* fix wandb hp search unable to resume from sweep_id
* format styles
---------
Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* 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>
* correctly slice
* check mask
* Update modular_gemma2.py
* fix
* add tests
* fix typo
* finally fix mask slicing
* Finally correctly slice in all cases!!
* add test for all attention functions
* small fix in tests
* trick around dynamo tracing issue
* last update
* more robust
* kwargs propagation
* make it explicit for checkpointing
* apply modular
* Added `segmentation_maps` support for DPT image processor
* Added tests for dpt image processor
* Moved preprocessing into separate functions
* Added # Copied from statements
* Fixed # Copied from statements
* Added `segmentation_maps` support for DPT image processor
* Added tests for dpt image processor
* Moved preprocessing into separate functions
* Added # Copied from statements
* Fixed # Copied from statements
* 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>
* use torch.testing.assertclose instead to get more details about error in cis
* fix
* style
* test_all
* revert for I bert
* fixes and updates
* more image processing fixes
* more image processors
* fix mamba and co
* style
* less strick
* ok I won't be strict
* skip and be done
* up
* Fix test_pipelines_video_classification that was always failing
* Update video pipeline docstring to reflect actual return type
---------
Co-authored-by: Louis Groux <louis.cal.groux@gmail.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>
`return unittest.skip()` used in the `test_model_parallel_beam_search` in
skip condition for xpu did not actually mark test to be skipped running
under pytest:
* 148 passed, 1 skipped
Other tests use `self.skipTest()`. Reusing this approach and moving the
condition outside the loop (since it does not depend on it) allows to skip
for xpu correctly:
* 148 skipped
Secondly, `device_map="auto"` is now implemented for XPU for IPEX>=2.5 and
torch>=2.6, so we can now enable these tests for XPU for new IPEX/torch
versions.
Fixes: 1ea3ad1ae ("[tests] use `torch_device` instead of `auto` for model testing (#29531)")
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.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
* An attempt to fix#29554. Include 'LayerNorm.' in gamma/beta rename scope, reduce number of characters searched on every load considerably.
* Fix fix on load issue
* Fix gamma/beta warning test
* A style complaint
* Improve efficiency of weight norm key rename. Add better comments about weight norm and layer norm renaming.
* Habitual elif redunant with the return
* add test
* augment test as suggested
* Update tests/utils/test_modeling_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* rerun tests
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* DataCollatorForLanguageModeling class was updated with new parameters that provides more control over the token masking and relacing
* DataCollatorForLanguageModeling class was updated with new parameters that provides more control over the token masking and relacing
* Addressed review comments, modified the docstring and made a test for the DataCollatorForLanguageModeling
* Add the helium model.
* Add a missing helium.
* And add another missing helium.
* Use float for the rmsnorm mul.
* Add the Helium tokenizer converter.
* Add the pad token as suggested by Arthur.
* Update the RMSNorm + some other tweaks.
* Fix more rebase issues.
* fix copies and style
* fixes and add helium.md
* add missing tests
* udpate the backlink
* oups
* style
* update init, and expected results
* small fixes
* match test outputs
* style fixup, fix doc builder
* add dummies and we should be good to go!z
* update sdpa and fa2 documentation
---------
Co-authored-by: laurent <laurent.mazare@gmail.com>
* model can convert to HF and be loaded back
* nit
* works in single batch generation but hallucinates
* use the image tokens
* add image generation
* now it works
* add tests
* update
* add modulare but it doesn't work for porting docstring :(
* skip some tests
* add slow tests
* modular removed the import?
* guess this works
* update
* update
* fix copies
* fix test
* fix copies
* update
* docs
* fix tests
* last fix tests?
* pls
* repo consistency
* more style
* style
* remove file
* address comments
* tiny bits
* update after the new modular
* fix tests
* add one more cond in check attributes
* decompose down/up/mid blocks
* allow static cache generation in VLMs
* nit
* fix copies
* Update docs/source/en/model_doc/emu3.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/emu3.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/emu3.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/emu3.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/emu3.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/emu3.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/emu3.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Update docs/source/en/model_doc/emu3.md
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* fix VAE upsampling
* Update src/transformers/models/emu3/modular_emu3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* address comments
* state overwritten stuff explicitly
* fix copies
* add the flag for flex attn
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Introduce 5 integration tests for the 4 model classes + torch export
* ModernBert: reuse GemmaRotaryEmbedding via modular
* Revert #35589, keep rope_kwargs; rely on them in modular_modernbert
* Revert "Revert #35589, keep rope_kwargs; rely on them in modular_modernbert"
This reverts commit 11b44b9ee8.
* Don't set rope_kwargs; override 'self.rope_init_fn' call instead
* Ensure that add_prefix_space is propagated to backend_tokenizer.pre_tokenizer
in PreTrainedTokenizerFast, rather than relying on subclasses to take care of this.
* Simplify setting self.add_prefix_space, ensure pre_tok exists
* Wrap in try-except to catch 'Custom PreTokenizer cannot be serialized'
862d1a346a/bindings/python/src/pre_tokenizers.rs (L672) produces the Exception. They're triggered by the roformer tests, as the RoFormerTokenizerFast uses a custom PreTokenizer.
* Propagate add_prefix_space in T5TokenizerFast to superclass
* look-ahead negation
* re add examples by default
* Fix the bug in topological sort
* Update create_dependency_mapping.py
* start adding test
* finalize test
* more tests
* style
* style
* update modular_modernbert -- add inputs_embeds param to ModernBertModel
* Fix implementation issues; extend to other classes; docstring
First of all, the inputs_embeds shouldn't fully replace `self.embeddings(input_ids)`, because this call also does layer normalization and dropout. So, now both input_ids and inputs_embeds is passed to the ModernBertEmbeddings, much like how BertEmbeddings is implemented.
I also added `inputs_embeds` to the docstring, and propagated the changes to the other model classes.
I also introduced an error if input_ids and input_embeds are both or neither provided.
Lastly, I fixed an issue with device being based solely on input_ids with attention_mask.
* Propagate inputs_embeds to ModernBertForMaskedLM correctly
Also reintroduce inputs_embeds test
---------
Co-authored-by: Tom Aarsen <Cubiegamedev@gmail.com>
* update codecarbon
* replace directly-specified-test-dirs with tmp_dir
* pass tmp_dir to all get_regression_trainer
* test_trainer.py: Use tmp_dir consistently for all output_dir arguments
* fix some with...as tmp_dir blocks
* reflect the comments to improve test_trainer.py
* refresh .gitignore
* update conversion script
* update for bias again
* remove pdv
* use my dir
* Update how we initialize the tokenizer
* Convert in bfloat16
* Undo that one again
* fix config dump
* .to() was broken for BatchMixFeature
* quick debug breakpoint
* put the breakpoint in the right place
* Add a config flag for the multimodal projector bias
* Add a config flag for the multimodal projector bias
* Conversion script can load chat templates
* Indent config for comparison
* Stop clobbering the config
* Re-enable the config clobber
* Get rid of the config manual save - it has no effect!
* Handle adapter bias correctly
* Default vision transformer activation to silu
* Remove legacy processing path
* One commit with all the debug breakpoints before I delete them all, in case I need to revert
* Update conversion
* Remove vLLM debugging instrumentation
* Drop xformers
* Remove debug enumerates
* make fixup
* make fixup
* Break copied from in pixtral
* Propagate multimodal_projector_bias change
* Propagate multimodal_projector_bias change
* Remove debug device .to()
* Restore attention weights output
* Fix Pixtral test
* Drop image_seq_length
* Drop image_seq_length
* Put the legacy processing code back
* Add the bias option to the llava_next_video config
* Add the bias option to the llava_next_video config
* Make certain args required in converter
* Make certain args required in converter
* typo
* make fixup
* Reverting some dtype changes since it seems to work without them
---------
Co-authored-by: arthur@huggingface.co <arthur@ip-26-0-166-244.ec2.internal>
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* Updated docstring for _determine_best_metric.
* Updated docstring for metric_for_best_model.
* Added test case for save strategy.
* Updated incorrect test case.
* Changed eval_strategy to match save_strategy.
* Separated test cases for metric.
* Allow load_best_model when save_strategy == "best".
* Updated docstring for metric_for_best_model.
* fix: processing odd number of frames
* feat: add test case
* update: test one frame
* feat: support custom patch size
* fix: test with videos
* revert: change on patch repeat
* fix: much wow
* update: fixups
* fixup pls
* ruff fixup
* fix typo at least
* Correctly list the chat template file in the saved files list
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add save file checking to test
* make fixup
* better filename handling
* make fixup
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add audio_token attribute to proc
* expand input_ids
* and legacy and expanded input_ids
* test update
* split lines
* add possibility not to provide eos and bos audio tokens
* raise errors
* test incorrect number of audio tokens
* add example
* fmt
* typo
* first adding diffllama
* add Diff Attention and other but still with errors
* complate make attention Diff-Attention
* fix some bugs which may be caused by transformer-cli while adding model
* fix a bug caused by forgetting KV cache...
* Update src/transformers/models/diffllama/modeling_diffllama.py
You don't need to divide by 2 if we use same number of attention heads as llama. instead you can just split in forward.
