1792 lines
80 KiB
Python
1792 lines
80 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
|
|
# This file was automatically generated from src/transformers/models/instructblipvideo/modular_instructblipvideo.py.
|
|
# Do NOT edit this file manually as any edits will be overwritten by the generation of
|
|
# the file from the modular. If any change should be done, please apply the change to the
|
|
# modular_instructblipvideo.py file directly. One of our CI enforces this.
|
|
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
|
|
# coding=utf-8
|
|
# Copyright 2024 HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import math
|
|
from dataclasses import dataclass
|
|
from typing import Any, Callable, Optional, Union
|
|
|
|
import torch
|
|
from torch import nn
|
|
|
|
from ...activations import ACT2FN
|
|
from ...cache_utils import Cache
|
|
from ...generation import GenerationMixin
|
|
from ...modeling_flash_attention_utils import FlashAttentionKwargs
|
|
from ...modeling_layers import GradientCheckpointingLayer
|
|
from ...modeling_outputs import (
|
|
BaseModelOutput,
|
|
BaseModelOutputWithPastAndCrossAttentions,
|
|
BaseModelOutputWithPooling,
|
|
BaseModelOutputWithPoolingAndCrossAttentions,
|
|
)
|
|
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
|
|
from ...processing_utils import Unpack
|
|
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
|
|
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple, logging, torch_int
|
|
from ...utils.deprecation import deprecate_kwarg
|
|
from ..auto import AutoModel, AutoModelForCausalLM, AutoModelForSeq2SeqLM
|
|
from .configuration_instructblipvideo import (
|
|
InstructBlipVideoConfig,
|
|
InstructBlipVideoQFormerConfig,
|
|
InstructBlipVideoVisionConfig,
|
|
)
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
class InstructBlipVideoVisionEmbeddings(nn.Module):
|
|
def __init__(self, config: InstructBlipVideoVisionConfig):
|
|
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 interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
|
|
"""
|
|
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
|
|
images. This method is also adapted to support torch.jit tracing.
|
|
|
|
Adapted from:
|
|
- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
|
|
- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
|
|
"""
|
|
|
|
num_patches = embeddings.shape[1] - 1
|
|
num_positions = self.position_embedding.shape[1] - 1
|
|
|
|
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
|
|
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
|
|
return self.position_embedding
|
|
|
|
class_pos_embed = self.position_embedding[:, :1]
|
|
patch_pos_embed = self.position_embedding[:, 1:]
|
|
|
|
dim = embeddings.shape[-1]
|
|
|
|
new_height = height // self.patch_size
|
|
new_width = width // self.patch_size
|
|
|
|
sqrt_num_positions = torch_int(num_positions**0.5)
|
|
patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
|
|
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
|
|
|
|
patch_pos_embed = nn.functional.interpolate(
|
|
patch_pos_embed,
|
|
size=(new_height, new_width),
|
|
mode="bicubic",
|
|
align_corners=False,
|
|
)
|
|
|
|
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
|
|
|
|
return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
|
|
|
|
def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
|
|
batch_size, _, height, width = pixel_values.shape
|
|
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)
|
|
if interpolate_pos_encoding:
|
|
position_embedding = self.interpolate_pos_encoding(embeddings, height, width)
|
|
else:
|
|
position_embedding = self.position_embedding
|
|
embeddings = embeddings + position_embedding[:, : embeddings.size(1), :].to(target_dtype)
|
|
return embeddings
|
|
|
|
|
|
# Adapted from transformers.models.siglip.modeling_siglip.eager_attention_forward -> InstructBlipVideo doesn't cast attn weights to fp32
|
|
def eager_attention_forward(
|
|
module: nn.Module,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor],
|
|
scaling: float,
|
|
dropout: float = 0.0,
|
|
**kwargs,
|
|
):
|
|
attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling
|
|
if attention_mask is not None:
|
|
attn_weights = attn_weights + attention_mask
|
|
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
|
|
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
|
|
|
|
attn_output = torch.matmul(attn_weights, value)
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
|
|
return attn_output, attn_weights
|
|
|
|
|
|
class InstructBlipVideoAttention(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.is_causal = False
|
|
