* refactor image processor slow got ocr
* add working image processor fast
* fix fast image processor, update doc
* use one big loop for processing patches
* decompose chat template docs
* add docs
* update model docs
* qwen2-5
* pixtral
* remove old chat template
* also video as list frames supported
* Update docs/source/en/chat_template_multimodal.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/chat_template_multimodal.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/chat_template_multimodal.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/chat_template_multimodal.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/chat_template_multimodal.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/chat_template_multimodal.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/chat_template_multimodal.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/chat_template_multimodal.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/chat_template_multimodal.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/chat_template_multimodal.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/chat_template_multimodal.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/chat_template_multimodal.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/chat_template_multimodal.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* remove audio for now
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* implement config and model building blocks
* refactor model architechture
* update model outputs
* update init param to include use_fov_model
* update param name in config
* fix hidden_states and attentions outputs for fov
* sort config
* complete minor todos
* update patching
* update config for encoder
* fix config
* use correct defaults in config
* update merge for compatibility with different image size
* restructure encoder for custom configuration
* make fov model compatible with custom config
* replace word "decoder" with "fusion"
* weight conversion script
* fix fov squeeze
* update conversion script (without test)
* upload ruff image processing
* create fast image processing
* use torch interpolation for image processing
* complete post_process_depth_estimation
* config: fix imports and sort args
* apply inference in weight conversion
* use mllama script instead for weight conversion
* clean weight conversion script
* add depth-pro status in other files
* fill docstring in config
* formatting
* more formatting
* formatting with ruff
* formatting with style
* fix copied classes
* add examples; update weight convert script
* fix using check_table.py and isort
* fix config docstring
* add depth pro to sdpa docs
* undo unintentional changes in configuration_gemma.py
* minor fixes
* test image processing
* fixes and tests
* more fixes
* use output states from image_encoder instead
* Revert "use output states from image_encoder instead"
This reverts commit 2408ec54e4.
* make embeddings dynamic
* reshape output hidden states and attentions as part of computation graph
* fix ruff formating
* fix docstring failure
* use num_fov_head_layers in tests
* update doc
* check consistency with config
* ruff formatting
* update test case
* fix ruff formatting
* add tests for fov
* use interpolation in postprocess
* run and fix slow tests locally
* use scaled_images_features for image and fov encoder
* return fused_hidden_states in fusion stage
* fix example
* fix ruff
* fix copyright license for all files
* add __all__ for each file
* minor fixes
- fix download spell
- add push_to_hub option
- fix Optional type hinting
- apply single loop for DepthProImageProcessor.preprocess
* return list in post_process_depth_estimation
* minor fixes
- capitalize start of docstring
- use ignore copy
- fix examples
- move docstring templates and custom output classes to top
- remove "-> None" typehinting from __init__
- type hinting for forward passes
- fix docstrings for custom output classes
* fix "ruff check"
* update upsample and projection
* major changes: (image size and merge optimization)
- add support for images of any size
- optimize merge operation
- remove image_size from config
- use full names instead of B, C, H, W
- remove interpolation from fusion stage
- add interpolation after merge
- move validations to config
- update integration test
- add type hints for functions
* fix push_to_hub option in weights conversion
* remove image_size in weights conversion
* major changes in the architecture
- remove all DepthProViT modules and support different backbones using the AutoModel API
- set default use_fov_model to False
- validate parameters in configuration
- update interpolate function: use "nearest" for faster computation
- update reshape_feature function: remove all special tokens, possible from different backbones
- update merge function: use padding from config instead of merge_out_size
- remove patch_to_batch and batch_to_patch conversions for now
- calculate out_size dynamically in the encoder
- leave head_mask calculation to the backbone
- fix bugs with merge
- add more comments
- update tests
* placeholder for unused config attributes
* improve docs amid review
* minor change in docs
* further optimize merge
* fix formatting
* remove unused patch/batch convertion functions
* use original F.interpolate
* improve function naming
* minor chages
- use torch_int instead of int
- use proper for newly initialized tensors
- use user provided return_dict for patch_encoder
- use if-else block instead in self.use_fov_model
* rearchitect upsample block for improved modularity
* update upsample keys in weight conversion
* improve padding in merge_patches
* use double-loop for merge
* update comments
* create feature_extractor, reduce some forward code
* introduce config.use_mask_token in dinov2
* minor fixes
* minor fixes for onnx
* update __init__ to latest format
* remove DepthProConfig.to_dict()
* major changes in backbone
* update config in weight conversion
* formatting
* converted model is fp32
* improve naming and docs for feature_extractor->reconstruct_feature_maps
* minor fixes; amid review
* create intermediate vars in func call
* use torch.testing.assert_close
* use ModuleList instead of Sequential and ModuleDict
* update docs
* include fov in integraiton tests
* update docs
* improve initialization of convolution layers
* fix unused fov keys
* update tests
* ruff format
* fix test, amid kaimming initialization
* add depthpro to toctree
* add residual layer to _no_split_modules
* architecture rework
* Update src/transformers/models/depth_pro/image_processing_depth_pro.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Update src/transformers/models/depth_pro/image_processing_depth_pro_fast.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* update docs
* improve merge_patches
* use flatten with fov_output
* ruff formatting
* update resources section in docs
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* fix typo "final_kernal_size"
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* fix output typehint for DepthProDepthEstimator
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* residual operation in 2 steps
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* use image_size instead of global patch_size in interpolation
* replace all Sequential with ModuleList
* update fov
* update heads
* fix and update conversion script for heads
* ruff formatting
* remove float32 conversion
* use "Fov" instead of "FOV" in class names
* use "Fov" instead of "FOV" in config docs
* remove prune_heads
* update fusion stage
* use device in examples
* update processor
* ruff fixes
* add do_rescale in image_processor_dict
* skip test: test_fast_is_faster_than_slow
* ruff formatting
* DepthProImageProcessorFast in other files
* revert antialias removal
* add antialias in BaseImageProcessorFast
* Revert "revert antialias removal"
This reverts commit 5caa0bd8f9.
* Revert "add antialias in BaseImageProcessorFast"
This reverts commit 3ae1134780.
* update processor for grouping and antialias
* try test_fast_is_faster_than_slow without "skip" or "flanky"
* update checkpoint
* update checkpoint
* use @is_flanky for processor test
* update checkpoint to "apple/DepthPro-hf"
---------
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* add init and base image processing functions
* add add_fast_image_processor to transformers-cli
* add working fast image processor clip
* add fast image processor to doc, working tests
* remove "to be implemented" SigLip
* fix unprotected import
* fix unprotected vision import
* update ViTImageProcessorFast
* increase threshold slow fast ewuivalence
* add fast img blip
* add fast class in tests with cli
* improve cli
* add fast image processor convnext
* add LlavaPatchingMixin and fast image processor for llava_next and llava_onevision
* add device kwarg to ImagesKwargs for fast processing on cuda
* cleanup
* fix unprotected import
* group images by sizes and add batch processing
* Add batch equivalence tests, skip when center_crop is used
* cleanup
* update init and cli
* fix-copies
* refactor convnext, cleanup base
* fix
* remove patching mixins, add piped torchvision transforms for ViT
* fix unbatched processing
* fix f strings
* protect imports
* change llava onevision to class transforms (test)
* fix convnext
* improve formatting (following Pavel review)
* fix handling device arg
* improve cli
* fix
* fix inits
* Add distinction between preprocess and _preprocess, and support for arbitrary kwargs through valid_extra_kwargs
* uniformize qwen2_vl fast
* fix docstrings
* add add fast image processor llava
* remove min_pixels max_pixels from accepted size
* nit
* nit
* refactor fast image processors docstrings
* cleanup and remove fast class transforms
* update add fast image processor transformers cli
* cleanup docstring
* uniformize pixtral fast and make _process_image explicit
* fix prepare image structure llava next/onevision
* Use typed kwargs instead of explicit args
* nit fix import Unpack
* clearly separate pops and gets in base preprocess. Use explicit typed kwargs
* make qwen2_vl preprocess arguments hashable
* initial commit
* encoder+decoder layer changes WIP
* architecture checks
* working version of detection + segmentation
* fix modeling outputs
* fix return dict + output att/hs
* found the position embedding masking bug
* pre-training version
* added iamge processors
* typo in init.py
* iterupdate set to false
* fixed num_labels in class_output linear layer bias init
* multihead attention shape fixes
* test improvements
* test update
* dab-detr model_doc update
* dab-detr model_doc update2
* test fix:test_retain_grad_hidden_states_attentions
* config file clean and renaming variables
* config file clean and renaming variables fix
* updated convert_to_hf file
* small fixes
* style and qulity checks
* return_dict fix
* Merge branch main into add_dab_detr
* small comment fix
* skip test_inputs_embeds test
* image processor updates + image processor test updates
* check copies test fix update
* updates for check_copies.py test
* updates for check_copies.py test2
* tied weights fix
* fixed image processing tests and fixed shared weights issues
* added numpy nd array option to get_Expected_values method in test_image_processing_dab_detr.py
* delete prints from test file
* SafeTensor modification to solve HF Trainer issue
* removing the safetensor modifications
* make fix copies and hf uplaod has been added.
* fixed index.md
* fixed repo consistency
* styel fix and dabdetrimageprocessor docstring update
* requested modifications after the first review
* Update src/transformers/models/dab_detr/image_processing_dab_detr.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* repo consistency has been fixed
* update copied NestedTensor function after main merge
* Update src/transformers/models/dab_detr/modeling_dab_detr.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* temp commit
* temp commit2
* temp commit 3
* unit tests are fixed
* fixed repo consistency
* updated expected_boxes varible values based on related notebook results in DABDETRIntegrationTests file.
* temporarialy config modifications and repo consistency fixes
* Put dilation parameter back to config
* pattern embeddings have been added to the rename_keys method
* add dilation comment to config + add as an exception in check_config_attributes SPECIAL CASES
* delete FeatureExtractor part from docs.md
* requested modifications in modeling_dab_detr.py
* [run_slow] dab_detr
* deleted last segmentation code part, updated conversion script and changed the hf path in test files
* temp commit of requested modifications
* temp commit of requested modifications 2
* updated config file, resolved codepaths and refactored conversion script
* updated decodelayer block types and refactored conversion script
* style and quality update
* small modifications based on the request
* attentions are refactored
* removed loss functions from modeling file, added loss function to lossutils, tried to move the MLP layer generation to config but it failed
* deleted imageprocessor
* fixed conversion script + quality and style
* fixed config_att
* [run_slow] dab_detr
* changing model path in conversion file and in test file
* fix Decoder variable naming
* testing the old loss function
* switched back to the new loss function and testing with the odl attention functions
* switched back to the new last good result modeling file
* moved back to the version when I asked the review
* missing new line at the end of the file
* old version test
* turn back to newest mdoel versino but change image processor
* style fix
* style fix after merge main
* [run_slow] dab_detr
* [run_slow] dab_detr
* added device and type for head bias data part
* [run_slow] dab_detr
* fixed model head bias data fill
* changed test_inference_object_detection_head assertTrues to torch test assert_close
* fixes part 1
* quality update
* self.bbox_embed in decoder has been restored
* changed Assert true torch closeall methods to torch testing assertclose
* modelcard markdown file has been updated
* deleted intemediate list from decoder module
---------
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* First commit
* Finish model implementation
* First commit
* Finish model implementation
* Register zamba2
* generated modeling and configuration
* generated modeling and configuration
* added hybrid cache
* fix attention_mask in mamba
* dropped unused loras
* fix flash2
* config docstrings
* fix config and fwd pass
* make fixup fixes
* text_modeling_zamba2
* small fixes
* make fixup fixes
* Fix modular model converter
* added inheritances in modular, renamed zamba cache
* modular rebase
* new modular conversion
* fix generated modeling file
* fixed import for Zamba2RMSNormGated
* modular file cleanup
* make fixup and model tests
* dropped inheritance for Zamba2PreTrainedModel
* make fixup and unit tests
* Add inheritance of rope from GemmaRotaryEmbedding
* moved rope to model init
* drop del self.self_attn and del self.feed_forward
* fix tests
* renamed lora -> adapter
* rewrote adapter implementation
* fixed tests
* Fix torch_forward in mamba2 layer
* Fix torch_forward in mamba2 layer
* Fix torch_forward in mamba2 layer
* Dropped adapter in-place sum
* removed rope from attention init
* updated rope
* created get_layers method
* make fixup fix
* make fixup fixes
* make fixup fixes
* update to new attention standard
* update to new attention standard
* make fixup fixes
* minor fixes
* cache_position
* removed cache_position postion_ids use_cache
* remove config from modular
* removed config from modular (2)
* import apply_rotary_pos_emb from llama
* fixed rope_kwargs
* Instantiate cache in Zamba2Model
* fix cache
* fix @slow decorator
* small fix in modular file
* Update docs/source/en/model_doc/zamba2.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* several minor fixes
* inherit mamba2decoder fwd and drop position_ids in mamba
* removed docstrings from modular
* reinstate zamba2 attention decoder fwd
* use regex for tied keys
* Revert "use regex for tied keys"
This reverts commit 9007a522b1.