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* Update src/transformers/models/diffllama/modeling_diffllama.py
fit to changeing "num_heads // 2" place
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* Update src/transformers/models/diffllama/modeling_diffllama.py
new codes are more meaningful than before
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* Update src/transformers/models/diffllama/modeling_diffllama.py
new codes are more meaningful than before
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* Update src/transformers/models/diffllama/modeling_diffllama.py
fit to changeing "num_heads // 2" place
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* Update src/transformers/models/diffllama/modeling_diffllama.py
fix 2times divide by sqrt(self.head_dim)
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* Update src/transformers/models/diffllama/modeling_diffllama.py
fix 2times divide by sqrt(self.head_dim)
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* Update src/transformers/models/diffllama/modeling_diffllama.py
fit to changeing "num_heads // 2" place.
and more visible
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* I found Attention missed implemented from paper still on e072544a3b.
* re-implemented
* adding groupnorm
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* align with transformers code style
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* fix typo
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* adding groupnorm
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* change SdpaAttention to DiffSdpaAttention
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* fix bug
* Update src/transformers/models/diffllama/modeling_diffllama.py
resolve "not same outputs" problem
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* fix bugs of places of "GroupNorm with scale" and etc
* Revert "fix bugs of places of "GroupNorm with scale" and etc"
This reverts commit 26307d92f6.
* simplify multiple of attention (matmul) operations into one by repeating value_states
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* simplify multiple of attention (matmul) operations into one by repeating value_states
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* simplify multiple of attention (matmul) operations into one by repeating value_states
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
* remove missed type
* add diffllama model_doc
* apply make style/quality
* apply review comment about model
* apply review comment about test
* place diffllama alphabetically on the src/transformers/__init__.py
* fix forgot code
* Supports parameters that are not initialized with standard deviation 0 in the conventional method
* add DiffLlamaConfig to CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK on utils/check_config_docstrings.py
* remove unused property of config
* add to supported model list
* add to spda supported model list
* fix copyright, remove pretraining_tensor_parallel, and modify for initialization test
* remove unused import and etc.
* empty commit
* empty commit
* empty commit
* apply modular transformers but with bugs
* revert prev commit
* create src/transformers/model/diffllama/modular_diffllama.py
* run utils/modular_model_converter.py
* empty commit
* leaner modular diffllama
* remove more and more in modular_diffllama.pt
* remove more and more in modular_diffllama.pt
* resolve missing docstring entries
* force reset
* convert modular
---------
Co-authored-by: Minho Ryu <ryumin93@gmail.com>
`parallelize()` API is deprecated in favor of accelerate's `device_map="auto"`
and therefore is not accepting new features. At the same time `parallelize()`
implementation is currently CUDA-specific. This commit marks respective
ci tests with `@require_torch_gpu`.
Fixes: #35252
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
* added logic for deleting adapters once loaded
* updated to the latest version of transformers, merged utility function into the source
* updated with missing check
* added peft version check
* Apply suggestions from code review
Co-authored-by: Anton Vlasjuk <73884904+vasqu@users.noreply.github.com>
* changes according to reviewer
* added test for deleting adapter(s)
* styling changes
* styling changes in test
* removed redundant code
* formatted my contributions with ruff
* optimized error handling
* ruff formatted with correct config
* resolved formatting issues
---------
Co-authored-by: Anton Vlasjuk <73884904+vasqu@users.noreply.github.com>
* Make kwargs uniform for SAM
* Remove unused attribute
* Make point_pad_value part of image_kwargs
* Update annotations
* Code review - use existing methods
* Use ProcessorTesterMixin
* Do not add ProcessorTesterMixin everywhere
* fixup mamba2 - caching and several other small fixes
* fixup cached forward
* correct fix this time
* fixup cache - we do not need to extend the attn mask it's handled by generate (gives total ids + mask at each step)
* remove unnecessary (un)squeeze
* fixup cache position
* simplify a few things
* [run-slow] mamba2
* multi gpu attempt two
* [run-slow] mamba2
* [run-slow] mamba2
* [run-slow] mamba2
* [run-slow] mamba2
* add newer slow path fix
* [run-slow] mamba2
* 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>
* initial commit for PR
Co-authored-by: Gabe Goodhart <gabe.l.hart@gmail.com>
* rename dynamic cache
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* add more unit tests
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* add integration test
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* add integration test
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* Add modular bamba file
* Remove trainer changes from unrelated PR
* Modify modular and cofig to get model running
* Fix some CI errors and beam search
* Fix a plethora of bugs from CI/docs/etc
* Add bamba to models with special caches
* Updat to newer mamba PR for mamba sublayer
* fix test_left_padding_compatibility
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* fix style
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* fix remaining tests
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* missed this test
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* ran make style
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* move slow tag to integration obj
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* make style
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* address comments
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* fix modular
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* left out one part of modular
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* change model
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* Make Rotary modular as well
* Update bamba.md
Added overview, update Model inference card and added config
* Update bamba.md
* Update bamba.md
* Update bamba.md
Minor fixes
* Add docs for config and model back
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
* Add warning when using fast kernels
* replaced generate example
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
* Address comments from PR
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
* Propagate attention fixes
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
* Fix attention interfaces to the new API
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
* Fix API for decoder layer
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
* Remove extra weights
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
---------
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com>
Signed-off-by: Antoni Viros i Martin <aviros@ibm.com>
Co-authored-by: Gabe Goodhart <gabe.l.hart@gmail.com>
Co-authored-by: Antoni Viros i Martin <aviros@ibm.com>
Co-authored-by: divya-kumari32 <72085811+divya-kumari32@users.noreply.github.com>
Co-authored-by: Antoni Viros <ani300@gmail.com>
* do not remove decoder_input_ids for the first segment
* do not remove eos token in generate_with_fallback
* when removing padding tokens, do not remove eos token
* remove eos token in generate (and not in generate_with_fallback!)
* reconciliate short-from/ long-form behavior
* correct avg_logprobs calculation
* handle eos token in segments
* handle decoder_input_ids and eos token in _prepare_decoder_input_ids
* fix incorrect time precision
* always remove eos token
* always remove decoder_input_ids
* no need to handle decoder_inputs_ids and eos token
* no need to remove decoder_input_ids
* no need to handle eos token
* fix num_beams in _retrieve_logit_processors
* remove todo unconsistency
* no need to add eos token
* last_timestamp_pos should indeed be timestamp token pos
* patch generate to enable compatibility with GenerationTesterMixin tests
* adapt test_generate_continue_from_past_key_values
* adapt test_prompt_lookup_decoding_matches_greedy_search
* adapt generic GenerationMixin tests to whisper's generate
* fix speculative decoding
* fix
* [run-slow] whisper
* change HF_HUB_TOKEN for require_read_token
* [run-slow] whisper
* prioritize kwargs over generation_config
* remove unnecessary args
* [run-slow] whisper
* update tests
* [run-slow] whisper
* add comment
* update test
* [run-slow] whisper
* update test + revert require_read_token
* docstring updates
* revert tokenizer decode args change
* do not use a patch + docstring updates
* [run-slow] whisper
* make
* [run-slow] whisper
* add a flag to force unique call to generate
* test update
* [run-slow] whisper
* add force_unique_generate_call arg
* do not use a patch
* correct the timestamps for the pad tokens
* docstring update
* docstring update
* docstring update
* upodate TF tests
* add require_read_token
* [run-slow] whisper
* test reset dynamo
* [run-slow] whisper
* fix
* [run-slow] whisper
* avoid iterating twice on current_segments
* [run-slow] whisper
* [run-slow] whisper
---------
Co-authored-by: Eustache Le Bihan <eustlb@users.noreply.huggingface.co>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* feat: add support for sdpa and gradient checkpointing
* fix: ruff format
* fix: config sdpa
* fix: sdpa layer naming convention
* fix: update test_eager_matches_sdpa_inference to handle vision_hidden_states
* test: skip incompatible tests and fix loading issue with sdpa
- Updated tests to skip cases flash and dynamic compile.
- Minor adjustment to ensure correct loading of model with sdpa for dispatch test.
* style: apply Ruff formatting
* ruff fix again after rebase
* [run-slow] sam
* [run-slow] sam
* refactor: Address review comments and improve sub-config handling in SAM model tests
- Added attributes for sub_configs as per PR #34410.
- Enabled tests for configs, ensuring the composite model (SAM) has several sub-configs in the main config.
- Added class attribute _is_composite=True to the tester class
- test_sdpa_can_dispatch_composite_models added
* [run-slow] sam
* style: ruff
* [run-slow] sam
* style: ruff again ...
