* Initial commit for Qwen3
* fix and add tests for qwen3 & qwen3_moe
* rename models for tests.
* fix
* fix
* fix and add docs.
* fix model name in docs.
* simplify modular and fix configuration issues
* Fix the red CI: ruff was updated
* revert ruff, version was wrong
* fix qwen3moe.
* fix
* make sure MOE can load
* fix copies
---------
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
* init commit
* style
* take comments into account
* add deepseekv3 modeling
* remove redundant code
* apply make style
* apply fix-copies
* make format
* add init files
* rename deepseekv3 into deepseek_v3 based on its model_type
* rename deepseekv3 into deepseek_v3 based on its model_type
* deepseek-v3 not deepseek_v3
* set model_type as deepseek_v3
* use default docs
* apply make
* fill type and docstring
* add rope_config_validation
* use custom DeepseekV3MLP
* hold code only for checkpoints congifuration; remove redundant
* revise rope yarn for DeepSeek variation
* rename DeepSeek-V3
* some refactoring
* revise load_hook to work properly; make moe func trainable; use llama instead of mixtral
* fix attention forward
* use -1 for not-changing dim when to use exapnd
* refactor DeepseekV3TopkRouter
* use reshape_for_rope instead of load_hook; revise attention forward for TP; rename q_head_dim with qk_head_dim
* register pre_hook and hook both
* make style
* use n_shared_experts
* Update src/transformers/models/deepseek_v3/configuration_deepseek_v3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add test file
* update modeling_file according to modular file
* make style
* add mapping for DeepseekV3ForSequenceClassification
* remove aux_loss_alpha
* add deepseek_v3 for perf
* add deepseek_v3
* rename test as deepseekv3
* use tiny-deepseek-v3
* remove DeepseekV3ForSequenceClassification
* cache before padding
* remote output_router_logits
* Revert "remote output_router_logits"
This reverts commit f264f800d0.
* remove output_router_logits
* make e_score_correction_bias as buffer
* skip tests not compatible
* make style
* make e_score_correction_bias as buffer
* use rope_interleave instead of load_hook
* skip tests not compatible with MLA
* add doc for rope_interleave
* fix typo
* remove torch.no_grad for selecting topk
* fix post merge issue
* mrege with main and simplify
* nits
* final
* small fixes
* fix
* support TP better
* stash
* changes currently requires
* remove synch
* more fixes for TP
* temp fix for TP : some attention layers's FP8 scales are too small + shared is local colwise and anything is local if FP8 because weights are used
* updates to have generation work!
* push most of the changes
* reorder functions + call for contributions!
* update readme
* nits
* update
* ruff was updated on main
* merge with main and fix copies
* revert unrelated changes
* route all tokens to all experts when testing to avoid no gradient iddues
* finish fixing all tests
* fixup
* nit
* clean config
* last readme changes
* nit
* do cnit
* typo
* last nit
* one more one more
---------
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: arthur@huggingface.co <arthur@ip-26-0-165-131.ec2.internal>
* no image
* test
* revert jax version updates
* make fixup
* update autodoc path for model_addition_debugger
* shieldgemma2
* add missing pages to toctree
* draft of model tracer visualiser
* add context manager in addition to decorator
* add debug utils to init
* move model debugging utils to dedicated file
* add documentation
* protect some imports
* format
* move and protect imports
* format
* doc: improve errors in case of broken dummy imports.
* format
* use automatic torch backend
* update doc
* fix backend
* (TEMP) move to dummies while backend wait
* update documentation
* doc
* add prompt depth anything model by modular transformer
* add prompt depth anything docs and imports
* update code style according transformers doc
* update code style: import order issue is fixed by custom_init_isort
* fix depth shape from B,1,H,W to B,H,W which is as the same as Depth Anything
* move prompt depth anything to vision models in _toctree.yml
* update backbone test; there is no need for resnet18 backbone test
* update init file & pass RUN_SLOW tests
* update len(prompt_depth) to prompt_depth.shape[0]
Co-authored-by: Joshua Lochner <admin@xenova.com>
* fix torch_int/model_doc
* fix typo
* update PromptDepthAnythingImageProcessor
* fix typo
* fix typo for prompt depth anything doc
* update promptda overview image link of huggingface repo
* fix some typos in promptda doc
* Update image processing to include pad_image, prompt depth position, and related explanations for better clarity and functionality.
