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Add BigBirdPegasus (#10991)

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* init bigbird pegasus

* add debugging nb ; update config

* init conversion

* update conversion script

* complete conversion script

* init forward()

* complete forward()

* add tokenizer

* add some slow tests

* commit current

* fix copies

* add docs

* add conversion script for bigbird-roberta-summarization

* remove TODO

* small fixups

* correct tokenizer

* add bigbird core for now

* fix config

* fix more

* revert pegasus-tokenizer back

* make style

* everything working for pubmed; yayygit status

* complete tests finally

* remove bigbird pegasus tok

* correct tokenizer

* correct tests

* add tokenizer files

* finish make style

* fix test

* update

* make style

* fix tok utils base file

* make fix-copies

* clean a bit

* small update

* fix some suggestions

* add to readme

* fix a bit, clean tests

* fix more tests

* Update src/transformers/__init__.py

* Update src/transformers/__init__.py

* make fix-copies

* complete attn switching, auto-padding left

* make style

* fix auto-padding test

* make style

* fix batched attention tests

* put tolerance at 1e-1 for stand-alone decoder test

* fix docs

* fix tests

* correct slow tokenizer conversion

* Apply suggestions from code review

Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* complete remaining suggestions

* fix test

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Vasudev Gupta
2021-05-07 12:57:43 +05:30
committed by GitHub
parent 6f40e31766
commit dc3f6758cf
22 changed files with 4592 additions and 92 deletions
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25
tests/test_generation_utils.py
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@@ -310,19 +310,18 @@ class GenerationTesterMixin:
logits_processor.append(InfNanRemoveLogitsProcessor())
with torch.no_grad():
with torch.no_grad():
output_sample = model.sample(
input_ids_clone,
attention_mask=attention_mask_clone,
max_length=max_length,
logits_processor=logits_processor,
logits_warper=logits_warper,
output_scores=output_scores,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
**kwargs,
)
output_sample = model.sample(
input_ids_clone,
attention_mask=attention_mask_clone,
max_length=max_length,
logits_processor=logits_processor,
logits_warper=logits_warper,
output_scores=output_scores,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
**kwargs,
)
return output_sample, output_generate
def _beam_search_generate(