mirror of
https://github.com/sd-webui/stable-diffusion-webui.git
synced 2024-12-15 15:22:55 +03:00
844 lines
36 KiB
Python
844 lines
36 KiB
Python
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import math
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import os
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import warnings
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from dataclasses import dataclass
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from typing import Optional, Tuple
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import torch
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from torch import Tensor, device, dtype, nn
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import CrossEntropyLoss
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import torch.nn.functional as F
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from transformers.activations import ACT2FN
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from transformers.file_utils import (
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ModelOutput,
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)
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from transformers.modeling_outputs import (
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BaseModelOutputWithPastAndCrossAttentions,
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BaseModelOutputWithPoolingAndCrossAttentions,
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CausalLMOutputWithCrossAttentions,
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MaskedLMOutput,
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MultipleChoiceModelOutput,
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NextSentencePredictorOutput,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutput,
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TokenClassifierOutput,
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)
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from transformers.modeling_utils import (
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PreTrainedModel,
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apply_chunking_to_forward,
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find_pruneable_heads_and_indices,
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prune_linear_layer,
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)
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from transformers.utils import logging
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from transformers.models.bert.configuration_bert import BertConfig
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logger = logging.get_logger(__name__)
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class BertEmbeddings(nn.Module):
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"""Construct the embeddings from word and position embeddings."""
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def __init__(self, config):
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super().__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
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# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
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# any TensorFlow checkpoint file
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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# position_ids (1, len position emb) is contiguous in memory and exported when serialized
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self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
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self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
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self.config = config
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def forward(
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self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
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):
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if input_ids is not None:
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input_shape = input_ids.size()
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else:
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input_shape = inputs_embeds.size()[:-1]
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seq_length = input_shape[1]
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if position_ids is None:
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position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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embeddings = inputs_embeds
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if self.position_embedding_type == "absolute":
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position_embeddings = self.position_embeddings(position_ids)
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embeddings += position_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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class BertSelfAttention(nn.Module):
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def __init__(self, config, is_cross_attention):
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super().__init__()
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self.config = config
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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"The hidden size (%d) is not a multiple of the number of attention "
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"heads (%d)" % (config.hidden_size, config.num_attention_heads)
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)
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.query = nn.Linear(config.hidden_size, self.all_head_size)
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if is_cross_attention:
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self.key = nn.Linear(config.encoder_width, self.all_head_size)
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self.value = nn.Linear(config.encoder_width, self.all_head_size)
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else:
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self.key = nn.Linear(config.hidden_size, self.all_head_size)
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self.value = nn.Linear(config.hidden_size, self.all_head_size)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
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if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
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self.max_position_embeddings = config.max_position_embeddings
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self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
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self.save_attention = False
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def save_attn_gradients(self, attn_gradients):
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self.attn_gradients = attn_gradients
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def get_attn_gradients(self):
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return self.attn_gradients
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def save_attention_map(self, attention_map):
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self.attention_map = attention_map
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def get_attention_map(self):
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return self.attention_map
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def transpose_for_scores(self, x):
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
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x = x.view(*new_x_shape)
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return x.permute(0, 2, 1, 3)
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
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encoder_hidden_states=None,
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encoder_attention_mask=None,
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past_key_value=None,
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output_attentions=False,
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):
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mixed_query_layer = self.query(hidden_states)
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# If this is instantiated as a cross-attention module, the keys
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# and values come from an encoder; the attention mask needs to be
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# such that the encoder's padding tokens are not attended to.
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is_cross_attention = encoder_hidden_states is not None
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if is_cross_attention:
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key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
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value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
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attention_mask = encoder_attention_mask
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elif past_key_value is not None:
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
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value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
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else:
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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query_layer = self.transpose_for_scores(mixed_query_layer)
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past_key_value = (key_layer, value_layer)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
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if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
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seq_length = hidden_states.size()[1]
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position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
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position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
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distance = position_ids_l - position_ids_r
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positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
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positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
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if self.position_embedding_type == "relative_key":
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relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
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attention_scores = attention_scores + relative_position_scores
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elif self.position_embedding_type == "relative_key_query":
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relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
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relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
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attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
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attention_scores = attention_scores / math.sqrt(self.attention_head_size)
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if attention_mask is not None:
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# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
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attention_scores = attention_scores + attention_mask
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# Normalize the attention scores to probabilities.
