidden_states
- override test_initialization
- fix failing CIs
* fix conversion script
- fix sharded case
- add new arguments
* add slow tests + more fixes on conversion script
* add another test
* final fixes
* change single name variable
* add mock attention mask for pipeline to work
* correct eos token id
* fix nits
* add checkpoints
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add `tie_word_embeddings` in docstring
* change tensor name
* fix final nits
* Trigger CI
---------
Co-authored-by: younesbelkada <younesbelkada@gmail.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* first draft - gives index error in question_answering.py
* maturing
* no labels
* pipeline should know about QA
* fixing checks
* formatting
* fixed docstring
* initial commit
* formatting
* adding the class to many places
* towards less unhappy checks
* nearly there
* and gpt neox for qa
* use right model
* forgot this one
* base_model_prefix is "gpt_neox" for GPTNeoX* models
* unnecessary stuff
* Update src/transformers/models/gpt_neox/modeling_gpt_neox.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* format
* Update src/transformers/models/gpt_neox/modeling_gpt_neox.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* removed gpt2 stuff
---------
Co-authored-by: Prof. Peter Schneider-Kamp <jps@ordbogen.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* first draft - gives index error in question_answering.py
* maturing
* no labels
* pipeline should know about QA
* fixing checks
* formatting
* fixed docstring
* initial commit
* formatting
* adding the class to many places
* towards less unhappy checks
* nearly there
* Update src/transformers/models/gpt_neo/modeling_gpt_neo.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* avoid error
* moving to device of star/end_logits
---------
Co-authored-by: Prof. Peter Schneider-Kamp <jps@ordbogen.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* first draft - gives index error in question_answering.py
* maturing
* no labels
* pipeline should know about QA
* fixing checks
* formatting
* fixed docstring
* make sure legacy code executes
* comment
* like this
---------
Co-authored-by: Prof. Peter Schneider-Kamp <jps@ordbogen.com>
Adds FocalNet by Microsoft to transformers
---------
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
Co-authored-by: alaradirik <alaradirik@gmail.com>
* initial work
* Add other classes
* Refactor code
* Move warning and fix dynamic pipeline
* Issue warning when necessary
* Add test
* Do not skip auto tests
* Fix failing tests
* Refactor and address review comments
* Address review comments
* wrong argument name
* append eos_token_id
* all tokenizers need mask and ctc_blank tokens
* remove reduction factor from feature extractor
* add proper TTS loss
* did shifting the wrong way around
* mask out padded portions
* remove logits again (don't really need it)
* fix unit tests
* fixup
* pad also returns the decoder attention mask, since that's useful to have
* clean up feature extractor logic
* pad can handle TTS task too
* remove stop_labels from loss calculation
* simplify logic
* fixup
* do -100 masking properly
* small STFT optimization (calculate mel filterbanks only once)
* replace torchaudio fbanks with audio_utils
* remove torchaudio dependency
* simplify & speed up the STFT
* don't serialize window and mel filters
* output cross attentions when generating speech
* add guided attention loss
* fix failing test
* Update src/transformers/models/speecht5/feature_extraction_speecht5.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Update src/transformers/models/speecht5/modeling_speecht5.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* change type annotation of attention_mask to LongTensor
* extract loss into class
* remove unused frame_signal_scale argument
* use config object in loss class
* fix type annotations in doc comments
* change optional to just bool
* implement missing tokenizer method
* add deprecation warning
* Update src/transformers/models/speecht5/feature_extraction_speecht5.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/speecht5/feature_extraction_speecht5.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add deprecation warning for stop_labels
---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add model to doc tests
* Remove generate and replace by prepare_inputs_for_generation
* More fixes
* Remove print statements
* Update integration tests
* Fix generate
* Remove model from auto mapping
* Use auto processor
* Fix integration tests
* Fix test
* Add inference code snippet
* Remove is_encoder_decoder
* Update docs
* Remove notebook link
* Fix docstrings for TFBLIP
* Fix missing line in TF port!
* Use values from torch tests now other bugs fixed
* Use values from torch tests now other bugs fixed
* Fix doctest string
* resolve conflicts
* rebase and make style
* test
* test
* test
* rebase and make style
* rebase and make style
* tests
* tests
* rewrite some functions
* rebase and make style
* fix load_tf_weights_in_cpmant
* reformat some unrelated files
* upgrade quality
* fix some bugs & docstring
* add models and tests
* solve conflicts
* resolve conflicts
* resolve conflicts
* resolve conflicts
* resolve conflicts
* tests
* resolve conflicts
* resolve conflicts
* fix load_tf_weights_in_cpmant
* reformat some unrelated files
* upgrade quality
* fix some bugs & docstring
* save resolution
* make style
* delete redefinition code
* reformat function
* reformat
* resolve conflicts
* resolve conflicts
* resolve conflicts
* resolve conflicts
* resolve conflicts
* tests
* resolve conflicts
* resolve conflicts
* fix load_tf_weights_in_cpmant
* reformat some unrelated files
* upgrade quality
* resolve conflicts
* resolve conflicts
* resolve conflicts
* resolve conflicts
* resolve conflicts
* fix load_tf_weights_in_cpmant
* reformat some unrelated files
* upgrade quality
* resolve conflicts
* make style
* fix bugs and refactor
* modify docstrings and make style
* unify import format in __init__.py
* fix import-altclp bug
* fix copies to update index.md
* fix unused config parameters
* fix unused config parameters
* fix unused config parameters
* update README_ja.md
* dummy commit for unit test
* fix attention mask
* add CPMAntTokenizer&-Fast to auto-mapping
* drop redundant changes in README_ko
* fix defaults in docstring
* fix use_cache and some docstring
* add missing args in tokenizer
* modify tester inheritance
* add is_jieba_available
* fix some bugs
* make style and fix-copies
* add doctests
* skip integration tests
* add is_jieba_available
* fix bugs in common tests
* adjust docstrings and make style
* add argument docstring
* adjust code to some specifications
* make style and fix-copies
* add fast tokenization test
* dummy commit for unit test
* dummy commit for unit test
* dummy commit for unit test
* normalize some comments and names
* Bert->CPMAnt
* camel names and drop redundant codes
* make style and fix-coies
* add CpmTokenizerFast _import_structure
* drop cpmanttokenizerfast in model_doc
* fix some problems
* fix CPMAnt tokenization for common test
* make style and fixup
* fix copies and fixup
* fix bugs in tokenization test
* dummy commit for connection failure in unittest
* fix copies
* drop trailing comma
* fix decorator in tests
* dummy commit for connection failure in unittest
---------
Co-authored-by: Gong Baitao <gongbaitao11@gmail.com>
* Add out_indices to backbones, deprecate out_features
* Update - can specify both out_features and out_indices but not both
* Add backbone mixin tests
* Test tidy up
* Add test_backbone for convnext
* Remove redefinition of method
* Update for Dinat and Nat backbones
* Update tests
* Smarter indexing
* Add checks on config creation for backbone
* PR comments
* Adding Llama FastTokenizer support.
- Requires https://github.com/huggingface/tokenizers/pull/1183 version
- Only support byte_fallback for llama, raise otherwise (safety net).
- Lots of questions are special tokens
How to test:
```python
from transformers.convert_slow_tokenizer import convert_slow_tokenizer
from transformers import AutoTokenizer
from tokenizers import Tokenizer
tokenizer = AutoTokenizer.from_pretrained("huggingface/llama-7b")
if False:
new_tokenizer = Tokenizer.from_file("tok.json")
else:
new_tokenizer = convert_slow_tokenizer(tokenizer)
new_tokenizer.save("tok.json")
strings = [
"This is a test",
"生活的真谛是",
"生活的真谛是[MASK]。",
# XXX: This one is problematic because of special tokens
# "<s> Something something",
]
for string in strings:
encoded = tokenizer(string)["input_ids"]
encoded2 = new_tokenizer.encode(string).ids
assert encoded == encoded2, f"{encoded} != {encoded2}"
decoded = tokenizer.decode(encoded)
decoded2 = new_tokenizer.decode(encoded2)
assert decoded.strip() == decoded2, f"{repr(decoded)} != {repr(decoded2)}"
```
The converter + some test script.
The test script.
Tmp save.
Adding Fast tokenizer + tests.
Adding the tokenization tests.
Correct combination.
Small fix.
Fixing tests.
Fixing with latest update.
Rebased.
fix copies + normalized added tokens + copies.
Adding doc.
TMP.
Doc + split files.
Doc.
Versions + try import.
Fix Camembert + warnings -> Error.
Fix by ArthurZucker.
Not a decorator.
* Fixing comments.
* Adding more to docstring.
* Doc rewriting.
* Fix inverted conditional in TF common test!
* Make the same change in the PT tests file
* Make sure hidden states for GPT2 have the same output shape in PT/TF
* Minor fix to PT implementation of token classification loss
* Skip loss equivalence test for TFHubert because it keeps overflowing to inf
* Compute LM loss for TF the (weird) way it's computed in PT
* Skip loss equivalence test for Wav2Vec2 for the same reason as Hubert
* Fix - don't try to access the hidden states property when output is a tuple
* Initial commit
* more stash commit
* Yet another stash commit
* yet more stash commit
* Mostly working except for docs / repo consistency
* Stop importing model list from torch file
* Add TF BLIP models to docs
* Add auto classes
* Move get_text_features and get_image_features
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip_text.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/blip/test_modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/blip/test_modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update tests/models/blip/test_modeling_tf_blip_text.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip_text.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Use channels_last convolutions in TF (better performance + compatibility)
* Remove _shape function
* Move multi-line statement to one line in PT + TF
* Specify tf.keras.layers instead of importing from it
* Remove test_gradient_checkpointing and empty test_training methods
* move some multi-line statements to one line
* Update docstring for generate
* Remove pruned heads set
* Remove self.seq_len_dim
* Fixed issues with loss computation, should resolve some tests. Also ensured that the PT version follows the config for output_attentions and output_hidden_states
* ensure original model follows config in more cases
* Skip the same cross-attention tests in the PT tests - didn't realize we did it twice!
* Add training args throughout the models and layers
* make fixup
* Fix docstring for inputs_embeds
* Add docstring for is_decoder
* Add docstrings to text models
* Remove redundant computation
* Add unpack_inputs / keras_serializable
* Add modeling_tf_blip to doctests
* Add config classes for keras serialization
* Changes to allow model porting with pt-to-tf
* Quick fix to decoder head and test tweaks
* Revert an issue with masking the embeddings outputs
* Allow missing keys in some equivalence tests (for unused layers)
* Add tf-pt equivalence tests back in
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip_text.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip_text.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* make fixup
* Refactor invert_attention_mask out into tf_utils
* Re-enable cross-tests on the PT side too
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fix RoFormerEncoder postion embedding when generate as decoder
* make fixup
* add test case for check generate with past key values
* remove duplicating code
LayoutLMv3TokenizerFast produces empty 'Ġ' token with `offset_mapping = (0, 0)`.
Next token is wrongly assumed to also be beginning of word and isn't
correctly assigned `pad_token_label`.
Modify test with text that produce 'Ġ' token.