* use regex for tied keys
* add cpu to slow forward tests
* dropped config.use_shared_mlp_adapter
* Update docs/source/en/model_doc/zamba2.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* re-convert from modular
---------
Co-authored-by: root <root@node-2.us-southcentral1-a.compute.internal>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* adding blog post to model doc
* Update docs/source/en/model_doc/timm_wrapper.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* review suggestions
* review suggestions
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Initial commit with template code generated by transformers-cli
* Multiple additions to SuperGlue implementation :
- Added the SuperGlueConfig
- Added the SuperGlueModel and its implementation
- Added basic weight conversion script
- Added new ImageMatchingOutput dataclass
* Few changes for SuperGlue
* Multiple changes :
- Added keypoint detection config to SuperGlueConfig
- Completed convert_superglue_to_pytorch and succesfully run inference
* Reverted unintentional change
* Multiple changes :
- Added SuperGlue to a bunch of places
- Divided SuperGlue into SuperGlueForImageMatching and SuperGlueModel
- Added testing images
* Moved things in init files
* Added docs (to be finished depending on the final implementation)
* Added necessary imports and some doc
* Removed unnecessary import
* Fixed make fix-copies bug and ran it
* Deleted SuperGlueModel
Fixed convert script
* Added SuperGlueImageProcessor
* Changed SuperGlue to support batching pairs of images and modified ImageMatchingOutput in consequences
* Changed convert_superglue_to_hf.py script to experiment different ways of reading an image and seeing its impact on performances
* Added initial tests for SuperGlueImageProcessor
* Added AutoModelForImageMatching in missing places and tests
* Fixed keypoint_detector_output instructions
* Fix style
* Adapted to latest main changes
* Added integration test
* Fixed bugs to pass tests
* Added keypoints returned by keypoint detector in the output of SuperGlue
* Added doc to SuperGlue
* SuperGlue returning all attention and hidden states for a fixed number of keypoints
* Make style
* Changed SuperGlueImageProcessor tests
* Revert "SuperGlue returning all attention and hidden states for a fixed number of keypoints"
Changed tests accordingly
This reverts commit 5b3b669c
* Added back hidden_states and attentions masked outputs with tests
* Renamed ImageMatching occurences into KeypointMatching
* Changed SuperGlueImageProcessor to raise error when batch_size is not even
* Added docs and clarity to hidden state and attention grouping function
* Fixed some code and done refactoring
* Fixed typo in SuperPoint output doc
* Fixed some of the formatting and variable naming problems
* Removed useless function call
* Removed AutoModelForKeypointMatching
* Fixed SuperGlueImageProcessor to only accept paris of images
* Added more fixes to SuperGlueImageProcessor
* Simplified the batching of attention and hidden states
* Simplified stack functions
* Moved attention instructions into class
* Removed unused do_batch_norm argument
* Moved weight initialization to the proper place
* Replaced deepcopy for instantiation
* Fixed small bug
* Changed from stevenbucaille to magic-leap repo
* Renamed London Bridge images to Tower Bridge
* Fixed formatting
* Renamed remaining "london" to "tower"
* Apply suggestions from code review
Small changes in the docs
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Added AutoModelForKeypointMatching
* Changed images used in example
* Several changes to image_processing_superglue and style
* Fixed resample type hint
* Changed SuperGlueImageProcessor and added test case for list of 2 images
* Changed list_of_tuples implementation
* Fix in dummy objects
* Added normalize_keypoint, log_sinkhorn_iterations and log_optimal_transport docstring
* Added missing docstring
* Apply suggestions from code review
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>
* Moved forward block at bottom
* Added docstring to forward method
* Added docstring to match_image_pair method
* Changed test_model_common_attributes to test_model_get_set_embeddings test method signature
* Removed AutoModelForKeypointMatching
* Removed image fixtures and added load_dataset
* Added padding of images in SuperGlueImageProcessor
* Cleaned up convert_superglue_to_hf script
* Added missing docs and fixed unused argument
* Fixed SuperGlueImageProcessor tests
* Transposed all hidden states from SuperGlue to reflect the standard (..., seq_len, feature_dim) shape
* Added SuperGlueForKeypointMatching back to modeling_auto
* Fixed image processor padding test
* Changed SuperGlue docs
* changes:
- Abstraction to batch, concat and stack of inconsistent tensors
- Changed conv1d's to linears to match standard attention implementations
- Renamed all tensors to be tensor0 and not tensor_0 and be consistent
- Changed match image pair to run keypoint detection on all image first, create batching tensors and then filling these tensors matches after matches
- Various changes in docs, etc
* Changes to SuperGlueImageProcessor:
- Reworked the input image pairs checking function and added tests accordingly
- Added Copied from statements
- Added do_grayscale tag (also for SuperPointImageProcessor)
- Misc changes for better code
* Formatting changes
* Reverted conv1d to linear conversion because of numerical differences
* fix: changed some code to be more straightforward (e.g. filtering keypoints) and converted plot from opencv to matplotlib
* fix: removed unnecessary test
* chore: removed commented code and added back hidden states transpositions
* chore: changed from "inconsistent" to "ragged" function names as suggested
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* docs: applied suggestions
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* docs: updated to display matched output
* chore: applied suggestion for check_image_pairs_input function
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* chore: changed check_image_pairs_input function name to validate_and_format_image_pairs and used validate_preprocess_arguments function
* tests: simplified tests for image input format and shapes
* feat: converted SuperGlue's use of Conv1d with kernel_size of 1 with Linear layers. Changed tests and conversion script accordingly
* feat: several changes to address comments
Conversion script:
- Reverted fuse batchnorm to linear conversion
- Changed all 'nn.Module' to respective SuperGlue models
- Changed conversion script to use regex mapping and match other recent scripts
Modeling SuperGlue:
- Added batching with mask and padding to attention
- Removed unnecessary concat, stack and batch ragged pairs functions
- Reverted batchnorm layer
- Renamed query, key, value and merge layers into q, k, v, out proj
- Removed Union of different Module into nn.Module in _init_weights method typehint
- Changed several method's signature to combine image0 and image1 inputs with appropriate doc changes
- Updated SuperGlue's doc with torch.no_grad()
Updated test to reflect changes in SuperGlue model
* refactor: changed validate_and_format_image_pairs function with clarity
* refactor: changed from one SuperGlueMLP class to a list of SuperGlueMLP class
* fix: fixed forgotten init weight change from last commit
* fix: fixed rebase mistake
* fix: removed leftover commented code
* fix: added typehint and changed some of arguments default values
* fix: fixed attribute default values for SuperGlueConfig
* feat: added SuperGlueImageProcessor post process keypoint matching method with tests
* fix: fixed SuperGlue attention and hidden state tuples aggregation
* chore: fixed mask optionality and reordered tensor reshapes to be cleaner
* chore: fixed docs and error message returned in validate_and_format_image_pairs function
* fix: fixed returned keypoints to be the ones that SuperPoint returns
* fix: fixed check on number of image sizes for post process compared to the pairs in outputs of SuperGlue
* fix: fixed check on number of image sizes for post process compared to the pairs in outputs of SuperGlue (bis)
* fix: Changed SuperGlueMultiLayerPerceptron instantiation to avoid if statement
* fix: Changed convert_superglue_to_hf script to reflect latest SuperGlue changes and got rid of nn.Modules
* WIP: implement Attention from an existing class (like BERT)
* docs: Changed docs to include more appealing matching plot
* WIP: Implement Attention
* chore: minor typehint change
* chore: changed convert superglue script by removing all classes and apply conv to linear conversion in state dict + rearrange keys to comply with changes in model's layers organisation
* Revert "Fixed typo in SuperPoint output doc"
This reverts commit 2120390e82.
* chore: added comments in SuperGlueImageProcessor
* chore: changed SuperGlue organization HF repo to magic-leap-community
* [run-slow] refactor: small change in layer instantiation
* [run-slow] chore: replaced remaining stevenbucaille org to magic-leap-community
* [run-slow] chore: make style
* chore: update image matching fixture dataset HF repository
* [run-slow] superglue
* tests: overwriting test_batching_equivalence
* [run-slow] superglue
* tests: changed test to cope with value changing depending on cuda version
* [run-slow] superglue
* tests: changed matching_threshold value
* [run-slow] superglue
* [run-slow] superglue
* tests: changed tests for integration
* [run-slow] superglue
* fix: Changed tensor view and permutations to match original implementation results
* fix: updated convert script and integration test to include last change in model
* fix: increase tolerance for CUDA variances
* Apply suggestions from code review
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* [run-slow] superglue
* chore: removed blank whitespaces
* [run-slow] superglue
* Revert SuperPoint image processor accident changes
* [run-slow] superglue
* refactor: reverted copy from BERT class
* tests: lower the tolerance in integration tests for SuperGlue
* [run-slow] superglue
* chore: set do_grayscale to False in SuperPoint and SuperGlue image processors
* [run-slow] superglue
* fix: fixed imports in SuperGlue files
* chore: changed do_grayscale SuperGlueImageProcessing default value to True
* docs: added typehint to post_process_keypoint_matching method in SuperGlueImageProcessor
* fix: set matching_threshold default value to 0.0 instead of 0.2
* feat: added matching_threshold to post_process_keypoint_matching method
* docs: update superglue.md to include matching_threshold parameter
* docs: updated SuperGlueConfig docstring for matching_threshold default value
* refactor: removed unnecessary parameters in SuperGlueConfig
* fix: changed from matching_threshold to threshold
* fix: re-revert changes to make SuperGlue attention classes copies of BERT
* [run-slow] superglue
* fix: added missing device argument in post_processing method
* [run-slow] superglue
* fix: add matches different from -1 to compute valid matches in post_process_keypoint_matching (and docstring)
* fix: add device to image_sizes tensor instantiation
* tests: added checks on do_grayscale test
* chore: reordered and added Optional typehint to KeypointMatchingOutput
* LightGluePR suggestions:
- use `post_process_keypoint_matching` as default docs example
- add `post_process_keypoint_matching` in autodoc
- add `SuperPointConfig` import under TYPE_CHECKING condition
- format SuperGlueConfig docstring
- add device in convert_superglue_to_hf
- Fix typo
- Fix KeypointMatchingOutput docstring
- Removed unnecessary line
- Added missing SuperGlueConfig in __init__ methods
* LightGluePR suggestions:
- use batching to get keypoint detection
* refactor: processing images done in 1 for loop instead of 4
* fix: use @ instead of torch.einsum for scores computation
* style: added #fmt skip to long tensor values
* refactor: rollbacked validate_and_format_image_pairs valid and invalid case to more simple ones
* refactor: prepare_imgs
* refactor: simplified `validate_and_format_image_pairs`
* docs: fixed doc
---------
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>
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Convert more checkpoints
* Update docs, convert huge variant
* Update model name
* Update src/transformers/models/vitpose/modeling_vitpose.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Remove print statements
* Update docs/source/en/model_doc/vitpose.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Link to collection
---------
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Add input ids to model output
* Add text preprocessing for processor
* Fix snippet
* Add test for equivalence
* Add type checking guard
* Fixing typehint
* Fix test for added `input_ids` in output
* Add deprecations and "text_labels" to output
* Adjust tests
* Fix test
* Update code examples
* Minor docs and code improvement
* Remove one-liner functions and rename class to CamelCase
* Update docstring
* Fixup
* Add the helium model.
* Add a missing helium.
* And add another missing helium.
* Use float for the rmsnorm mul.
* Add the Helium tokenizer converter.
* Add the pad token as suggested by Arthur.
* Update the RMSNorm + some other tweaks.
