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vllm-dev/vllm/model_executor/models/idefics2_vision_model.py
2025-08-29 07:42:57 -07:00

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Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# adapted from https://github.com/huggingface/transformers/blob/v4.43.2/src/transformers/models/idefics2/modeling_idefics2.py
# Copyright 2024 The vLLM team.
# Copyright 2024 the 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.
"""PyTorch Idefics2 model."""
from collections.abc import Iterable
from typing import Optional
import torch
from torch import nn
from transformers.models.idefics2.configuration_idefics2 import (
Idefics2Config, Idefics2VisionConfig)
from vllm.attention.layer import MultiHeadAttention
from vllm.distributed import 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,
ReplicatedLinear,
RowParallelLinear)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.multimodal.utils import run_dp_sharded_vision_model
class Idefics2VisionEmbeddings(nn.Module):
"""
This is a modified version of `siglip.modelign_siglip.SiglipVisionEmbeddings
` to enable images of variable
resolution.
The modifications are adapted from [Patch n' Pack: NaViT, a Vision
Transformer for any Aspect Ratio and Resolution](https://arxiv.org/abs/2307.06304)
which allows treating images in their native aspect ratio and without the
need to resize them to the same fixed size. In particular, we start from the
original pre-trained SigLIP model(which uses images of fixed-size square
images) and adapt it by training on images of variable resolutions.
"""
def __init__(self, config: Idefics2VisionConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
padding="valid",
)
self.num_patches_per_side = self.image_size // self.patch_size
self.num_patches = self.num_patches_per_side**2
self.num_positions = self.num_patches
self.position_embedding = nn.Embedding(self.num_positions,
self.embed_dim)
def forward(self,
pixel_values: torch.FloatTensor,
patch_attention_mask: torch.BoolTensor,
tgt_sizes: Optional[torch.IntTensor] = None) -> torch.Tensor:
batch_size, _, max_im_h, max_im_w = pixel_values.shape
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values.to(target_dtype))
embeddings = patch_embeds.flatten(2).transpose(1, 2)
max_nb_patches_h, max_nb_patches_w = (
max_im_h // self.patch_size,
max_im_w // self.patch_size,
)
boundaries = torch.arange(1 / self.num_patches_per_side, 1.0,
1 / self.num_patches_per_side)
position_ids = torch.full(size=(batch_size,
max_nb_patches_h * max_nb_patches_w),
fill_value=0)
for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
if tgt_sizes is not None:
nb_patches_h = tgt_sizes[batch_idx][0]
nb_patches_w = tgt_sizes[batch_idx][1]
else:
nb_patches_h = p_attn_mask[:, 0].sum()
nb_patches_w = p_attn_mask[0].sum()
fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h)
fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w)
bucket_coords_h = torch.bucketize(fractional_coords_h,
boundaries,
right=True)
bucket_coords_w = torch.bucketize(fractional_coords_w,
boundaries,
right=True)
pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side +
bucket_coords_w).flatten()
position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids
position_ids = position_ids.to(self.position_embedding.weight.device)
embeddings += self.position_embedding(position_ids)
return embeddings
class Idefics2VisionAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
config: Idefics2VisionConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
use_data_parallel: bool = False,
) -> 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 (got `embed_dim`: {self.embed_dim} and `num_heads`:" # noqa: E501
f" {self.num_heads}).")
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
tp_size = (1 if use_data_parallel else
get_tensor_model_parallel_world_size())
assert self.num_heads % tp_size == 0
self.num_heads_per_partition = self.num_heads // tp_size
if use_data_parallel:
self.q_size = self.num_heads * self.head_dim
self.qkv_proj = ReplicatedLinear(
self.embed_dim,
3 * self.q_size,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.qkv_proj",
)
self.out_proj = ReplicatedLinear(
self.embed_dim,
self.embed_dim,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.out_proj",
)
else:
self.qkv_proj = QKVParallelLinear(
self.embed_dim,
self.head_dim,
self.num_heads,
quant_config=quant_config,
prefix=f"{prefix}.qkv_proj",
)
self.out_proj = RowParallelLinear(
self.embed_dim,
self.embed_dim,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.out_proj",
)
self.attn = MultiHeadAttention(self.num_heads_per_partition,
self.head_dim, self.scale)
def forward(
self,
hidden_states: torch.Tensor,
) -> torch.Tensor:
qkv, _ = self.qkv_proj(
hidden_states
) # batch_size, q_len, 3 * num_heads_per_partition * head_dim
query_states, key_states, value_states = qkv.chunk(3, dim=-1)
out = self.attn(query_states, key_states, value_states)
attn_output, _ = self.out_proj(out)
return attn_output
class Idefics2VisionMLP(nn.Module):
def __init__(
self,
config: Idefics2VisionConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
use_data_parallel: bool = False,
) -> None:
super().__init__()
self.config = config
self.activation_fn = get_act_fn(config.hidden_act)
cls_fc1 = (ReplicatedLinear
if use_data_parallel else ColumnParallelLinear)
self.fc1 = cls_fc1(
config.hidden_size,
config.intermediate_size,
bias=True,
quant_config=quant_config,
prefix=f"{prefix}.fc1",
)
cls_fc2 = (ReplicatedLinear
if use_data_parallel else RowParallelLinear)
self.fc2 = cls_fc2(
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 Idefics2EncoderLayer(nn.Module):
def __init__(
self,
config: Idefics2Config,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
use_data_parallel: bool = False,
) -> None:
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = Idefics2VisionAttention(
config,
quant_config=quant_config,
prefix=f"{prefix}.self_attn",
use_data_parallel=use_data_parallel)
self.layer_norm1 = nn.LayerNorm(self.embed_dim,
eps=config.layer_norm_eps)
self.mlp = Idefics2VisionMLP(config,
quant_config=quant_config,
prefix=f"{prefix}.mlp",
use_data_parallel=use_data_parallel)
self.layer_norm2 = nn.LayerNorm(self.embed_dim,
eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`):
Input to the layer of shape `(batch, seq_len, embed_dim)`.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states = self.self_attn(hidden_states)
hidden_states += residual
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states += residual
return hidden_states
class Idefics2Encoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention
layers. Each layer is a
[`Idefics2EncoderLayer`].
