GitLab

* Fix contrastive_search for new cache structure, and improve performance by removing inneficient torch.stack(torch.split(x, top_k, dim=0))

* Fix _contrastive_search for non-standard cache using ellipsis slicing

* Fix all outputs.logits memory leaks for all decoding strategies!

* Fix small error in _contrastive_search()

* Make all necessary change and revert for the new class

* Apply coding style

* Remove pipes in type hints for compatibility

* correct type hint

* apply style

* Use DynamicCache by default and solve conflicts

* Fix rebase issues

* Add `_supports_dynamic_cache_class` in models for models that support DynamicCache but not other caches to make DynamicCache the default for more models

* Create generation config to return legacy format by default, or to choose not to

* style

* Fix case when use_cache is False

* Remove default DynamicCache in assiste_decoding if assistant_model does not support it + fix _seen_tokens when cropping cache

* Update prepare_inputs_for_generation() for case with empty DynamicCache

* Correct return of args in _assisted_decoding

* Remove EfficientDynamicCache as it is no longer needed

* Correct mistake in generation config

* Move cache logic of assisted decoding to AssistedCandidateGenerator.__init__

* change DynamicCache function names from "split" to "batch_split" for readability + apply coding style

* Remove `_supports_dynamic_cache_class` attribute after rebase

* Correct missing line lost in conflict resolution during rebasing

* Add special case for Jamba

* Fix jamba test

* Coding style

* coding style

* Correct missing import in rebasing

* Simplify _validate_model_kwargs based on removal of _supports_dynamic_cache attribute

* Simplify code paths in _contrastive_search

* coding style

* Update docstrings of cache methods

* Update prepare_inputs_for_generation() -> past_key_values are always Cache objects

* add flaubert tokenization test, enrich inheritance in FlaubertTokenizer.

* fix quality code ci

* ensure parameter consistency

* fix ci

* fix copyright year and flatten vocab list.

* fix style

* Move label validation checks - fail early

* Remove some formatting changes - add back labels change wav2vec2

Fixed torch definition error

The StoppingCriteriaList allocates is_done without specifying dtype=torch.bool. On XLA this allocates a float tensor and causes a failure on the following line:

is_done = is_done | criteria(input_ids, scores, **kwargs)

by attempting to OR float with bool.

* Move out common validation

* Add missing backbone config arguments

* deprecate blip

* mention deprecation on docs

✨

 Add new line switch before logging "***** Running {description} *****".
Signed-off-by: jacklanda <yonyonlau@gmail.com>

* Fix pipeline tests - torch imports

* Frameowrk dependant float conversion

* fix logits dtype

* Add bf16/fp16 tests for text_classification pipeline

* Update test_pipelines_text_classification.py

* fix

* fix

* Added interpolate pos encoding feature and test to deit

* Added interpolate pos encoding feature and test for deit TF model

* readded accidentally delted test for multi_gpu

* storing only patch_size instead of entire config and removed commented code

* Update modeling_tf_deit.py to remove extra line
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

video-llava can handle more frames

* fix(PatchTST): Wrong dropout used for PretainHead

* feat(PatchTST): remove unused config.dropout

---------
Co-authored-by: Strobel Maximilian (IFAG PSS SIS SCE ACM) <Maximilian.Strobel@infineon.com>

* add device in logits processor

* remove device when not needed

* codestyle

* tests

* forgot `melody` version

* Update src/transformers/models/whisper/generation_whisper.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* codestyle

* updates

---------
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* update to not(endswith(loss))

* ruff formatting

Update modeling_cohere.py

* Rename sanity_evaluation to eval_on_start

* move arg back to last

fix typo

* fix the get_size_with_aspect_ratio in max_size situation

* make fix-up

* add more general solution

* consider when max_size is not defined

* fix typo

* fix typo

* simple fix

* fix error

* fix if else error

* fix error of size overwrite

* fix yolos image processing

* fix detr image processing

* make

* add longest related test script

* Update src/transformers/models/yolos/image_processing_yolos.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* add more test