* Fix more rebase issues.
* fix copies and style
* fixes and add helium.md
* add missing tests
* udpate the backlink
* oups
* style
* update init, and expected results
* small fixes
* match test outputs
* style fixup, fix doc builder
* add dummies and we should be good to go!z
* update sdpa and fa2 documentation
---------
Co-authored-by: laurent <laurent.mazare@gmail.com>
* model can convert to HF and be loaded back
* nit
* works in single batch generation but hallucinates
* use the image tokens
* add image generation
* now it works
* add tests
* update
* add modulare but it doesn't work for porting docstring :(
* skip some tests
* add slow tests
* modular removed the import?
* guess this works
* update
* update
* fix copies
* fix test
* fix copies
* update
* docs
* fix tests
* last fix tests?
* pls
* repo consistency
* more style
* style
* remove file
* address comments
* tiny bits
* update after the new modular
* fix tests
* add one more cond in check attributes
* decompose down/up/mid blocks
* allow static cache generation in VLMs
* nit
* fix copies
* Update docs/source/en/model_doc/emu3.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/emu3.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/emu3.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/emu3.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/emu3.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/emu3.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/emu3.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/emu3.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* fix VAE upsampling
* Update src/transformers/models/emu3/modular_emu3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* address comments
* state overwritten stuff explicitly
* fix copies
* add the flag for flex attn
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* bug fixes
* organize imports
* wrap cpu warning in reference_compile
* Avoid needing repad_logits_with_grad, always repad with grads when training
I'm not 100% that the conditional with "or labels is None" makes sense though - not sure what the intention is there. Perhaps we can remove that?
* Revert "Avoid needing repad_logits_with_grad, always repad with grads when training"
This reverts commit cedcb4e89b.
* Fix grammar: keep -> keeps
* Propagate grammar fix with modular_model_converter
---------
Co-authored-by: Tom Aarsen <Cubiegamedev@gmail.com>
Co-authored-by: Tom Aarsen <37621491+tomaarsen@users.noreply.github.com>
* add audio_token attribute to proc
* expand input_ids
* and legacy and expanded input_ids
* test update
* split lines
* add possibility not to provide eos and bos audio tokens
* raise errors
* test incorrect number of audio tokens
* add example
* fmt
* typo
* first adding diffllama
* add Diff Attention and other but still with errors
* complate make attention Diff-Attention
* fix some bugs which may be caused by transformer-cli while adding model
* fix a bug caused by forgetting KV cache...
* Update src/transformers/models/diffllama/modeling_diffllama.py
You don't need to divide by 2 if we use same number of attention heads as llama. instead you can just split in forward.
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* Update src/transformers/models/diffllama/modeling_diffllama.py
fit to changeing "num_heads // 2" place
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* Update src/transformers/models/diffllama/modeling_diffllama.py
new codes are more meaningful than before
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* Update src/transformers/models/diffllama/modeling_diffllama.py
new codes are more meaningful than before
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* Update src/transformers/models/diffllama/modeling_diffllama.py
fit to changeing "num_heads // 2" place
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* Update src/transformers/models/diffllama/modeling_diffllama.py
fix 2times divide by sqrt(self.head_dim)
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* Update src/transformers/models/diffllama/modeling_diffllama.py
fix 2times divide by sqrt(self.head_dim)
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* Update src/transformers/models/diffllama/modeling_diffllama.py
fit to changeing "num_heads // 2" place.
and more visible
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* I found Attention missed implemented from paper still on e072544a3b.
* re-implemented
* adding groupnorm
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* align with transformers code style
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* fix typo
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* adding groupnorm
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* change SdpaAttention to DiffSdpaAttention
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* fix bug
* Update src/transformers/models/diffllama/modeling_diffllama.py
resolve "not same outputs" problem
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* fix bugs of places of "GroupNorm with scale" and etc
* Revert "fix bugs of places of "GroupNorm with scale" and etc"
This reverts commit 26307d92f6.
* simplify multiple of attention (matmul) operations into one by repeating value_states
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* simplify multiple of attention (matmul) operations into one by repeating value_states
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* simplify multiple of attention (matmul) operations into one by repeating value_states
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* remove missed type
* add diffllama model_doc
* apply make style/quality
* apply review comment about model
* apply review comment about test
* place diffllama alphabetically on the src/transformers/__init__.py
* fix forgot code
* Supports parameters that are not initialized with standard deviation 0 in the conventional method
* add DiffLlamaConfig to CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK on utils/check_config_docstrings.py
* remove unused property of config
* add to supported model list
* add to spda supported model list
* fix copyright, remove pretraining_tensor_parallel, and modify for initialization test
* remove unused import and etc.
* empty commit
* empty commit
* empty commit
* apply modular transformers but with bugs
* revert prev commit
* create src/transformers/model/diffllama/modular_diffllama.py
* run utils/modular_model_converter.py
* empty commit
* leaner modular diffllama
* remove more and more in modular_diffllama.pt
* remove more and more in modular_diffllama.pt
* resolve missing docstring entries
* force reset
* convert modular
---------
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* initial cut of modernbert for transformers
* small bug fixes
* fixes
* Update import
* Use compiled mlp->mlp_norm to match research implementation
* Propagate changes in modular to modeling
* Replace duplicate attn_out_dropout in favor of attention_dropout
cc @warner-benjamin let me know if the two should remain separate!
* Update BOS to CLS and EOS to SEP
Please confirm @warner-benjamin
* Set default classifier bias to False, matching research repo
* Update tie_word_embeddings description
* Fix _init_weights for ForMaskedLM
* Match base_model_prefix
* Add compiled_head to match research repo outputs
* Fix imports for ModernBertForMaskedLM
* Just use "gelu" default outright for classifier
* Fix config name typo: initalizer -> initializer
* Remove some unused parameters in docstring. Still lots to edit there!
* Compile the embeddings forward
Not having this resulted in very slight differences - so small it wasn't even noticed for the base model, only for the large model.
But the tiny difference for large propagated at the embedding layer through the rest of the model, leading to notable differences of ~0.0084 average per value, up to 0.2343 for the worst case.
* Add drafts for ForSequenceClassification/ForTokenClassification
* Add initial SDPA support (not exactly equivalent to FA2 yet!)
During testing, FA2 and SDPA still differ by about 0.0098 per value in the token embeddings. It still predicts the correct mask fills, but I'd like to get it fully 1-1 if possible.
* Only use attention dropout if training
* Add initial eager attention support (also not equivalent to FA2 yet!)
Frustratingly, I also can't get eager to be equivalent to FA2 (or sdpa), but it does get really close, i.e. avg ~0.010 difference per value.
Especially if I use fp32 for both FA2&eager, avg ~0.0029 difference per value
The fill-mask results are good with eager.
* Add initial tests, output_attentions, output_hidden_states, prune_heads
Tests are based on BERT, not all tests pass yet: 23 failed, 79 passed, 100 skipped
* Remove kwargs from ModernBertForMaskedLM
Disable sparse_prediction by default to match the normal HF, can be enabled via config
* Remove/adjust/skip improper tests; warn if padding but no attn mask
* Run formatting etc.
* Run python utils/custom_init_isort.py
* FlexAttention with unpadded sequences(matches FA2 within bf16 numerics)
* Reformat init_weights based on review
* self -> module in attention forwards
* Remove if config.tie_word_embeddings
* Reformat output projection on a different line
* Remove pruning
* Remove assert
* Call contiguous() to simplify paths
* Remove prune_qkv_linear_layer
* Format code
* Keep as kwargs, only use if needed
* Remove unused codepaths & related config options
* Remove 3d attn_mask test; fix token classification tuple output
* Reorder: attention_mask above position_ids, fixes gradient checkpointing
* Fix usage if no FA2 or torch v2.5+
* Make torch.compile/triton optional
Should we rename 'compile'? It's a bit vague
* Separate pooling options into separate functions (cls, mean) - cls as default
* Simplify _pad_modernbert_output, remove unused labels path
* Update tied weights to remove decoder.weight, simplify decoder loading
* Adaptively set config.compile based on hf_device_map/device/resize, etc.
* Update ModernBertConfig docstring
* Satisfy some consistency checks, add unfinished docs
* Only set compile to False if there's more than 1 device
* Add docstrings for public ModernBert classes
* Dont replace docstring returns - ends up being duplicate
* Fix mistake in toctree
* Reformat toctree
* Patched FlexAttention, SDPA, Eager with Local Attention
* Implement FA2 -> SDPA -> Eager attn_impl defaulting, crucial
both to match the original performance, and to get the highest inference speed without requiring users to manually pick FA2
* Patch test edge case with Idefics3 not working with 'attn_implementation="sdpa"'
* Repad all_hidden_states as well
* rename config.compile to reference_compile
* disable flex_attention since it crashes
* Update modernbert.md
* Using dtype min to mask in eager
* Fully remove flex attention for now
It's only compatible with the nightly torch 2.6, so we'll leave it be for now. It's also slower than eager/sdpa.
Also, update compile -> reference_compile in one more case
* Call contiguous to allow for .view()
* Copyright 2020 -> 2024
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update/simplify __init__ structure
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Remove "... if dropout_prob > 0 else identity"
As dropout with 0.0 should be efficient like identity
* re-use existing pad/unpad functions instead of creating new ones
* remove flexattention method
* Compute attention_mask and local_attention_mask once in modeling
* Simplify sequence classification prediction heads, only CLS now
Users can make custom heads if they feel like it
Also removes the unnecessary pool parameter
* Simplify module.training in eager attn
* Also export ModernBertPreTrainedModel
* Update the documentation with links to finetuning scripts
* Explain local_attention_mask parameter in docstring
* Simplify _autoset_attn_implementation, rely on super()
* Keep "in" to initialize Prediction head
Doublechecked with Benjamin that it's correct/what we used for pretraining
* add back mean pooling
* Use the pooling head in TokenClassification
* update copyright
* Reset config._attn_implementation_internal on failure
* Allow optional attention_mask in ForMaskedLM head
* fix failing run_slow tests
* Add links to the paper
* Remove unpad_no_grad, always pad/unpad without gradients
* local_attention_mask -> sliding_window_mask
* Revert "Use the pooling head in TokenClassification"
This reverts commit 99c38badd1.
There was no real motivation, no info on whether having this bigger head does anything useful.
