When supplied by Keras deserialization, the config parameter to initializers
will be a dict. So intercept it and convert to PretrainedConfig object (and
store in instance attribute for get_config to get at it) before passing to the
actual initializer. To accomplish this, and repeat as little code as possible,
use a class decorator on TF*MainLayer classes.
* add first copy past test to tf 2 generate
* add tf top_k_top_p_filter fn
* add generate function for TF
* add generate function for TF
* implemented generate for all models expect transfoXL
* implemented generate for all models expect transfoXL
* implemented generate for all models expect transfoXL
* make style
* change permission of test file to correct ones
* delete ipdb
* delete ipdb
* fix bug and finish simple gpt2 integration test
* clean test file
* clean test file
* make style
* make style
* make style
* make style
* change import style
* change import style
* make style
* make style
* add decorators
* add decorators
* fix tf ctrl bug dim => axis in TF
* make style
* make style
* refactored test file
* refactored test file
* take out test_torch_tf_conversion if nothing is defined
* take out test_torch_tf_conversion if nothing is defined
* remove useless files
* remove useless files
* fix conflicts
* fix conflicts
* fix conflicts
* fix conflicts
* fix conflicts
* solve conflicts
* solve conflicts
* fix conflicts
* fix conflicts
* merge conflicts
* delete ipdb
* exposed top_k_top_p_filtering fns
* delete weirdly created w! file
* add comment to test tf common modeling
* fix conflicts
* fix conflicts
* make style
* merge conflicts
* make style
* change tf.tensor.shape to shape_list(tensor)
* Pipeline doc initial commit
* pipeline abstraction
* Remove modelcard argument from pipeline
* Task-specific pipelines can be instantiated with no model or tokenizer
* All pipelines doc
* * Added support for Albert when fine-tuning for NER
* Added support for Albert in NER pipeline
* Added command-line options to examples/ner/run_ner.py to better control tokenization
* Added class AlbertForTokenClassification
* Changed output for NerPipeline to use .convert_ids_to_tokens(...) instead of .decode(...) to better reflect tokens
* Added ,
* Now passes style guide enforcement
* Changes from reviews.
* Code now passes style enforcement
* Added test for AlbertForTokenClassification
* Added test for AlbertForTokenClassification
* Renamed file generate by tokenizers when calling save_pretrained to match python.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added save_vocabulary tests.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Remove python quick and dirty fix for clean Rust impl.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Bump tokenizers dependency to 0.5.1
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* TransfoXLTokenizerFast uses a json vocabulary file + warning about incompatibility between Python and Rust
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added some save_pretrained / from_pretrained unittests.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Update tokenizers to 0.5.2
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Quality and format.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* flake8
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Making sure there is really a bug in unittest
* Fix TransfoXL constructor vocab_file / pretrained_vocab_file mixin.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Testing that encode_plus and batch_encode_plus behave the same way
Spoiler alert: they don't
* Testing rest of arguments in batch_encode_plus
* Test tensor return in batch_encode_plus
* Addressing Sam's comments
* flake8
* Simplified with `num_added_tokens`
* enable_padding should pad up to max_length if set.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added more testing on padding.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* improving generation
* finalized special token behaviour for no_beam_search generation
* solved modeling_utils merge conflict
* solve merge conflicts in modeling_utils.py
* add run_generation improvements from PR #2749
* adapted language generation to not use hardcoded -1 if no padding token is available
* remove the -1 removal as hard coded -1`s are not necessary anymore
* add lightweight language generation testing for randomely initialized models - just checking whether no errors are thrown
* add slow language generation tests for pretrained models using hardcoded output with pytorch seed
* delete ipdb
* check that all generated tokens are valid
* renaming
* renaming Generation -> Generate
* make style
* updated so that generate_beam_search has same token behavior than generate_no_beam_search
* consistent return format for run_generation.py
* deleted pretrain lm generate tests -> will be added in another PR
* cleaning of unused if statements and renaming
* run_generate will always return an iterable
* make style
* consistent renaming
* improve naming, make sure generate function always returns the same tensor, add docstring
* add slow tests for all lmhead models
* make style and improve example comments modeling_utils
* better naming and refactoring in modeling_utils
* improving generation
* finalized special token behaviour for no_beam_search generation
* solved modeling_utils merge conflict
* solve merge conflicts in modeling_utils.py
* add run_generation improvements from PR #2749
* adapted language generation to not use hardcoded -1 if no padding token is available
* remove the -1 removal as hard coded -1`s are not necessary anymore
* add lightweight language generation testing for randomely initialized models - just checking whether no errors are thrown
* add slow language generation tests for pretrained models using hardcoded output with pytorch seed
* delete ipdb
* check that all generated tokens are valid
* renaming
* renaming Generation -> Generate
* make style
* updated so that generate_beam_search has same token behavior than generate_no_beam_search
* consistent return format for run_generation.py
* deleted pretrain lm generate tests -> will be added in another PR
* cleaning of unused if statements and renaming
