1634 lines
72 KiB
Python
Executable File
1634 lines
72 KiB
Python
Executable File
# coding=utf-8
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# Copyright 2020 The Allen Institute for AI team and The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch Longformer model. """
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import math
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import warnings
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import torch
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import torch.nn as nn
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from torch.nn import CrossEntropyLoss, MSELoss
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from torch.nn import functional as F
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from .configuration_longformer import LongformerConfig
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from .file_utils import (
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_callable,
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replace_return_docstrings,
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)
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from .modeling_bert import BertIntermediate, BertLayerNorm, BertOutput, BertPooler, BertPreTrainedModel, BertSelfOutput
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from .modeling_outputs import (
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BaseModelOutput,
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BaseModelOutputWithPooling,
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MaskedLMOutput,
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MultipleChoiceModelOutput,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutput,
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TokenClassifierOutput,
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)
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from .modeling_roberta import RobertaEmbeddings, RobertaLMHead
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from .modeling_utils import (
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PreTrainedModel,
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apply_chunking_to_forward,
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find_pruneable_heads_and_indices,
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prune_linear_layer,
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)
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from .utils import logging
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "LongformerConfig"
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_TOKENIZER_FOR_DOC = "LongformerTokenizer"
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LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
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"allenai/longformer-base-4096",
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"allenai/longformer-large-4096",
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"allenai/longformer-large-4096-finetuned-triviaqa",
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"allenai/longformer-base-4096-extra.pos.embd.only",
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"allenai/longformer-large-4096-extra.pos.embd.only",
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# See all Longformer models at https://huggingface.co/models?filter=longformer
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]
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def _get_question_end_index(input_ids, sep_token_id):
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"""
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Computes the index of the first occurance of `sep_token_id`.
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"""
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sep_token_indices = (input_ids == sep_token_id).nonzero()
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batch_size = input_ids.shape[0]
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assert sep_token_indices.shape[1] == 2, "`input_ids` should have two dimensions"
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assert (
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sep_token_indices.shape[0] == 3 * batch_size
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), f"There should be exactly three separator tokens: {sep_token_id} in every sample for questions answering. You might also consider to set `global_attention_mask` manually in the forward function to avoid this error."
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return sep_token_indices.view(batch_size, 3, 2)[:, 0, 1]
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def _compute_global_attention_mask(input_ids, sep_token_id, before_sep_token=True):
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"""
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Computes global attention mask by putting attention on all tokens
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before `sep_token_id` if `before_sep_token is True` else after
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`sep_token_id`.
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"""
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question_end_index = _get_question_end_index(input_ids, sep_token_id)
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question_end_index = question_end_index.unsqueeze(dim=1) # size: batch_size x 1
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# bool attention mask with True in locations of global attention
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attention_mask = torch.arange(input_ids.shape[1], device=input_ids.device)
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if before_sep_token is True:
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attention_mask = (attention_mask.expand_as(input_ids) < question_end_index).to(torch.uint8)
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else:
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# last token is separation token and should not be counted and in the middle are two separation tokens
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attention_mask = (attention_mask.expand_as(input_ids) > (question_end_index + 1)).to(torch.uint8) * (
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attention_mask.expand_as(input_ids) < input_ids.shape[-1]
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).to(torch.uint8)
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return attention_mask
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class LongformerSelfAttention(nn.Module):
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def __init__(self, config, layer_id):
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0:
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raise ValueError(
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"The hidden size (%d) is not a multiple of the number of attention "
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"heads (%d)" % (config.hidden_size, config.num_attention_heads)
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)
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self.num_heads = config.num_attention_heads
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self.head_dim = int(config.hidden_size / config.num_attention_heads)
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self.embed_dim = config.hidden_size
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self.query = nn.Linear(config.hidden_size, self.embed_dim)
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self.key = nn.Linear(config.hidden_size, self.embed_dim)
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self.value = nn.Linear(config.hidden_size, self.embed_dim)
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# separate projection layers for tokens with global attention
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self.query_global = nn.Linear(config.hidden_size, self.embed_dim)
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self.key_global = nn.Linear(config.hidden_size, self.embed_dim)
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self.value_global = nn.Linear(config.hidden_size, self.embed_dim)
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self.dropout = config.attention_probs_dropout_prob
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self.layer_id = layer_id
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attention_window = config.attention_window[self.layer_id]
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assert (
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attention_window % 2 == 0
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), f"`attention_window` for layer {self.layer_id} has to be an even value. Given {attention_window}"
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assert (
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attention_window > 0
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), f"`attention_window` for layer {self.layer_id} has to be positive. Given {attention_window}"
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self.one_sided_attn_window_size = attention_window // 2
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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output_attentions=False,
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):
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"""
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LongformerSelfAttention expects `len(hidden_states)` to be multiple of `attention_window`.
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Padding to `attention_window` happens in LongformerModel.forward to avoid redoing the padding on each layer.