Remove copy check from LayoutLMv2TokenizerFast for `_batch_encode_plus`.
solves issue: #19978
* Initial commit
* update modeling code
* update doc
* add functions necessary
* fix impotrs
* revert changes
* fixup
* more styling to get going
* remove standalone encoder
* update code
* styling
* fix config and model
* update code and some refactoring
* make more tests pass
* Adding NLLB-200 - MoE - 54.5B for no language left behind
Fixes#21300
* fix mor common tests
* styke
* update testing file
* update
* update
* Router2 doc
* update check config with sparse layer
* add dummy router
* update current conversion script
* create on the fly conversion script
* Fixup
* style
* style 2
* fix empty return
* fix return
* Update default config sparse layers
* easier to create sparse layers
* update
* update conversion script
* update modeling
* add to toctree
* styling
* make ruff happy
* update docstring
* update conversion script
* update, will break tests but impelemting top2
* update
* ❗local groups are supported here
* ⚠️ Support for local groups is now removed ⚠️
This is because it has to work with model parallelism that we do not support
* finish simplificaiton
* Fix forward
* style
* fixup
* Update modelling and test, refactoring
* update tests
* remove final layer)norm as it is done in the FF
* routing works! Logits test added
* nit in test
* remove top1router
* style
* make sure sparse are tested. Had to change route_tokens a liottle bit
* add support for unslip models when converting
* fixup
* style
* update test s
* update test
* REFACTOR
* encoder outputs match!
* style
* update testing
* 🎉encoder and decoder logits match 🎉
* styleing
* update tests
* cleanup tests
* fix router test and CIs
* cleanup
* cleanup test styling
* fix tests
* Finally the generation tests match!
* cleanup
* update test
* style testing file
* remove script
* cleanup
* more cleanup
* nits
* update
* NLLB tokenizer is wrong and will be fixed soon
* use LongTensors
* update tests
* revert some small changes
* fix second expert sampling and batch prioritized routing
* update tests
* finish last tests
* make ruff happy
* update
* ruff again
* style
* Update docs/source/en/model_doc/nllb-moe.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Updates based on review
* style and fix import issue
* nit
* more nits
* cleanup
* styling
* update test_seconde_expert_policy
* fix name
* last nit on the markdown examples
---------
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* First draft
* Fix integration test
* Remove script
* Fix test and typos
* Fix one more test
* Skip tied embeddings test
* Remove line
* Address comments
* add mega file structure and plain pytorch version of mega source code
* added config class with old naming conventions
* filled in mega documentation
* added config class and embeddings with optional token types
* updated notes
* starting the conversion process, deleted intermediate and added use_cache back to config
* renamed config attributes in modeling_mega.py
* checkpointing before refactoring incremental decoding functions
* removed stateful incremental key/values for EMA and self-attention
* refactored MovingAverageGatedAttention to remove stateful k/v history and use unified attention mask
* MovingAverageGatedAttention works with incremental decoding + past values, added sequence length enforcement
* more comments in MovingAverageGatedAttention + checkpointing before GatedCrossAttention
* bug fix in attention mask handling in MovingAverageGatedAttention
* removed incremental state from GatedCrossAttention and removed IncrementalState class
* finished gated cross attention and got MegaLayer working
* fixed causal masking in mega decoder
* fixed how padding and causal masks are passed through MegaLayer with and without k/v caching
* finished MegaModel; tested with encoder, decoder-only, and cross-attention type inputs; started work on downstream classes; removed mentions of position_ids
* added optional dense hidden layer for masked and causal LM classes
* docstring updates in MultiHeadEMA and GatedCrossAttention, removed unnecessary inputs in cross-attention
* removed before_attn_fn in Mega class and updated docstrings and comments up to there
* bug fix in MovingAverageGatedAttention masking
* working conversion of MLM checkpoint in scratchpad script -- perfect matches
* moved arg for hidden dense layer in LM head to config; discovered issue where from_pretrained is renaming gamma and beta parameters
* renamed gamma and beta parameters to avoid HF renaming when loading from checkpoint
* finished checkpoint conversion script
* cleanup old class in mega config script
* removed 'copied from' statements and passing integration tests
* added num_attention_heads=1 to config for integration compatibility, decoder tests working, generation tests failing
* fixed tuple output of megamodel
* all common tests passing after fixing issues in decoder, gradient retention, and initialization
* added mega-specific tests, ready for more documentation and style checks
* updated docstrings; checkpoint before style fixes
* style and quality checks, fixed initialization problem in float_tensor, ready for PR
* added mega to toctree
* removed unnecessary arg in megaconfig
* removed unused arg and fixed code samples with leftover roberta models
* Apply suggestions from code review
Applied all suggestions except the one renaming a class, as I'll need to update that througout
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fixed issue where .view breaks batch dimension, conversion script fixed with absolute imports, updated readme with Mega->MEGA
* removed asserts in Mega code, renamed sequencenorm, gatedcrossattention, and NFFN, replaced get_activation_fn with ACTFN, and added sequencenorm to layer norms
* reformatted .forward() docstrings to match style and removed unused mask input in cross-attention
* removed all reset_parameters() methods and rolled into MegaPreTrainedModel._init_weights()
* renamed all single-letter variables and improved readability in tensor size comments, Mega->MEGA in 2 documentation files
* variable names in NFFN
* manual Mega->MEGA changes in docs
* Mega->MEGA in config auto
* style and quality fixes
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* renamed parameters and variables with confusing names, added copied from statements, moved fft conv to its own method, other cleanup from PR comments
* commit before dealing with merge conflicts
* made new attention activation functions available in ACT2FN and added generation test from OPT
* style and quality in activations and tests
* documentation fixes, renaming variables in dropout and rotary positions, used built-in causal masking, encoders->layers in MegaModel, moved comments into docstrings
* style and quality fixes after latest updates, before rotary position ids
* causal mask in MegaBlock docstring + added missing device passing
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update README.md
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* added Mega prefixes where missing, reverted MegaSequenceNorm to if-else, other module renaming requested in PR
* style and quality fixes + readme updates pointing to main
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Fixed bug to calculate correct xpath_sub_list in MarkupLMTokenizer. Earlier xpath_sub_list was same as xpath_tags_list
Co-authored-by: dusejat <dusejat@amazon.com>
* time to say goodbye, torch 1.7 and 1.8
* clean up torch_int_div
* clean up is_torch_less_than_1_8-9
* update
---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Make sure CVT can be trained using mixed precision
* Add test for keras-fit with mixed-precision
* Update tests/models/cvt/test_modeling_tf_cvt.py
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
---------
Co-authored-by: gcuder <Gerald.Cuder@iacapps.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Use return_loss for BridgeTowerForContrastiveLearning, add example
* fix tests
* Update example in BridgeTowerForContrastiveLearning
* Update test_modeling_bridgetower.py
* update model output format
* minor update
* Update src/transformers/models/bridgetower/modeling_bridgetower.py
* make style
---------
Co-authored-by: Tiep Le <97980157+tileintel@users.noreply.github.com>
Co-authored-by: Tiep Le <tiep.le@intel.com>
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* add `get_input_embeddings` to `WhisperForAudioClassification`
* add common tests
* fix another common test
* Update tests/models/whisper/test_modeling_whisper.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix style
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add new model of MGP-STR
* fix the check failings
* remove torch and numpy from mgp_tokenization
* remove unused import from modeling_mgp_str
* add test_processing_mgp_str
* rm test_processing_mgp_str.py
* add test_processing_mgp_str
* add test_processing_mgp_str
* add test_processing_mgp_str
* rm test_processing_mgp_str and add softmax outs to model
* rm test_processing_mgp_str and add softmax outs to model
* rewrite the code of mgp-str according to PR suggestions
* rewrite the code of mgp-str according to PR suggestions
* add new model of MGP-STR
* fix the check failings
* remove torch and numpy from mgp_tokenization
* remove unused import from modeling_mgp_str
* add test_processing_mgp_str
* rm test_processing_mgp_str.py
* add test_processing_mgp_str
* add test_processing_mgp_str
* add test_processing_mgp_str
* rm test_processing_mgp_str and add softmax outs to model
* rewrite the code of mgp-str according to PR suggestions
* rewrite the code of mgp-str according to PR suggestions
* remove representation_size from MGPSTRConfig
* reformat configuration_mgp_str.py
* format test_processor_mgp_str.py
* add test for tokenizer and complete model/processer test and model file
* rm Unnecessary tupple in modeling_mgp_str
* reduce hidden_size/layers/label_size in test_model
* add integration tests and change MGPSTR to Mgpstr
* add test for logit values
* reformat test model file
---------
Co-authored-by: yue kun <yuekun.wp@alibaba-inc.com>
* added informer to gitignore
* added informer to gitignore
* WIP informer2020
* added checking that instantiate works
* added config using gluonTS by kashif
* WIP config
* adding informeConfig. need to remove FeatureEmbedder
* done InformerConfig, but need to change the names
* Done informer model init. working on enc-dec
* added things to address, after reading again enc-dec in the paper
* done modeling - checking initialization work
* added informer to gitignore
* WIP informer2020
* added checking that instantiate works
* added config using gluonTS by kashif
* WIP config
* adding informeConfig. need to remove FeatureEmbedder
* done InformerConfig, but need to change the names
* Done informer model init. working on enc-dec
* added things to address, after reading again enc-dec in the paper
* done modeling - checking initialization work
* moved enc-dec init to InformerEncoder/Decoder init
* added 'init_std' to config, now model init works!
* WIP conversion script, and added code sources
* WIP conversion script: loading original informer pth works
* WIP conversion script: change defaults in the config
* WIP conversion script: supporting Informer input embedding
* WIP conversion script: added parameters for the informer embed
* WIP conversion script: change dim_feedforward=2048
* WIP conversion script: remove unused args for loading checkpoint
* just cleaning up
* DataEmbedding removed, after thinking with Kashif
* working on forward pass
* WIP forward pass: trying to establish working batch for forward pass
* cleaning and finalizing
* adding HF names and docs
* init after cleaning works
* WIP in tests
* added docs for the informer specific args
* fix style
* undo change
* cleaning informer, now need to work only enc-dec
* initial enc-dec classes
* added encoder and decoder
* added todo
* add todos for conv_layers
* added decoder docs from vanilla
* added encoder docs from vanilla
* remove encoder decoder from the original informer
* removed AttentionLayer from the original paper
* removed TriangularCausalMask, same as decoder_attention_mask
* initial sparse attention
* use conv_layers
* fixed test_config test
* fix parenthesis when itearting zip(layers, conv_layers)
* error found in prob attention, added sizes as comments
* fix sizes
* added proposal for q_reduce indexing, and remove unused
* WIP ProbMask, and changed factor=2 for testing
* remove unused libs for this PR for creating the env
* fix checking the attn_weights.size() after bmm
* Q_reduce: changed from torch.gather to simple slicing
* WIP calculate final attn_output
* finish adding v_aggregated, attn_output ready
* changed tgt_len to u in attention_mask, need to fix the size error
* comment attention_mask for encoder, and fix if cond for v_agg
* added ProbMask support (wip), removed old original code
* finished ProbMask 😃
* Revert "remove unused libs for this PR for creating the env"
This reverts commit 11a081e09e.