* run_generate will always return an iterable
* make style
* consistent renaming
* improve naming, make sure generate function always returns the same tensor, add docstring
* add slow tests for all lmhead models
* make style and improve example comments modeling_utils
* better naming and refactoring in modeling_utils
* changed fast random lm generation testing design to more general one
* delete in old testing design in gpt2
* correct old variable name
* temporary fix for encoder_decoder lm generation tests - has to be updated when t5 is fixed
* adapted all fast random generate tests to new design
* better warning description in modeling_utils
* better comment
* better comment and error message
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
* Correctly return the tuple of generated file(s) when calling save_pretrained
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Quality and format.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Override build_inputs_with_special_tokens for fast impl + unittest.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Quality + format.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Implemented fast version of tokenizers
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Bumped tokenizers version requirements to latest 0.2.1
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added matching tests
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Matching OpenAI GPT tokenization !
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Matching GPT2 on tokenizers
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Expose add_prefix_space as constructor parameter for GPT2
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Matching Roberta tokenization !
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Removed fast implementation of CTRL.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Binding TransformerXL tokenizers to Rust.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Updating tests accordingly.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added tokenizers as top-level modules.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Black & isort.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Rename LookupTable to WordLevel to match Rust side.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Black.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Use "fast" suffix instead of "ru" for rust tokenizers implementations.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Introduce tokenize() method on fast tokenizers.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* encode_plus dispatchs to batch_encode_plus
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* batch_encode_plus now dispatchs to encode if there is only one input element.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Bind all the encode_plus parameter to the forwarded batch_encode_plus call.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Bump tokenizers dependency to 0.3.0
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Formatting.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Fix tokenization_auto with support for new (python, fast) mapping schema.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Give correct fixtures path in test_tokenization_fast.py for the CLI.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Expose max_len_ properties on BertTokenizerFast
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Move max_len_ properties to PreTrainedTokenizerFast and override in specific subclasses.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* _convert_encoding should keep the batch axis tensor if only one sample in the batch.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Add warning message for RobertaTokenizerFast if used for MLM.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added use_fast (bool) parameter on AutoTokenizer.from_pretrained().
This allows to easily enable/disable Rust-based tokenizer instantiation.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Let's tokenizers handle all the truncation and padding stuff.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Allow to provide tokenizer arguments during pipeline creation.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Update test_fill_mask pipeline to not use fast tokenizers.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Fix too much parameters for convert_encoding.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* When enabling padding, max_length should be set to None.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Avoid returning nested tensors of length 1 when calling encode_plus
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Ensure output is padded when return_tensor is not None.
Tensor creation requires the inital list input to be of the exact same size.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Disable transfoxl unittest if pytorch is not available (required to load the model)
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* encode_plus should not remove the leading batch axis if return_tensor is set
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Temporary disable fast tokenizers on QA pipelines.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Fix formatting issues.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Update tokenizers to 0.4.0
* Update style
* Enable truncation + stride unit test on fast tokenizers.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Add unittest ensuring special_tokens set match between Python and Rust.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Ensure special_tokens are correctly set during construction.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Give more warning feedback to the user in case of padding without pad_token.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* quality & format.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added possibility to add a single token as str
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Added unittest for add_tokens and add_special_tokens on fast tokenizers.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Fix rebase mismatch on pipelines qa default model.