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attention_probs = nn.Softmax(dim=-1)(attention_scores)
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if is_cross_attention and self.save_attention:
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self.save_attention_map(attention_probs)
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attention_probs.register_hook(self.save_attn_gradients)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs_dropped = self.dropout(attention_probs)
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# Mask heads if we want to
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if head_mask is not None:
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attention_probs_dropped = attention_probs_dropped * head_mask
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context_layer = torch.matmul(attention_probs_dropped, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.view(*new_context_layer_shape)
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outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
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outputs = outputs + (past_key_value,)
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return outputs
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class BertSelfOutput(nn.Module):
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def __init__(self, config, twin=False, merge=False):
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super().__init__()
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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if twin:
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self.dense0 = nn.Linear(config.hidden_size, config.hidden_size)
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self.dense1 = nn.Linear(config.hidden_size, config.hidden_size)
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else:
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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if merge:
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self.act = ACT2FN[config.hidden_act]
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self.merge_layer = nn.Linear(config.hidden_size * 2, config.hidden_size)
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self.merge = True
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else:
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self.merge = False
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def forward(self, hidden_states, input_tensor):
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if type(hidden_states) == list:
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hidden_states0 = self.dense0(hidden_states[0])
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hidden_states1 = self.dense1(hidden_states[1])
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if self.merge:
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#hidden_states = self.merge_layer(self.act(torch.cat([hidden_states0,hidden_states1],dim=-1)))
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hidden_states = self.merge_layer(torch.cat([hidden_states0,hidden_states1],dim=-1))
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else:
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hidden_states = (hidden_states0+hidden_states1)/2
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else:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class BertAttention(nn.Module):
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def __init__(self, config, is_cross_attention=False, layer_num=-1):
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super().__init__()
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if is_cross_attention:
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self.self0 = BertSelfAttention(config, is_cross_attention)
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self.self1 = BertSelfAttention(config, is_cross_attention)
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else:
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self.self = BertSelfAttention(config, is_cross_attention)
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self.output = BertSelfOutput(config, twin=is_cross_attention, merge=(is_cross_attention and layer_num>=6))
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self.pruned_heads = set()
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def prune_heads(self, heads):
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(
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heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
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)
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# Prune linear layers
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self.self.query = prune_linear_layer(self.self.query, index)
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self.self.key = prune_linear_layer(self.self.key, index)
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self.self.value = prune_linear_layer(self.self.value, index)
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self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
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# Update hyper params and store pruned heads
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self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
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self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
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encoder_hidden_states=None,
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encoder_attention_mask=None,
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past_key_value=None,
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output_attentions=False,
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):
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if type(encoder_hidden_states)==list:
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self_outputs0 = self.self0(
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hidden_states,
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attention_mask,
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head_mask,