* add copy disclaimer for prompt depth anything image processing
* fix some format typos in image processing and conversion scripts
* fix nn.ReLU(False) to nn.ReLU()
* rename residual layer as it's a sequential layer
* move size compute to a separate line/variable for easier debug in modular prompt depth anything
* fix modular format for prompt depth anything
* update modular prompt depth anything
* fix scale to meter and some internal funcs warp
* fix code style in image_processing_prompt_depth_anything.py
* fix issues in image_processing_prompt_depth_anything.py
* fix issues in image_processing_prompt_depth_anything.py
* fix issues in prompt depth anything
* update converting script similar to mllamma
* update testing for modeling prompt depth anything
* update testing for image_processing_prompt_depth_anything
* fix assertion in image_processing_prompt_depth_anything
* Update src/transformers/models/prompt_depth_anything/modular_prompt_depth_anything.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Update src/transformers/models/prompt_depth_anything/modular_prompt_depth_anything.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Update src/transformers/models/prompt_depth_anything/image_processing_prompt_depth_anything.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Update src/transformers/models/prompt_depth_anything/image_processing_prompt_depth_anything.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Update src/transformers/models/prompt_depth_anything/image_processing_prompt_depth_anything.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Update docs/source/en/model_doc/prompt_depth_anything.md
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Update docs/source/en/model_doc/prompt_depth_anything.md
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* update some testing
* fix testing
* fix
* add return doc for forward of prompt depth anything
* Update src/transformers/models/prompt_depth_anything/modular_prompt_depth_anything.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Update tests/models/prompt_depth_anything/test_modeling_prompt_depth_anything.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* fix prompt depth order
* fix format for testing prompt depth anything
* fix minor issues in prompt depth anything doc
* fix format for modular prompt depth anything
* revert format for modular prompt depth anything
* revert format for modular prompt depth anything
* update format for modular prompt depth anything
* fix parallel testing errors
* fix doc for prompt depth anything
* Add header
* Fix imports
* Licence header
---------
Co-authored-by: Joshua Lochner <admin@xenova.com>
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Disable inductor config setter by default
This is hard to debug and should be off by default
* remove default settings in autoquant too
* Add info to torchao.md about recommended settings
* satisfying Ruff format
Summary:
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Just import torch AdamW instead
* Update docs too
* Make AdamW undocumented
* make fixup
* Add a basic wrapper class
* Add it back to the docs
* Just remove AdamW entirely
* Remove some AdamW references
* Drop AdamW from the public init
* make fix-copies
* Cleanup some references
* make fixup
* Delete lots of transformers.AdamW references
* Remove extra references to adamw_hf
* add support for fast image processors in add-new-model-like
* fix header not found add-fast-image-processor-cli
* Encourage adding fast image processor
* nit
* start improve doc
* update docs
* make requested modifs
* Fix converter
* [Broken] Adds Gemma 3 to Hugging Face Transformers
* Consolidating Config and Processor params across impls
* Sorting out configuration parameters. Adds qk_norm before RoPE. Still not sure if RoPE is right.
* Additional plumbing for CausalLM and ConditionalGeneration variants
* incomplete draft of Orbax conversion script
* More complete checkpoint conversion
* Supporting Gemma 3 1B checkpoints
* Updating RoPE for multiple frequencies
* Adjustments to rotary embedder
* Proof of life for text-only operation
* Updating the conversion script to handle multimodal projection weights
* Fixing tet-only conversions
* Cleaner conversion script with multimodal support and a simpler processor
* Additional refatcors to the Gemma3Processor
* Simplified Processor to work over text representations
* Updated conversion script to join text and vision embeddings at converion time
* Logging for debugging
* Update src/transformers/models/gemma2/modeling_gemma2.py
Co-authored-by: Joshua Lochner <admin@xenova.com>
* Removed extraneous Config params
* Switching to fast tokenizer for checkpoint conversions
* isolating siglip for performance tetsing
* Minor changes for debugging tests against baselines
* Adding average pooling for soft tokens
* Updating processor code to enable simpler embedding interleaving for arbitrary number of images in prompts
* Updating conversion script for ShieldGemma 2 conversion compatibility
* Allow disable_compile to be provided as a kwarg
* Refresh from modular
* Updated conversion script and corrected sliding window
* Fix type mismatch in cache_position (#4)
* Fix dtype (#5)
* Fix type mismatch in cache_position
* Actually fix in the modular file
Co-authored-by: Aritra Roy Gosthipaty <aritra.born2fly@gmail.com>
---------
Co-authored-by: Aritra Roy Gosthipaty <aritra.born2fly@gmail.com>
* fixes for embedding table overflow and missing image_soft_token_mask from Gemma3Processor
* Adding 2D pooling for image embeddings
* Revert "Adding 2D pooling for image embeddings"
This reverts commit 65350cf531.