QA requires cased input while the tokenizers would be uncased.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Using offset mapping relative to the original string + unittest.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: save_vocabulary requires folder and file name
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Simplify import for Bert.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: truncate_and_pad disables padding according to the same heuristic than the one enabling padding.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Remove private member access in tokenize()
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Bump tokenizers dependency to 0.4.2
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* format & quality.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Use named arguments when applicable.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Add Github link to Roberta/GPT2 space issue on masked input.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Move max_len_single_sentence / max_len_sentences_pair to PreTrainedTokenizerFast + tests.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Relax type checking to include tuple and list object.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Addressing review comment: Document the truncate_and_pad manager behavior.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Raise an exception if return_offsets_mapping is not available with the current tokenizer.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Ensure padding is set on the tokenizers before setting any padding strategy + unittest.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* On pytorch we need to stack tensor to get proper new axis.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Generalize tests to different framework removing hard written return_tensors="..."
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Bump tokenizer dependency for num_special_tokens_to_add
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Overflowing tokens in batch_encode_plus are now stacked over the batch axis.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Improved error message for padding strategy without pad token.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Bumping tokenizers dependency to 0.5.0 for release.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Optimizing convert_encoding around 4x improvement. 🚀
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* expose pad_to_max_length in encode_plus to avoid duplicating the parameters in kwargs
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Generate a proper overflow_to_sampling_mapping when return_overflowing_tokens is True.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Fix unittests for overflow_to_sampling_mapping not being returned as tensor.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Format & quality.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Remove perfect alignment constraint for Roberta (allowing 1% difference max)
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
* Triggering final CI
Co-authored-by: MOI Anthony <xn1t0x@gmail.com>
* Preserve spaces in GPT-2 tokenizers
Preserves spaces after special tokens in GPT-2 and inhereted (RoBERTa)
tokenizers, enabling correct BPE encoding. Automatically inserts a space
in front of first token in encode function when adding special tokens.
* Add tokenization preprocessing method
* Add framework argument to pipeline factory
Also fixes pipeline test issue. Each test input now treated as a
distinct sequence.
PyTorch < 1.3 requires multiplication operands to be of the same type.
This was violated when using default attention mask (i.e.,
attention_mask=None in arguments) given BERT in the decoder mode.
In particular, this was breaking Model2Model and made tutorial
from the quickstart failing.
* fill_mask helper
* [poc] FillMaskPipeline
* Revert "[poc] FillMaskPipeline"
This reverts commit 67eeea55b0.
* Revert "fill_mask helper"
This reverts commit cacc17b884.
* README: clarify that Pipelines can also do text-classification
cf. question at the AI&ML meetup last week, @mfuntowicz
* Fix test: test feature-extraction pipeline
* Test tweaks
* Slight refactor of existing pipeline (in preparation of new FillMaskPipeline)
* Extraneous doc
* More robust way of doing this
@mfuntowicz as we don't rely on the model name anymore (see AutoConfig)
* Also add RobertaConfig as a quickfix for wrong token_type_ids
* cs
* [BIG] FillMaskPipeline
Created a link between the linear layer bias and the model attribute bias. This does not change anything for the user nor for the conversion scripts, but allows the `resize_token_embeddings` method to resize the bias as well as the weights of the decoder.
Added a test.
I suspect the wrapper classes were created in order to prevent the
abstract base class (TF)CommonModelTester from being included in test
discovery and running, because that would fail.
I solved this by replacing the abstract base class with a mixin.
Code changes are just de-indenting and automatic reformattings
performed by black to use the extra line space.
This construct isn't used anymore these days.
Running python tests/test_foo.py puts the tests/ directory on
PYTHONPATH, which isn't representative of how we run tests.
Use python -m unittest tests/test_foo.py instead.
