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340 lines
12 KiB
Python
340 lines
12 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Minimal implementation of BlipVisionModel intended to be only used
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within a vision language model."""
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from collections.abc import Iterable
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from typing import Optional, Union
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import torch
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import torch.nn as nn
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from transformers import Blip2VisionConfig, BlipVisionConfig
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from vllm.attention.layer import MultiHeadAttention
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from vllm.distributed import divide, get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from .interfaces import SupportsQuant
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def get_blip_patch_grid_length(*, image_size: int, patch_size: int) -> int:
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assert image_size % patch_size == 0
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return image_size // patch_size
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def get_blip_num_patches(*, image_size: int, patch_size: int) -> int:
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grid_length = get_blip_patch_grid_length(image_size=image_size,
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patch_size=patch_size)
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return grid_length * grid_length
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# Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/blip/modeling_blip.py#L164 # noqa
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class BlipVisionEmbeddings(nn.Module):
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def __init__(self, config: Union[BlipVisionConfig, Blip2VisionConfig]):
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.image_size = config.image_size
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self.patch_size = config.patch_size
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self.class_embedding = nn.Parameter(torch.randn(1, 1, self.embed_dim))
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self.patch_embedding = nn.Conv2d(
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in_channels=3,
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out_channels=self.embed_dim,
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kernel_size=self.patch_size,
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stride=self.patch_size,
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)
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self.num_patches = get_blip_num_patches(image_size=self.image_size,
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patch_size=self.patch_size)
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self.num_positions = self.num_patches + 1
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self.position_embedding = nn.Parameter(
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torch.randn(1, self.num_positions, self.embed_dim))
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def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
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batch_size = pixel_values.shape[0]
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target_dtype = self.patch_embedding.weight.dtype
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patch_embeds = self.patch_embedding(pixel_values.to(
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dtype=target_dtype)) # shape = [*, width, grid, grid]
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patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
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class_embeds = self.class_embedding.expand(batch_size, 1, -1)
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embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
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position_embeds = self.position_embedding.to(target_dtype)
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embeddings = embeddings + position_embeds[:, :embeddings.size(1), :]
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return embeddings
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class BlipAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(
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self,
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config: Union[BlipVisionConfig, Blip2VisionConfig],
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.embed_dim // self.num_heads
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if self.head_dim * self.num_heads != self.embed_dim:
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raise ValueError(
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"embed_dim must be divisible by num_heads "
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f"(got `embed_dim`: {self.embed_dim} and `num_heads`:"
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f" {self.num_heads}).")
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self.scale = self.head_dim**-0.5
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self.dropout = config.attention_dropout
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self.qkv = QKVParallelLinear(
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self.embed_dim,
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self.head_dim,
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self.num_heads,
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bias=config.qkv_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv",
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)
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self.projection = RowParallelLinear(
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self.embed_dim,
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self.embed_dim,
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quant_config=quant_config,
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prefix=f"{prefix}.projection",
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)
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self.tp_size = get_tensor_model_parallel_world_size()
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self.num_heads_per_partition = divide(self.num_heads, self.tp_size)
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self.attn = MultiHeadAttention(self.num_heads_per_partition,
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self.head_dim, self.scale)
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return tensor.view(bsz, seq_len, self.num_heads,
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self.head_dim).transpose(1, 2).contiguous()
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def forward(
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self,
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hidden_states: torch.Tensor,
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):
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"""Input shape: Batch x Time x Channel"""
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qkv_states, _ = self.qkv(hidden_states)
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query_states, key_states, value_states = qkv_states.chunk(3, dim=-1)
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out = self.attn(query_states, key_states, value_states)
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attn_output, _ = self.projection(out)
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return attn_output, None
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class BlipMLP(nn.Module):
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def __init__(
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self,
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config: BlipVisionConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.activation_fn = get_act_fn(config.hidden_act)
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self.fc1 = ColumnParallelLinear(config.hidden_size,
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config.intermediate_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.fc1")
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self.fc2 = RowParallelLinear(config.intermediate_size,
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config.hidden_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.fc2")
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.fc1(hidden_states)
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hidden_states = self.activation_fn(hidden_states)
