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| # coding=utf-8 | |
| # Copyright 2022 The Salesforce Team Authors and The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the BSD-3-clause license (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # https://opensource.org/licenses/BSD-3-Clause | |
| # | |
| # 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. | |
| import math | |
| from typing import List, Optional, Tuple, Union | |
| import torch | |
| import torch.utils.checkpoint | |
| from torch import Tensor, device, nn | |
| from torch.nn import CrossEntropyLoss | |
| from ...activations import ACT2FN | |
| from ...modeling_outputs import ( | |
| BaseModelOutputWithPastAndCrossAttentions, | |
| BaseModelOutputWithPoolingAndCrossAttentions, | |
| CausalLMOutputWithCrossAttentions, | |
| ) | |
| from ...modeling_utils import ( | |
| PreTrainedModel, | |
| apply_chunking_to_forward, | |
| find_pruneable_heads_and_indices, | |
| prune_linear_layer, | |
| ) | |
| from ...utils import logging | |
| from .configuration_blip import BlipTextConfig | |
| logger = logging.get_logger(__name__) | |
| # Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L52 | |
| class BlipTextEmbeddings(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)), persistent=False | |
| ) | |
| self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") | |
| self.config = config | |
| def forward( | |
| self, | |
| input_ids: Optional[torch.LongTensor] = None, | |
| position_ids: Optional[torch.LongTensor] = None, | |
| inputs_embeds: Optional[torch.FloatTensor] = None, | |
| past_key_values_length: int = 0, | |
| ) -> torch.Tensor: | |
| 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: | |
| input_ids = input_ids.to(self.word_embeddings.weight.device) | |
| inputs_embeds = self.word_embeddings(input_ids) | |
| embeddings = inputs_embeds | |
| if self.position_embedding_type == "absolute": | |
| position_embeddings = self.position_embeddings(position_ids) | |
| embeddings += position_embeddings | |
| embeddings = self.LayerNorm(embeddings) | |
| embeddings = self.dropout(embeddings) | |
| return embeddings | |
| # Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L97 | |
| class BlipTextSelfAttention(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_hidden_size, self.all_head_size) | |
| self.value = nn.Linear(config.encoder_hidden_size, 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) | |
| 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: torch.Tensor, | |
| attention_mask: Optional[torch.FloatTensor] = None, | |
| head_mask: Optional[torch.FloatTensor] = None, | |
| encoder_hidden_states: Optional[torch.FloatTensor] = None, | |
| encoder_attention_mask: Optional[torch.FloatTensor] = None, | |
| past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, | |
| output_attentions: Optional[bool] = False, | |
| ) -> Tuple[torch.Tensor]: | |
| 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: | |
| 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) | |
| # 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 BlipTextModel forward() function) | |
| attention_scores = attention_scores + attention_mask.to(attention_scores.device) | |
| # Normalize the attention scores to probabilities. | |
| attention_probs = nn.Softmax(dim=-1)(attention_scores) | |
| # 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 | |
| # Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert -> BlipText | |
| class BlipTextSelfOutput(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: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: | |
| hidden_states = self.dense(hidden_states) | |
| hidden_states = self.dropout(hidden_states) | |
| hidden_states = self.LayerNorm(hidden_states + input_tensor) | |
| return hidden_states | |
| # Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#242 | |
| class BlipTextAttention(nn.Module): | |
| def __init__(self, config, is_cross_attention=False): | |
| super().__init__() | |
| self.self = BlipTextSelfAttention(config, is_cross_attention) | |
| self.output = BlipTextSelfOutput(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: torch.Tensor, | |
| attention_mask: Optional[torch.FloatTensor] = None, | |
| head_mask: Optional[torch.FloatTensor] = None, | |
| encoder_hidden_states: Optional[torch.FloatTensor] = None, | |
| encoder_attention_mask: Optional[torch.FloatTensor] = None, | |
| past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, | |
| output_attentions: Optional[bool] = False, | |
| ) -> Tuple[torch.Tensor]: | |
| 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 | |
| # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert -> BlipText | |
| class BlipTextIntermediate(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: torch.Tensor) -> torch.Tensor: | |
| hidden_states = self.dense(hidden_states) | |
