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Convert formatting to use ruff instead of yapf + isort (#26247)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-05 15:06:22 +01:00
committed by GitHub
parent 17edd8a807
commit d6953beb91
1508 changed files with 115244 additions and 94146 deletions
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256
vllm/model_executor/models/glm4v.py
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@@ -4,6 +4,7 @@
# Adapted from
# https://github.com/zai-org/CogAgent
"""Inference-only CogAgent model compatible with THUDM weights."""
from argparse import Namespace
from collections.abc import Mapping, Sequence
from typing import Annotated, Literal, Optional, Union
@@ -22,28 +23,40 @@ from vllm.config import VllmConfig
from vllm.config.multimodal import BaseDummyOptions
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.model_executor.layers.activation import SiluAndMul, get_act_fn
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
MergedColumnParallelLinear,
QKVParallelLinear,
ReplicatedLinear,
RowParallelLinear)
from vllm.model_executor.layers.linear import (
ColumnParallelLinear,
MergedColumnParallelLinear,
QKVParallelLinear,
ReplicatedLinear,
RowParallelLinear,
)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.models.module_mapping import MultiModelKeys
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
MultiModalKwargsItems)
from vllm.multimodal.inputs import (
MultiModalDataDict,
MultiModalFieldConfig,
MultiModalKwargsItems,
)
from vllm.multimodal.parse import MultiModalDataItems
from vllm.multimodal.processing import (BaseMultiModalProcessor,
BaseProcessingInfo, PromptReplacement,
PromptUpdate)
from vllm.multimodal.processing import (
BaseMultiModalProcessor,
BaseProcessingInfo,
PromptReplacement,
PromptUpdate,
)
from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs import ChatGLMConfig
from vllm.utils.tensor_schema import TensorSchema, TensorShape
from .chatglm import ChatGLMBaseModel, ChatGLMModel
from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
SupportsMultiModal, SupportsPP)
from .interfaces import (
MultiModalEmbeddings,
SupportsLoRA,
SupportsMultiModal,
SupportsPP,
)
class GLMVImagePixelInputs(TensorSchema):
@@ -54,21 +67,22 @@ class GLMVImagePixelInputs(TensorSchema):
- h: Height of image
- w: Width of image
"""
type: Literal["pixel_values"] = "pixel_values"
data: Annotated[torch.Tensor, TensorShape("b", 3, "h", "w")]
class EVA2CLIPPatchEmbedding(nn.Module):
def __init__(self, config):
super().__init__()
self.proj = nn.Conv2d(config.in_channels,
config.hidden_size,
kernel_size=config.patch_size,
stride=config.patch_size)
self.proj = nn.Conv2d(
config.in_channels,
config.hidden_size,
kernel_size=config.patch_size,
stride=config.patch_size,
)
self.cls_embedding = nn.Parameter(torch.zeros(1, config.hidden_size))
self.position_embedding = nn.Embedding(config.num_positions,
config.hidden_size)
self.position_embedding = nn.Embedding(config.num_positions, config.hidden_size)
def forward(self, images: torch.Tensor) -> torch.Tensor:
"""
@@ -80,8 +94,7 @@ class EVA2CLIPPatchEmbedding(nn.Module):
torch.Tensor
Transformed tensor with shape (B, L, D)
"""
images = images.to(device=self.proj.weight.device,
dtype=self.proj.weight.dtype)
images = images.to(device=self.proj.weight.device, dtype=self.proj.weight.dtype)
x = self.proj(images)
x = x.flatten(2).transpose(1, 2)
cls_token = self.cls_embedding.expand(x.shape[0], -1, -1)
@@ -91,12 +104,11 @@ class EVA2CLIPPatchEmbedding(nn.Module):