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The `attention_mask` is changed in `BertModel.forward` from 0, 1, 2 to
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-ve: no attention
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0: local attention
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+ve: global attention
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"""
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attention_mask = attention_mask.squeeze(dim=2).squeeze(dim=1)
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# is index masked or global attention
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is_index_masked = attention_mask < 0
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is_index_global_attn = attention_mask > 0
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is_global_attn = is_index_global_attn.flatten().any().item()
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hidden_states = hidden_states.transpose(0, 1)
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# project hidden states
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query_vectors = self.query(hidden_states)
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key_vectors = self.key(hidden_states)
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value_vectors = self.value(hidden_states)
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seq_len, batch_size, embed_dim = hidden_states.size()
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assert (
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embed_dim == self.embed_dim
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), f"hidden_states should have embed_dim = {self.embed_dim}, but has {embed_dim}"
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# normalize query
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query_vectors /= math.sqrt(self.head_dim)
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query_vectors = query_vectors.view(seq_len, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
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key_vectors = key_vectors.view(seq_len, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
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# attn_probs = (batch_size, seq_len, num_heads, window*2+1)
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attn_scores = self._sliding_chunks_query_key_matmul(
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query_vectors, key_vectors, self.one_sided_attn_window_size
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)
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# values to pad for attention probs
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remove_from_windowed_attention_mask = (attention_mask != 0)[:, :, None, None]
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# cast to fp32/fp16 then replace 1's with -inf
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float_mask = remove_from_windowed_attention_mask.type_as(query_vectors).masked_fill(
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remove_from_windowed_attention_mask, -10000.0
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)
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# diagonal mask with zeros everywhere and -inf inplace of padding
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diagonal_mask = self._sliding_chunks_query_key_matmul(
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float_mask.new_ones(size=float_mask.size()), float_mask, self.one_sided_attn_window_size
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)
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# pad local attention probs
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attn_scores += diagonal_mask
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assert list(attn_scores.size()) == [
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batch_size,
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seq_len,
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self.num_heads,
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self.one_sided_attn_window_size * 2 + 1,
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], f"attn_probs should be of size ({batch_size}, {seq_len}, {self.num_heads}, {self.one_sided_attn_window_size * 2 + 1}), but is of size {attn_scores.size()}"
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# compute local attention probs from global attention keys and contact over window dim
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if is_global_attn:
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# compute global attn indices required through out forward fn
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(
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max_num_global_attn_indices,
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is_index_global_attn_nonzero,
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is_local_index_global_attn_nonzero,
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is_local_index_no_global_attn_nonzero,
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) = self._get_global_attn_indices(is_index_global_attn)
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# calculate global attn probs from global key
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global_key_attn_scores = self._concat_with_global_key_attn_probs(
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query_vectors=query_vectors,
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key_vectors=key_vectors,
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max_num_global_attn_indices=max_num_global_attn_indices,
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is_index_global_attn_nonzero=is_index_global_attn_nonzero,
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is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
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is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero,
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)
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# concat to attn_probs
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# (batch_size, seq_len, num_heads, extra attention count + 2*window+1)
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attn_scores = torch.cat((global_key_attn_scores, attn_scores), dim=-1)
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# free memory
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del global_key_attn_scores
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attn_probs_fp32 = F.softmax(attn_scores, dim=-1, dtype=torch.float32) # use fp32 for numerical stability
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attn_probs = attn_probs_fp32.type_as(attn_scores)
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# free memory
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del attn_probs_fp32
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# softmax sometimes inserts NaN if all positions are masked, replace them with 0
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attn_probs = torch.masked_fill(attn_probs, is_index_masked[:, :, None, None], 0.0)
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# apply dropout
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attn_probs = F.dropout(attn_probs, p=self.dropout, training=self.training)
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value_vectors = value_vectors.view(seq_len, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
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# compute local attention output with global attention value and add
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if is_global_attn:
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# compute sum of global and local attn
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attn_output = self._compute_attn_output_with_global_indices(
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value_vectors=value_vectors,
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attn_probs=attn_probs,
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max_num_global_attn_indices=max_num_global_attn_indices,
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is_index_global_attn_nonzero=is_index_global_attn_nonzero,
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is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
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)
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else:
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# compute local attn only
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attn_output = self._sliding_chunks_matmul_attn_probs_value(
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attn_probs, value_vectors, self.one_sided_attn_window_size
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)
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assert attn_output.size() == (batch_size, seq_len, self.num_heads, self.head_dim), "Unexpected size"
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attn_output = attn_output.transpose(0, 1).reshape(seq_len, batch_size, embed_dim).contiguous()
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# compute value for global attention and overwrite to attention output
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# TODO: remove the redundant computation
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if is_global_attn:
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global_attn_output = self._compute_global_attn_output_from_hidden(
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hidden_states=hidden_states,
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max_num_global_attn_indices=max_num_global_attn_indices,
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is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
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is_index_global_attn_nonzero=is_index_global_attn_nonzero,
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is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero,
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is_index_masked=is_index_masked,
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)
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# get only non zero global attn output
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nonzero_global_attn_output = global_attn_output[
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is_local_index_global_attn_nonzero[0], :, is_local_index_global_attn_nonzero[1]
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]
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# overwrite values with global attention
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attn_output[is_index_global_attn_nonzero[::-1]] = nonzero_global_attn_output.view(
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len(is_local_index_global_attn_nonzero[0]), -1
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)
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attn_output = attn_output.transpose(0, 1)
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if output_attentions:
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if is_global_attn:
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# With global attention, return global attention probabilities only
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# batch_size x num_heads x max_num_global_attention_tokens x sequence_length
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# which is the attention weights from tokens with global attention to all tokens
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# It doesn't not return local attention
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# In case of variable number of global attantion in the rows of a batch,
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# attn_probs are padded with -10000.0 attention scores
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attn_probs = attn_probs.view(batch_size, self.num_heads, max_num_global_attn_indices, seq_len)
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else:
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# without global attention, return local attention probabilities
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# batch_size x num_heads x sequence_length x window_size
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# which is the attention weights of every token attending to its neighbours
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attn_probs = attn_probs.permute(0, 2, 1, 3)
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outputs = (attn_output, attn_probs) if output_attentions else (attn_output,)
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return outputs
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@staticmethod
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def _pad_and_transpose_last_two_dims(hidden_states_padded, padding):
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"""pads rows and then flips rows and columns"""
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hidden_states_padded = F.pad(
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hidden_states_padded, padding
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) # padding value is not important because it will be overwritten
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hidden_states_padded = hidden_states_padded.view(
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*hidden_states_padded.size()[:-2], hidden_states_padded.size(-1), hidden_states_padded.size(-2)
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)
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return hidden_states_padded
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@staticmethod
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def _pad_and_diagonalize(chunked_hidden_states):
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"""shift every row 1 step right, converting columns into diagonals.