Args:
config: Idefics2Config
"""
def __init__(
self,
config: Idefics2Config,
quant_config: Optional[QuantizationConfig] = None,
*,
num_hidden_layers_override: Optional[int] = None,
prefix: str = "",
use_data_parallel: bool = False,
) -> 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([
Idefics2EncoderLayer(config,
quant_config=quant_config,
prefix=f"{prefix}.layers.{layer_idx}",
use_data_parallel=use_data_parallel)
for layer_idx in range(num_hidden_layers)
])
def forward(
self,
inputs_embeds: torch.Tensor,
) -> torch.Tensor:
r"""
Args:
inputs_embeds (torch.Tensor):
Optionally, instead of passing `input_ids` you can choose to
directly pass an embedded representation.
This is useful if you want more control over how to convert
`input_ids` indices into associated vectorsthan the model's
internal embedding lookup matrix.
"""
hidden_states = inputs_embeds
for encoder_layer in self.layers:
layer_outputs = encoder_layer(hidden_states)
hidden_states = layer_outputs
return hidden_states
class Idefics2VisionTransformer(nn.Module):
def __init__(
self,
config: Idefics2VisionConfig,
quant_config: Optional[QuantizationConfig] = None,
*,
num_hidden_layers_override: Optional[int] = None,
require_post_norm: bool = True,
prefix: str = "",
use_data_parallel: bool = False,
) -> None:
super().__init__()
embed_dim = config.hidden_size
self.config = config
self.use_data_parallel = use_data_parallel
self.embeddings = Idefics2VisionEmbeddings(config)
self.encoder = Idefics2Encoder(
config,
quant_config=quant_config,
num_hidden_layers_override=num_hidden_layers_override,
prefix=f"{prefix}.encoder",
use_data_parallel=use_data_parallel)
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."
)
self.require_post_norm = require_post_norm
self.post_layernorm = nn.LayerNorm(
embed_dim,
eps=config.layer_norm_eps,
) if require_post_norm else nn.Identity()
def get_input_embeddings(self):
return self.embeddings
def forward(
self,
pixel_values,
patch_attention_mask: Optional[torch.BoolTensor] = None,
tgt_sizes: Optional[torch.IntTensor] = None,
) -> torch.Tensor:
hidden_states = self.embeddings(
pixel_values=pixel_values,
patch_attention_mask=patch_attention_mask,
tgt_sizes=tgt_sizes,
)
if self.use_data_parallel:
encoder_outputs = run_dp_sharded_vision_model(
hidden_states, self.encoder)
else:
encoder_outputs = self.encoder(hidden_states)
last_hidden_state = self.post_layernorm(encoder_outputs)
return last_hidden_state
def _consolidate_qkv_weights(
self, weights: Iterable[tuple[str, torch.Tensor]]
) -> Iterable[tuple[str, torch.Tensor]]:
qkv_idx_mappings = {
".self_attn.q_proj": 0,
".self_attn.k_proj": 1,
".self_attn.v_proj": 2,
}
qkv_weights = {}
for name, loaded_weight in weights:
for weight_name, idx in qkv_idx_mappings.items():
if weight_name not in name:
continue
new_name = name.replace(weight_name, ".self_attn.qkv_proj")
if new_name not in qkv_weights:
qkv_weights[new_name] = [None] * 3
qkv_weights[new_name][idx] = loaded_weight
break
else:
yield name, loaded_weight
for key, weight in qkv_weights.items():
qkv_weight = torch.cat(weight, dim=0)
yield key, qkv_weight
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)
if self.use_data_parallel:
weights = self._consolidate_qkv_weights(weights)
for name, loaded_weight in weights:
# skip pooling header
if name.startswith("head."):
continue
# post_layernorm is optional
if (name.startswith("post_layernorm.")
and not self.require_post_norm):
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 or self.use_data_parallel:
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