* [run-slow] sam
* 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 more cases
* fix method not found in unittest
Signed-off-by: Lin, Fanli <fanli.lin@intel.com>
* fix more cases
* add more models
* add all
* no unittest.case
* remove for oneformer
* fix style
---------
Signed-off-by: Lin, Fanli <fanli.lin@intel.com>
* draft, run model as compreszed/uncompressed mode
* draft
* run run_compressed=False
* run_compressed as attr
* set run_compressed=False using quantization_config
* remove redundant line
* make is_qat_trainable dependent on run_compressed status
* add tests
* lint
* full in docstring
* add decompress
* comments
* decompress if model is compresssed and not run_compressed
* apply_quant_config logic fix -- populate statedict properly
* comments
* remove non compressed model
* make is_compressed as property
* cosmetic
* run apply_quant_config for non-compressed models -- popualte scales and zeropoints
* add pahtway for decompressing sparse models
* typo on is_quantization_compressed
* lint
* fix typo
* 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>
Original issue: https://github.com/huggingface/peft/issues/2256
There is a potential error when using load_best_model_at_end=True with a
prompt learning PEFT method. This is because Trainer uses load_adapter
under the hood but with some prompt learning methods, there is an
optimization on the saved model to remove parameters that are not
required for inference, which in turn requires a change to the model
architecture. This is why load_adapter will fail in such cases and users
should instead set load_best_model_at_end=False and use
PeftModel.from_pretrained. As this is not obvious, we now intercept the
error and add a helpful error message.
* Support BatchNorm in Hubert pos_conv_emb as in fairseq
* Correct the new defaults (#34377)
* Correct the new defaults
* CIs
* add check
* Update utils.py
* Update utils.py
* Add the max_length in generate test checking shape without passing length
* style
* CIs
* fix fx CI issue
* [auto. ping] Avoid sending empty info + add more team members (#34383)
* update
* update
---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Fix glm (#34388)
* Fix duplicated
* fix import
* Use non nested images and batched text Idefics2/3 (#34222)
* add support for non nested images and add tests
* add tests error scenario
* fix style
* added single and no image to error tests
* Fix onnx non-expotable inplace aten op (#34376)
* fix onnx non-expotable inplace op
* mistral, qwen2, qwen2_vl, starcoder2
* fixup copies
* Fix right padding in LLaVA models (#34305)
* fix right pad llavas
* device mismatch
* no filter (#34391)
* no filter
* no filter
* no filter
---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* SynthID: better example (#34372)
* better example
* Update src/transformers/generation/configuration_utils.py
* Update src/transformers/generation/logits_process.py
* nits
* Tests: upgrade `test_eager_matches_sdpa_generate` (#34386)
* Fix bnb training test failure (#34414)
* Fix bnb training test: compatibility with OPTSdpaAttention
* Avoid check expected exception when it is on CUDA (#34408)
* update
* update
---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Fix typos in agents_advanced.md (#34405)
* [docs] Cache implementations (#34325)
cache
* [run-slow] hubert
* Support BatchNorm in Hubert pos_conv_emb as in fairseq
Add conversion integration test, and make batchnorm explicit variable
* Support BatchNorm in Hubert pos_conv_emb as in fairseq
fix make fixup styling changes
* [run-slow] hubert
* Support BatchNorm in Hubert pos_conv_emb as in fairseq
* [run-slow] hubert
* Support BatchNorm in Hubert pos_conv_emb as in fairseq
Add conversion integration test, and make batchnorm explicit variable
* Support BatchNorm in Hubert pos_conv_emb as in fairseq
fix make fixup styling changes
* [run-slow] hubert
* [run-slow] hubert
---------
Co-authored-by: Cyril Vallez <cyril.vallez@huggingface.co>
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
Co-authored-by: Ilyas Moutawwakil <57442720+IlyasMoutawwakil@users.noreply.github.com>
Co-authored-by: Raushan Turganbay <raushan@huggingface.co>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: Matthew Douglas <38992547+matthewdouglas@users.noreply.github.com>
Co-authored-by: Rudy Delouya <rudy.delouya@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>
* fix GA bugs and add unit test
* narrow down model loss unit test diff gap
* format code to make ruff happy
* send num_items_in_batch argument to decoder
* fix GA loss bug in BertLMHeadModel
* use TinyStories-33M to narrow down diff gap
* fotmat code
* missing .config
* avoid add extra args
---------
Co-authored-by: kangsheng <kangsheng@meituan.com>
* gpt neox flex attention + refactor
* some formatting
* small fix on dropout
* add assertion on flex attn test
* flaky ci :(
* add head mask support
* style
* handle dtype, replace torch where
* fixup flex with output attns
* code review and several other fixes
* Update src/transformers/modeling_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* style
* remove unnecessary comment
* remove incorrect comment
* make flex attn check more agnostic tor versions and centralized
* change peft input dtype check to value since q and k could be affected by other stuff like RoPE
* i forgor
* flaky
* code review and small fixes
* Update src/transformers/models/gpt_neox/modeling_gpt_neox.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Use torch.nn.attention.sdpa_kernel instead of deprecated torch.backends.cuda.sdp_kernel
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
* Fix test_eager_matches_sdpa_inference for XPU backend
As of PyTorch 2.5 XPU backend supports only torch.nn.attention.SDPBackend.MATH
which is implemented on PyTorch level using aten operators and is device
agnostic with respect to implementation of each aten operator. Thus, we can
reuse CUDA (or CPU) MATH weights for XPU.
Fixes: #34888
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
* Use torch.amp.autocast instead of deprecated torch.cuda.amp.autocast in nemotron
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
---------
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
* [PEFT] Set eval mode when loading PEFT adapter
Resolves#34469
When calling model.load_adapter to load a PEFT adapter, by default the
adapter should be set to eval mode. This is now correctly done. Users
can still pass is_trainable=True to load the adapter in training mode.
* Linter
* Initial draft
* Add .jinja file loading for processors
* Add processor saving of naked chat template files
* make fixup
* Add save-load test for tokenizers
* Add save-load test for tokenizers
* stash commit
* Try popping the file
* make fixup
* Pop the arg correctly
* Pop the arg correctly
* Add processor test
* Fix processor code
* stash commit
* Processor clobbers child tokenizer's chat template
* Processor clobbers child tokenizer's chat template
* make fixup
* Split processor/tokenizer files to avoid interactions
* fix test
* Expand processor tests
* Rename arg to "save_raw_chat_template" across all classes
* Update processor warning
* Move templates to single file
* Move templates to single file
* Improve testing for processor/tokenizer clashes
* Improve testing for processor/tokenizer clashes
* Extend saving test
* Test file priority correctly
* make fixup
* Don't pop the chat template file before the slow tokenizer gets a look
* Remove breakpoint
* make fixup
* Fix error
* fix test_tiny_timestamp_generation
* fix test_large_timestamp_generation
* fix test_whisper_shortform_single_batch_prev_cond
* fix test_whisper_shortform_multi_batch_hard_prev_cond
* return_timestamps necessary with long form
* fix test_default_multilingual_transcription_long_form
* fix test_tiny_token_timestamp_generation_longform
* fix test_whisper_longform_multi_batch_hard
* Update tests/models/whisper/test_modeling_whisper.py
Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>
* fix typo
* do not expect special tokens
* fix test_whisper_longform_single_batch_beam
* fix test_whisper_longform_multi_batch_hard_prev_cond
* update test_whisper_longform_multi_batch_hard_prev_cond
* update test_whisper_longform_multi_batch_hard_prev_cond
* these tests does not make sense anymore
* this test does not make sense anymore
* make fixup
* suggested nits
* add test with forced_decoder_ids
* this test does not make sense anymore
* change assert for unittest test cases
* make fixup
* test with prompt_ids and task and language
* fix unittest test case call
* fix test_tiny_generation
* fix test_tiny_en_generation
* fix test_tiny_en_batched_generation
* fix test_tiny_longform_timestamps_generation
* fix test_tiny_timestamp_generation
* fix test_large_generation
* fix test_large_batched_generation
* fix test_large_generation_multilingual
* fix test_large_timestamp_generation
* fix test_large_timestamp_generation
* fix test_tiny_token_timestamp_generation_longform
* fix test_tiny_en_batched_generation
* make fixup
* [run-slow] whisper
---------
Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>
* allow unused parameter passthrough when chunking in asr pipelines
* format code
* format
* run fixup
* update tests
* update parameters to pipline in test
* updates parametrs in tests
* change spelling in gitignore
* revert .gitignore to main
* add git ignore of devcontainer folder
* assert asr output follows expected inference output type
* run fixup
* Remove .devcontainer from .gitignore
* remove compliance check
* Add Nemotron GGUF Loading Support
* fix the Nemotron architecture assignation
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Do not load for meta device
* Make some minor improvements
* Add test
* Update tests/utils/test_modeling_utils.py
Update test parameters
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Make the test simpler
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* add deformable detr image processor fast
* add fast processor to doc
* fix copies
* nit docstring
* Add tests gpu/cpu and fix docstrings
* fix docstring
* import changes from detr
* fix imports
* rebase and fix
* fix input data format change in detr and rtdetr fast
* add support for openai api image_url input
* change continue to elif
* Explicitely add support for OpenAI/TGI chat format
* rewrite content to transformers chat format and add tests
* Add support for typing of image type in chat templates
* add base64 to possible image types
* refactor nesting
* softcapping
* soft cap before the mask
* style
* ...
