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# coding=utf-8 | |
# Copyright 2023 The Salesforce Authors and The HuggingFace Team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
""" PyTorch BLIP-2 model.""" | |
import math | |
from dataclasses import dataclass | |
from typing import Any, Optional, Tuple, Union | |
import torch | |
import torch.utils.checkpoint | |
from torch import nn | |
from torch.nn import CrossEntropyLoss | |
from ...activations import ACT2FN | |
from ...modeling_outputs import ( | |
BaseModelOutput, | |
BaseModelOutputWithPastAndCrossAttentions, | |
BaseModelOutputWithPooling, | |
BaseModelOutputWithPoolingAndCrossAttentions, | |
) | |
from ...modeling_utils import PreTrainedModel | |
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer | |
from ...utils import ( | |
ModelOutput, | |
add_start_docstrings, | |
add_start_docstrings_to_model_forward, | |
logging, | |
replace_return_docstrings, | |
) | |
from ..auto import AutoModelForCausalLM, AutoModelForSeq2SeqLM | |
from .configuration_blip_2 import Blip2Config, Blip2QFormerConfig, Blip2VisionConfig | |
logger = logging.get_logger(__name__) | |
_CHECKPOINT_FOR_DOC = "Salesforce/blip2-opt-2.7b" | |
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST = [ | |
"Salesforce/blip2-opt-2.7b", | |
# See all BLIP-2 models at https://huggingface.co/models?filter=blip | |
] | |
class Blip2ForConditionalGenerationModelOutput(ModelOutput): | |
""" | |
Class defining the outputs of [`Blip2ForConditionalGeneration`]. | |
Args: | |
loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): | |
Language modeling loss from the language model. | |
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): | |
Prediction scores of the language modeling head of the language model. | |
vision_outputs (`BaseModelOutputWithPooling`): | |
Outputs of the vision encoder. | |
qformer_outputs (`BaseModelOutputWithPoolingAndCrossAttentions`): | |
Outputs of the Q-Former (Querying Transformer). | |
language_model_outputs (`CausalLMOutputWithPast` or `Seq2SeqLMOutput`): | |
Outputs of the language model. | |
""" | |
loss: Optional[Tuple[torch.FloatTensor]] = None | |
logits: Optional[Tuple[torch.FloatTensor]] = None | |
vision_outputs: Optional[torch.FloatTensor] = None | |
qformer_outputs: Optional[Tuple[torch.FloatTensor]] = None | |
language_model_outputs: Optional[Tuple[torch.FloatTensor]] = None | |
def to_tuple(self) -> Tuple[Any]: | |
return tuple( | |
self[k] | |
if k not in ["vision_outputs", "qformer_outputs", "language_model_outputs"] | |
else getattr(self, k).to_tuple() | |
for k in self.keys() | |
) | |
# Copied from transformers.models.blip.modeling_blip.BlipVisionEmbeddings with Blip->Blip2 | |
class Blip2VisionEmbeddings(nn.Module): | |
def __init__(self, config: Blip2VisionConfig): | |
super().__init__() | |
self.config = config | |
self.embed_dim = config.hidden_size | |
self.image_size = config.image_size | |
self.patch_size = config.patch_size | |
self.class_embedding = nn.Parameter(torch.randn(1, 1, self.embed_dim)) | |
self.patch_embedding = nn.Conv2d( | |
in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size | |
) | |
self.num_patches = (self.image_size // self.patch_size) ** 2 | |
self.num_positions = self.num_patches + 1 | |
self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim)) | |
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: | |
batch_size = pixel_values.shape[0] | |
target_dtype = self.patch_embedding.weight.dtype | |
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid] | |
patch_embeds = patch_embeds.flatten(2).transpose(1, 2) | |
class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype) | |
embeddings = torch.cat([class_embeds, patch_embeds], dim=1) | |
embeddings = embeddings + self.position_embedding[:, : embeddings.size(1), :].to(target_dtype) | |
return embeddings | |
class Blip2Attention(nn.Module): | |
"""Multi-headed attention from 'Attention Is All You Need' paper""" | |
def __init__(self, config): | |
super().__init__() | |
self.config = config | |
self.embed_dim = config.hidden_size | |
self.num_heads = config.num_attention_heads | |
self.head_dim = self.embed_dim // self.num_heads | |
if self.head_dim * self.num_heads != self.embed_dim: | |
raise ValueError( | |
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" | |
f" {self.num_heads})." | |
) | |
self.scale = self.head_dim**-0.5 | |
self.dropout = nn.Dropout(config.attention_dropout) | |
# small tweak here compared to CLIP, no bias here | |
self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=False) | |
if config.qkv_bias: | |
q_bias = nn.Parameter(torch.zeros(self.embed_dim)) | |
v_bias = nn.Parameter(torch.zeros(self.embed_dim)) | |
else: | |
q_bias = None | |
v_bias = None | |
if q_bias is not None: | |
qkv_bias = torch.cat((q_bias, torch.zeros_like(v_bias, requires_grad=False), v_bias)) | |
self.qkv.bias = nn.Parameter(qkv_bias) | |
self.projection = nn.Linear(self.embed_dim, self.embed_dim) | |
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): | |
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
head_mask: Optional[torch.Tensor] = None, | |
output_attentions: Optional[bool] = False, | |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: | |
"""Input shape: Batch x Time x Channel""" | |
bsz, tgt_len, embed_dim = hidden_states.size() | |
mixed_qkv = self.qkv(hidden_states) | |
mixed_qkv = mixed_qkv.reshape(bsz, tgt_len, 3, self.num_heads, embed_dim // self.num_heads).permute( | |
2, 0, 3, 1, 4 | |
) | |
query_states, key_states, value_states = mixed_qkv[0], mixed_qkv[1], mixed_qkv[2] | |
# Take the dot product between "query" and "key" to get the raw attention scores. | |
attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2)) | |
attention_scores = attention_scores * self.scale | |
# Normalize the attention scores to probabilities. | |
attention_probs = nn.functional.softmax(attention_scores, dim=-1) | |
# This is actually dropping out entire tokens to attend to, which might | |
# seem a bit unusual, but is taken from the original Transformer paper. | |
attention_probs = self.dropout(attention_probs) | |