* add test script about longest size

* remove deprecated

---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* add qwen2 gguf support

* Update docs

* fix qwen2 tokenizer

* add qwen2 gguf test

* fix typo in qwen2 gguf test

* format code

* Remove mistral, clarify the error message

* format code

* add typing and update docstring

Update MLP

* tmp commit

* sliding window with fewer differences

* make fixup + rebase

* missing overwrite

* update non-causal mask for sdpa

* add test

* update docstrings

* add one more test

* fix cross attention bug

* gentler atol/rtol

While running the model.prepare_tf_dataset() method,
it raises the error below:
```
TypeError: Cannot convert [array([322.,   1.])] to EagerTensor of dtype int64
```

This happens, in  "DataCollatorForSeq2Seq" function when we are try
to convert the labels to tensors. While converting the labels to tensors,
the labels can be in the format of list of list or list of ndarrays.
There is no problem converting the list of list lables. There is a problem
when the list of ndarrays are float values(like below).

```
[array([322.,   1.])]
```

so the exception raises while trying to convert this label to tensors using
below code.

```
batch["labels"] = tf.constant(batch["labels"], dtype=tf.int64)
```

The labels are always integer values, so this got converted to float
values in the label padding operation below.
```
batch["labels"] = [
                    call(label)
                    if padding_side == "right"
                    else np.concatenate([[self.label_pad_token_id] * (max_label_length - len(label)), label])
                    for label in labels
                    ]
```
Here we have 2 cases:
1 - Concatenating an array having integer padding token value with labels.
2 - Concatenating an empty array with labels.

----------------------------------------------------------------------------------------
case 1: Concatenating an array having integer padding token value with labels.
WORKS EXPECTED:
----------------------------------------------------------------------------------------
```
label = np.array([233, 1])
max_label_length = 4
label_pad_token_id = -100
np.concatenate([[label_pad_token_id] * (max_label_length - len(label)), label])
o/p:
array([-100, -100,  233,    1])
```

----------------------------------------------------------------------------------------
Case 2: Concatenating an empty array with labels.
GIVES THE ISSUE:
This scenorio can happen when the label has the maximum label length -- No padding needed.
----------------------------------------------------------------------------------------
```
label = np.array([233, 1])
max_label_length = 2
label_pad_token_id = -100
np.concatenate([[label_pad_token_id] * (max_label_length - len(label)), label])
o/p:
array([233.,   1.])
```

----------------------------------------------------------------------------------------
Solution:
----------------------------------------------------------------------------------------
We need to concatenate a ndarray of dtype int with labels.

AFTER FIX:
----------
case 1:
```

label = np.array([233, 1])
max_label_length = 4
label_pad_token_id = -100
np.concatenate([np.array([label_pad_token_id] * (max_label_length - len(label)), dtype=np.int64),label])

o/p:
array([-100, -100,  233,    1])
```

case 2:
```

label = np.array([233, 1])
max_label_length = 2
label_pad_token_id = -100
np.concatenate([np.array([label_pad_token_id] * (max_label_length - len(label)), dtype=np.int64),label])

o/p:
array([233,   1])
```

* fixes

* fix-copies

* token healing impl + trie with extensions

* make fixup

* prefix-robust space tokenization

* examples readme and requirements

* make fixup

* allow input prompt and model

* redundant defaults

* Specialized Trie

* make fixup

* updated tests with new inherited Tree

* input ids to auto device_map

* rm unused import

* Update src/transformers/generation/utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* naming convention

* Revert "naming convention"

This reverts commit dd39d9c5b7a969e2d8a8d2a8e54f121b82dc44f0.

* naming convention

* last -hopefully- changes

---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Remove copied froms for deprecated models

* Remove automatically in script

* current working example!