* Gemma3 average pooling changed from 1D to 2D
* Major refactor to Gemma3MultimodalInputProjection
* Updating Gemm 3 Auto* registrations
* Add option to save Gemma 3 chat template with tokenizer during weights conversion
* Removing unused imports
* Moving out-of-vocab handling from Gemma3Processor to Gemma3ForConditionalGeneration
* Removing duplicate config property
* Removing final logit softcapping and 1-indexing of position ids
* Fixing image processor config and none --> None typo
* Fixing sliding window size for 1B
* Updating image_mean and image_std in Image Processor
* Attention masking changed to lower triangular
* Moving image special tokens to conversion script
* Mirror image processor defaults from conversion script into Gemma3ProcessorKwargs
* Remove special token variables from symbol space
* Moving image soft token mask computation from Gemma3Processor to Gemma3ForConditionalGeneration
* tie lm_head and embedding weights
Co-authored-by: Matthew Douglas <38992547+matthewdouglas@users.noreply.github.com>
* Correct tied weights in Gemma3CausalLM
* iterative bidirectional attention
* resolving merge conflicts
* Reverting to Gemma 2 HybridCache with sldiing window support and a sliding_window_pattern of 6
* Correcting RoPE scaling
* clean up first pass, dummy model geenration works
* final clean up before fixing tests
* causal lm test works, so fine
* Fix conversion
* Update src/transformers/models/gemma3/processing_gemma3.py
* model tests are happy
* processor tests are happy
* image processing tests added
* fixup
* Fix pre-processing in conversion
* Inputs merging
* Do not normalize vision embeddings
* Apply Ryan's (and team) changes to attention
* token type ids + mask
* template
* move embed scale, add rope scale, fix tests
* Add chat template to tokenizer
* Use prefix for causal model loading
* use existing code for sliding mask from gemma2
* self.embed_tokens already normalizes
* Correcting Gemma3TextConfig parameters in conversion script
* typo, modular overwrites my fixes
* enable device map for text model
* Conversion updates
* ultra nit: no einsums
* update image token
* copy deepcopy config + some docs
* add some test, still WIP
* Refactoring --include_chat_tempalte logic in converter
* Update src/transformers/models/gemma3/modular_gemma3.py
Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
* Add eos tokens for instruct models
* dump so i can work on dgx
* Removing add_bos by default
* dump
* add fast im proc
* docs for PaS + fixup
* another fixup
* one more fixup
* fix tests
* Inverting prior BOS change
* ultra nit
* Reverting to Tokenizer saved with add_bos_token=True and chat template starting with BOS
* resize embeds, remove sqrt, add slow test outputs
* FA2 but quality is meh
* nit
* skip FA2, no idea what happened
* last bit for green CI
* please, green CI for docs
* T_T
* Fix for Gemma3 logits
* Support both options for system prompt
* Update src/transformers/models/gemma3/image_processing_gemma3_fast.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update docs/source/en/model_doc/gemma3.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update docs/source/en/model_doc/gemma3.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update docs/source/en/model_doc/gemma3.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update docs/source/en/model_doc/gemma3.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Update docs/source/en/model_doc/gemma3.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Docs updates now that assets are live
* Style fixes
---------
Co-authored-by: Joshua Lochner <admin@xenova.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Aritra Roy Gosthipaty <aritra.born2fly@gmail.com>
Co-authored-by: Mayank Chaturvedi <imayank@google.com>
Co-authored-by: Matthew Douglas <38992547+matthewdouglas@users.noreply.github.com>
Co-authored-by: raushan <raushan@huggingface.co>
Co-authored-by: Raushan Turganbay <raushan.turganbay@alumni.nu.edu.kz>
Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
Co-authored-by: Lysandre <hi@lysand.re>
* update
* doc
* update
* Update docs/source/en/gguf.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* fix
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* add swanlab integration
* feat(integrate): add SwanLab as an optional experiment tracking tool in transformers
- Integrated SwanLab into the transformers library as an alternative for experiment tracking.
- Users can now log training metrics, hyperparameters, and other experiment details to SwanLab by setting `report_to="swanlab"` in the `TrainingArguments`.