* super nit
* update
* fixes
* update
* small issue with modular
* fix modular imports
* update
* fixup
* simplify a hell lot
* simplify cleaning imports
* finish fixing
* update our design
* nits
* use a deprecation cycle
* updates
* Fix modular (recursive deps need to always be computed after merges!)
* push
* fix
* update
* fix modular order
* make fix-copies
* updates
* update
* ?
* don't compile for now
* ?
* fix some stuff
* donc!
* fix copies
* update
* fixup
* ?
* fix two tests
* fix?
* for now, don't use head info
* eager when output attentoin and sdpa or flash as it's the simplest behaviour (for our tests as well :))
* fix-copies
* revert sdpa check
* Apply suggestions from code review
Co-authored-by: Cyril Vallez <cyril.vallez@huggingface.co>
* rebase, fix-copies and push
* add a slow integration test
* update the test
* fix left padding issue
* fix test
* remove duplicate scaling
* quality
* add a small test and make sure it works
* 2b
---------
Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>
Co-authored-by: Cyril Vallez <cyril.vallez@huggingface.co>
19d58d31f has introduced a context manager to manage subtests of
test_training_gradient_checkpointing. However, test body was not
moved under "with" statement. Thus, while tests are correctly
marked as skipped, test bodies were still executed. In some cases,
as with llama this caused attribute errors.
Fixes: #34722
Fixes: 19d58d31f ("Add MLLama (#33703)")
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
* 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
* remove v4.44 deprecations
* PR comments
* deprecations scheduled for v4.50
* hub version update
* make fiuxp
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Remove FSDP wrapping from sub-models.
* solve conflict trainer.py
* make fixup
* add unit test for fsdp_auto_wrap_policy when using auto_find_batch_size
* put back extract_model_from_parallel
* use transformers unwrap_model
* Retain newlines in chat template when
* Add try/except
* Add regression test
* Simplify test
* Apply suggestions from code review
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
---------
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* save/load sub-configs
* nit forgot these
* fix copies
* move test to common
* use dict for sub-configs
* add load-save-laod test
* clean up modeling check
* oops this are correct keys
* fix some tests, missed some composite configs
* this model was missed
FIX Broken repr of TorchAoConfig
The __repr__ method references a non-existent self.kwargs. This is now
fixed.
There does not appear to be a uniform way of defining __repr__ for
quantization configs. I copied the method as implemented for HQQ:
e2ac16b28a/src/transformers/utils/quantization_config.py (L285-L287)
* 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
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update docs
* test for load time adding tokens
* fix some more tests which are now fetched better
* one more fix
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update trainer for easier handling of accumulate + proper reporting
* test
* Fixup tests
* Full fix
* Fix style
* rm comment
* Fix tests
* Minimize test + remove py 311 check
* Unused import
* Forward contrib credits from discussions
* Fix reported metrics
* Refactor, good as it's going to get
* rm pad tok id check
* object detection and audio are being annoying
* Fin
* Fin x2
---------
Co-authored-by: Gyanateet Dutta <Ryukijano@users.noreply.github.com>
* blip2 tests
* instructblips
* copies
* fix slow tests
* fix
* uncomment this
* clean up after rebase
* should be model main input
* fix overwritten tests
* oops len should be multiple of frame number
* style
* fix some tests
* 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
* 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
* add mamba architecture for gguf
* add logic for weights conversion, some fixes and refactoring
* add lm_head layers, unit test refactoring
* more fixes for tests
* remove lm_head creation
* remove unused comments
* tmp commit
* tmp commit
* cull overwrites of deleted tests
* typo
* more specific docstring
* make fixup
* parameterize at the top?
* correction
* more deletions :D
* tmp commit
* for VLMs too
* fix _check_outputs
* test nit
* make fixup
* fix another flaky
* test_generate_from_inputs_embeds -- handle missing attention mask
* feat: Added int conversion and unwrapping
* test: added tests for post_process_keypoint_detection of SuperPointImageProcessor
* docs: changed docs to include post_process_keypoint_detection method and switched from opencv to matplotlib
* test: changed test to not depend on SuperPointModel forward
* test: added missing require_torch decorator
* docs: changed pyplot parameters for the keypoints to be more visible in the example
* tests: changed import torch location to make test_flax and test_tf
* Revert "tests: changed import torch location to make test_flax and test_tf"
This reverts commit 39b32a2f69.
* tests: fixed import
* chore: applied suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* tests: fixed import
* tests: fixed import (bis)
* tests: fixed import (ter)
* feat: added choice of type for target_size and changed tests accordingly
* docs: updated code snippet to reflect the addition of target size type choice in post process method
* tests: fixed imports (...)
* tests: fixed imports (...)
* style: formatting file
* docs: fixed typo from image[0] to image.size[0]
* docs: added output image and fixed some tests
* Update docs/source/en/model_doc/superpoint.md
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* fix: included SuperPointKeypointDescriptionOutput in TYPE_CHECKING if statement and changed tests results to reflect changes to SuperPoint from absolute keypoints coordinates to relative
* docs: changed SuperPoint's docs to print output instead of just accessing
* style: applied make style
* docs: added missing output type and precision in docstring of post_process_keypoint_detection
* perf: deleted loop to perform keypoint conversion in one statement
* fix: moved keypoint conversion at the end of model forward
* docs: changed SuperPointInterestPointDecoder to SuperPointKeypointDecoder class name and added relative (x, y) coordinates information to its method
* fix: changed type hint
* refactor: removed unnecessary brackets
* revert: SuperPointKeypointDecoder to SuperPointInterestPointDecoder
* Update docs/source/en/model_doc/superpoint.md
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
---------
Co-authored-by: Steven Bucaille <steven.bucaille@buawei.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Add _determine_best_metric and new saving logic.
1. Logic to determine the best logic was separated out from
`_save_checkpoint`.
2. In `_maybe_log_save_evaluate`, whether or not a new best metric was
achieved is determined after each evaluation, and if the save strategy
is "best' then the TrainerControl is updated accordingly.
* Added SaveStrategy.
Same as IntervalStrategy, but with a new attribute called BEST.
* IntervalStrategy -> SaveStrategy
* IntervalStratgy -> SaveStrategy for save_strat.
* Interval -> Save in docstring.
* Updated docstring for save_strategy.
* Added SaveStrategy and made according changes.
`save_strategy` previously followed `IntervalStrategy` but now follows
`SaveStrategy`.
Changes were made accordingly to the code and the docstring.
* Changes from `make fixup`.
* Removed redundant metrics argument.
* Added new test_save_best_checkpoint test.
1. Checks for both cases where `metric_for_best_model` is explicitly
provided and when it's not provided.
2. The first case should have two checkpoints saved, whereas the second
should have three saved.
* Changed should_training_end saving logic.
The Trainer saves a checkpoints at the end of training by default as
long as `save_strategy != SaveStrategy.NO`. This condition was modified
to include `SaveStrategy.BEST` because it would be counterintuitive that
we'd only want the best checkpoint to be saved but the last one is as
well.
* `args.metric_for_best_model` default to loss.
* Undo metric_for_best_model update.
* Remove checking metric_for_best_model.
* Added test cases for loss and no metric.
* Added error for metric and changed default best_metric.
* Removed unused import.
* `new_best_metric` -> `is_new_best_metric`
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Applied `is_new_best_metric` to all.
Changes were made for consistency and also to fix a potential bug.
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Zach Mueller <muellerzr@gmail.com>
* exclude fsdp from delay_optimizer_creation
* add test case for trainer: FSDP mode and fp8 as mixed precision
* rearrange imports
* ruff formatted
* adapt _init_fsdp to fp8
* use _init_fsdp only when resume_from_checkpoint
* In case of FDP, self.layer will be CheckpointWrapper which has no len() method
* delete _init_fsdp
* solve conflict
* fix conflict
* make fixup
* Correct the new defaults
* CIs
* add check
* Update utils.py
* Update utils.py
* Add the max_length in generate test checking shape without passing length
* style
* CIs
* fix fx CI issue
When loading a LoRA adapter, so far, there was only a warning when there
were unexpected keys in the checkpoint. Now, there is also a warning
when there are missing keys.
This change is consistent with
https://github.com/huggingface/peft/pull/2118 in PEFT and the planned PR
https://github.com/huggingface/diffusers/pull/9622 in diffusers.
Apart from this change, the error message for unexpected keys was
slightly altered for consistency (it should be more readable now). Also,
besides adding a test for the missing keys warning, a test for
unexpected keys warning was also added, as it was missing so far.
* Add SynthIDTextWatermarkLogitsProcessor
* esolving comments.
* Resolving comments.
* esolving commits,
* Improving SynthIDWatermark tests.
* switch to PT version
* detector as pretrained model + style
* update training + style
* rebase
* Update logits_process.py
* Improving SynthIDWatermark tests.
* Shift detector training to wikitext negatives and stabilize with lower learning rate.
* Clean up.
* in for 7B
* cleanup
* upport python 3.8.
* README and final cleanup.
* HF Hub upload and initiaze.
* Update requirements for synthid_text.
* Adding SynthIDTextWatermarkDetector.
* Detector testing.
* Documentation changes.
* Copyrights fix.
* Fix detector api.