* Simplify pooling, 2 options via if-else
---------
Co-authored-by: Tom Aarsen <37621491+tomaarsen@users.noreply.github.com>
Co-authored-by: Tom Aarsen <Cubiegamedev@gmail.com>
Co-authored-by: Said Taghadouini <taghadouinisaid@gmail.com>
Co-authored-by: Benjamin Clavié <ben@clavie.eu>
Co-authored-by: Antoine Chaffin <ant54600@hotmail.fr>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* docs: fix typo quickstart snippet in ColPali's model card
* docs: clean the ColPali's model card
* docs: make the `ColPaliForRetrieval`'s docstring more concise
* docs: add missing bash command used to convert weights for `vidore/colpali-v1.3-hf`
* initial commit for PR
Co-authored-by: Gabe Goodhart <gabe.l.hart@gmail.com>
* rename dynamic cache
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* add more unit tests
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* add integration test
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* add integration test
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* Add modular bamba file
* Remove trainer changes from unrelated PR
* Modify modular and cofig to get model running
* Fix some CI errors and beam search
* Fix a plethora of bugs from CI/docs/etc
* Add bamba to models with special caches
* Updat to newer mamba PR for mamba sublayer
* fix test_left_padding_compatibility
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* fix style
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* fix remaining tests
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* missed this test
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* ran make style
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* move slow tag to integration obj
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* make style
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* address comments
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* fix modular
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* left out one part of modular
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* change model
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* Make Rotary modular as well
* Update bamba.md
Added overview, update Model inference card and added config
* Update bamba.md
* Update bamba.md
* Update bamba.md
Minor fixes
* Add docs for config and model back
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
* Add warning when using fast kernels
* replaced generate example
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* Address comments from PR
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
* Propagate attention fixes
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
* Fix attention interfaces to the new API
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
* Fix API for decoder layer
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
* Remove extra weights
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
---------
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
Co-authored-by: Gabe Goodhart <gabe.l.hart@gmail.com>
Co-authored-by: Antoni Viros i Martin <aviros@ibm.com>
Co-authored-by: divya-kumari32 <72085811+divya-kumari32@users.noreply.github.com>
Co-authored-by: Antoni Viros <ani300@gmail.com>
* Add Cohere2 docs details
* Update docs/source/en/model_doc/cohere2.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Add Falcon3 documentation
* Update Falcon3 documentation
* Change Falcon to Falcon3
* Update docs and run make fix-copies
* Add blog post and huggingface models links
* Add files
* Init
* Add TimmWrapperModel
* Fix up
* Some fixes
* Fix up
* Remove old file
* Sort out import orders
* Fix some model loading
* Compatible with pipeline and trainer
* Fix up
* Delete test_timm_model_1/config.json
* Remove accidentally commited files
* Delete src/transformers/models/modeling_timm_wrapper.py
* Remove empty imports; fix transformations applied
* Tidy up
* Add image classifcation model to special cases
* Create pretrained model; enable device_map='auto'
* Enable most tests; fix init order
* Sort imports
* [run-slow] timm_wrapper
* Pass num_classes into timm.create_model
* Remove train transforms from image processor
* Update timm creation with pretrained=False
* Fix gamma/beta issue for timm models
* Fixing gamma and beta renaming for timm models
* Simplify config and model creation
* Remove attn_implementation diff
* Fixup
* Docstrings
* Fix warning msg text according to test case
* Fix device_map auto
* Set dtype and device for pixel_values in forward
* Enable output hidden states
* Enable tests for hidden_states and model parallel
* Remove default scriptable arg
* Refactor inner model
* Update timm version
* Fix _find_mismatched_keys function
* Change inheritance for Classification model (fix weights loading with device_map)
* Minor bugfix
* Disable save pretrained for image processor
* Rename hook method for loaded keys correction
* Rename state dict keys on save, remove `timm_model` prefix, make checkpoint compatible with `timm`
* Managing num_labels <-> num_classes attributes
* Enable loading checkpoints in Trainer to resume training
* Update error message for output_hidden_states
* Add output hidden states test
* Decouple base and classification models
* Add more test cases
* Add save-load-to-timm test
* Fix test name
* Fixup
* Add do_pooling
* Add test for do_pooling
* Fix doc
* Add tests for TimmWrapperModel
* Add validation for `num_classes=0` in timm config + test for DINO checkpoint
* Adjust atol for test
* Fix docs
* dev-ci
* dev-ci
* Add tests for image processor
* Update docs
* Update init to new format
* Update docs in configuration
* Fix some docs in image processor
* Improve docs for modeling
* fix for is_timm_checkpoint
* Update code examples
* Fix header
* Fix typehint
* Increase tolerance a bit
* Fix Path
* Fixing model parallel tests
* Disable "parallel" tests
* Add comment for metadata
* Refactor AutoImageProcessor for timm wrapper loading
* Remove custom test_model_outputs_equivalence
* Add require_timm decorator
* Fix comment
* Make image processor work with older timm versions and tensor input
* Save config instead of whole model in image processor tests
* Add docstring for `image_processor_filename`
* Sanitize kwargs for timm image processor
* Fix doc style
* Update check for tensor input
* Update normalize
* Remove _load_timm_model function
---------
Co-authored-by: Amy Roberts <22614925+amyeroberts@users.noreply.github.com>
* add deformable detr image processor fast
* add fast processor to doc
* fix copies
* nit docstring
* Add tests gpu/cpu and fix docstrings
* fix docstring
* import changes from detr
* fix imports
* rebase and fix
* fix input data format change in detr and rtdetr fast
* Fix post process function called in the instance segmentation example of mask2former
* fix description and additional notes for post_process_instance_segmentation of maskformers
* remove white space in maskformers post_process_instance_segmentation doc
* change image.size[::-1] to height and width for clarity in segmentation examples
* Add model skeletion with transformers-cli add-new-model-like
* Convert config to modular, add rms_norm_eps, delete clip_qkv
* Convert model to modular, add RMSNorm
* Add flash attention with qk norm and no qkv clipping
* Add decoder layer with RMSNorm after attention/feedforward layers
* Add base and causal model
* Add converter improvements from OLMo repo
* Update weight loading in OLMo to HF converter
* Set correct default for rms_norm_eps
* Set correct pipeline_model_mapping in test
* Run make fixup
* Fix model type
* Re-run modular conversion
* Manually set config docs to fix build errors
* Convert olmo-1124 to olmo_1124 to fix flash attention docs errors
* Start updating tests
* Update tests
* Copy upstream test_eager_matches_sdpa_inference_1_bfloat16 changes to olmo_1124
* Rename input_layernorm and post_attention_layernorm to reflect their ops better
* Use correct tokenizer
* Remove test unsupported by GPT2 tokenizer
* Create GenerationConfig outside of from_pretrained call
* Use simpler init file structure
* Add explicit __all__ to support simplified init
* Make safetensor serialization the default
* Update OLMo November 2024 docs
* add fast image processor rtdetr
* add gpu/cpu test and fix docstring
* remove prints
* add to doc
* nit docstring
* avoid iterating over images/annotations several times
* change torch typing
* Add image processor fast documentation
* feat: Added int conversion and unwrapping
* test: added tests for post_process_keypoint_detection of SuperPointImageProcessor
* docs: changed docs to include post_process_keypoint_detection method and switched from opencv to matplotlib
* test: changed test to not depend on SuperPointModel forward
* test: added missing require_torch decorator
* docs: changed pyplot parameters for the keypoints to be more visible in the example
* tests: changed import torch location to make test_flax and test_tf
* Revert "tests: changed import torch location to make test_flax and test_tf"
This reverts commit 39b32a2f69.
* tests: fixed import
* chore: applied suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* tests: fixed import
* tests: fixed import (bis)
* tests: fixed import (ter)
* feat: added choice of type for target_size and changed tests accordingly
* docs: updated code snippet to reflect the addition of target size type choice in post process method
* tests: fixed imports (...)
* tests: fixed imports (...)
* style: formatting file
* docs: fixed typo from image[0] to image.size[0]
* docs: added output image and fixed some tests
* Update docs/source/en/model_doc/superpoint.md
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* fix: included SuperPointKeypointDescriptionOutput in TYPE_CHECKING if statement and changed tests results to reflect changes to SuperPoint from absolute keypoints coordinates to relative
* docs: changed SuperPoint's docs to print output instead of just accessing
* style: applied make style
* docs: added missing output type and precision in docstring of post_process_keypoint_detection
* perf: deleted loop to perform keypoint conversion in one statement
* fix: moved keypoint conversion at the end of model forward
* docs: changed SuperPointInterestPointDecoder to SuperPointKeypointDecoder class name and added relative (x, y) coordinates information to its method
* fix: changed type hint
* refactor: removed unnecessary brackets
* revert: SuperPointKeypointDecoder to SuperPointInterestPointDecoder
* Update docs/source/en/model_doc/superpoint.md
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
---------
Co-authored-by: Steven Bucaille <steven.bucaille@buawei.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* add colorize_depth and matplotlib availability check
* add post_process_depth_estimation for zoedepth + tests
* add post_process_depth_estimation for DPT + tests
* add post_process_depth_estimation in DepthEstimationPipeline & special case for zoedepth
* run `make fixup`
* fix import related error on tests
* fix more import related errors on test
* forgot some `torch` calls in declerations
* remove `torch` call in zoedepth tests that caused error
* updated docs for depth estimation
* small fix for `colorize` input/output types
* remove `colorize_depth`, fix various names, remove matplotlib dependency
* fix formatting
* run fixup
* different images for test
* update examples in `forward` functions
* fixed broken links
* fix output types for docs
* possible format fix inside `<Tip>`
* Readability related updates
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Readability related update
* cleanup after merge
* refactor `post_process_depth_estimation` to return dict; simplify ZoeDepth's `post_process_depth_estimation`
* rewrite dict merging to support python 3.8
---------
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* mistral qna start
* mixtral qna
* oops
* qwen2 qna
* qwen2moe qna
* add missing input embed methods
* add copied to all methods, can't directly from llama due to the prefix
* make top level copied from
* add sdpa to OPT
* chore: remove redundant whitespace in OPTDecoder class
* fixup
* bug fix
* add sdpa and attention generate test
* fixup
* Refactor OPTAttention forward method for improved readability and maintainability
* undo refactor for _shape and key,val states
* add OPT to doc, fixup didn't find it for some reason
* change order
* change default attn_implemntation in testing to eager
* [run-slow] opt
* change test_eager_matches_sdpa_generate to the one llama
* Update default attention implementation in testing common
* [run-slow] opt
* remove uneeded print
* [run-slow] opt
* refactor model testers to have attn_implementation="eager"
* [run-slow] opt
* convert test_eager_matches_sdpa_generate to opt-350M
* bug fix when creating mask for opt
* [run-slow] opt
* if layer head mask default to eager
* if head mask is not none fall to eager
* [run-slow] opt
* Update src/transformers/models/opt/modeling_opt.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Clean up Unpack imports (#33631)
clean up Unpack imports
* Fix DPT /Dinov2 sdpa regression on main (#33660)
* fallback to eager if output attentions.
* fix copies
* handle dependency errors in check_imports (#33622)
* handle dependency errors in check_imports
* change log level to warning
* add back self.max_position_embeddings = config.max_position_embeddings (#33550)
* add back self.max_position_embeddings = config.max_position_embeddings
* fix-copies
* Fix Llava conversion for LlavaQwen2ForCausalLM with Clip vision tower (#33613)
fix llavaqwen2 model conversion
* Uniformize kwargs for Udop processor and update docs (#33628)
* Add optional kwargs and uniformize udop
* cleanup Unpack
* nit Udop
* Generation: deprecate `PreTrainedModel` inheriting from `GenerationMixin` (#33203)
* Enable BNB multi-backend support (#31098)
* enable cpu bnb path
* fix style
* fix code style
* fix 4 bit path
* Update src/transformers/utils/import_utils.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* add multi backend refactor tests
* fix style
* tweak 4bit quantizer + fix corresponding tests
* tweak 8bit quantizer + *try* fixing corresponding tests
* fix dequant bnb 8bit
* account for Intel CPU in variability of expected outputs
* enable cpu and xpu device map
* further tweaks to account for Intel CPU
* fix autocast to work with both cpu + cuda
* fix comments
* fix comments
* switch to testing_utils.torch_device
* allow for xpu in multi-gpu tests
* fix tests 4bit for CPU NF4
* fix bug with is_torch_xpu_available needing to be called as func
* avoid issue where test reports attr err due to other failure
* fix formatting
* fix typo from resolving of merge conflict
* polish based on last PR review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* fix CI
* Update src/transformers/integrations/integration_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/integrations/integration_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix error log
* fix error msg
* add \n in error log
* make quality
* rm bnb cuda restriction in doc
* cpu model don't need dispatch
* fix doc
* fix style
* check cuda avaliable in testing
* fix tests
* Update docs/source/en/model_doc/chameleon.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update docs/source/en/model_doc/llava_next.md
Co-authored-by: Aarni Koskela <akx@iki.fi>
* Update tests/quantization/bnb/test_4bit.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* Update tests/quantization/bnb/test_4bit.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* fix doc
* fix check multibackends
* fix import sort
* remove check torch in bnb
* docs: update bitsandbytes references with multi-backend info
* docs: fix small mistakes in bnb paragraph
* run formatting
* reveret bnb check
* move bnb multi-backend check to import_utils
* Update src/transformers/utils/import_utils.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* fix bnb check
* minor fix for bnb
* check lib first
* fix code style
* Revert "run formatting"
This reverts commit ac108c6d6b.
* fix format
* give warning when bnb version is low and no cuda found]
* fix device assignment check to be multi-device capable
* address akx feedback on get_avlbl_dev fn
* revert partially, as we don't want the function that public, as docs would be too much (enforced)
---------
Co-authored-by: Aarni Koskela <akx@iki.fi>
Co-authored-by: Titus von Koeller <9048635+Titus-von-Koeller@users.noreply.github.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Fix error string after refactoring into get_chat_template (#33652)
* Fix error string after refactoring into get_chat_template
* Take suggestion from CR
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
---------
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* uniformize git processor (#33668)
* uniformize git processor
* update doctring
* Modular `transformers`: modularity and inheritance for new model additions (#33248)
* update exampel
* update
* push the converted diff files for testing and ci
* correct one example
* fix class attributes and docstring
* nits
* oups
* fixed config!
