GitLab

* Fix sagemaker/distributed state

* Fix correctly

* Bring back -1

* Bring back local rank for distributed check

* better version

* Cleanest option

* fix

* fix

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

Use config to set name and descriptiob if not present

* rwkv fix for 8bit inference

* add comment

* First commit

* Add auto-translation with GPT-4

* make fixup

* Add a functional layernorm for TF

* Add all the auxiliary imports etc.

* Add the extra processor and tests

* rebase to main

* Add all the needed fixes to the GPT code

* make fixup

* Make convolutions channels-last so they run on CPU

* make fixup

* Fix final issues

* Fix other models affected by test change

* Clarify comment on the sparse_prompt_embeddings check

* Refactor functional_layernorm, use shape_list in place of .shape in some places

* Remove deprecated torch-alike code

* Update tests/models/sam/test_modeling_tf_sam.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/sam/test_modeling_tf_sam.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Refactor processor with common methods and separated private methods

* make fixup

* Quietly delete the file that didn't do anything (sorry Sylvain)

* Refactor the processor tests into one file

* make fixup

* Clean up some unnecessary indirection

* Fix TF mask postprocessing

* Add more processor equivalence tests

* Refactor generate_crop_boxes to use framework-neutral np code

* Make the serving output correctly conditional

* Fix error message line length

* Use dict keys rather than indices internally in both TF and PT SAM call/forward

* Return dicts internally in the call/forward methods

* Revert changes to common tests and just override check_pt_tf_outputs

* Revert changes to other model tests

* Clarify comments for functional layernorm

* Add missing transpose from PT code

* Removed unused copied from in PT code

* Remove overrides for tests that don't exist in TF

* Fix transpose and update tests for PT and TF to check pred_masks

* Add training flag

* Update tests to use TF checkpoints

* Update index.mdx

* Add missing cross-test decorator

* Remove optional extra asterisks

* Revert return_dict changes in PT code

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

* Remove None return annotations on init methods

* Update tests/models/sam/test_processor_sam.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Fix input_boxes shapes

* make fixup

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Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Patched the optimizers

* README: Fix affiliation for MEGA

* Fix quality

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Co-authored-by: Lysandre <lysandre@huggingface.co>

* initial working additions

* clean and rename, add cond stripping initial prompt to decode

* cleanup, edit create_initial_prompt_ids, add tests

* repo consistency, flip order of conditional

* fix error, move the processor fn to the tokenizer

* repo consistency, update test ids to corresponding tokenizer

* use convert_tokens_to_ids not get_vocab...

* use actual conditional in generate

* make sytle

* initial address comments

* initial working add new params to pipeline

* first draft of sequential generation for condition_on_previous_text

* add/update tests, make compatible with timestamps

* make compatible with diff. input kwargs and max length

* add None check

* add temperature check

* flip temp check operand

* refocusing to prev pr scope

* remove the params too

* make style

* edits, move max length incorporating prompt to whisper

* address comments

* remove asr pipeline prompt decoding, fix indexing

* address comments (more tests, validate prompt)

* un-comment out tests (from debug)

* remove old comment

* address comments

* fix typo

* remove timestamp token from test

* make style

* cleanup

* copy method to fast tokenizer, set max_new_tokens for test

* prompt_ids type just pt

* address Amy's comments

* make style

* fix bug in group_texts function, that was inserting short batches

* fully exclude short batches and return empty dict instead

* fix style

* add tool to awesome-transformers list

* add keyword list

* sgugger wording suggestion
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

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Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Properly guard PyTorch stuff

* [all-test]

* [all-test] Fix model imports as well

* Making sure StoppingCriteria is always defined

* [all-test]

* fix

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

* fix

* fix

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

* fix

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

* Add local agent

* Document LocalAgent

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

docs: ko: zero_shot_object_detection

Fix device issue in `SwiftFormerModelIntegrationTest::test_inference_image_classification_head` (#23435)

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

* fix

* fix

* fix

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

Update error

Fix + link

* Awesome Transformers

* Update

* Update

* Keywords

* Keywords

* Complete document

* Add lm-evaluation-harness

* Edit txtai according to David's comments

* Update awesome-transformers.md

* Create test_tokenization_electra.py

* Update tests/models/electra/test_tokenization_electra.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

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Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

search model buffers for dtype as the last resort

Previously even after finding a stop token, other stop tokens were considered, which is unnecessary and slows down processing.

Currently, this unnecessary overhead is negligible since there are usually 2 stop tokens considered and they are fairly short, but in future it may become more expensive.

* fix sam slow test

* oops

* fix error message

* fix

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

It's more efficient to iterate over key, value dict pairs instead of iterating over keys and performing value lookups on each iteration. It's also more idiomatic.

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