# Mask heads if we want to | |
if head_mask is not None: | |
attention_probs = attention_probs * head_mask | |
context_layer = torch.matmul(attention_probs, value_states).permute(0, 2, 1, 3) | |
new_context_layer_shape = context_layer.size()[:-2] + (self.embed_dim,) | |
context_layer = context_layer.reshape(new_context_layer_shape) | |
output = self.projection(context_layer) | |
outputs = (output, attention_probs) if output_attentions else (output, None) | |
return outputs | |
# Copied from transformers.models.blip.modeling_blip.BlipMLP | |
class Blip2MLP(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.config = config | |
self.activation_fn = ACT2FN[config.hidden_act] | |
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) | |
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) | |
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: | |
hidden_states = self.fc1(hidden_states) | |
hidden_states = self.activation_fn(hidden_states) | |
hidden_states = self.fc2(hidden_states) | |
return hidden_states | |
# Copied from transformers.models.blip.modeling_blip.BlipEncoderLayer with Blip->Blip2 | |
class Blip2EncoderLayer(nn.Module): | |
def __init__(self, config: Blip2Config): | |
super().__init__() | |
self.embed_dim = config.hidden_size | |
self.self_attn = Blip2Attention(config) | |
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) | |
self.mlp = Blip2MLP(config) | |
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
attention_mask: torch.Tensor, | |
output_attentions: Optional[bool] = False, | |
) -> Tuple[torch.FloatTensor]: | |
""" | |
Args: | |
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` | |
attention_mask (`torch.FloatTensor`): attention mask of size | |
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. | |
`(config.encoder_attention_heads,)`. | |
output_attentions (`bool`, *optional*): | |
Whether or not to return the attentions tensors of all attention layers. See `attentions` under | |
returned tensors for more detail. | |
""" | |
residual = hidden_states | |
hidden_states = self.layer_norm1(hidden_states) | |
hidden_states, attn_weights = self.self_attn( | |
hidden_states=hidden_states, | |
head_mask=attention_mask, | |
output_attentions=output_attentions, | |
) | |
hidden_states = hidden_states + residual | |
residual = hidden_states | |
hidden_states = self.layer_norm2(hidden_states) | |
hidden_states = self.mlp(hidden_states) | |
hidden_states = hidden_states + residual | |
outputs = (hidden_states,) | |
if output_attentions: | |
outputs += (attn_weights,) | |
return outputs | |
class Blip2PreTrainedModel(PreTrainedModel): | |
""" | |
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained | |
models. | |
""" | |
config_class = Blip2Config | |
base_model_prefix = "blip" | |
supports_gradient_checkpointing = True | |
_no_split_modules = ["Blip2Attention", "T5Block", "OPTDecoderLayer"] | |
_skip_keys_device_placement = "past_key_values" | |
_keep_in_fp32_modules = ["wo"] | |
def _init_weights(self, module): | |
"""Initialize the weights""" | |
factor = self.config.initializer_range | |
if isinstance(module, nn.Conv2d) or isinstance(module, nn.Embedding) or isinstance(module, nn.Linear): | |
module.weight.data.normal_(mean=0.0, std=factor) | |
if hasattr(module, "bias") and module.bias is not None: | |
module.bias.data.zero_() | |
if isinstance(module, Blip2VisionEmbeddings): | |
if hasattr(self.config, "vision_config"): | |
factor = self.config.vision_config.initializer_range | |
nn.init.trunc_normal_(module.position_embedding, mean=0.0, std=factor) | |
nn.init.trunc_normal_(module.class_embedding, mean=0.0, std=factor) | |
elif isinstance(module, nn.LayerNorm): | |
module.bias.data.zero_() | |
module.weight.data.fill_(1.0) | |
elif isinstance(module, nn.Linear) and module.bias is not None: | |
module.bias.data.zero_() | |
def _set_gradient_checkpointing(self, module, value=False): | |
if isinstance(module, Blip2Encoder): | |
module.gradient_checkpointing = value | |
BLIP_2_START_DOCSTRING = r""" | |
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the | |
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads | |
etc.) | |
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. | |
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage | |
and behavior. | |
Parameters: | |
config ([`Blip2Config`]): Model configuration class with all the parameters of the model. | |
Initializing with a config file does not load the weights associated with the model, only the | |
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. | |
""" | |
BLIP_2_VISION_INPUTS_DOCSTRING = r""" | |
Args: | |
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): | |
Pixel values. Pixel values can be obtained using [`Blip2Processor`]. See [`Blip2Processor.__call__`] for | |
details. | |
output_attentions (`bool`, *optional*): | |
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned | |
tensors for more detail. | |
output_hidden_states (`bool`, *optional*): | |
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for | |
more detail. | |
return_dict (`bool`, *optional*): | |
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. | |
""" | |
BLIP_2_TEXT_INPUTS_DOCSTRING = r""" | |
Args: | |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): | |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide | |
it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and | |
[`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) | |
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: | |
- 1 for tokens that are **not masked**, | |
- 0 for tokens that are **masked**. | |
[What are attention masks?](../glossary#attention-mask) | |
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): | |
Indices of decoder input sequence tokens in the vocabulary. | |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and | |
[`PreTrainedTokenizer.__call__`] for details. | |
[What are decoder input IDs?](../glossary#decoder-input-ids) | |
T5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` | |
is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). | |
To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5 | |
