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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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303
vllm/model_executor/models/deepseek_vl2.py
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@@ -3,6 +3,7 @@
# adapted from https://github.com/deepseek-ai/DeepSeek-VL2/blob/faf18023f24b962b32d9f0a2d89e402a8d383a78/deepseek_vl2/models/modeling_deepseek_vl_v2.py
"""Inference-only Deepseek-VL2 model compatible with HuggingFace weights."""
import math
from collections.abc import Iterable, Mapping, Sequence
from typing import Annotated, Literal, Optional, Union
@@ -20,28 +21,44 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.model_loader.utils import set_default_torch_dtype
from vllm.model_executor.models.transformers import replace_linear_class
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
MultiModalKwargsItems, MultiModalUUIDDict)
from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
ImageSize, MultiModalDataItems)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
BaseProcessingInfo,
MultiModalProcessingInfo,
PromptReplacement, PromptUpdate)
from vllm.multimodal.inputs import (
MultiModalDataDict,
MultiModalFieldConfig,
MultiModalKwargsItems,
MultiModalUUIDDict,
)
from vllm.multimodal.parse import (
ImageEmbeddingItems,
ImageProcessorItems,
ImageSize,
MultiModalDataItems,
)
from vllm.multimodal.processing import (
BaseMultiModalProcessor,
BaseProcessingInfo,
MultiModalProcessingInfo,
PromptReplacement,
PromptUpdate,
)
from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs.deepseek_vl2 import (DeepseekVLV2Config,
MlpProjectorConfig,
VisionEncoderConfig)
from vllm.transformers_utils.processors.deepseek_vl2 import (
DeepseekVLV2Processor)
from vllm.transformers_utils.configs.deepseek_vl2 import (
DeepseekVLV2Config,
MlpProjectorConfig,
VisionEncoderConfig,
)
from vllm.transformers_utils.processors.deepseek_vl2 import DeepseekVLV2Processor
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from vllm.utils import is_list_of
from vllm.utils.tensor_schema import TensorSchema, TensorShape
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .utils import (AutoWeightsLoader, WeightsMapper,
init_vllm_registered_model, maybe_prefix)
from .utils import (
AutoWeightsLoader,
WeightsMapper,
init_vllm_registered_model,
maybe_prefix,
)
# The image token id may be various
_IMAGE_TOKEN = "<image>"
@@ -56,9 +73,9 @@ class DeepseekVL2ImagePixelInputs(TensorSchema):
- h: Height of each image
- w: Width of each image
"""
type: Literal["pixel_values"]
data: Annotated[torch.Tensor,
TensorShape("bnp", 3, "h", "w", dynamic_dims={"bnp"})]
data: Annotated[torch.Tensor, TensorShape("bnp", 3, "h", "w", dynamic_dims={"bnp"})]
images_spatial_crop: Annotated[torch.Tensor, TensorShape("bn", 2)]
@@ -69,51 +86,53 @@ class DeepseekVL2VImageEmbeddingInputs(TensorSchema):
- f: Image feature size
- h: Hidden size (must match language model backbone)
"""
type: Literal["image_embeds"]
data: Annotated[Union[torch.Tensor, list[torch.Tensor]],
TensorShape("bn", "f", "h")]
data: Annotated[
Union[torch.Tensor, list[torch.Tensor]], TensorShape("bn", "f", "h")
]
DeepseekVL2ImageInputs = Union[DeepseekVL2ImagePixelInputs,
DeepseekVL2VImageEmbeddingInputs]
DeepseekVL2ImageInputs = Union[
DeepseekVL2ImagePixelInputs, DeepseekVL2VImageEmbeddingInputs
]
class MlpProjector(nn.Module):
def __init__(self, cfg: MlpProjectorConfig):
super().__init__()
self.cfg = cfg
assert not cfg.token_pooling, (
"Token pooling is not supported currently.")
assert not cfg.token_pooling, "Token pooling is not supported currently."
