GitLab

* Move convert_to_rgb to image_transforms module

* Fix tests

* generate from config mvp

* fix failing tests

* max_time test

* Load default gen config at model load time; Update docs

* further documentation; add tests

* adapt rag to the new structure

* handle models not instantiated with from_pretained (like in tests)

* better default generation config

* add can_generate fn

* handle legacy use case of ad hoc model config changes

* initialize gen config from config in individual methods, if gen config is none

* fix _get_decoder_start_token_id when called outside GenerationMixin

* correct model config load order (set attr > model config > decoder config)

* update rag to match latest changes

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* load gen config from model config in model.from_pretrained

* fix can_generate fn

* handle generate calls without a previous from_pretrained (e.g. tests)

* add legacy behavior (and a warning)

* lower logger severity
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Fixing object detection with layoutlm.

* Fixup.

fix failing `pipeline` test

* Workaround for #20287: FlanT5-XXL 8bit support

* Make fix-copies

* revert unrelated change

* Dont apply to longt5 and switch transformers

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Even more validation.

* Fixing order.

* Add Swin backbone

* Remove line

* Add code example
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Replaces xxx_required with requires_backends

* Fixup

* weight -> weights

* model embedding resize does not work with both v2 and noraml

* remove useless test

fix: 修复Trainer无法使用use_legacy_prediction_loop参数的问题

解决使用use_legacy_prediction_loop参数在predict阶段使用prediction_loop进行预测时，遇到AttributeError: 'PredictionOutput' object has no attribute 'num_samples'的问题
Co-authored-by: ZhouHang <zhouhang@idataway.com>

* Improve tests

* Improve TF tests

* Apply suggestion

* Fix test
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

Uninstall torch_tensorrt for now
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* added model resources for xlm-roberta

* added model resources for xlm-roberta

* resolve suggested changes

* add resources to xlm-roberta

* Add first draft

* Add out_features attribute to config

* Add corresponding test

* Add Dinat backbone

* Add BackboneMixin

* Add Backbone mixin, improve tests

* Fix embeddings

* Fix bug

* Improve backbones

* Fix Nat backbone tests

* Fix Dinat backbone tests

* Apply suggestions
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

`image = to_channel_dimension_format(image, ChannelDimension.LAST)`
is redundant as this same conversion is also applied in to_pil_image().

This redundant call actually makes the training fail in rare cases.
The problem can be reproduced with the following code snippet:
```
from transformers.models.clip import CLIPFeatureExtractor
vision_processor = CLIPFeatureExtractor.from_pretrained('openai/clip-vit-large-patch14')
images = [
    torch.rand(size=(3, 2, 10), dtype=torch.float),
    torch.rand(size=(3, 10, 1), dtype=torch.float),
    torch.rand(size=(3, 1, 10), dtype=torch.float)
]
for image in images:
    processed_image = vision_processor(images=image, return_tensors="pt")['pixel_values']
    print(processed_image.shape)
    assert processed_image.shape == torch.Size([1, 3, 224, 224])
```

The last image has a height of 1 pixel.
The second call to to_channel_dimesion_format() will transpose the image, and the height
dimension is wrongly treated as the channels dimension afterwards.
Because of this, the following normalize() step will result in an
exception.

* Fix AdamWeightDecay for TF

* Fix AdamWeightDecay for TF

* make fixup

* Fix the pipeline test regarding TF

* Fix the pipeline test regarding TF

* update comment
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* add `keep_in_fp32_modules` support

* pass it as class attribute

* few modifs

- make tests `slow`
- fix logic

* better logic

* fix failing test

* `bfloat16` support

* Update src/transformers/modeling_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix

* simplify tests

* simplify tests

* fix test

* modify message

* more checks

* fix failing tests

* add more conditions

- add `is_accelerate_available`
- fixes pipleine tests that failed

* add suggestions

* Update src/transformers/modeling_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix failing `bnb` test

* add last safety checker
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Add decorator for flaky tests

* Fix up

* Disambiguate test for required_input in tokenization base file.