- Added necessary dependencies and documentation for SwanLab integration.
* Fix the spelling error of SwanLabCallback in callback.md
* Apply suggestions from code review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Fix typo in comment
* Fix typo in comment
* Fix typos and update comments
* fix annotation
* chore: opt some comments
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: AAssets <20010618@qq.com>
Co-authored-by: ZeYi Lin <944270057@qq.com>
Co-authored-by: KAAANG <79990647+SAKURA-CAT@users.noreply.github.com>
* initial commit
* small fix
* move stuff to image processing file
* remove stuff in validate turn and fix return tensor
* remove liquid stuff
* in the process of addressing comments
* changes to get the right tokenization
* new __init__ works
* fixing defulat std and mean
* works
* small testing scipt -- to be deleted before merge
* remove redundant code
* addressing comments
* fix inits, add docs templates
* refactor processor, switch to gotocr image processor
* remove image proc from init
* refactor to working llava-style architecture
* Change AyaVisionModel to AyaVisionForConditionalGeneration
* add tests
* fixups
* update doc
* Adding logits_to_keep explicitly in ayavision forward to enable compatibility with cohere model
* better variable names + remove code paths
* Updates to aya_vision.md
* address comments
* adding copied from
* make style and remove unused projector_hidden_act from config
* sort init
* include usage of fast image proc and proc on cuda in doc
* update checkpoint iin test processor
* update checkpoint in test processor 2
* remove test_model and update docstring
* skip failing tests
---------
Co-authored-by: Saurabh Dash <saurabh@cohere.com>
Co-authored-by: yonigozlan <yoni.gozlan@huggingface.co>
* 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>
* Add implementation for DataCollatorForMultipleChoice based on docs.
* Add DataCollatorForMultipleChoice to import structure.
* Remove custom DataCollatorForMultipleChoice implementations from example scripts.
* Remove custom implementations of DataCollatorForMultipleChoice from docs in English, Spanish, Japanese and Korean.
* Refactor torch version of DataCollatorForMultipleChoice to be more easily understandable.
* Apply suggested changes and run make fixup.
* fix copies, style and fixup
* add missing documentation
* nits
* fix docstring
* style
* nits
* isort
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
* 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>
* update awq doc
* Update docs/source/en/quantization/awq.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/quantization/awq.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/quantization/awq.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/quantization/awq.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* add note for inference
---------
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>
* change cuda to DEVICE
* Update docs/source/en/llm_tutorial.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.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
* 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>
* إضافة الترجمة العربية: masked_language_modeling.md
* Update docs/source/ar/tasks/masked_language_modeling.md
Co-authored-by: Abdullah Mohammed <554032+abodacs@users.noreply.github.com>
* Update docs/source/ar/tasks/masked_language_modeling.md
Co-authored-by: Abdullah Mohammed <554032+abodacs@users.noreply.github.com>
* Update docs/source/ar/tasks/masked_language_modeling.md
Co-authored-by: Abdullah Mohammed <554032+abodacs@users.noreply.github.com>
* Update docs/source/ar/tasks/masked_language_modeling.md
Co-authored-by: Abdullah Mohammed <554032+abodacs@users.noreply.github.com>
* Update docs/source/ar/tasks/masked_language_modeling.md
Co-authored-by: Abdullah Mohammed <554032+abodacs@users.noreply.github.com>
* Update docs/source/ar/tasks/masked_language_modeling.md
Co-authored-by: Abdullah Mohammed <554032+abodacs@users.noreply.github.com>
* Update docs/source/ar/tasks/masked_language_modeling.md
Co-authored-by: Abdullah Mohammed <554032+abodacs@users.noreply.github.com>
* Update docs/source/ar/tasks/masked_language_modeling.md
Co-authored-by: Abdullah Mohammed <554032+abodacs@users.noreply.github.com>
* Update docs/source/ar/tasks/masked_language_modeling.md
Co-authored-by: Abdullah Mohammed <554032+abodacs@users.noreply.github.com>
* Update docs/source/ar/tasks/masked_language_modeling.md
Co-authored-by: Abdullah Mohammed <554032+abodacs@users.noreply.github.com>
* Update docs/source/ar/tasks/masked_language_modeling.md
Co-authored-by: Abdullah Mohammed <554032+abodacs@users.noreply.github.com>
* Update docs/source/ar/tasks/masked_language_modeling.md
Co-authored-by: Abdullah Mohammed <554032+abodacs@users.noreply.github.com>
* Update docs/source/ar/tasks/masked_language_modeling.md
Co-authored-by: Abdullah Mohammed <554032+abodacs@users.noreply.github.com>
* Update _toctree.yml
* Update _toctree.yml
* Add language_modeling.md
* Add Sequence_classifiation.md
* Update _toctree.yml
---------
Co-authored-by: Abdullah Mohammed <554032+abodacs@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
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
Enhanced installation section with troubleshooting, GPU setup, and OS-specific details.
