mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-17 12:55:36 +08:00
434 lines
15 KiB
Python
434 lines
15 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
# adapted from https://huggingface.co/OpenGVLab/InternVL2-4B/blob/main/modeling_intern_vit.py
|
|
# --------------------------------------------------------
|
|
# InternVL
|
|
# Copyright (c) 2023 OpenGVLab
|
|
# Licensed under The MIT License [see LICENSE for details]
|
|
# --------------------------------------------------------
|
|
from collections.abc import Iterable
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
from transformers import PretrainedConfig
|
|
from transformers.utils import torch_int
|
|
|
|
from vllm.attention.layer import MultiHeadAttention
|
|
from vllm.model_executor.layers.activation import get_act_fn
|
|
from vllm.model_executor.layers.layernorm import RMSNorm
|
|
from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
|
|
from vllm.model_executor.layers.quantization import QuantizationConfig
|
|
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
|
|
|
|
NORM2FN = {
|
|
"rms_norm": RMSNorm,
|
|
"layer_norm": nn.LayerNorm,
|
|
}
|
|
|
|
|
|
class InternS1VisionPatchEmbeddings(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
image_size, patch_size = config.image_size, config.patch_size
|
|
num_channels, hidden_size = config.num_channels, config.hidden_size
|
|
|
|
num_patches = (image_size[1] // patch_size[1]) * (
|
|
image_size[0] // patch_size[0]
|
|
)
|
|
patch_shape = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])
|
|
self.image_size = image_size
|
|
self.patch_size = patch_size
|
|
self.num_channels = num_channels
|
|
self.num_patches = num_patches
|
|
self.patch_shape = patch_shape
|
|
|
|
self.projection = nn.Conv2d(
|
|
num_channels, hidden_size, kernel_size=patch_size, stride=patch_size
|
|
)
|
|
|
|
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
|
|
batch_size, num_channels, height, width = pixel_values.shape
|
|
if num_channels != self.num_channels:
|
|
raise ValueError(
|
|
"Make sure that the channel dimension of the pixel values "
|
|
"match with the one set in the configuration."
|
|
)
|
|
|
|
embeddings = self.projection(pixel_values.to(self.projection.weight.dtype))
|
|
patch_height, patch_width = embeddings.shape[2], embeddings.shape[3]
|
|
embeddings = embeddings.flatten(2).transpose(1, 2)
|
|
|
|
return embeddings, (patch_height, patch_width)
|
|
|
|
|
|
class InternS1VisionEmbeddings(nn.Module):
|
|
def __init__(self, config: PretrainedConfig):
|
|
super().__init__()
|
|
self.config = config
|
|
self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
|
|
if config.use_mask_token:
|
|
self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
|
|
else:
|
|
self.mask_token = None
|
|
self.patch_embeddings = InternS1VisionPatchEmbeddings(config)
|
|
self.patch_size = config.patch_size
|
|
self.image_size = (
|
|
config.image_size
|
|
if isinstance(config.image_size, Iterable)
|
|
else (config.image_size, config.image_size)
|
|
)
|
|
num_patches = self.patch_embeddings.num_patches
|
|
if config.use_absolute_position_embeddings:
|
|
self.position_embeddings = nn.Parameter(
|
|
torch.zeros(1, num_patches + 1, config.hidden_size)
|
|
)
|
|
else:
|
|
self.position_embeddings = None
|
|
|
|
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
|
|
""" # noqa: E501
|
|
|
|
num_patches = embeddings.shape[1] - 1
|
|
num_positions = self.position_embeddings.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_embeddings
|