class EVA2CLIPAttention(nn.Module):
def __init__(
self,
config,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = '',
prefix: str = "",
):
super().__init__()
self.hidden_size = config.hidden_size
@@ -119,8 +131,9 @@ class EVA2CLIPAttention(nn.Module):
prefix=f"{prefix}.dense",
)
self.attn = MultiHeadAttention(self.num_heads_per_rank, self.head_dim,
self.scale)
self.attn = MultiHeadAttention(
self.num_heads_per_rank, self.head_dim, self.scale
)
self.output_dropout = torch.nn.Dropout(config.dropout_prob)
def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -134,12 +147,11 @@ class EVA2CLIPAttention(nn.Module):
class EVA2CLIPMLP(nn.Module):
def __init__(
self,
config,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = '',
prefix: str = "",
):
super().__init__()
self.config = config
@@ -165,29 +177,27 @@ class EVA2CLIPMLP(nn.Module):
class EVA2CLIPTransformerLayer(nn.Module):
def __init__(
self,
config,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = '',
prefix: str = "",
):
super().__init__()
self.input_layernorm = LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
self.attention = EVA2CLIPAttention(config,
quant_config=quant_config,
prefix=f"{prefix}.attention")
self.mlp = EVA2CLIPMLP(config,
quant_config=quant_config,
prefix=f"{prefix}.mlp")
self.post_attention_layernorm = LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
self.input_layernorm = LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.attention = EVA2CLIPAttention(
config, quant_config=quant_config, prefix=f"{prefix}.attention"
)
self.mlp = EVA2CLIPMLP(
config, quant_config=quant_config, prefix=f"{prefix}.mlp"
)
self.post_attention_layernorm = LayerNorm(
config.hidden_size, eps=config.layer_norm_eps
)
def forward(self, hidden_states):
attention_input = hidden_states
attention_output = self.input_layernorm(
self.attention(attention_input))
attention_output = self.input_layernorm(self.attention(attention_input))
hidden_states = attention_input + attention_output
mlp_input = hidden_states
mlp_output = self.post_attention_layernorm(self.mlp(mlp_input))
@@ -196,20 +206,23 @@ class EVA2CLIPTransformerLayer(nn.Module):
class EVA2CLIPTransformer(nn.Module):
def __init__(
self,
config,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = '',
prefix: str = "",
):
super().__init__()
self.layers = nn.ModuleList([
EVA2CLIPTransformerLayer(config,
quant_config=quant_config,
prefix=f"{prefix}.layers.{layer_idx}")
for layer_idx in range(config.num_hidden_layers)
])
self.layers = nn.ModuleList(
[
EVA2CLIPTransformerLayer(
config,
quant_config=quant_config,
prefix=f"{prefix}.layers.{layer_idx}",
)
for layer_idx in range(config.num_hidden_layers)
]
)
def forward(self, hidden_states):
for layer_module in self.layers:
@@ -218,13 +231,12 @@ class EVA2CLIPTransformer(nn.Module):
class EVA2CLIPGLU(nn.Module):
def __init__(
self,
config,
in_features,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = '',
prefix: str = "",
):
"""
The original implementation is the same as:
@@ -233,14 +245,14 @@ class EVA2CLIPGLU(nn.Module):
config.hidden_size,
config.ffn_hidden_size,
bias=False,
quant_config=quant_config
quant_config=quant_config,
)
self.gate_proj = ColumnParallelLinear(
config.hidden_size,
config.ffn_hidden_size,
bias=False,
quant_config=quant_config
quant_config=quant_config,
)
```
```
@@ -255,7 +267,7 @@ class EVA2CLIPGLU(nn.Module):
config.hidden_size,
[config.ffn_hidden_size] * 2,
bias=False,
quant_config=quant_config
quant_config=quant_config,
)
```
```
@@ -263,27 +275,32 @@ class EVA2CLIPGLU(nn.Module):
```