* commit regex and result file

* update

* nit

* push the conversion file

* oups

* roadmap and nits

* attempt diffs for 3 files

* persimmon

* nit

* add diff file that is the same as the modeling_llama.py

* fix rope nits

* updates

* updates with converted versions

* give some breathing space to the code

* delete

* update

* update

* push the actual result

* update regex patterns

* update regex patterns

* fix some issues

* fix some issues

* fix some issues

* updates

* updates

* updates

* updates

* updates

* revert changes done to llama

* updates

* update gemma

* updates

* oups

* current state

* current state

* update

* ouiiii

* nit

* clear diffs

* nit

* fixup

* update

* doc 🚀

* 🔥

* for now use gemma

* deal with comments

* style

* handle funtions

* deal with assigns

* todos

* process inheritage

* keep decorators?

* 🤗

* deal with duplicates

* fixup

* correctly remove duplicate code

* run ruff post script

* ruff deals pretty well with imports, let's leave it to him

* ah maybe not lol

* for now remove all imports from child.

* nit

* conversion of llama

* okay

* convert starcoder2

* synch with main

* update llama diff

* updates

* https://docs.astral.sh/ruff/rules/redefined-while-unused/

 fixes the imports, bit needs later version of ruff

* updates

* okay actual state

* non zero exit

* update!

* revert unrelated

* remove other diff files

* updates

* cleanup

* update

* less diff!

* stash

* current updates

* updates

* No need for call

* finished fining deps

* update

* current changes

* current state

* current state

* new status

* nit

* finally

* fixes

* nits

* order is now expected

* use logger info instead of prints

* fixup

* up

* nit

* update

* nits

* update

* correct merge

* update

* update

* update

* add warning

* update caution message

* update

* better merging strategy

* copy class statements :wink

* fixups

* nits

* update

* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* nits

* smaller header

* do cleanup some stuff

* even simpler header?

* fixup

* updates

* ruff

* update examples

* nit

* TODO

* state

* OUUUUUUF

* current state

* nits

* final state

* add a readme

* fixup

* remove diff llama

* fix

* nit

* dummy noy funny

* ruff format tests src utils --check

* everless diffs

* less diffs and fix test

* fixes

* naming nit?

* update converter and add supper example

* nits

* updated for function signatures

* update

* update

* add converted dummies

* autoformat

* single target assign fix

* fixup

* fix some imports

* fixes

* don't push them

* `# noqa: F841`

---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Description of quantization_config

Added missing description about quantization_config in replace_with_bnb_linear for better readability.

* Removed trailing spaces

* Initial setup

* Metrics

* Overfit on two batches

* Train 40 epochs

* Memory leak debugging

* Trainer fine-tuning

* Draft

* Fixup

* Trained end-to-end

* Add requirements

* Rewrite evaluator

* nits

* Add readme

* Add instance-segmentation to the table

* Support void masks

* Remove sh

* Update docs

* Add pytorch test

* Add accelerate test

* Update examples/pytorch/instance-segmentation/README.md

* Update examples/pytorch/instance-segmentation/run_instance_segmentation.py

* Update examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py

* Update examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py

* Update examples/pytorch/instance-segmentation/run_instance_segmentation.py

* Fix consistency oneformer

* Fix imports

* Fix imports sort

* Apply suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update examples/pytorch/instance-segmentation/run_instance_segmentation.py
Co-authored-by: Sangbum Daniel Choi <34004152+SangbumChoi@users.noreply.github.com>

* Add resources to docs

* Update examples/pytorch/instance-segmentation/README.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update examples/pytorch/instance-segmentation/README.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Remove explicit model_type argument

* Fix tests

* Update readme

* Note about other models

---------
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Sangbum Daniel Choi <34004152+SangbumChoi@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Implement streaming run in ReAct agents
* Allow additional imports in code agents
* Python interpreter: support classes and exceptions, fixes

* add sanity evaluation

* fix

* Apply suggestions from code review
Co-authored-by: Zach Mueller <muellerzr@gmail.com>

* fix

---------
Co-authored-by: Zach Mueller <muellerzr@gmail.com>

enhance error message

fix get_scheduler args

add validation for bnb config

* Modified test

* Added on_optimizer_step to callbacks

* Move callback after step is called

* Added on optimizer step callback