Training](./t5#training). | |
decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): | |
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also | |
be used by default. | |
output_attentions (`bool`, *optional*): | |
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned | |
tensors for more detail. | |
output_hidden_states (`bool`, *optional*): | |
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for | |
more detail. | |
return_dict (`bool`, *optional*): | |
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. | |
""" | |
BLIP_2_INPUTS_DOCSTRING = r""" | |
Args: | |
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): | |
Pixel values. Pixel values can be obtained using [`Blip2Processor`]. See [`Blip2Processor.__call__`] for | |
details. | |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Indices of input sequence tokens in the vocabulary of the language model. Input tokens can optionally be | |
provided to serve as text prompt, which the language model can continue. | |
Indices can be obtained using [`Blip2Processor`]. See [`Blip2Processor.__call__`] for details. | |
[What are input IDs?](../glossary#input-ids) | |
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: | |
- 1 for tokens that are **not masked**, | |
- 0 for tokens that are **masked**. | |
[What are attention masks?](../glossary#attention-mask) | |
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): | |
Indices of decoder input sequence tokens in the vocabulary of the language model. Only relevant in case an | |
encoder-decoder language model (like T5) is used. | |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and | |
[`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) | |
decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): | |
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also | |
be used by default. | |
Only relevant in case an encoder-decoder language model (like T5) is used. | |
output_attentions (`bool`, *optional*): | |
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned | |
tensors for more detail. | |
output_hidden_states (`bool`, *optional*): | |
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for | |
more detail. | |
return_dict (`bool`, *optional*): | |
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. | |
""" | |
# Copied from transformers.models.blip.modeling_blip.BlipEncoder with Blip->Blip2 | |
class Blip2Encoder(nn.Module): | |
""" | |
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a | |
[`Blip2EncoderLayer`]. | |
Args: | |
config (`Blip2Config`): | |
The corresponding vision configuration for the `Blip2Encoder`. | |
""" | |
def __init__(self, config: Blip2Config): | |
super().__init__() | |
self.config = config | |
self.layers = nn.ModuleList([Blip2EncoderLayer(config) for _ in range(config.num_hidden_layers)]) | |
self.gradient_checkpointing = False | |
def forward( | |
self, | |
inputs_embeds, | |
attention_mask: Optional[torch.Tensor] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple, BaseModelOutput]: | |
r""" | |
Args: | |
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): | |
Embedded representation of the inputs. Should be float, not int tokens. | |
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: | |
- 1 for tokens that are **not masked**, | |
- 0 for tokens that are **masked**. | |
[What are attention masks?](../glossary#attention-mask) | |
output_attentions (`bool`, *optional*): | |
Whether or not to return the attentions tensors of all attention layers. See `attentions` under | |
returned tensors for more detail. | |
output_hidden_states (`bool`, *optional*): | |
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors | |
for more detail. | |
return_dict (`bool`, *optional*): | |
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. | |
""" | |
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 | |
encoder_states = () if output_hidden_states else None | |
all_attentions = () if output_attentions else None | |
hidden_states = inputs_embeds | |
for idx, encoder_layer in enumerate(self.layers): | |
if output_hidden_states: | |
encoder_states = encoder_states + (hidden_states,) | |
if self.gradient_checkpointing and self.training: | |
def create_custom_forward(module): | |
def custom_forward(*inputs): | |
return module(*inputs, output_attentions) | |
return custom_forward | |
layer_outputs = torch.utils.checkpoint.checkpoint( | |
create_custom_forward(encoder_layer), | |
hidden_states, | |
attention_mask, | |
) | |
else: | |
layer_outputs = encoder_layer( | |
hidden_states, | |
attention_mask, | |
output_attentions=output_attentions, | |
) | |
hidden_states = layer_outputs[0] | |
if output_attentions: | |
all_attentions = all_attentions + (layer_outputs[1],) | |
if output_hidden_states: | |
encoder_states = encoder_states + (hidden_states,) | |
if not return_dict: | |
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) | |
return BaseModelOutput( | |
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions | |
) | |
# Copied from transformers.models.blip.modeling_blip.BlipVisionModel with Blip->Blip2, BLIP->BLIP_2 | |
class Blip2VisionModel(Blip2PreTrainedModel): | |
main_input_name = "pixel_values" | |
config_class = Blip2VisionConfig | |
def __init__(self, config: Blip2VisionConfig): | |
super().__init__(config) | |
self.config = config | |
embed_dim = config.hidden_size | |
self.embeddings = Blip2VisionEmbeddings(config) | |
self.encoder = Blip2Encoder(config) | |
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) | |
self.post_init() | |
def forward( | |
self, | |
pixel_values: Optional[torch.FloatTensor] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple, BaseModelOutputWithPooling]: | |
r""" | |
Returns: | |
""" | |
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 pixel_values is None: | |
raise ValueError("You have to specify pixel_values") | |
hidden_states = self.embeddings(pixel_values) | |
encoder_outputs = self.encoder( | |
inputs_embeds=hidden_states, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
last_hidden_state = encoder_outputs[0] | |