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hidden_states, _ = self.fc2(hidden_states)
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return hidden_states
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class BlipEncoderLayer(nn.Module):
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def __init__(
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self,
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config: BlipVisionConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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# fallback to sdpa attention if tp unavailable
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self.self_attn = BlipAttention(
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config,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn",
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)
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self.layer_norm1 = nn.LayerNorm(config.hidden_size,
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eps=config.layer_norm_eps)
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self.mlp = BlipMLP(config,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp")
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self.layer_norm2 = nn.LayerNorm(config.hidden_size,
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eps=config.layer_norm_eps)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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residual = hidden_states
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hidden_states = self.layer_norm1(hidden_states)
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hidden_states, _ = self.self_attn(hidden_states=hidden_states)
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hidden_states = residual + hidden_states
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residual = hidden_states
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hidden_states = self.layer_norm2(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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class BlipEncoder(nn.Module):
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"""
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Transformer encoder consisting of `config.num_hidden_layers` self
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attention layers. Each layer is a [`BlipEncoderLayer`].
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Args:
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config: BlipConfig
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"""
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def __init__(
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self,
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config: BlipVisionConfig,
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quant_config: Optional[QuantizationConfig] = None,
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num_hidden_layers_override: Optional[int] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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if num_hidden_layers_override is None:
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num_hidden_layers = config.num_hidden_layers
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else:
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num_hidden_layers = num_hidden_layers_override
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self.layers = nn.ModuleList([
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BlipEncoderLayer(config=config,
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quant_config=quant_config,
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prefix=f"{prefix}.layers.{layer_idx}")
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for layer_idx in range(num_hidden_layers)
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])
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def forward(self, inputs_embeds: torch.Tensor):
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hidden_states = inputs_embeds
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for encoder_layer in self.layers:
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hidden_states = encoder_layer(hidden_states)
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return hidden_states
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class BlipVisionModel(nn.Module, SupportsQuant):
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config_class = BlipVisionConfig
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main_input_name = "pixel_values"
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packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
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def __init__(
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self,
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config: BlipVisionConfig,
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quant_config: Optional[QuantizationConfig] = None,
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*,
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num_hidden_layers_override: Optional[int] = None,
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require_post_norm: Optional[bool] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.embeddings = BlipVisionEmbeddings(config)
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self.encoder = BlipEncoder(
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config=config,
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quant_config=quant_config,
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num_hidden_layers_override=num_hidden_layers_override,
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prefix=f"{prefix}.encoder",
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)
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num_hidden_layers = config.num_hidden_layers
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if len(self.encoder.layers) > config.num_hidden_layers:
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raise ValueError(
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f"The original encoder only has {num_hidden_layers} "
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f"layers, but you requested {len(self.encoder.layers)} layers."
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)
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# If possible, skip post_layernorm to conserve memory
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if require_post_norm is None:
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require_post_norm = len(self.encoder.layers) == num_hidden_layers
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if require_post_norm:
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self.post_layernorm = nn.LayerNorm(config.hidden_size,
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eps=config.layer_norm_eps)
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else:
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self.post_layernorm = None
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def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
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hidden_states = self.embeddings(pixel_values)
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hidden_states = self.encoder(inputs_embeds=hidden_states)
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if self.post_layernorm is None:
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return hidden_states
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return self.post_layernorm(hidden_states)
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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]
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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layer_count = len(self.encoder.layers)
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for name, loaded_weight in weights:
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# post_layernorm is not needed in BlipVisionModel
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if (name.startswith("post_layernorm")
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and self.post_layernorm is None):
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continue
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# omit layers when num_hidden_layers_override is set
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if name.startswith("encoder.layers"):
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layer_idx = int(name.split(".")[2])
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if layer_idx >= layer_count:
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continue
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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