"""
super().__init__()
self.linear_proj = ReplicatedLinear(in_features,
config.hidden_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.linear_proj")
self.linear_proj = ReplicatedLinear(
in_features,
config.hidden_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.linear_proj",
)
self.norm1 = nn.LayerNorm(config.hidden_size)
self.act1 = nn.GELU()
self.act2 = SiluAndMul()
self.merged_proj = MergedColumnParallelLinear(
config.hidden_size, [config.ffn_hidden_size] * 2,
config.hidden_size,
[config.ffn_hidden_size] * 2,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.merged_proj")
prefix=f"{prefix}.merged_proj",
)
self.dense_4h_to_h = RowParallelLinear(
config.ffn_hidden_size,
config.hidden_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.dense_4h_to_h")
prefix=f"{prefix}.dense_4h_to_h",
)
def forward(self, x):
x, _ = self.linear_proj(x)
@@ -295,27 +312,30 @@ class EVA2CLIPGLU(nn.Module):
class EVA2CLIPModel(nn.Module):
def __init__(
self,
config,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = '',
prefix: str = "",
):
super().__init__()
vision_config = Namespace(**config.vision_config)
self.patch_embedding = EVA2CLIPPatchEmbedding(vision_config)
self.transformer = EVA2CLIPTransformer(vision_config,
quant_config=quant_config,
prefix=f"{prefix}.transformer")
self.linear_proj = EVA2CLIPGLU(config,
in_features=config.hidden_size,
quant_config=quant_config,
prefix=f"{prefix}.linear_proj")
self.conv = nn.Conv2d(in_channels=vision_config.hidden_size,
out_channels=config.hidden_size,
kernel_size=2,
stride=2)
self.transformer = EVA2CLIPTransformer(
vision_config, quant_config=quant_config, prefix=f"{prefix}.transformer"
)
self.linear_proj = EVA2CLIPGLU(
config,
in_features=config.hidden_size,
quant_config=quant_config,
prefix=f"{prefix}.linear_proj",
)
self.conv = nn.Conv2d(
in_channels=vision_config.hidden_size,
out_channels=config.hidden_size,
kernel_size=2,
stride=2,
)
self.boi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
self.eoi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
self.scaling_factor = vision_config.scaling_factor
@@ -349,15 +369,14 @@ class EVA2CLIPModel(nn.Module):
class GLM4VModel(ChatGLMModel):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__(vllm_config=vllm_config, prefix=prefix)
quant_config = vllm_config.quant_config
self.vision = EVA2CLIPModel(self.config,
quant_config,
prefix=f"{prefix}.vision")
self.vision = EVA2CLIPModel(
self.config, quant_config, prefix=f"{prefix}.vision"
)
class GLM4VProcessor:
@@ -379,17 +398,19 @@ class GLM4VProcessor:
vision_config = config.vision_config
image_size = vision_config["image_size"]
self.image_transform = transforms.Compose([
transforms.Resize(
(image_size, image_size),
interpolation=InterpolationMode.BICUBIC,
),
transforms.ToTensor(),
transforms.Normalize(
mean=(0.48145466, 0.4578275, 0.40821073),
std=(0.26862954, 0.26130258, 0.27577711),
),
])
self.image_transform = transforms.Compose(
[
transforms.Resize(
(image_size, image_size),
interpolation=InterpolationMode.BICUBIC,
),
transforms.ToTensor(),
transforms.Normalize(
mean=(0.48145466, 0.4578275, 0.40821073),
std=(0.26862954, 0.26130258, 0.27577711),
),
]
)
def __call__(
self,
@@ -424,7 +445,6 @@ class GLM4VProcessor:
class GLM4VProcessingInfo(BaseProcessingInfo):
def get_hf_config(self):
return self.ctx.get_hf_config(ChatGLMConfig)
@@ -454,7 +474,6 @@ class GLM4VProcessingInfo(BaseProcessingInfo):
class GLM4VDummyInputsBuilder(BaseDummyInputsBuilder[GLM4VProcessingInfo]):
def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
