""" * Copyright (c) 2022, salesforce.com, inc. * All rights reserved. * SPDX-License-Identifier: BSD-3-Clause * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause * By Junnan Li * Based on huggingface code base * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert """ import math import os import warnings from dataclasses import dataclass from typing import Optional from typing import Tuple import torch.nn.functional as F import torch.utils.checkpoint from torch import device from torch import dtype from torch import nn from torch import Tensor from torch.nn import CrossEntropyLoss from transformers.activations import ACT2FN from transformers.file_utils import ( ModelOutput, ) from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions from transformers.modeling_outputs import MaskedLMOutput from transformers.modeling_outputs import MultipleChoiceModelOutput from transformers.modeling_outputs import NextSentencePredictorOutput from transformers.modeling_outputs import QuestionAnsweringModelOutput from transformers.modeling_outputs import SequenceClassifierOutput from transformers.modeling_outputs import TokenClassifierOutput from transformers.modeling_utils import apply_chunking_to_forward from transformers.modeling_utils import find_pruneable_heads_and_indices from transformers.modeling_utils import PreTrainedModel from transformers.modeling_utils import prune_linear_layer from transformers.models.bert.configuration_bert import BertConfig from transformers.utils import logging logger = logging.get_logger(__name__) class BertEmbeddings_nopos(nn.Module): """Construct the embeddings from word and position embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding( config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id ) # self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized # self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) # self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.config = config def forward( self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0, ): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] # if position_ids is None: # position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) embeddings = inputs_embeds # if self.position_embedding_type == "absolute": # position_embeddings = self.position_embeddings(position_ids) # # print('add position_embeddings!!!!') # embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class BertEmbeddings(nn.Module): """Construct the embeddings from word and position embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding( config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id ) self.position_embeddings = nn.Embedding( config.max_position_embeddings, config.hidden_size ) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer( "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) ) self.position_embedding_type = getattr( config, "position_embedding_type", "absolute" ) self.config = config def forward( self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0, ): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[ :, past_key_values_length : seq_length + past_key_values_length ] if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) embeddings = inputs_embeds if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) # print('add position_embeddings!!!!') embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class BertSelfAttention(nn.Module): def __init__(self, config, is_cross_attention): super().__init__() self.config = config if config.hidden_size % config.num_attention_heads != 0 and not hasattr( config, "embedding_size" ): raise ValueError( "The hidden size (%d) is not a multiple of the number of attention " "heads (%d)" % (config.hidden_size, 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) if is_cross_attention: self.key = nn.Linear(config.encoder_width, self.all_head_size) self.value = nn.Linear(config.encoder_width, self.all_head_size) else: self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = getattr( config, "position_embedding_type", "absolute" ) if ( self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query" ): self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding( 2 * config.max_position_embeddings - 1, self.attention_head_size ) self.save_attention = False def save_attn_gradients(self, attn_gradients): self.attn_gradients = attn_gradients def get_attn_gradients(self): return self.attn_gradients def save_attention_map(self, attention_map): self.attention_map = attention_map def get_attention_map(self): return self.attention_map 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: # print(self.key.weight.shape) 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) past_key_value = (key_layer, value_layer) # compatible with higher versions of transformers if key_layer.shape[0] > query_layer.shape[0]: key_layer = key_layer[: query_layer.shape[0], :, :, :] attention_mask = attention_mask[: query_layer.shape[0], :, :] value_layer = value_layer[: query_layer.shape[0], :, :, :] # 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)) if ( self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query" ): seq_length = hidden_states.size()[1] position_ids_l = torch.arange( seq_length, dtype=torch.long, device=hidden_states.device ).view(-1, 1) position_ids_r = torch.arange( seq_length, dtype=torch.long, device=hidden_states.device ).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding( distance + self.max_position_embeddings - 1 ) positional_embedding = positional_embedding.to( dtype=query_layer.dtype ) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum( "bhld,lrd->bhlr", query_layer, positional_embedding ) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum( "bhld,lrd->bhlr", query_layer, positional_embedding ) relative_position_scores_key = torch.einsum( "bhrd,lrd->bhlr", key_layer, positional_embedding ) attention_scores = ( attention_scores + relative_position_scores_query + relative_position_scores_key ) 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 BertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.Softmax(dim=-1)(attention_scores) if is_cross_attention and self.save_attention: self.save_attention_map(attention_probs) attention_probs.register_hook(self.save_attn_gradients) # 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_dropped = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs_dropped = attention_probs_dropped * head_mask context_layer = torch.matmul(attention_probs_dropped, 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,) ) outputs = outputs + (past_key_value,) return outputs class BertSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class BertAttention(nn.Module): def __init__(self, config, is_cross_attention=False): super().__init__() self.self = BertSelfAttention(config, is_cross_attention) self.output = BertSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads, ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = ( self.self.attention_head_size * self.self.num_attention_heads ) self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): self_outputs = self.self( hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[ 1: ] # add attentions if we output them return outputs class BertIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class BertOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class BertLayer(nn.Module): def __init__(self, config, layer_num): super().__init__() self.config = config self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = BertAttention(config) self.layer_num = layer_num if self.config.add_cross_attention: self.crossattention = BertAttention( config, is_cross_attention=self.config.add_cross_attention ) self.intermediate = BertIntermediate(config) self.output = BertOutput(config) 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, mode=None, ): if mode == "tagging": assert ( encoder_hidden_states is not None ), "encoder_hidden_states must be given for cross-attention layers" cross_attention_outputs = self.crossattention( hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, output_attentions=output_attentions, ) attention_output = cross_attention_outputs[0] outputs = cross_attention_outputs[ 1:-1 ] # add cross attentions if we output attention weights present_key_value = cross_attention_outputs[-1] else: # 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 ) self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, 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 `__ 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, ) class BertLMHeadModel(BertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] def __init__(self, config): super().__init__(config) self.bert = BertModel(config, add_pooling_layer=False) self.cls = BertOnlyMLMHead(config) self.init_weights() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings def forward( self, input_ids=None, attention_mask=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, labels=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, return_logits=False, is_decoder=True, reduction="mean", 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**. labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]`` 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`). Returns: Example:: >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig >>> import torch >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') >>> config = BertConfig.from_pretrained("bert-base-cased") >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config) >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.logits """ return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict ) if labels is not None: use_cache = False outputs = self.bert( input_ids, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_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, is_decoder=is_decoder, mode=mode, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) # sequence_output.shape torch.Size([85, 30, 768]) # prediction_scores.shape torch.Size([85, 30, 30524]) # labels.shape torch.Size([85, 30]) if return_logits: return prediction_scores[:, :-1, :].contiguous() lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) lm_loss = loss_fct( shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1), ) if reduction == "none": lm_loss = lm_loss.view(prediction_scores.size(0), -1).sum(1) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((lm_loss,) + output) if lm_loss is not None else output return CausalLMOutputWithCrossAttentions( loss=lm_loss, logits=prediction_scores, 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=None, attention_mask=None, **model_kwargs ): input_shape = input_ids.shape # 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_shape) # cut decoder_input_ids if past is used if past is not None: input_ids = input_ids[:, -1:] return { "input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past, "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None), "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None), "is_decoder": True, } def _reorder_cache(self, past, beam_idx): reordered_past = () for layer_past in past: reordered_past += ( tuple( past_state.index_select(0, beam_idx) for past_state in layer_past ), ) return reordered_past