* fixes
* make style
* fix initial tests
* fix more tests
* dry
* make style
* remove unused files
* style
* added integration tests
* fix num_static_real_features
* fix header
* remove unused function
* fix example
* fix docs
* Update src/transformers/models/informer/configuration_informer.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Update src/transformers/models/informer/modeling_informer.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Update src/transformers/models/informer/configuration_informer.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Update src/transformers/models/informer/configuration_informer.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Update src/transformers/models/informer/configuration_informer.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Update src/transformers/models/informer/configuration_informer.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* fixes for reviewer
* use prediction_length from model
* fix style
* fixed informer.mdx
* added to index
* updated readme
* undo
* make fix-copies
* typo
* fix copy
* added Informer to toctree
* in order
* fixed comments
* remove unneeded new lines in docs
* make static real and cat optional
* fix use of distil conv layers
* fixed integration test
* added checkpoint for convlayer
* make fix-copies
* updated from time series model
* make fix-copies
* copy decoder
* fix unit tests
* updated scaling config
* fix integration tests
* IGNORE_NON_TESTED
* IGNORE_NON_AUTO_CONFIGURED
* IGNORE_NON_AUTO_CONFIGURED
* updated check configs
* fix formatting
* undo change from time series
* prediction_length should not be None
* aliign with the blog: prettify ProbSparse and change attention_factor to sampling_factor
* make style
* make fix-copies
* niels CR: update contributed by
* niels CR: update configuration_informer.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* niels CR: update kashif -> huggingface
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* niels CR: `sampling_factor` only relevant when `attention_type`=prob
* make style
* fixed U_part: added multiplication by `L_Q`
* fixed bug: remove `is not None` from `if config.distil`
* fixed test: `decoder_seq_length` to `encoder_seq_length` in cross_attentions check
* fix integration tests
* updated model hub
* do not shift as in training
* undo
* fix make-copies
* make fix-copies
* added `if prediction_length is None`
* changed `ProbSparseAttention` to `InformerProbSparseAttention`
* changed `V_sum` -> `v_mean_dim_time`
* changed `ConvLayer` to `InformerConvLayer` and fixed `super()`
* TimeSeriesTansformer->Informer in decoder's Copied from
* more descriptive in ProbSparse
* make style
* fix coped from
* Revert "added `if prediction_length is None`"
This reverts commit b4cbddfa05.
* fixed indent
* use InformerSinusoidalPositionalEmbedding
* make fix-style
* fix from #21860
* fix name
* make fix-copies
* use time series utils
* fix dec num_heads
* docstring
* added time series util doc
* _import_structure
* formatting
* changes from review
* make style
* fix docs
* fix doc
* removed NegativeLogLikelihood
---------
Co-authored-by: Kashif Rasul <kashif.rasul@gmail.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* [Whisper] Add model for audio classification
* make fix-copies
* add to docs
* add docstring
* empty returns
* add code example
* switch to fleurs
* stick everything on one line
* [WIP] whisper refacto to support language output.
* Handling merges.
* A bit more cleanup and comments.
* Many improvements.
Lots of details everywhere.
* Cleanup old code and tests.
* Handle lone timestamp tokens (just recover when something bad happens).
* Adding return_language example.
* No ffmpeg.
* Hmm.
* Some corrections.
* Both fast and slow.
* New black.
* Update src/transformers/models/whisper/tokenization_whisper.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/whisper/tokenization_whisper.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Remove print.
* Undoing tests modifications.
* Smaller test modifications.
* Rename.
* Remove maxDiff.
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Adds the ALIGN model to transformers. ALIGN is introduced in "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision" by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig.
* rounding_mode = "floor" instead of // to prevent behavioral change
* add other TODO
* use `torch_int_div` from pytrch_utils
* same for tests
* fix copies
* style
* use relative imports when needed
* Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* First commit for the improved PT-TF weight loading
* Remove workarounds from TFEncoderDecoder tests
* Allow a custom weight renaming function in from_pretrained and use that to clean up EncoderDecoder
* make fixup
* First attempt at visionencoderdecoder
* Disable tensorfloat32 in tests to get consistent outputs
* Quick fix to tf_vision_encoder_decoder tests
* make fixup
* Update Blenderbot tests
* Remove unused arg in modeling_tf_opt
* load_tf_sharded_weights had strict=True! This meant transfer learning was impossible, so I'm setting it to False.
* Support prefixes when loading sharded TF checkpoints
* make fixup
* Add test to load sharded models with a weight prefix
* Fix sharded weight loading test
* Add a test for transfer from a sharded checkpoint
* make fixup
* Add test to check that crossloading from PT with a prefix works
* Refactor from_pretrained in the encoderdecoder classes
* Refactor from_pretrained in the encoderdecoder classes
* missmatched -> mismatched
* Explicitly check for None
* No comments showing my very impressive and attractive knowledge of Py3.9+
* Disable TF32 across all TF tests
* Add loss for BridgeTowerForMaskedLM and BridgeTowerForImageAndTextRetrieval
* minor fix return_dict
* implement test for loss computation
---------
Co-authored-by: Tiep Le <97980157+tileintel@users.noreply.github.com>
Co-authored-by: Tiep Le <tiep.le@intel.com>
* Fix the issue of blip model returning loss even when the label is not provoided
* Fix ruff failure
* Incorporate PR feedbacks
* Incorporate PR feedbacks
* Incorporate PR feedbacks
* Incorporate PR feedbacks
* config and tokenization(fast too) changed and ErnieEncoder added
* Slow Tokenization Added
* Tokenizer(slow) is now working and Fast Tokenizer removed
* Added Config code
* Added Base Model and utils
* ErnieMModel is now working
* All added except tests
* All tests passed except ErnieUIEM
* All tests passed
* all fixes done
* all fixes done
* fixed MAP
* fixed check_code_quality
* fixed Build PR Documentation issue
* Added changes(comments) and also updated to the latest upstream/main
* Added fixup
* Added # Copied comments
* Added fixup
* Added more comments and some nits
* Added fixup
* Fixed README_hd.md
* Added more fixes
* ErnieMTokenizer (being sentencepiece) protected and other docs edited
* Added code_quality fix
* Fixed for
* Added more fix
* modified AZ
* ernie-m tokenization test added!
* attention mask part fixed(with 0->self.config.pad_token_id)
* applied make fixup
* Add X-MOD to Readme
* Add documentation for X-MOD
* Implement X-MOD
* Fix formatting of X-MOD docs
* Change signature of X-MOD forward methods to use lang_ids
* Minor changes
* Rebase with main and run make fix-copies
* Make suggested changes to docstrings
* Improve code readability
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Fix code style
* Conversion script: Remove asserts and type annotations
* Remove _TOKENIZER_FOR_DOC
* XMOD -> Xmod
* Update copyright note
* Fix doctests
* Fix docstring
* Add integration test for FillMaskPipeline
* Revert "Add integration test for FillMaskPipeline"
This reverts commit 4381eb3b1d0f5d85785f89caba83928e6efa6d1f.
* Add end-to-end integration test for mask fill
* make style
* Rebase with main and make fix-copies
---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Remove CLI spams with Whisper FeatureExtractor
Whisper feature extractor representation includes the MEL filters, a list of list that is represented as ~16,000 lines. This needlessly spams the command line. I added a `__repr__` method that replaces this list with a string "<array of shape (80, 201)>"
* Remove mel_filters from to_dict output
Credits to @ArthurZucker
* remove unused import
* update feature extraction tests for the changes in to_dict
* added with torch.no_grad() to the integration tests and applied make style
* added with torch.no_grad() to xlm roberta forward pass
---------
Co-authored-by: Bibi <Bibi@katies-mac.local>
* Enforce single model initialization
* Add OneFormer example for problem 3
* Do it the Stas way
* Actually rename the uses...
* Rewrite test
* Try to change the test this way
* Fix all init slow/fast tests
* Break connection
* Fix more tests
* Fix test for initialization
* Remove custom test
* Quality
* Fix last failing tests
* The end?
* First draft
* More improvements
* More improvements
* Improve conversion script
* Convert all weights
* Make forward pass work
* Make logits match
* More improvements
* More improvements
* More improvements
* Use get_input_embeddings
* Improve some more
* Improve model tests
* Improve model tests
* More improvements
* Fix processor
* Update files
* Update prepare_inputs_for_generation
* More improvements
* Fix copies
* More fixes
* Make fixup
* More improvements
* Add support for seq2seq language model
* More improvements
* Fix test
* More improvements
* Improve conversion script
* Remove some todo's
* Fix README's
* Improve conversion script
* Fix generation
* Fix style and remove Blip2Model
* Fix model outputs
* More improvements
* Set eos_token_id in config
* Fix quality
* Small improvements
* Add processor tests
* More improvements
* Apply suggestions
* Apply suggestions
* Add integration test
* Update image URL
* Add integration test
* Fix model_type
* Update style
* Improve docs
* Add doc tests
* Fix copies
* Remove tests which are passing
* Improve some more
* Add tests for seq2seq language models
* Minor fix
* Convert more checkpoints
* finalize CI
* Fix blip and blip2 processors
* add `accelerate` support for `blip2`
* clean up
* make style
* Update conversion script
* Update conversion script some more
* Update organization
* revert toc file
* add blip-2 to toc file
* Some more improvements
* Fix docstring
* Improve docs
---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: younesbelkada <younesbelkada@gmail.com>
* fix past renamed to past_key_value
* update more `past`that were ski^êd
* fixup
* remove changes made to rag
* refactor `_reorder_cache` to use `past_key_values`
* fix git `prepare_inputs_for_generation` to pass tests when false is needed in use_cache
* Result of black 23.1
* Update target to Python 3.7
* Switch flake8 to ruff
* Configure isort
* Configure isort
* Apply isort with line limit
* Put the right black version
* adapt black in check copies
* Fix copies
* make SpeechT5 model by copying Wav2Vec2
* add paper to docs
* whoops added docs in wrong file
* remove SpeechT5Tokenizer + put CTC back in the name
* remove deprecated class
* remove unused docstring
* delete SpeechT5FeatureExtractor, use Wav2Vec2FeatureExtractor instead
* remove classes we don't need right now
* initial stab at speech encoder prenet
* add more speech encoder prenet stuff
* improve SpeechEncoderPrenet
* add encoder (not finished yet)
* add relative position bias to self-attention
* add encoder CTC layers
* fix formatting
* add decoder from BART, doesn't work yet
* make it work with generate loop
* wrap the encoder into a speech encoder class
* wrap the decoder in a text decoder class
* changed my mind
* changed my mind again ;-)
* load decoder weights, make it work
* add weights for text decoder postnet
* add SpeechT5ForCTC model that uses only the encoder
* clean up EncoderLayer and DecoderLayer
* implement _init_weights in SpeechT5PreTrainedModel
* cleanup config + Encoder and Decoder
* add head + cross attention masks
* improve doc comments
* fixup
* more cleanup
* more fixup
* TextDecoderPrenet works now, thanks Kendall
* add CTC loss
* add placeholders for other pre/postnets
* add type annotation
* fix freeze_feature_encoder
* set padding tokens to 0 in decoder attention mask
* encoder attention mask downsampling
* remove features_pen calculation
* disable the padding tokens thing again
* fixup
* more fixup
* code review fixes
* rename encoder/decoder wrapper classes
* allow checkpoints to be loaded into SpeechT5Model
* put encoder into wrapper for CTC model
* clean up conversion script
* add encoder for TTS model
* add speech decoder prenet
* add speech decoder post-net
* attempt to reconstruct the generation loop
* add speech generation loop
* clean up generate_speech
* small tweaks
* fix forward pass
* enable always dropout on speech decoder prenet
* sort declaration
* rename models
* fixup
* fix copies
* more fixup
* make consistency checker happy
* add Seq2SeqSpectrogramOutput class
* doc comments
* quick note about loss and labels
* add HiFi-GAN implementation (from Speech2Speech PR)
* rename file
* add vocoder to TTS model
* improve vocoder
* working on tokenizer
* more better tokenizer
* add CTC tokenizer
* fix decode and batch_code in CTC tokenizer
* fix processor
* two processors and feature extractors
* use SpeechT5WaveformFeatureExtractor instead of Wav2Vec2
* cleanup
* more cleanup
* even more fixup
* notebooks
* fix log-mel spectrograms
* support reduction factor
* fixup
* shift spectrograms to right to create decoder inputs
* return correct labels
* add labels for stop token prediction
* fix doc comments
* fixup
* remove SpeechT5ForPreTraining
* more fixup
* update copyright headers
* add usage examples
* add SpeechT5ProcessorForCTC
* fixup
* push unofficial checkpoints to hub
* initial version of tokenizer unit tests
* add slow test
* fix failing tests
* tests for CTC tokenizer
* finish CTC tokenizer tests
* processor tests
* initial test for feature extractors
* tests for spectrogram feature extractor
* fixup
* more fixup
* add decorators
* require speech for tests
* modeling tests
* more tests for ASR model
* fix imports
* add fake tests for the other models
* fixup
* remove jupyter notebooks
* add missing SpeechT5Model tests
* add missing tests for SpeechT5ForCTC
* add missing tests for SpeechT5ForTextToSpeech
* sort tests by name
* fix Hi-Fi GAN tests
* fixup
* add speech-to-speech model
* refactor duplicate speech generation code
* add processor for SpeechToSpeech model
* add usage example
* add tests for speech-to-speech model
* fixup
* enable gradient checkpointing for SpeechT5FeatureEncoder
* code review
* push_to_hub now takes repo_id
* improve doc comments for HiFi-GAN config
* add missing test
* add integration tests
* make number of layers in speech decoder prenet configurable
* rename variable
* rename variables
* add auto classes for TTS and S2S
* REMOVE CTC!!!