|
|
|
class_pos_embed = self.position_embeddings[:, :1]
|
|
patch_pos_embed = self.position_embeddings[:, 1:]
|
|
|
|
dim = embeddings.shape[-1]
|
|
|
|
new_height = height // self.patch_size[0]
|
|
new_width = width // self.patch_size[1]
|
|
|
|
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.Tensor,
|
|
bool_masked_pos: torch.BoolTensor | None = None,
|
|
) -> torch.Tensor:
|
|
_, _, height, width = pixel_values.shape
|
|
embeddings, (patch_height, patch_width) = self.patch_embeddings(pixel_values)
|
|
batch_size, seq_len, _ = embeddings.size()
|
|
|
|
if bool_masked_pos is not None:
|
|
mask_tokens = self.mask_token.expand(batch_size, seq_len, -1)
|
|
# replace the masked visual tokens by mask_tokens
|
|
w = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
|
|
embeddings = embeddings * (1 - w) + mask_tokens * w
|
|
|
|
cls_tokens = self.cls_token.expand(batch_size, -1, -1)
|
|
embeddings = torch.cat((cls_tokens, embeddings), dim=1)
|
|
|
|
if self.position_embeddings is not None:
|
|
embeddings = embeddings + self.interpolate_pos_encoding(
|
|
embeddings, height, width
|
|
)
|
|
|
|
return embeddings, (patch_height, patch_width)
|
|
|
|
|
|
class InternSdpaAttention(nn.Module):
|
|
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
*,
|
|
num_dummy_heads: int = 0,
|
|
) -> 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(
|
|
f"embed_dim must be divisible by num_heads "
|
|
f"(got `embed_dim`: {self.embed_dim} and `num_heads`:"
|
|
f" {self.num_heads})."
|
|
)
|
|
|
|
# Additional dummy heads are used to enable TP for common GPU counts.
|
|
self.dummy_dim = (num_dummy_heads + self.num_heads) * self.head_dim
|
|
|
|
self.scale = self.head_dim**-0.5
|
|
|
|
self.q_proj = nn.Linear(
|
|
self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias
|
|
)
|
|
self.k_proj = nn.Linear(
|
|
self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias
|
|
)
|
|
self.v_proj = nn.Linear(
|
|
self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias
|
|
)
|
|
|
|
self.qk_normalization = config.use_qk_norm
|
|
if self.qk_normalization:
|
|
self.q_norm = RMSNorm(
|
|
self.dummy_dim,
|
|
eps=config.layer_norm_eps,
|
|
var_hidden_size=self.embed_dim,
|
|
)
|
|
self.k_norm = RMSNorm(
|
|
self.dummy_dim,
|
|
eps=config.layer_norm_eps,
|
|
var_hidden_size=self.embed_dim,
|
|
)
|
|
|
|
self.projection_layer = nn.Linear(self.dummy_dim, self.embed_dim)
|
|
|
|
# Use unified MultiHeadAttention with automatic backend selection
|
|
self.attn = MultiHeadAttention(self.num_heads, self.head_dim, self.scale)
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
B, N, C = x.shape
|
|
|
|
q = self.q_proj(x)
|
|
k = self.k_proj(x)
|
|
v = self.v_proj(x)
|
|
|
|
if self.qk_normalization:
|
|
B_, N_, H_, D_ = q.shape
|
|
q = self.q_norm(q.flatten(-2, -1)).view(B_, N_, H_, D_)
|
|
k = self.k_norm(k.flatten(-2, -1)).view(B_, N_, H_, D_)
|
|
|
|
# Use unified MultiHeadAttention with automatic backend selection
|
|
x = self.attn(q, k, v)
|
|
|
|
x = self.projection_layer(x)
|
|
return x
|
|
|
|
|
|
class InternS1VisionMLP(nn.Module):
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
quant_config: QuantizationConfig | None = 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 InternS1VisionLayer(nn.Module):
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
quant_config: QuantizationConfig | None = None,
|
|
*,
|
|
num_dummy_heads: int = 0,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
self.attention = self._init_attn(
|
|
config,
|
|
quant_config,
