* Fix: Change tensors to integers in torch.split() for torch.dynamo and torch.compile compatibility
* Applied the suggested fix to the utils/check_copies.py test
* Applied the suggested fix by changing the original function that gets copied
* 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
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* 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
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.
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.
It's more idiomatic and significantly more efficient because
1) it avoids repeated `append` call that Python has to resolve on each iteration
2) can preallocate the size of the final list avoiding resizing
