* Fixed nll with label_smoothing to nll
* Resolved conflict by rebase
* Fixed nll with label_smoothing to nll
* Resolved conflict by rebase
* Added label_smoothing to config file
* Fixed nits
* generated text on A10G
* generated text in CI
* Apply suggestions from code review
add explanatory comments
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
output_logits option behaves like output_scores, but returns the raw, unprocessed prediction logit scores,
ie. the values before they undergo logit processing and/or warping. The latter happens by default for the
regular output scores.
It's useful to have the unprocessed logit scores in certain circumstances. For example, unprocessed logit scores
are very useful with causallm models when one wants to determine the probability of a certain answer, e.g.
when asking a question with a yes/no answer. In that case getting the next-token probabilities of both "yes" and
"no" (and/or their relative ratio) is of interest for classification. The reason for getting these _before_ logit
processing and/or warping is b/c a) that can change the probabilities or b) reject the tokens of interest / reduce
the number of tokens to just 1.
For an example use-case see paper TabLLM: Few-shot Classification of Tabular Data with Large Language Models
by Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, and David Sontag.
https://arxiv.org/abs/2210.10723
In addition:
- added dedicated unit test: tests/generation/test_utils/test_return_unprocessed_logit_scores
which tests return of logics with output_logits=True in generation.
- set output_logits=True in all other generation unit tests, that also have output_scores=True.
Implemented @gante's and @amyeroberts review feedback
Co-authored-by: kx79wq <max.baak@ing.com>
* change version
* nuke
* this doesn't make sense
* update some requirements.py
* revert + no main
* nits
* change cache number
* more pin
* revert
---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
The link in evaluation was missing a hyphen between post and processing. I fixed this, for English only. Someone with the ability to do a global search/replace should fix the other languages (if indeed they have this issue)/
* Add task_summary to es/_toctree.yml
* Add task_summary.md to docs/es
* Change title of task_summary.md
* Translate firsts paragraphs
* Translate middle paragraphs
* Translte the rest of the doc
* Edit firts paragraph
* Add chat support to text generation pipeline
* Better handling of single elements
* Deprecate ConversationalPipeline
* stash commit
* Add missing add_special_tokens kwarg
* Update chat templating docs to refer to TextGenerationPipeline instead of ConversationalPipeline
* Add ✨TF✨ tests
* @require_tf
* Add type hint
* Add specific deprecation version
* Remove unnecessary do_sample
* Remove todo - the discrepancy has been resolved
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/pipelines/text_generation.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* pass through trust_remote_code for dynamically loading unregistered tokenizers specified by config
add test
* change directories back to previous directory after test
* fix ruff check
* Add a note to that block for future in case we want to remove it later
---------
Co-authored-by: Matt <rocketknight1@gmail.com>
* enable graident checkpointing in DetaObjectDetection
* fix missing part in original DETA
* make style
* make fix-copies
* Revert "make fix-copies"
This reverts commit 4041c86c29248f1673e8173b677c20b5a4511358.
* remove fix-copies of DetaDecoder
* enable swin gradient checkpointing
* fix gradient checkpointing in donut_swin
* add tests for deta/swin/donut
* Revert "fix gradient checkpointing in donut_swin"
This reverts commit 1cf345e34d3cc0e09eb800d9895805b1dd9b474d.
* change supports_gradient_checkpointing pipeline to PreTrainedModel
* Revert "add tests for deta/swin/donut"
This reverts commit 6056ffbb1eddc3cb3a99e4ebb231ae3edf295f5b.
* Revert "Revert "fix gradient checkpointing in donut_swin""
This reverts commit 24e25d0a14891241de58a0d86f817d0b5d2a341f.
* Simple revert
* enable deformable detr gradient checkpointing
* add gradient in encoder
* add cuda_custom_kernel function in MSDA
* make style and fix input of DetaMSDA
* make fix-copies
* remove n_levels in input of DetaMSDA
* minor changes
* refactor custom_cuda_kernel like yoso format
0507e69d34/src/transformers/models/yoso/modeling_yoso.py (L53)
* wow I was scared!
* fix everything
* nits
* make it BC?
* add todo
* nits
* is_tracing should still be used to pass tracing tests
* nits
* some nits to make sure genration works with static cache uncompiled
* fix sdpa
* fix FA2 for both static and dynamic in a better way?
* style
* fix-copies
* fix fix copies
* fix sequential beam searcg
* style
* use `keys_to_ignore`
* nit
* correct dtype inference when init
* :( the fix for FA2 is still not optimal to investigate!
* styling
* nits
* nit
* this might work better
* add comment
* Update src/transformers/models/llama/modeling_llama.py
* "position_ids" -> "cache_position"
* style
* nit
* Remove changes that should no be propagatted just yet
* Apply suggestions from code review
* Styling
* make sure we raise an errir for static cache with FA2 enabled
* move to the bottom of the signature
* style
* Update src/transformers/models/llama/modeling_llama.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Update src/transformers/models/llama/modeling_llama.py
* nit in the name
---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Initial commit
* Add guards for the global mesh
* Address more comments
* Move the dataloader into integrations/tpu.py
* Fix linters
* Make karg more explicitly
* Remove the move device logic
* Fix the CI
* Fix linters
* Re-enable checkpointing
* Add tie_weights() to LM heads and set bias in set_output_embeddings()
The bias were not tied correctly in some LM heads, and this change should fix that.
* Moving test_save_and_load_low_cpu_mem_usage to ModelTesterMixin
* Adding _tie_weights() to MPNet and Vilt
* Skip test for low cpu mem usage for Deta/DeformableDetr since they cannot init on meta device
* Rename to test name to save_load to match the convention
