* [trainer] fix ignored columns logger
This PR fixes a confusing log entry that says:
```
The following columns in the evaluation set don't have a corresponding argument in `T5ForConditionalGeneration.forward` and have been ignored: .
```
when everything is in order.
* Update src/transformers/trainer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add check-ops script
* Finish to implement check_tf_ops and start the test
* Make the test mandatory only for BERT
* Update tf_ops folder
* Remove useless classes
* Add the ONNX test for GPT2 and BART
* Add a onnxruntime slow test + better opset flexibility
* Fix test + apply style
* fix tests
* Switch min opset from 12 to 10
* Update src/transformers/file_utils.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Fix GPT2
* Remove extra shape_list usage
* Fix GPT2
* Address Morgan's comments
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
* Conversion from slow to fast for BPE spm vocabs contained an error.
- There is only 1 test currently (tokenizers + slow) that used the modified path
and it's reformer, which does not contain any ids modification so the
bug was silent for now.
- The real issue is that vocab variable was overloaded by
SentencePieceExtractor, leading to Slow specific vocab oddities to be
completely ignored
- The bug was reported here https://github.com/huggingface/transformers/issues/9518
- Ran the complete tokenization test suite with slow without error
(`RUN_SLOW=1 pytest -sv tests/test_tokenization_*`)
* Remove rebase error.
* Adding the fixture.
* fix rag generate and tests
* put back adjust_logits_during_generation
* tests are okay
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Unify logging with f-strings
* Get limits from MLflow rather than hardcode
* Add a check for parameter length overflow
Also constants are marked as internal
* Don't stop run in on_train_end
This causes bad behaviour when there is a seprarte validation step:
validation gets recorded as separate run.
* Fix style
* Cleaning up `ConversationalPipeline` to support more than DialoGPT.
Currently ConversationalPipeline was heavily biased towards DialoGPT
,which is the default model for this pipeline.
This PR proposes changes to put back the modifications specific to
DialoGPT into tokenizer-specific behavior wherever possible, by
creating `_build_conversation_input_ids` function that takes
conversation as input, and returns a list of ints corresponding
to the tokens. It feels natural to put here because all models
have probably different strategies to build input_ids from the
full conversation and it's the tokenizer's job to transform strings
into tokens (and vice-versa)
If `_build_conversation_input_ids` is missing, previous behavior is
used so we don't break anything so far (except for blenderbot where it's a fix).
This PR also contains a fix for too long inputs. There used
to be dead code for trying to limit the size of incoming input.
The introduced fixed is that we limit
within `_build_conversation_input_ids` to `tokenizer.model_max_length`.
It corresponds to the intent of the removed dead code and is actually
better because it corresponds to `model_max_length` which is different
from `max_length` (which is a default parameter for `generate`).
- Removed `history` logic from the Conversation as it's not relevant
anymore because tokenization logic has been moved to tokenizer.
And tokenizer cannot save any cache, and conversation cannot know
what is relevant or not.
Also it's not usable from `blenderbot` because the input_ids are
not append only (EOS tokens is always at the end).
- Added `iter_texts` method on `Conversation` because all
the code was literred with some form of this iteration of
past/generated_responses.
* Removing torch mention in types.
* Adding type checking to `_build_conversation_input_ids`.
* Fixing import in strings.