last_hidden_state = self.post_layernorm(last_hidden_state) | |
pooled_output = last_hidden_state[:, 0, :] | |
pooled_output = self.post_layernorm(pooled_output) | |
if not return_dict: | |
return (last_hidden_state, pooled_output) + encoder_outputs[1:] | |
return BaseModelOutputWithPooling( | |
last_hidden_state=last_hidden_state, | |
pooler_output=pooled_output, | |
hidden_states=encoder_outputs.hidden_states, | |
attentions=encoder_outputs.attentions, | |
) | |
def get_input_embeddings(self): | |
return self.embeddings | |
class Blip2QFormerMultiHeadAttention(nn.Module): | |
def __init__(self, config, is_cross_attention=False): | |
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) | |
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, | |
): | |
# 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)) | |
mixed_query_layer = self.query(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 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 | |
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->Blip2QFormer | |
class Blip2QFormerSelfOutput(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 | |
class Blip2QFormerAttention(nn.Module): | |
def __init__(self, config, is_cross_attention=False): | |
super().__init__() | |
self.attention = Blip2QFormerMultiHeadAttention(config, is_cross_attention) | |
self.output = Blip2QFormerSelfOutput(config) | |
self.pruned_heads = set() | |
def prune_heads(self, heads): | |
if len(heads) == 0: | |
return | |
heads, index = find_pruneable_heads_and_indices( | |
heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads | |
) | |
# Prune linear layers | |
self.attention.query = prune_linear_layer(self.attention.query, index) | |
self.attention.key = prune_linear_layer(self.attention.key, index) | |
self.attention.value = prune_linear_layer(self.attention.value, index) | |
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) | |
# Update hyper params and store pruned heads | |
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) | |
self.attention.all_head_size = self.attention.attention_head_size * self.attention.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.attention( | |
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->Blip2QFormer | |
class Blip2QFormerIntermediate(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->Blip2QFormer | |
class Blip2QFormerOutput(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 Blip2QFormerLayer(nn.Module): | |
def __init__(self, config, layer_idx): | |
super().__init__() | |
self.chunk_size_feed_forward = config.chunk_size_feed_forward | |
self.seq_len_dim = 1 | |
self.attention = Blip2QFormerAttention(config) | |
self.layer_idx = layer_idx | |
if layer_idx % config.cross_attention_frequency == 0: | |
self.crossattention = Blip2QFormerAttention(config, is_cross_attention=True) | |
self.has_cross_attention = True | |
else: | |
self.has_cross_attention = False | |
self.intermediate_query = Blip2QFormerIntermediate(config) | |
self.output_query = Blip2QFormerOutput(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, | |
query_length=0, | |
): | |
# 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 query_length > 0: | |
query_attention_output = attention_output[:, :query_length, :] | |
if self.has_cross_attention: | |
if encoder_hidden_states is None: | |
raise ValueError("encoder_hidden_states must be given for cross-attention layers") | |
cross_attention_outputs = self.crossattention( | |
query_attention_output, | |
attention_mask, | |
head_mask, | |
encoder_hidden_states, | |
encoder_attention_mask, | |
output_attentions=output_attentions, | |
) | |
query_attention_output = cross_attention_outputs[0] | |
# add cross attentions if we output attention weights | |
outputs = outputs + cross_attention_outputs[1:-1] | |
layer_output = apply_chunking_to_forward( | |
self.feed_forward_chunk_query, | |
self.chunk_size_feed_forward, | |
self.seq_len_dim, | |
query_attention_output, | |
) | |
if attention_output.shape[1] > query_length: | |
layer_output_text = apply_chunking_to_forward( | |
self.feed_forward_chunk, | |
self.chunk_size_feed_forward, | |
self.seq_len_dim, | |
attention_output[:, query_length:, :], | |
) | |
layer_output = torch.cat([layer_output, layer_output_text], dim=1) | |
else: | |
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 | |
def feed_forward_chunk_query(self, attention_output): | |
intermediate_output = self.intermediate_query(attention_output) | |
layer_output = self.output_query(intermediate_output, attention_output) | |
return layer_output | |
class Blip2QFormerEncoder(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.config = config | |
self.layer = nn.ModuleList( | |
[Blip2QFormerLayer(config, layer_idx) for layer_idx 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, | |
query_length=0, | |
): | |
all_hidden_states = () if output_hidden_states else None | |
all_self_attentions = () if output_attentions else None | |
all_cross_attentions = () if output_attentions 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 getattr(self.config, "gradient_checkpointing", False) and self.training: | |
if use_cache: | |
logger.warning( | |
"`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, query_length) | |
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, | |
query_length, | |
) | |
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 layer_module.has_cross_attention: | |
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, | |
) | |
class Blip2QFormerModel(Blip2PreTrainedModel): | |
""" | |
Querying Transformer (Q-Former), used in BLIP-2. | |
""" | |
def __init__(self, config: Blip2QFormerConfig): | |
super().__init__(config) | |
self.config = config | |
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) | |
self.dropout = nn.Dropout(config.hidden_dropout_prob) | |
self.encoder = Blip2QFormerEncoder(config) | |
self.post_init() | |
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: torch.Tensor, | |
input_shape: Tuple[int], | |
device: torch.device, | |
has_query: bool = False, | |
) -> torch.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] | |
