* Don't accidentally mutate the base_model_tp_plan
* Co-authored by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Trigger tests
* Marking grad accum test as slow
* Add a flaky decorator
* Add a flaky decorator
* Use cyril's codeblock
* Don't copy() when it's None
* Use cyril's new codeblock
* make fixup
* test
* fix
* fix
* skip some and run some first
* test fsdp
* fix
* patches for generate
* test distributed
* copy
* don't test distributed loss for hpu
* require fp16 and run first
* changes from marc's PR fixing zero3
* better alternative
* return True when fp16 support on gaudi without creating bridge
* fix
* fix tested dtype in deepspeed inference test
* test
* fix
* test
* fix
* skip
* require fp16
* run first fsdp
* Apply suggestions from code review
* address comments
* address comments and refactor test
* reduce precison
* avoid doing gaudi1 specific stuff in the genreation loop
* document test_gradient_accumulation_loss_alignment_with_model_loss test a bit more
* 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>
* fix: handle input_channel_dim == channels_last
Signed-off-by: Travis Johnson <tsjohnso@us.ibm.com>
* fix: default PIL images to channels_last
Signed-off-by: Travis Johnson <tsjohnso@us.ibm.com>
* Apply suggestions from code review
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* fixup from review batch
Signed-off-by: Travis Johnson <tsjohnso@us.ibm.com>
* test: add 1x1 PIL image to ambiguous channel test
Signed-off-by: Travis Johnson <tsjohnso@us.ibm.com>
* fix(mllama): avoid 0 dimension for image with impractical aspect ratio
Signed-off-by: Travis Johnson <tsjohnso@us.ibm.com>
---------
Signed-off-by: Travis Johnson <tsjohnso@us.ibm.com>
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
Fixed 2 issues regarding `tests/trainer/test_data_collator.py::TFDataCollatorIntegrationTest::test_all_mask_replacement`:
1. I got the error `RuntimeError: "bernoulli_tensor_cpu_p_" not implemented for 'Long'`. This is because the `mask_replacement_prob=1` and `torch.bernoulli` doesn't accept this type (which would be a `torch.long` dtype instead. I fixed this by manually casting the probability arguments in the `__post_init__` function of `DataCollatorForLanguageModeling`.
2. I also got the error `tensorflow.python.framework.errors_impl.InvalidArgumentError: cannot compute Equal as input #1(zero-based) was expected to be a int64 tensor but is a int32 tensor [Op:Equal]` due to the line `tf.reduce_all((batch["input_ids"] == inputs) | (batch["input_ids"] == tokenizer.mask_token_id))` in `test_data_collator.py`. This occurs because the type of the `inputs` variable is `tf.int32`. Solved this by manually casting it to `tf.int64` in the test, as the expected return type of `batch["input_ids"]` is `tf.int64`.
* 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>
* Fix edge case for continue_final_message
* lstrip() correctly
* Add regression test
* Add a clearer error message when the final message is not present
* Add a clearer error message when the final message is not present
* Fix massive bug!
* Fix pipeline-peft interaction
* once again you have committed a debug breakpoint
* Remove extra testing line
* Add a test to check adapter loading
* Correct adapter path
* make fixup
* Remove unnecessary check
* Make check a little more stringent
* refactor image processor slow got ocr
* add working image processor fast
* fix fast image processor, update doc
* use one big loop for processing patches
* test
* docstring
* prepare distributed cache data
* fix cat dim
* test mvp
* add test checks
* like this?
* working test and solution
* nit
* nit
* add shape info
* clean code
* oups
* fix merge
* yups
* fix if
* now you can play
* fix shape issue
* try non blocking
* fix
* updates
* up
* updates
* fix most of thetests
* update
* update
* small updates
* up
* fix the remaining bug?
* update
* rename when you read from the file
* buffer issues
* current status
* cleanup
* properly allocate dumb memory
* update a small bug
* fix colwise rep issue
* fix keep in float 32 that was keeping everything in float 32
* typo
* more fixes with keep_in_fp32_modules as we use to serach on it
* fix ROPE dtype for TP
* remove what's breaking the tests
* updates
* update and fixes
* small cleanup after merging
* allocate 2x to be safe
* style, auto
* update
* yup nit
* fix
* remove slow as fuck torch api :(
* work
* fixup
* update
* brting the fix back
* fix and update
* fixes
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* updates because some suggestions were wrong 👀
* update?
* fuck this bloated function
* typo
* fix the dumb prefix thing once and forall
* fixes here and there
* updates
* remove prints
* fix strict cases
* styel
* properly fix keys on load!
* update
* fix base model prefix issue
* style
* update
* fix all?
* remoce 1 print
* fix the final etsts
* fixup
* last nits
* fix the detach issue which cause a 2x slowdown
* fixup
* small fixes
* ultra nit
* fix
* fix
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* fix: prevent model access error during Optuna hyperparameter tuning
The `transformers.integrations.integration_utils.run_hp_search_optuna` function releases model memory and sets trainer.model to None after each trial. This causes an AttributeError when subsequent Trainer.train calls attempt to access the model before reinitialization. This is only an issue when `fp16_full_eval` or `bf16_full_eval` flags are enabled.