self.attention_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,
|
|
**kwargs,
|
|
) -> 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]
|
|
|
|
attention_interface: Callable = eager_attention_forward
|
|
|
|
if self.config._attn_implementation != "eager":
|
|
if self.config._attn_implementation == "sdpa" and output_attentions:
|
|
logger.warning_once(
|
|
"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
|
|
'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
|
|
)
|
|
else:
|
|
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
|
|
|
|
attn_output, attn_weights = attention_interface(
|
|
self,
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
attention_mask=None,
|
|
dropout=0.0 if not self.training else self.attention_dropout,
|
|
scaling=self.scale,
|
|
**kwargs,
|
|
)
|
|
|
|
attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
|
|
attn_output = self.projection(attn_output)
|
|
|
|
outputs = (attn_output, attn_weights) if output_attentions else (attn_output, None)
|
|
return outputs
|
|
|
|
|
|
class InstructBlipVideoMLP(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
|
|
|
|
|
|
class InstructBlipVideoEncoderLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config: InstructBlipVideoConfig):
|
|
super().__init__()
|
|
self.embed_dim = config.hidden_size
|
|
self.self_attn = InstructBlipVideoAttention(config)
|
|
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
|
|
self.mlp = InstructBlipVideoMLP(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 InstructBlipVideoEncoder(nn.Module):
|
|
"""
|
|
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
|
|
[`InstructBlipVideoEncoderLayer`].
|
|
|
|
Args:
|
|
config (`InstructBlipVideoConfig`):
|
|
The corresponding vision configuration for the `InstructBlipVideoEncoder`.
|
|
"""
|
|
|
|
def __init__(self, config: InstructBlipVideoConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layers = nn.ModuleList([InstructBlipVideoEncoderLayer(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,)
|
|
|
|
layer_outputs = encoder_layer(
|
|
hidden_states,
|
|
attention_mask=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
|
|
)
|
|
|
|
|
|
class InstructBlipVideoQFormerMultiHeadAttention(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)
|
|
|
|
@deprecate_kwarg("past_key_value", version="4.55.0")
|
|
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
|
|
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)
|
|
|
|
# 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)
|
|
attention_scores_dtype = attention_scores.dtype
|
|
|
|
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).to(attention_scores_dtype)
|
|
|
|
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,)
|
|
|
|
return outputs
|
|
|
|
|
|
class InstructBlipVideoQFormerSelfOutput(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 InstructBlipVideoQFormerAttention(nn.Module):
|
|
def __init__(self, config, is_cross_attention=False):
|
|
super().__init__()
|
|
self.attention = InstructBlipVideoQFormerMultiHeadAttention(config, is_cross_attention)
|
|
self.output = InstructBlipVideoQFormerSelfOutput(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)
|
|
|
|
@deprecate_kwarg("past_key_value", version="4.55.0")
|
|
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[Cache] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> tuple[torch.Tensor]:
|
|
self_outputs = self.attention(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
attention_output = self.output(self_outputs[0], hidden_states)
|
|
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
class InstructBlipVideoQFormerIntermediate(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
|
|
|
|
|
|
class InstructBlipVideoQFormerOutput(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 InstructBlipVideoQFormerLayer(GradientCheckpointingLayer):
|
|
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 = InstructBlipVideoQFormerAttention(config)
|
|
|
|
self.layer_idx = layer_idx
|
|
|
|
if layer_idx % config.cross_attention_frequency == 0:
|
|
self.crossattention = InstructBlipVideoQFormerAttention(config, is_cross_attention=True)
|
|
self.has_cross_attention = True
|
|
else:
|
|
self.has_cross_attention = False
|
|
|
|
self.intermediate = InstructBlipVideoQFormerIntermediate(config)
|
|
self.output = InstructBlipVideoQFormerOutput(config)
|
|
|
|
self.intermediate_query = InstructBlipVideoQFormerIntermediate(config)
|
|
self.output_query = InstructBlipVideoQFormerOutput(config)
|
|
|
|
@deprecate_kwarg("past_key_value", version="4.55.0")
|
|
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,
|
|
):
|
|
