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* init bigbird * model.__init__ working, conversion script ready, config updated * add conversion script * BigBirdEmbeddings working :) * slightly update conversion script * BigBirdAttention working :) ; some bug in layer.output.dense * add debugger-notebook * forward() working for BigBirdModel :) ; replaced gelu with gelu_fast * tf code adapted to torch till rand_attn in bigbird_block_sparse_attention ; till now everything working :) * BigBirdModel working in block-sparse attention mode :) * add BigBirdForPreTraining * small fix * add tokenizer for BigBirdModel * fix config & hence modeling * fix base prefix * init testing * init tokenizer test * pos_embed must be absolute, attn_type=original_full when add_cross_attn=True , nsp loss is optional in BigBirdForPreTraining, add assert statements * remove position_embedding_type arg * complete normal tests * add comments to block sparse attention * add attn_probs for sliding & global tokens * create fn for block sparse attn mask creation * add special tests * restore pos embed arg * minor fix * attn probs update * make big bird fully gpu friendly * fix tests * remove pruning * correct tokenzier & minor fixes * update conversion script , remove norm_type * tokenizer-inference test add * remove extra comments * add docs * save intermediate * finish trivia_qa conversion * small update to forward * correct qa and layer * better error message * BigBird QA ready * fix rebased * add triva-qa debugger notebook * qa setup * fixed till embeddings * some issue in q/k/v_layer * fix bug in conversion-script * fixed till self-attn * qa fixed except layer norm * add qa end2end test * fix gradient ckpting ; other qa test * speed-up big bird a bit * hub_id=google * clean up * make quality * speed up einsum with bmm * finish perf improvements for big bird * remove wav2vec2 tok * fix tokenizer * include docs * correct docs * add helper to auto pad block size * make style * remove fast tokenizer for now * fix some * add pad test * finish * fix some bugs * fix another bug * fix buffer tokens * fix comment and merge from master * add comments * make style * commit some suggestions Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Fix typos * fix some more suggestions * add another patch Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * fix copies * another path Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * update * update nit suggestions * make style Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
129 lines
5.9 KiB
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129 lines
5.9 KiB
ReStructuredText
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Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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BigBird
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-----------------------------------------------------------------------------------------------------------------------
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The BigBird model was proposed in `Big Bird: Transformers for Longer Sequences <https://arxiv.org/abs/2007.14062>`__ by
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Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon,
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Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a sparse-attention
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based transformer which extends Transformer based models, such as BERT to much longer sequences. In addition to sparse
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attention, BigBird also applies global attention as well as random attention to the input sequence. Theoretically, it
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has been shown that applying sparse, global, and random attention approximates full attention, while being
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computationally much more efficient for longer sequences. As a consequence of the capability to handle longer context,
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BigBird has shown improved performance on various long document NLP tasks, such as question answering and
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summarization, compared to BERT or RoBERTa.
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The abstract from the paper is the following:
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*Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP.
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Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence
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length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that
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reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and
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is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our
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theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire
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sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to
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8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context,
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BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also
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propose novel applications to genomics data.*
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Tips:
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- BigBird comes with 2 implementations: **original_full** & **block_sparse**. For the sequence length < 1024, using
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**original_full** is advised as there is no benefit in using **block_sparse** attention.
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- The code currently uses window size of 3 blocks and 2 global blocks.
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- Sequence length must be divisible by block size.
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- Current implementation supports only **ITC**.
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- Current implementation doesn't support **num_random_blocks = 0**
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The original code can be found `here <https://github.com/google-research/bigbird>`__.
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BigBirdConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BigBirdConfig
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:members:
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BigBirdTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BigBirdTokenizer
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:members: build_inputs_with_special_tokens, get_special_tokens_mask,
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create_token_type_ids_from_sequences, save_vocabulary
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BigBird specific outputs
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput
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:members:
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BigBirdModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BigBirdModel
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:members: forward
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BigBirdForPreTraining
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BigBirdForPreTraining
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:members: forward
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BigBirdForCausalLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BigBirdForCausalLM
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:members: forward
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BigBirdForMaskedLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BigBirdForMaskedLM
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:members: forward
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BigBirdForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BigBirdForSequenceClassification
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:members: forward
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BigBirdForMultipleChoice
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BigBirdForMultipleChoice
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:members: forward
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BigBirdForTokenClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BigBirdForTokenClassification
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:members: forward
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BigBirdForQuestionAnswering
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BigBirdForQuestionAnswering
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:members: forward
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