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Example:
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chunked_hidden_states: [ 0.4983, 2.6918, -0.0071, 1.0492,
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-1.8348, 0.7672, 0.2986, 0.0285,
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-0.7584, 0.4206, -0.0405, 0.1599,
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2.0514, -1.1600, 0.5372, 0.2629 ]
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window_overlap = num_rows = 4
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(pad & diagonilize) =>
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[ 0.4983, 2.6918, -0.0071, 1.0492, 0.0000, 0.0000, 0.0000
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0.0000, -1.8348, 0.7672, 0.2986, 0.0285, 0.0000, 0.0000
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0.0000, 0.0000, -0.7584, 0.4206, -0.0405, 0.1599, 0.0000
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0.0000, 0.0000, 0.0000, 2.0514, -1.1600, 0.5372, 0.2629 ]
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"""
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total_num_heads, num_chunks, window_overlap, hidden_dim = chunked_hidden_states.size()
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chunked_hidden_states = F.pad(
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chunked_hidden_states, (0, window_overlap + 1)
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) # total_num_heads x num_chunks x window_overlap x (hidden_dim+window_overlap+1). Padding value is not important because it'll be overwritten
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chunked_hidden_states = chunked_hidden_states.view(
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total_num_heads, num_chunks, -1
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) # total_num_heads x num_chunks x window_overlapL+window_overlapwindow_overlap+window_overlap
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chunked_hidden_states = chunked_hidden_states[
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:, :, :-window_overlap
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] # total_num_heads x num_chunks x window_overlapL+window_overlapwindow_overlap
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chunked_hidden_states = chunked_hidden_states.view(
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total_num_heads, num_chunks, window_overlap, window_overlap + hidden_dim
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) # total_num_heads x num_chunks, window_overlap x hidden_dim+window_overlap
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chunked_hidden_states = chunked_hidden_states[:, :, :, :-1]
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return chunked_hidden_states
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@staticmethod
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def _chunk(hidden_states, window_overlap):
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"""convert into overlapping chunkings. Chunk size = 2w, overlap size = w"""
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# non-overlapping chunks of size = 2w
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hidden_states = hidden_states.view(
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hidden_states.size(0),
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hidden_states.size(1) // (window_overlap * 2),
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window_overlap * 2,
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hidden_states.size(2),
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)
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# use `as_strided` to make the chunks overlap with an overlap size = window_overlap
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chunk_size = list(hidden_states.size())
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chunk_size[1] = chunk_size[1] * 2 - 1
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chunk_stride = list(hidden_states.stride())
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chunk_stride[1] = chunk_stride[1] // 2
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return hidden_states.as_strided(size=chunk_size, stride=chunk_stride)
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@staticmethod
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def _mask_invalid_locations(input_tensor, affected_seq_len) -> torch.Tensor:
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beginning_mask_2d = input_tensor.new_ones(affected_seq_len, affected_seq_len + 1).tril().flip(dims=[0])
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beginning_mask = beginning_mask_2d[None, :, None, :]
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ending_mask = beginning_mask.flip(dims=(1, 3))
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beginning_input = input_tensor[:, :affected_seq_len, :, : affected_seq_len + 1]
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beginning_mask = beginning_mask.expand(beginning_input.size())
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beginning_input.masked_fill_(beginning_mask == 1, -float("inf")) # `== 1` converts to bool or uint8
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ending_input = input_tensor[:, -affected_seq_len:, :, -(affected_seq_len + 1) :]
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ending_mask = ending_mask.expand(ending_input.size())
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ending_input.masked_fill_(ending_mask == 1, -float("inf")) # `== 1` converts to bool or uint8
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def _sliding_chunks_query_key_matmul(self, query: torch.Tensor, key: torch.Tensor, window_overlap: int):
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"""Matrix multiplication of query and key tensors using with a sliding window attention pattern.
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This implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained Longformer)
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with an overlap of size window_overlap"""
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batch_size, seq_len, num_heads, head_dim = query.size()
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assert (
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seq_len % (window_overlap * 2) == 0
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), f"Sequence length should be multiple of {window_overlap * 2}. Given {seq_len}"
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assert query.size() == key.size()
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chunks_count = seq_len // window_overlap - 1
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# group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size window_overlap * 2
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query = query.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
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key = key.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
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chunked_query = self._chunk(query, window_overlap)
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chunked_key = self._chunk(key, window_overlap)
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# matrix multipication
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# bcxd: batch_size * num_heads x chunks x 2window_overlap x head_dim
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# bcyd: batch_size * num_heads x chunks x 2window_overlap x head_dim
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# bcxy: batch_size * num_heads x chunks x 2window_overlap x window_overlap
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|
chunked_attention_scores = torch.einsum("bcxd,bcyd->bcxy", (chunked_query, chunked_key)) # multiply
|
|
|
|
# convert diagonals into columns
|
|
diagonal_chunked_attention_scores = self._pad_and_transpose_last_two_dims(
|
|
chunked_attention_scores, padding=(0, 0, 0, 1)
|
|
)
|
|
|
|
# allocate space for the overall attention matrix where the chunks are combined. The last dimension
|
|
# has (window_overlap * 2 + 1) columns. The first (window_overlap) columns are the window_overlap lower triangles (attention from a word to
|
|
# window_overlap previous words). The following column is attention score from each word to itself, then
|
|
# followed by window_overlap columns for the upper triangle.
|
|
|
|
diagonal_attention_scores = diagonal_chunked_attention_scores.new_empty(
|
|
(batch_size * num_heads, chunks_count + 1, window_overlap, window_overlap * 2 + 1)
|
|
)
|
|
|
|
# copy parts from diagonal_chunked_attention_scores into the combined matrix of attentions
|
|
# - copying the main diagonal and the upper triangle
|
|
diagonal_attention_scores[:, :-1, :, window_overlap:] = diagonal_chunked_attention_scores[
|
|
:, :, :window_overlap, : window_overlap + 1
|
|
]
|
|
diagonal_attention_scores[:, -1, :, window_overlap:] = diagonal_chunked_attention_scores[
|
|
:, -1, window_overlap:, : window_overlap + 1
|
|
]
|
|
# - copying the lower triangle
|
|
diagonal_attention_scores[:, 1:, :, :window_overlap] = diagonal_chunked_attention_scores[
|
|
:, :, -(window_overlap + 1) : -1, window_overlap + 1 :
|
|
]
|
|
|
|
diagonal_attention_scores[:, 0, 1:window_overlap, 1:window_overlap] = diagonal_chunked_attention_scores[
|
|
:, 0, : window_overlap - 1, 1 - window_overlap :
|
|
]
|
|
|
|
# separate batch_size and num_heads dimensions again
|
|
diagonal_attention_scores = diagonal_attention_scores.view(
|
|
batch_size, num_heads, seq_len, 2 * window_overlap + 1
|
|
).transpose(2, 1)
|
|
|
|
self._mask_invalid_locations(diagonal_attention_scores, window_overlap)
|
|
return diagonal_attention_scores
|
|
|
|
def _sliding_chunks_matmul_attn_probs_value(
|
|
self, attn_probs: torch.Tensor, value: torch.Tensor, window_overlap: int
|
|
):
|
|
"""Same as _sliding_chunks_query_key_matmul but for attn_probs and value tensors.