self_attention_outputs = self.attention(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
outputs = 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=attention_mask,
|
|
head_mask=head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=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:]
|
|
|
|
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
|
|
|
|
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 InstructBlipVideoQFormerEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList(
|
|
[InstructBlipVideoQFormerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.gradient_checkpointing = False
|
|
|
|
@deprecate_kwarg("past_key_value", version="4.55.0")
|
|
@deprecate_kwarg("use_cache", version="4.55.0")
|
|
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
|
|
|
|
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
|
|
|
|
layer_outputs = layer_module(
|
|
hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
encoder_hidden_states, # as a positional argument for gradient checkpointing
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
output_attentions=output_attentions,
|
|
query_length=query_length,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
if query_length > 0 and 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,
|
|
all_hidden_states,
|
|
all_self_attentions,
|
|
all_cross_attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
|
|
class InstructBlipVideoQFormerEmbeddings(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 = 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=None,
|
|
position_ids=None,
|
|
query_embeds=None,
|
|
past_key_values_length=0,
|
|
):
|
|
if input_ids is not None:
|
|
seq_length = input_ids.size()[1]
|
|
else:
|
|
seq_length = 0
|
|
|
|
if position_ids is None:
|
|
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length].clone()
|
|
|
|
if input_ids is not None:
|
|
embeddings = self.word_embeddings(input_ids)
|
|
if self.position_embedding_type == "absolute":
|
|
position_embeddings = self.position_embeddings(position_ids.to(embeddings.device))
|
|
embeddings = embeddings + position_embeddings
|
|
|
|
if query_embeds is not None:
|
|
embeddings = torch.cat((query_embeds, embeddings), dim=1)
|
|
else:
|
|
embeddings = query_embeds
|
|
|
|
embeddings = embeddings.to(self.layernorm.weight.dtype)
|
|
embeddings = self.layernorm(embeddings)
|
|
embeddings = self.dropout(embeddings)
|
|
return embeddings
|
|
|
|
|
|
@auto_docstring
|
|
class InstructBlipVideoPreTrainedModel(PreTrainedModel):
|
|
config: InstructBlipVideoConfig
|
|
base_model_prefix = "blip"
|
|
supports_gradient_checkpointing = True
|
|
_supports_attention_backend = True
|
|
_supports_flash_attn = True
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = True
|
|
|
|
_can_compile_fullgraph = True
|
|
|
|
_no_split_modules = [
|
|
"InstructBlipVideoQFormerEmbeddings",
|
|
"InstructBlipVideoAttention",
|
|
"InstructBlipVideoQFormerMultiHeadAttention",
|
|
"InstructBlipVideoQFormerSelfOutput",
|
|
]
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
factor = self.config.initializer_range
|
|
|
|
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
|
module.weight.data.normal_(mean=0.0, std=factor)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=factor)
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
elif isinstance(module, InstructBlipVideoVisionEmbeddings):
|
|
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, (InstructBlipVideoForConditionalGeneration, InstructBlipVideoModel)):
|
|
module.query_tokens.data.zero_()
|
|
|
|
|
|
class InstructBlipVideoVisionModel(InstructBlipVideoPreTrainedModel):
|
|
main_input_name = "pixel_values"
|
|
config: InstructBlipVideoVisionConfig
|
|
|
|
def __init__(self, config: InstructBlipVideoVisionConfig):
|
|
super().__init__(config)
|
|
self.config = config
|
|
embed_dim = config.hidden_size
|
|
|
|
self.embeddings = InstructBlipVideoVisionEmbeddings(config)
|
|
self.encoder = InstructBlipVideoEncoder(config)
|
|
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
|
|
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
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,
|
|
interpolate_pos_encoding: bool = False,
|
|
) -> Union[tuple, BaseModelOutputWithPooling]:
|
|
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, interpolate_pos_encoding=interpolate_pos_encoding)
|
|
|
|
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 InstructBlipVideoQFormerModel(InstructBlipVideoPreTrainedModel):
|
|
"""
|
|
Querying Transformer (Q-Former), used in InstructBlipVideo. Slightly modified from BLIP-2 as it also takes the
|
|
instruction as input.
