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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Minimal implementation of BlipVisionModel intended to be only used
within a vision language model."""
from collections.abc import Iterable
from typing import Optional, Union
import torch
import torch.nn as nn
from transformers import Blip2VisionConfig, BlipVisionConfig
from vllm.attention.layer import MultiHeadAttention
from vllm.distributed import divide, get_tensor_model_parallel_world_size
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
QKVParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from .interfaces import SupportsQuant
def get_blip_patch_grid_length(*, image_size: int, patch_size: int) -> int:
assert image_size % patch_size == 0
return image_size // patch_size
def get_blip_num_patches(*, image_size: int, patch_size: int) -> int:
grid_length = get_blip_patch_grid_length(image_size=image_size,
patch_size=patch_size)
return grid_length * grid_length
# Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/blip/modeling_blip.py#L164 # noqa
class BlipVisionEmbeddings(nn.Module):
def __init__(self, config: Union[BlipVisionConfig, 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 = get_blip_num_patches(image_size=self.image_size,
patch_size=self.patch_size)
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.Tensor) -> 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)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
position_embeds = self.position_embedding.to(target_dtype)
embeddings = embeddings + position_embeds[:, :embeddings.size(1), :]
return embeddings
class BlipAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
config: Union[BlipVisionConfig, Blip2VisionConfig],
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
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(
"embed_dim must be divisible by num_heads "
f"(got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads}).")
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
self.qkv = QKVParallelLinear(
self.embed_dim,
self.head_dim,
self.num_heads,
bias=config.qkv_bias,
quant_config=quant_config,
prefix=f"{prefix}.qkv",
)
self.projection = RowParallelLinear(
self.embed_dim,
self.embed_dim,
quant_config=quant_config,
prefix=f"{prefix}.projection",
)
self.tp_size = get_tensor_model_parallel_world_size()
self.num_heads_per_partition = divide(self.num_heads, self.tp_size)
self.attn = MultiHeadAttention(self.num_heads_per_partition,
self.head_dim, self.scale)
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,
):
"""Input shape: Batch x Time x Channel"""
qkv_states, _ = self.qkv(hidden_states)
query_states, key_states, value_states = qkv_states.chunk(3, dim=-1)
out = self.attn(query_states, key_states, value_states)
attn_output, _ = self.projection(out)
return attn_output, None
class BlipMLP(nn.Module):
def __init__(
self,
config: BlipVisionConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
self.config = config
self.activation_fn = get_act_fn(config.hidden_act)
self.fc1 = ColumnParallelLinear(config.hidden_size,
config.intermediate_size,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.fc1")
self.fc2 = RowParallelLinear(config.intermediate_size,
config.hidden_size,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.fc2")
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 BlipEncoderLayer(nn.Module):
def __init__(
self,
config: BlipVisionConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
# fallback to sdpa attention if tp unavailable
self.self_attn = BlipAttention(
config,
quant_config=quant_config,
prefix=f"{prefix}.self_attn",
)
self.layer_norm1 = nn.LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
self.mlp = BlipMLP(config,
quant_config=quant_config,
prefix=f"{prefix}.mlp")
self.layer_norm2 = nn.LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, _ = self.self_attn(hidden_states=hidden_states)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
return hidden_states
class BlipEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self
attention layers. Each layer is a [`BlipEncoderLayer`].
Args:
config: BlipConfig
"""
def __init__(
self,
config: BlipVisionConfig,
quant_config: Optional[QuantizationConfig] = None,
num_hidden_layers_override: Optional[int] = None,
prefix: str = "",
) -> None:
super().__init__()
self.config = config
if num_hidden_layers_override is None:
num_hidden_layers = config.num_hidden_layers
else:
num_hidden_layers = num_hidden_layers_override
self.layers = nn.ModuleList([
BlipEncoderLayer(config=config,
quant_config=quant_config,
prefix=f"{prefix}.layers.{layer_idx}")
for layer_idx in range(num_hidden_layers)
])
def forward(self, inputs_embeds: torch.Tensor):
hidden_states = inputs_embeds
for encoder_layer in self.layers:
hidden_states = encoder_layer(hidden_states)
return hidden_states
class BlipVisionModel(nn.Module, SupportsQuant):
config_class = BlipVisionConfig
main_input_name = "pixel_values"
packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
def __init__(
self,
config: BlipVisionConfig,
quant_config: Optional[QuantizationConfig] = None,
*,
num_hidden_layers_override: Optional[int] = None,
require_post_norm: Optional[bool] = None,
prefix: str = "",
) -> None:
super().__init__()
self.config = config
self.embeddings = BlipVisionEmbeddings(config)
self.encoder = BlipEncoder(
config=config,
quant_config=quant_config,
num_hidden_layers_override=num_hidden_layers_override,
prefix=f"{prefix}.encoder",
)
num_hidden_layers = config.num_hidden_layers
if len(self.encoder.layers) > config.num_hidden_layers:
raise ValueError(
f"The original encoder only has {num_hidden_layers} "
f"layers, but you requested {len(self.encoder.layers)} layers."
)
# If possible, skip post_layernorm to conserve memory
if require_post_norm is None:
require_post_norm = len(self.encoder.layers) == num_hidden_layers
if require_post_norm:
self.post_layernorm = nn.LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
else:
self.post_layernorm = None
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
hidden_states = self.embeddings(pixel_values)
hidden_states = self.encoder(inputs_embeds=hidden_states)
if self.post_layernorm is None:
return hidden_states
return self.post_layernorm(hidden_states)
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("qkv_proj", "q_proj", "q"),
("qkv_proj", "k_proj", "k"),
("qkv_proj", "v_proj", "v"),
]
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
layer_count = len(self.encoder.layers)
for name, loaded_weight in weights:
# post_layernorm is not needed in BlipVisionModel
if (name.startswith("post_layernorm")
and self.post_layernorm is None):
continue
# omit layers when num_hidden_layers_override is set
if name.startswith("encoder.layers"):
layer_idx = int(name.split(".")[2])
if layer_idx >= layer_count:
continue
for (param_name, weight_name, shard_id) in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
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