| hidden_states = self.intermediate_act_fn(hidden_states) | |
| return hidden_states | |
| # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert -> BlipText | |
| class BlipTextOutput(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: torch.Tensor, input_tensor: torch.Tensor) -> torch.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 BlipTextLayer(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 = BlipTextAttention(config) | |
| self.layer_num = layer_num | |
| if self.config.is_decoder: | |
| self.crossattention = BlipTextAttention(config, is_cross_attention=self.config.is_decoder) | |
| self.intermediate = BlipTextIntermediate(config) | |
| self.output = BlipTextOutput(config) | |
| def forward( | |
| self, | |
| hidden_states: torch.Tensor, | |
| attention_mask: Optional[torch.FloatTensor] = None, | |
| head_mask: Optional[torch.FloatTensor] = None, | |
| encoder_hidden_states: Optional[torch.FloatTensor] = None, | |
| encoder_attention_mask: Optional[torch.FloatTensor] = None, | |
| past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, | |
| output_attentions: Optional[bool] = False, | |
| ) -> Tuple[torch.Tensor]: | |
| # 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 encoder_hidden_states is not None: | |
| 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 | |
| # Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L386 | |
| class BlipTextEncoder(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.config = config | |
| self.layer = nn.ModuleList([BlipTextLayer(config, i) for i in range(config.num_hidden_layers)]) | |
| self.gradient_checkpointing = False | |
| def forward( | |
| self, | |
| hidden_states: torch.Tensor, | |
| attention_mask: Optional[torch.FloatTensor] = None, | |
| head_mask: Optional[torch.FloatTensor] = None, | |
| encoder_hidden_states: Optional[torch.FloatTensor] = None, | |
| encoder_attention_mask: Optional[torch.FloatTensor] = None, | |
| past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, | |
| use_cache: Optional[bool] = None, | |
| output_attentions: Optional[bool] = False, | |
| output_hidden_states: Optional[bool] = False, | |
| return_dict: Optional[bool] = True, | |
| ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: | |
| if self.gradient_checkpointing and self.training: | |
| if use_cache: | |
| logger.warning( | |
| "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." | |
| ) | |
| use_cache = False | |
| 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.is_decoder 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: | |
| 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, | |
| ) | |
| else: | |
| layer_outputs = layer_module( | |
| hidden_states, | |
| attention_mask, | |
| layer_head_mask, | |
| encoder_hidden_states, | |
| encoder_attention_mask, | |
| past_key_value, | |
| output_attentions, | |
| ) | |
| 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],) | |
| all_cross_attentions = all_cross_attentions + (layer_outputs[2],) | |
| 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, | |
| ) | |
| # Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->BlipText | |
| class BlipTextPooler(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: torch.Tensor) -> torch.Tensor: | |
| # 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 | |
| # Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->BlipText | |
| class BlipTextPredictionHeadTransform(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: torch.Tensor) -> torch.Tensor: | |
| hidden_states = self.dense(hidden_states) | |
| hidden_states = self.transform_act_fn(hidden_states) | |
| hidden_states = self.LayerNorm(hidden_states) | |
| return hidden_states | |
| # Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->BlipText | |
| class BlipTextLMPredictionHead(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.transform = BlipTextPredictionHeadTransform(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 | |
| # Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->BlipText | |
| class BlipTextOnlyMLMHead(nn.Module): | |
| def __init__(self, config): | |
| super().__init__() | |
| self.predictions = BlipTextLMPredictionHead(config) | |
| def forward(self, sequence_output: torch.Tensor) -> torch.Tensor: | |
| prediction_scores = self.predictions(sequence_output) | |
| return prediction_scores | |
| # Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L548 | |
| class BlipTextPreTrainedModel(PreTrainedModel): | |
| """ | |
| An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained | |
| models. | |
| """ | |
| config_class = BlipTextConfig | |
| base_model_prefix = "bert" | |
| 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_() | |
| # Adapted from https://github.com/salesforce/BLIP/blob/3a29b7410476bf5f2ba0955827390eb6ea1f4f9d/models/med.py#L571 | |
| class BlipTextModel(BlipTextPreTrainedModel): | |