* ironing out errors
* ironing out errors
* training checks
* make fixup and make fix-copies
* docstrings and add to docs
* copyright
* BC
* test docstrings
* move import
* protect type hints
* top level imports
* watermarking example
* direct imports
* tpr fpr meaning
* process_kwargs
* SynthIDTextWatermarkingConfig docstring
* assert -> exception
* example updates
* no immutable dict (cant be serialized)
* pack fn
* einsum equivalent
* import order
* fix test on gpu
* add detector example
---------
Co-authored-by: Sumedh Ghaisas <sumedhg@google.com>
Co-authored-by: Marc Sun <marc@huggingface.co>
Co-authored-by: sumedhghaisas2 <138781311+sumedhghaisas2@users.noreply.github.com>
Co-authored-by: raushan <raushan@huggingface.co>
* Enable grad accum fix across all models + trainer fully in forward()
* handle peft case
* Account for DDP: need to run scale tests
* Use accelerator state
* Quality
* Guard
* Experiment w/ only fairseq fix
* Fairseq only
* Revert multiply_grads fix
* Mult by grad accum to fully bring back solution
* Style
* Good to go now
* Skip fx tests for now
* Bookmark
* Working now
* Add option for running ffmpeg_microphone_live as a background process
* Code quality checks for audio_utils
* Code clean up for audio_utils
* Fixing logic in ffmpeg_microphone calls in audio_utils
* Allowing any arbitrary arguments to be passed to ffmpeg_microphone_live
* Formatting
* Fixing last problems with adding ffmpeg_additional_args
* Fixing default arguments and formatting issues
* Fixing comments for ffmpeg_additional_args
* Adding two shorts tests for ffmpeg_microphone_live
* Fixing test bug
* add colorize_depth and matplotlib availability check
* add post_process_depth_estimation for zoedepth + tests
* add post_process_depth_estimation for DPT + tests
* add post_process_depth_estimation in DepthEstimationPipeline & special case for zoedepth
* run `make fixup`
* fix import related error on tests
* fix more import related errors on test
* forgot some `torch` calls in declerations
* remove `torch` call in zoedepth tests that caused error
* updated docs for depth estimation
* small fix for `colorize` input/output types
* remove `colorize_depth`, fix various names, remove matplotlib dependency
* fix formatting
* run fixup
* different images for test
* update examples in `forward` functions
* fixed broken links
* fix output types for docs
* possible format fix inside `<Tip>`
* Readability related updates
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* Readability related update
* cleanup after merge
* refactor `post_process_depth_estimation` to return dict; simplify ZoeDepth's `post_process_depth_estimation`
* rewrite dict merging to support python 3.8
---------
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
* this worked in normal generation, needs more tests
* fix almost all tests in t5
* nit
* longt5, umt5, mt5
* style
* udop, pix2struct
* more models
* fix some tests
* fix onnx tests
* tracing tests fixed
* compile enabled and tested for t5 models
* fix small bug in slow tests
* [run-slow] t5
* uncomment
* style
* update with new generation refactoring
* nit
* fix copies
* this is the fix, had to change t5 to fix copies
* update
* [run-slow] t5
* [run-slow] t5
* update
* add test for encoder only T5
* clean up after rebase
* fix pop2piano
* add comment
* style
* fix copies after rebase
* fix copies missed this one
* first try
* codestyle
* idefics2 is happy
* [run-slow] llava, llava_next, video_llava, vipllava, llava_next_video, idefics, idefics2, kosmos2, fuyu, blip, blip_2, instructblip, instructblipvideo, paligemma
* fix-copies
* [run-slow] llava, llava_next, video_llava, vipllava, llava_next_video, idefics, idefics2, kosmos2, fuyu, blip, blip_2, instructblip, instructblipvideo
* blip-2 needs to init vision from config
* when was this removed O_o
* minor fix
* tests
* this way?
* tests
* model-agnostic code
* codestyle
* add tests for idefics
* modify general test for VLMs
* no generation test for vlm yet!
* no generation test here also
* wanr in VIT-SDPA if output attn
* add more tests
* user can pass dict as attn impl
* repo consistency
* update
* muicgen
* no prints
* forgot speech enc-dec and clip
* how many composite models we have?
* musicgen meelody is same as mudicgen
* +siglip
* fix tests + add some more
* remove idefics custom overriden code
* make idefics2 automappable
* nits
* skip tests
* doctests
* Update src/transformers/models/idefics2/configuration_idefics2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/clip/test_modeling_clip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/idefics2/test_modeling_idefics2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/idefics2/test_modeling_idefics2.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/configuration_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* major update, no need for automap
* clean up
* add FA2 test
* more tests
* style
* skip tests
* why did these started failing now?
* no attributes for FA2 needed
* one tiny test
* address comment about FA2 false warning
* style
* add new models and resolve conflicts
* fix copies
* let it be this way for now, come back tomorrow to review
* some more fixes
* update
* more updates
* update
* fix copies
* style and tests
* another big update
* fix tests
* fix tests
* update
* another update
* fix tests
* fix copies
* fix tests
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* bookmark
* Bookmark
* Bookmark
* Actually implement
* Pass in kwarg explicitly
* Adjust for if we do or don't have labels
* Bookmark fix for od
* bookmark
* Fin
* closer
* Negate accelerate grad accum div
* Fixup not training long enough
* Add in compute_loss to take full model output
* Document
* compute_loss -> compute_loss_fn
* Add a test
* Refactor
* Refactor
* Uncomment tests
* Update tests/trainer/test_trainer.py
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* auto-gptq requirement is removed & model is changed & tokenizer pad token is assigned
* values func is changed with extensions & sequence key value bug is fixed
* map key value check is added in ExtensionsTree
* empty trimmed_ids bug is fixed
* tail_id IndexError is fixed
* empty trimmed_ids bug fix is updated for failed test
* too much specific case for specific tokenizer is removed
* input_ids check is updated
* require auto-gptq import is removed
* key error check is changed with empty list check
* empty input_ids check is added
* empty trimmed_ids fix is checked with numel function
* usage change comments are added
* test changes are commented
* comment style and quality bugs are fixed
* test comment style and quality bug is fixed
* Fix FSDP Initialization for resume training
* Added init_fsdp function to work with dummy values
* Fix FSDP initialization for resuming training
* Added CUDA decorator for tests
* Added torch_gpu decorator to FSDP tests
* Fixup for failing code quality tests
* add idefics
* conflicts after merging main
* enable tests but need to fix some
* fix tests
* no print
* fix/skip some slow tests
* continue not skip
* rebasing broken smth, this is the fix
* mistral qna start
* mixtral qna
* oops
* qwen2 qna
* qwen2moe qna
* add missing input embed methods
* add copied to all methods, can't directly from llama due to the prefix
* make top level copied from
* Generate using exported model and enable gemma2-2b in ExecuTorch
* [run_slow] gemma, gemma2
* truncate expected output message
* Bump required torch version to support gemma2 export
* [run_slow] gemma, gemma2
---------
Co-authored-by: Guang Yang <guangyang@fb.com>
* Default synced_gpus to True when using FullyShardedDataParallel
Fixes#30228
Related:
* https://github.com/pytorch/pytorch/issues/100069
* https://github.com/pytorch/pytorch/issues/123962
Similar to DeepSpeed ZeRO Stage 3, when using FSDP with multiple GPUs and differently sized data per rank, the ranks reach different synchronization points at the same time, leading to deadlock
To avoid this, we can automatically set synced_gpus to True if we detect that a PreTrainedModel is being managed by FSDP using _is_fsdp_managed_module, which was added in 2.0.0 for torch.compile: https://github.com/pytorch/pytorch/blob/v2.0.0/torch/distributed/fsdp/_dynamo_utils.py
* Remove test file
* ruff formatting
* ruff format
* Update copyright year
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add test for FSDP-wrapped model generation
Before #33483, these tests would have hung for 10 minutes before crashing due to a timeout error
* Ruff format
* Move argparse import
* Remove barrier
I think this might cause more problems if one of the workers was killed
* Move import into function to decrease load time
https://github.com/huggingface/transformers/pull/33483#discussion_r1787972735
* Add test for accelerate and Trainer
https://github.com/huggingface/transformers/pull/33483#discussion_r1790309675
* Refactor imports
* Ruff format
* Use nullcontext
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Allow for hyphenated field names in long-options
argparse converts hyphens into underscores before assignment (e.g., an
option passed as `--long-option` will be stored under `long_option`), So
there is no need to pass options as literal attributes, as in
`--long_option` (with an underscore instead of a hyphen). This commit
ensures that this behavior is respected by `parse_args_into_dataclasses`
as well.