* update
* nitd
* class attributes are not matched against the other, this is missing
* fixed overwriting self.xxx now onto the attributes I think
* partial fix, now order with docstring
* fix docstring order?
* more fixes
* update
* fix missing docstrings!
* examples don't all work yet
* fixup
* nit
* updated
* hick
* update
* delete
* update
* update
* update
* fix
* all default
* no local import
* fix more diff
* some fix related to "safe imports"
* push fixed
* add helper!
* style
* add a check
* all by default
* add the
* update
* FINALLY!
* nit
* fix config dependencies
* man that is it
* fix fix
* update diffs
* fix the last issue
* re-default to all
* alll the fixes
* nice
* fix properties vs setter
* fixup
* updates
* update dependencies
* make sure to install what needs to be installed
* fixup
* quick fix for now
* fix!
* fixup
* update
* update
* updates
* whitespaces
* nit
* fix
* simplify everything, and make it file agnostic (should work for image processors)
* style
* finish fixing all import issues
* fixup
* empty modeling should not be written!
* Add logic to find who depends on what
* update
* cleanup
* update
* update gemma to support positions
* some small nits
* this is the correct docstring for gemma2
* fix merging of docstrings
* update
* fixup
* update
* take doc into account
* styling
* update
* fix hidden activation
* more fixes
* final fixes!
* fixup
* fixup instruct blip video
* update
* fix bugs
* align gemma2 with the rest as well
* updats
* revert
* update
* more reversiom
* grind
* more
* arf
* update
* order will matter
* finish del stuff
* update
* rename to modular
* fixup
* nits
* update makefile
* fixup
* update order of the checks!
* fix
* fix docstring that has a call inside
* fiix conversion check
* style
* add some initial documentation
* update
* update doc
* some fixup
* updates
* yups
* Mostly todo gimme a minut
* update
* fixup
* revert some stuff
* Review docs for the modular transformers (#33472)
Docs
* good update
* fixup
* mmm current updates lead to this code
* okay, this fixes it
* cool
* fixes
* update
* nit
* updates
* nits
* fix doc
* update
* revert bad changes
* update
* updates
* proper update
* update
* update?
* up
* update
* cool
* nits
* nits
* bon bon
* fix
* ?
* minimise changes
* update
* update
* update
* updates?
* fixed gemma2
* kind of a hack
* nits
* update
* remove `diffs` in favor of `modular`
* fix make fix copies
---------
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Fix CIs post merging modular transformers (#33681)
update
* Fixed docstring for cohere model regarding unavailability of prune_he… (#33253)
* Fixed docstring for cohere model regarding unavailability of prune_head() methods
The docstring mentions that cohere model supports prune_heads() methods. I have fixed the docstring by explicitly mentioning that it doesn't support that functionality.
* Update src/transformers/models/cohere/modeling_cohere.py
---------
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Generation tests: update imagegpt input name, remove unused functions (#33663)
* Improve Error Messaging for Flash Attention 2 on CPU (#33655)
Update flash-attn error message on CPU
Rebased to latest branch
* Gemma2: fix config initialization (`cache_implementation`) (#33684)
* Fix ByteLevel alphabet missing when Sequence pretokenizer is used (#33556)
* Fix ByteLevel alphabet missing when Sequence pretokenizer is used
* Fixed formatting with `ruff`.
* Uniformize kwargs for image-text-to-text processors (#32544)
* uniformize FUYU processor kwargs
* Uniformize instructblip processor kwargs
* Fix processor kwargs and tests Fuyu, InstructBlip, Kosmos2
* Uniformize llava_next processor
* Fix save_load test for processor with chat_template only as extra init args
* Fix import Unpack
* Fix Fuyu Processor import
* Fix FuyuProcessor import
* Fix FuyuProcessor
* Add defaults for specific kwargs kosmos2
* Fix Udop to return BatchFeature instead of BatchEncoding and uniformize kwargs
* Add tests processor Udop
* remove Copied from in processing Udop as change of input orders caused by BatchEncoding -> BatchFeature
* Fix overwrite tests kwargs processors
* Add warnings and BC for changes in processor inputs order, change docs, add BC for text_pair as arg for Udop
* Fix processing test fuyu
* remove unnecessary pad_token check in instructblip ProcessorTest
* Fix BC tests and cleanup
* FIx imports fuyu
* Uniformize Pix2Struct
* Fix wrong name for FuyuProcessorKwargs
* Fix slow tests reversed inputs align fuyu llava-next, change udop warning
* Fix wrong logging import udop
* Add check images text input order
* Fix copies
* change text pair handling when positional arg
* rebase on main, fix imports in test_processing_common
* remove optional args and udop uniformization from this PR
* fix failing tests
* remove unnecessary test, fix processing utils and test processing common
* cleanup Unpack
* cleanup
* fix conflict grounding dino
* 🚨🚨 Setting default behavior of assisted decoding (#33657)
* tests: fix pytorch tensor placement errors (#33485)
This commit fixes the following errors:
* Fix "expected all tensors to be on the same device" error
* Fix "can't convert device type tensor to numpy"
According to pytorch documentation torch.Tensor.numpy(force=False)
performs conversion only if tensor is on CPU (plus few other restrictions)
which is not the case. For our case we need force=True since we just
need a data and don't care about tensors coherency.
Fixes: #33517
See: https://pytorch.org/docs/2.4/generated/torch.Tensor.numpy.html
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
* bump tokenizers, fix added tokens fast (#32535)
* update based on tokenizers release
* update
* nits
* update
* revert re addition
* don't break that yet
* fmt
* revert unwanted
* update tokenizers version
* update dep table
* update
* update in conversion script as well
* some fix
* revert
* fully revert
* fix training
* remove set trace
* fixup
* update
* update
* [Pixtral] Improve docs, rename model (#33491)
* Improve docs, rename model
* Fix style
* Update repo id
* fix code quality after merge
* HFQuantizer implementation for compressed-tensors library (#31704)
* Add compressed-tensors HFQuantizer implementation
* flag serializable as False
* run
* revive lines deleted by ruff
* fixes to load+save from sparseml, edit config to quantization_config, and load back
* address satrat comment
* compressed_tensors to compressed-tensors and revert back is_serializable
* rename quant_method from sparseml to compressed-tensors
* tests
* edit tests
* clean up tests
* make style
* cleanup
* cleanup
* add test skip for when compressed tensors is not installed
* remove pydantic import + style
* delay torch import in test
* initial docs
* update main init for compressed tensors config
* make fix-copies
* docstring
* remove fill_docstring
* Apply suggestions from code review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* review comments
* review comments
* comments - suppress warnings on state dict load, tests, fixes
* bug-fix - remove unnecessary call to apply quant lifecycle
* run_compressed compatability
* revert changes not needed for compression
* no longer need unexpected keys fn
* unexpected keys not needed either
* Apply suggestions from code review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* add to_diff_dict
* update docs and expand testing
* Update _toctree.yml with compressed-tensors
* Update src/transformers/utils/quantization_config.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update doc
* add note about saving a loaded model
---------
Co-authored-by: George Ohashi <george@neuralmagic.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Sara Adkins <sara@neuralmagic.com>
Co-authored-by: Sara Adkins <sara.adkins65@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Dipika Sikka <ds3822@columbia.edu>
Co-authored-by: Dipika <dipikasikka1@gmail.com>
* update model card for opt
* add batch size to inference table
* [slow-run] opt
* [run-slow] opt
---------
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
Co-authored-by: Avishai Elmakies <avishai.elma@cs.huji.ac.il>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
Co-authored-by: chengchengpei <5881383+chengchengpei@users.noreply.github.com>
Co-authored-by: Isotr0py <2037008807@qq.com>
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: jiqing-feng <jiqing.feng@intel.com>
Co-authored-by: Aarni Koskela <akx@iki.fi>
Co-authored-by: Titus von Koeller <9048635+Titus-von-Koeller@users.noreply.github.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Tibor Reiss <75096465+tibor-reiss@users.noreply.github.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
Co-authored-by: Lysandre Debut <hi@lysand.re>
Co-authored-by: Muhammad Naufil <m.naufil1@gmail.com>
Co-authored-by: sizhky <yyeshr@gmail.com>
Co-authored-by: Umar Butler <umar@umar.au>
Co-authored-by: Jonathan Mamou <jonathan.mamou@intel.com>
Co-authored-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
Co-authored-by: Benjamin Fineran <bfineran@users.noreply.github.com>
Co-authored-by: George Ohashi <george@neuralmagic.com>
Co-authored-by: Sara Adkins <sara@neuralmagic.com>
Co-authored-by: Sara Adkins <sara.adkins65@gmail.com>
Co-authored-by: Dipika Sikka <ds3822@columbia.edu>
Co-authored-by: Dipika <dipikasikka1@gmail.com>
* Add Auto model for image-text-to-text
* Remove donut from processing auto, add chameleon ti image text to text models
* add qwen2_vl and llava_onevision
* add pixtral to auto model for image-text-to-text
* add mllama and idefics3
* remove models in IGNORE_NON_AUTO_CONFIGURED
* add AutoModelForImageTextToText to tests and doc
* docs: add example for separating q, k, v projections in SAM
* docs: How to Hack Any Transformers Model
* docs: remove changes from sam model docs
* 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 for MyT5 model
* custom implementation of MyT5 tokenizer, unused files deleted
* unittest for myt5 tokenizer
* upadate of import structure and style
* removed remmanents of MyT5Config
* fixed docstrings
* Updates after review: filled documentaion file, new docstrings and tests added
* Fixed code style issues
* fixed copied from to refer to function
* updated loading myt5 tokenizer in tests, added sample byte map file to fixtures
* changes after review
* removed redundant copied from
* removed redundant copied from
* optimalization and loading model from hf
* [run_slow] myt5
* [run-slow] myt5
* Updated en documentation for myt5
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* onboard phimoe model
* removed debug code
* added unit tests
* updated docs
* formatted
* fixed unit tests
* fixed test case
* fixed format
* refactored code
* fixed expected outputs in the integration tests
* Added a warning msg
* Addressed comments
* Addressed comments
* fixed test cases
* added paper link
* Addressed comments
* Refactored PhimoeForCausalLM forward fn
* Refactored PhimoeRotaryEmbedding class
* fixed test cases
* fixed testcase
* fixed test case
* Addressed comments
* fixed test cases
* fixed testcases
* Used cache position instead to get the seq len
* Trainer - deprecate tokenizer for processing_class
* Extend chage across Seq2Seq trainer and docs
* Add tests
* Update to FutureWarning and add deprecation version
Update siglip.md
This was already partially fixed relative to the deployed docs. But the partial fix made it inconsistent. Additionally, giving the full text ("This is a photo of...") is likely not the desired output.
* Add Idefics 3!
* fixes to make both pipelines identical
* fix for quantized models
* First pass at the review
* remove vocab size from the main config (it's still in the text_config)
* hot fix for merve
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* re-add model_type for text_config
* remove support for old_cache
* remove hidden_size from main config
* rename idefics3 HF repo
* few changes suggested in the PR
* fix to input_data_format computation
* remove overwrite of _autoset_attn_implementation following @zucchini-nlp suggestion
* improve example
* few improvements from amy's review
* big change to enable processing input images as numpy arrays
* Changes to the code to uniformize processor kwargs
* image processing tests
* image processing tests fixes and some bugs they discovered
* addressed review comments from Yoni
* fix modeling tests
* remove special tokens that are not special
* fixes tests
* skip failing tests - they also fail for idefics2
* added paper and readded the tests with multi gpu, who knows
* Update docs/source/en/model_doc/idefics3.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>
* review amy until image_processing_idefics3
* last comments from Amy
* review amy
* Update src/transformers/models/idefics3/image_processing_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/idefics3/modeling_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update docs/source/en/model_doc/idefics3.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* doc improvement - amy review
* fix runtime error during fine-tuning
* amy's review
* Update src/transformers/models/idefics3/image_processing_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/idefics3/image_processing_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/idefics3/modeling_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* ruff
* amy's comment on the order
* ruff ruff
* fix copies
* square images when they are not splitted
* ruff :(
* Update src/transformers/models/idefics3/image_processing_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/idefics3/test_processing_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix small bug introduced in refactor
* amy's image processing changes
* fixes peft tests and ruff
* modify to_pil_image from transformers. and review from emanuele.
