* 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
* Initial commit (still lots of unfinished bits)
* (Still untested) add safetensors sharding to save_pretrained
* Fix savetensors saving, update default shard size to match PT
* Add proper loading of TF-format safetensors
* Revert default size in case that changes things
* Fix incorrect index name
* Update loading priority
* Update tests
* Make the tests a little more stringent
* Expand tests
* Add sharded cross-test
* Fix argument name
* One more test fix
* Adding mlx to the list of allowed formats
* Remove irrelevant block for safetensors
* Refactor warning logging into a separate function
* Remove unused skip_logger_warnings arg
* Update src/transformers/modeling_tf_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Move function def
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* 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`
* add galore v1
* add import
* add tests and doc
* fix doctest
* forward contrib credits from discussions
* forward contrib credits from discussions
* Apply suggestions from code review
Co-authored-by: Zach Mueller <muellerzr@gmail.com>
* fix failing tests'
* switch to `optim_target_modules` and clarify docs
* more clarification
* enhance lookup logic
* update a test to add peak memory
* add regex, all-linear and single string support
* add layer-wise optimization through DummyOptimizers and LRSchedulers
* forward contrib credits from discussions and original idea
* add a section about DDP not supported in layerwise
* Update src/transformers/trainer.py
Co-authored-by: Zach Mueller <muellerzr@gmail.com>
* fix self
* check only if layer_wise
* Update src/transformers/training_args.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* oops
* make use of intervals
* clarify comment
* add matching tests
* GaLoRe -> GaLore
* move to `get_scheduler`
* add note on docs
* add a warning
* adapt a bit the docs
* update docstring
* support original API
* Update docs/source/en/trainer.md
* slightly refactor
* Update docs/source/en/trainer.md
Co-authored-by: Matthew Douglas <38992547+matthewdouglas@users.noreply.github.com>
* Update src/transformers/training_args.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix args parsing and add tests
* remove warning for regex
* fix type hint
* add note about extra args
* make `is_regex` return optional
---------
Co-authored-by: Maxime <maximegmd @users.noreply.github.com>
Co-authored-by: Wing Lian <winglian @users.noreply.github.com>
Co-authored-by: Zach Mueller <muellerzr@gmail.com>
Co-authored-by: hiyouga <hiyouga@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Matthew Douglas <38992547+matthewdouglas@users.noreply.github.com>
* 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>
* Allow apply_chat_template to pass kwargs to the template
* Fix priority for template_kwargs
* Fix docstring
* style fix
* Add the option for the model to have a dict of templates
* Error message cleanup
* Add test for chat template dicts
* Simplify the chat template dict test and apply it to all tokenizers in self.get_tokenizers()
* Save chat template dicts as lists with fixed key names
* Add test for serialization/reloading
* Add require_jinja just to be safe, even though I don't think we use it
* 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>
* fix image-to-text batch incorrect output issue
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* add ci test
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* update ci test
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Signed-off-by: Wang, Yi <yi.a.wang@intel.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>
* added exllama kernels support for awq models
* doc
* style
* Update src/transformers/modeling_utils.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* refactor
* moved exllama post init to after device dispatching
* bump autoawq version
* added exllama test
* style
* configurable exllama kernels
* copy exllama_config from gptq
* moved exllama version check to post init
* moved to quantization dockerfile
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* 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
* draft processor arg capture
* add missing vivit model
* add new common test for image preprocess signature
* fix quality
* fix up
* add back missing validations
* quality
* move info level to warning for unused kwargs
* 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
* generated text on A10G
* generated text in CI
* Apply suggestions from code review
add explanatory comments
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
output_logits option behaves like output_scores, but returns the raw, unprocessed prediction logit scores,
ie. the values before they undergo logit processing and/or warping. The latter happens by default for the
regular output scores.