# - the model is an encoder, so make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] | |
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, | |
query_embeds: torch.FloatTensor, | |
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] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: | |
r""" | |
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, `optional`): | |
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if | |
the model is configured as a decoder. | |
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, `optional`): | |
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in | |
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: | |
- 1 for tokens that are **not masked**, | |
- 0 for tokens that are **masked**. | |
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of: | |
shape `(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 `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 | |
# past_key_values_length | |
past_key_values_length = ( | |
past_key_values[0][0].shape[2] - self.config.query_length if past_key_values is not None else 0 | |
) | |
query_length = query_embeds.shape[1] if query_embeds is not None else 0 | |
embedding_output = self.layernorm(query_embeds) | |
embedding_output = self.dropout(embedding_output) | |
input_shape = embedding_output.size()[:-1] | |
batch_size, seq_length = input_shape | |
device = embedding_output.device | |
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 = self.get_extended_attention_mask(attention_mask, input_shape, device) | |
# 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) | |
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, | |
query_length=query_length, | |
) | |
sequence_output = encoder_outputs[0] | |
pooled_output = sequence_output[:, 0, :] | |
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 Blip2Model(Blip2PreTrainedModel): | |
config_class = Blip2Config | |
main_input_name = "pixel_values" | |
def __init__(self, config: Blip2Config): | |
super().__init__(config) | |
self.vision_model = Blip2VisionModel(config.vision_config) | |
self.query_tokens = nn.Parameter(torch.zeros(1, config.num_query_tokens, config.qformer_config.hidden_size)) | |
self.qformer = Blip2QFormerModel(config.qformer_config) | |
self.language_projection = nn.Linear(config.qformer_config.hidden_size, config.text_config.hidden_size) | |
if config.use_decoder_only_language_model: | |
language_model = AutoModelForCausalLM.from_config(config.text_config) | |
else: | |
language_model = AutoModelForSeq2SeqLM.from_config(config.text_config) | |
# Update _tied_weights_keys using the base model used. | |
if language_model._tied_weights_keys is not None: | |
self._tied_weights_keys = [f"language_model.{k}" for k in language_model._tied_weights_keys] | |
self.language_model = language_model | |
# Initialize weights and apply final processing | |
self.post_init() | |
def get_input_embeddings(self): | |
return self.language_model.get_input_embeddings() | |
def set_input_embeddings(self, value): | |
self.language_model.set_input_embeddings(value) | |
def set_output_embeddings(self, new_embeddings): | |
self.language_model.set_output_embeddings(new_embeddings) | |
def get_output_embeddings(self) -> nn.Module: | |
return self.language_model.get_output_embeddings() | |
def get_encoder(self): | |
return self.language_model.get_encoder() | |
def get_decoder(self): | |
return self.language_model.get_decoder() | |
def _tie_weights(self): | |
if not self.config.use_decoder_only_language_model: | |
self.language_model.encoder.embed_tokens = self.language_model.shared | |
self.language_model.decoder.embed_tokens = self.language_model.shared | |
def get_text_features( | |
self, | |
input_ids: Optional[torch.Tensor] = None, | |
attention_mask: Optional[torch.Tensor] = None, | |
decoder_input_ids: Optional[torch.Tensor] = None, | |
decoder_attention_mask: Optional[torch.Tensor] = None, | |
labels: Optional[torch.Tensor] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
): | |
r""" | |
Returns: | |
text_outputs (`CausalLMOutputWithPast`, or `tuple(torch.FloatTensor)` if `return_dict=False`): | |
The language model outputs. If `return_dict=True`, the output is a [`CausalLMOutputWithPast`] that | |
contains the language model logits, the past key values and the hidden states if | |
`output_hidden_states=True`. | |
Examples: | |
```python | |
>>> import torch | |
>>> from transformers import AutoTokenizer, Blip2Model | |
>>> device = "cuda" if torch.cuda.is_available() else "cpu" | |
>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16) | |
>>> model.to(device) # doctest: +IGNORE_RESULT | |
>>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/blip2-opt-2.7b") | |
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt").to(device) | |
>>> text_features = model.get_text_features(**inputs) | |
```""" | |
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 self.config.use_decoder_only_language_model: | |
text_outputs = self.language_model( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
else: | |
inputs_embeds = self.language_model.get_input_embeddings()(input_ids) | |
text_outputs = self.language_model( | |
inputs_embeds=inputs_embeds, | |
attention_mask=attention_mask, | |
decoder_input_ids=decoder_input_ids, | |
decoder_attention_mask=decoder_attention_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
labels=labels, | |
) | |
return text_outputs | |
def get_image_features( | |
self, | |
pixel_values: Optional[torch.FloatTensor] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
): | |
r""" | |
Returns: | |
vision_outputs (`BaseModelOutputWithPooling` or tuple of `torch.FloatTensor`): | |
The vision model outputs. If `return_dict=True`, the output is a [`BaseModelOutputWithPooling`] that | |
contains the image features, the pooled image features and the hidden states if | |
`output_hidden_states=True`. | |
Examples: | |
```python | |
>>> import torch | |
>>> from PIL import Image | |
>>> import requests | |
>>> from transformers import AutoProcessor, Blip2Model | |
>>> device = "cuda" if torch.cuda.is_available() else "cpu" | |