* Update src/transformers/trainer.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* size tuple
* delete original input_size
* use zip
* process the other case
* Update src/transformers/models/vitdet/modeling_vitdet.py
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* [VITDET] Test non-square image
* [Fix] Make Quality
* make fix style
* Update src/transformers/models/vitdet/modeling_vitdet.py
---------
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* tests: revert change of torch_require_multi_gpu to be device agnostic
The 11c27dd33 modified `torch_require_multi_gpu()` to be device agnostic
instead of being CUDA specific. This broke some tests which are rightfully
CUDA specific, such as:
* `tests/trainer/test_trainer_distributed.py::TestTrainerDistributed`
In the current Transformers tests architecture `require_torch_multi_accelerator()`
should be used to mark multi-GPU tests agnostic to device.
This change addresses the issue introduced by 11c27dd33 and reverts
modification of `torch_require_multi_gpu()`.
Fixes: 11c27dd33 ("Enable BNB multi-backend support (#31098)")
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
* fix bug: modification of frozen set
---------
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
Co-authored-by: Titus von Koeller <9048635+Titus-von-Koeller@users.noreply.github.com>
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* Disable warnings for stacked compressors
* Introduce two new hooks in HfQuantizer lifecycle
to allow updates to missing and unexpected keys
* Update missing and unexpected keys
for stacked compressors
* Add tests
* Fix: run_compressed cases
* Fix: uncompressed cases
* Rename compressed_tensor folder to compressed_tensors
Move RunCompressedTest to the same file
Update tests to unittest
* fix: prevent second save in the end of training
* fix: prevent second save in the end of training
* test: added test for no duplicate save on epoch save strategy
* fix: removed TrainerControl
* chore: style formatting
---------
Co-authored-by: JaktensTid <jaktenstid1@gmail.com>
* Add dithering to the `Speech2TextFeatureExtractor` API.
- in kaldi : 4a8b7f6732/src/feat/feature-window.cc (L145)
- with dithering without a seed, the features become non-deterministic due
to small Gaussian noise added to the audio (i.e. 2 runs lead to little
different outputs)
* update the PR
- add dithering also for WhisperFeatureExtractor
- not adding to Wav2Vec2FeatureExtractor (no FBANK computation)
* add unit-tests for dithering, fix docstrings
* ruff
* utils/check_copies.py --fix_and_overwrite
* update code, add seed to unit-test
* adding explanation of dithering
* Fix XGLM loss computation (PyTorch and TensorFlow)
* Update expected output string in XGLM sample test
This updates the expected output string of test_xglm_sample for torch
2.0 to the correct one and removes the one for torch 1.13.1 + cu116
(transformers moved to torch 2.0 with PR #35358).
* Update expected output IDs in XGLM generation test
**Summary:** TorchAoConfig optionally contains a
`torchao.dtypes.Layout` object which is a dataclass and not
JSON serializable, and so the following fails:
```
import json
from torchao.dtypes import TensorCoreTiledLayout
from transformers import TorchAoConfig
config = TorchAoConfig("int4_weight_only", layout=TensorCoreTiledLayout())
config.to_json_string()
json.dumps(config.to_dict())
```
This also causes `quantized_model.save_pretrained(...)` to
fail because the first step of this call is to JSON serialize
the config. Fixes https://github.com/pytorch/ao/issues/1704.
**Test Plan:**
python tests/quantization/torchao_integration/test_torchao.py -k test_json_serializable
Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* archive_file may not be specified
When loading a pre-trained model from a gguf file, resolved_archive_file may not be set. Guard against that case in the safetensors availability check.
* Remap partial disk offload to cpu for GGUF files
GGUF files don't support disk offload so attempt to remap them to the CPU when device_map is auto. If device_map is anything else but None, raise a NotImplementedError.
* Don't remap auto device_map and raise RuntimeError
If device_map=auto and modules are selected for disk offload, don't attempt to map them to any other device. Raise a runtime error when a GGUF model is configured to map any modules to disk.
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* new flute
* new higgs working
* small adjustments
* progress and quallity
* small updates
* style
---------
Co-authored-by: Andrey Panferov <panferov.andrey3@wb.ru>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>
* allow processor to preprocess conversation + video metadata
* allow callable
* add test
* fix test
* nit: fix
* add metadata frames_indices
* Update src/transformers/processing_utils.py
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
* Update src/transformers/processing_utils.py
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
* port updates from Orr and add one more test
* Update src/transformers/processing_utils.py
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
* typo
* as dataclass
* style
* docstring + maek sure tests green
---------
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
* tmp commit
* move tests to the right class
* remove ALL all_generative_model_classes = ...
* skip tf roberta
* skip InstructBlipForConditionalGenerationDecoderOnlyTest
* videollava
* reduce diff
* reduce diff
* remove on vlms
* fix a few more
* manual rebase bits
* more manual rebase
* remove all manual generative model class test entries
* fix up to ernie
* a few more removals
* handle remaining cases
* recurrent gemma
* it's better here
* make fixup
* tf idefics is broken
* tf bert + generate is broken
* don't touch tf :()
* don't touch tf :(
* make fixup
* better comments for test skips
* revert tf changes
* remove empty line removal
* one more
* missing one
* 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>
* change order of unmasking of tokens
* library import
* class setup
* test function
* refactor
* add commit message
* test modified
* explict initiliasation of weights + made model smaller
* removed sepete testing file
* fixup
* fixup core
* test attention mask with token types
* tests fixup
* removed PaliGemmaAttentionMaskTest class
---------
Co-authored-by: sambhavnoobcoder <indosambahv@gmail.com>
* Adding option to save/reload scaler
* Removing duplicate variable
* Adding save/reload test
* Small fixes on deterministic algorithm call
* Moving LLM test to another file to isolate its environment
* Moving back to old file and using subprocess to run test isolated
* Reverting back accidental change
* Reverting back accidental change
* 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 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>
* 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>