|
|
Returned tensor will be of the same shape as `attn_probs`"""
|
|
batch_size, seq_len, num_heads, head_dim = value.size()
|
|
|
|
assert seq_len % (window_overlap * 2) == 0
|
|
assert attn_probs.size()[:3] == value.size()[:3]
|
|
assert attn_probs.size(3) == 2 * window_overlap + 1
|
|
chunks_count = seq_len // window_overlap - 1
|
|
# group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size 2 window overlap
|
|
|
|
chunked_attn_probs = attn_probs.transpose(1, 2).reshape(
|
|
batch_size * num_heads, seq_len // window_overlap, window_overlap, 2 * window_overlap + 1
|
|
)
|
|
|
|
# group batch_size and num_heads dimensions into one
|
|
value = value.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
|
|
|
|
# pad seq_len with w at the beginning of the sequence and another window overlap at the end
|
|
padded_value = F.pad(value, (0, 0, window_overlap, window_overlap), value=-1)
|
|
|
|
# chunk padded_value into chunks of size 3 window overlap and an overlap of size window overlap
|
|
chunked_value_size = (batch_size * num_heads, chunks_count + 1, 3 * window_overlap, head_dim)
|
|
chunked_value_stride = padded_value.stride()
|
|
chunked_value_stride = (
|
|
chunked_value_stride[0],
|
|
window_overlap * chunked_value_stride[1],
|
|
chunked_value_stride[1],
|
|
chunked_value_stride[2],
|
|
)
|
|
chunked_value = padded_value.as_strided(size=chunked_value_size, stride=chunked_value_stride)
|
|
|
|
chunked_attn_probs = self._pad_and_diagonalize(chunked_attn_probs)
|
|
|
|
context = torch.einsum("bcwd,bcdh->bcwh", (chunked_attn_probs, chunked_value))
|
|
return context.view(batch_size, num_heads, seq_len, head_dim).transpose(1, 2)
|
|
|
|
@staticmethod
|
|
def _get_global_attn_indices(is_index_global_attn):
|
|
""" compute global attn indices required throughout forward pass """
|
|
# helper variable
|
|
num_global_attn_indices = is_index_global_attn.long().sum(dim=1)
|
|
|
|
# max number of global attn indices in batch
|
|
max_num_global_attn_indices = num_global_attn_indices.max()
|
|
|
|
# indices of global attn
|
|
is_index_global_attn_nonzero = is_index_global_attn.nonzero(as_tuple=True)
|
|
|
|
# helper variable
|
|
is_local_index_global_attn = torch.arange(
|
|
max_num_global_attn_indices, device=is_index_global_attn.device
|
|
) < num_global_attn_indices.unsqueeze(dim=-1)
|
|
|
|
# location of the non-padding values within global attention indices
|
|
is_local_index_global_attn_nonzero = is_local_index_global_attn.nonzero(as_tuple=True)
|
|
|
|
# location of the padding values within global attention indices
|
|
is_local_index_no_global_attn_nonzero = (is_local_index_global_attn == 0).nonzero(as_tuple=True)
|
|
return (
|
|
max_num_global_attn_indices,
|
|
is_index_global_attn_nonzero,
|
|
is_local_index_global_attn_nonzero,
|
|
is_local_index_no_global_attn_nonzero,
|
|
)
|
|
|
|
def _concat_with_global_key_attn_probs(
|
|
self,
|
|
key_vectors,
|
|
query_vectors,
|
|
max_num_global_attn_indices,
|
|
is_index_global_attn_nonzero,
|
|
is_local_index_global_attn_nonzero,
|
|
is_local_index_no_global_attn_nonzero,
|
|
):
|
|
batch_size = key_vectors.shape[0]
|
|
|
|
# create only global key vectors
|
|
key_vectors_only_global = key_vectors.new_zeros(
|
|
batch_size, max_num_global_attn_indices, self.num_heads, self.head_dim
|
|
)
|
|
|
|
key_vectors_only_global[is_local_index_global_attn_nonzero] = key_vectors[is_index_global_attn_nonzero]
|
|
|
|
# (batch_size, seq_len, num_heads, max_num_global_attn_indices)
|
|
attn_probs_from_global_key = torch.einsum("blhd,bshd->blhs", (query_vectors, key_vectors_only_global))
|
|
|
|
attn_probs_from_global_key[
|
|
is_local_index_no_global_attn_nonzero[0], :, :, is_local_index_no_global_attn_nonzero[1]
|
|
] = -10000.0
|
|
|
|
return attn_probs_from_global_key
|
|
|
|
def _compute_attn_output_with_global_indices(
|
|
self,
|
|
value_vectors,
|
|
attn_probs,
|
|
max_num_global_attn_indices,
|
|
is_index_global_attn_nonzero,
|
|
is_local_index_global_attn_nonzero,
|
|
):
|
|
batch_size = attn_probs.shape[0]
|
|
|
|
# cut local attn probs to global only
|
|
attn_probs_only_global = attn_probs.narrow(-1, 0, max_num_global_attn_indices)
|
|
# get value vectors for global only
|
|
value_vectors_only_global = value_vectors.new_zeros(
|
|
batch_size, max_num_global_attn_indices, self.num_heads, self.head_dim
|
|
)
|
|
value_vectors_only_global[is_local_index_global_attn_nonzero] = value_vectors[is_index_global_attn_nonzero]
|
|
|
|
# use `matmul` because `einsum` crashes sometimes with fp16
|
|
# attn = torch.einsum('blhs,bshd->blhd', (selected_attn_probs, selected_v))
|
|
# compute attn output only global
|
|
attn_output_only_global = torch.matmul(
|
|
attn_probs_only_global.transpose(1, 2), value_vectors_only_global.transpose(1, 2)
|
|
).transpose(1, 2)
|
|
|
|
# reshape attn probs
|
|
attn_probs_without_global = attn_probs.narrow(
|
|
-1, max_num_global_attn_indices, attn_probs.size(-1) - max_num_global_attn_indices
|
|
).contiguous()
|
|
|
|
# compute attn output with global
|
|
attn_output_without_global = self._sliding_chunks_matmul_attn_probs_value(
|
|
attn_probs_without_global, value_vectors, self.one_sided_attn_window_size
|
|
)
|
|
return attn_output_only_global + attn_output_without_global
|
|
|
|
def _compute_global_attn_output_from_hidden(
|
|
self,
|
|
hidden_states,
|
|
max_num_global_attn_indices,
|
|
is_local_index_global_attn_nonzero,
|
|
is_index_global_attn_nonzero,
|
|
is_local_index_no_global_attn_nonzero,
|
|
is_index_masked,
|
|
):
|
|
seq_len, batch_size = hidden_states.shape[:2]
|
|
|
|
# prepare global hidden states
|
|
global_attn_hidden_states = hidden_states.new_zeros(max_num_global_attn_indices, batch_size, self.embed_dim)
|
|
global_attn_hidden_states[is_local_index_global_attn_nonzero[::-1]] = hidden_states[
|
|
is_index_global_attn_nonzero[::-1]
|
|
]
|
|
|
|
# global key, query, value
|
|
global_query_vectors_only_global = self.query_global(global_attn_hidden_states)
|
|
global_key_vectors = self.key_global(hidden_states)
|
|
global_value_vectors = self.value_global(hidden_states)
|
|
|
|
# normalize
|
|
global_query_vectors_only_global /= math.sqrt(self.head_dim)
|
|
|
|
# reshape
|
|
global_query_vectors_only_global = (
|
|
global_query_vectors_only_global.contiguous()
|
|
.view(max_num_global_attn_indices, batch_size * self.num_heads, self.head_dim)
|
|
.transpose(0, 1)
|
|
) # (batch_size * self.num_heads, max_num_global_attn_indices, head_dim)
|
|
global_key_vectors = (
|
|
global_key_vectors.contiguous().view(-1, batch_size * self.num_heads, self.head_dim).transpose(0, 1)
|
|
) # batch_size * self.num_heads, seq_len, head_dim)
|
|
global_value_vectors = (
|
|
global_value_vectors.contiguous().view(-1, batch_size * self.num_heads, self.head_dim).transpose(0, 1)
|
|
) # batch_size * self.num_heads, seq_len, head_dim)
|
|
|
|
# compute attn scores
|
|
global_attn_scores = torch.bmm(global_query_vectors_only_global, global_key_vectors.transpose(1, 2))
|
|
|
|
assert list(global_attn_scores.size()) == [
|
|
batch_size * self.num_heads,
|
|
max_num_global_attn_indices,
|
|
seq_len,
|
|
], f"global_attn_scores have the wrong size. Size should be {(batch_size * self.num_heads, max_num_global_attn_indices, seq_len)}, but is {global_attn_scores.size()}."