Issue: #33933
Co-authored-by: Daniel Marti <mrtidm@amazon.com>
* add sdpa to OPT
* chore: remove redundant whitespace in OPTDecoder class
* fixup
* bug fix
* add sdpa and attention generate test
* fixup
* Refactor OPTAttention forward method for improved readability and maintainability
* undo refactor for _shape and key,val states
* add OPT to doc, fixup didn't find it for some reason
* change order
* change default attn_implemntation in testing to eager
* [run-slow] opt
* change test_eager_matches_sdpa_generate to the one llama
* Update default attention implementation in testing common
* [run-slow] opt
* remove uneeded print
* [run-slow] opt
* refactor model testers to have attn_implementation="eager"
* [run-slow] opt
* convert test_eager_matches_sdpa_generate to opt-350M
* bug fix when creating mask for opt
* [run-slow] opt
* if layer head mask default to eager
* if head mask is not none fall to eager
* [run-slow] opt
* Update src/transformers/models/opt/modeling_opt.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Clean up Unpack imports (#33631)
clean up Unpack imports
* Fix DPT /Dinov2 sdpa regression on main (#33660)
* fallback to eager if output attentions.
* fix copies
* handle dependency errors in check_imports (#33622)
* handle dependency errors in check_imports
* change log level to warning
* add back self.max_position_embeddings = config.max_position_embeddings (#33550)
* add back self.max_position_embeddings = config.max_position_embeddings
* fix-copies
* Fix Llava conversion for LlavaQwen2ForCausalLM with Clip vision tower (#33613)
fix llavaqwen2 model conversion
* Uniformize kwargs for Udop processor and update docs (#33628)
* Add optional kwargs and uniformize udop
* cleanup Unpack
* nit Udop
* Generation: deprecate `PreTrainedModel` inheriting from `GenerationMixin` (#33203)
* Enable BNB multi-backend support (#31098)
* enable cpu bnb path
* fix style
* fix code style
* fix 4 bit path
* Update src/transformers/utils/import_utils.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* add multi backend refactor tests
* fix style
* tweak 4bit quantizer + fix corresponding tests
* tweak 8bit quantizer + *try* fixing corresponding tests
* fix dequant bnb 8bit
* account for Intel CPU in variability of expected outputs
* enable cpu and xpu device map
* further tweaks to account for Intel CPU
* fix autocast to work with both cpu + cuda
* fix comments
* fix comments
* switch to testing_utils.torch_device
* allow for xpu in multi-gpu tests
* fix tests 4bit for CPU NF4
* fix bug with is_torch_xpu_available needing to be called as func
* avoid issue where test reports attr err due to other failure
* fix formatting
* fix typo from resolving of merge conflict
* polish based on last PR review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* fix CI
* Update src/transformers/integrations/integration_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/integrations/integration_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix error log
* fix error msg
* add \n in error log
* make quality
* rm bnb cuda restriction in doc
* cpu model don't need dispatch
* fix doc
* fix style
* check cuda avaliable in testing
* fix tests
* Update docs/source/en/model_doc/chameleon.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update docs/source/en/model_doc/llava_next.md
Co-authored-by: Aarni Koskela <akx@iki.fi>
* Update tests/quantization/bnb/test_4bit.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* Update tests/quantization/bnb/test_4bit.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* fix doc
* fix check multibackends
* fix import sort
* remove check torch in bnb
* docs: update bitsandbytes references with multi-backend info
* docs: fix small mistakes in bnb paragraph
* run formatting
* reveret bnb check
* move bnb multi-backend check to import_utils
* Update src/transformers/utils/import_utils.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* fix bnb check
* minor fix for bnb
* check lib first
* fix code style
* Revert "run formatting"
This reverts commit ac108c6d6b.
* fix format
* give warning when bnb version is low and no cuda found]
* fix device assignment check to be multi-device capable
* address akx feedback on get_avlbl_dev fn
* revert partially, as we don't want the function that public, as docs would be too much (enforced)
---------
Co-authored-by: Aarni Koskela <akx@iki.fi>
Co-authored-by: Titus von Koeller <9048635+Titus-von-Koeller@users.noreply.github.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Fix error string after refactoring into get_chat_template (#33652)
* Fix error string after refactoring into get_chat_template
* Take suggestion from CR
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
---------
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
* uniformize git processor (#33668)
* uniformize git processor
* update doctring
* Modular `transformers`: modularity and inheritance for new model additions (#33248)
* update exampel
* update
* push the converted diff files for testing and ci
* correct one example
* fix class attributes and docstring
* nits
* oups
* fixed config!
* update
* nitd
* class attributes are not matched against the other, this is missing
* fixed overwriting self.xxx now onto the attributes I think
* partial fix, now order with docstring
* fix docstring order?
* more fixes
* update
* fix missing docstrings!
* examples don't all work yet
* fixup
* nit
* updated
* hick
* update
* delete
* update
* update
* update
* fix
* all default
* no local import
* fix more diff
* some fix related to "safe imports"
* push fixed
* add helper!
* style
* add a check
* all by default
* add the
* update
* FINALLY!
* nit
* fix config dependencies
* man that is it
* fix fix
* update diffs
* fix the last issue
* re-default to all
* alll the fixes
* nice
* fix properties vs setter
* fixup
* updates
* update dependencies
* make sure to install what needs to be installed
* fixup
* quick fix for now
* fix!
* fixup
* update
* update
* updates
* whitespaces
* nit
* fix
* simplify everything, and make it file agnostic (should work for image processors)
* style
* finish fixing all import issues
* fixup
* empty modeling should not be written!
* Add logic to find who depends on what
* update
* cleanup
* update
* update gemma to support positions
* some small nits
* this is the correct docstring for gemma2
* fix merging of docstrings
* update
* fixup
* update
* take doc into account
* styling
* update
* fix hidden activation
* more fixes
* final fixes!
* fixup
* fixup instruct blip video
* update
* fix bugs
* align gemma2 with the rest as well
* updats
* revert
* update
* more reversiom
* grind
* more
* arf
* update
* order will matter
* finish del stuff
* update
* rename to modular
* fixup
* nits
* update makefile
* fixup
* update order of the checks!
* fix
* fix docstring that has a call inside
* fiix conversion check
* style
* add some initial documentation
* update
* update doc
* some fixup
* updates
* yups
* Mostly todo gimme a minut
* update
* fixup
* revert some stuff
* Review docs for the modular transformers (#33472)
Docs
* good update
* fixup
* mmm current updates lead to this code
* okay, this fixes it
* cool
* fixes
* update
* nit
* updates
* nits
* fix doc
* update
* revert bad changes
* update
* updates
* proper update
* update
* update?
* up
* update
* cool
* nits
* nits
* bon bon
* fix
* ?
* minimise changes
* update
* update
* update
* updates?
* fixed gemma2
* kind of a hack
* nits
* update
* remove `diffs` in favor of `modular`
* fix make fix copies
---------
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Fix CIs post merging modular transformers (#33681)
update
* Fixed docstring for cohere model regarding unavailability of prune_he… (#33253)
* Fixed docstring for cohere model regarding unavailability of prune_head() methods
The docstring mentions that cohere model supports prune_heads() methods. I have fixed the docstring by explicitly mentioning that it doesn't support that functionality.
* Update src/transformers/models/cohere/modeling_cohere.py
---------
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Generation tests: update imagegpt input name, remove unused functions (#33663)
* Improve Error Messaging for Flash Attention 2 on CPU (#33655)
Update flash-attn error message on CPU
Rebased to latest branch
* Gemma2: fix config initialization (`cache_implementation`) (#33684)
* Fix ByteLevel alphabet missing when Sequence pretokenizer is used (#33556)
* Fix ByteLevel alphabet missing when Sequence pretokenizer is used
* Fixed formatting with `ruff`.
* Uniformize kwargs for image-text-to-text processors (#32544)
* uniformize FUYU processor kwargs
* Uniformize instructblip processor kwargs
* Fix processor kwargs and tests Fuyu, InstructBlip, Kosmos2
* Uniformize llava_next processor
* Fix save_load test for processor with chat_template only as extra init args
* Fix import Unpack
* Fix Fuyu Processor import
* Fix FuyuProcessor import
* Fix FuyuProcessor
* Add defaults for specific kwargs kosmos2
* Fix Udop to return BatchFeature instead of BatchEncoding and uniformize kwargs
* Add tests processor Udop
* remove Copied from in processing Udop as change of input orders caused by BatchEncoding -> BatchFeature
* Fix overwrite tests kwargs processors
* Add warnings and BC for changes in processor inputs order, change docs, add BC for text_pair as arg for Udop
* Fix processing test fuyu
* remove unnecessary pad_token check in instructblip ProcessorTest
* Fix BC tests and cleanup
* FIx imports fuyu
* Uniformize Pix2Struct
* Fix wrong name for FuyuProcessorKwargs
* Fix slow tests reversed inputs align fuyu llava-next, change udop warning
* Fix wrong logging import udop
* Add check images text input order
* Fix copies
* change text pair handling when positional arg
* rebase on main, fix imports in test_processing_common
* remove optional args and udop uniformization from this PR
* fix failing tests
* remove unnecessary test, fix processing utils and test processing common
* cleanup Unpack
* cleanup
* fix conflict grounding dino
* 🚨🚨 Setting default behavior of assisted decoding (#33657)
* tests: fix pytorch tensor placement errors (#33485)
This commit fixes the following errors:
* Fix "expected all tensors to be on the same device" error
* Fix "can't convert device type tensor to numpy"
According to pytorch documentation torch.Tensor.numpy(force=False)
performs conversion only if tensor is on CPU (plus few other restrictions)
which is not the case. For our case we need force=True since we just
need a data and don't care about tensors coherency.