|
|
"""
|
|
|
|
_supports_attention_backend = False # adds position on attn weights before last matmul
|
|
_supports_flash_attn = False
|
|
_supports_sdpa = False
|
|
_supports_flex_attn = False
|
|
|
|
def __init__(self, config: InstructBlipVideoQFormerConfig):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = InstructBlipVideoQFormerEmbeddings(config)
|
|
|
|
self.encoder = InstructBlipVideoQFormerEncoder(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(
|
|
f"Wrong shape for input_ids (shape {input_shape}) or attention_mask (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
|
|
|
|
@deprecate_kwarg("past_key_value", version="4.55.0")
|
|
@deprecate_kwarg("use_cache", version="4.55.0")
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
query_embeds: Optional[torch.Tensor] = 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[Cache] = 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.FloatTensor], 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 (`Cache` 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
|
|
|
|
if input_ids is None and query_embeds is None:
|
|
raise ValueError("You have to specify query_embeds when input_ids is None")
|
|
|
|
query_length = query_embeds.shape[1] if query_embeds is not None else 0
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
query_embeds=query_embeds,
|
|
)
|
|
|
|
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)), 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 isinstance(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 isinstance(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,
|
|
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,
|
|
)
|
|
|
|
|
|
@dataclass
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Class defining the outputs of [`InstructBlipVideoForConditionalGeneration`].
|
|
"""
|
|
)
|
|
class InstructBlipVideoForConditionalGenerationModelOutput(ModelOutput):
|
|
r"""
|
|
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()
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
InstructBlipVideo base Model consisting of language model, qformer and vision encoder.
|
|
"""
|
|
)
|
|
class InstructBlipVideoModel(InstructBlipVideoPreTrainedModel):
|
|
main_input_name = "pixel_values"
|
|
_keep_in_fp32_modules = ["query_tokens"] # TODO @ArthurZucker I don't know why this is required for FP8
|
|
|
|
def __init__(self, config: InstructBlipVideoConfig):
|
|
super().__init__(config)
|
|
|
|
self.vision_model = InstructBlipVideoVisionModel(config.vision_config)
|
|
self.query_tokens = nn.Parameter(torch.zeros(1, config.num_query_tokens, config.qformer_config.hidden_size))
|
|
self.qformer = InstructBlipVideoQFormerModel(config.qformer_config)
|
|
|
|
self.language_projection = nn.Linear(config.qformer_config.hidden_size, config.text_config.hidden_size)
|
|
self.language_model = AutoModel.from_config(config.text_config)
|
|
|
|
if self.language_model._no_split_modules is not None:
|
|
self._no_split_modules.extend(self.language_model._no_split_modules)
|
|
|
|
if self.language_model._keep_in_fp32_modules is not None:
|
|
self._keep_in_fp32_modules.extend(self.language_model._keep_in_fp32_modules)
|
|
|
|
# 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 _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 + InstructBlipVideo + `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
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
pixel_values: torch.FloatTensor,
|
|
qformer_input_ids: torch.FloatTensor,
|
|
qformer_attention_mask: Optional[torch.LongTensor] = None,
|
|
input_ids: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.LongTensor] = None,
|
|
decoder_input_ids: Optional[torch.LongTensor] = None,
|
|
decoder_attention_mask: Optional[torch.LongTensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
interpolate_pos_encoding: bool = False,
|
|
use_cache: Optional[bool] = None,
|
|
**kwargs: Unpack[FlashAttentionKwargs],
|
|
) -> Union[tuple, InstructBlipVideoForConditionalGenerationModelOutput]:
|
|
r"""
|
|
qformer_input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Indices of input sequence tokens in the vocabulary of the Q-Former. Input tokens can optionally be provided
|
|
to serve as text prompt, which the Q-Former model will encode.
|
|
|
|
Indices can be obtained using [`InstructBlipVideoProcessor`]. See [`InstructBlipVideoProcessor.__call__`] for
|
|
details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
qformer_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)
|
|
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 [`InstructBlipVideoProcessor`]. See [`InstructBlipVideoProcessor.__call__`] for
|
|
details.
|
|
|
|
[What are input IDs?](../glossary#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.
