# coding=utf-8 # Copyright 2021 Google Research The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch BigBirdPegasus model.""" import copy import math from typing import List, Optional, Tuple, Union import numpy as np import torch from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput, Seq2SeqQuestionAnsweringModelOutput, Seq2SeqSequenceClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_bigbird_pegasus import BigBirdPegasusConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "google/bigbird-pegasus-large-arxiv" _CONFIG_FOR_DOC = "BigBirdPegasusConfig" _EXPECTED_OUTPUT_SHAPE = [1, 7, 1024] BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/bigbird-pegasus-large-arxiv", "google/bigbird-pegasus-large-pubmed", "google/bigbird-pegasus-large-bigpatent", # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus ] def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): """ Shift input ids one token to the right. """ shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() shifted_input_ids[:, 0] = decoder_start_token_id if pad_token_id is None: raise ValueError("self.model.config.pad_token_id has to be defined.") # replace possible -100 values in labels by `pad_token_id` shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) return shifted_input_ids # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask( input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 ): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) mask_cond = torch.arange(mask.size(-1), device=device) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) if past_key_values_length > 0: mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min) class BigBirdPegasusLearnedPositionalEmbedding(nn.Embedding): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, num_embeddings: int, embedding_dim: int): super().__init__(num_embeddings, embedding_dim) def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0): """`input_ids_shape` is expected to be [bsz x seqlen].""" bsz, seq_len = input_ids_shape[:2] positions = torch.arange( past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device ) return super().forward(positions) # Copied from transformers.models.big_bird.modeling_big_bird.BigBirdSelfAttention with BigBird->BigBirdPegasus class BigBirdPegasusSelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.is_decoder = config.is_decoder def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): mixed_query_layer = self.query(hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in BigBirdPegasusModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs # Copied from transformers.models.big_bird.modeling_big_bird.BigBirdBlockSparseAttention with BigBird->BigBirdPegasus class BigBirdPegasusBlockSparseAttention(nn.Module): def __init__(self, config, seed=None): super().__init__() self.max_seqlen = config.max_position_embeddings self.seed = seed if config.hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size {config.hidden_size} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.num_random_blocks = config.num_random_blocks self.block_size = config.block_size self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, band_mask=None, from_mask=None, to_mask=None, from_blocked_mask=None, to_blocked_mask=None, output_attentions=None, ): # Currently this `class` can't be used in decoder. batch_size, seqlen, _ = hidden_states.size() to_seq_length = from_seq_length = seqlen from_block_size = to_block_size = self.block_size if from_seq_length % from_block_size != 0: raise ValueError("Query sided sequence length must be multiple of block size") if to_seq_length % to_block_size != 0: raise ValueError("Key/Value sided sequence length must be multiple of block size") query_layer = self.transpose_for_scores(self.query(hidden_states)) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) context_layer, attention_probs = self.bigbird_block_sparse_attention( query_layer, key_layer, value_layer, band_mask, from_mask, to_mask, from_blocked_mask, to_blocked_mask, self.num_attention_heads, self.num_random_blocks, self.attention_head_size, from_block_size, to_block_size, batch_size, from_seq_length, to_seq_length, seed=self.seed, plan_from_length=None, plan_num_rand_blocks=None, output_attentions=output_attentions, ) context_layer = context_layer.contiguous().view(batch_size, from_seq_length, -1) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs @staticmethod def torch_bmm_nd(inp_1, inp_2, ndim=None): """Fast nd matrix multiplication""" # faster replacement of torch.einsum ("bhqk,bhkd->bhqd") return torch.bmm(inp_1.reshape((-1,) + inp_1.shape[-2:]), inp_2.reshape((-1,) + inp_2.shape[-2:])).view( inp_1.shape[: ndim - 2] + (inp_1.shape[ndim - 2], inp_2.shape[ndim - 1]) ) @staticmethod def torch_bmm_nd_transpose(inp_1, inp_2, ndim=None): """Fast nd matrix multiplication with transpose""" # faster replacement of torch.einsum (bhqd,bhkd->bhqk) return torch.bmm( inp_1.reshape((-1,) + inp_1.shape[-2:]), inp_2.reshape((-1,) + inp_2.shape[-2:]).transpose(1, 2) ).view(inp_1.shape[: ndim - 2] + (inp_1.shape[ndim - 2], inp_2.shape[ndim - 2])) def bigbird_block_sparse_attention( self, query_layer, key_layer, value_layer, band_mask, from_mask, to_mask, from_blocked_mask, to_blocked_mask, n_heads, n_rand_blocks, attention_head_size, from_block_size, to_block_size, batch_size, from_seq_len, to_seq_len, seed, plan_from_length, plan_num_rand_blocks, output_attentions, ): # BigBirdPegasus block-sparse attention as suggested in paper # ITC: # global tokens: 2 x block_size # window tokens: 3 x block_size # random tokens: num_rand_tokens x block_size # ETC: # global tokens: extra_globals_tokens + 2 x block_size # window tokens: 3 x block_size # random tokens: num_rand_tokens x block_size # Note: # 1) Currently, ETC is not supported. # 2) Window size is fixed to 3 blocks & it can be changed only by # changing `block_size`. # 3) Number of global blocks are fixed (2 blocks here) & global tokens can be # controlled only by `block_size`. # attention is calculated separately for q[0], q[1], q[2:-2], q[-2], q[-1] in order to use special trick of shifting tokens (for calculating sliding attention) # hence following code can be divided into 5 parts. if from_seq_len // from_block_size != to_seq_len // to_block_size: raise ValueError("Error the number of blocks needs to be same!") rsqrt_d = 1 / math.sqrt(attention_head_size) bsz = batch_size attn_mask_penalty = -10000.0 # generate random attention and corresponding masks np.random.seed(seed) if from_seq_len in [1024, 3072, 4096]: # old plans used in paper rand_attn = [ self._bigbird_block_rand_mask( self.max_seqlen, self.max_seqlen, from_block_size, to_block_size, n_rand_blocks, last_idx=1024 )[: (from_seq_len // from_block_size - 2)] for _ in range(n_heads) ] else: if plan_from_length is None: plan_from_length, plan_num_rand_blocks = self._get_rand_attn_plan( from_seq_len, from_block_size, n_rand_blocks ) rand_attn = self._bigbird_block_rand_mask_with_head( from_seq_length=from_seq_len, to_seq_length=to_seq_len, from_block_size=from_block_size, to_block_size=to_block_size, num_heads=n_heads, plan_from_length=plan_from_length, plan_num_rand_blocks=plan_num_rand_blocks, ) rand_attn = np.stack(rand_attn, axis=0) rand_attn = torch.tensor(rand_attn, device=query_layer.device, dtype=torch.long) rand_attn.unsqueeze_(0) rand_attn = torch.cat([rand_attn for _ in range(batch_size)], dim=0) rand_mask = self._create_rand_mask_from_inputs( from_blocked_mask, to_blocked_mask, rand_attn, n_heads, n_rand_blocks, bsz, from_seq_len, from_block_size ) blocked_query_matrix = query_layer.view(bsz, n_heads, from_seq_len // from_block_size, from_block_size, -1) blocked_key_matrix = key_layer.view(bsz, n_heads, to_seq_len // to_block_size, to_block_size, -1) blocked_value_matrix = value_layer.view(bsz, n_heads, to_seq_len // to_block_size, to_block_size, -1) # preparing block for randn attn gathered_key = self.torch_gather_b2(blocked_key_matrix, rand_attn) gathered_key = gathered_key.view( bsz, n_heads, to_seq_len // to_block_size - 2, n_rand_blocks * to_block_size, -1 ) # [bsz, n_heads, to_seq_len//to_block_size-2, n_rand_blocks, to_block_size, -1] gathered_value = self.torch_gather_b2(blocked_value_matrix, rand_attn) gathered_value = gathered_value.view( bsz, n_heads, to_seq_len // to_block_size - 2, n_rand_blocks * to_block_size, -1 ) # [bsz, n_heads, to_seq_len//to_block_size-2, n_rand_blocks, to_block_size, -1] # 1st PART # 1st block (global block) attention scores # q[0] x (k[0], k[1], k[2], k[3], k[4] .... ) # [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, to_seq_len] first_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, 0], key_layer, ndim=4) first_product = first_product * rsqrt_d first_product += (1.0 - to_mask) * attn_mask_penalty first_attn_weights = nn.functional.softmax( first_product, dim=-1 ) # [bsz, n_heads, from_block_size, to_seq_len] # [bsz, n_heads, from_block_size, to_seq_len] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, -1] first_context_layer = self.torch_bmm_nd(first_attn_weights, value_layer, ndim=4) first_context_layer.unsqueeze_(2) # 2nd PART # 2nd block attention scores # q[1] x (sliding_keys, random_keys, global_keys) # sliding key blocks -> 2nd, 3rd blocks # global key blocks -> 1st block second_key_mat = torch.cat( [ blocked_key_matrix[:, :, 0], blocked_key_matrix[:, :, 1], blocked_key_matrix[:, :, 2], blocked_key_matrix[:, :, -1], gathered_key[:, :, 0], ], dim=2, ) # [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] second_value_mat = torch.cat( [ blocked_value_matrix[:, :, 0], blocked_value_matrix[:, :, 1], blocked_value_matrix[:, :, 2], blocked_value_matrix[:, :, -1], gathered_value[:, :, 0], ], dim=2, ) # [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] # [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size] second_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, 1], second_key_mat, ndim=4) second_seq_pad = torch.cat( [ to_mask[:, :, :, : 3 * to_block_size], to_mask[:, :, :, -to_block_size:], to_mask.new_ones([bsz, 1, 1, n_rand_blocks * to_block_size]), ], dim=3, ) second_rand_pad = torch.cat( [ rand_mask.new_ones([bsz, n_heads, from_block_size, 4 * to_block_size]), rand_mask[:, :, 0], ], dim=3, ) second_product = second_product * rsqrt_d second_product += (1.0 - torch.minimum(second_seq_pad, second_rand_pad)) * attn_mask_penalty second_attn_weights = nn.functional.softmax( second_product, dim=-1 ) # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size] # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, -1] second_context_layer = self.torch_bmm_nd(second_attn_weights, second_value_mat, ndim=4) second_context_layer.unsqueeze_(2) # 3rd PART # Middle blocks attention scores # q[-2:2] x (sliding_keys, random_keys, global_keys) # sliding attn is calculated using special trick of shifting tokens as discussed in paper # random keys are generated by taking random indices as per `rand_attn` # global keys -> 1st & last block exp_blocked_key_matrix = torch.cat( [blocked_key_matrix[:, :, 1:-3], blocked_key_matrix[:, :, 2:-2], blocked_key_matrix[:, :, 3:-1]], dim=3 ) # [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1] exp_blocked_value_matrix = torch.cat( [blocked_value_matrix[:, :, 1:-3], blocked_value_matrix[:, :, 2:-2], blocked_value_matrix[:, :, 3:-1]], dim=3, ) # [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1] middle_query_matrix = blocked_query_matrix[:, :, 2:-2] # sliding attention scores for q[-2:2] # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [b, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1] inner_band_product = self.torch_bmm_nd_transpose(middle_query_matrix, exp_blocked_key_matrix, ndim=5) # ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, 3*to_block_size] inner_band_product = inner_band_product * rsqrt_d # randn attention scores for q[-2:2] # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, from_seq_len//from_block_size-4, n_rand_blocks*to_block_size, -1] rand_band_product = self.torch_bmm_nd_transpose(middle_query_matrix, gathered_key[:, :, 1:-1], ndim=5) # ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, n_rand_blocks*to_block_size] rand_band_product = rand_band_product * rsqrt_d # Including 1st block (since it's global) first_band_product = torch.einsum( "bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, 0] ) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size] first_band_product = first_band_product * rsqrt_d # Including last block (since it's global) last_band_product = torch.einsum( "bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, -1] ) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size] last_band_product = last_band_product * rsqrt_d # masking padded tokens inner_band_product += (1.0 - band_mask) * attn_mask_penalty first_band_product += (1.0 - to_mask[:, :, :, :to_block_size].unsqueeze(3)) * attn_mask_penalty last_band_product += (1.0 - to_mask[:, :, :, -to_block_size:].unsqueeze(3)) * attn_mask_penalty rand_band_product += (1.0 - rand_mask[:, :, 1:-1]) * attn_mask_penalty # completing attention scores matrix for all q[-2:2] band_product = torch.cat( [first_band_product, inner_band_product, rand_band_product, last_band_product], dim=-1 ) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, (5+n_rand_blocks)*to_block_size] # safely doing softmax since attention matrix is completed attn_weights = nn.functional.softmax( band_product, dim=-1 ) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, (5+n_rand_blocks)*to_block_size] # contribution of sliding keys # [bsz, n_heads, m//from_block_size-4, from_block_size, 3*to_block_size] x [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1] context_layer = self.torch_bmm_nd( attn_weights[:, :, :, :, to_block_size : 4 * to_block_size], exp_blocked_value_matrix, ndim=5 ) # ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] # adding contribution of random keys # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, n_rand_blocks*to_block_size] x [bsz, n_heads, from_seq_len//from_block_size-4, n_rand_blocks*to_block_size, -1] context_layer += self.torch_bmm_nd( attn_weights[:, :, :, :, 4 * to_block_size : -to_block_size], gathered_value[:, :, 1:-1], ndim=5 ) # ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] # adding contribution of global keys context_layer += torch.einsum( "bhlqk,bhkd->bhlqd", attn_weights[:, :, :, :, :to_block_size], blocked_value_matrix[:, :, 0] ) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] context_layer += torch.einsum( "bhlqk,bhkd->bhlqd", attn_weights[:, :, :, :, -to_block_size:], blocked_value_matrix[:, :, -1] ) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] # 4th PART # last 2nd token attention scores # q[-2] x (sliding_keys, random_keys, global_keys) # sliding key blocks -> last 3 blocks # global key block -> 1st block # random key block -> based on indices stored in `randn_attn` second_last_key_mat = torch.cat( [ blocked_key_matrix[:, :, 0], blocked_key_matrix[:, :, -3], blocked_key_matrix[:, :, -2], blocked_key_matrix[:, :, -1], gathered_key[:, :, -1], ], dim=2, ) # [bsz, n_heads, (4+n_random_blocks)*to_block_size, -1] second_last_value_mat = torch.cat( [ blocked_value_matrix[:, :, 0], blocked_value_matrix[:, :, -3], blocked_value_matrix[:, :, -2], blocked_value_matrix[:, :, -1], gathered_value[:, :, -1], ], dim=2, ) # [bsz, n_heads, (4+r)*to_block_size, -1] # [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size] second_last_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, -2], second_last_key_mat, ndim=4) second_last_seq_pad = torch.cat( [ to_mask[:, :, :, :to_block_size], to_mask[:, :, :, -3 * to_block_size :], to_mask.new_ones([bsz, 1, 1, n_rand_blocks * to_block_size]), ], dim=3, ) second_last_rand_pad = torch.cat( [ rand_mask.new_ones([bsz, n_heads, from_block_size, 4 * to_block_size]), rand_mask[:, :, -1], ], dim=3, ) second_last_product = second_last_product * rsqrt_d second_last_product += (1.0 - torch.minimum(second_last_seq_pad, second_last_rand_pad)) * attn_mask_penalty second_last_attn_weights = nn.functional.softmax( second_last_product, dim=-1 ) # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size] # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, -1] second_last_context_layer = self.torch_bmm_nd(second_last_attn_weights, second_last_value_mat, ndim=4) second_last_context_layer.unsqueeze_(2) # 5th PART # last block (global) attention scores # q[-1] x (k[0], k[1], k[2], k[3], .... ) # [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, to_seq_len] last_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, -1], key_layer, ndim=4) last_product = last_product * rsqrt_d last_product += (1.0 - to_mask) * attn_mask_penalty last_attn_weights = nn.functional.softmax(last_product, dim=-1) # [bsz, n_heads, from_block_size, n] # [bsz, n_heads, from_block_size, to_seq_len] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, -1] last_context_layer = self.torch_bmm_nd(last_attn_weights, value_layer, ndim=4) last_context_layer.unsqueeze_(2) # combining representations of all tokens context_layer = torch.cat( [first_context_layer, second_context_layer, context_layer, second_last_context_layer, last_context_layer], dim=2, ) context_layer = context_layer.view((bsz, n_heads, from_seq_len, -1)) * from_mask context_layer = torch.transpose(context_layer, 1, 2) # this is just for visualizing; forward pass doesn't depend on following code if output_attentions: # TODO(PVP): need to verify if below code is correct attention_probs = torch.zeros( bsz, n_heads, from_seq_len, to_seq_len, dtype=torch.float, device=context_layer.device ) # 1st query block # corresponding to `first_context_layer` attention_probs[:, :, :from_block_size, :] = first_attn_weights # all keys global # 2nd query block # corresponding to `second_context_layer` attention_probs[:, :, from_block_size : 2 * from_block_size, : 3 * to_block_size] = second_attn_weights[ :, :, :, : 3 * to_block_size ] # 1st three key blocks (global + sliding) attention_probs[:, :, from_block_size : 2 * from_block_size, -to_block_size:] = second_attn_weights[ :, :, :, 3 * to_block_size : 4 * to_block_size ] # last key block (global) # random keys for p1, i1, w1 in zip(range(bsz), rand_attn, second_attn_weights): # p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch for p2, i2, w2 in zip(range(n_heads), i1, w1): # p2, i2, w2 corresponds to head_dim i.e. following operation is done for each heads attn_probs_view = attention_probs.view( bsz, n_heads, from_seq_len // from_block_size, from_block_size, to_seq_len // to_block_size, to_block_size, ) right_slice = w2[:, 4 * to_block_size :] attn_probs_view[p1, p2, 1, :, i2[0]] = right_slice.view( from_block_size, n_rand_blocks, to_block_size ) # Middle query blocks # corresponding to `context_layer` # sliding keys for q_idx in range(from_seq_len // from_block_size - 4): attn_probs_view = attention_probs.view( bsz, n_heads, from_seq_len // from_block_size, from_block_size, to_seq_len // to_block_size, to_block_size, )[:, :, 2:-2, :, 1:-1, :] right_slice = attn_weights[:, :, q_idx, :, to_block_size : 4 * to_block_size] attn_probs_view[:, :, q_idx, :, q_idx : q_idx + 3, :] = right_slice.view( bsz, n_heads, from_block_size, 3, to_block_size ) # inner_band_product # global keys (corresponding to 1st key block) attention_probs[:, :, 2 * from_block_size : -2 * from_block_size, :to_block_size] = attn_weights[ :, :, :, :, :to_block_size ].view( bsz, n_heads, -1, to_block_size ) # first_band_product # global keys (corresponding to last key block) attention_probs[:, :, 2 * from_block_size : -2 * from_block_size, -to_block_size:] = attn_weights[ :, :, :, :, -to_block_size: ].view( bsz, n_heads, -1, to_block_size ) # last_band_product # random keys for p1, i1, w1 in zip(range(bsz), rand_attn, attn_weights): # p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch for p2, i2, w2 in zip(range(n_heads), i1, w1): # p2, i2, w2 corresponds to head_dim i.e. following operation is done for each heads for q_idx in range(1, len(i2) - 1): attn_probs_view = attention_probs.view( bsz, n_heads, from_seq_len // from_block_size, from_block_size, to_seq_len // to_block_size, to_block_size, ) right_slice = w2[q_idx - 1, :, 4 * to_block_size : -to_block_size] attn_probs_view[p1, p2, q_idx + 1, :, i2[q_idx]] = right_slice.view( from_block_size, n_rand_blocks, to_block_size ) # Second-last query block # corresponding to `second_last_context_layer` attention_probs[:, :, -2 * from_block_size : -from_block_size, :to_block_size] = second_last_attn_weights[ :, :, :, :to_block_size ] # 1st key block (global) attention_probs[ :, :, -2 * from_block_size : -from_block_size, -3 * to_block_size : ] = second_last_attn_weights[ :, :, :, to_block_size : 4 * to_block_size ] # last three blocks (global + sliding) # random keys for p1, i1, w1 in zip(range(bsz), rand_attn, second_last_attn_weights): # p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch for p2, i2, w2 in zip(range(n_heads), i1, w1): # p2, i2, w2 corresponds to head_dim i.e. following operation is done for each heads attn_probs_view = attention_probs.view( bsz, n_heads, from_seq_len // from_block_size, from_block_size, to_seq_len // to_block_size, to_block_size, ) right_slice = w2[:, 4 * to_block_size :] attn_probs_view[p1, p2, -2, :, i2[-1]] = right_slice.view( from_block_size, n_rand_blocks, to_block_size ) # last query block # corresponding to `last_context_layer` attention_probs[:, :, -from_block_size:, :] = last_attn_weights # all keys global else: attention_probs = None return context_layer, attention_probs @staticmethod def torch_gather_b2(params, indices): # this operation is equivalent to tf.gather when batch_dims=2 if params.shape[:2] != indices.shape[:2]: raise ValueError( "Make sure that the first two dimensions of params and indices are identical, but" f" they are params: {params.shape[:2]} vs. indices: {indices.shape[:2]}" ) num_indices_to_gather = indices.shape[-2] * indices.shape[-1] num_indices_to_pick_from = params.shape[2] shift = torch.arange(indices.shape[0] * indices.shape[1] * num_indices_to_gather, device=indices.device) indices_shift = torch.div(shift, num_indices_to_gather, rounding_mode="floor") * num_indices_to_pick_from flattened_indices = indices.view(-1) + indices_shift flattened_params = params.reshape(-1, params.shape[-2], params.shape[-1]) out_flattened = flattened_params.index_select(0, flattened_indices) out = out_flattened.reshape(params.shape[:2] + (num_indices_to_gather,) + params.shape[3:]) return out @staticmethod def _create_rand_mask_from_inputs( from_blocked_mask, to_blocked_mask, rand_attn, num_attention_heads, num_rand_blocks, batch_size, from_seq_length, from_block_size, ): """ Create 3D attention mask from a 2D tensor mask. Args: from_blocked_mask: 2D Tensor of shape [batch_size, from_seq_length//from_block_size, from_block_size]. to_blocked_mask: int32 Tensor of shape [batch_size, to_seq_length//to_block_size, to_block_size]. rand_attn: [batch_size, num_attention_heads, from_seq_length//from_block_size-2, num_rand_blocks] num_attention_heads: int. Number of attention heads. num_rand_blocks: int. Number of random chunks per row. batch_size: int. Batch size for computation. from_seq_length: int. length of from sequence. from_block_size: int. size of block in from sequence. Returns: float Tensor of shape [batch_size, num_attention_heads, from_seq_length//from_block_size-2, from_block_size, num_rand_blocks*to_block_size]. """ num_windows = from_seq_length // from_block_size - 2 rand_mask = torch.stack([p1[i1.flatten()] for p1, i1 in zip(to_blocked_mask, rand_attn)]) rand_mask = rand_mask.view(batch_size, num_attention_heads, num_windows, num_rand_blocks * from_block_size) rand_mask = torch.einsum("blq,bhlk->bhlqk", from_blocked_mask[:, 1:-1], rand_mask) return rand_mask @staticmethod def _get_rand_attn_plan(from_seq_length, from_block_size, num_rand_blocks): """ Gives the plan of where to put random attention. Args: from_seq_length: int. length of from sequence. from_block_size: int. size of block in from sequence. num_rand_blocks: int. Number of random chunks per row. Returns: plan_from_length: ending location of from block plan_num_rand_blocks: number of random ending location for each block """ plan_from_length = [] plan_num_rand_blocks = [] if (2 * num_rand_blocks + 5) < (from_seq_length // from_block_size): plan_from_length.append(int((2 * num_rand_blocks + 5) * from_block_size)) plan_num_rand_blocks.append(num_rand_blocks) plan_from_length.append(from_seq_length) plan_num_rand_blocks.append(0) elif (num_rand_blocks + 5) < (from_seq_length // from_block_size): plan_from_length.append(int((num_rand_blocks + 5) * from_block_size)) plan_num_rand_blocks.append(num_rand_blocks // 2) plan_from_length.append(from_seq_length) plan_num_rand_blocks.append(num_rand_blocks - (num_rand_blocks // 2)) else: plan_from_length.append(from_seq_length) plan_num_rand_blocks.append(num_rand_blocks) return plan_from_length, plan_num_rand_blocks def _bigbird_block_rand_mask( self, from_seq_length, to_seq_length, from_block_size, to_block_size, num_rand_blocks, last_idx=-1 ): """ Create adjacency list