|
|
|
|
global_attn_scores = global_attn_scores.view(batch_size, self.num_heads, max_num_global_attn_indices, seq_len)
|
|
|
|
global_attn_scores[
|
|
is_local_index_no_global_attn_nonzero[0], :, is_local_index_no_global_attn_nonzero[1], :
|
|
] = -10000.0
|
|
|
|
global_attn_scores = global_attn_scores.masked_fill(
|
|
is_index_masked[:, None, None, :],
|
|
-10000.0,
|
|
)
|
|
|
|
global_attn_scores = global_attn_scores.view(batch_size * self.num_heads, max_num_global_attn_indices, seq_len)
|
|
|
|
# compute global attn probs
|
|
global_attn_probs_float = F.softmax(
|
|
global_attn_scores, dim=-1, dtype=torch.float32
|
|
) # use fp32 for numerical stability
|
|
|
|
global_attn_probs = F.dropout(
|
|
global_attn_probs_float.type_as(global_attn_scores), p=self.dropout, training=self.training
|
|
)
|
|
|
|
# global attn output
|
|
global_attn_output = torch.bmm(global_attn_probs, global_value_vectors)
|
|
|
|
assert list(global_attn_output.size()) == [
|
|
batch_size * self.num_heads,
|
|
max_num_global_attn_indices,
|
|
self.head_dim,
|
|
], f"global_attn_output tensor has the wrong size. Size should be {(batch_size * self.num_heads, max_num_global_attn_indices, self.head_dim)}, but is {global_attn_output.size()}."
|
|
|
|
global_attn_output = global_attn_output.view(
|
|
batch_size, self.num_heads, max_num_global_attn_indices, self.head_dim
|
|
)
|
|
return global_attn_output
|
|
|
|
|
|
class LongformerAttention(nn.Module):
|
|
def __init__(self, config, layer_id=0):
|
|
super().__init__()
|
|
self.self = LongformerSelfAttention(config, layer_id)
|
|
self.output = BertSelfOutput(config)
|
|
self.pruned_heads = set()
|
|
|
|
def prune_heads(self, heads):
|
|
if len(heads) == 0:
|
|
return
|
|
heads, index = find_pruneable_heads_and_indices(
|
|
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
|
|
)
|
|
|
|
# Prune linear layers
|
|
self.self.query = prune_linear_layer(self.self.query, index)
|
|
self.self.key = prune_linear_layer(self.self.key, index)
|
|
self.self.value = prune_linear_layer(self.self.value, index)
|
|
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
|
|
|
|
# Update hyper params and store pruned heads
|
|
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
|
|
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
output_attentions=False,
|
|
):
|
|
self_outputs = self.self(
|
|
hidden_states,
|
|
attention_mask,
|
|
output_attentions,
|
|
)
|
|
attn_output = self.output(self_outputs[0], hidden_states)
|
|
outputs = (attn_output,) + self_outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
class LongformerLayer(nn.Module):
|
|
def __init__(self, config, layer_id=0):
|
|
super().__init__()
|
|
self.attention = LongformerAttention(config, layer_id)
|
|
self.intermediate = BertIntermediate(config)
|
|
self.output = BertOutput(config)
|
|
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.seq_len_dim = 1
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
output_attentions=False,
|
|
):
|
|
self_attn_outputs = self.attention(
|
|
hidden_states,
|
|
attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
attn_output = self_attn_outputs[0]
|
|
outputs = self_attn_outputs[1:] # add self attentions if we output attention weights
|
|
|
|
layer_output = apply_chunking_to_forward(
|
|
self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attn_output
|
|
)
|
|
outputs = (layer_output,) + outputs
|
|
return outputs
|
|
|
|
def ff_chunk(self, attn_output):
|
|
intermediate_output = self.intermediate(attn_output)
|
|
layer_output = self.output(intermediate_output, attn_output)
|
|
return layer_output
|
|
|
|
|
|
class LongformerEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([LongformerLayer(config, layer_id=i) for i in range(config.num_hidden_layers)])
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
output_attentions=False,
|
|
output_hidden_states=False,
|
|
return_dict=False,
|
|
):
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_attentions = () if output_attentions else None
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if getattr(self.config, "gradient_checkpointing", False):
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
return module(*inputs, output_attentions)
|
|
|
|
return custom_forward
|
|
|
|
layer_outputs = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(layer_module),
|
|
hidden_states,
|
|
attention_mask,
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(
|
|
hidden_states,
|
|
attention_mask,
|
|
output_attentions,
|
|
)
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_attentions = all_attentions + (layer_outputs[1],)
|
|
|
|
# Add last layer
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
|
|
)
|
|
|
|
|
|
class LongformerPreTrainedModel(PreTrainedModel):
|
|
"""An abstract class to handle weights initialization and
|
|
a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = LongformerConfig
|
|
base_model_prefix = "longformer"
|
|
|
|
def _init_weights(self, module):
|
|
""" Initialize the weights """
|
|
if isinstance(module, (nn.Linear, nn.Embedding)):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
elif isinstance(module, BertLayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
if isinstance(module, nn.Linear) and module.bias is not None:
|
|
module.bias.data.zero_()
|
|
|
|
|
|
LONGFORMER_START_DOCSTRING = r"""
|
|
This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__ sub-class.
|
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
|
|
usage and behavior.