|
|
num_dummy_heads=num_dummy_heads,
|
|
prefix=f"{prefix}.attention",
|
|
)
|
|
|
|
self.mlp = InternS1VisionMLP(
|
|
config, quant_config=quant_config, prefix=f"{prefix}.mlp"
|
|
)
|
|
self.layernorm_before = NORM2FN[config.norm_type](
|
|
config.hidden_size, eps=config.layer_norm_eps
|
|
)
|
|
self.layernorm_after = NORM2FN[config.norm_type](
|
|
config.hidden_size, eps=config.layer_norm_eps
|
|
)
|
|
|
|
init_values = config.layer_scale_init_value
|
|
self.lambda_1 = nn.Parameter(
|
|
init_values * torch.ones(config.hidden_size), requires_grad=True
|
|
)
|
|
self.lambda_2 = nn.Parameter(
|
|
init_values * torch.ones(config.hidden_size), requires_grad=True
|
|
)
|
|
|
|
def _init_attn(
|
|
self,
|
|
config: PretrainedConfig,
|
|
quant_config: QuantizationConfig | None,
|
|
*,
|
|
num_dummy_heads: int,
|
|
prefix: str = "",
|
|
):
|
|
return InternSdpaAttention(config, num_dummy_heads=num_dummy_heads)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
):
|
|
hidden_states = (
|
|
hidden_states
|
|
+ self.attention(self.layernorm_before(hidden_states)) * self.lambda_1
|
|
)
|
|
|
|
hidden_states = (
|
|
hidden_states
|
|
+ self.mlp(self.layernorm_after(hidden_states)) * self.lambda_2
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class InternS1VisionEncoder(nn.Module):
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
quant_config: QuantizationConfig | None = None,
|
|
*,
|
|
num_hidden_layers_override: int | None = None,
|
|
num_dummy_heads: int = 0,
|
|
prefix: str = "",
|
|
):
|
|
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.layer = nn.ModuleList(
|
|
[
|
|
InternS1VisionLayer(
|
|
config,
|
|
quant_config,
|
|
num_dummy_heads=num_dummy_heads,
|
|
prefix=f"{prefix}.layer.{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.layer:
|
|
hidden_states = encoder_layer(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class InternS1VisionModel(nn.Module):
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
quant_config: QuantizationConfig | None = None,
|
|
*,
|
|
num_hidden_layers_override: int | None = None,
|
|
num_dummy_heads: int = 0,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
self.config = config
|
|
|
|
self.embeddings = InternS1VisionEmbeddings(config)
|
|
self.encoder = InternS1VisionEncoder(
|
|
config=config,
|
|
num_hidden_layers_override=num_hidden_layers_override,
|
|
num_dummy_heads=num_dummy_heads,
|
|
prefix=f"{prefix}.encoder",
|
|
)
|
|
self.layernorm = (
|
|
nn.Identity()
|
|
if config.use_mean_pooling
|
|
else nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
)
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.patch_embeddings
|
|
|
|
def forward(
|
|
self,
|
|
pixel_values: torch.Tensor | None = None,
|
|
pixel_embeds: torch.Tensor | None = None,
|
|
) -> torch.FloatTensor:
|
|
if pixel_values is None and pixel_embeds is None:
|
|
raise ValueError("You have to specify pixel_values or pixel_embeds")
|
|
|
|
if pixel_embeds is not None:
|
|
hidden_states = pixel_embeds
|
|
elif pixel_values is not None:
|
|
if pixel_values.ndim == 4:
|
|
hidden_states, _ = self.embeddings(pixel_values)
|
|
else:
|
|
raise ValueError(f"wrong pixel_values size: {pixel_values.shape}")
|
|
|
|
encoder_outputs = self.encoder(inputs_embeds=hidden_states)
|
|
encoder_outputs = self.layernorm(encoder_outputs)
|
|
|
|
return encoder_outputs
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: set[str] = set()
|
|
for name, loaded_weight in weights:
|
|
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
|