* Update README.md
Enhanced installation section with troubleshooting, GPU setup, and OS-specific details.
* Update installation.md
Updated installation.md to include virtual environment and GPU setup instructions.
* Update installation.md
Updated installation.md to include virtual environment and GPU setup instructions.
* Update installation.md
Updated installation.md to include virtual environment, troubleshooting and GPU setup instructions.
* Update installation.md
* Update installation.md
* Update installation.md
* Update installation.md
Updated installation.md to include virtual environment, troubleshooting functions and GPU setup instructions.
* Update installation.md
Updated installation.md to include virtual environment, troubleshooting functions and GPU setup instructions.
* Update installation.md
Updated installation.md to include virtual environment, troubleshooting functions and GPU setup instructions.
* Update README.md
Removed numbering from README.md.
* Update README.md
Removed unnecessary "a)" formatting as per maintainer feedback.
* Update README.md
Added blank lines around code snippets for better readability.
* Update README.md
Removed the line "b) Install a backend framework:" from README.md as per feedback.
* Update README.md
Simplified "For Windows:" to "Windows" in README.md as per feedback as well as "For macOS/Linux:" to "macOS/Linux"
* Update README.md
Removed unnecessary heading and retained valid code snippet.
* Update README.md
Removed unnecessary heading "d) Optional: Install from source for the latest updates" as per feedback.
* Update README.md
Removed "GPU Setup (Optional)" section to align with minimal design feedback.
* Update installation.md
Removed "Create and Activate a Virtual Environment" section from installation.md as per feedback.
* Update installation.md
Adjusted "Troubleshooting" to a second-level heading and added an introductory line as per feedback.
* Update installation.md
Updated troubleshooting section with simplified headings and formatted code blocks as per feedback.
* Update installation.md
Integrated GPU setup instructions into the "Install with pip" section for better content flow.
* Update README.md
Removed Troubleshooting section from README.md for minimalism as per maintainer feedback.
* Update torchao.md: use auto-compilation
* Update torchao.md: indicate updating transformers to the latest
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* 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>
* Create token_classification.md
* Update token_classification.md
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* 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
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* Update docs/source/en/model_doc/emu3.md
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* Update docs/source/en/model_doc/emu3.md
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* Update docs/source/en/model_doc/emu3.md
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* Update docs/source/en/model_doc/emu3.md
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* Update docs/source/en/model_doc/emu3.md
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* Update docs/source/en/model_doc/emu3.md
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* Update docs/source/en/model_doc/emu3.md
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* 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>
* universal checkpoint
* Update docs/source/en/deepspeed.md
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* Update docs/source/en/deepspeed.md
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* Update docs/source/en/deepspeed.md
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---------
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* Update _toctree.yml
* Add files via upload
* Update _toctree.yml
---------
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* 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
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* 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>
* Add French translation of task_summary and tasks_explained
---------
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* Update _toctree.yml
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* Improve modular transformers documentation
- Adds hints to general contribution guides
- Lists which utils scripts are available to generate single-files from modular files and check their content
* Show commands in copyable code cells
---------
Co-authored-by: Joel Koch <joel@bitcrowd.net>
* 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>
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* Add Cohere2 docs details
* Update docs/source/en/model_doc/cohere2.md
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---------
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* Add Falcon3 documentation
* Update Falcon3 documentation
* Change Falcon to Falcon3
* Update docs and run make fix-copies
* Add blog post and huggingface models links
* refactor image_processing_auto logic
* fix fast image processor tests
* Fix tests fast vit image processor
* Add safeguard when use_fast True and torchvision not available
* change default use_fast back to None, add warnings
* remove debugging print
* call get_image_processor_class_from_name once
* 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 "Translating Benchmarks.md to Chinese "
* Removed all the English original text (which was previously kept as comments in the document) and refined some of the Chinese expressions.