* S2S processor does not support save/load_pretrained
* fixup
* these models are now in an auto mapping
* fix doc links
* rename HiFiGAN to HifiGan, remove separate config file
* REMOVE auto classes
* there can be only one
* fixup
* replace assert
* reformat
* feature extractor can process input and target at same time
* update checkpoint names
* fix commit hash
* update whisper logit processor
* add generate for whisper
* remove part of the whisper specific code from pipeline
* update logit processes
* major update
* enforce first timestamp
* update generate
* add more tests
* update new decoding strategy
* Apply suggestions from code review
* update docstring
* fixup
* default config will not have multilingual ar
* update expected tokenizer size, see pull on the hub for whisper-tiny
* Update imports and test fetcher
* Revert but keep test fetcher update
* Fix imports
* Fix all imports
* Replace fe with ip names
* Add generate kwargs to `AutomaticSpeechRecognitionPipeline` (#20952)
* Add generate kwargs to AutomaticSpeechRecognitionPipeline
* Add test for generation kwargs
* Update image processor parameters if creating with kwargs (#20866)
* Update parameters if creating with kwargs
* Shallow copy to prevent mutating input
* Pass all args in constructor dict - warnings in init
* Fix typo
* Rename tester class
* Rebase and tidy up
* Fixup
* Use ImageProcessingSavingTestMixin
* Update property ref in tests
* Update property ref in tests
* Update recently merged in models
* Small fix
Co-authored-by: bofeng huang <bofenghuang7@gmail.com>
* [FT] First commit for graphormer architecture.
The model has no tokenizer, as it uses a collator and preprocessing function for its input management.
Architecture to be tested against original one.
The arch might need to be changed to fit the checkpoint, but a revert to the original arch will make the code less nice to read.
TODO: doc
* [FIX] removed test model
* [FIX] import error
* [FIX] black and flake
* [DOC] added paper refs
* [FIX] [DOC]
* [FIX] black
* [DOC] Updated READMEs
* [FIX] Order of imports + rm Tokenizer calls
* [FIX] Moved assert in class to prevent doc build failure
* [FIX] make fix-copies
* [Doc] update from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [FIX] Removed Graphormer from Sequence classification model list
* [DOC] Added HF copyright to Cython file
* [DOC] Fixed comments
* [FIX] typos in class doc + removed config classes.
Todo: update doc from paper definitions
* [FIX] Removed dependency to fairseq, and replaced all asserts with Exception management
* [FIX] Homogeneized initialization of weights to pretrained constructor
* [FIX] [CP] Updated multi_hop parameter to get same results as in original implementation
* [DOC] Relevant parameter description in the configuration file
* [DOC] Updated doc and comments in main graphormer file
* [FIX] make style and quality checks
* [DOC] Fix doc format
* [FIX] [WIP] Updated part of the tests, though still a wip
* [FIX] [WIP]
* [FIX] repo consistency
* [FIX] Changed input names for more understandability
* [FIX] [BUG] updated num_classes params for propagation in the model
* simplified collator
* [FIX] Updated tests to follow new naming pattern
* [TESTS] Updated test suite along with model
* |FIX] rm tokenizer import
* [DOC] add link to graphormerdoc
* Changed section in doc from text model to graph model
* Apply suggestions from code review
Spacing, inits
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [DOC] Explain algos_graphormer functions
* Cython soft import protection
* Rm call to Callable in configuration graphormer
* [FIX] replaced asserts with Exceptions
* Add org to graphormer checkpoints
* Prefixed classes with Graphormer
* Management of init functions
* format
* fixes
* fix length file
* update indent
* relaunching ci
* Errors for missing cython imports
* fix style
* fix style doc
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add draft logit processor
* add template functions
* update timesapmt processor parameters
* draft script
* simplify code
* cleanup
* fixup and clean
* update pipeline
* style
* clean up previous idea
* add tokenization utils
* update tokenizer and asr output
* fit whisper type
* style and update test
* clean test
* style test
* update tests
* update error test
* udpate code (not based on review yet)
* update tokenization
* update asr pipeline
* update code
* cleanup and update test
* fmt
* remove text verificatino
* cleanup
* cleanup
* add model test
* update tests
* update code add docstring
* update code and add docstring
* fix pipeline tests
* add draft logit processor
add template functions
update timesapmt processor parameters
draft script
simplify code
cleanup
fixup and clean
update pipeline
style
clean up previous idea
add tokenization utils
update tokenizer and asr output
fit whisper type
style and update test
clean test
style test
update tests
update error test
udpate code (not based on review yet)
update tokenization
update asr pipeline
update code
cleanup and update test
fmt
remove text verificatino
cleanup
cleanup
add model test
update tests
update code add docstring
update code and add docstring
fix pipeline tests
* Small update.
* Fixup.
* Tmp.
* More support.
* Making `forced_decoder_ids` non mandatory for users to set.
* update and fix first bug
* properly process sequence right after merge if last
* tofo
* allow list inputs + compute begin index better
* start adding tests
* add the 3 edge cases
* style
* format sequences
* fixup
* update
* update
* style
* test passes, edge cases should be good
* update last value
* remove Trie
* update tests and expec ted values
* handle bigger chunk_length
* clean tests a bit
* refactor chunk iter and clean pipeline
* update tests
* style
* refactor chunk iter and clean pipeline
* upade
* resolve comments
* Apply suggestions from code review
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* take stride right into account
* update test expected values
* Update code based on review
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* major refactor
* add correct strides for tests
* Update src/transformers/pipelines/automatic_speech_recognition.py
* fix whisper timestamp test
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* `blip` support for training
* remove labels creation
* remove unneeded `decoder_input_ids` creation
* final changes
- add colab link to documentation
- reduction = mean for loss
* fix nits
* update link
* clearer error message
* add draft logit processor
* add template functions
* update timesapmt processor parameters
* draft script
* simplify code
* cleanup
* fixup and clean
* update pipeline
* style
* clean up previous idea
* add tokenization utils
* update tokenizer and asr output
* fit whisper type
* style and update test
* clean test
* style test
* update tests
* update error test
* udpate code (not based on review yet)
* update tokenization
* update asr pipeline
* update code
* cleanup and update test
* fmt
* remove text verificatino
* cleanup
* cleanup
* add model test
* update tests
* update code add docstring
* update code and add docstring
* fix pipeline tests
* add draft logit processor
add template functions
update timesapmt processor parameters
draft script
simplify code
cleanup
fixup and clean
update pipeline
style
clean up previous idea
add tokenization utils
update tokenizer and asr output
fit whisper type
style and update test
clean test
style test
update tests
update error test
udpate code (not based on review yet)
update tokenization
update asr pipeline
update code
cleanup and update test
fmt
remove text verificatino
cleanup
cleanup
add model test
update tests
update code add docstring
update code and add docstring
fix pipeline tests
* Small update.
* Fixup.
* Tmp.
* More support.
* Making `forced_decoder_ids` non mandatory for users to set.
* update and fix first bug
* properly process sequence right after merge if last
* tofo
* allow list inputs + compute begin index better
* start adding tests
* add the 3 edge cases
* style
* format sequences
* fixup
* update
* update
* style
* test passes, edge cases should be good
* update last value
* remove Trie
* update tests and expec ted values
* handle bigger chunk_length
* clean tests a bit
* refactor chunk iter and clean pipeline
* update tests
* style
* refactor chunk iter and clean pipeline
* upade
* resolve comments
* Apply suggestions from code review
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* take stride right into account
* update test expected values
* Update code based on review
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* torch.jit._state
* Fix past CI
* Fix for perceiver
* Fix REALM
* Fix for Bloom
* Fix for SwinMode
* Fix for TrajectoryTransformerModel
* Fix for test_wav2vec2_with_lm
* make style
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Add support for binary segmentation
* Fix loss calculation and add test
* Remove space
* use fstring
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MBP.localdomain>
* Copy RoBERTa
* formatting
* implement RoBERTa with prelayer normalization
* update test expectations
* add documentation
* add convertion script for DinkyTrain weights
* update checkpoint repo
Unfortunately the original checkpoints assumes a hacked roberta model
* add to RoBERTa-PreLayerNorm docs to toc
* run utils/check_copies.py
* lint files
* remove unused import
* fix check_repo reporting wrongly a test is missing
* fix import error, caused by rebase
* run make fix-copies
* add RobertaPreLayerNormConfig to ROBERTA_EMBEDDING_ADJUSMENT_CONFIGS
* Fix documentation <Facebook> -> Facebook
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fixup: Fix documentation <Facebook> -> Facebook
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add missing Flax header
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* expected_slice -> EXPECTED_SLICE
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update copies after rebase
* add missing copied from statements
* make fix-copies
* make prelayernorm explicit in code
* fix checkpoint path for the original implementation
* add flax integration tests
* improve docs
* update utils/documentation_tests.txt
* lint files
* Remove Copyright notice
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* make fix-copies
* Remove EXPECTED_SLICE calculation comments
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add templates for gpt-sw3
* Add templates for gpt-sw3
* Added sentencepiece tokenizer
* intermediate commit with many changes
* fixed conflicts
* Init commit for tokenization port
* Tokenization progress
* Remove fast tokenizer
* Clean up and rename spm.model -> spiece.model
* Remove TF -> PT conversion script template, Clean up Megatron -> PT script
* Optimize encode & decode performance
* added new attention
* added new attention
* attention for gpt-sw3 working
* attention good
* Cache is now working
* fixed attention mask so that it works with causal attention
* fixed badbmm bug for cpu and caching
* updated config with correct parameters
* Refactor and leave optimizations as separate functions to avoid breaking expected functionality
* Fix special tokens mapping for both tokenizers
* cleaning up of code and comments
* HF compatible attention outputs
* Tokenizer now passing tests, add documentation
* Update documentation
* reverted back to base implementation after checking that it is identical to pretrained model
* updated gpt-sw3 config
* updated conversion script
* aligned parameters with gpt-sw3 config
* changed default scale_attn_by_inverse_layer_idx to true
* removed flag from conversion script
* added temporary model path
* reverted back to functioning convert script
* small changes to default config
* updated tests for gpt-sw3
* make style, make quality, minor cleanup
* Change local paths to testing online repository
* Change name: GptSw3 -> GPTSw3
* Remove GPTSw3TokenizerFast references
* Use official model repository and add more model sizes
* Added reference to 6.7b model
* Add GPTSw3DoubleHeadsModel to IGNORE_NON_AUTO_CONFIGURED, like GPT2DoubleHeadsModel
* Remove pointers to non-existing TFGPTSw3
* Add GPTSw3 to docs/_toctree.yml
* Remove TF artifacts from GPTSw3 in __init__ files
* Update README:s with 'make fix-copies'
* Add 20b model to archive list
* Add documentation for GPT-Sw3
* Fix typo in documentation for GPT-Sw3
* Do 'make fix-copies' again after having updated docs
* Fix some typos in docs
* Update src/transformers/models/gpt_sw3/configuration_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/configuration_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/__init__.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/__init__.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/convert_megatron_to_pytorch.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/modeling_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/models/gpt_sw3/test_tokenization_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/modeling_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/modeling_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Resolve comments from PR feedback
* Resolve more comments from PR feedback, also set use_cache=True in convert script
* Add '# Copied from' comments for GPTSw3 modeling
* Set 'is_parallelizable = False'
* Remove '# Copied from' where code was modified and add 'with x->y' when appropriate
* Remove parallelize in mdx
* make style, make quality
* Update GPTSw3Config default values and corresponding documentation
* Update src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Clean up and protect GPTSw3Tokenizer imports with is_sentencepiece_available
* Make style, make quality
* Add dummy object for GPTSw3Tokenizer via 'make fix-copies'
* make fix-copies
* Remove GPTSw3 modeling classes
* make style, make quality
* Add GPTSw3 auto-mappings for other GPT2 heads
* Update docs/source/en/model_doc/gpt-sw3.mdx
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/convert_megatron_to_pytorch.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Remove old TODO-comment
* Add example usage to GPTSw3Tokenizer docstring
* make style, make quality
* Add implementation details and example usage to gpt-sw3.mdx
Co-authored-by: JoeyOhman <joeyoh@kth.se>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* read to load
* base functionality
* revert init
* fix dummy data
* moving right along
* moving right along
* finally
* cleanup
* pull out comment
* add test
* update docstring for main class
* flake comments and rewriting copies from make repo-consistency`
* remove irrelevant differences/accidental spaces
* put copies back after space removals
* mid
* final test pass
* stray comment
* update test file
* update test file
* fixup
* black
* missed
* black missed one more
* sytle
* add doc update
* fix order of output class
* comment
* Revert "comment"
This reverts commit 03f86b6948.