of random attention. Args: from_seq_length: int. length of from sequence. to_seq_length: int. length of to sequence. from_block_size: int. size of block in from sequence. to_block_size: int. size of block in to sequence. num_rand_blocks: int. Number of random chunks per row. last_idx: if -1 then num_rand_blocks blocks chosen anywhere in to sequence, if positive then num_rand_blocks blocks chosen only up to last_idx. Returns: adjacency list of size from_seq_length//from_block_size-2 by num_rand_blocks """ # using this method when from_seq_length in [1024, 3072, 4096] if from_seq_length // from_block_size != to_seq_length // to_block_size: raise ValueError("Error the number of blocks needs to be same!") rand_attn = np.zeros((from_seq_length // from_block_size - 2, num_rand_blocks), dtype=np.int32) # During inference (eval) no randomness if not self.training: return rand_attn middle_seq = np.arange(1, to_seq_length // to_block_size - 1, dtype=np.int32) last = to_seq_length // to_block_size - 1 if last_idx > (2 * to_block_size): last = (last_idx // to_block_size) - 1 r = num_rand_blocks # shorthand for i in range(1, from_seq_length // from_block_size - 1): start = i - 2 end = i if i == 1: rand_attn[i - 1, :] = np.random.permutation(middle_seq[2:last])[:r] elif i == 2: rand_attn[i - 1, :] = np.random.permutation(middle_seq[3:last])[:r] elif i == from_seq_length // from_block_size - 3: rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r] # Missing -3: should have been sliced till last-3 elif i == from_seq_length // from_block_size - 2: rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r] # Missing -4: should have been sliced till last-4 else: if start > last: start = last rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[:r] elif (end + 1) == last: rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[:r] else: rand_attn[i - 1, :] = np.random.permutation( np.concatenate((middle_seq[:start], middle_seq[end + 1 : last])) )[:r] return rand_attn def _bigbird_block_rand_mask_with_head( self, from_seq_length, to_seq_length, from_block_size, to_block_size, num_heads, plan_from_length, plan_num_rand_blocks, window_block_left=1, window_block_right=1, global_block_top=1, global_block_bottom=1, global_block_left=1, global_block_right=1, ): """ Create adjacency list of random attention. Args: from_seq_length: int. length of from sequence. to_seq_length: int. length of to sequence. from_block_size: int. size of block in from sequence. to_block_size: int. size of block in to sequence. num_heads: int. total number of heads. plan_from_length: list. plan from length where num_random_blocks are chosen from. plan_num_rand_blocks: list. number of rand blocks within the plan. window_block_left: int. number of blocks of window to left of a block. window_block_right: int. number of blocks of window to right of a block. global_block_top: int. number of blocks at the top. global_block_bottom: int. number of blocks at the bottom. global_block_left: int. Number of blocks globally used to the left. global_block_right: int. Number of blocks globally used to the right. Returns: adjacency list of size num_head where each element is of size from_seq_length//from_block_size-2 by num_rand_blocks """ # using this method when from_seq_length not in [1024, 3072, 4096] if from_seq_length // from_block_size != to_seq_length // to_block_size: raise ValueError("Error the number of blocks needs to be same!") if from_seq_length not in plan_from_length: raise ValueError("Error from sequence length not in plan!") # Total number of blocks in the mmask num_blocks = from_seq_length // from_block_size # Number of blocks per plan plan_block_length = np.array(plan_from_length) // from_block_size # till when to follow plan max_plan_idx = plan_from_length.index(from_seq_length) # Random Attention adjacency list rand_attn = [ np.zeros((num_blocks, np.sum(plan_num_rand_blocks[: max_plan_idx + 1])), dtype=np.int32) for i in range(num_heads) ] # During inference (eval) no randomness if not self.training: for nh in range(num_heads): rand_attn[nh] = rand_attn[nh][global_block_top : num_blocks - global_block_bottom, :] return rand_attn # We will go iteratively over the plan blocks and pick random number of # Attention blocks from the legally allowed blocks for plan_idx in range(max_plan_idx + 1): rnd_r_cnt = 0 if plan_idx > 0: # set the row for all from_blocks starting from 0 to # plan_block_length[plan_idx-1] # column indx start fromm plan_block_length[plan_idx-1] and ends at # plan_block_length[plan_idx] if plan_num_rand_blocks[plan_idx] > 0: rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx])) curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1])) for blk_rw_idx in range(global_block_top, plan_block_length[plan_idx - 1]): for h in range(num_heads): rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention( block_id=blk_rw_idx, to_start_block_id=plan_block_length[plan_idx - 1], to_end_block_id=plan_block_length[plan_idx], num_rand_blocks=plan_num_rand_blocks[plan_idx], window_block_left=window_block_left, window_block_right=window_block_right, global_block_left=global_block_left, global_block_right=global_block_right, ) for pl_id in range(plan_idx): if plan_num_rand_blocks[pl_id] == 0: continue for blk_rw_idx in range(plan_block_length[plan_idx - 1], plan_block_length[plan_idx]): rnd_r_cnt = 0 to_start_block_id = 0 if pl_id > 0: rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:pl_id])) to_start_block_id = plan_block_length[pl_id - 1] curr_r_cnt = int(np.sum(plan_num_rand_blocks[: pl_id + 1])) for h in range(num_heads): rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention( block_id=blk_rw_idx, to_start_block_id=to_start_block_id, to_end_block_id=plan_block_length[pl_id], num_rand_blocks=plan_num_rand_blocks[pl_id], window_block_left=window_block_left, window_block_right=window_block_right, global_block_left=global_block_left, global_block_right=global_block_right, ) if plan_num_rand_blocks[plan_idx] == 0: continue curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1])) from_start_block_id = global_block_top to_start_block_id = 0 if plan_idx > 0: rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx])) from_start_block_id = plan_block_length[plan_idx - 1] to_start_block_id = plan_block_length[plan_idx - 1] for blk_rw_idx in range(from_start_block_id, plan_block_length[plan_idx]): for h in range(num_heads): rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention( block_id=blk_rw_idx, to_start_block_id=to_start_block_id, to_end_block_id=plan_block_length[plan_idx], num_rand_blocks=plan_num_rand_blocks[plan_idx], window_block_left=window_block_left, window_block_right=window_block_right, global_block_left=global_block_left, global_block_right=global_block_right, ) for nh in range(num_heads): rand_attn[nh] = rand_attn[nh][global_block_top : num_blocks - global_block_bottom, :] return rand_attn @staticmethod def _get_single_block_row_attention( block_id, to_start_block_id, to_end_block_id, num_rand_blocks, window_block_left=1, window_block_right=1, global_block_left=1, global_block_right=1, ): """ For a single row block get random row attention. Args: block_id: int. block id of row. to_start_block_id: int. random attention column start id. to_end_block_id: int. random attention column end id. num_rand_blocks: int. number of random blocks to be selected. window_block_left: int. number of blocks of window to left of a block. window_block_right: int. number of blocks of window to right of a block. global_block_left: int. Number of blocks globally used to the left. global_block_right: int. Number of blocks globally used to the right. Returns: row containing the random attention vector of size num_rand_blocks. """ # list of to_blocks from which to choose random attention to_block_list = np.arange(to_start_block_id, to_end_block_id, dtype=np.int32) # permute the blocks perm_block = np.random.permutation(to_block_list) # illegal blocks for the current block id, using window illegal_blocks = list(range(block_id - window_block_left, block_id + window_block_right + 1)) # Add blocks at the start and at the end illegal_blocks.extend(list(range(global_block_left))) illegal_blocks.extend(list(range(to_end_block_id - global_block_right, to_end_block_id))) # The second from_block cannot choose random attention on second last to_block if block_id == 1: illegal_blocks.append(to_end_block_id - 2) # The second last from_block cannot choose random attention on second to_block if block_id == to_end_block_id - 2: illegal_blocks.append(1) selected_random_blokcs = [] for i in range(to_end_block_id - to_start_block_id): if perm_block[i] not in illegal_blocks: selected_random_blokcs.append(perm_block[i]) if len(selected_random_blokcs) == num_rand_blocks: break return np.array(selected_random_blokcs, dtype=np.int32) class BigBirdPegasusEncoderAttention(nn.Module): def __init__(self, config, seed=None): super().