Fixes: #33517
See: https://pytorch.org/docs/2.4/generated/torch.Tensor.numpy.html
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
* bump tokenizers, fix added tokens fast (#32535)
* update based on tokenizers release
* update
* nits
* update
* revert re addition
* don't break that yet
* fmt
* revert unwanted
* update tokenizers version
* update dep table
* update
* update in conversion script as well
* some fix
* revert
* fully revert
* fix training
* remove set trace
* fixup
* update
* update
* [Pixtral] Improve docs, rename model (#33491)
* Improve docs, rename model
* Fix style
* Update repo id
* fix code quality after merge
* HFQuantizer implementation for compressed-tensors library (#31704)
* Add compressed-tensors HFQuantizer implementation
* flag serializable as False
* run
* revive lines deleted by ruff
* fixes to load+save from sparseml, edit config to quantization_config, and load back
* address satrat comment
* compressed_tensors to compressed-tensors and revert back is_serializable
* rename quant_method from sparseml to compressed-tensors
* tests
* edit tests
* clean up tests
* make style
* cleanup
* cleanup
* add test skip for when compressed tensors is not installed
* remove pydantic import + style
* delay torch import in test
* initial docs
* update main init for compressed tensors config
* make fix-copies
* docstring
* remove fill_docstring
* Apply suggestions from code review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* review comments
* review comments
* comments - suppress warnings on state dict load, tests, fixes
* bug-fix - remove unnecessary call to apply quant lifecycle
* run_compressed compatability
* revert changes not needed for compression
* no longer need unexpected keys fn
* unexpected keys not needed either
* Apply suggestions from code review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* add to_diff_dict
* update docs and expand testing
* Update _toctree.yml with compressed-tensors
* Update src/transformers/utils/quantization_config.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update doc
* add note about saving a loaded model
---------
Co-authored-by: George Ohashi <george@neuralmagic.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Sara Adkins <sara@neuralmagic.com>
Co-authored-by: Sara Adkins <sara.adkins65@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Dipika Sikka <ds3822@columbia.edu>
Co-authored-by: Dipika <dipikasikka1@gmail.com>
* update model card for opt
* add batch size to inference table
* [slow-run] opt
* [run-slow] opt
---------
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
Co-authored-by: Avishai Elmakies <avishai.elma@cs.huji.ac.il>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
Co-authored-by: chengchengpei <5881383+chengchengpei@users.noreply.github.com>
Co-authored-by: Isotr0py <2037008807@qq.com>
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: jiqing-feng <jiqing.feng@intel.com>
Co-authored-by: Aarni Koskela <akx@iki.fi>
Co-authored-by: Titus von Koeller <9048635+Titus-von-Koeller@users.noreply.github.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Tibor Reiss <75096465+tibor-reiss@users.noreply.github.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
Co-authored-by: Lysandre Debut <hi@lysand.re>
Co-authored-by: Muhammad Naufil <m.naufil1@gmail.com>
Co-authored-by: sizhky <yyeshr@gmail.com>
Co-authored-by: Umar Butler <umar@umar.au>
Co-authored-by: Jonathan Mamou <jonathan.mamou@intel.com>
Co-authored-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
Co-authored-by: Benjamin Fineran <bfineran@users.noreply.github.com>
Co-authored-by: George Ohashi <george@neuralmagic.com>
Co-authored-by: Sara Adkins <sara@neuralmagic.com>
Co-authored-by: Sara Adkins <sara.adkins65@gmail.com>
Co-authored-by: Dipika Sikka <ds3822@columbia.edu>
Co-authored-by: Dipika <dipikasikka1@gmail.com>
* rebasing changes
* fixing style
* adding some doc to functions
* remove bitblas
* change dtype
* fixing check_code_quality
* fixing import order
* adding doc to tree
* Small update on BitLinear
* adding some tests
* sorting imports
* small update
* reformatting
* reformatting
* reformatting with ruff
* adding assert
* changes after review
* update disk offloading
* adapting after review
* Update after review
* add is_serializable back
* fixing style
* adding serialization test
* make style
* small updates after review
* Fix Failed tests with mobile bert
* Cast to the correct dtype
* Code fixup
* Fix padding_idx larger that embedding_size
* Reduce covariance more. use 1e-7 instead of 1e-5
* Comment fix
* Reduce covariance more. use 1e-9 instead of 1e-7
* Copy new config
* all but MRA fixed
* fix mra
* very flaky
* skip instead
* make fixup
---------
Co-authored-by: Joao Gante <joao@huggingface.co>
* Update many similar visual pipelines
* Add input tests
* Add ImageToText as well
* Add output tests
* Add output tests
* Add output tests
* OutputElement -> Output
* Correctly test elements
* make fixup
* fix typo in the task list
* Fix VQA testing
* Add copyright to image_classification.py
* Revert changes to VQA pipeline because outputs have differences - will move to another PR
* make fixup
* Remove deprecation warnings
* Add Auto model for image-text-to-text
* Remove donut from processing auto, add chameleon ti image text to text models
* add qwen2_vl and llava_onevision
* add pixtral to auto model for image-text-to-text
* add mllama and idefics3
* remove models in IGNORE_NON_AUTO_CONFIGURED
* add AutoModelForImageTextToText to tests and doc
* Initial commit for MyT5 model
* custom implementation of MyT5 tokenizer, unused files deleted
* unittest for myt5 tokenizer
* upadate of import structure and style
* removed remmanents of MyT5Config
* fixed docstrings
* Updates after review: filled documentaion file, new docstrings and tests added
* Fixed code style issues
* fixed copied from to refer to function
* updated loading myt5 tokenizer in tests, added sample byte map file to fixtures
* changes after review
* removed redundant copied from
* removed redundant copied from
* optimalization and loading model from hf
* [run_slow] myt5
* [run-slow] myt5
* Updated en documentation for myt5
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* onboard phimoe model
* removed debug code
* added unit tests
* updated docs
* formatted
* fixed unit tests
* fixed test case
* fixed format
* refactored code
* fixed expected outputs in the integration tests
* Added a warning msg
* Addressed comments
* Addressed comments
* fixed test cases
* added paper link
* Addressed comments
* Refactored PhimoeForCausalLM forward fn
* Refactored PhimoeRotaryEmbedding class
* fixed test cases
* fixed testcase
* fixed test case
* Addressed comments
* fixed test cases
* fixed testcases
* Used cache position instead to get the seq len
* intilize new embeddings from normal distrib
* Fix typo in comments
* Fix typo in comments
* Fix style
* Fix variables naming
* Add tests
* Fix style
* code consistency nit
* Add deepspeed support
* Add deepspeed support
* Conver embeddings weights to float32 before computations
* Add deepspeed tests
* Cover when vocab_size is smaller than embedding_size
* Style fix
* Add tests for vocab_size smaller than hiddin_size
* Style fix
* Nits in tests
* Nits in tests
* Check for deepspeed before importing it
* Increase vocab_size for positive definite covariance matrix test
* Add warning
* Add multivariate_resizing flag and implement resizing for lm_heads
* Fix typo
* Fix wrong bias indexing
* Fix bias is zero check
* remove multivariate_resizing flag from tests
* Intialize bias from old bias normal distribution
* Fixup
* Code usability
* Use mean_resizing instead of multivariate_resizing
* Fix up
* Fix comments and docs
* enable cpu awq ipex linear
* add doc for cpu awq with ipex kernel
* add tests for cpu awq
* fix code style
* fix doc and tests
* Update docs/source/en/quantization/awq.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update tests/quantization/autoawq/test_awq.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* fix comments
* fix log
* fix log
* fix style
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* [PEFT] Support low_cpu_mem_usage for PEFT loading
PEFT added support for low_cpu_mem_usage=True when loading adapters in
https://github.com/huggingface/peft/pull/1961. This feature is now
available when installing PEFT v0.13.0. With this PR, this option is
also supported when loading PEFT adapters directly into transformers
models.
Additionally, with this PR,
https://github.com/huggingface/diffusers/pull/9510 will be unblocked,
which implements this option in diffusers.
* Fix typo
* fix beam indices in token_timestamps
* fix attention_mask in FA2
* correct translation example with the right example
* correct how somes tests are using outputs + correct num_frames
* fix shortform batch prev cond tests
* make fix-copies
* make fix-copies
* take care of shifting beam indices
* [run-slow] whisper
* [run-slow] whisper
* add unit tests for splinter_tokenizer
* add unit test for splinter tokenizer, pass in the question_token to be saved on save_pretrained called
* remove unused import
* remove vocab_splinter.txt, add Copied from, use fmt:on and fmt:off to prevent autoformatting on long lines
* remove all the spaces
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Trainer - deprecate tokenizer for processing_class
* Extend chage across Seq2Seq trainer and docs
* Add tests
* Update to FutureWarning and add deprecation version
* add support for custom inputs and batched inputs in ProcessorTesterMixin
* Fix batch_size behavior ProcessorTesterMixin
* Change format prepare inputs batched
* Remove override test pixtral processor
* Remove unnecessary tests and cleanup after new prepare_inputs functions
* Fix instructBlipVideo image processor
* Remove max_new_tokens arg
* Add ASR pipeline to testing
* make fixup
* Factor the output test out into a util
* Full error reporting
* Full error reporting
* Update src/transformers/pipelines/automatic_speech_recognition.py
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Small comment
---------
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Fix Mamba slow path bug with dtype mismatch.