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encoder_hidden_states[0],
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encoder_attention_mask[0],
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past_key_value,
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output_attentions,
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)
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self_outputs1 = self.self1(
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hidden_states,
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attention_mask,
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head_mask,
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encoder_hidden_states[1],
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encoder_attention_mask[1],
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past_key_value,
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output_attentions,
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)
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attention_output = self.output([self_outputs0[0],self_outputs1[0]], hidden_states)
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outputs = (attention_output,) + self_outputs0[1:] # add attentions if we output them
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else:
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self_outputs = self.self(
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hidden_states,
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attention_mask,
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head_mask,
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encoder_hidden_states,
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encoder_attention_mask,
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past_key_value,
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output_attentions,
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)
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attention_output = self.output(self_outputs[0], hidden_states)
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outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
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return outputs
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class BertIntermediate(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
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self.intermediate_act_fn = config.hidden_act
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def forward(self, hidden_states):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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class BertOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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class BertLayer(nn.Module):
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def __init__(self, config, layer_num):
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super().__init__()
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self.config = config
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self.chunk_size_feed_forward = config.chunk_size_feed_forward
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self.seq_len_dim = 1
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self.attention = BertAttention(config)
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self.layer_num = layer_num
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if self.config.add_cross_attention:
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self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention, layer_num=layer_num)
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self.intermediate = BertIntermediate(config)
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self.output = BertOutput(config)
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
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encoder_hidden_states=None,
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encoder_attention_mask=None,
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past_key_value=None,
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output_attentions=False,
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mode=None,
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):
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# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
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self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
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self_attention_outputs = self.attention(
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hidden_states,
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attention_mask,
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head_mask,
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||
|
output_attentions=output_attentions,
|
||
|
past_key_value=self_attn_past_key_value,
|
||
|
)
|
||
|
attention_output = self_attention_outputs[0]
|
||
|
|
||
|
outputs = self_attention_outputs[1:-1]
|
||
|
present_key_value = self_attention_outputs[-1]
|
||
|
|
||
|
if mode=='multimodal':
|
||
|
assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
|
||
|
cross_attention_outputs = self.crossattention(
|
||
|
attention_output,
|
||
|
attention_mask,
|
||
|
head_mask,
|
||
|
encoder_hidden_states,
|
||
|
encoder_attention_mask,
|
||
|
output_attentions=output_attentions,
|
||
|
)
|
||
|
attention_output = cross_attention_outputs[0]
|
||
|
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
|
||
|
layer_output = apply_chunking_to_forward(
|
||
|
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
|
||
|
)
|
||
|
outputs = (layer_output,) + outputs
|
||
|
|
||
|
outputs = outputs + (present_key_value,)
|
||
|
|
||
|
return outputs
|
||
|
|
||
|
def feed_forward_chunk(self, attention_output):
|
||
|
intermediate_output = self.intermediate(attention_output)
|
||
|
layer_output = self.output(intermediate_output, attention_output)
|
||
|
return layer_output
|
||
|
|
||
|
|
||
|
class BertEncoder(nn.Module):
|
||
|
def __init__(self, config):
|
||
|
super().__init__()
|
||
|
self.config = config
|
||
|
self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)])
