* add RAdamScheduleFree optimizer
* revert schedulefree version to the minimum requirement
* refine is_schedulefree_available so that it can take min_version
* refine documents
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* make output_dir optional
* inintaied a basic testing module to validate and verify the changes
* Test output_dir default to 'tmp_trainer' when unspecified.
* test existing functionality of output_dir.
* test that output dir only created when needed
* final check
* added doc string and changed the tmp_trainer to trainer_output
* amke style fixes to test file.
* another round of fixup
---------
Co-authored-by: sambhavnoobcoder <indosambahv@gmail.com>
* Add is_torch_greater_or_equal test decorator
* Add common test for torch.export
* Fix bit
* Fix focalnet
* Fix imagegpt
* Fix seggpt
* Fix swin2sr
* Enable torch.export test for vision models
* Enable test for video models
* Remove json
* Enable for hiera
* Enable for ijepa
* Fix detr
* Fic conditional_detr
* Fix maskformer
* Enable test maskformer
* Fix test for deformable detr
* Fix custom kernels for export in rt-detr and deformable-detr
* Enable test for all DPT
* Remove custom test for deformable detr
* Simplify test to use only kwargs for export
* Add comment
* Move compile_compatible_method_lru_cache to utils
* Fix beit export
* Fix deformable detr
* Fix copies data2vec<->beit
* Fix typos, update test to work with dict
* Add seed to the test
* Enable test for vit_mae
* Fix beit tests
* [run-slow] beit, bit, conditional_detr, data2vec, deformable_detr, detr, focalnet, imagegpt, maskformer, rt_detr, seggpt, swin2sr
* Add vitpose test
* Add textnet test
* Add dinov2 with registers
* Update tests/test_modeling_common.py
* Switch to torch.testing.assert_close
* Fix masformer
* Remove save-load from test
* Add dab_detr
* Add depth_pro
* Fix and test RT-DETRv2
* Fix dab_detr
* Add support for constant learning rate with cooldown
* Add support for constant learning rate with cooldown
* Add support for constant learning rate with cooldown
* Add support for constant learning rate with cooldown
* Add support for constant learning rate with cooldown
* Add support for constant learning rate with cooldown
* Add support for constant learning rate with cooldown
* Add more warmup and cooldown methods to 'get_wsc_schedule'
* Add more warmup and cooldown methods to 'get_wsc_schedule'
* Add more warmup and cooldown methods to 'get_wsc_schedule'
* Add more warmup and cooldown methods to 'get_wsc_schedule'
* Add more warmup and decay methods to 'get_wsd_schedule'
* support num_training_steps and num_stable_steps for get_wsd_schedule
* support num_training_steps and num_stable_steps for get_wsd_schedule
* get wsd scheduler before the `num_training_steps` decision
* fix code_quality
* Update stable branch logic
* fix code_quality
* Move stable stage decide to `get_wsd_schedule`
* Update docstring of `get_wsd_schedule`
* Update `num_train_steps` to optional
* Update `num_train_steps` to optional
* Update docstring of `get_wsd_schedule`
* Update src/transformers/optimization.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
---------
Co-authored-by: Marc Sun <57196510+SunMarc@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 2408ec54e4f27d2abbecdb8374e58f34d91d8e96.
* 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 5caa0bd8f9f7463b98410c04e6cfe8fef3adee18.
* Revert "add antialias in BaseImageProcessorFast"
This reverts commit 3ae1134780ae236872985523d9c0a444eabcc179.
* 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>
* Fix StopStringCriteria to handle tokens above len(tokenizer)
This fixes#35244 by clipping token IDs to be within the tokenizer's vocabulary size before performing the embedding lookup. This prevents index errors when model.config.vocab_size > len(tokenizer).
The fix:
1. Adds a clamp operation to ensure token IDs are within bounds
2. Adds a test case to verify the behavior
* Use self.stop_strings instead of stop_strings
* Handle clipping correctly
* make fixup
* Update test to the new embedding vecs
* Use much bigger values in the mismatch test
* Typo fix
* Slight simplification
---------
Co-authored-by: openhands <openhands@all-hands.dev>
* Save state
* Make a failing test
* Better test
* mpt -> done, many more to go
* Rm extranious
* Bamba
* Bert
* big_bird
* biogpt
* bloom
* codegen
* ctrl
* data2vec
* dbrx
* Through up to Dbrx
* electra
* ernie
* falcon
* Fuyu/persimmon
* Include noop kwargs to base models
* Rebase
* Skip musigen
* Refactor/skip mllama
* Revert makefile
* Rm file
* Fix PT failing, need to modify rest of loss funcs to not resize
* Propagate some
* Continue
* More
* More options
* Mostly fixed
* Proved that it's the same
* Bloom is good
* Make ability to override loss func possible
* Fixup
* Clean
* Fix xglm
* Quality tests
* Skip OCR2
* Make specific loss for xglm
* Make order the same/line up 1:1
* xglm
* Skip fx output loss bloom model
* Didn't pass in pad_token_id
* Fix quality
* Nail in edge case of torch dtype
* Rm unused func
* Apply suggestions from code review
Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>
* Refactor tests to only mock what we need, don't introduce injection functions
* SetUp/TearDown
* Do super
---------
Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>
* added condition for top_k Doc mismatch fix
* initilation of test file for top_k changes
* added test for returning all labels
* added test for few labels
* tests/test_audio_classification_top_k.py
* final fix
* ruff fix
---------
Co-authored-by: sambhavnoobcoder <indosambahv@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>
* layernorm_decay_fix
* W293 fix
* ruff format fix
* black format
* ruff format
* erase last layer
* add test_get_parameter_names_rmsnorm
* rmsnorm fix
* apply_chat_template: consistent return_tensors behaviour with return_assistant_tokens_mask flag
* test_chat_template_return_assistant_tokens_mask: support tokenizers with no attention mask
* test_chat_template_return_assistant_tokens_mask: skip tokenizers with no padding token
* test_chat_template_return_assistant_tokens_mask: force tokenizer padding_side=right
---------
Co-authored-by: Eduard Allakhverdov <goncharova@airi.net>
Co-authored-by: d.tarasov <d.tarasov@airi.net>
* Handle empty change indices in RLE conversion for masks
* [test] Add unit tests for RLE encoding of masks in SamProcessor
* [test] Update RLE conversion tests to use TensorFlow implementation
* [test] Fix formatting in SamProcessorTest according to check_code_quality action
* [test] Fix formatting in SamProcessorTest according to check_code_quality
* [test] Refactored rle test cases into one test and used tf tensors in tf test cases
* [test] Fix: removed self parameter from refactored methods
* [test] Removed nested methods in run-length encoding tests for PyTorch and TensorFlow
* [test] Added description to individual to run-length encoding tests for PyTorch and TensorFlow.
* initial POC
* - batch mix feature
* fix tests
* fix tests
* make style
* do not skip and instead fix tests
* update
* return back the test
* correct text with the correct ckpt
* Support for generate_argument: return_dict_in_generate=True, instead of returning a error
* fix: call test with return_dict_in_generate=True
* fix: Only import torch if it is present
* update: Encapsulate output_dict changes
* fix: added back original comments
---------
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