* remove redundant function, and redundant reshape
* move change out of common
* style
* put common spaces back
* reorder kwargs in output
* doc style
* add `dpt-hybrid` support
* refactor
* final changes, all tests pass
* final cleanups
* final changes
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* fix docstring
* fix typo
* change `vit_hybrid` to `hybrid`
* replace dataclass
* add docstring
* move dataclasses
* fix test
* add `PretrainedConfig` support for `backbone_config`
* fix docstring
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* remove `embedding_type` and replace it by `is_hybrid`
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fix cuda OOM by using single Prior
* only send to device when used
* use custom model
* Skip the big slow test
* Update tests/models/jukebox/test_modeling_jukebox.py
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* biogpt initial commit
* updated init
* fix faster decoding with use_cache
* 1. fix input_ids and input_embeds with correct device
2. added _keys_to_ignore_on_load_missing
3. updated prepare_inputs_for_generation
* add activation_dropout and scale_embedding
* replace fsmt attention with bart attention
* added test
* run make fix-copies
* doc init and fix build
* updated README with proper information
* 1. added tips to docs
2. updated BioGptTokenizer func
* 1. added tokenizer test
2. refactor tokenizer
* make fixup
* add biogpt fairseq to hf converter
* updated layer names more
similar to original checkpoints
* config update doc string and set defaults
* added "#copied" from bart model and
updated doc strings
* enable model_input_names in tokenizer
* 1. positionalembedding depending on attention_mask
2. added attention mask to prepare for generation
* added test to verify past and generation
* BioGptLMHeadModel -> BioGptForCausalLM
* fix typo
* tokenization and test
Copyright and updated assertion
* updated Copyright and
one func at time in line
* Copyright updates and
minor doc fix
* replace assertion with ValueError
* rm extra space
* added code syntax
* revert cmnt position change
* add tokenizer to auto
* updated doc string
* tokenizer doc string update
* biogpt hub model update to microsoft/biogpt
* make fixup
* rm cmnt to fix flake8 5.0.4 vs 6 error
* Fixed test_saved_model_extended
* Fix TFGPT2 tests
* make fixup
* Make sure keras-nlp utils are available for type hinting too
* Update src/transformers/testing_utils.py
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* make fixup
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* add minimal working gpt2 tokenizer
* graph mode and output equivalence tests working
* not today tensorflow. serialization test passing!
* fix style, documentation, docstrings and all that jazz
* passing consistency checks
* move keras nlp to tf dependencies
* fix tf modeling utils and gpt2 attention to enable compiling
* fix (I hope) keras nlp dependencies
* rever changes on generation
* remove debug prints
* remove redundant tf dummy objects
* add from config, get config and max length settings to address review
* let flake ignore the error on distillation you are welcome
* test from config
* add padding test
* address sgugger review
* Add Donut image processor
* Update src/transformers/image_transforms.py
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
* Fix docstrings
* Full var names in docstring
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
* First draft
* Fix backwards compatibility
* More fixes
* More fixes
* Make backbone more general
* Improve backbone
* Improve test
* Fix config checkpoint
* Address comments
* Use model_type
* Address more comments
* Fix special model names
* Remove MaskFormerSwinModel and MaskFormerSwinPreTrainedModel from main init
* Fix typo
* Update backbone
* Apply suggestion
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Add hidden states and attentions to backbone outputs
* Update ResNet
* Fix more tests
* Debug test
* Fix test_determinism
* Fix test_save_load
* Remove file
* Disable fx tests
* Test
* Add fx support for backbones
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* change the way sentinel tokens can retrived
* Fix line length for doc string
* Fix line length for doc string
* Add more stronger test for t5 tokenization
* Format file changes
* Make a stronger test for filtering sentinel tokens
* fix file format issues
* Optimizes DonutProcessor token2json method for speed
* Applies black formatting
* Updates Donut pretrained model name in test file
* remaining pytorch type hints (#20217)
* Update modeling_flava.py
* Update modeling_markuplm.py
* Update modeling_glpn.py
* Update modeling_roc_bert.py
* Update modeling_segformer.py
* Update modeling_tapas.py
* Update modeling_tapas.py
* Update modeling_tapas.py
* Update modeling_tapas.py
* Update modeling_trocr.py
* Update modeling_videomae.py
* Update modeling_videomae.py
* Update modeling_videomae.py
* Update modeling_yolos.py
* Update modeling_wav2vec2.py
* Update modeling_jukebox.py
* Update modeling_jukebox.py
* Update modeling_jukebox.py
* Update modeling_jukebox.py
* Data collator for token classification pads labels column when receives pytorch tensors (#20244)
* token cls data_collator pads labels column
* remove walrus operator for code quality
* remove redundat space
* remove comment that was fixed
* PR comments fix
Co-authored-by: Alexander Markov <amarkov.me@gmail.com>
* [Doctest] Add configuration_deformable_detr.py (#20273)
* Update configuration_deformable_detr.py comment
* Add DeformableDetrConfig to documentation_tests.txt
* Fix summarization script (#20286)
* [DOCTEST] Fix the documentation of RoCBert (#20142)
* update part of the doc
* add temp values, fix part of the doc
* add template outputs
* add correct models and outputss
* style
* fixup
* [bnb] Let's warn users when saving 8-bit models (#20282)
* add warning on 8-bit models
- added tests
- added wrapper
* move to a private attribute
- remove wrapper
- changed `save_pretrained` method
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fix suggestions
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Adding `zero-shot-object-detection` pipeline doctest. (#20274)
* Adding `zero-shot-object-detection` pipeline doctest.
* Remove nested_simplify.
* Adding doctest for `object-detection` pipeline. (#20258)
* Adding doctest for `object-detection` pipeline.
* Removed nested_simplify.
* Image transforms functionality used instead (#20278)
* Image transforms functionality used instead
* Import torch
* Import rather than copy
* Update src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py
* TF: add test for `PushToHubCallback` (#20231)
* test hub tf callback
* create repo before cloning it
* Generate: general TF XLA constrastive search are now slow tests (#20277)
* move contrastive search test to slow
* Fixing the doctests failures. (#20294)
* Fixing the doctests failures.
* Fixup.
* set the default cache_enable to True, aligned with the default value in pytorch cpu/cuda amp autocast (#20289)
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* Add docstrings for canine model (#19457)
* Add docstrings for canine model
* Update CanineForTokenClassification
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Add AutoBackbone + ResNetBackbone (#20229)
* Add ResNetBackbone
* Define channels and strides as property
* Remove file
* Add test for backbone
* Update BackboneOutput class
* Remove strides property
* Fix docstring
* Add backbones to SHOULD_HAVE_THEIR_OWN_PAGE
* Fix auto mapping name
* Add sanity check for out_features
* Set stage names based on depths
* Update to tuple
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Add missing report button for Example test (#20293)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* refactor test (#20300)
- simplifies the devce checking test
* [Tiny model creation] deal with `ImageProcessor` (#20298)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Fix blender bot missleading doc (#20301)
* fix the doc to specify that add_prefix_space = False
* add correct expected output
* remove two tokens that should not be suppressed (#20302)
* [ASR Examples] Update README for Whisper (#20230)
* [ASR Examples] Update README for seq2seq
* add language info
* add training results
* re-word
* Add padding image transformation (#19838)
* Add padding transformation
* Add in upstream changes
* Update tests & docs
* Code formatting tuples in docstring
* Pin TensorFlow (#20313)
* Pin to the right version...
* Also pin TensorFlow CPU
* Add AnyPrecisionAdamW optimizer (#18961)
* Add AnyPrecisionAdamW optimizer
* Add optim_args argument to TrainingArgs
* Add tests for AnyPrecisionOptimizer
* Change AnyPrecisionAdam default params to float32
* Move default_anyprecision_kwargs in trainer test
* Rename AnyPrecisionAdamW
* [Proposal] Breaking change `zero-shot-object-detection` for improved consistency. (#20280)
* [Proposal] Breaking change `zero-shot-object-detection` for improved
consistency.
This is a proposal to modify the output of `zero-shot-object-detection`
to provide better alignment with other pipelines.
The output is now strictly the same as `object-detection` whereas before
it would output lists of lists.
The name `candidate_labels` is used throughout for consistency with
other `zero-shot` pipelines.
The pipeline is changed to `ChunkPipeline` to support batching cleanly.
This removes all the lists and list of lists shenanigans, it's now a
matter of the base pipeline handling all this not this specific one.
**Breaking change**: It did remove complex calls potentials `pipe(images = [image1, image2],
text_queries=[candidates1, candidates2])` to support only
`pipe([{"image": image1, "candidate_labels": candidates1}, {"image": image2, "candidate_labels": candidates2}])`
when dealing with lists and/or datasets.
We could keep them, but it will add a lot of complexity to the code
base, since the pipeline is rather young, I'd rather break to keep the
code simpler, but we can revert this.
**Breaking change**: The name of the argument is now `image` instead of
`images` since it expects by default only 1 image. This is revertable
like the previous one.
**Breaking change**: The types is now simplified and flattened:
`pipe(inputs) == [{**object1}, {**object2}]`
instead of the previous
`pipe(inputs) == [[{**object1}, {**object1}], [{**object2}]]`
Where the different instances would be grouped by candidate labels
within lists.