>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16) | |
>>> model.to(device) # doctest: +IGNORE_RESULT | |
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b") | |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" | |
>>> image = Image.open(requests.get(url, stream=True).raw) | |
>>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16) | |
>>> image_outputs = model.get_image_features(**inputs) | |
```""" | |
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 | |
vision_outputs = self.vision_model( | |
pixel_values=pixel_values, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
return vision_outputs | |
def get_qformer_features( | |
self, | |
pixel_values: Optional[torch.FloatTensor] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
): | |
r""" | |
Returns: | |
vision_outputs (`BaseModelOutputWithPooling` or tuple of `torch.FloatTensor`): | |
The vision model outputs. If `return_dict=True`, the output is a [`BaseModelOutputWithPooling`] that | |
contains the image features, the pooled image features and the hidden states if | |
`output_hidden_states=True`. | |
Examples: | |
```python | |
>>> import torch | |
>>> from PIL import Image | |
>>> import requests | |
>>> from transformers import Blip2Processor, Blip2Model | |
>>> device = "cuda" if torch.cuda.is_available() else "cpu" | |
>>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") | |
>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16) | |
>>> model.to(device) # doctest: +IGNORE_RESULT | |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" | |
>>> image = Image.open(requests.get(url, stream=True).raw) | |
>>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16) | |
>>> qformer_outputs = model.get_qformer_features(**inputs) | |
```""" | |
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 | |
vision_outputs = self.vision_model( | |
pixel_values=pixel_values, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
image_embeds = vision_outputs[0] | |
# step 2: forward the query tokens through the QFormer, using the image embeddings for cross-attention | |
image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device) | |
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1) | |
query_outputs = self.qformer( | |
query_embeds=query_tokens, | |
encoder_hidden_states=image_embeds, | |
encoder_attention_mask=image_attention_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
return query_outputs | |
def forward( | |
self, | |
pixel_values: torch.FloatTensor, | |
input_ids: torch.FloatTensor, | |
attention_mask: Optional[torch.LongTensor] = None, | |
decoder_input_ids: Optional[torch.LongTensor] = None, | |
decoder_attention_mask: Optional[torch.LongTensor] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
labels: Optional[torch.LongTensor] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple, Blip2ForConditionalGenerationModelOutput]: | |
r""" | |
Returns: | |
Examples: | |
```python | |
>>> from PIL import Image | |
>>> import requests | |
>>> from transformers import Blip2Processor, Blip2Model | |
>>> import torch | |
>>> device = "cuda" if torch.cuda.is_available() else "cpu" | |
>>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") | |
>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16) | |
>>> model.to(device) # doctest: +IGNORE_RESULT | |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" | |
>>> image = Image.open(requests.get(url, stream=True).raw) | |
>>> prompt = "Question: how many cats are there? Answer:" | |
>>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device, torch.float16) | |
>>> outputs = model(**inputs) | |
```""" | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
# step 1: forward the images through the vision encoder, | |
# to get image embeddings of shape (batch_size, seq_len, hidden_size) | |
vision_outputs = self.vision_model( | |
pixel_values=pixel_values, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
image_embeds = vision_outputs[0] | |
# step 2: forward the query tokens through the QFormer, using the image embeddings for cross-attention | |
image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device) | |
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1) | |
query_outputs = self.qformer( | |
query_embeds=query_tokens, | |
encoder_hidden_states=image_embeds, | |
encoder_attention_mask=image_attention_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
query_output = query_outputs[0] | |
# step 3: use the language model, conditioned on the query outputs and the prompt | |
language_model_inputs = self.language_projection(query_output) | |
language_model_attention_mask = torch.ones( | |
language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device | |
) | |
inputs_embeds = self.language_model.get_input_embeddings()(input_ids) | |
inputs_embeds = torch.cat([language_model_inputs, inputs_embeds], dim=1) | |
if attention_mask is None: | |
attention_mask = torch.ones_like(input_ids) | |
expected_device = language_model_attention_mask.device | |
attention_mask = torch.cat([language_model_attention_mask, attention_mask.to(expected_device)], dim=1) | |
if self.config.use_decoder_only_language_model: | |
outputs = self.language_model( | |
inputs_embeds=inputs_embeds, | |
attention_mask=attention_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
logits = outputs.logits if return_dict else outputs[0] | |
loss = None | |
# we compute the loss here since we need to take into account the sequence length of the query embeds | |
if labels is not None: | |
labels = labels.to(logits.device) | |
logits = logits[:, -labels.size(1) :, :] | |
# Shift so that tokens < n predict n | |
shift_logits = logits[..., :-1, :].contiguous() | |
shift_labels = labels[..., 1:].contiguous().to(logits.device) | |
# Flatten the tokens | |
loss_fct = CrossEntropyLoss(reduction="mean") | |
loss = loss_fct(shift_logits.view(-1, self.config.text_config.vocab_size), shift_labels.view(-1)) | |
else: | |
outputs = self.language_model( | |
inputs_embeds=inputs_embeds, | |
attention_mask=attention_mask, | |
decoder_input_ids=decoder_input_ids, | |
decoder_attention_mask=decoder_attention_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