|
|
|
|
Parameters:
|
|
config (:class:`~transformers.LongformerConfig`): Model configuration class with all the parameters of the
|
|
model. Initializing with a config file does not load the weights associated with the model, only the configuration.
|
|
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
|
|
"""
|
|
|
|
LONGFORMER_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using :class:`transformers.LonmgformerTokenizer`.
|
|
See :func:`transformers.PreTrainedTokenizer.encode` and
|
|
:func:`transformers.PreTrainedTokenizer.__call__` for details.
|
|
|
|
`What are input IDs? <../glossary.html#input-ids>`__
|
|
attention_mask (:obj:`torch.FloatTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
|
|
Mask to avoid performing attention on padding token indices.
|
|
Mask values selected in ``[0, 1]``:
|
|
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
|
|
|
|
`What are attention masks? <../glossary.html#attention-mask>`__
|
|
|
|
global_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
|
|
Mask to decide the attention given on each token, local attention or global attenion.
|
|
Tokens with global attention attends to all other tokens, and all other tokens attend to them. This is important for
|
|
task-specific finetuning because it makes the model more flexible at representing the task. For example,
|
|
for classification, the <s> token should be given global attention. For QA, all question tokens should also have
|
|
global attention. Please refer to the `Longformer paper <https://arxiv.org/abs/2004.05150>`__ for more details.
|
|
Mask values selected in ``[0, 1]``:
|
|
``0`` for local attention (a sliding window attention),
|
|
``1`` for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
|
|
|
|
token_type_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
|
|
Segment token indices to indicate first and second portions of the inputs.
|
|
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
|
|
corresponds to a `sentence B` token
|
|
|
|
`What are token type IDs? <../glossary.html#token-type-ids>`_
|
|
position_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`, defaults to :obj:`None`):
|
|
Indices of positions of each input sequence tokens in the position embeddings.
|
|
Selected in the range ``[0, config.max_position_embeddings - 1]``.
|
|
|
|
`What are position IDs? <../glossary.html#position-ids>`_
|
|
inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
|
|
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
|
|
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
|
than the model's internal embedding lookup matrix.
|
|
output_attentions (:obj:`bool`, `optional`, defaults to :obj:`None`):
|
|
If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail.
|
|
output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`None`):
|
|
If set to ``True``, the hidden states of all layers are returned. See ``hidden_states`` under returned tensors for more detail.
|
|
return_dict (:obj:`bool`, `optional`, defaults to :obj:`None`):
|
|
If set to ``True``, the model will return a :class:`~transformers.file_utils.ModelOutput` instead of a
|
|
plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare Longformer Model outputting raw hidden-states without any specific head on top.",
|
|
LONGFORMER_START_DOCSTRING,
|
|
)
|
|
class LongformerModel(LongformerPreTrainedModel):
|
|
"""
|
|
This class copied code from :class:`~transformers.RobertaModel` and overwrote standard self-attention with longformer self-attention to provide the ability to process
|
|
long sequences following the self-attention approach described in `Longformer: the Long-Document Transformer
|
|
<https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, and Arman Cohan. Longformer self-attention
|
|
combines a local (sliding window) and global attention to extend to long documents without the O(n^2) increase in
|
|
memory and compute.
|
|
|
|
The self-attention module `LongformerSelfAttention` implemented here supports the combination of local and
|
|
global attention but it lacks support for autoregressive attention and dilated attention. Autoregressive
|
|
and dilated attention are more relevant for autoregressive language modeling than finetuning on downstream
|
|
tasks. Future release will add support for autoregressive attention, but the support for dilated attention
|
|
requires a custom CUDA kernel to be memory and compute efficient.
|
|
|
|
"""
|
|
|
|
config_class = LongformerConfig
|
|
base_model_prefix = "longformer"
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
if isinstance(config.attention_window, int):
|
|
assert config.attention_window % 2 == 0, "`config.attention_window` has to be an even value"
|
|
assert config.attention_window > 0, "`config.attention_window` has to be positive"
|
|
config.attention_window = [config.attention_window] * config.num_hidden_layers # one value per layer
|
|
else:
|
|
assert len(config.attention_window) == config.num_hidden_layers, (
|
|
"`len(config.attention_window)` should equal `config.num_hidden_layers`. "
|
|
f"Expected {config.num_hidden_layers}, given {len(config.attention_window)}"
|
|
)
|
|
|
|
self.embeddings = RobertaEmbeddings(config)
|
|
self.encoder = LongformerEncoder(config)
|
|
self.pooler = BertPooler(config)
|
|
|
|
self.init_weights()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
def _prune_heads(self, heads_to_prune):
|
|
"""Prunes heads of the model.
|
|
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
|
|
See base class PreTrainedModel
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
|
|
|
def _pad_to_window_size(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
attention_mask: torch.Tensor,
|
|
token_type_ids: torch.Tensor,
|
|
position_ids: torch.Tensor,
|
|
inputs_embeds: torch.Tensor,
|
|
pad_token_id: int,
|
|
):
|
|
"""A helper function to pad tokens and mask to work with implementation of Longformer self-attention."""