__init__() self.config = config self.seed = seed self.attention_type = config.attention_type if self.attention_type == "original_full": self.self = BigBirdPegasusSelfAttention(config) elif self.attention_type == "block_sparse": self.self = BigBirdPegasusBlockSparseAttention(config, seed) else: raise ValueError( f"attention_type can either be original_full or block_sparse, but is {self.config.attention_type}" ) self.output = nn.Linear(config.hidden_size, config.hidden_size, bias=config.use_bias) def set_attention_type(self, value: str): if value not in ["original_full", "block_sparse"]: raise ValueError( f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}" ) # attention type is already correctly set if value == self.attention_type: return self.attention_type = value if value == "original_full": # copy all weights to new full attention class attn_weights = BigBirdPegasusSelfAttention(self.config) else: # copy all weights to new sparse attention class attn_weights = BigBirdPegasusBlockSparseAttention(self.config, self.seed) attn_weights.query = self.self.query attn_weights.value = self.self.value attn_weights.key = self.self.key self.self = attn_weights self.attention_type = value if not self.training: self.self.eval() def forward( self, hidden_states, attention_mask=None, head_mask=None, past_key_value=None, output_attentions=False, band_mask=None, from_mask=None, to_mask=None, from_blocked_mask=None, to_blocked_mask=None, ): # Expand dims to enable multiplication in the self-attention module head_mask = head_mask.reshape(1, -1, 1, 1) if head_mask is not None else None if self.config.attention_type == "original_full": self_outputs = self.self( hidden_states, attention_mask, head_mask, past_key_value=past_key_value, output_attentions=output_attentions, ) else: self_outputs = self.self( hidden_states, band_mask, from_mask, to_mask, from_blocked_mask, to_blocked_mask, output_attentions ) attention_output = self.output(self_outputs[0]) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->BigBirdPegasusDecoder class BigBirdPegasusDecoderAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, _ = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj # `past_key_value[0].shape[2] == key_value_states.shape[1]` # is checking that the `sequence_length` of the `past_key_value` is the same as # the provided `key_value_states` to support prefix tuning if ( is_cross_attention and past_key_value is not None and past_key_value[0].shape[2] == key_value_states.shape[1] ): # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.reshape(*proj_shape) value_states = value_states.reshape(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if layer_head_mask is not None: if layer_head_mask.size() != (self.num_heads,): raise ValueError( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" f" {layer_head_mask.size()}" ) attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to be reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned across GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped, past_key_value class BigBirdPegasusEncoderLayer(nn.Module): def __init__(self, config: BigBirdPegasusConfig, seed=None): super().__init__() self.attention_type = config.attention_type self.embed_dim = config.d_model self.self_attn = BigBirdPegasusEncoderAttention(config, seed=seed) self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, layer_head_mask: torch.Tensor, band_mask=None, from_mask=None, to_mask=None, from_blocked_mask=None, to_blocked_mask=None, output_attentions: bool = False, ): """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) self_attention_outputs = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, head_mask=layer_head_mask, output_attentions=output_attentions, band_mask=band_mask, from_mask=from_mask, to_mask=to_mask, from_blocked_mask=from_blocked_mask, to_blocked_mask=to_blocked_mask, ) hidden_states = self_attention_outputs[0] hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states if hidden_states.dtype == torch.float16 and ( torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any() ): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if output_attentions: outputs += (self_attention_outputs[1],) return outputs def set_attention_type(self, value: str): if value not in ["original_full", "block_sparse"]: raise ValueError( f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}" ) # attention type is already correctly set if value == self.attention_type: return self.attention_type = value self.self_attn.set_attention_type(value) class BigBirdPegasusDecoderLayer(nn.Module): def __init__(self, config: BigBirdPegasusConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = BigBirdPegasusDecoderAttention( embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, bias=config.use_bias, ) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.encoder_attn = BigBirdPegasusDecoderAttention( self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, bias=config.use_bias, ) self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) # Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer.forward def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, cross_attn_layer_head_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = True, ) -> torch.Tensor: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. encoder_hidden_states (`torch.FloatTensor`): cross attention input to the layer of shape `(batch, seq_len, embed_dim)` encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of size `(decoder_attention_heads,)`. past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Self Attention # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None # add present self-attn cache to positions 1,2 of present_key_value tuple hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states # Cross-Attention Block cross_attn_present_key_value = None cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states # add cross-attn to positions 3,4 of present_key_value tuple present_key_value = present_key_value + cross_attn_present_key_value # Fully Connected residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) if use_cache: outputs += (present_key_value,) return outputs # Copied from transformers.models.bart.modeling_bart.BartClassificationHead with Bart->BigBirdPegasus class BigBirdPegasusClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__( self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float, ): super().__init__() self.dense = nn.Linear(input_dim, inner_dim) self.dropout = nn.Dropout(p=pooler_dropout) self.out_proj = nn.Linear(inner_dim, num_classes) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dropout(hidden_states) hidden_states = self.dense(hidden_states) hidden_states = torch.tanh(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.out_proj(hidden_states) return hidden_states class BigBirdPegasusPreTrainedModel(PreTrainedModel): config_class = BigBirdPegasusConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["BigBirdPegasusEncoderLayer", "BigBirdPegasusDecoderLayer"] _skip_keys_device_placement = "past_key_values" def _init_weights(self, module): std = self.config.init_std if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (BigBirdPegasusDecoder, BigBirdPegasusEncoder)): module.gradient_checkpointing = value @property def dummy_inputs(self): pad_token = self.config.pad_token_id input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device) dummy_inputs = { "attention_mask": input_ids.ne(pad_token), "input_ids": input_ids, } return dummy_inputs BIGBIRD_PEGASUS_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`BigBirdPegasusConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ BIGBIRD_PEGASUS_GENERATION_EXAMPLE = r""" Summarization example: ```python >>> from transformers import AutoTokenizer, BigBirdPegasusForConditionalGeneration >>> model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-arxiv") >>> tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv") >>> ARTICLE_TO_SUMMARIZE = ( ... "The dominant sequence transduction models are based on complex recurrent or convolutional neural " ... "networks in an encoder-decoder configuration. The best performing models also connect the encoder " ... "and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, " ... "based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. " ... "Experiments on two machine translation tasks show these models to be superior in quality " ... "while being more parallelizable and requiring significantly less time to train." ... ) >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=4096, return_tensors="pt", truncation=True) >>> # Generate Summary >>> summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=15) >>> tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] 'dominant sequence models are based on recurrent or convolutional neural networks .' ``` """ BIGBIRD_PEGASUS_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Provide for translation and summarization training. By default, the model will create this tensor by shifting the `input_ids` to the right, following the paper. decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. If you want to change padding behavior, you should read [`modeling_bigbird_pegasus._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. decoder_head_mask (`torch.Tensor` of shape `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `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. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of `inputs_embeds`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ BIGBIRD_PEGASUS_STANDALONE_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`ProphetNetTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class BigBirdPegasusEncoder(BigBirdPegasusPreTrainedModel): """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`BigBirdPegasusEncoderLayer`]. Args: config: BigBirdPegasusConfig embed_tokens (nn.Embedding): output embedding """ def __init__(self, config: BigBirdPegasusConfig, embed_tokens: Optional[nn.Embedding] = None): super().__init__(config) self.attention_type = config.attention_type self.block_size = config.block_size self.dropout = config.dropout self.layerdrop = config.encoder_layerdrop embed_dim = config.d_model self.padding_idx = config.pad_token_id self.max_source_positions = config.max_position_embeddings self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx) if embed_tokens is not None: self.embed_tokens.weight = embed_tokens.weight self.embed_positions = BigBirdPegasusLearnedPositionalEmbedding( config.max_position_embeddings, embed_dim, ) self.layers = nn.ModuleList([BigBirdPegasusEncoderLayer(config, seed=i) for i in range(config.encoder_layers)]) self.layernorm_embedding = nn.LayerNorm(embed_dim) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `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 (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ 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 # retrieve input_ids and inputs_embeds 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: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) 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") if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale embed_pos = self.embed_positions(input_shape) hidden_states = inputs_embeds + embed_pos hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) if attention_mask is None: attention_mask = torch.ones(input_shape, device=hidden_states.device) attention_mask = attention_mask.long() # in order to use block_sparse attention, sequence_length has to be at least # bigger than all global attentions: 2 * block_size # + sliding tokens: 3 * block_size # + random tokens: 2 * num_random_blocks * block_size max_tokens_to_attend = (5 + 2 * self.config.num_random_blocks) * self.config.block_size if self.attention_type == "block_sparse" and input_shape[1] <= max_tokens_to_attend: # change attention_type from block_sparse to original_full sequence_length = input_shape[1] logger.warning( "Attention type 'block_sparse' is not possible if sequence_length: " f"{sequence_length} <= num global tokens: 2 * config.block_size " "+ min. num sliding tokens: 3 * config.block_size " "+ config.num_random_blocks * config.block_size " "+ additional buffer: config.num_random_blocks * config.block_size " f"= {max_tokens_to_attend} with config.block_size " f"= {self.config.block_size}, config.num_random_blocks " f"= {self.config.num_random_blocks}. " "Changing attention type to 'original_full'..." ) self.set_attention_type("original_full") if self.attention_type == "block_sparse": padding_len, hidden_states, attention_mask = self._pad_to_block_size(hidden_states, attention_mask) else: padding_len = 0 # expand attention_mask if self.attention_type == "original_full": # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype) blocked_encoder_mask = band_mask = from_mask = to_mask = None elif self.attention_type == "block_sparse": blocked_encoder_mask, band_mask, from_mask, to_mask = self.create_masks_for_block_sparse_attn( attention_mask, self.block_size ) attention_mask = None else: raise ValueError( f"attention_type can either be original_full or block_sparse, but is {self.attention_type}" ) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None # check if head_mask has a correct number of layers specified if desired if head_mask is not None: if head_mask.size()[0] != len(self.layers): raise ValueError( f"The head_mask should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) to_drop = False if self.training: dropout_probability = torch.rand([]) if dropout_probability < self.layerdrop: # skip the layer to_drop = True if to_drop: layer_outputs = (None, None) else: if self.gradient_checkpointing and self.training: 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(encoder_layer), hidden_states, attention_mask, (head_mask[idx] if head_mask is not None else None), band_mask, from_mask, to_mask, blocked_encoder_mask, blocked_encoder_mask, ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), band_mask=band_mask, from_mask=from_mask, to_mask=to_mask, from_blocked_mask=blocked_encoder_mask, to_blocked_mask=blocked_encoder_mask, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) hidden_states = self.layernorm_embedding(hidden_states) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if padding_len > 0: # unpad `sequence_output` because the calling function is expecting a length == input_ids.size(1) hidden_states = hidden_states[:, :-padding_len] if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) self.encoder_o = hidden_states return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) def set_attention_type(self, value: str): if value not in ["original_full", "block_sparse"]: raise ValueError( f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}" ) # attention type is already correctly set if value == self.attention_type: return self.attention_type = value for layer in self.layers: layer.set_attention_type(value) @staticmethod # Copied from transformers.models.big_bird.modeling_big_bird.BigBirdModel.create_masks_for_block_sparse_attn def create_masks_for_block_sparse_attn(attention_mask: torch.Tensor, block_size: int): batch_size, seq_length = attention_mask.size() if seq_length % block_size != 0: raise ValueError( f"Sequence length must be multiple of block size, but sequence length is {seq_length}, while block" f" size is {block_size}." ) def create_band_mask_from_inputs(from_blocked_mask, to_blocked_mask): """ Create 3D attention mask from a 2D tensor mask. Args: from_blocked_mask: 2D Tensor of shape [batch_size, from_seq_length//from_block_size, from_block_size]. to_blocked_mask: int32 Tensor of shape [batch_size, to_seq_length//to_block_size, to_block_size]. Returns: float Tensor of shape [batch_size, 1, from_seq_length//from_block_size-4, from_block_size, 3*to_block_size]. """ exp_blocked_to_pad = torch.cat( [to_blocked_mask[:, 1:-3], to_blocked_mask[:, 2:-2], to_blocked_mask[:, 3:-1]], dim=2 ) band_mask = torch.einsum("blq,blk->blqk", from_blocked_mask[:, 2:-2], exp_blocked_to_pad) band_mask.unsqueeze_(1) return band_mask blocked_encoder_mask = attention_mask.view(batch_size, seq_length // block_size, block_size) band_mask = create_band_mask_from_inputs(blocked_encoder_mask, blocked_encoder_mask) from_mask = attention_mask.view(batch_size, 1, seq_length, 1) to_mask = attention_mask.view(batch_size, 1, 1, seq_length) return blocked_encoder_mask, band_mask, from_mask, to_mask def _pad_to_block_size(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor): """A helper function to pad tokens and mask to work with implementation of BigBird block-sparse attention.""" # padding block_size = self.config.block_size batch_size, seq_len = hidden_states.shape[:2] padding_len = (block_size - seq_len % block_size) % block_size if padding_len > 0: logger.info( f"Input ids are automatically padded from {seq_len} to {seq_len + padding_len} to be a multiple of " f"`config.block_size`: {block_size}" ) pad_id = self.config.pad_token_id device = hidden_states.device input_ids_padding = torch.ones((batch_size, padding_len), dtype=torch.long, device=device) * pad_id inputs_embeds_padding = self.embed_tokens(input_ids_padding) hidden_states = torch.cat([hidden_states, inputs_embeds_padding], dim=-2) attention_mask = nn.functional.pad( attention_mask, (0, padding_len), value=0 ) # no attention on the padding tokens return padding_len, hidden_states, attention_mask class BigBirdPegasusDecoder(BigBirdPegasusPreTrainedModel): """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BigBirdPegasusDecoderLayer`] Args: config: BigBirdPegasusConfig embed_tokens (nn.Embedding): output embedding """ def __init__(self, config: BigBirdPegasusConfig, embed_tokens: Optional[nn.Embedding] = None): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.decoder_layerdrop self.padding_idx = config.pad_token_id self.max_target_positions = config.max_position_embeddings self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx) if embed_tokens is not None: self.embed_tokens.weight = embed_tokens.weight self.embed_positions = BigBirdPegasusLearnedPositionalEmbedding( config.max_position_embeddings, config.d_model, ) self.layers = nn.ModuleList([BigBirdPegasusDecoderLayer(config) for _ in range(config.decoder_layers)]) self.layernorm_embedding = nn.LayerNorm(config.d_model) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, device=inputs_embeds.device, past_key_values_length=past_key_values_length, ) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( inputs_embeds.device ) combined_attention_mask = ( expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask ) return combined_attention_mask def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in decoder to avoid performing cross-attention on hidden heads. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `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 (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ 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 ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale attention_mask = self._prepare_decoder_attention_mask( attention_mask, input_shape, inputs_embeds, past_key_values_length ) # expand encoder attention mask if encoder_hidden_states is not None and encoder_attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]) # embed positions positions = self.embed_positions(input_shape, past_key_values_length) positions = positions.to(inputs_embeds.device) hidden_states = inputs_embeds + positions hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None next_decoder_cache = () if use_cache else None # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]): if attn_mask is not None: if attn_mask.size()[0] != len(self.layers): raise ValueError( f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) if self.training: dropout_probability = torch.rand([]) if dropout_probability < self.layerdrop: continue past_key_value = past_key_values[idx] if past_key_values is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): # None for past_key_value return module(*inputs, output_attentions, use_cache) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(decoder_layer), hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, head_mask[idx] if head_mask is not None else None, cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, None, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), cross_attn_layer_head_mask=( cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None ), past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[3 if output_attentions else 1],) if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) hidden_states = self.layernorm_embedding(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None if not return_dict: return tuple( v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) @add_start_docstrings( "The bare BigBirdPegasus Model outputting raw hidden-states without any specific head on top.", BIGBIRD_PEGASUS_START_DOCSTRING, ) class BigBirdPegasusModel(BigBirdPegasusPreTrainedModel): _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"] def __init__(self, config: BigBirdPegasusConfig): super().__init__(config) padding_idx, vocab_size = config.pad_token_id, config.vocab_size self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx) self.encoder = BigBirdPegasusEncoder(config, self.shared) self.decoder = BigBirdPegasusDecoder(config, self.shared) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.shared def set_input_embeddings(self, value): self.shared = value self.encoder.embed_tokens = self.shared self.decoder.embed_tokens = self.shared def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder @add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC, expected_output=_EXPECTED_OUTPUT_SHAPE, ) # Copied from transformers.models.bart.modeling_bart.BartModel.forward with Bart->BigBirdPegasus def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, Seq2SeqModelOutput]: # different to other models, BigBirdPegasus automatically creates decoder_input_ids from # input_ids if no decoder_input_ids are provided if decoder_input_ids is None and decoder_inputs_embeds is None: if input_ids is None: raise ValueError( "If no `decoder_input_ids` or `decoder_inputs_embeds` are " "passed, `input_ids` cannot be `None`. Please pass either " "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`." ) decoder_input_ids = shift_tokens_right( input_ids, self.config.pad_token_id, self.config.decoder_start_token_id ) 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 ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if encoder_outputs is None: encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn) decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return decoder_outputs + encoder_outputs return Seq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The BigBirdPegasus Model with a language modeling head. Can be used for summarization.", BIGBIRD_PEGASUS_START_DOCSTRING, ) # Copied from transformers.models.bart.modeling_bart.BartForConditionalGeneration with Bart->BigBirdPegasus, BART->BIGBIRD_PEGASUS class BigBirdPegasusForConditionalGeneration(BigBirdPegasusPreTrainedModel): base_model_prefix = "model" _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"] _keys_to_ignore_on_load_missing = ["final_logits_bias"] def __init__(self, config: BigBirdPegasusConfig): super().