labels=labels, | |
) | |
loss = outputs.loss if return_dict else outputs[0] | |
logits = outputs.logits if return_dict else outputs[1] | |
if not return_dict: | |
output = (logits, vision_outputs, query_outputs, outputs) | |
return ((loss,) + output) if loss is not None else output | |
return Blip2ForConditionalGenerationModelOutput( | |
loss=loss, | |
logits=logits, | |
vision_outputs=vision_outputs, | |
qformer_outputs=query_outputs, | |
language_model_outputs=outputs, | |
) | |
class Blip2ForConditionalGeneration(Blip2PreTrainedModel): | |
config_class = Blip2Config | |
main_input_name = "pixel_values" | |
def __init__(self, config: Blip2Config): | |
super().__init__(config) | |
self.vision_model = Blip2VisionModel(config.vision_config) | |
self.query_tokens = nn.Parameter(torch.zeros(1, config.num_query_tokens, config.qformer_config.hidden_size)) | |
self.qformer = Blip2QFormerModel(config.qformer_config) | |
self.language_projection = nn.Linear(config.qformer_config.hidden_size, config.text_config.hidden_size) | |
if config.use_decoder_only_language_model: | |
language_model = AutoModelForCausalLM.from_config(config.text_config) | |
else: | |
language_model = AutoModelForSeq2SeqLM.from_config(config.text_config) | |
# Update _tied_weights_keys using the base model used. | |
if language_model._tied_weights_keys is not None: | |
self._tied_weights_keys = [f"language_model.{k}" for k in language_model._tied_weights_keys] | |
self.language_model = language_model | |
# Initialize weights and apply final processing | |
self.post_init() | |
def get_input_embeddings(self): | |
return self.language_model.get_input_embeddings() | |
def set_input_embeddings(self, value): | |
self.language_model.set_input_embeddings(value) | |
def set_output_embeddings(self, new_embeddings): | |
self.language_model.set_output_embeddings(new_embeddings) | |
def get_output_embeddings(self) -> nn.Module: | |
return self.language_model.get_output_embeddings() | |
def get_encoder(self): | |
return self.language_model.get_encoder() | |
def get_decoder(self): | |
return self.language_model.get_decoder() | |
def _tie_weights(self): | |
if not self.config.use_decoder_only_language_model: | |
self.language_model.encoder.embed_tokens = self.language_model.shared | |
self.language_model.decoder.embed_tokens = self.language_model.shared | |
def _preprocess_accelerate(self): | |
r""" | |
Some pre-processing hacks to make the model `accelerate` compatible. Check | |
https://github.com/huggingface/transformers/pull/21707 for more details. | |
""" | |
hf_device_map = self.hf_device_map | |
if len(hf_device_map) > 1 and "language_model" not in hf_device_map and torch.cuda.device_count() > 1: | |
# warn users about unexpected behavior when using multi-GPU + BLIP-2 + `accelerate`. | |
logger.warning( | |
"The `language_model` is not in the `hf_device_map` dictionary and you are running your script" | |
" in a multi-GPU environment. this may lead to unexpected behavior when using `accelerate`." | |
" Please pass a `device_map` that contains `language_model` to remove this warning." | |
" Please refer to https://github.com/huggingface/blog/blob/main/accelerate-large-models.md for" | |
" more details on creating a `device_map` for large models.", | |
) | |
if hasattr(self.language_model, "_hf_hook"): | |
self.language_model._hf_hook.io_same_device = True # For `generate` compatibility | |
def forward( | |
self, | |
pixel_values: torch.FloatTensor, | |
input_ids: torch.FloatTensor, | |
attention_mask: Optional[torch.LongTensor] = None, | |
decoder_input_ids: Optional[torch.LongTensor] = None, | |
decoder_attention_mask: Optional[torch.LongTensor] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
labels: Optional[torch.LongTensor] = None, | |
return_dict: Optional[bool] = None, | |
) -> Union[Tuple, Blip2ForConditionalGenerationModelOutput]: | |
r""" | |
Returns: | |
Examples: | |
Image captioning (without providing a text prompt): | |
```python | |
>>> from PIL import Image | |
>>> import requests | |
>>> from transformers import Blip2Processor, Blip2ForConditionalGeneration | |
>>> import torch | |
>>> device = "cuda" if torch.cuda.is_available() else "cpu" | |
>>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") | |
>>> model = Blip2ForConditionalGeneration.from_pretrained( | |
... "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16 | |
... ) | |
>>> model.to(device) # doctest: +IGNORE_RESULT | |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" | |
>>> image = Image.open(requests.get(url, stream=True).raw) | |
>>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16) | |
>>> generated_ids = model.generate(**inputs) | |
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() | |
>>> print(generated_text) | |
two cats laying on a couch | |
``` | |
Visual question answering (prompt = question): | |
```python | |
>>> from PIL import Image | |
>>> import requests | |
>>> from transformers import Blip2Processor, Blip2ForConditionalGeneration | |
>>> import torch | |
>>> device = "cuda" if torch.cuda.is_available() else "cpu" | |
>>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") | |
>>> model = Blip2ForConditionalGeneration.from_pretrained( | |
... "Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map={"": 0}, torch_dtype=torch.float16 | |
... ) # doctest: +IGNORE_RESULT | |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" | |
>>> image = Image.open(requests.get(url, stream=True).raw) | |
>>> prompt = "Question: how many cats are there? Answer:" | |
>>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device="cuda", dtype=torch.float16) | |
>>> generated_ids = model.generate(**inputs) | |
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() | |
>>> print(generated_text) | |
two | |
``` | |
Note that int8 inference is also supported through [bitsandbytes](https://github.com/TimDettmers/bitsandbytes). | |
This greatly reduces the amount of memory used by the model while maintaining the same performance. | |
```python | |
>>> from PIL import Image | |
>>> import requests | |