|
|
# padding
|
|
attention_window = (
|
|
self.config.attention_window
|
|
if isinstance(self.config.attention_window, int)
|
|
else max(self.config.attention_window)
|
|
)
|
|
|
|
assert attention_window % 2 == 0, f"`attention_window` should be an even value. Given {attention_window}"
|
|
input_shape = input_ids.shape if input_ids is not None else inputs_embeds.shape
|
|
batch_size, seq_len = input_shape[:2]
|
|
|
|
padding_len = (attention_window - seq_len % attention_window) % attention_window
|
|
if padding_len > 0:
|
|
logger.info(
|
|
"Input ids are automatically padded from {} to {} to be a multiple of `config.attention_window`: {}".format(
|
|
seq_len, seq_len + padding_len, attention_window
|
|
)
|
|
)
|
|
if input_ids is not None:
|
|
input_ids = F.pad(input_ids, (0, padding_len), value=pad_token_id)
|
|
if position_ids is not None:
|
|
# pad with position_id = pad_token_id as in modeling_roberta.RobertaEmbeddings
|
|
position_ids = F.pad(position_ids, (0, padding_len), value=pad_token_id)
|
|
if inputs_embeds is not None:
|
|
input_ids_padding = inputs_embeds.new_full(
|
|
(batch_size, padding_len),
|
|
self.config.pad_token_id,
|
|
dtype=torch.long,
|
|
)
|
|
inputs_embeds_padding = self.embeddings(input_ids_padding)
|
|
inputs_embeds = torch.cat([inputs_embeds, inputs_embeds_padding], dim=-2)
|
|
|
|
attention_mask = F.pad(attention_mask, (0, padding_len), value=False) # no attention on the padding tokens
|
|
token_type_ids = F.pad(token_type_ids, (0, padding_len), value=0) # pad with token_type_id = 0
|
|
|
|
return padding_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds
|
|
|
|
def _merge_to_attention_mask(self, attention_mask: torch.Tensor, global_attention_mask: torch.Tensor):
|
|
# longformer self attention expects attention mask to have 0 (no attn), 1 (local attn), 2 (global attn)
|
|
# (global_attention_mask + 1) => 1 for local attention, 2 for global attention
|
|
# => final attention_mask => 0 for no attention, 1 for local attention 2 for global attention
|
|
if attention_mask is not None:
|
|
attention_mask = attention_mask * (global_attention_mask + 1)
|
|
else:
|
|
# simply use `global_attention_mask` as `attention_mask`
|
|
# if no `attention_mask` is given
|
|
attention_mask = global_attention_mask + 1
|
|
return attention_mask
|
|
|
|
@add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
|
|
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
global_attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
r"""
|
|
|
|
Returns:
|
|
|
|
Examples::
|
|
|
|
>>> import torch
|
|
>>> from transformers import LongformerModel, LongformerTokenizer
|
|
|
|
>>> model = LongformerModel.from_pretrained('allenai/longformer-base-4096', return_dict=True)
|
|
>>> tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')
|
|
|
|
>>> SAMPLE_TEXT = ' '.join(['Hello world! '] * 1000) # long input document
|
|
>>> input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0) # batch of size 1
|
|
|
|
>>> # Attention mask values -- 0: no attention, 1: local attention, 2: global attention
|
|
>>> attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device) # initialize to local attention
|
|
>>> attention_mask[:, [1, 4, 21,]] = 2 # Set global attention based on the task. For example,
|
|
... # classification: the <s> token
|
|
... # QA: question tokens
|
|
... # LM: potentially on the beginning of sentences and paragraphs
|
|
>>> outputs = model(input_ids, attention_mask=attention_mask)
|
|
>>> sequence_output = outputs.last_hidden_state
|
|
>>> pooled_output = outputs.pooler_output
|
|
"""
|
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(input_shape, device=device)
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
# merge `global_attention_mask` and `attention_mask`
|
|
if global_attention_mask is not None:
|
|
attention_mask = self._merge_to_attention_mask(attention_mask, global_attention_mask)
|
|
|
|
padding_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds = self._pad_to_window_size(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
pad_token_id=self.config.pad_token_id,
|
|
)
|
|
|
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
|
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
|
|
)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask=extended_attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output)
|
|
|
|
# undo padding
|
|
if padding_len > 0:
|
|
# unpad `sequence_output` because the calling function is expecting a length == input_ids.size(1)
|
|
sequence_output = sequence_output[:, :-padding_len]
|
|
|
|
if not return_dict:
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings("""Longformer Model with a `language modeling` head on top. """, LONGFORMER_START_DOCSTRING)
|
|
class LongformerForMaskedLM(LongformerPreTrainedModel):
|
|
config_class = LongformerConfig
|
|
base_model_prefix = "longformer"
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.longformer = LongformerModel(config)
|
|
self.lm_head = RobertaLMHead(config)
|
|
|
|
self.init_weights()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head.decoder
|
|
|
|
@add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
|
|
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
global_attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
**kwargs
|
|
):
|
|
r"""
|
|
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
|
|
Labels for computing the masked language modeling loss.
|
|
Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
|
|
Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
|
|
in ``[0, ..., config.vocab_size]``
|
|
kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
|
|
Used to hide legacy arguments that have been deprecated.
|
|
|
|
Returns:
|
|
|
|
Examples::
|
|
|
|
>>> import torch
|
|
>>> from transformers import LongformerForMaskedLM, LongformerTokenizer
|
|
|
|
>>> model = LongformerForMaskedLM.from_pretrained('allenai/longformer-base-4096', return_dict=True)
|
|
>>> tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')
|
|
|
|
>>> SAMPLE_TEXT = ' '.join(['Hello world! '] * 1000) # long input document
|
|
>>> input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0) # batch of size 1
|
|
|
|
>>> attention_mask = None # default is local attention everywhere, which is a good choice for MaskedLM
|
|
... # check ``LongformerModel.forward`` for more details how to set `attention_mask`
|
|
>>> outputs = model(input_ids, attention_mask=attention_mask, labels=input_ids)
|
|
>>> loss = outputs.loss
|
|
>>> prediction_logits = output.logits
|
|
"""
|
|
|
|
if "masked_lm_labels" in kwargs:
|
|
warnings.warn(
|
|
"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
|
|
FutureWarning,
|
|
)
|
|
labels = kwargs.pop("masked_lm_labels")
|
|
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.longformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
global_attention_mask=global_attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.lm_head(sequence_output)
|
|
|
|
masked_lm_loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (prediction_scores,) + outputs[2:]
|
|
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
|
|
|
|
return MaskedLMOutput(
|
|
loss=masked_lm_loss,
|
|
logits=prediction_scores,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""Longformer Model transformer with a sequence classification/regression head on top (a linear layer
|
|
on top of the pooled output) e.g. for GLUE tasks. """,
|
|
LONGFORMER_START_DOCSTRING,
|
|
)
|
|
class LongformerForSequenceClassification(BertPreTrainedModel):
|
|
config_class = LongformerConfig
|
|
base_model_prefix = "longformer"
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.longformer = LongformerModel(config)
|
|
self.classifier = LongformerClassificationHead(config)
|
|
|
|
self.init_weights()
|
|
|
|
@add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
|
|
@add_code_sample_docstrings(
|
|
tokenizer_class=_TOKENIZER_FOR_DOC,
|
|
checkpoint="allenai/longformer-base-4096",
|
|
output_type=SequenceClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
global_attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
r"""
|
|
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
|
|
Labels for computing the sequence classification/regression loss.
|
|
Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
|
|
If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
|
|
If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if global_attention_mask is None:
|
|
logger.info("Initializing global attention on CLS token...")