num_images = mm_counts.get("image", 0)
@@ -477,16 +496,16 @@ class GLM4VDummyInputsBuilder(BaseDummyInputsBuilder[GLM4VProcessingInfo]):
image_overrides = mm_options.get("image") if mm_options else None
return {
"image":
self._get_dummy_images(width=target_width,
height=target_height,
num_images=num_images,
overrides=image_overrides)
"image": self._get_dummy_images(
width=target_width,
height=target_height,
num_images=num_images,
overrides=image_overrides,
)
}
class GLM4VMultiModalProcessor(BaseMultiModalProcessor[GLM4VProcessingInfo]):
def _hf_processor_applies_updates(
self,
prompt_text: str,
@@ -530,17 +549,18 @@ class GLM4VMultiModalProcessor(BaseMultiModalProcessor[GLM4VProcessingInfo]):
]
@MULTIMODAL_REGISTRY.register_processor(GLM4VMultiModalProcessor,
info=GLM4VProcessingInfo,
dummy_inputs=GLM4VDummyInputsBuilder)
class GLM4VForCausalLM(ChatGLMBaseModel, SupportsMultiModal, SupportsLoRA,
SupportsPP):
@MULTIMODAL_REGISTRY.register_processor(
GLM4VMultiModalProcessor,
info=GLM4VProcessingInfo,
dummy_inputs=GLM4VDummyInputsBuilder,
)
class GLM4VForCausalLM(ChatGLMBaseModel, SupportsMultiModal, SupportsLoRA, SupportsPP):
merge_by_field_config = True
packed_modules_mapping = {
"query_key_value": ["query_key_value"],
"dense_h_to_4h": ["dense_h_to_4h"],
"merged_proj": ["gate_proj", "dense_h_to_4h"]
"merged_proj": ["gate_proj", "dense_h_to_4h"],
}
def get_mm_mapping(self) -> MultiModelKeys:
@@ -550,7 +570,8 @@ class GLM4VForCausalLM(ChatGLMBaseModel, SupportsMultiModal, SupportsLoRA,
return MultiModelKeys.from_string_field(
language_model="transformer.encoder",
connector="transformer.vision.linear_proj",
tower_model="transformer.vision.transformer")
tower_model="transformer.vision.transformer",
)
@classmethod
def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
@@ -575,22 +596,21 @@ class GLM4VForCausalLM(ChatGLMBaseModel, SupportsMultiModal, SupportsLoRA,
self.transformer: GLM4VModel
def _parse_and_validate_image_input(
self, **kwargs: object) -> Optional[GLMVImagePixelInputs]:
self, **kwargs: object
) -> Optional[GLMVImagePixelInputs]:
pixel_values = kwargs.pop("pixel_values", None)
if pixel_values is not None:
expected_h = expected_w = self.config.vision_config["image_size"]
return GLMVImagePixelInputs(type="pixel_values",
data=pixel_values,
resolve_bindings={
"h": expected_h,
"w": expected_w
})
return GLMVImagePixelInputs(
type="pixel_values",
data=pixel_values,
resolve_bindings={"h": expected_h, "w": expected_w},
)
return None
def _process_image_input(
self, image_input: GLMVImagePixelInputs) -> torch.Tensor:
def _process_image_input(self, image_input: GLMVImagePixelInputs) -> torch.Tensor:
pixel_values = image_input["data"].to(dtype=self.config.torch_dtype)
return self.transformer.vision(pixel_values)
@@ -600,8 +620,7 @@ class GLM4VForCausalLM(ChatGLMBaseModel, SupportsMultiModal, SupportsLoRA,
get_input_embeddings = SupportsMultiModal.get_input_embeddings
def get_multimodal_embeddings(self,
**kwargs: object) -> MultiModalEmbeddings:
def get_multimodal_embeddings(self, **kwargs: object) -> MultiModalEmbeddings:
image_input = self._parse_and_validate_image_input(**kwargs)
if image_input is None:
return []
@@ -620,7 +639,8 @@ class GLM4VForCausalLM(ChatGLMBaseModel, SupportsMultiModal, SupportsLoRA,
if intermediate_tensors is not None:
inputs_embeds = None
hidden_states = self.transformer(input_ids, positions,
intermediate_tensors, inputs_embeds)
hidden_states = self.transformer(
input_ids, positions, intermediate_tensors, inputs_embeds
)
return hidden_states