|
|
"""
|
|
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,
|
|
# we process in a batched way, later unbatch it back (video has frames=4 always)
|
|
batch_size, frames, channel, height, width = pixel_values.shape
|
|
pixel_values = pixel_values.reshape(batch_size * frames, channel, height, width)
|
|
|
|
vision_outputs = self.vision_model(
|
|
pixel_values=pixel_values,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
)
|
|
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)
|
|
|
|
# difference with BLIP-2 here: we also feed the instruction prompt to the Q-Former
|
|
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
|
|
query_attention_mask = torch.ones(query_tokens.size()[:-1], dtype=torch.long, device=image_embeds.device)
|
|
|
|
if qformer_attention_mask is None:
|
|
qformer_attention_mask = torch.ones_like(qformer_input_ids)
|
|
|
|
qformer_input_ids = qformer_input_ids.repeat_interleave(frames, dim=0)
|
|
qformer_attention_mask = qformer_attention_mask.repeat_interleave(frames, dim=0)
|
|
qformer_attention_mask = torch.cat([query_attention_mask, qformer_attention_mask], dim=1)
|
|
query_outputs = self.qformer(
|
|
input_ids=qformer_input_ids,
|
|
attention_mask=qformer_attention_mask,
|
|
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][:, : query_tokens.size(1), :]
|
|
|
|
# step 3: use the language model, conditioned on the query outputs and the prompt
|
|
language_model_inputs = self.language_projection(query_output)
|
|
|
|
# unbatch inputs back, each video-frame gets `num_query_tokens` seq length
|
|
language_model_inputs = language_model_inputs.reshape(batch_size, self.config.num_query_tokens * frames, -1)
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.language_model.get_input_embeddings()(input_ids)
|
|
special_image_mask = input_ids == self.config.video_token_id
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones_like(input_ids)
|
|
else:
|
|
special_image_mask = inputs_embeds == self.get_input_embeddings()(
|
|
torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device)
|
|
)
|
|
special_image_mask = special_image_mask.all(-1)
|
|
|
|
special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
|
|
language_model_inputs = language_model_inputs.to(inputs_embeds.device, inputs_embeds.dtype)
|
|
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, language_model_inputs)
|
|
|
|
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,
|
|
use_cache=use_cache,
|
|
**kwargs,
|
|
)
|
|
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,
|
|
use_cache=use_cache,
|
|
**kwargs,
|
|
)
|
|
|
|
return InstructBlipVideoForConditionalGenerationModelOutput(
|
|
vision_outputs=vision_outputs,
|
|
qformer_outputs=query_outputs,
|
|
language_model_outputs=outputs,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
InstructBlipVideo Model for generating text given an image and an optional text prompt. The model consists of a vision
|
|
encoder, Querying Transformer (Q-Former) and a language model.
|
|
|
|
One can optionally pass `input_ids` to the model, which serve as a text prompt, to make the language model continue
|
|
the prompt. Otherwise, the language model starts generating text from the [BOS] (beginning-of-sequence) token.
|
|
"""
|
|
)
|
|
class InstructBlipVideoForConditionalGeneration(InstructBlipVideoPreTrainedModel, GenerationMixin):
|
|
config: InstructBlipVideoConfig
|
|
main_input_name = "pixel_values"
|
|
|
|
_can_compile_fullgraph = True
|
|
_keep_in_fp32_modules = ["query_tokens"] # TODO @ArthurZucker I don't know why this is required for FP8
|
|
|
|
def __init__(self, config: InstructBlipVideoConfig):
|
|
super().__init__(config)
|
|
|
|
self.vision_model = InstructBlipVideoVisionModel._from_config(config.vision_config)
|
|
|
|
self.query_tokens = nn.Parameter(torch.zeros(1, config.num_query_tokens, config.qformer_config.hidden_size))
|
|
self.qformer = InstructBlipVideoQFormerModel._from_config(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)
|
|
|
|
if language_model._no_split_modules is not None:
|
|
self._no_split_modules.extend(language_model._no_split_modules)
|
|
|
|
if language_model._keep_in_fp32_modules is not None:
|
|
self._keep_in_fp32_modules.extend(language_model._keep_in_fp32_modules)
|
|
|
|
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 + InstructBlipVideo + `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 get_image_features(
|
|
self,
|
|
pixel_values: torch.FloatTensor,
|
|
qformer_input_ids: torch.LongTensor,
|
|
qformer_attention_mask: Optional[torch.LongTensor] = None,
|
|
interpolate_pos_encoding: Optional[bool] = False,
|
|
return_dict: Optional[bool] = False,
|
|
):
|
|
"""
|
|
Encodes images into continuous embeddings that can be forwarded to the language model.
|
|
|
|
Args:
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
|
|
The tensors corresponding to the input images.