|
|
global_attention_mask = torch.zeros_like(input_ids)
|
|
# global attention on cls token
|
|
global_attention_mask[:, 0] = 1
|
|
|
|
outputs = self.longformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
global_attention_mask=global_attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = outputs[0]
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.num_labels == 1:
|
|
# We are doing regression
|
|
loss_fct = MSELoss()
|
|
loss = loss_fct(logits.view(-1), labels.view(-1))
|
|
else:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class LongformerClassificationHead(nn.Module):
|
|
"""Head for sentence-level classification tasks."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
def forward(self, hidden_states, **kwargs):
|
|
hidden_states = hidden_states[:, 0, :] # take <s> token (equiv. to [CLS])
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = torch.tanh(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
output = self.out_proj(hidden_states)
|
|
return output
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""Longformer Model with a span classification head on top for extractive question-answering tasks like SQuAD / TriviaQA (a linear layers on top of
|
|
the hidden-states output to compute `span start logits` and `span end logits`). """,
|
|
LONGFORMER_START_DOCSTRING,
|
|
)
|
|
class LongformerForQuestionAnswering(BertPreTrainedModel):
|
|
config_class = LongformerConfig
|
|
base_model_prefix = "longformer"
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.longformer = LongformerModel(config)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
self.init_weights()
|
|
|
|
@add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
|
|
@replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
global_attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
start_positions=None,
|
|
end_positions=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
r"""
|
|
start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
|
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`).
|
|
Position outside of the sequence are not taken into account for computing the loss.
|
|
end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
|
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`).
|
|
Position outside of the sequence are not taken into account for computing the loss.
|
|
Returns:
|
|
|
|
Examples::
|
|
|
|
>>> from transformers import LongformerTokenizer, LongformerForQuestionAnswering
|
|
>>> import torch
|
|
|
|
>>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-large-4096-finetuned-triviaqa")
|
|
>>> model = LongformerForQuestionAnswering.from_pretrained("allenai/longformer-large-4096-finetuned-triviaqa", return_dict=True)
|
|
|
|
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
|
|
>>> encoding = tokenizer(question, text, return_tensors="pt")
|
|
>>> input_ids = encoding["input_ids"]
|
|
|
|
>>> # default is local attention everywhere
|
|
>>> # the forward method will automatically set global attention on question tokens
|
|
>>> attention_mask = encoding["attention_mask"]
|
|
|
|
>>> outputs = model(input_ids, attention_mask=attention_mask)
|
|
>>> start_logits = outputs.start_logits
|
|
>>> end_logits = outputs.end_logits
|
|
>>> all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())
|
|
|
|
>>> answer_tokens = all_tokens[torch.argmax(start_logits) :torch.argmax(end_logits)+1]
|
|
>>> answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens)) # remove space prepending space token
|
|
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if global_attention_mask is None:
|
|
if input_ids is None:
|
|
logger.warning(
|
|
"It is not possible to automatically generate the `global_attention_mask` because input_ids is None. Please make sure that it is correctly set."
|
|
)
|
|
else:
|
|
# set global attention on question tokens automatically
|
|
global_attention_mask = _compute_global_attention_mask(input_ids, self.config.sep_token_id)
|
|
|
|
outputs = self.longformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
global_attention_mask=global_attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1)
|
|
end_logits = end_logits.squeeze(-1)
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if len(start_positions.size()) > 1:
|
|
start_positions = start_positions.squeeze(-1)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1)
|
|
start_positions.clamp_(0, ignored_index)
|
|
end_positions.clamp_(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + outputs[2:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""Longformer Model with a token classification head on top (a linear layer on top of
|
|
the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
|
|
LONGFORMER_START_DOCSTRING,
|
|
)
|
|
class LongformerForTokenClassification(BertPreTrainedModel):
|
|
config_class = LongformerConfig
|
|
base_model_prefix = "longformer"
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.longformer = LongformerModel(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
self.init_weights()
|
|
|
|
@add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
|
|
@add_code_sample_docstrings(
|
|
tokenizer_class=_TOKENIZER_FOR_DOC,
|
|
checkpoint="allenai/longformer-base-4096",
|
|
output_type=TokenClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
global_attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
r"""
|
|
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
|
|
Labels for computing the token classification loss.
|
|
Indices should be in ``[0, ..., config.num_labels - 1]``.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.longformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
global_attention_mask=global_attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
# Only keep active parts of the loss
|
|
if attention_mask is not None:
|
|
active_loss = attention_mask.view(-1) == 1
|
|
active_logits = logits.view(-1, self.num_labels)
|
|
active_labels = torch.where(
|
|
active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
|
|
)
|
|
loss = loss_fct(active_logits, active_labels)
|
|
else:
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""Longformer Model with a multiple choice classification head on top (a linear layer on top of
|
|
the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
|
|
LONGFORMER_START_DOCSTRING,
|
|
)
|
|
class LongformerForMultipleChoice(BertPreTrainedModel):
|
|
config_class = LongformerConfig
|
|
base_model_prefix = "longformer"
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.longformer = LongformerModel(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, 1)
|
|
|
|
self.init_weights()
|
|
|
|
@add_start_docstrings_to_callable(LONGFORMER_INPUTS_DOCSTRING.format("(batch_size, num_choices, sequence_length)"))
|
|
@add_code_sample_docstrings(
|
|
tokenizer_class=_TOKENIZER_FOR_DOC,
|
|
checkpoint="allenai/longformer-base-4096",
|
|
output_type=MultipleChoiceModelOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
token_type_ids=None,
|
|
attention_mask=None,
|
|
global_attention_mask=None,
|
|
labels=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
r"""
|
|
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
|
|
Labels for computing the multiple choice classification loss.
|
|
Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
|
|
of the input tensors. (see `input_ids` above)
|
|
"""
|
|
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# set global attention on question tokens
|
|
if global_attention_mask is None and input_ids is not None:
|
|
logger.info("Initializing global attention on multiple choice...")
|
|
# put global attention on all tokens after `config.sep_token_id`
|
|
global_attention_mask = torch.stack(
|
|
[
|
|
_compute_global_attention_mask(input_ids[:, i], self.config.sep_token_id, before_sep_token=False)
|
|
for i in range(num_choices)
|
|
],
|
|
dim=1,
|
|
)
|
|
|
|
flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
|
|
flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
|
|
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
|
|
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
|
|
flat_global_attention_mask = (
|
|
global_attention_mask.view(-1, global_attention_mask.size(-1))
|
|
if global_attention_mask is not None
|
|
else None
|
|
)
|
|
flat_inputs_embeds = (
|
|
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
|
|
if inputs_embeds is not None
|
|
else None
|
|
)
|
|
|
|
outputs = self.longformer(
|
|
flat_input_ids,
|
|
position_ids=flat_position_ids,
|
|
token_type_ids=flat_token_type_ids,
|
|
attention_mask=flat_attention_mask,
|
|
global_attention_mask=flat_global_attention_mask,
|
|
inputs_embeds=flat_inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
pooled_output = outputs[1]
|
|
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
reshaped_logits = logits.view(-1, num_choices)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(reshaped_logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (reshaped_logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return MultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|