|
||
|
self.gradient_checkpointing = False
|
||
|
|
||
|
def forward(
|
||
|
self,
|
||
|
hidden_states,
|
||
|
attention_mask=None,
|
||
|
head_mask=None,
|
||
|
encoder_hidden_states=None,
|
||
|
encoder_attention_mask=None,
|
||
|
past_key_values=None,
|
||
|
use_cache=None,
|
||
|
output_attentions=False,
|
||
|
output_hidden_states=False,
|
||
|
return_dict=True,
|
||
|
mode='multimodal',
|
||
|
):
|
||
|
all_hidden_states = () if output_hidden_states else None
|
||
|
all_self_attentions = () if output_attentions else None
|
||
|
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
|
||
|
|
||
|
next_decoder_cache = () if use_cache else None
|
||
|
|
||
|
for i in range(self.config.num_hidden_layers):
|
||
|
layer_module = self.layer[i]
|
||
|
if output_hidden_states:
|
||
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
||
|
|
||
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
||
|
past_key_value = past_key_values[i] if past_key_values is not None else None
|
||
|
|
||
|
if self.gradient_checkpointing and self.training:
|
||
|
|
||
|
if use_cache:
|
||
|
logger.warn(
|
||
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
||
|
)
|
||
|
use_cache = False
|
||
|
|
||
|
def create_custom_forward(module):
|
||
|
def custom_forward(*inputs):
|
||
|
return module(*inputs, past_key_value, output_attentions)
|
||
|
|
||
|
return custom_forward
|
||
|
|
||
|
layer_outputs = torch.utils.checkpoint.checkpoint(
|
||
|
create_custom_forward(layer_module),
|
||
|
hidden_states,
|
||
|
attention_mask,
|
||
|
layer_head_mask,
|
||
|
encoder_hidden_states,
|
||
|
encoder_attention_mask,
|
||
|
mode=mode,
|
||
|
)
|
||
|
else:
|
||
|
layer_outputs = layer_module(
|
||
|
hidden_states,
|
||
|
attention_mask,
|
||
|
layer_head_mask,
|
||
|
encoder_hidden_states,
|
||
|
encoder_attention_mask,
|
||
|
past_key_value,
|
||
|
output_attentions,
|
||
|
mode=mode,
|
||
|
)
|
||
|
|
||
|
hidden_states = layer_outputs[0]
|
||
|
if use_cache:
|
||
|
next_decoder_cache += (layer_outputs[-1],)
|
||
|
if output_attentions:
|
||
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
||
|
|
||
|
if output_hidden_states:
|
||
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
||
|
|
||
|
if not return_dict:
|
||
|
return tuple(
|
||
|
v
|
||
|
for v in [
|
||
|
hidden_states,
|
||
|
next_decoder_cache,
|
||
|
all_hidden_states,
|
||
|
all_self_attentions,
|
||
|
all_cross_attentions,
|
||
|
]
|
||
|
if v is not None
|
||
|
)
|
||
|
return BaseModelOutputWithPastAndCrossAttentions(
|
||
|
last_hidden_state=hidden_states,
|
||
|
past_key_values=next_decoder_cache,
|
||
|
hidden_states=all_hidden_states,
|
||
|
attentions=all_self_attentions,
|
||
|
cross_attentions=all_cross_attentions,
|
||
|
)
|
||
|
|
||
|
|
||
|
class BertPooler(nn.Module):
|
||
|
def __init__(self, config):
|
||
|
super().__init__()
|
||
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
||
|
self.activation = nn.Tanh()
|
||
|
|
||
|
def forward(self, hidden_states):
|
||
|
# We "pool" the model by simply taking the hidden state corresponding
|
||
|
# to the first token.
|
||
|
first_token_tensor = hidden_states[:, 0]
|
||
|
pooled_output = self.dense(first_token_tensor)
|
||
|
pooled_output = self.activation(pooled_output)
|
||
|
return pooled_output
|
||
|
|
||
|
|
||
|
class BertPredictionHeadTransform(nn.Module):
|
||
|
def __init__(self, config):
|
||
|
super().__init__()
|
||
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
||
|
if isinstance(config.hidden_act, str):
|
||
|
self.transform_act_fn = ACT2FN[config.hidden_act]
|
||
|
else:
|
||
|
self.transform_act_fn = config.hidden_act
|
||
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
||
|
|
||
|
def forward(self, hidden_states):
|
||
|
hidden_states = self.dense(hidden_states)
|
||
|
hidden_states = self.transform_act_fn(hidden_states)
|
||
|
hidden_states = self.LayerNorm(hidden_states)
|
||
|
return hidden_states
|
||
|
|
||
|
|
||
|
class BertLMPredictionHead(nn.Module):
|
||
|
def __init__(self, config):
|
||
|
super().__init__()
|
||
|
self.transform = BertPredictionHeadTransform(config)
|
||
|
|
||
|
# The output weights are the same as the input embeddings, but there is
|
||
|
# an output-only bias for each token.
|
||
|
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||
|
|
||
|
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
|
||
|
|
||
|
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
|
||
|
self.decoder.bias = self.bias
|
||
|
|
||
|
def forward(self, hidden_states):
|
||
|
hidden_states = self.transform(hidden_states)
|
||
|
hidden_states = self.decoder(hidden_states)
|
||
|
return hidden_states
|
||
|
|
||
|
|
||
|
class BertOnlyMLMHead(nn.Module):
|
||
|
def __init__(self, config):
|
||
|
super().__init__()
|
||
|
self.predictions = BertLMPredictionHead(config)
|
||
|
|
||
|
def forward(self, sequence_output):
|
||
|
prediction_scores = self.predictions(sequence_output)
|
||
|
return prediction_scores
|
||
|
|
||
|
|
||
|
class BertPreTrainedModel(PreTrainedModel):
|
||
|
"""
|
||
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
||
|
models.
|
||
|
"""
|
||
|
|
||
|
config_class = BertConfig
|
||
|
base_model_prefix = "bert"
|
||
|
_keys_to_ignore_on_load_missing = [r"position_ids"]
|
||
|
|
||
|
def _init_weights(self, module):
|
||
|
""" Initialize the weights """
|
||
|
if isinstance(module, (nn.Linear, nn.Embedding)):
|
||
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
||
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
||
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
||
|
elif isinstance(module, nn.LayerNorm):
|
||
|
module.bias.data.zero_()
|
||
|
module.weight.data.fill_(1.0)
|
||
|
if isinstance(module, nn.Linear) and module.bias is not None:
|
||
|
module.bias.data.zero_()
|
||
|
|
||
|
|
||
|
class BertModel(BertPreTrainedModel):
|
||
|
"""
|
||
|
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
|
||
|
cross-attention is added between the self-attention layers, following the architecture described in `Attention is
|
||
|
all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
|
||
|
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
|
||
|
argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
|
||
|
input to the forward pass.
