* 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.
Adding new `encoder_no_repeat_ngram_size` to `generate`.
Blenderbot results seemed off compared to original ParlAI script:
`https://parl.ai/projects/recipes/`. Notably the model seems
to repeat a lot what was said during the conversation.
The actual problem was that `no_repeat_ngram_size` actually applies
to the `encoder_input_ids` but HF's `no_repeat_ngram_size` applies
to the previously generated ids (within the decoder). The history
conversation of blenderbot is within the `encoder` part so that
explains why HF's implementation had the repetitions.
This fix was focused on blenderbot *not* small and added tests
for those because they are quite different in configuration.
This change includes:
- Adding a new EncoderNoRepeatLogitProcessor.
- Adding 1 new arg to `generate` (`encoder_no_repeat_ngram_size`)
- Adding 1 new config parameter `encoder_no_repeat_ngram_size`.
- Adding 2 tests, one for the pipeline (high level, inputs exhibited
repeat behavior, one low level for EncoderNoRepeatLogitProcessor)
- Factored NoRepeatLogitProcessor so that logic could be reused.
Further work:
- Blenderbot conversational pipeline still does not behave correctly
as they way input is prepared within the pipeline is still incorrect
(follow up PR)
- Blenderbot allows the bot to have personas, which is done by
prepending "your personna: XXXX" to the input, this could be explored
too in a follow up PR.
@patrickvonplaten
@LysandreJik
* Update src/transformers/generation_logits_process.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update src/transformers/configuration_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Doc quality.
* Fixing test.
* Last fixes.
* Fixing to account for batch_size.
* Update src/transformers/configuration_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/generation_utils.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add {decoder_,}head_mask to LED
* Fix create_custom_forward signatue in encoder
* Add head_mask to longformer
* Add head_mask to longformer to fix dependencies
of LED on Longformer.
* Not working yet
* Add mising one input in longofrmer_modeling.py
* make fix-copies
* change tokenizer requirement
* split line
* Correct typo from list to str
* improve style
* make other function pretty as well
* add comment
* correct typo
* add new test
* pass tests for tok without padding token
* Apply suggestions from code review
* Add {decoder_,}head_mask to fsmt_modeling.py
* Enable test_headmasking and some changes to docs
* Remove test_head_masking flag from fsmt test file
Remove test_head_masking flag from test_modeling_fsmt.py
since test_head_masking is set to be True by default (thus it is redundant to store).
* Merge master and remove test_head_masking = True
* Rebase necessary due to an update of jaxlib
* Remove test_head_masking=True in tests/test_modeling_fsmt.py
as it is redundant.
* Adding a new `return_full_text` parameter to TextGenerationPipeline.
For text-generation, it's sometimes used as prompting text.
In that context, prefixing `generated_text` with the actual input
forces the caller to take an extra step to remove it.
The proposed change adds a new parameter (for backward compatibility).
`return_full_text` that enables the caller to prevent adding the prefix.
* Doc quality.
* Remove redundant test_head_masking = True flags
* Remove all redundant test_head_masking flags in PyTorch test_modeling_* files
* Make test_head_masking = True as a default choice in test_modeling_tf_commong.py
* Remove all redundant test_head_masking flags in TensorFlow
test_modeling_tf_* files
* Put back test_head_masking=False fot TFT5 models
* fix --lr_scheduler_type choices
* rewrite to fix for all enum-based cl args
* cleanup
* adjust test
* style
* Proposal that should work
* Remove needless code
* Fix test
Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>
pipeline.
- If table is empty then the line that contain `answer[0]` will fail.
- This PR add a check to prevent `answer[0]`.
- Also adds an early check for presence of `table` and `query` to
prevent late failure and give better error message.
- Adds a few tests to make sure these errors are correctly raised.
* We most likely don't want special tokens in this output.
* Adding `skip_special_tokens=True` to FillMaskPipeline
- It's backward incompatible.
- It makes for sense for pipelines to remove references to
special_tokens (all of the other pipelines do that).
- Keeping special tokens makes it hard for users to actually remove them
because all models have different tokens (<s>, <cls>, [CLS], ....)
* Fixing `token_str` in the same vein, and actually fix the tests too !
* Add head_mask/decoder_head_mask for TF BART models
* Add head_mask and decoder_head_mask input arguments for TF BART-based
models as a TF counterpart to the PR #9569
* Add test_headmasking functionality to tests/test_modeling_tf_common.py
* TODO: Add a test to verify that we can get a gradient back for
importance score computation
* Remove redundant #TODO note
Remove redundant #TODO note from tests/test_modeling_tf_common.py
* Fix assertions
* Make style
* Fix ...Model input args and adjust one new test
* Add back head_mask and decoder_head_mask to BART-based ...Model
after the last commit
* Remove head_mask ande decoder_head_mask from input_dict
in TF test_train_pipeline_custom_model as these two have different
shape than other input args (Necessary for passing this test)
* Revert adding global_rng in test_modeling_tf_common.py
* Add decoder_head_mask for PyTorch T5 model
* Add decoder_head_mask args into T5Model and T5ForConditionalGeneration
* Slightly change the order of input args to be in accordance
with the convention from BART-based models introduced within the PR #9569.
* Make style for modeling_t5.py
* Add decoder_head_mask for TF T5 models
* Separate head_mask and decoder_head_mask args in TF T5 models
* Slightly change the order of input args to follow convention
of BART-based models updated in PR #9569
* Update test_forward_signature tests/test_modeling_tf_common.py
w.r.t. the changed order of input args
* Add FutureWarnings for T5 and TFT5 models
* Add FutureWarnings for T5 and TFT5 models warning a user that
input argument `head_mask` was split into two arguments -
`head_mask` and `decoder_head_mask`
* Add default behaviour - `decoder_head_mask` is set to copy
`head_mask`
* Fix T5 modeling and FutureWarning
* Make proper usage of head_mask and decoder_head_mask
in cross_attention
* Fix conditions for raising FutureWarning
* Reformat FutureWarning in T5 modeling
* Refactor the warning message
