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[PyTorch Bart] Split Bart into different models (#9343)

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* first try

* remove old template

* finish bart

* finish mbart

* delete unnecessary line

* init pegasus

* save intermediate

* correct pegasus

* finish pegasus

* remove cookie cutter leftover

* add marian

* finish blenderbot

* replace in file

* correctly split blenderbot

* delete "old" folder

* correct "add statement"

* adapt config for tf comp

* correct configs for tf

* remove ipdb

* fix more stuff

* fix mbart

* push pegasus fix

* fix mbart

* more fixes

* fix research projects code

* finish docs for bart, mbart, and marian

* delete unnecessary file

* correct attn typo

* correct configs

* remove pegasus for seq class

* correct peg docs

* correct peg docs

* finish configs

* further improve docs

* add copied from statements to mbart

* fix copied from in mbart

* add copy statements to marian

* add copied from to marian

* add pegasus copied from

* finish pegasus

* finish copied from

* Apply suggestions from code review

* make style

* backward comp blenderbot

* apply lysandres and sylvains suggestions

* apply suggestions

* push last fixes

* fix docs

* fix tok tests

* fix imports code style

* fix doc
This commit is contained in:
Patrick von Platen
2021-01-05 22:00:05 +01:00
committed by GitHub
parent 4eec5d0cf6
commit eef66035a2
59 changed files with 9273 additions and 2271 deletions
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13
src/transformers/models/encoder_decoder/modeling_encoder_decoder.py
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@@ -77,10 +77,21 @@ ENCODER_DECODER_INPUTS_DOCSTRING = r"""
`What are attention masks? <../glossary.html#attention-mask>`__
decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using :class:`~transformers.PreTrainedTokenizer`. See
:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
details.
`What are input IDs? <../glossary.html#input-ids>`__
If :obj:`past_key_values` is used, optionally only the last :obj:`decoder_input_ids` have to be input (see
:obj:`past_key_values`).
Provide for sequence to sequence training to the decoder. Indices can be obtained using
:class:`~transformers.PretrainedTokenizer`. See :meth:`transformers.PreTrainedTokenizer.encode` and
:meth:`transformers.PreTrainedTokenizer.__call__` for details.
decoder_attention_mask (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`):
decoder_attention_mask (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
also be used by default.
encoder_outputs (:obj:`tuple(torch.FloatTensor)`, `optional`):