|
|
"""
|
|
pass
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
pixel_values: torch.FloatTensor,
|
|
qformer_input_ids: torch.FloatTensor,
|
|
qformer_attention_mask: Optional[torch.LongTensor] = None,
|
|
input_ids: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.LongTensor] = None,
|
|
decoder_input_ids: Optional[torch.LongTensor] = None,
|
|
decoder_attention_mask: Optional[torch.LongTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
return_dict: Optional[bool] = None,
|
|
interpolate_pos_encoding: bool = False,
|
|
use_cache: Optional[bool] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> Union[tuple, InstructBlipVideoForConditionalGenerationModelOutput]:
|
|
r"""
|
|
qformer_input_ids (`torch.LongTensor` of shape (batch_size, sequence_length)):
|
|
The sequence used as a prompt to be fed to the Q-Former module.
|
|
qformer_attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
|
|
Mask to avoid performing attention on padding token indices.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import InstructBlipVideoProcessor, InstructBlipVideoForConditionalGeneration
|
|
>>> import torch
|
|
>>> from huggingface_hub import hf_hub_download
|
|
>>> import av
|
|
>>> import numpy as np
|
|
|
|
>>> def read_video_pyav(container, indices):
|
|
... '''
|
|
... Decode the video with PyAV decoder.
|
|
... Args:
|
|
... container (`av.container.input.InputContainer`): PyAV container.
|
|
... indices (`list[int]`): List of frame indices to decode.
|
|
... Returns:
|
|
... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
|
|
... '''
|
|
... frames = []
|
|
... container.seek(0)
|
|
... start_index = indices[0]
|
|
... end_index = indices[-1]
|
|
... for i, frame in enumerate(container.decode(video=0)):
|
|
... if i > end_index:
|
|
... break
|
|
... if i >= start_index and i in indices:
|
|
... frames.append(frame)
|
|
... return np.stack([x.to_ndarray(format="rgb24") for x in frames])
|
|
|
|
>>> model = InstructBlipVideoForConditionalGeneration.from_pretrained("Salesforce/instructblip-vicuna-7b", device_map="auto")
|
|
>>> processor = InstructBlipVideoProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b")
|
|
|
|
>>> file_path = hf_hub_download(
|
|
... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset"
|
|
... )
|
|
>>> container = av.open(file_path)
|
|
|
|
>>> # sample uniformly 4 frames from the videWhy is this video funny?o
|
|
>>> total_frames = container.streams.video[0].frames
|
|
>>> indices = np.arange(0, total_frames, total_frames / 4).astype(int)
|
|
>>> clip = read_video_pyav(container, indices)
|
|
|
|
>>> prompt = "What is happening in the video?"
|
|
>>> inputs = processor(text=prompt, images=clip, return_tensors="pt").to(model.device)
|
|
|
|
>>> outputs = model.generate(
|
|
... **inputs,
|
|
... do_sample=False,
|
|
... num_beams=5,
|
|
... max_length=256,
|
|
... repetition_penalty=1.5,
|
|
... length_penalty=1.0,
|
|
... )
|
|
>>> generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()
|
|
>>> print(generated_text)
|
|
"A person is eating a bowl of pasta, and they are using a fork to eat it. The person is sitting at a table, and the plate of pasta is on the table in front"
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
language_model_inputs, vision_outputs, query_outputs = self.get_video_features(
|
|
pixel_values,
|
|
qformer_input_ids=qformer_input_ids,
|
|
qformer_attention_mask=qformer_attention_mask,
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
return_dict=True,
|
|
)
|
|
vision_outputs = vision_outputs.to_tuple() if not return_dict else vision_outputs
|
|
query_outputs = query_outputs.to_tuple() if not return_dict else query_outputs
|
|
language_model_attention_mask = torch.ones(
|
|
language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device
|
|
)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.get_input_embeddings()(input_ids)
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones_like(input_ids)
|
|
|
|
# if the model already has "video_token_id" then the input is expanded to account for image embeds
|
|
# otherwise we expand manually by concatenating
|
|
if getattr(self.config, "video_token_id", None) is not None:
|
|
if input_ids is None:
|
|
special_image_mask = inputs_embeds == self.get_input_embeddings()(
|
|
torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device)
|
|
)
|
|
special_image_mask = special_image_mask.all(-1)
|
|
else:
|
|
special_image_mask = input_ids == self.config.video_token_id
|
|
|
|
special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
|
|
language_model_inputs = language_model_inputs.to(inputs_embeds.device, inputs_embeds.dtype)
|
|
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, language_model_inputs)
|
|
else:
|
|
logger.warning_once(
|
|
"Expanding inputs for video tokens in InstructBLIPVideo should be done in processing. "
|
|
"Please follow instruction here (https://gist.github.com/zucchini-nlp/65f22892b054dc0d68228af56fbeaac2) to update your InstructBLIPVideo model. "
|
|
"Using processors without these attributes in the config is deprecated and will throw an error in v4.54."