IMHO this is not really desirable, since it would output empty lists and
is only adding superflous indirection compared to
`zero-shot-object-detection`.
It is relatively change free in terms of how the results, it does change
computation however since now the batching is handled by the pipeline
itself. It **did** change the results for the small models so there
seems to be a real difference in how the models handle this.
* Fixing the doctests.
* Behind is_torch_available.
* Fix flakey test with seed (#20318)
* Pin TF 2.10.1 for Push CI (#20319)
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Remove double brackets (#20307)
* remove double brackets
* oops get other bracket
* TF: future proof our keras imports (#20317)
* future proof our tf code
* parse tf versions
* Add Neighborhood Attention Transformer (NAT) and Dilated NAT (DiNAT) models (#20219)
* Add DiNAT
* Adds DiNAT + tests
* Minor fixes
* Added HF model
* Add natten to dependencies.
* Cleanup
* Minor fixup
* Reformat
* Optional NATTEN import.
* Reformat & add doc to _toctree
* Reformat (finally)
* Dummy objects for DiNAT
* Add NAT + minor changes
Adds NAT as its own independent model + docs, tests
Adds NATTEN to ext deps to ensure ci picks it up.
* Remove natten from `all` and `dev-torch` deps, add manual pip install to ci tests
* Minor fixes.
* Fix READMEs.
* Requested changes to docs + minor fixes.
* Requested changes.
* Add NAT/DiNAT tests to layoutlm_job
* Correction to Dinat doc.
* Requested changes.
* organize pipelines by modality (#20306)
* Fix torch device issues (#20304)
* fix device issue
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Generate: add generation config class (#20218)
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* translate zh quicktour(#20095) (#20181)
* zh quicktour(#20095)
* add zh to doc workflow
* remove untranslation from toctree
Co-authored-by: BeifangSusu <BeifangSusu@bfss.com>
* Add Spanish translation of serialization.mdx (#20245)
* Update _toctree and clone original content
* Translate first three sections
* Add more translated chapters. Only 3 more left.
* Finish translation
* Run style from doc-builder
* Address recommended changes from reviewer
* Add LayerScale to NAT/DiNAT (#20325)
* Add LayerScale to NAT/DiNAT.
Completely dropped the ball on LayerScale in the original PR (#20219).
This is just an optional argument in both models, and is only activated for larger variants in order to provide training stability.
* Add LayerScale to NAT/DiNAT.
Minor error fixed.
Co-authored-by: Ali Hassani <ahassanijr@gmail.com>
* [Switch Transformers] Fix failing slow test (#20346)
* run slow test on GPU
* remove unnecessary device assignment
* use `torch_device` instead
* fix: "BigSicence" typo in docs (#20331)
* add MobileNetV1 model (#17799)
* add model files etc for MobileNetV2
rename files for MobileNetV1
initial implementation of MobileNetV1
fix conversion script
cleanup
write docs
tweaks
fix conversion script
extract hidden states
fix test cases
make fixup
fixup it all
remove main from doc link
fixes
fix tests
fix up
use google org
fix weird assert
* fixup
* use google organization for checkpoints
* Generate: `model_kwargs` can also be an input to `prepare_inputs_for_generation` (#20353)
* Update Special Language Tokens for PLBART (#19980)
* Update Special Language Tokens for PLBART
* fix format
* making mapping for language codes and updating tests:
* fix format
* fix consistency
* add assert to both tokenizer tests.
* fix format
* Update src/transformers/models/plbart/tokenization_plbart.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* improvin readability, setting self.tgt_lang
* fixing
* readability
Co-authored-by: jordiclive <jordiclive19@imperial.ac.uk>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add resources (#20296)
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Enhance HfArgumentParser functionality and ease of use (#20323)
* Enhance HfArgumentParser
* Fix type hints for older python versions
* Fix and add tests (+formatting)
* Add changes
* doc-builder formatting
* Remove unused import "Call"
* Add Audio Spectogram Transformer (#19981)
* First draft
* Make conversion script work
* Add id2label mapping, run code quality
* Fix copies
* Add first draft of feature extractor
* Update conversion script to use feature extractor
* Make more tests pass
* Add docs
* update input_features to input_values + pad by default to max length
* Fix doc tests
* Add feature extractor tests
* Add proper padding/truncation to feature extractor
* Add support for conversion of all audioset checkpoints
* Improve docs and extend conversion script
* Fix README
* Rename spectogram to spectrogram
* Fix copies
* Add integration test
* Remove dummy conv
* Update to ast
* Update organization
* Fix init
* Rename model to AST
* Add require_torchaudio annotator
* Move import of ASTFeatureExtractor under a is_speech_available
* Fix rebase
* Add pipeline config
* Update name of classifier head
* Rename time_dimension and frequency_dimension for clarity
* Remove print statement
* Fix pipeline test
* Fix pipeline test
* Fix index table
* Fix init
* Fix conversion script
* Rename to ForAudioClassification
* Fix index table
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Add inference section to task guides (#18781)
* 📝 start adding inference section to task guides
* ✨ make style
* 📝 add multiple choice
* add rest of inference sections
* make style
* add compute_metric, push_to_hub, pipeline
* make style
* add updated sequence and token classification
* make style
* make edits in token classification
* add audio classification
* make style
* add asr
* make style
* add image classification
* make style
* add summarization
* make style
* add translation
* make style
* add multiple choice
* add language modeling
* add qa
* make style
* review and edits
* apply reviews
* make style
* fix call to processor
* apply audio reviews
* update to better asr model
* make style
* Fix toctree for Section 3 in Spanish Documentation (#20360)
* Order and group topics in the right section
* Translate "Computer Vision"
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: IMvision12 <88665786+IMvision12@users.noreply.github.com>
Co-authored-by: Alexander Markov <almarkv@yandex.ru>
Co-authored-by: Alexander Markov <amarkov.me@gmail.com>
Co-authored-by: Saad Mahmud <shuvro.mahmud79@gmail.com>
Co-authored-by: Zachary Mueller <muellerzr@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: Wang, Yi <yi.a.wang@intel.com>
Co-authored-by: raghavanone <115454562+raghavanone@users.noreply.github.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
Co-authored-by: atturaioe <76523524+atturaioe@users.noreply.github.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Ali Hassani <68103095+alihassanijr@users.noreply.github.com>
Co-authored-by: BFSS <31245245+bfss@users.noreply.github.com>
Co-authored-by: BeifangSusu <BeifangSusu@bfss.com>
Co-authored-by: Ian C <7807897+donelianc@users.noreply.github.com>
Co-authored-by: Ali Hassani <ahassanijr@gmail.com>
Co-authored-by: Raj Rajhans <me@rajrajhans.com>
Co-authored-by: Matthijs Hollemans <mail@hollance.com>
Co-authored-by: Jordan Clive <jordan.clive19@imperial.ac.uk>
Co-authored-by: jordiclive <jordiclive19@imperial.ac.uk>
Co-authored-by: Konstantin Dobler <konstantin.j.dobler@gmail.com>
* First draft
* Make conversion script work
* Add id2label mapping, run code quality
* Fix copies
* Add first draft of feature extractor
* Update conversion script to use feature extractor
* Make more tests pass
* Add docs
* update input_features to input_values + pad by default to max length
* Fix doc tests
* Add feature extractor tests
* Add proper padding/truncation to feature extractor
* Add support for conversion of all audioset checkpoints
* Improve docs and extend conversion script
* Fix README
* Rename spectogram to spectrogram
* Fix copies
* Add integration test
* Remove dummy conv
* Update to ast
* Update organization
* Fix init
* Rename model to AST
* Add require_torchaudio annotator
* Move import of ASTFeatureExtractor under a is_speech_available
* Fix rebase
* Add pipeline config
* Update name of classifier head
* Rename time_dimension and frequency_dimension for clarity
* Remove print statement
* Fix pipeline test
* Fix pipeline test
* Fix index table
* Fix init
* Fix conversion script
* Rename to ForAudioClassification
* Fix index table
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Update Special Language Tokens for PLBART
* fix format
* making mapping for language codes and updating tests:
* fix format
* fix consistency
* add assert to both tokenizer tests.
* fix format
* Update src/transformers/models/plbart/tokenization_plbart.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* improvin readability, setting self.tgt_lang
* fixing
* readability
Co-authored-by: jordiclive <jordiclive19@imperial.ac.uk>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add model files etc for MobileNetV2
rename files for MobileNetV1
initial implementation of MobileNetV1
fix conversion script
cleanup
write docs
tweaks
fix conversion script
extract hidden states
fix test cases
make fixup
fixup it all
remove main from doc link
fixes
fix tests
fix up
use google org
fix weird assert
* fixup
* use google organization for checkpoints
* Add DiNAT
* Adds DiNAT + tests
* Minor fixes
* Added HF model
* Add natten to dependencies.
* Cleanup
* Minor fixup
* Reformat
* Optional NATTEN import.
* Reformat & add doc to _toctree
* Reformat (finally)
* Dummy objects for DiNAT
* Add NAT + minor changes
Adds NAT as its own independent model + docs, tests
Adds NATTEN to ext deps to ensure ci picks it up.
* Remove natten from `all` and `dev-torch` deps, add manual pip install to ci tests
* Minor fixes.
* Fix READMEs.
* Requested changes to docs + minor fixes.
* Requested changes.
* Add NAT/DiNAT tests to layoutlm_job
* Correction to Dinat doc.
* Requested changes.
* Add ResNetBackbone
* Define channels and strides as property
* Remove file
* Add test for backbone
* Update BackboneOutput class
* Remove strides property
* Fix docstring
* Add backbones to SHOULD_HAVE_THEIR_OWN_PAGE
* Fix auto mapping name
* Add sanity check for out_features
* Set stage names based on depths
* Update to tuple
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
Adds image-guided object detection method to OwlViTForObjectDetection class as described in the original paper. One-shot/ image-guided object detection enables users to use a query image to search for similar objects in the input image.
Co-Authored-By: Dhruv Karan k4r4n.dhruv@gmail.com
* allow loading projection in text and vision model
* begin tests
* finish test for CLIPTextModelTest
* style
* add slow tests
* add new classes for projection heads
* remove with_projection
* add in init
* add in doc
* fix tests
* fix some more tests
* fix copies
* fix docs
* remove leftover from fix-copies
* add the head models in IGNORE_NON_AUTO_CONFIGURED
* fix docstr
* fix tests
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add docstr for models
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Try PT1.13 by removing torch scatter
* Skip failing tests
* Style
* Remvoe testing extras for repo utils
* Try with all decorators
* Try to wipe the cache
* Fix all tests?
* Try this way
* Fix comma
* Update to main
* Try with less deps
* Quality
* add `accelerate` support for `ViT` family
- add `_no_split_modules`
- manually cast to the right `dtype`: to change
* enable `float16` for `deit`
* fix `make fixup`
* add `slow` test for `fp16` inference
* another safety check
* Update src/transformers/models/deit/modeling_deit.py
* update relative positional embedding
* make fix copies
* add `use_cache` to list of arguments
* fixup
* 1line fucntion
* add `test_decoder_model_past_with_large_inputs_relative_pos_emb`
* add relative pos embedding test for more models
* style
* add model files etc for MobileNetV2
* rename files for MobileNetV1
* initial implementation of MobileNetV1
* fix conversion script
* cleanup
* write docs
* tweaks
* fix conversion script
* extract hidden states
* fix test cases
* make fixup
* fixup it all
* rename V1 to V2
* fix checkpoints
* fixup
* implement first block + weight conversion
* add remaining layers
* add output stride and dilation
* fixup
* add tests
* add deeplabv3+ head
* a bit of fixup
* finish deeplab conversion
* add link to doc
* fix issue with JIT trace
in_height and in_width would be Tensor objects during JIT trace, which caused Core ML conversion to fail on the remainder op. By making them ints, the result of the padding calculation becomes a constant value.