| """ | |
| 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 `is_decoder` set to `True`; an | |
| `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 = BlipTextEmbeddings(config) | |
| self.encoder = BlipTextEncoder(config) | |
| self.pooler = BlipTextPooler(config) if add_pooling_layer else None | |
| self.post_init() | |
| def get_input_embeddings(self): | |
| return self.embeddings.word_embeddings | |
| def set_input_embeddings(self, value): | |
| self.embeddings.word_embeddings = value | |
| # Copied from transformers.models.bert.modeling_bert.BertModel._prune_heads | |
| 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 (`torch.Tensor`): | |
| Mask with ones indicating tokens to attend to, zeros for tokens to ignore. | |
| input_shape (`Tuple[int]`): | |
| The shape of the input to the model. | |
| device (`torch.device`): | |
| The device of the input to the model. | |
| Returns: | |
| `torch.Tensor` The extended attention mask, with a the same dtype as `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: Optional[torch.Tensor] = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| position_ids: Optional[torch.Tensor] = None, | |
| head_mask: Optional[torch.Tensor] = None, | |
| inputs_embeds: Optional[torch.Tensor] = None, | |
| encoder_embeds: Optional[torch.Tensor] = None, | |
| encoder_hidden_states: Optional[torch.Tensor] = None, | |
| encoder_attention_mask: Optional[torch.Tensor] = None, | |
| past_key_values: Optional[List[torch.FloatTensor]] = None, | |
| use_cache: Optional[bool] = None, | |
| output_attentions: Optional[bool] = None, | |
| output_hidden_states: Optional[bool] = None, | |
| return_dict: Optional[bool] = None, | |
| is_decoder: Optional[bool] = False, | |
| ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: | |
| r""" | |
| encoder_hidden_states (`torch.FloatTensor`, *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`, *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 (`tuple(tuple(torch.FloatTensor))`, *optional*): | |
| Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up 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)`. | |
| 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 = 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: | |
| self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) | |
| 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))).to(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, | |
| ) | |
| 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, | |
| ) | |
| # Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L811 | |
| class BlipTextLMHeadModel(BlipTextPreTrainedModel): | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.bert = BlipTextModel(config, add_pooling_layer=False) | |
| self.cls = BlipTextOnlyMLMHead(config) | |
| 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: Optional[torch.Tensor] = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| position_ids: Optional[torch.Tensor] = None, | |
| head_mask: Optional[torch.Tensor] = None, | |
| inputs_embeds: Optional[torch.Tensor] = None, | |
| encoder_hidden_states: Optional[torch.Tensor] = None, | |
| encoder_attention_mask: Optional[torch.Tensor] = None, | |
| labels: Optional[torch.Tensor] = None, | |
| past_key_values: Optional[List[torch.Tensor]] = None, | |
| use_cache: Optional[bool] = None, | |
| output_attentions: Optional[bool] = None, | |
| output_hidden_states: Optional[bool] = None, | |
| return_dict: Optional[bool] = None, | |
| return_logits: Optional[bool] = False, | |
| is_decoder: Optional[bool] = True, | |
| reduction: Optional[str] = "mean", | |
| ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: | |
| r""" | |
| encoder_hidden_states (`torch.FloatTensor`, *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`, *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 (`torch.LongTensor`, *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 (`tuple(tuple(torch.FloatTensor))`, *optional*): | |
| Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up 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)`. | |
| 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`). | |
| """ | |
| 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, | |
| ) | |
| sequence_output = outputs[0] | |
| prediction_scores = self.cls(sequence_output) | |
| 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().to(shifted_prediction_scores.device) | |
| 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_key_values=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_key_values is used | |
| if past_key_values is not None: | |
| input_ids = input_ids[:, -1:] | |
| return { | |
| "input_ids": input_ids, | |
| "attention_mask": attention_mask, | |
| "past_key_values": past_key_values, | |
| "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_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 | |