|
|
)
|
|
inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1)
|
|
attention_mask = torch.cat(
|
|
[language_model_attention_mask, attention_mask.to(language_model_attention_mask.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,
|
|
use_cache=use_cache,
|
|
**kwargs,
|
|
)
|
|
logits = outputs.logits if return_dict else outputs[0]
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(
|
|
logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
|
|
)
|
|
|
|
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,
|
|
use_cache=use_cache,
|
|
**kwargs,
|
|
)
|
|
loss = outputs.loss if return_dict else outputs[0]
|
|
logits = outputs.logits if return_dict else outputs[1]
|
|
|
|
return InstructBlipVideoForConditionalGenerationModelOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
vision_outputs=vision_outputs,
|
|
qformer_outputs=query_outputs,
|
|
language_model_outputs=outputs,
|
|
)
|
|
|
|
@torch.no_grad()
|
|
def generate(
|
|
self,
|
|
pixel_values: torch.FloatTensor,
|
|
qformer_input_ids: Optional[torch.LongTensor] = None,
|
|
qformer_attention_mask: Optional[torch.LongTensor] = None,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.LongTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
interpolate_pos_encoding: bool = False,
|
|
**generate_kwargs,
|
|
) -> torch.LongTensor:
|
|
r"""
|
|
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) or
|
|
(batch_size, num_frames, num_channels, height, width)): Input images or videos to be processed.
|
|
qformer_input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
|
|
The sequence used as a prompt to be fed to the Q-Former module.
|
|
qformer_attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
|
|
Mask to avoid performing attention on padding token indices.
|
|
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.
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
|
Embedded representation of the inputs. Should be float, not int tokens.
|
|
interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
|
|
Whether to interpolate the positional encoding of the image embeddings.
|
|
|
|
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]
|
|
language_model_inputs, vision_outputs, query_outputs = self.get_video_features(
|
|
pixel_values,
|
|
qformer_input_ids=qformer_input_ids,
|
|
qformer_attention_mask=qformer_attention_mask,
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
return_dict=True,
|
|
)
|
|
|
|
language_attention_mask = torch.ones(
|
|
language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device
|
|
)
|
|
|
|
if inputs_embeds is None:
|
|
if input_ids is None:
|
|
start_tokens = [self.config.text_config.bos_token_id]
|
|
if getattr(self.config, "video_token_id", None) is not None:
|
|
start_tokens = [self.config.video_token_id] * self.config.num_query_tokens * 4 + start_tokens
|
|
input_ids = torch.tensor([start_tokens], dtype=torch.long, device=language_model_inputs.device)
|
|
input_ids = input_ids.repeat(batch_size, 1)
|
|
inputs_embeds = self.get_input_embeddings()(input_ids)
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones_like(input_ids)
|
|
|
|
# if the model already has "video_token_id" then the input is expanded to account for image embeds
|
|
# otherwise we expand manually by concatenating
|
|
if getattr(self.config, "video_token_id", None) is not None:
|
|
if input_ids is None:
|
|
special_image_mask = inputs_embeds == self.get_input_embeddings()(
|
|
torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device)
|
|
)
|
|
special_image_mask = special_image_mask.all(-1)
|
|
else:
|
|
special_image_mask = input_ids == self.config.video_token_id
|
|
|
|
special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
|
|
language_model_inputs = language_model_inputs.to(inputs_embeds.device, inputs_embeds.dtype)
|
|
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, language_model_inputs)
|
|
else:
|
|
logger.warning_once(
|
|
"Expanding inputs for video tokens in InstructBLIPVideo should be done in processing. "
|
|
"Please follow instruction here (https://gist.github.com/zucchini-nlp/65f22892b054dc0d68228af56fbeaac2) to update your InstructBLIPVideo model. "
|
|
"Using processors without these attributes in the config is deprecated and will throw an error in v4.54."