* add modified to_pil_image
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* enable cpu bnb path
* fix style
* fix code style
* fix 4 bit path
* Update src/transformers/utils/import_utils.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* add multi backend refactor tests
* fix style
* tweak 4bit quantizer + fix corresponding tests
* tweak 8bit quantizer + *try* fixing corresponding tests
* fix dequant bnb 8bit
* account for Intel CPU in variability of expected outputs
* enable cpu and xpu device map
* further tweaks to account for Intel CPU
* fix autocast to work with both cpu + cuda
* fix comments
* fix comments
* switch to testing_utils.torch_device
* allow for xpu in multi-gpu tests
* fix tests 4bit for CPU NF4
* fix bug with is_torch_xpu_available needing to be called as func
* avoid issue where test reports attr err due to other failure
* fix formatting
* fix typo from resolving of merge conflict
* polish based on last PR review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* fix CI
* Update src/transformers/integrations/integration_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/integrations/integration_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix error log
* fix error msg
* add \n in error log
* make quality
* rm bnb cuda restriction in doc
* cpu model don't need dispatch
* fix doc
* fix style
* check cuda avaliable in testing
* fix tests
* Update docs/source/en/model_doc/chameleon.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update docs/source/en/model_doc/llava_next.md
Co-authored-by: Aarni Koskela <akx@iki.fi>
* Update tests/quantization/bnb/test_4bit.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* Update tests/quantization/bnb/test_4bit.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* fix doc
* fix check multibackends
* fix import sort
* remove check torch in bnb
* docs: update bitsandbytes references with multi-backend info
* docs: fix small mistakes in bnb paragraph
* run formatting
* reveret bnb check
* move bnb multi-backend check to import_utils
* Update src/transformers/utils/import_utils.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* fix bnb check
* minor fix for bnb
* check lib first
* fix code style
* Revert "run formatting"
This reverts commit ac108c6d6b.
* fix format
* give warning when bnb version is low and no cuda found]
* fix device assignment check to be multi-device capable
* address akx feedback on get_avlbl_dev fn
* revert partially, as we don't want the function that public, as docs would be too much (enforced)
---------
Co-authored-by: Aarni Koskela <akx@iki.fi>
Co-authored-by: Titus von Koeller <9048635+Titus-von-Koeller@users.noreply.github.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* clean mimi commit
* some nits suggestions from Arthur
* make fixup
* rename repo id + change readme
* Update docs/source/en/model_doc/mimi.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add flaky flag to batching equivalence due to audio_codes failing sometimes
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* initial commit
* gloups
* updates
* work
* weights match
* nits
* nits
* updates to support the tokenizer :)
* updates
* Pixtral processor (#33454)
* rough outline
* Add in image break and end tokens
* Fix
* Udo some formatting changes
* Set patch_size default
* Fix
* Fix token expansion
* nit in conversion script
* Fix image token list creation
* done
* add expected results
* Process list of list of images (#33465)
* updates
* working image and processor
* this is the expected format
* some fixes
* push current updated
* working mult images!
* add a small integration test
* Uodate configuration docstring
* Formatting
* Config docstring fix
* simplify model test
* fixup modeling and etests
* Return BatchMixFeature in image processor
* fix some copies
* update
* nits
* Update model docstring
* Apply suggestions from code review
* Fix up
* updates
* revert modeling changes
* update
* update
* fix load safe
* addd liscence
* update
* use pixel_values as required by the model
* skip some tests and refactor
* Add pixtral image processing tests (#33476)
* Image processing tests
* Add processing tests
* woops
* defaults reflect pixtral image processor
* fixup post merge
* images -> pixel values
* oups sorry Mr docbuilder
* isort
* fix
* fix processor tests
* small fixes
* nit
* update
* last nits
* oups this was really breaking!
* nits
* is composition needs to be true
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Fixed typo: insted to instead
* Fixed typo: relase to release
* Fixed typo: nighlty to nightly
* Fixed typos: versatible, benchamarks, becnhmark to versatile, benchmark, benchmarks
* Fixed typo in comment: quantizd to quantized
* Fixed typo: architecutre to architecture
* Fixed typo: contibution to contribution
* Fixed typo: Presequities to Prerequisites
* Fixed typo: faste to faster
* Fixed typo: extendeding to extending
* Fixed typo: segmetantion_maps to segmentation_maps
* Fixed typo: Alternativelly to Alternatively
* Fixed incorrectly defined variable: output to output_disabled
* Fixed typo in library name: tranformers.onnx to transformers.onnx
* Fixed missing import: import tensorflow as tf
* Fixed incorrectly defined variable: token_tensor to tokens_tensor
* Fixed missing import: import torch
* Fixed incorrectly defined variable and typo: uromaize to uromanize
* Fixed incorrectly defined variable and typo: uromaize to uromanize
* Fixed typo in function args: numpy.ndarry to numpy.ndarray
* Fixed Inconsistent Library Name: Torchscript to TorchScript
* Fixed Inconsistent Class Name: OneformerProcessor to OneFormerProcessor
* Fixed Inconsistent Class Named Typo: TFLNetForMultipleChoice to TFXLNetForMultipleChoice
* Fixed Inconsistent Library Name Typo: Pytorch to PyTorch
* Fixed Inconsistent Function Name Typo: captureWarning to captureWarnings
* Fixed Inconsistent Library Name Typo: Pytorch to PyTorch
* Fixed Inconsistent Class Name Typo: TrainingArgument to TrainingArguments
* Fixed Inconsistent Model Name Typo: Swin2R to Swin2SR
* Fixed Inconsistent Model Name Typo: EART to BERT
* Fixed Inconsistent Library Name Typo: TensorFLow to TensorFlow
* Fixed Broken Link for Speech Emotion Classification with Wav2Vec2
* Fixed minor missing word Typo
* Fixed minor missing word Typo
* Fixed minor missing word Typo
* Fixed minor missing word Typo
* Fixed minor missing word Typo
* Fixed minor missing word Typo
* Fixed minor missing word Typo
* Fixed minor missing word Typo
* Fixed Punctuation: Two commas
* Fixed Punctuation: No Space between XLM-R and is
* Fixed Punctuation: No Space between [~accelerate.Accelerator.backward] and method
* Added backticks to display model.fit() in codeblock
* Added backticks to display openai-community/gpt2 in codeblock
* Fixed Minor Typo: will to with
* Fixed Minor Typo: is to are
* Fixed Minor Typo: in to on
* Fixed Minor Typo: inhibits to exhibits
* Fixed Minor Typo: they need to it needs
* Fixed Minor Typo: cast the load the checkpoints To load the checkpoints
* Fixed Inconsistent Class Name Typo: TFCamembertForCasualLM to TFCamembertForCausalLM
* Fixed typo in attribute name: outputs.last_hidden_states to outputs.last_hidden_state
* Added missing verbosity level: fatal
* Fixed Minor Typo: take To takes
* Fixed Minor Typo: heuristic To heuristics
* Fixed Minor Typo: setting To settings
* Fixed Minor Typo: Content To Contents
* Fixed Minor Typo: millions To million
* Fixed Minor Typo: difference To differences
* Fixed Minor Typo: while extract To which extracts
* Fixed Minor Typo: Hereby To Here
* Fixed Minor Typo: addition To additional
* Fixed Minor Typo: supports To supported
* Fixed Minor Typo: so that benchmark results TO as a consequence, benchmark
* Fixed Minor Typo: a To an
* Fixed Minor Typo: a To an
* Fixed Minor Typo: Chain-of-though To Chain-of-thought
* add Blip2ForImageTextRetrieval
* use one line and remove unnecessary space in tests
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* use value from the config, rather than hardcoded
* change order of params in Blip2QFormerModel.forward
* update docstring
* fix style
* update test_inference_opt
* move embeddings out of Blip2QFormerModel
* remove from_vision_qformer_configs
* remove autocast float16 in Blip2QFormerModel
* rename fiels into vision_projection,text_projection,use_image_text_matching_head
* use CLIPOutput for Blip2ImageTextMatchingModelOutput
* remove past_key_values_length from Blip2TextEmbeddings
* fix small typo in the CLIPOutput docstring
* add Blip2ForImageTextRetrieval to Zero Shot Image Classification mapping
* update docstring and add require_torch_fp16
* rollback test_inference_opt
* use use_image_text_matching_head=True in convert
* skip test_model_get_set_embeddings
* fix create_rename_keys error on new itm fields
* revert to do scale after dot product between "query" and "key"
* fix ValueError on convert script for blip2-opt-2.7b
* update org of paths to Salesforce
* add is_pipeline_test_to_skip for VisualQuestionAnsweringPipelineTests
* [run_slow] blip_2
* removed Blip2ForImageTextRetrieval from IGNORE_NON_AUTO_CONFIGURED
* fix docstring of Blip2ImageTextMatchingModelOutput
* [run_slow] blip_2
* fix multi-gpu tests
* [run_slow] blip_2
* [run_slow] blip_2
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add changes for uroman package to handle non-Roman characters
* Update docs for uroman changes
* Modifying error message to warning, for backward compatibility
* Update instruction for user to install uroman
* Update docs for uroman python version dependency and backward compatibility
* Update warning message for python version compatibility with uroman
* Refine docs
* Fix: fix all model_type of Llava-Next-Video to llava_next_video
* Fix doc for llava_next_video
* * Fix formatting issues
* Change llava-next-video.md file name into llava_next_video.md to make it compatible with implementation
* Fix docs TOC for llava-next-video
* add new model like
* draft cuda forward - mismatched keys (sharding on conv1)
* match keys successfully
* fix split
* get generation/forward running (wrong gens, norm?)
* :update
* some refactoring
* fixes
* works up until copy to cache
* fix
* update
* NON WORKING VERSION
* version that work?
* nit
* fix config
* fix conversion script
* working cuda forward
* nit
* update
* simplifcation
* make mamba slow simple work
* no einops
* todo
* fix style
* no einops
* update fix no einsum
* nit
* remove einops
* bug: scan_output differs strongly
* add rms norm option
* fix fast + slow generation with and w/o cache ✔️
* draft integration tests
* remove a big chunk of the einsum
* fix slow, fast generations, without any einsum
* fix copies
* fix structure
* fix up modeling and tests
* fix tests
* clamping is indeed worse
* recover mamba2 cache test
* fix copies
* no cache position (yet)
* fix tf tests
* fix matmul for generate
* fixup
* skip cache tests for now
* [run-slow]mamba2
* tune out hidden states for padding
* test batched generation
* propagate attention mask changes
* fix past length
* fix integration test
* style
* address comments
* update readme
* add mamba2 version check
* fix tests
* [run-slow]mamba2
* skip edge tests
* [run-slow]mamba2
* last fixup
* [run-slow]mamba2
* update README
---------
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
* Add llama3-llava-next-8b to llava_next conversion script
Adds support for the lmms-lab/llama3-llava-next-8b model to the
convert_llava_next_weights_to_hf.py script, along with an example
prompt generated from the llava_llama_3 conv_template in the LLaVA-NeXT
repo.
* Exclude <|begin_of_text|> from prompt example
This token gets added automatically, so it should not be included in the
prompt example.
* Add llava-next-72b and llava-next-110b
Adds the Qwen-based LLaVA-Next models to the conversion script, along
with changes to load the models on multiple GPUs for inference.
* Add llama3 and qwen prompt formats to docs
* Chat prompt and padding side left for llama3 batched
* update
* Update src/transformers/models/llava_next/convert_llava_next_weights_to_hf.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/llava_next/convert_llava_next_weights_to_hf.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* remove code
* better naming
---------
Co-authored-by: raushan <raushan@huggingface.co>
Co-authored-by: Raushan Turganbay <raushan.turganbay@alumni.nu.edu.kz>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* minor edits and clarifications
* address comment
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Add siglip loss function
* Update docs
* Enable training tests
[experimental] enable GC training tests as it has worked for my own data
* Remove test_training* overrides to enable training tests
[run_slow] siglip
* Skip training tests for Siglip text model and ImageClassificationModel
[run_slow] siglip
* Skip GC training tests for SiglipForImageClassification
* Explicitly skip training tests for SiglipVisionModel
Add skip reason for training tests for SiglipTextModel
* Remove copied from to fix CI
* Fix documentation for Gemma2.
Model sizes and Blog post URL are wrong in the documentation.
