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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# A modified implementation of the AIMv2 Transformer
# inserted here also the image tokenizer used by Ovis2
from collections.abc import Iterable
from typing import Optional
import torch
import torch.nn as nn
from vllm.attention.layer import MultiHeadAttention
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.distributed.utils import divide
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
QKVParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.transformers_utils.configs.ovis import AIMv2Config
class AIMv2SwiGLUFFN(nn.Module):
def __init__(self, config: AIMv2Config, quant_config: QuantizationConfig,
prefix: str):
super().__init__()
hidden_features = config.intermediate_size
in_features = config.hidden_size
bias = config.use_bias
self.fc13 = MergedColumnParallelLinear(
in_features,
[hidden_features] * 2,
bias=bias,
quant_config=quant_config,
prefix=f"{prefix}.fc13",
)
self.fc2 = RowParallelLinear(
input_size=hidden_features,
output_size=in_features,
bias=bias,
quant_config=quant_config,
prefix=f"{prefix}.fc2",
)
self.act_fn = SiluAndMul()
def forward(self, x: torch.Tensor) -> torch.Tensor:
x, _ = self.fc13(x)
x = self.act_fn(x)
x, _ = self.fc2(x)
return x
class AIMv2PatchEmbed(nn.Module):
def __init__(self, config: AIMv2Config):
super().__init__()
self.proj = nn.Conv2d(
config.num_channels,
config.hidden_size,
kernel_size=(config.patch_size, config.patch_size),
stride=(config.patch_size, config.patch_size),
)
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.proj(x).flatten(2).transpose(1, 2)
x = self.norm.forward_native(x)
return x
class AIMv2ViTPreprocessor(nn.Module):
def __init__(self, config: AIMv2Config):
super().__init__()
num_patches = (config.image_size // config.patch_size)**2
self.patchifier = AIMv2PatchEmbed(config)
self.pos_embed = nn.Parameter(
torch.zeros((1, num_patches, config.hidden_size)))
def forward(self, x: torch.Tensor) -> torch.Tensor:
tokens = self.patchifier(x)
_, N, _ = tokens.shape
pos_embed = self.pos_embed.to(tokens.device)
tokens = tokens + pos_embed[:, :N]
return tokens
class AIMv2Attention(nn.Module):
def __init__(self, config: AIMv2Config, quant_config: QuantizationConfig,
prefix: str):
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.qkv = QKVParallelLinear(
hidden_size=self.embed_dim,
head_size=self.head_dim,
total_num_heads=self.num_heads,
bias=config.qkv_bias,
quant_config=quant_config,
prefix=f"{prefix}.qkv",
)
self.proj = RowParallelLinear(
input_size=self.embed_dim,
output_size=self.embed_dim,
bias=config.use_bias,
quant_config=quant_config,
prefix=f"{prefix}.proj",
)
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 forward(self, x: torch.Tensor) -> torch.Tensor:
qkv, _ = self.qkv(x)
q, k, v = qkv.chunk(3, dim=-1)
x = self.attn(q, k, v)
x, _ = self.proj(x)
return x
class AIMv2Block(nn.Module):
def __init__(self, config: AIMv2Config, quant_config: QuantizationConfig,
prefix: str):
super().__init__()
self.attn = AIMv2Attention(config,
quant_config=quant_config,
prefix=f"{prefix}.attn")
self.norm_1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.mlp = AIMv2SwiGLUFFN(config,
quant_config=quant_config,
prefix=f"{prefix}.mlp")
self.norm_2 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x + self.attn(self.norm_1.forward_native(x))
x = x + self.mlp(self.norm_2.forward_native(x))
return x
class AIMv2Transformer(nn.Module):
def __init__(
self,
config: AIMv2Config,
quant_config: QuantizationConfig,
*,
require_post_norm: Optional[bool] = None,
prefix: str = "",
):
super().__init__()
self.blocks = nn.ModuleList([
AIMv2Block(config, quant_config, prefix=f"{prefix}.blocks.{i}")
for i in range(config.num_hidden_layers)
])
if require_post_norm:
self.post_trunk_norm = RMSNorm(config.hidden_size,
eps=config.rms_norm_eps)
else:
self.post_trunk_norm = None
def forward(self, tokens: torch.Tensor) -> torch.Tensor:
# they take the -1 as the ref embeddings, like a clip skip
for block in self.blocks:
tokens = block(tokens)
if self.post_trunk_norm is not None:
tokens = self.post_trunk_norm(tokens)
return tokens
class AIMv2Model(torch.nn.Module):
def __init__(self,
config: AIMv2Config,
quant_config: QuantizationConfig,
*,
require_post_norm: Optional[bool] = None,
prefix: str = ""):
super().__init__()
self.preprocessor = AIMv2ViTPreprocessor(config)
self.trunk = AIMv2Transformer(config,
quant_config=quant_config,
require_post_norm=require_post_norm,
prefix=f"{prefix}.trunk")
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
x = self.preprocessor(pixel_values)
x = self.trunk(x)
return x
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
(".fc13", ".fc1", 0),
(".fc13", ".fc3", 1),
]
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
for name, loaded_weight in weights:
# post_layernorm is optional in SiglipVisionModel
if (name.startswith("trunk.post_trunk_norm")
and self.trunk.post_trunk_norm is None):
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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