* Update test_modeling_mamba.py
* Improve style.
* Fix issue with cache position of dtype mismatch test.
* Change test for slow path.
* Revert changes.
* Switch to buggy code and add test to catch it.
* Fix the dtype mismatch bug and add test code to verify it.
* Fix minor bug with test.
* Fix incorrect dtype of model output.
* Fix incorrect dtype of cache.
* Fix incorrect dtype of ssm cache.
* Fix incorrect dtype of conv state.
* Remove assertion for ssm state.
* Add assertion for conv state dtype.
* Fix all issues with dtype mismatch test.
* HQQ model serialization attempt
* fix hqq dispatch and unexpected keys
* style
* remove check_old_param
* revert to check HQQLinear in quantizer_hqq.py
* revert to check HQQLinear in quantizer_hqq.py
* update HqqConfig default params
* make ci happy
* make ci happy
* revert to HQQLinear check in quantizer_hqq.py
* check hqq_min version 0.2.0
* set axis=1 as default in quantization_config.py
* validate_env with hqq>=0.2.0 version message
* deprecated hqq kwargs message
* make ci happy
* remove run_expected_keys_check hack + bump to 0.2.1 min hqq version
* fix unexpected_keys hqq update
* add pre_quantized check
* add update_expected_keys to base quantizerr
* ci base.py fix?
* ci base.py fix?
* fix "quantization typo" src/transformers/utils/quantization_config.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix post merge
---------
Co-authored-by: Marc Sun <marc@huggingface.co>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Make audio classification pipeline spec-compliant and add test
* Check that test actually running in CI
* Try a different pipeline for the CI
* Move the test so it gets triggered
* Move it again, this time into task_tests!
* make fixup
* indentation fix
* comment
* Move everything from testing_utils to test_pipeline_mixin
* Add output testing too
* revert small diff with main
* make fixup
* Clarify comment
* Update tests/pipelines/test_pipelines_audio_classification.py
Co-authored-by: Lucain <lucainp@gmail.com>
* Update tests/test_pipeline_mixin.py
Co-authored-by: Lucain <lucainp@gmail.com>
* Rename function and js_args -> hub_args
* Cleanup the spec recursion
* Check keys for all outputs
---------
Co-authored-by: Lucain <lucainp@gmail.com>
* add bloom arch support for gguf
* apply format
* small refactoring, bug fix in GGUF_TENSOR_MAPPING naming
* optimize bloom GGUF_TENSOR_MAPPING
* implement reverse reshaping for bloom gguf
* add qkv weights test
* add q_8 test for bloom
* clean_up_tokenization_spaces=False if unset
* deprecate warning
* updating param for old models
* update models
* make fix-copies
* fix-copies and update bert models
* warning msg
* update prophet and clvp
* updating test since space before is arbitrarily removed
* remove warning for 4.45
* Add Idefics 3!
* fixes to make both pipelines identical
* fix for quantized models
* First pass at the review
* remove vocab size from the main config (it's still in the text_config)
* hot fix for merve
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* re-add model_type for text_config
* remove support for old_cache
* remove hidden_size from main config
* rename idefics3 HF repo
* few changes suggested in the PR
* fix to input_data_format computation
* remove overwrite of _autoset_attn_implementation following @zucchini-nlp suggestion
* improve example
* few improvements from amy's review
* big change to enable processing input images as numpy arrays
* Changes to the code to uniformize processor kwargs
* image processing tests
* image processing tests fixes and some bugs they discovered
* addressed review comments from Yoni
* fix modeling tests
* remove special tokens that are not special
* fixes tests
* skip failing tests - they also fail for idefics2
* added paper and readded the tests with multi gpu, who knows
* Update docs/source/en/model_doc/idefics3.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* review amy until image_processing_idefics3
* last comments from Amy
* review amy
* Update src/transformers/models/idefics3/image_processing_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/idefics3/modeling_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update docs/source/en/model_doc/idefics3.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* doc improvement - amy review
* fix runtime error during fine-tuning
* amy's review
* Update src/transformers/models/idefics3/image_processing_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/idefics3/image_processing_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/idefics3/modeling_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* ruff
* amy's comment on the order
* ruff ruff
* fix copies
* square images when they are not splitted
* ruff :(
* Update src/transformers/models/idefics3/image_processing_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/idefics3/test_processing_idefics3.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fix small bug introduced in refactor
* amy's image processing changes
* fixes peft tests and ruff
* modify to_pil_image from transformers. and review from emanuele.
* add modified to_pil_image
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Add AdEMAMix optimizer
* Fix test
* Update tests/trainer/test_trainer.py
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Add compressed-tensors HFQuantizer implementation
* flag serializable as False
* run
* revive lines deleted by ruff
* fixes to load+save from sparseml, edit config to quantization_config, and load back
* address satrat comment
* compressed_tensors to compressed-tensors and revert back is_serializable
* rename quant_method from sparseml to compressed-tensors
* tests
* edit tests
* clean up tests
* make style
* cleanup
* cleanup
* add test skip for when compressed tensors is not installed
* remove pydantic import + style
* delay torch import in test
* initial docs
* update main init for compressed tensors config
* make fix-copies
* docstring
* remove fill_docstring
* Apply suggestions from code review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* review comments
* review comments
* comments - suppress warnings on state dict load, tests, fixes
* bug-fix - remove unnecessary call to apply quant lifecycle
* run_compressed compatability
* revert changes not needed for compression
* no longer need unexpected keys fn
* unexpected keys not needed either
* Apply suggestions from code review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* add to_diff_dict
* update docs and expand testing
* Update _toctree.yml with compressed-tensors
* Update src/transformers/utils/quantization_config.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update doc
* add note about saving a loaded model
---------
Co-authored-by: George Ohashi <george@neuralmagic.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Sara Adkins <sara@neuralmagic.com>
Co-authored-by: Sara Adkins <sara.adkins65@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Dipika Sikka <ds3822@columbia.edu>
Co-authored-by: Dipika <dipikasikka1@gmail.com>
This commit fixes the following errors:
* Fix "expected all tensors to be on the same device" error
* Fix "can't convert device type tensor to numpy"
According to pytorch documentation torch.Tensor.numpy(force=False)
performs conversion only if tensor is on CPU (plus few other restrictions)
which is not the case. For our case we need force=True since we just
need a data and don't care about tensors coherency.
Fixes: #33517
See: https://pytorch.org/docs/2.4/generated/torch.Tensor.numpy.html
Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
* enable cpu bnb path
* fix style
* fix code style
* fix 4 bit path
* Update src/transformers/utils/import_utils.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* add multi backend refactor tests
* fix style
* tweak 4bit quantizer + fix corresponding tests
* tweak 8bit quantizer + *try* fixing corresponding tests
* fix dequant bnb 8bit
* account for Intel CPU in variability of expected outputs
* enable cpu and xpu device map
* further tweaks to account for Intel CPU
* fix autocast to work with both cpu + cuda
* fix comments
* fix comments
* switch to testing_utils.torch_device
* allow for xpu in multi-gpu tests
* fix tests 4bit for CPU NF4
* fix bug with is_torch_xpu_available needing to be called as func
* avoid issue where test reports attr err due to other failure
* fix formatting
* fix typo from resolving of merge conflict
* polish based on last PR review
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* fix CI
* Update src/transformers/integrations/integration_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/integrations/integration_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fix error log
* fix error msg
* add \n in error log
* make quality
* rm bnb cuda restriction in doc
* cpu model don't need dispatch
* fix doc
* fix style
* check cuda avaliable in testing
* fix tests
* Update docs/source/en/model_doc/chameleon.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
* Update docs/source/en/model_doc/llava_next.md
Co-authored-by: Aarni Koskela <akx@iki.fi>
* Update tests/quantization/bnb/test_4bit.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* Update tests/quantization/bnb/test_4bit.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* fix doc
* fix check multibackends
* fix import sort
* remove check torch in bnb
* docs: update bitsandbytes references with multi-backend info
* docs: fix small mistakes in bnb paragraph
* run formatting
* reveret bnb check
* move bnb multi-backend check to import_utils
* Update src/transformers/utils/import_utils.py
Co-authored-by: Aarni Koskela <akx@iki.fi>
* fix bnb check
* minor fix for bnb
* check lib first
* fix code style
* Revert "run formatting"
This reverts commit ac108c6d6b.
* fix format
* give warning when bnb version is low and no cuda found]
* fix device assignment check to be multi-device capable
* address akx feedback on get_avlbl_dev fn
* revert partially, as we don't want the function that public, as docs would be too much (enforced)
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Co-authored-by: Aarni Koskela <akx@iki.fi>
Co-authored-by: Titus von Koeller <9048635+Titus-von-Koeller@users.noreply.github.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