|
|
)
|
|
inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1)
|
|
attention_mask = torch.cat(
|
|
[language_attention_mask, attention_mask.to(language_attention_mask.device)], dim=1
|
|
)
|
|
|
|
# add image_embeds length to max_length, so that the final max_length in counted only on token embeds
|
|
# -1 is to account for the prepended BOS after `generate.`
|
|
if not self.language_model.config.is_encoder_decoder:
|
|
generate_kwargs["max_length"] = (
|
|
generate_kwargs.get("max_length", 20) + language_model_inputs.shape[1] - 1
|
|
)
|
|
generate_kwargs["min_length"] = generate_kwargs.get("min_length", 0) + language_model_inputs.shape[1]
|
|
|
|
inputs = {"inputs_embeds": inputs_embeds, "attention_mask": attention_mask}
|
|
if not self.language_model.config.is_encoder_decoder:
|
|
inputs["input_ids"] = input_ids
|
|
|
|
outputs = self.language_model.generate(**inputs, **generate_kwargs)
|
|
|
|
return outputs
|
|
|
|
def get_video_features(
|
|
self,
|
|
pixel_values: torch.FloatTensor,
|
|
qformer_input_ids: torch.LongTensor,
|
|
qformer_attention_mask: Optional[torch.LongTensor] = None,
|
|
interpolate_pos_encoding: Optional[bool] = False,
|
|
return_dict: Optional[bool] = False,
|
|
):
|
|
"""
|
|
Encodes images into continuous embeddings that can be forwarded to the language model.
|
|
|
|
Args:
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
|
|
The tensors corresponding to the input images.
|
|
"""
|
|
# step 1: forward the images through the vision encoder,
|
|
# we process in a batched way, later unbatch it back (video has frames=4 always)
|
|
batch_size, frames, channel, height, width = pixel_values.shape
|
|
pixel_values = pixel_values.reshape(batch_size * frames, channel, height, width)
|
|
|
|
vision_outputs = self.vision_model(
|
|
pixel_values=pixel_values,
|
|
interpolate_pos_encoding=interpolate_pos_encoding,
|
|
return_dict=True,
|
|
)
|
|
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)
|
|
|
|
# difference with BLIP-2 here: we also feed the instruction prompt to the Q-Former
|
|
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
|
|
query_attention_mask = torch.ones(query_tokens.size()[:-1], dtype=torch.long, device=image_embeds.device)
|
|
|
|
if qformer_attention_mask is None:
|
|
qformer_attention_mask = torch.ones_like(qformer_input_ids)
|
|
|
|
qformer_input_ids = qformer_input_ids.repeat_interleave(frames, dim=0)
|
|
qformer_attention_mask = qformer_attention_mask.repeat_interleave(frames, dim=0)
|
|
qformer_attention_mask = torch.cat([query_attention_mask, qformer_attention_mask], dim=1)
|
|
query_outputs = self.qformer(
|
|
input_ids=qformer_input_ids,
|
|
attention_mask=qformer_attention_mask,
|
|
query_embeds=query_tokens,
|
|
encoder_hidden_states=image_embeds,
|
|
encoder_attention_mask=image_attention_mask,
|
|
return_dict=True,
|
|
)
|
|
query_output = query_outputs[0][:, : query_tokens.size(1), :]
|
|
|
|
# step 3: use the language model, conditioned on the query outputs and the prompt
|
|
language_model_inputs = self.language_projection(query_output)
|
|
|
|
# unbatch inputs back, each video-frame gets `num_query_tokens` seq length
|
|
language_model_inputs = language_model_inputs.reshape(batch_size, self.config.num_query_tokens * frames, -1)
|
|
if return_dict:
|
|
return language_model_inputs, vision_outputs, query_outputs
|
|
return language_model_inputs
|
|
|
|
|
|
__all__ = [
|
|
"InstructBlipVideoVisionModel",
|
|
"InstructBlipVideoPreTrainedModel",
|
|
"InstructBlipVideoQFormerModel",
|
|
"InstructBlipVideoModel",
|
|
"InstructBlipVideoForConditionalGeneration",
|
|
]
|