>>> from transformers import Blip2Processor, Blip2ForConditionalGeneration | |
>>> import torch | |
>>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl") | |
>>> model = Blip2ForConditionalGeneration.from_pretrained( | |
... "Salesforce/blip2-flan-t5-xl", load_in_8bit=True, device_map={"": 0}, torch_dtype=torch.bfloat16 | |
... ) # doctest: +IGNORE_RESULT | |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" | |
>>> image = Image.open(requests.get(url, stream=True).raw) | |
>>> prompt = "Question: how many cats are there? Answer:" | |
>>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device="cuda", dtype=torch.bfloat16) | |
>>> generated_ids = model.generate(**inputs) | |
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() | |
>>> print(generated_text) | |
two | |
```""" | |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
# step 1: forward the images through the vision encoder, | |
# to get image embeddings of shape (batch_size, seq_len, hidden_size) | |
vision_outputs = self.vision_model( | |
pixel_values=pixel_values, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
image_embeds = vision_outputs[0] | |
# step 2: forward the query tokens through the QFormer, using the image embeddings for cross-attention | |
image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device) | |
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1) | |
query_outputs = self.qformer( | |
query_embeds=query_tokens, | |
encoder_hidden_states=image_embeds, | |
encoder_attention_mask=image_attention_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
query_output = query_outputs[0] | |
# step 3: use the language model, conditioned on the query outputs and the prompt | |
language_model_inputs = self.language_projection(query_output) | |
language_model_attention_mask = torch.ones( | |
language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device | |
) | |
inputs_embeds = self.language_model.get_input_embeddings()(input_ids) | |
inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1) | |
if attention_mask is None: | |
attention_mask = torch.ones_like(input_ids) | |
expected_device = language_model_attention_mask.device | |
attention_mask = torch.cat([language_model_attention_mask, attention_mask.to(expected_device)], dim=1) | |
if self.config.use_decoder_only_language_model: | |
outputs = self.language_model( | |
inputs_embeds=inputs_embeds, | |
attention_mask=attention_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
logits = outputs.logits if return_dict else outputs[0] | |
loss = None | |
# we compute the loss here since we need to take into account the sequence length of the query embeds | |
if labels is not None: | |
labels = labels.to(logits.device) | |
logits = logits[:, -labels.size(1) :, :] | |
# Shift so that tokens < n predict n | |
shift_logits = logits[..., :-1, :].contiguous() | |
shift_labels = labels[..., 1:].contiguous().to(logits.device) | |
# Flatten the tokens | |
loss_fct = CrossEntropyLoss(reduction="mean") | |
loss = loss_fct(shift_logits.view(-1, self.config.text_config.vocab_size), shift_labels.view(-1)) | |
else: | |
outputs = self.language_model( | |
inputs_embeds=inputs_embeds, | |
attention_mask=attention_mask, | |
decoder_input_ids=decoder_input_ids, | |
decoder_attention_mask=decoder_attention_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
labels=labels, | |
) | |
loss = outputs.loss if return_dict else outputs[0] | |
logits = outputs.logits if return_dict else outputs[1] | |
if not return_dict: | |
output = (logits, vision_outputs, query_outputs, outputs) | |
return ((loss,) + output) if loss is not None else output | |
return Blip2ForConditionalGenerationModelOutput( | |
loss=loss, | |
logits=logits, | |
vision_outputs=vision_outputs, | |
qformer_outputs=query_outputs, | |
language_model_outputs=outputs, | |
) | |
def generate( | |
self, | |
pixel_values: torch.FloatTensor, | |
input_ids: Optional[torch.LongTensor] = None, | |
attention_mask: Optional[torch.LongTensor] = None, | |
**generate_kwargs, | |
) -> torch.LongTensor: | |
""" | |
Overrides `generate` function to be able to use the model as a conditional generator. | |
Args: | |
pixel_values (`torch.FloatTensor` of shape (batch_size, num_channels, height, width)): | |
Input images to be processed. | |
input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*): | |
The sequence used as a prompt for the generation. | |
attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*): | |
Mask to avoid performing attention on padding token indices | |
Returns: | |
captions (list): A list of strings of length batch_size * num_captions. | |
""" | |
if hasattr(self, "hf_device_map"): | |
# preprocess for `accelerate` | |
self._preprocess_accelerate() | |
batch_size = pixel_values.shape[0] | |
image_embeds = self.vision_model(pixel_values, return_dict=True).last_hidden_state | |
image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device) | |
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1) | |
query_outputs = self.qformer( | |
query_embeds=query_tokens, | |
encoder_hidden_states=image_embeds, | |
encoder_attention_mask=image_attention_mask, | |
return_dict=True, | |
) | |
query_output = query_outputs.last_hidden_state | |
language_model_inputs = self.language_projection(query_output) | |
language_attention_mask = torch.ones( | |
language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device | |
) | |
if input_ids is None: | |
input_ids = ( | |
torch.LongTensor([[self.config.text_config.bos_token_id]]) | |
.repeat(batch_size, 1) | |
.to(image_embeds.device) | |
) | |
if attention_mask is None: | |
attention_mask = torch.ones_like(input_ids) | |
attention_mask = torch.cat([language_attention_mask, attention_mask.to(language_attention_mask.device)], dim=1) | |
# concatenate query embeddings with prompt embeddings | |
inputs_embeds = self.get_input_embeddings()(input_ids) | |
inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1) | |
outputs = self.language_model.generate( | |
inputs_embeds=inputs_embeds, | |
attention_mask=attention_mask, | |
**generate_kwargs, | |
) | |
return outputs | |