* Kill model archive maps
* Fixup
* Also kill model_archive_map for MaskedBertPreTrainedModel
* Unhook config_archive_map
* Tokenizers: align with model id changes
* make style && make quality
* Fix CI
* remove output_past from pt
* make style
* add optional input length for gpt2
* add use cache to prepare input
* save memory in gpt2
* correct gpt2 test inputs
* make past input optional for gpt2
* finish use_cache for all models
* make style
* delete modeling_gpt2 change in test file
* correct docstring
* correct is true statements for gpt2
* add some t5 integration tests
* finish summarization and translation integration tests for T5 - results loook good
* add tf test
* fix == vs is bug
* fix tf beam search error and make tf t5 tests pass
* make decoder input ids optional for t5 training
* lm_lables should not be shifted in t5
* add tests
* finish shift right functionality for PT T5
* move shift right to correct class
* cleaner code
* replace -100 values with pad token id
* add assert statement
* remove unnecessary for loop
* make style
* fix conflicts
* update bart max length test
* correct spelling mistakes
* implemented model specific encode function
* fix merge conflicts
* better naming
* save intermediate state -> need to rethink strucuture a bit
* leave tf problem as it is for now
* current version
* add layers.pop
* remove ipdb
* make style
* clean return cut decoding
* remove ipdbs
* Fix restoring layers in the decoders that doesnt exists.
* push good intermediate solution for now
* fix conflicts
* always good to refuse to merge conflicts when rebasing
* fix small bug
* improve function calls
* remove unused file
* add correct scope behavior for t5_generate
Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
Be explicit that this is config for the transformers package (as these
layers may coexist with other custom stuff in a Keras model, plus the
Keras container itself is called config, and config["config"] is not
great)
Add explicit error handling for initializer calls that have neither
the `config` nor the `transformers_config` argument, or have both.
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)