* Update docs/source/en/model_doc/gemma2.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* squash into single commit
* run diff once more
* docstring
* tests
* minor chnages and ready to go
* Update src/transformers/models/llava_next_video/processing_llava_next_video.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/vipllava/test_modeling_vipllava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* [run-slow] llava-next-video
* [run-slow] llava-next-video
* [run-slow] llava_next_video
* fix two tests
* fix slow tests
* remove logit checks due to numeric errors
* run test once more
* [run-slow] llava_next_video
* final try to pass the test
* [run-slow] llava_next_video
* [run-slow] llava_next_video
* [run-slow] llava_next_video
* style
* fix
* style
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* starting support for sdpa in `gptneox` models
* small comment on tests
* fix dropout
* documentation and style
* clarify concrete paths for reference
* generalise attn projections and rope application
added head mask check to sdpa mask creation
handle sdpa memory backend bug via own version flag
* update docs and style
* move dtype casting outside of general attn_projection_and_rope function
fix flash_attn_2 stuff
* more generic attn warning if output_attns or head_mask
* simplify head mask check by moving head mask creation to a later point
* remove copied llama artifact
* remove padding_mask from attention function signature
* removing unnecessary comments, only "save" attn implementation once
* [run_slow] gpt_neox
* Draft fast image processors
* Draft working fast version
* py3.8 compatible cache
* Enable loading fast image processors through auto
* Tidy up; rescale behaviour based on input type
* Enable tests for fast image processors
* Smarter rescaling
* Don't default to Fast
* Safer imports
* Add necessary Pillow requirement
* Woops
* Add AutoImageProcessor test
* Fix up
* Fix test for imagegpt
* Fix test
* Review comments
* Add warning for TF and JAX input types
* Rearrange
* Return transforms
* NumpyToTensor transformation
* Rebase - include changes from upstream in ImageProcessingMixin
* Safe typing
* Fix up
* convert mean/std to tesnor to rescale
* Don't store transforms in state
* Fix up
* Update src/transformers/image_processing_utils_fast.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/auto/image_processing_auto.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/auto/image_processing_auto.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/auto/image_processing_auto.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Warn if fast image processor available
* Update src/transformers/models/vit/image_processing_vit_fast.py
* Transpose incoming numpy images to be in CHW format
* Update mapping names based on packages, auto set fast to None
* Fix up
* Fix
* Add AutoImageProcessor.from_pretrained(checkpoint, use_fast=True) test
* Update src/transformers/models/vit/image_processing_vit_fast.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Add equivalence and speed tests
* Fix up
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
`mask` variable is not defined. probably a writing mistake. it should be `segmentation_map`. `segmentation_map` should be a `1` channel image rather than `RGB`.
[on a different note, the `mask_url` is the same as `raw_image`. could provide a better example.
* Add MistralForTokenClassification
* Add tests and docs
* Add token classification for Mixtral and Qwen2
* Save llma for token classification draft
* Add token classification support for Llama, Gemma, Persimmon, StableLm and StarCoder2
* Formatting
* Add token classification support for Qwen2Moe model
* Add dropout layer to each ForTokenClassification model
* Add copied from in tests
* Update src/transformers/models/llama/modeling_llama.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Propagate suggested changes
* Style
---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Initial commit
* Just a copy of modeling_idefics.py that will be ported to TF
* - Prepend TF to the name of all classes
- Convert pytorch ops to TF (not all operations are converted yet)
* Add TF imports
* Add autotranslated files
* Add TF classes to model_tf_auto.py
* Add the TF classes in model_doc
* include auto-translated code
* Adopted from auto-translated version
* Add a forgotten super().build
* Add test code for TF version.
* Fix indentation and load pytorch weights for now
* Some fixes. Many tests are still failing but some are passing now.
- I have added TODO's for some of the hacks I made to unblock me
and I will address them soon
- I have the processing_idefics.py hacked in my view to support TF temporarily
* Add ALL_LAYERNORM_LAYERS to match pytorch
* Revert "Add ALL_LAYERNORM_LAYERS to match pytorch"
This reverts commit 7e0a35119b4d7a6284d04d8c543fba1b29e573c9 as it
is not needed in the tf implementation.
* Fix freeze_relevant_params()
* Some more fixes
* Fix test_attention_outputs
* Add tf stuff to processing_idefics.py
processing_idefics.py supports both pytorch and tf now.
test_processor_idefics.py for pytorch is passing, so i didn't break anything
but still some issues with tf. I also need to add tf tests in
test_processor_idefics.py.
* Pass return_tensors to image processing code and fix test
* Pass return_tensors to the image processor __init__
* Fix several test cases
- Make input to some of the forward pass of type `TFModelInputType`
- Decorate main layer forward pass with `@unpack_inputs`
- Decorate main layer with `@keras_serializable`
- Pass `inputs` to TFIdeficsModel
* Some more fixes forgotten in last commit
* Fix processing code and vision_tf.py
* Fix perceiver bug
* Import from
* Auto-add build() methods + style pass
* Fix build() errors due to `None` being passed as shape to some layers
* Change name in TFIdeficsForVisionText2Text to attribute in IdeficsForVisionText2Text
* Fix pytorch weights load for tf2
There were a lot of `name=` missing in weight initialization code.
* Attempt to fix CI
* Add back accidently removed line
* Remove torch-specific stuff from the TF test file
* make fix-copies, make style, remove autotranslated files
* Fixes to imports/docstrings
* Let's try the from future import in desperation
* Fix the core random_attention_mask fn to match the torch/flax behaviour
* Clean random_attention_mask up correctly
* Remove torch-only test
* Fix loss shape, couple of nits
* make style
* Don't test for OOB embeddings because IDEFICS uses those deliberately
* Fix loss computation to handle masking
* Fix test failures when flattening
* Fix some test failures
- Add cross attention gate which was missing and wasn't being passed arround
- Fix overwriting of image_attention_mask due to hack I had for dummy inputs
* Add a proper stateless scaled_dot_product_attention
* make style
* Adding missing attribute from the PyTorch version
* Small cleanups to decoupledlinearlayer in case that helps
* Pass epsilon to LayerNormalization
* Attemp to fix pytorch weight cross-loading for TFIdeficsEmbedding
* Fix a bug in TFIdeficsGatedCrossAttentionLayer
* Patching up build() methods
* Constant self.inv_freq
* Constant self.inv_freq
* First working version
The TF implementation works now, there was a bug in the TFIdeficsDecoupledLinear
where the weights were mis-intialized (in_features,out_features)
when it should be: (out_features, in_features)
I have tested this so far with tiny-random and idefics-9b-instruct
and gives correct output.
I also dumped the final outputs for both pytorch and TF
and they are identical.
* Fix some test failures
* remove print statement
* Fix return_tensors
* Fix CI test failure check_code_quality
* Attempt to fix CI failures by running `make fixup`
The hardcoded IDs in test_modeling_tf_idefics.py are for the integration
test and makes that file unreadable and should probably be moved to a seperate file.
* Attempt to fix tests_pr_documentation_tests
* Fix a test failure in test_image_processing_idefics.py
* Fix test test_pt_tf_model_equivalence
* Fix a few failures
* Tiny fix
* Some minor fixes
* Remove a duplicate test
* Override a few test failures for IDEFICS
- `test_keras_save_load` is passing now
- `test_compile_tf_model` is still failing
* Fix processing_idefics.py after rebase
* Guard import keras with is_tf_available
* fix check code quality
* fix check code quality
* Minor fixes
* Skip test_save_load temporarily
This test passed on my local box but fails on the CI, skipping
for now to see if there are other remaining failures on the CI.
* Run `ruff format tests src utils`
* Fix last failing test, `test_compile_tf_model`
* Add fixes for vision_tf.py
I forgot to add this file in last commit.
* Minor fixes
* Replace "<<<" with "<<" for doc tests
IDEFICS-9B is too big for doctest runner, so don't run it there
* Make code more readable
* Fix bug after code review
I added a layer_norm_eps to IdeficsConfig but I don't even need it
since the vision config has a layer_norm_eps.
* Fix after code review
Use original code tokenizer.convert_tokens_to_ids
* Keep PyTorch as the default return_tensors
* Fixes to modeling_tf after code review
* Fixes from code review
- Remove all references of `TF_IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST`
- Pass 1e-5 to LayerNormalization in perceiver
* Run ruff
* Undo a change
* Refactor processing code after Matt's suggestion
* Remove TODO's that aren't needed anymore
* For pytorch, Use original pytorch processing code from main
Since this PR is a TF port it shouldn't make any modifications
to pytorch IDEFICS code. This changes undo's the pytorch processing
modifications I made and uses original code from main.
* Update tests/models/idefics/test_modeling_idefics.py
* Update tests/models/idefics/test_modeling_tf_idefics.py
* Add missing imports for is_pt_tf_cross_test
* [DO NOT MERGE]: This is a commit for debugging and will be reverted
The cross test `test_pt_tf_model_equivalence` passes locally but
fails when running on the CI. This commit is to help debug that
and will be reverted.
* Revert "[DO NOT MERGE]: This is a commit for debugging and will be reverted"
This reverts commit 8f0d709ec5bd46685fb0b4259d914ffee794875b.
* [DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted
* [DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted
* Revert "[DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted"
This reverts commit 998cc38b8c3d313bf5e5eb55a7f5b7b881897b89.
* Revert "[DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted"
This reverts commit 1c695ac4219c4ae4d39b330b01744dc27deb7dd4.
* Don't skip test_save_load
IIRC test_save_load was also failing on the CI but not on my local
box, it might be easier to debug that on the CI first than the cross tests
* Debugging commit, will be reverted
* Revert "Debugging commit, will be reverted"
This reverts commit 8eafc8e41e20c4e95a3a90834f06a6e9f445e2d5.
* Override `test_save_load` and push model to save
Maybe this will help me repro this weird bug
* pass my repo_id
* add endpoint
* Pass a temp (write) token just for this CI
* Undo last few commits, still pushing to hub for model debugging
The issue seems to be with save_pretrained(), when I looked at the model saved
from the CI test failure it is basically empty and has no weights.
`self.save_weights(..)` seems to be failing in save_pretrained but needs
more debugging
* Add logging to modeling tf utils, will be reverted just for debugging
* Debugging, will revert
* Revert "Debugging, will revert"
This reverts commit 9d0d3075fb7c82d8cde3a5c76bc8f3876c5c55d3.
* Revert "Add logging to modeling tf utils, will be reverted just for debugging"
This reverts commit 774b6b7b1c17b3ce5d7634ade768f2f686cee617.
* Remove `test_save_load`
The CI failures are gone after my latest rebase, no idea why
but I was still saving the model to my hub on HF and the tf_model.h5
file now has everything.
* Run make fix-copies
* Run ruff format tests src utils
* Debugging commit, will be reverted
* Run ruff, also trigger CI run
* Run ruff again
* Undo debugging commit
---------
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Adding SDPA support for BERT
* Using the proper input name for testing model input in inference()
* Adding documentation for SDPA in BERT model page
* Use the stable link for the documentation
* Adding a gate to only call .contiguous() for torch < 2.2.0
* Additions and fixes to the documentation
* Minor updates to documentation
* Adding extra requirements needed for the contiguous() bug
* Adding "Adapted from" in plcae of the "Copied from"
* Add benchmark speedup tables to the documentation
* Minor fixes to the documentation
* Use ClapText as a replacemenet for Bert in the Copied-From
* Some more fixes for the fix-copies references
* Overriding the test_eager_matches_sdpa_generate in bert tests to not load with low_cpu_mem_usage
[test all]
* Undo changes to separate test
* Refactored SDPA self attention code for KV projections
* Change use_sdpa to attn_implementation
* Fix test_sdpa_can_dispatch_on_flash by preparing input (required for MultipleChoice models)
* chore(root): Initial commit of Phi-3 files.
* fix(root): Fixes Phi-3 missing on readme.
* fix(root): Ensures files are consistent.
* fix(phi3): Fixes unit tests.
* fix(tests): Fixes style of phi-3 test file.
* chore(tests): Adds integration tests for Phi-3.
* fix(phi3): Removes additional flash-attention usage, .e.g, swiglu and rmsnorm.
* fix(phi3): Fixes incorrect docstrings.
* fix(phi3): Fixes docstring typos.
* fix(phi3): Adds support for Su and Yarn embeddings.
* fix(phi3): Improves according first batch of reviews.
* fix(phi3): Uses up_states instead of y in Phi3MLP.