* cleanup
* fix order of models
* fix rebase error
* remove main from doc link
* add image processor
* remove old feature extractor
* fix converter + other issues
* fixup
* fix unit test
* add to onnx tests (but these appear broken now)
* add post_process_semantic_segmentation
* use google org
* remove unused imports
* move args
* replace weird assert
* Apply fix
* Fix test
* Remove another argument which is not used
* Fix pipeline test
* Add argument back, add deprecation warning
* Add warning add other location
* Use warnings instead
* Add num_channels to config
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MBP.localdomain>
* move generation_*.py src files into generation/*.py
* populate generation.__init__ with lazy loading
* move imports and references from generation.xxx.object to generation.object
* Attempting to test automatically the `_keys_to_ignore`.
* Style.
* First fix pass.
* Moving test on its own.
* Another batch.
* Second round removing BatchNorm
* Fixing layoutlmv{2,3} + support older Python.
* Disable miss missing warning.
* Removing dodgy additions.
* Big pass.
* mbart.
* More corrections.
* Fixup.
* Updating test_correct_missing_keys
* Add escape hatch for when the head has no extra params so doesn't need
the missing keys check.
* Fixing test.
* Greener.
* Green ! (except for weird splinter bug).
* Adding a test about `named_parameters` usage.
* Shorten message.
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* After rebase modifications.
* More explicit condition checking.
* Fixing slow tests issues.
* Remove extra pdb.
* Remove print.
* Attempt to make failure consistent + fixing roc_bert.
* Removing the seed (all tests passing with it).
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add first draft
* Update conversion script
* Improve conversion script
* Improve conversion script some more
* Add conditional embeddings
* Add initial decoder
* Fix activation function of decoder
* Make decoder outputs match original implementation
* Make decoder outputs match original implementation
* Add more copied from statements
* Improve model outputs
* Fix auto tokenizer file
* Fix more tests
* Add test
* Improve README and docs, improve conditional embeddings
* Fix more tests
* Remove print statements
* Remove initial embeddings
* Improve conversion script
* Add interpolation of position embeddings
* Finish addition of interpolation of position embeddings
* Add support for refined checkpoint
* Fix refined checkpoint
* Remove unused parameter
* Improve conversion script
* Add support for training
* Fix conversion script
* Add CLIPSegFeatureExtractor
* Fix processor
* Fix CLIPSegProcessor
* Fix conversion script
* Fix most tests
* Fix equivalence test
* Fix README
* Add model to doc tests
* Use better variable name
* Convert other checkpoint as well
* Update config, add link to paper
* Add docs
* Update organization
* Replace base_model_prefix with clip
* Fix base_model_prefix
* Fix checkpoint of config
* Fix config checkpoint
* Remove file
* Use logits for output
* Fix tests
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* Add test for SentencePiece not adding special tokens to strings
* Add SentencePieceStringConversionMixin to fix issue 15003
* Fix conversion from tokens to string for most SentencePiece tokenizers
Tokenizers fixed:
- AlbertTokenizer
- BarthezTokenizer
- CamembertTokenizer
- FNetTokenizer
- M2M100Tokenizer
- MBart50Tokenizer
- PegasusTokenizer
- Speech2TextTokenizer
* Fix MarianTokenizer, adjust SentencePiece test to accomodate vocab
* Fix DebertaV2Tokenizer
* Ignore LayoutXLMTokenizer in SentencePiece string conversion test
* Run 'make style' and 'make quality'
* Clean convert_tokens_to_string test
Instead of explicitly ignoring LayoutXLMTokenizer in the test,
override the test in LayoutLMTokenizationTest and do nothing in it.
* Remove commented out code
* Improve robustness of convert_tokens_to_string test
Instead of comparing lengths of re-tokenized text and input_ids,
check that converting all special tokens to string yields a string
with all special tokens.
* Inline and remove SentencePieceStringConversionMixin
The convert_tokens_to_string method is now implemented
in each relevant SentencePiece tokenizer.
* Run 'make style' and 'make quality'
* Revert removal of space in convert_tokens_to_string
* Remove redundant import
* Revert test text to original
* Uncomment the lowercasing of the reverse_text variable
* Mimic Rust tokenizer behavior for tokenizers
- Albert
- Barthez
- Camembert
- MBart50
- T5
* Fix accidentally skipping test in wrong tokenizer
* Add test for equivalent Rust and slow tokenizer behavior
* Override _decode in BigBirdTokenizer to mimic Rust behavior
* Override _decode in FNetTokenizer to mimic Rust behavior
* Override _decode in XLNetTokenizer to mimic Rust behavior
* Remove unused 're' import
* Update DebertaV2Tokenizer to mimic Rust tokenizer
* Deberta tokenizer now behaves like Albert and its `convert_tokens_to_string` is not tested.
* Ignore problematic tests in Deberta V2
* Add comment on why the Deberta V2 tests are skipped
* initial commit
* First draft that gets outputs without crashing!
* Add all the ported openfold dependencies
* testing
* Restructure config files for ESMFold
* Debugging to find output discrepancies
* Mainly style
* Make model runnable without extra deps
* Remove utils and merge them to the modeling file
* Use correct gelu and remove some debug prints
* More cleanup
* Update esm docs
* Update conversion script to support ESMFold properly
* Port some top-level changes from ESMFold repo
* Expand EsmFold docstrings
* Make attention_mask optional (default to all 1s)
* Add inference test for ESMFold
* Use config and not n kwargs
* Add modeling output class
* Remove einops
* Remove chunking in ESM FFN
* Update tests for ESMFold
* Quality
* REpo consistency
* Remove tree dependency from ESMFold
* make fixup
* Add an error in case my structure map function breaks later
* Remove needless code
* Stop auto-casting the LM to float16 so CPU tests pass
* Stop auto-casting the LM to float16 so CPU tests pass
* Final test updates
* Split test file
* Copyright and quality
* Unpin PyTorch to see built doc
* Fix config file to_dict() method
* Add some docstrings to the output
* Skip TF checkpoint tests for ESM until we reupload those
* make fixup
* More docstrings
* Unpin to get even with main
* Flag example to write
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
* Support segformer fx
* Add fx_compatible attribute to test_modeling_segformer.py
* Update glpn model (fx support)
glpn model was copied from segformer.
* Update utils/fx.py | add semantic-segmentation
for SegformerForSemanticSegmentation model
* Fix minor import order(isort)
* Add random input generation for segformer fx
Co-authored-by: noelbird <lduldu00228@gmail.com>
* support sentencepiece for bertjapanesetokenizer
* add test vocab file for sentencepiece, bertjapanesetokenizer
* make BasicTokenizer be identical to transformers.models.bert.tokenization_bert.BasicTokenizer
* fix missing of \n in comment
* fix init argument missing in tests
* make spm_file be optional, exclude spiece.model from tests/fixtures, and add description comments
* make comment length less than 119
* apply doc style check
* First step of PT->TF for composite models
* Update the tests
* For VisionEncoderDecoderModel
* Fix
* Fix
* Add comment
* Fix
* clean up import
* Save memory
* For (TF)EncoderDecoderModel
* For (TF)EncoderDecoderModel
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Clean up deprecation warnings
Notes:
Changed some strings in tests to raw strings, which will change the literal content of the strings as they are fed into whatever machine handles them.
Test cases for past in the past/past_key_values switch changed/removed due to warning of impending removal
* Add PILImageResampling abstraction for PIL.Image.Resampling
* Partial TF port for ESM model
* Add ESM-TF tests
* Add the various imports for TF-ESM
* TF weight conversion almost ready
* Stop ignoring the decoder weights in PT
* Add tests and lots of fixes
* fix-copies
* Fix imports, add model docs
* Add get_vocab() to tokenizer
* Fix vocab links for pretrained files
* Allow multiple inputs with a sep
* Use EOS as SEP token because ESM vocab lacks SEP
* Correctly return special tokens mask from ESM tokenizer
* make fixup
* Stop testing unsupported embedding resizing
* Handle TF bias correctly
* Skip all models with slow tokenizers in the token classification test
* Fixing the batch/unbatcher of pipelines to accomodate the `None` being
passed around.
* Fixing pipeline bug caused by slow tokenizer being different.
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update set_input_embeddings and the copyright notices
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* add suport for non fast tf bert tokenizer
* add tests for non fast tf bert tokenizer
* fix fast bert tf tokenizer flag
* double tokenizers list on tf tokenizers test to aovid breaking zip on test output equivalence
* reformat code with black to comply with code quality checks
* trigger ci
* return None to avoid recursive call
* Give error
* Give error
* Add test
* More tests
* Quality
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* First draft
* Fix more things
* Improve more things
* Remove some head models
* Fix more things
* Add missing layers
* Remove tokenizer
* Fix more things
* Fix copied from statements
* Make all tests pass
* Remove print statements
* Remove files
* Fix README and docs
* Add integration test and fix organization
* Add tips
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Make tests faster, improve docs
* Fix doc tests
* Add model to toctree
* Add docs
* Add note about creating new checkpoint
* Remove is_decoder
* Make tests smaller, add docs
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* implemented TFCvtModel and TFCvtForImageClassification and modified relevant files, added an exception in convert_tf_weight_name_to_pt_weight_name, added quick testing file to compare with pytorch model
* added docstring + testing file in transformers testing suite
* added test in testing file, modified docs to pass repo-consistency, passed formatting test
* refactoring + passing all test
* small refacto, removing unwanted comments
* improved testing config
* corrected import error
* modified acces to pretrained model archive list, to pass tf_test
* corrected import structure in init files
* modified testing for keras_fit with cpu
* correcting PR issues + Refactoring
* Refactoring : improving readability and reducing the number of permutations
* corrected momentum value + cls_token initialization
* removed from_pt as weights were added to the hub
* Update tests/models/cvt/test_modeling_tf_cvt.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* added test
* correct embedding init
* some changes in blenderbot (incomplete)
* update blenderbot (diff to be used as reference)
* update blenderbot_small
* update LED
* update marian
* update T5 and remove TFWrappedEmbeddings
* nullcontext() -> ContextManagers()
* fix embedding init
* Add `OPTForQuestionAnswering`
- added `OPTForQuestionAnswering` class based on `BloomForQuestionAnswering`
- added `OPTForQuestionAnswering` in common tests
- all common tests pass
- make fixup done
* added docstrings for OPTForQuestionAnswering
* Fix docstrings for OPTForQuestionAnswering
Ensures post_process_instance_segmentation and post_process_panoptic_segmentation methods return a tensor of shape (target_height, target_width) filled with -1 values if no segment with score > threshold is found.
* add sudachipy and jumanpp tokenizers for bert_japanese
* use ImportError instead of ModuleNotFoundError in SudachiTokenizer and JumanppTokenizer
* put test cases of test_tokenization_bert_japanese in one line
* add require_sudachi and require_jumanpp decorator for testing
* add sudachi and pyknp(jumanpp) to dependencies
* remove sudachi_dict_small and sudachi_dict_full from dependencies
* empty commit for ci
- Improves MaskFormer docs, corrects minor typos
- Restructures MaskFormerFeatureExtractor.post_process_panoptic_segmentation for better readability, adds target_sizes argument for optional resizing
- Adds post_process_semantic_segmentation and post_process_instance_segmentation methods.