* Add test for size

* Add templates for gpt-sw3

* Add templates for gpt-sw3

* Added sentencepiece tokenizer

* intermediate commit with many changes

* fixed conflicts

* Init commit for tokenization port

* Tokenization progress

* Remove fast tokenizer

* Clean up and rename spm.model -> spiece.model

* Remove TF -> PT conversion script template, Clean up Megatron -> PT script

* Optimize encode & decode performance

* added new attention

* added new attention

* attention for gpt-sw3 working

* attention good

* Cache is now working

* fixed attention mask so that it works with causal attention

* fixed badbmm bug for cpu and caching

* updated config with correct parameters

* Refactor and leave optimizations as separate functions to avoid breaking expected functionality

* Fix special tokens mapping for both tokenizers

* cleaning up of code and comments

* HF compatible attention outputs

* Tokenizer now passing tests, add documentation

* Update documentation

* reverted back to base implementation after checking that it is identical to pretrained model

* updated gpt-sw3 config

* updated conversion script

* aligned parameters with gpt-sw3 config

* changed default scale_attn_by_inverse_layer_idx to true

* removed flag from conversion script

* added temporary model path

* reverted back to functioning convert script

* small changes to default config

* updated tests for gpt-sw3

* make style, make quality, minor cleanup

* Change local paths to testing online repository

* Change name: GptSw3 -> GPTSw3

* Remove GPTSw3TokenizerFast references

* Use official model repository and add more model sizes

* Added reference to 6.7b model

* Add GPTSw3DoubleHeadsModel to IGNORE_NON_AUTO_CONFIGURED, like GPT2DoubleHeadsModel

* Remove pointers to non-existing TFGPTSw3

* Add GPTSw3 to docs/_toctree.yml

* Remove TF artifacts from GPTSw3 in __init__ files

* Update README:s with 'make fix-copies'

* Add 20b model to archive list

* Add documentation for GPT-Sw3

* Fix typo in documentation for GPT-Sw3

* Do 'make fix-copies' again after having updated docs

* Fix some typos in docs

* Update src/transformers/models/gpt_sw3/configuration_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/gpt_sw3/configuration_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/gpt_sw3/__init__.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/gpt_sw3/__init__.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/gpt_sw3/convert_megatron_to_pytorch.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/gpt_sw3/modeling_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update tests/models/gpt_sw3/test_tokenization_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/gpt_sw3/modeling_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/gpt_sw3/modeling_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Resolve comments from PR feedback

* Resolve more comments from PR feedback, also set use_cache=True in convert script

* Add '# Copied from' comments for GPTSw3 modeling

* Set 'is_parallelizable = False'

* Remove '# Copied from' where code was modified and add 'with x->y' when appropriate

* Remove parallelize in mdx

* make style, make quality

* Update GPTSw3Config default values and corresponding documentation

* Update src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/gpt_sw3/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Clean up and protect GPTSw3Tokenizer imports with is_sentencepiece_available

* Make style, make quality

* Add dummy object for GPTSw3Tokenizer via 'make fix-copies'

* make fix-copies

* Remove GPTSw3 modeling classes

* make style, make quality

* Add GPTSw3 auto-mappings for other GPT2 heads

* Update docs/source/en/model_doc/gpt-sw3.mdx
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/gpt_sw3/convert_megatron_to_pytorch.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Remove old TODO-comment

* Add example usage to GPTSw3Tokenizer docstring

* make style, make quality

* Add implementation details and example usage to gpt-sw3.mdx
Co-authored-by: JoeyOhman <joeyoh@kth.se>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Add require_vision decorator

* Fixup

* Use requires_backends

* Add requires_backend to utils functions

* clarify docstring

* make style

* Convert tokenizer outputs for Keras in doc example

* Das deutsche Beispiel auch korrigieren

* Create and translate to Spanish debugging.mdx

* solved typo error in a header

* Update debugging.mdx

* Update debugging.mdx

* Update docs/source/es/debugging.mdx
Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>

* Update docs/source/es/debugging.mdx
Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>

* Update docs/source/es/debugging.mdx
Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>

* Update docs/source/es/debugging.mdx
Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>

* Update docs/source/es/debugging.mdx
Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>

* Update _toctree.yml
Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Initial commit

* Add type hints for two major classes

* Run make fixup

* Fix output type for Whisper

* Run isort to fix imports

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* ✨

 add accelerate support for LongT5 models
Signed-off-by: peter szemraj <peterszemraj@gmail.com>

* fix `accelerate` tests

* Trigger CI test
Signed-off-by: peter szemraj <peterszemraj@gmail.com>
Co-authored-by: younesbelkada <younesbelkada@gmail.com>