It's useful to have the unprocessed logit scores in certain circumstances. For example, unprocessed logit scores
are very useful with causallm models when one wants to determine the probability of a certain answer, e.g.
when asking a question with a yes/no answer. In that case getting the next-token probabilities of both "yes" and
"no" (and/or their relative ratio) is of interest for classification. The reason for getting these _before_ logit
processing and/or warping is b/c a) that can change the probabilities or b) reject the tokens of interest / reduce
the number of tokens to just 1.
For an example use-case see paper TabLLM: Few-shot Classification of Tabular Data with Large Language Models
by Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, and David Sontag.
https://arxiv.org/abs/2210.10723
In addition:
- added dedicated unit test: tests/generation/test_utils/test_return_unprocessed_logit_scores
which tests return of logics with output_logits=True in generation.
- set output_logits=True in all other generation unit tests, that also have output_scores=True.
Implemented @gante's and @amyeroberts review feedback
Co-authored-by: kx79wq <max.baak@ing.com>
* Add chat support to text generation pipeline
* Better handling of single elements
* Deprecate ConversationalPipeline
* stash commit
* Add missing add_special_tokens kwarg
* Update chat templating docs to refer to TextGenerationPipeline instead of ConversationalPipeline
* Add ✨TF✨ tests
* @require_tf
* Add type hint
* Add specific deprecation version
* Remove unnecessary do_sample
* Remove todo - the discrepancy has been resolved
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/pipelines/text_generation.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* 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>
* wow I was scared!
* fix everything
* nits
* make it BC?
* add todo
* nits
* is_tracing should still be used to pass tracing tests
* nits
* some nits to make sure genration works with static cache uncompiled
* fix sdpa
* fix FA2 for both static and dynamic in a better way?
* style
* fix-copies
* fix fix copies
* fix sequential beam searcg
* style
* use `keys_to_ignore`
* nit
* correct dtype inference when init
* :( the fix for FA2 is still not optimal to investigate!
* styling
* nits
* nit
* this might work better
* add comment
* Update src/transformers/models/llama/modeling_llama.py
* "position_ids" -> "cache_position"
* style
* nit
* Remove changes that should no be propagatted just yet
* Apply suggestions from code review
* Styling
* make sure we raise an errir for static cache with FA2 enabled
* move to the bottom of the signature
* style
* Update src/transformers/models/llama/modeling_llama.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Update src/transformers/models/llama/modeling_llama.py
* nit in the name
---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.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
* [WIP] Hard error when ignoring tensors.
* Better selection/error when saving a checkpoint.
- Find all names we should normally drop (those are in the transformers
config)
- Find all disjoint tensors (for those we can safely trigger a copy to
get rid of the sharing before saving)
- Clone those disjoint tensors getting rid of the issue
- Find all identical names (those should be declared in the config
but we try to find them all anyway.)
- For all identical names:
- If they are in the config, just ignore them everything is fine
- If they are not, warn about them.
- For all remainder tensors which are shared yet neither identical NOR
disjoint. raise a hard error.
* Adding a failing test on `main` that passes here.
* We don't need to keep the subfolder logic in this test.
* 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>
* 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>
* test that tied output embeddings aren't initialized on load
* don't initialize the output embeddings if we're going to tie them to the input embeddings
* 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
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Co-authored-by: Your Name <you@example.com>
* fix adding special tokens when the token is already there.
* add a test
* add a test
* nit
* fix the test: make sure the order is preserved
* Update tests/test_tokenization_common.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.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
* fix mismatching behavior in from_pretrained with/without accelerate
* meaningful refactor
* remove added space
* add test
* fix model on the hub
* comment
* use tiny model
* style
* added args to the pipeline
* added test
* more sensical tests
* fixup
* docs
* typo
;
* docs
* made changes to support named args
* fixed test
* docs update
* styles
* docs
* docs
* Correct the implementation of auxiliary loss of mixtrtal
* correct the implementation of auxiliary loss of mixtrtal
* Implement a simpler calculation method
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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
Matt