|
||
|
"""
|
||
|
|
||
|
def __init__(self, config, add_pooling_layer=True):
|
||
|
super().__init__(config)
|
||
|
self.config = config
|
||
|
|
||
|
self.embeddings = BertEmbeddings(config)
|
||
|
|
||
|
self.encoder = BertEncoder(config)
|
||
|
|
||
|
self.pooler = BertPooler(config) if add_pooling_layer else None
|
||
|
|
||
|
self.init_weights()
|
||
|
|
||
|
|
||
|
def get_input_embeddings(self):
|
||
|
return self.embeddings.word_embeddings
|
||
|
|
||
|
def set_input_embeddings(self, value):
|
||
|
self.embeddings.word_embeddings = value
|
||
|
|
||
|
def _prune_heads(self, heads_to_prune):
|
||
|
"""
|
||
|
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
|
||
|
class PreTrainedModel
|
||
|
"""
|
||
|
for layer, heads in heads_to_prune.items():
|
||
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
||
|
|
||
|
|
||
|
def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:
|
||
|
"""
|
||
|
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
|
||
|
|
||
|
Arguments:
|
||
|
attention_mask (:obj:`torch.Tensor`):
|
||
|
Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
|
||
|
input_shape (:obj:`Tuple[int]`):
|
||
|
The shape of the input to the model.
|
||
|
device: (:obj:`torch.device`):
|
||
|
The device of the input to the model.
|
||
|
|
||
|
Returns:
|
||
|
:obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
|
||
|
"""
|
||
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
||
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
||
|
if attention_mask.dim() == 3:
|
||
|
extended_attention_mask = attention_mask[:, None, :, :]
|
||
|
elif attention_mask.dim() == 2:
|
||
|
# Provided a padding mask of dimensions [batch_size, seq_length]
|
||
|
# - if the model is a decoder, apply a causal mask in addition to the padding mask
|
||
|
# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
||
|
if is_decoder:
|
||
|
batch_size, seq_length = input_shape
|
||
|
|
||
|
seq_ids = torch.arange(seq_length, device=device)
|
||
|
causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
|
||
|
# in case past_key_values are used we need to add a prefix ones mask to the causal mask
|
||
|
# causal and attention masks must have same type with pytorch version < 1.3
|
||
|
causal_mask = causal_mask.to(attention_mask.dtype)
|
||
|
|
||
|
if causal_mask.shape[1] < attention_mask.shape[1]:
|
||
|
prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
|
||
|
causal_mask = torch.cat(
|
||
|
[
|
||
|
torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),
|
||
|
causal_mask,
|
||
|
],
|
||
|
axis=-1,
|
||
|
)
|
||
|
|
||
|
extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
|
||
|
else:
|
||
|
extended_attention_mask = attention_mask[:, None, None, :]
|
||
|
else:
|
||
|
raise ValueError(
|
||
|
"Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
|
||
|
input_shape, attention_mask.shape
|
||
|
)
|
||
|
)
|
||
|
|
||
|
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
|
||
|
# masked positions, this operation will create a tensor which is 0.0 for
|
||
|
# positions we want to attend and -10000.0 for masked positions.
|
||
|
# Since we are adding it to the raw scores before the softmax, this is
|
||
|
# effectively the same as removing these entirely.
|
||
|
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
|
||
|
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
|
||
|
return extended_attention_mask
|
||
|
|
||
|
def forward(
|
||
|
self,
|
||
|
input_ids=None,
|
||
|
attention_mask=None,
|
||
|
position_ids=None,
|
||
|
head_mask=None,
|
||
|
inputs_embeds=None,
|
||
|
encoder_embeds=None,
|
||
|
encoder_hidden_states=None,
|
||
|
encoder_attention_mask=None,
|
||
|
past_key_values=None,
|
||
|
use_cache=None,
|
||
|
output_attentions=None,
|
||
|
output_hidden_states=None,
|
||
|
return_dict=None,
|
||
|
is_decoder=False,
|
||
|
mode='multimodal',
|
||
|
):
|
||
|
r"""
|
||
|
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(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 (:obj:`torch.FloatTensor` of shape :obj:`(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]``:
|
||
|
- 1 for tokens that are **not masked**,
|
||
|
- 0 for tokens that are **masked**.