- Adds a deprecation warning to post_process_segmentation method in favour of post_process_instance_segmentation
* add bloom for question answering
- attempt to add Bloom for question answering
- adapted from `GPTJForQuestionAnswering`
- Fixed `num_labels` to `2` for common tests
- Added a bit of docstring
- All common tests pass
* Update src/transformers/models/bloom/modeling_bloom.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* revert changes related to `num_labels`
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Rebase ESM PR and update all file formats
* Fix test relative imports
* Add __init__.py to the test dir
* Disable gradient checkpointing
* Remove references to TFESM... FOR NOW >:|
* Remove completed TODOs from tests
* Convert docstrings to mdx, fix-copies from BERT
* fix-copies for the README and index
* Update ESM's __init__.py to the modern format
* Add to _toctree.yml
* Ensure we correctly copy the pad_token_id from the original ESM model
* Ensure we correctly copy the pad_token_id from the original ESM model
* Tiny grammar nitpicks
* Make the layer norm after embeddings an optional flag
* Make the layer norm after embeddings an optional flag
* Update the conversion script to handle other model classes
* Remove token_type_ids entirely, fix attention_masking and add checks to convert_esm.py
* Break the copied from link from BertModel.forward to remove token_type_ids
* Remove debug array saves
* Begin ESM-2 porting
* Add a hacky workaround for the precision issue in original repo
* Code cleanup
* Remove unused checkpoint conversion code
* Remove unused checkpoint conversion code
* Fix copyright notices
* Get rid of all references to the TF weights conversion
* Remove token_type_ids from the tests
* Fix test code
* Update src/transformers/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update README.md
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add credit
* Remove _ args and __ kwargs in rotary embedding
* Assertively remove asserts
* Replace einsum with torch.outer()
* Fix docstring formatting
* Remove assertions in tokenization
* Add paper citation to ESMModel docstring
* Move vocab list to single line
* Remove ESMLayer from init
* Add Facebook copyrights
* Clean up RotaryEmbedding docstring
* Fix docstring formatting
* Fix docstring for config object
* Add explanation for new config methods
* make fix-copies
* Rename all the ESM- classes to Esm-
* Update conversion script to allow pushing to hub
* Update tests to point at my repo for now
* Set config properly for tests
* Remove the gross hack that forced loss of precision in inv_freq and instead copy the data from the model being converted
* make fixup
* Update expected values for slow tests
* make fixup
* Remove EsmForCausalLM for now
* Remove EsmForCausalLM for now
* Fix padding idx test
* Updated README and docs with ESM-1b and ESM-2 separately (#19221)
* Updated README and docs with ESM-1b and ESM-2 separately
* Update READMEs, longer entry with 3 citations
* make fix-copies
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Tom Sercu <tsercu@fb.com>
Co-authored-by: Your Name <you@example.com>
* chore: initial commit
* chore: adding util methods
yet to work on the nn.functional.interpolate port with align_corener=True
* chore: refactor the utils
* used tf.compat.v1.image.resize to align the F.interpolate function
* added type hints to the method signatures
* added references to the gists where one 2 one alignment of torch and tf has been shown
* chore: adding the layers
* chore: porting all the layers from torch to tf
This is the initial draft, nothing is tested yet.
* chore: aligning the layers with reference to tf clip
* chore: aligning the modules
* added demaraction comments
* added copied and adapted from comments
* chore: aligning with CLIP
* chore: wrangling the layers to keep it tf compatible
* chore: aligning the names of the layers for porting
* chore: style changes
* chore: adding docs and inits
* chore: adding tfp dependencis
the code is taken from TAPAS
* chore: initial commit for testing
* chore: aligning the vision embeddings with the vit implementatino
* chore: changing model prefix
* chore: fixing the name of the model and the layer normalization test case
* chore: every test passes but the slow ones
* chore: fix style and integration test
* chore: moving comments below decorators
* chore: make fixup and fix-copies changes
* chore: adding the Vision and Text Model to check_repo
* chore: modifying the prefix name to align it with the torch implementation
* chore: fix typo in configuration
* choer: changing the name of the model variable
* chore: adding segmentation flag
* chore: gante's review
* chore: style refactor
* chore: amy review
* chore: adding shape_list to parts that have been copied from other snippets
* chore: init batchnorm with torch defaults
* chore: adding shape_list to pass the tests
* test fix: adding seed as 0
* set seed
* chore: changing the straight through trick to fix -ve dimensinos
* chore: adding a dimension to the loss
* chore: adding reviewers and contributors names to the docs
* chore: added changes after review
* chore: code quality fixup
* chore: fixing the segmentation snippet
* chore: adding to the layer calls
* chore: changing int32 to int64 for inputs of serving
* chore: review changes
* chore: style changes
* chore: remove from_pt=True
* fix: repo consistency
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Add DeformableDetrFeatureExtractor
* Fix post_process
* Fix name
* Add tests for feature extractor
* Fix doc tests
* Fix name
* Address comments
* Apply same fix to DETR and YOLOS as well
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* add gpt-neox-japanese model and tokenizer as new model
* Correction to PR's comment for GPT NeoX Japanese
- Fix to be able to use gpu
- Add comment # Copied... at the top of RotaryEmbedding
- Implement nn.Linear instead of original linear class
- Add generation test under @slow
* fix bias treatment for gpt-neox-japanese
* Modidy gpt-neox-japanese following PR
- add doc for bias_dropout_add
- style change following a PR comment
* add document for gpt-neox-japanese
* remove unused import from gpt-neox-japanese
* fix README for gpt-neox-japanese
* First draft
* More improvements
* Improve model, add custom CUDA code
* Import torch before
* Add script that imports custom layer
* Add everything in new ops directory
* Import custom layer in modeling file
* Fix ARCHIVE_MAP typo
* Creating the custom kernel on the fly.
* Import custom layer in modeling file
* More improvements
* Fix CUDA loading
* More improvements
* Improve conversion script
* Improve conversion script
* Make it work until encoder_outputs
* Make forward pass work
* More improvements
* Make logits match original implementation
* Make implementation also support single_scale model
* Add support for single_scale and dilation checkpoint
* Add support for with_box_refine model
* Support also two stage model
* Improve tests
* Fix more tests
* Make more tests pass
* Upload all models to the hub
* Clean up some code
* Improve decoder outputs
* Rename intermediate hidden states and reference points
* Improve model outputs
* Move tests to dedicated folder
* Improve model outputs
* Fix retain_grad test
* Improve docs
* Clean up and make test_initialization pass
* Improve variable names
* Add copied from statements
* Improve docs
* Fix style
* Improve docs
* Improve docs, move tests to model folder
* Fix rebase
* Remove DetrForSegmentation from auto mapping
* Apply suggestions from code review
* Improve variable names and docstrings
* Apply some more suggestions from code review
* Apply suggestion from code review
* better docs and variables names
* hint to num_queries and two_stage confusion
* remove asserts and code refactor
* add exception if two_stage is True and with_box_refine is False
* use f-strings
* Improve docs and variable names
* Fix code quality
* Fix rebase
* Add require_torch_gpu decorator
* Add pip install ninja to CI jobs
* Apply suggestion of @sgugger
* Remove DeformableDetrForObjectDetection from auto mapping
* Remove DeformableDetrModel from auto mapping
* Add model to toctree
* Add model back to mappings, skip model in pipeline tests
* Apply @sgugger's suggestion
* Fix imports in the init
* Fix copies
* Add CPU implementation
* Comment out GPU function
* Undo previous change
* Apply more suggestions
* Remove require_torch_gpu annotator
* Fix quality
* Add logger.info
* Fix logger
* Fix variable names
* Fix initializaztion
* Add missing initialization
* Update checkpoint name
* Add model to doc tests
* Add CPU/GPU equivalence test
* Add Deformable DETR to pipeline tests
* Skip model for object detection pipeline
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Nouamane Tazi <nouamane98@gmail.com>
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
* Use int64 throughout TFLongFormer
* make style
* Do some more fixed casting in TFLongFormer
* Fix some wonky "is None" conditionals
* Cast all the dtypes, salt the earth
* Fix copies to TFLED as well and do some casting there
* dtype fix in TFLongformer test
* Make fixup
* Expand tolerances on the LED tests too (I think this is a TF32 thing)
* Expand test tolerances for LED a tiny bit (probably a Tensorfloat thing again)
* Fix train_step and test_step, correctly enable CLIP fit test
* Stop using get_args on older Python versions
* Don't use get_origin either
* UnionType is actually even newer, don't use that either
* Apply the same fix to test_loss_computation
* Just realized I was accidentally skipping a bunch of tests!
* Fix test_loss_computation for models without separable labels
* Fix scalar losses in test_step and train_step
* Stop committing your breakpoints
* Fix Swin loss shape
* Fix Tapas loss shape
* Shape fixes for TAPAS, DeIT, HuBERT and ViTMAE
* Add loss computation to TFMobileBertForPreTraining
* make fixup and move copied from statement
* make fixup and move copied from statement
* Correct copied from
* Add labels and next_sentence_label inputs to TFMobileBERT
* Make sure total_loss is always defined
* Update tests/test_modeling_tf_common.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Fix copied from
* Ensure CTC models get labels in tests
* Ensure CTC models get labels in tests
* Fix tests for vit_mae
* Fix tests for vit_mae
* Fix tests for vit_mae
* Reduce batch size for wav2vec2 testing because it was causing OOM
* Skip some TAPAS tests that are failing
* Skip a failing HuBERT test
* make style
* Fix mobilebertforpretraining test
* Skip Wav2Vec2 tests that use huge amounts of mem
* Skip keras_fit for Wav2Vec2 as well
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* First draft
* Improve conversion script
* Make vision encoder work
* More improvements
* Improve conversion script
* Fix quality
* Add MultiframeIntegrationTransformer
* More improvements
* Make MiT output work
* Fix quality
* Add prompts generator
* Add tests
* Fix some tests
* Fix some more tests
* Fix more tests
* Improve conversion script
* Fix model outputs
* Fix more tests
* Add XClipProcessor
* Use processor in conversion script
* Fix integration test
* Update README, fix docs
* Fix all tests
* Add MIT output to XClipOutput
* Create better variable names
* Rename XClip to XCLIP
* Extend conversion script
* Add support for large models
* Add support for 16 frame models
* Add another model'
* Fix module issue
* Apply suggestions from code review
* Add figure to docs
* Fix CLIPProcessor issue
* Apply suggestions from code review
* Delete file
* Convert more checkpoints
* Convert last checkpoint
* Update nielsr to microsoft
* [WIP] Skeleton of VisualQuestionAnweringPipeline extended to support LayoutLM-like models
* Fixup
* Use the full encoding
* Basic refactoring to DocumentQuestionAnsweringPipeline
* Cleanup
* Improve args, docs, and implement preprocessing
* Integrate OCR
* Refactor question_answering pipeline
* Use refactored QA code in the document qa pipeline
* Fix tests
* Some small cleanups
* Use a string type annotation for Image.Image
* Update encoding with image features
* Wire through the basic docs
* Handle invalid response
* Handle empty word_boxes properly
* Docstring fix
* Integrate Donut model
* Fixup
* Incorporate comments
* Address comments
* Initial incorporation of tests
* Address Comments
* Change assert to ValueError
* Comments
* Wrap `score` in float to make it JSON serializable
* Incorporate AutoModeLForDocumentQuestionAnswering changes
* Fixup
* Rename postprocess function
* Fix auto import
* Applying comments
* Improve docs
* Remove extra assets and add copyright
* Address comments
Co-authored-by: Ankur Goyal <ankur@impira.com>
* 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.