* Add docs/source/ar/community.md to Add_docs_source_ar_community.md
* Update community.md
* Update community.md
* Update community.md
* Update _toctree.yml - add community.md
* Update docs/source/ar/community.md
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* Create how_to_hack_models.md
* Create modular_transformers.md
* Create tiktoken.md
* Update _toctree.yml
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---------
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* add commen to offloading
* Update docs/source/en/kv_cache.md
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---------
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* Fixed typo in multi gpu docs and OLMoE version
* Fixed typos in docs for agents, agents advanced, knowledge distillation, and image feature extraction
* Fixed incorrect usage of model.image_guided_detection in zero shot object detection docs
* explain release_memory
* Update docs/source/en/llm_tutorial_optimization.md
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---------
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* Add docs/source/ar/benchmarks.md to Add_docs_source_ar_benchmarks.md
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* Update _toctree.yml
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---------
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* Updated documentation and added conversion utility
* Update docs/source/en/tiktoken.md
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* Update docs/source/en/tiktoken.md
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* Moved util function to integration folder + allow for str
* Update formatting
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Updated formatting
* style changes
---------
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Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add Nemotron GGUF Loading Support
* fix the Nemotron architecture assignation
---------
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* Added image-text-to-text pipeline to task guide
* Update docs/source/en/tasks/image_text_to_text.md
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* Merge codeblocks
---------
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* 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 XPU path
* use accelerate API
* Update docs/source/en/tasks/semantic_segmentation.md
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* update more places with accelerate API
---------
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* Add docs/source/ar/torchscript.md to Add_docs_source_ar_torchscript.md
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* Create _toctree.yml
* Delete docs/source/ar/_toctree.yml
* Update _toctree.yml - add trainer
* Update _toctree.yml
* merge serialization.md into this branch
* merge sagemaker.md into this PR
* Update _toctree.yml
* Update docs/source/ar/trainer.md
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* kinda works
* update
* add tests
* update
* use special tokens in processors
* typo
* fix copies
* fix
* fix moshi after rebase
* update
* fix tests
* update
* Update docs/source/en/main_classes/tokenizer.md
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* update docs
* test for load time adding tokens
* fix some more tests which are now fetched better
* one more fix
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* Add docs/source/ar/multilingual.md to Add_docs_source_ar_multilingual.md
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* Update _toctree.yml
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* Add Translated files to branch for merg
* Update _toctree.yml
* Update _toctree.yml
* Update custom_models.md
* Update chat_templating.md
* Update docs/source/ar/create_a_model.md
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* Update create_a_model.md
* Update gguf.md
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* Update gguf.md
* Update gguf.md
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* update doc
* Update docs/source/en/perf_train_cpu.md
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* delete closing tip
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* Standardize image-text-to-text-models-output
add post_process_image_text_to_text to chameleon and cleanup
Fix legacy kwarg behavior and deprecation warning
add post_process_image_text_to_text to qwen2_vl and llava_onevision
Add post_process_image_text_to_text to idefics3, mllama, pixtral processor
* nit var name post_process_image_text_to_text udop
* nit fix deprecation warnings
* Add image-text-to-text pipeline
* add support for image url in chat template for pipeline
* Reformat to be fully compatible with chat templates
* Add tests chat template
* Fix imports and tests
* Add pipeline tag
* change logic handling of single prompt ans multiple images
* add pipeline mapping to models
* fix batched inference
* fix tests
* Add manual batching for preprocessing
* Fix outputs with nested images
* Add support for all common processing kwargs
* Add default padding when multiple text inputs (batch size>1)
* nit change version deprecation warning
* Add support for text only inference
* add chat_template warnings
* Add pipeline tests and add copied from post process function
* Fix batched pipeline tests
* nit
* Fix pipeline tests blip2
* remove unnecessary max_new_tokens
* revert processing kosmos2 and remove unnecessary max_new_tokens
* fix pipeline tests idefics
* Force try loading processor if pipeline supports it
* revert load_processor change
* hardcode loading only processor
* remove unnecessary try except
* skip imagetexttotext tests for kosmos2 as tiny model causes problems
* Make code clearer
* Address review comments
* remove preprocessing logic from pipeline
* fix fuyu
* add BC resize fuyu
* Move post_process_image_text_to_text to ProcessorMixin
* add guard in post_process
* fix zero shot object detection pipeline
* add support for generator input in pipeline
* nit
* change default image-text-to-text model to llava onevision
* fix owlv2 size dict
* Change legacy deprecation warning to only show when True
* Update TRANSLATING.md
* Apply suggestions from code review
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* Update TRANSLATING.md
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* 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