|
||
|
past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
|
||
|
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
|
||
|
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
|
||
|
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
|
||
|
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
|
||
|
use_cache (:obj:`bool`, `optional`):
|
||
|
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
|
||
|
decoding (see :obj:`past_key_values`).
|
||
|
"""
|
||
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||
|
output_hidden_states = (
|
||
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||
|
)
|
||
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
|
||
|
if is_decoder:
|
||
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||
|
else:
|
||
|
use_cache = False
|
||
|
|
||
|
if input_ids is not None and inputs_embeds is not None:
|
||
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
||
|
elif input_ids is not None:
|
||
|
input_shape = input_ids.size()
|
||
|
batch_size, seq_length = input_shape
|
||
|
device = input_ids.device
|
||
|
elif inputs_embeds is not None:
|
||
|
input_shape = inputs_embeds.size()[:-1]
|
||
|
batch_size, seq_length = input_shape
|
||
|
device = inputs_embeds.device
|
||
|
elif encoder_embeds is not None:
|
||
|
input_shape = encoder_embeds.size()[:-1]
|
||
|
batch_size, seq_length = input_shape
|
||
|
device = encoder_embeds.device
|
||
|
else:
|
||
|
raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_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 attention_mask is None:
|
||
|
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
|
||
|
|
||
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
||
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
||
|
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape,
|
||
|
device, is_decoder)
|
||
|
|
||
|
# If a 2D or 3D attention mask is provided for the cross-attention
|
||
|
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
||
|
if encoder_hidden_states is not None:
|
||
|
if type(encoder_hidden_states) == list:
|
||
|
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
|
||
|
else:
|
||
|
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
|
||
|
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
|
||
|
|
||
|
if type(encoder_attention_mask) == list:
|
||
|
encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
|
||
|
elif encoder_attention_mask is None:
|
||
|
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
|
||
|
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
||
|
else:
|
||
|
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
||
|
else:
|
||
|
encoder_extended_attention_mask = None
|
||
|
|
||
|
# Prepare head mask if needed
|
||
|
# 1.0 in head_mask indicate we keep the head
|
||
|
# attention_probs has shape bsz x n_heads x N x N
|
||
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
||
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
||
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
||
|
|
||
|
if encoder_embeds is None:
|
||
|
embedding_output = self.embeddings(
|
||
|
input_ids=input_ids,
|
||
|
position_ids=position_ids,
|
||
|
inputs_embeds=inputs_embeds,
|
||
|
past_key_values_length=past_key_values_length,
|
||
|
)
|
||
|
else:
|
||
|
embedding_output = encoder_embeds
|
||
|
|
||
|
encoder_outputs = self.encoder(
|
||
|
embedding_output,
|
||
|
attention_mask=extended_attention_mask,
|
||
|
head_mask=head_mask,
|
||
|
encoder_hidden_states=encoder_hidden_states,
|
||
|
encoder_attention_mask=encoder_extended_attention_mask,
|
||
|
past_key_values=past_key_values,
|
||
|
use_cache=use_cache,
|
||
|
output_attentions=output_attentions,
|
||
|
output_hidden_states=output_hidden_states,
|
||
|
return_dict=return_dict,
|
||
|
mode=mode,
|
||
|
)
|
||
|
sequence_output = encoder_outputs[0]
|
||
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
||
|
|
||
|
if not return_dict:
|
||
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
||
|
|
||
|
return BaseModelOutputWithPoolingAndCrossAttentions(
|
||
|
last_hidden_state=sequence_output,
|
||
|
pooler_output=pooled_output,
|
||
|
past_key_values=encoder_outputs.past_key_values,
|
||
|
hidden_states=encoder_outputs.hidden_states,
|
||
|
attentions=encoder_outputs.attentions,
|
||
|
cross_attentions=encoder_outputs.cross_attentions,
|
||
|
)
|
||
|
|