* fix(phi3): Uses gemma rotary embedding to support torch.compile.
* fix(phi3): Improves how rotary embedding classes are defined.
* fix(phi3): Fixes inv_freq not being re-computed for extended RoPE.
* fix(phi3): Adds last suggestions to modeling file.
* fix(phi3): Splits inv_freq calculation in two lines.
* Duplicate swiftformer
* Convert SwiftFormerPatchEmbedding
* Convert SwiftFormerEmbeddings
* Convert TFSwiftFormerMlp
* Convert TFSwiftFormerConvEncoder
* Convert TFSwiftFormerLocalRepresentation
* convert TFSwiftFormerEncoderBlock
* Convert SwiftFormerStage
* Convert SwiftFormerEncoder
* Add TFSWiftFormerPreTrainedModel
* Convert SwiftFormerForImageClassification
* Add kwargs and start drop path
* Fix syntax
* Change Model class name
* Add TFSwiftFormer to __init__
* Duplicate test_modeling_swiftformer
* First test conversions
* Change require_torch to require_tf
* Add exports to swiftformer __init__
* Add TFSwiftFormerModel wrapper
* Fix __init__ and run black
* Remove docstring from MainLayer, fix padding
* Use keras.layers.Activation on keras.Sequential
* Fix swiftformer exports
* Fix activation layer from config
* Remove post_inits
* Use tf.keras.layers.ZeroPadding2D
* Convert torch normalize
* Change tf test input shape
* Fix softmax and reduce_sum
* Convert expand_dims and repeat
* Add missing reshape and tranpose
* Simplify TFSwiftFormerEncoderBlock.call
* Fix mismatch in patch embeddings
* Fix expected output shape to match channels last
* Fix swiftformer typo
* Disable test_onnx
* Fix TFSwiftFormerForImageClassification call
* Add unpack inputs
* Convert flatten(2).mean(-1)
* Change vision dummy inputs (to be reviewed)
* Change test_forward_signature to use .call
* Fix @unpack_inputs
* Set return_tensors="tf" and rename class
* Rename wrongly named patch_embeddings layer
* Add serving_output and change dummy_input shape
* Make dimensions BCHW and transpose inside embedding layer
* Change SwiftFormerEncoderBlock
* Fix ruff problems
* Add image size to swiftformer config
* Change tranpose to MainLayer and use -1 for reshape
* Remove serving_outputs and dummy_inputs
* Remove test_initialization test from tf model
* Make Sequential component a separate layer
* Fix layers' names
* Tranpose encoder outputs
* Fix tests and check if hidden states is not None
* Fix TFSwiftFormerForImageClassification
* Run make fixup
* Run make fix-copies
* Update modeling_tf_auto
* Update docs
* Fix modeling auto mapping
* Update modelint_tf_swiftformer docs
* Fill image_size doc and type
* Add reduction=None to loss computation
* Update docs
* make style
* Debug: Delete the tip to see if that changes anything
* Re-add tip
* Remove add_code_sample_docstrings
* Remove unused import
* Get the debug to actually tell us the problem it has with the docs
* Try a substitution to match the PyTorch file?
* Add swiftformer to ignore list
* Add build() methods
* Update copyright year
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove FIXME comment
* Remove from_pt
* Update copyright year
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Rename one-letter variables
* Remove FIXMEs related to momentum
* Remove old TODO comment
* Remove outstanding FIXME comments
* Get dropout rate from config
* Add specific dropout config for MLP
* Add convencoder dropout to config
* Pass config to SwiftFormerDropPath layer
* Fix drop_path variable name and add Adapted from comment
* Run ruff
* Removed copied from comment
* Run fix copies
* Change drop_path to identity to match pt
* Cleanup build() methods and move to new keras imports
* Update docs/source/en/model_doc/swiftformer.md
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Raise error if drop_path_rate > 0.0
* Apply suggestions from code review
Replace (self.dim), with self.dim,
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Remove drop_path function
* Add training to TFSwiftFormerEncoder
* Set self.built = True last
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Should have been added to previous commit
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>
* Change default_feature_extractor to default_image_processor
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Import Keras from modeling_tf_utils
* Remove relative import
* Run ruff --fix
* Move import keras to tf_available
* Add copied from comment to test_forward_signature
* Reduce batch size and num_labels
* Extract loss logic to hf_compute_loss
* Run ruff format
---------
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* wip
* fix __init__.py
* add docs
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* address comments 1
* work on make fixup
* pass configs down
* add sdpa attention
* remove DbrxBlock
* add to configuration_auto
* docstring now passes formatting test
* fix style
* update READMEs
* add dbrx to modeling_auto
* make fix-copies generated this
* add DBRX_PRETRAINED_CONFIG_ARCHIVE_MAP
* config docstring passes formatting test
* rename moe_loss_weight to router_aux_loss_coef
* add to flash-attn documentation
* fix model-path in tests
* Explicitly make `"suli"` the default `ffn_act_fn`
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* default to using router_aux_loss_coef over ffn_config[moe_loss_weight]
* fix _flash_attn_uses_top_left_mask and is_causal
* fix tests path
* don't use token type IDs
* follow Llama and remove token_type_ids from test
* init ConfigTester differently so tests pass
* remove multiple choice test
* remove question + answer test
* remove sequence classification test
* remove token classification test
* copy Llama tests and remove token_type_ids from test inputs
* do not test pruning or headmasking; style code
* add _tied_weights_keys parameter to pass test
* add type hints
* fix type check
* update config tester
* remove masked_lm test
* remove encoder tests
* initialize DbrxModelTester with correct params
* style
* torch_dtype does not rely on torch
* run make fixup, fix-copies
* use https://huggingface.co/v2ray/dbrx-base-fixed/blob/main/modeling_dbrx.py
* add copyright info
* fix imports and DbrxRotaryEmbedding
* update DbrxModel docstring
* use copies
* change model path in docstring
* use config in DbrxFFN
* fix flashattention2, sdpaattention
* input config to DbrXAttention, DbrxNormAttentionNorm
* more fixes
* fix
* fix again!
* add informative comment
* fix ruff?
* remove print statement + style
* change doc-test
* fix doc-test
* fix docstring
* delete commented out text
* make defaults match dbrx-instruct
* replace `router_aux_loss_coef` with `moe_loss_weight`
* is_decoder=True
* remove is_decoder from configtester
* implement sdpa properly
* make is_decoder pass tests
* start on the GenerationTesterMixin tests
* add dbrx to sdpa documentation
* skip weight typing test
* style
* initialize smaller model
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Add DBRX to toctree
* skip test_new_cache_format
* make config defaults smaller again
* add pad_token_id
* remove pad_token_id from config
* Remove all references to DBRX_PRETRAINED_CONFIG_ARCHIVE_MAP
* Update src/transformers/models/dbrx/__init__.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/dbrx/modeling_dbrx.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update docs/source/en/model_doc/dbrx.md
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Update src/transformers/models/dbrx/configuration_dbrx.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update docs/source/en/model_doc/dbrx.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix typo
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update docs, fix configuration_auto.py
* address pr comments
* remove is_decoder flag
* slice
* fix requires grad
* remove grad
* disconnect differently
* remove grad
* enable grads
* patch
* detach expert
* nissan al ghaib
* Update modeling_dbrx.py
* Update src/transformers/models/dbrx/modeling_dbrx.py
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* replace "Gemma" with "Dbrx"
* remove # type: ignore
* don't hardcode vocab_size
* remove ToDo
* Re-add removed idefics2 line
* Update test to use tiny-random!
* Remove TODO
* Remove one more case of loading the entire dbrx-instruct in the tests
* Update src/transformers/models/dbrx/modeling_dbrx.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* address some comments
* small model
* add dbrx to tokenization_auto
* More docstrings with add_start_docstrings
* Dbrx for now
* add PipelineTesterMixin
* Update src/transformers/models/dbrx/configuration_dbrx.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* remove flash-attn2 import error
* fix docstring
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add useage example
* put on one line
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix ffn_act_fn
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* change "dbrx" to "DBRX" for display purposes.
* fix __init__.py?
* fix __init__.py
* fix README
* return the aux_loss
* remove extra spaces
* fix configuration_auto.py
* fix format in tokenization_auto
* remove new line
* add more useage examples
---------
Co-authored-by: Abhi Venigalla <abhi.venigalla@databricks.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Eitan Turok <eitan.turok@databricks.com>
Co-authored-by: Eitan Turok <150733043+eitanturok@users.noreply.github.com>
Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: Eitan Turok <eitanturok@gmail.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Mihir Patel <mihir.v.patel7@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add jamba arch
* apply "make fix-copies" changes
* fix link to model in JambaConfig docstring
* Add n_ctx in modeling file because repo-consistency wants that
* Add jamba to flash attention and sdpa documentation
* mamba dt_proj quant fix now works for LoRA as well
* override test_left_padding_compatibility and use a more permissive tolerance. left padding numerical difference are accentuated by mamba layers
* add jamba to tokenization auto
* fix comments of shape (PR #24 in the model page: https://huggingface.co/ai21labs/Jamba-v0.1/discussions/24)
* simple PR fixes
* remove unnecessary kwargs from JambaAttentionDecoderLayer and JambaMambaDecoderLayer
* remove the LoRA hack for the mamba dt_proj bias. It was solved in huggingface/peft#1530 (https://github.com/huggingface/peft/pull/1530)
* Add copied comment on JambaMLP (it's the same as MixtralMLP)
* remove padding_mask warnings. It's not supported anymore
* fix docstring. Float instead of int
* A few more minor PR fixes
* (1) lowercase names for mamba layernorms (2) remove _apply_inner_layernorms and do it directly in the forward pass
* Return None attention weights from mamba layers. Append to all attentions only if not None.
* remove some leftover jamba archive lists
* Better separation between expert vs non-expert layers. non-expert layers return None as router_logits, and it is not concatenated to all_router_logits returned from JambaModel
* no need to take router_logits at config.expert_layer_offset anymore. result.router_logits now holds results only for expert layers
* Add Jamba paper on READMEs
* (1) rename n_ctx -> max_position_embeddings (2) don't use it in the modeling file since it's not needed (set it as an exception to check_config_attributes)
* Add copied from comment
* remove the code path for apply_inner_layernorms=False. Jamba always has the inner mamba layernorms
* clearer docstring for _convert_to_standard_cache
* style fixes
* Change calc_logits_for_entire_prompt (bool) to num_logits_to_keep (int). Adapt assisted decoding code tp use it. Also small change in low memory beam search decoding path to support this new int value in model_inputs
* rename test so it still overrides what its meant to override
* draft
* oups
* nit
* remove more complexe logic
* fix names used in config
* fix fix fix
* style
* fix some more failing tests
* generate did not init the cache 🙃
* more small nits
* typo
* config.mamba_expand * config.hidden_size for the intermediate size of the mamba shapes
* fix init of pkv with torch.tensor()
* empty tensor
* fix some init issues
* stupid changes required by generate because it does not even support it's own DynamicCache class
* more fixes
* fix general assisted gen cache_position bug
* tests passing
* Add offsets and periods as SPECIAL_CASES_TO_ALLOW in check_config_attributes.py
* fix reorder_cache to reorder mamba states and override some more functions in HybridMambaAttentionDynamicCache
* no need to override test_past_key_values_format() and _check_past_key_values_for_generate() in tests anymore
* fix docstrings and typehints for past_key_values
* style fixes
* fix docs
* change typehint due to copy from Mixtral
* forgot import
* import order
* Add configuration_jamba and modeling_jamba to not_doctested because the model is too big to download (in docstring of JambaForCausalLM.forward)
* Add integration test with tiny tandom Jamba model on hub
* fix flash attention cache shapes
* bring back forgotten hidden states
* rename HybridMambaAttentionDynamicCache.seqlen_offset to has_previous_state (and make bool) and bugfix - it should be set to True after a finished forward pass of the entire model
* align integration test after modeling fixes
* bugfix - mamba can use precomputed states only of forward pass is on a single token
* bugfix - mamba can use precomputed states only if they match the batch size
* typo
* remove making _prepare_4d_causal_attention_mask a leaf function
* stop using past_seq_len.get_seq_length(). Use cache positions instead. Adjust test (test_decoder_model_past_with_large_inputs) accordingly
---------
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
Co-authored-by: Joao Gante <joao@huggingface.co>