__init__(config) self.model = BigBirdPegasusModel(config) self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings))) self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False) # Initialize weights and apply final processing self.post_init() def get_encoder(self): return self.model.get_encoder() def get_decoder(self): return self.model.get_decoder() def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding: new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of) self._resize_final_logits_bias(new_embeddings.weight.shape[0]) return new_embeddings def _resize_final_logits_bias(self, new_num_tokens: int) -> None: old_num_tokens = self.final_logits_bias.shape[-1] if new_num_tokens <= old_num_tokens: new_bias = self.final_logits_bias[:, :new_num_tokens] else: extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device) new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1) self.register_buffer("final_logits_bias", new_bias) def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings @add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) @add_end_docstrings(BIGBIRD_PEGASUS_GENERATION_EXAMPLE) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, Seq2SeqLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (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]`. Returns: """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: if use_cache: logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") use_cache = False if decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = shift_tokens_right( labels, self.config.pad_token_id, self.config.decoder_start_token_id ) outputs = self.model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, encoder_outputs=encoder_outputs, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) lm_logits = self.lm_head(outputs[0]) lm_logits = lm_logits + self.final_logits_bias.to(lm_logits.device) masked_lm_loss = None if labels is not None: labels = labels.to(lm_logits.device) loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (lm_logits,) + outputs[1:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return Seq2SeqLMOutput( loss=masked_lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) def prepare_inputs_for_generation( self, decoder_input_ids, past_key_values=None, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, use_cache=None, encoder_outputs=None, **kwargs, ): # cut decoder_input_ids if past_key_values is used if past_key_values is not None: decoder_input_ids = decoder_input_ids[:, -1:] return { "input_ids": None, # encoder_outputs is defined. input_ids not needed "encoder_outputs": encoder_outputs, "past_key_values": past_key_values, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache, # change this to avoid caching (presumably for debugging) } def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id) @staticmethod def _reorder_cache(past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: # cached cross_attention states don't have to be reordered -> they are always the same reordered_past += ( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2]) + layer_past[2:], ) return reordered_past @add_start_docstrings( """ BigBirdPegasus model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, BIGBIRD_PEGASUS_START_DOCSTRING, ) class BigBirdPegasusForSequenceClassification(BigBirdPegasusPreTrainedModel): _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"] def __init__(self, config: BigBirdPegasusConfig, **kwargs): super().__init__(config, **kwargs) self.model = BigBirdPegasusModel(config) self.classification_head = BigBirdPegasusClassificationHead( config.d_model, config.d_model, config.num_labels, config.classifier_dropout, ) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=Seq2SeqSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) # Copied from transformers.models.bart.modeling_bart.BartForSequenceClassification.forward def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, Seq2SeqSequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `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 labels is not None: use_cache = False if input_ids is None and inputs_embeds is not None: raise NotImplementedError( f"Passing input embeddings is currently not supported for {self.__class__.__name__}" ) outputs = self.model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, encoder_outputs=encoder_outputs, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] # last hidden state eos_mask = input_ids.eq(self.config.eos_token_id).to(hidden_states.device) if len(torch.unique_consecutive(eos_mask.sum(1))) > 1: raise ValueError("All examples must have the same number of tokens.") sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[ :, -1, : ] logits = self.classification_head(sentence_representation) loss = None if labels is not None: labels = labels.to(logits.device) if self.config.problem_type is None: if self.config.num_labels == 1: self.config.problem_type = "regression" elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.config.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return Seq2SeqSequenceClassifierOutput( loss=loss, logits=logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( """ BigBirdPegasus Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """, BIGBIRD_PEGASUS_START_DOCSTRING, ) class BigBirdPegasusForQuestionAnswering(BigBirdPegasusPreTrainedModel): _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"] def __init__(self, config): super().__init__(config) config.num_labels = 2 self.num_labels = config.num_labels self.model = BigBirdPegasusModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=Seq2SeqQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) # Copied from transformers.models.bart.modeling_bart.BartForQuestionAnswering.forward def forward( self, input_ids: torch.Tensor = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, Seq2SeqQuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): 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 (`torch.LongTensor` of shape `(batch_size,)`, *optional*): 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. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if start_positions is not None and end_positions is not None: use_cache = False outputs = self.model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, encoder_outputs=encoder_outputs, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, 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).contiguous() end_logits = end_logits.squeeze(-1).contiguous() 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 = start_positions.clamp(0, ignored_index) end_positions = 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[1:] return ((total_loss,) + output) if total_loss is not None else output return Seq2SeqQuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) # Copied from transformers.models.pegasus.modeling_pegasus.PegasusDecoderWrapper with Pegasus->BigBirdPegasus class BigBirdPegasusDecoderWrapper(BigBirdPegasusPreTrainedModel): """ This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is used in combination with the [`EncoderDecoderModel`] framework. """ def __init__(self, config): super().__init__(config) self.decoder = BigBirdPegasusDecoder(config) def forward(self, *args, **kwargs): return self.decoder(*args, **kwargs) class BigBirdPegasusForCausalLM(BigBirdPegasusPreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): config = copy.deepcopy(config) config.is_decoder = True config.is_encoder_decoder = False super().__init__(config) self.model = BigBirdPegasusDecoderWrapper(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.decoder.embed_tokens def set_input_embeddings(self, value): self.model.decoder.embed_tokens = value def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def set_decoder(self, decoder): self.model.decoder = decoder def get_decoder(self): return self.model.decoder @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]: r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are only required when the model is used as a decoder in a Sequence to Sequence model. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (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]`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. Returns: Example: ```python >>> from transformers import AutoTokenizer, BigBirdPegasusForCausalLM >>> tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv") >>> model = BigBirdPegasusForCausalLM.from_pretrained( ... "google/bigbird-pegasus-large-arxiv", add_cross_attention=False ... ) >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder." >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> logits = outputs.logits ```""" 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 # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model.decoder( input_ids=input_ids, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, head_mask=head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) logits = self.lm_head(outputs[0]) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithCrossAttentions( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs ): # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_ids.shape) if past_key_values: input_ids = input_ids[:, -1:] # first step, decoder_cached_states are empty return { "input_ids": input_ids, # encoder_outputs is defined. input_ids not needed "attention_mask": attention_mask, "past_key_values": past_key_values, "use_cache": use_cache, } @staticmethod def _reorder_cache(past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += ( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), ) return reordered_past