* Fork.
* RecurrentGemma initial commit.
* Updating __init__.py.
* Minor modification to how we initialize the cache.
Changing how the config specifies the architecture.
* Reformat code to 4 spaces.
Fixed a few typos.
* Fixed the forward pass.
Still unclear on the cache?
* Fixed the RecurrentGemmaForCausalLM
* Minor comment that we might not need attention_mask and output_attention arguments.
* Now cache should work as well.
* Adding a temporary example to check whether the model generation works.
* Adding the tests and updating imports.
* Adding the example file missing in the previous commit.
* First working example.
* Removing .gitignore and reverting parts of __init__.
* Re-add .gitignore.
* Addressing comments for configuration.
* Move mask creation to `_prepare_inputs_for_generation`.
* First try at integration tests:
1. AttributeError: 'GriffinCausalLMOutput' object has no attribute 'attentions'.
2. `cache_position` not passed
* Transfoering between machines.
* Running normal tests.
* Minor fix.
* More fixes.
* Addressing more comments.
* Minor fixes.
* first stab at cleanup
* more refactoring
* fix copies and else
* renaming and get init to work
* fix causal mask creation
* update
* nit
* fix a hell lot of things
* updates
* update conversion script
* make all keys importable
* nits
* add auto mappings
* properly convert ffw_up and down
* add scaling
* fix generations
* for recurrent dtype
* update
* fix going beyong window
* fixup
* add missing files
* current updates to remove last einops
* finish modeling refactor
* TADA
* fix compile
* fix most failing testt ? ?
* update tests
* refactor and update
* update
* nits, fixup and update tests
* more fixup
* nits
* fix imports
* test format
* fixups
* nits
* tuple typing
* fix code quality
* add model card
* fix doc
* skip most generation tests
* nits
* style
* doc fixes
* fix pr and check_copies?
* last nit
* oupsy
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <hi@lysand.re>
* update
* Update src/transformers/models/recurrent_gemma/convert_recurrent_gemma_to_hf.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* update based on review
* doc nit
* fix quality
* quality
* fix slow test model path
* update default dype
* ignore attributes that can be safely ignored in check config attributes
* 0lallalala come on
* save nit
* style
* remove to dict update
* make sure we can also run in float16
* style
---------
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
Co-authored-by: Aleksandar Botev <botev@google.com>
Co-authored-by: Leonard Berrada <lberrada@users.noreply.github.com>
Co-authored-by: anushanf <anushanf@google.com>
Co-authored-by: botev <botevmg@gmail.com>
Co-authored-by: Lysandre Debut <hi@lysand.re>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* ImportError: Trainer with PyTorch requires accelerate>=0.20.1 Fix
Adding the evaluate and accelerate installs at the beginning of the cell to fix the issue
* ImportError Fix: Trainer with PyTorch requires accelerate>=0.20.1
* Import Error Fix
* Update installation.md
* Update quicktour.md
* rollback other lang changes
* Update _config.py
* updates for other languages
* fixing error
* Tutorial Update
* Update tokenization_utils_base.py
* Just use an optimizer string to pass the doctest?
---------
Co-authored-by: Matt <rocketknight1@gmail.com>
* add FA2 to o.g Musicgen
* make style
* add FA2 support to Musicgen Melody
* add generation FA2 tests to o.g Musicgen
* make style and fix copies
* add Musicgen to FA2 docs + deprecate list
* add sdpa supports to Musicgen's
* make style and fix copies
* refactor attention implementation arguments
* add Copied from to sdpa tests
* add copied form in sdpa tests melody
* add copied for FA2 generation tests
* add FA2 inference copied from
* make style
* 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>
* model_summary.md - Add link to Harvard's Annotated Transformer.
* model_summary.md - slight wording change + capitalize name of the paper
* model_summary.md - moves the Annotated Transformer link in a praenthesis next to the link to the original paper (great idea, stevhliu!)
* model_summary.md - moves the Annotated Transformer link in a praenthesis next to the link to the original paper (commit pt. 2, accidentally removed "has" in pt. 1)
* 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>
* 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>
* Update pipeline_tutorial.md to include gradio
* Update pipeline_tutorial.md
* Update docs/source/en/pipeline_tutorial.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/pipeline_tutorial.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/pipeline_tutorial.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/pipeline_tutorial.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update pipeline_tutorial.md
* Update docs/source/en/pipeline_tutorial.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@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>
* 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>
* torchscript and trainer md es translation
* corrected md es files and even corrected spelling in en md
* made es corrections to trainer.md
* deleted entrenamiento... title on yml
* placed entrenamiento in right place
* translated es chat_templating.md w/ yml addition
* requested es changes to md and yml
* last es changes to md
* 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>
* 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>
* torchscript and trainer md es translation
* corrected md es files and even corrected spelling in en md
* made es corrections to trainer.md
* deleted entrenamiento... title on yml
* placed entrenamiento in right place
* 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>
* Add tasks_explained.md to es/
* Fix little typo in en/ version
* translate speach/audio section
* translate part of vision computer section | fix little typo in en/
* Fix little typo in en/
* Translate vision computer section | remove ** ** to * * in both files
* Translate NLP section | fix link to task/translation in en/
* Updete link in es/tasks_summary.md
* Fix task_summary title link
The link in evaluation was missing a hyphen between post and processing. I fixed this, for English only. Someone with the ability to do a global search/replace should fix the other languages (if indeed they have this issue)/
* Add task_summary to es/_toctree.yml
* Add task_summary.md to docs/es
* Change title of task_summary.md
* Translate firsts paragraphs
* Translate middle paragraphs
* Translte the rest of the doc
* Edit firts paragraph
* 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>
* Add missing entries to the language selector
* Add links to the Colab and AWS Studio notebooks for ONNX
* Use anchor links in CONTRIBUTING.md
* Fix broken hyperlinks due to spaces
* Fix links to OpenAI research articles
* Remove confusing footnote symbols from author names, as they are also considered invalid markup
* 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