if cfg.projector_type == "downsample_mlp_gelu":
mlp_depth = cfg.depth
mlp_ratio = cfg.mlp_ratio
modules = [
nn.Linear(
cfg.input_dim * cfg.downsample_ratio *
cfg.downsample_ratio, cfg.n_embed * mlp_ratio)
cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio,
cfg.n_embed * mlp_ratio,
)
]
for _ in range(1, mlp_depth - 1):
modules.append(nn.GELU())
modules.append(
nn.Linear(cfg.n_embed * mlp_ratio,
cfg.n_embed * mlp_ratio))
nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio)
)
modules.append(nn.GELU())
modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
modules = nn.Sequential(*modules)
else:
raise NotImplementedError(
f"Unsupported projector type: {cfg.projector_type}")
f"Unsupported projector type: {cfg.projector_type}"
)
self.layers = modules
def forward(self, x):
bs, hw, input_dim = x.shape
h = w = int((hw)**0.5)
h = w = int((hw) ** 0.5)
"""compute padding"""
if h % self.cfg.downsample_ratio:
pad = self.cfg.downsample_ratio - h % self.cfg.downsample_ratio
@@ -124,17 +143,18 @@ class MlpProjector(nn.Module):
x = F.pad(x, (0, 0, 0, pad, 0, pad), "constant", 0)
"""4 to 1 concat"""
x = x.permute(0, 3, 1, 2) # B, C, H, W
x = F.unfold(x,
kernel_size=self.cfg.downsample_ratio,
stride=self.cfg.downsample_ratio,
padding=0) # B, C*4, HW // 4
x = F.unfold(
x,
kernel_size=self.cfg.downsample_ratio,
stride=self.cfg.downsample_ratio,
padding=0,
) # B, C*4, HW // 4
x = x.permute(0, 2, 1)
return self.layers(x)
class DeepseekVL2ProcessingInfo(BaseProcessingInfo):
def get_hf_config(self):
return self.ctx.get_hf_config(DeepseekVLV2Config)
@@ -144,11 +164,9 @@ class DeepseekVL2ProcessingInfo(BaseProcessingInfo):
def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
return {"image": None}
def get_num_image_tokens(self,
*,
image_width: int,
image_height: int,
cropping: bool = True) -> int:
def get_num_image_tokens(
self, *, image_width: int, image_height: int, cropping: bool = True
) -> int:
hf_processor = self.get_hf_processor()
image_size = hf_processor.image_size
patch_size = hf_processor.patch_size
@@ -156,9 +174,12 @@ class DeepseekVL2ProcessingInfo(BaseProcessingInfo):
if cropping:
best_width, best_height = hf_processor.select_best_resolution(
(image_width, image_height))
num_width_tiles, num_height_tiles = (best_width // image_size,
best_height // image_size)
(image_width, image_height)
)
num_width_tiles, num_height_tiles = (
best_width // image_size,
best_height // image_size,
)
else:
num_width_tiles = num_height_tiles = 1
@@ -171,15 +192,16 @@ class DeepseekVL2ProcessingInfo(BaseProcessingInfo):
def get_image_size_with_most_features(self) -> ImageSize:
hf_config = self.get_hf_config()
candidate_resolutions = hf_config.candidate_resolutions
height, width = max(candidate_resolutions,
key=lambda x: self.get_num_image_tokens(
image_width=x[1], image_height=x[0]))
height, width = max(
candidate_resolutions,
key=lambda x: self.get_num_image_tokens(
image_width=x[1], image_height=x[0]
),
)
return ImageSize(width=width, height=height)
class DeepseekVL2DummyInputsBuilder(
BaseDummyInputsBuilder[DeepseekVL2ProcessingInfo]):
class DeepseekVL2DummyInputsBuilder(BaseDummyInputsBuilder[DeepseekVL2ProcessingInfo]):
def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
num_images = mm_counts.get("image", 0)
@@ -201,17 +223,18 @@ class DeepseekVL2DummyInputsBuilder(
image_overrides = mm_options.get("image") if mm_options else None
return {
"image":
self._get_dummy_images(width=max_image_size.width,
height=max_image_size.height,
num_images=num_images,
overrides=image_overrides)
"image": self._get_dummy_images(
width=max_image_size.width,
height=max_image_size.height,
num_images=num_images,
overrides=image_overrides,
)
}
class DeepseekVL2MultiModalProcessor(
BaseMultiModalProcessor[DeepseekVL2ProcessingInfo]):
BaseMultiModalProcessor[DeepseekVL2ProcessingInfo]
):
def _call_hf_processor(
self,
prompt: str,
@@ -221,9 +244,7 @@ class DeepseekVL2MultiModalProcessor(
) -> BatchFeature:
if not mm_data:
tokenizer = self.info.get_tokenizer()
return tokenizer(prompt,
add_special_tokens=True,
return_tensors="pt")
return tokenizer(prompt, add_special_tokens=True, return_tensors="pt")
processed_outputs = super()._call_hf_processor(
prompt=prompt,
@@ -233,7 +254,8 @@ class DeepseekVL2MultiModalProcessor(
)
processed_outputs["num_patches"] = (
processed_outputs["images_spatial_crop"].prod(-1) + 1)
processed_outputs["images_spatial_crop"].prod(-1) + 1
)
return processed_outputs
@@ -245,8 +267,7 @@ class DeepseekVL2MultiModalProcessor(
num_patches = hf_inputs.get("num_patches", torch.empty(0))
return dict(
pixel_values=MultiModalFieldConfig.flat_from_sizes(
"image", num_patches),
pixel_values=MultiModalFieldConfig.flat_from_sizes("image", num_patches),
images_spatial_crop=MultiModalFieldConfig.batched("image"),
image_embeds=MultiModalFieldConfig.batched("image"),
)
@@ -264,7 +285,8 @@ class DeepseekVL2MultiModalProcessor(
def get_replacement_deepseek_vl2(item_idx: int):
images = mm_items.get_items(
"image", (ImageEmbeddingItems, ImageProcessorItems))
"image", (ImageEmbeddingItems, ImageProcessorItems)
)
if isinstance(images, ImageEmbeddingItems):
num_image_tokens = images.get_feature_size(item_idx)
@@ -319,13 +341,16 @@ class DeepseekVL2MultiModalProcessor(
@MULTIMODAL_REGISTRY.register_processor(
DeepseekVL2MultiModalProcessor,
info=DeepseekVL2ProcessingInfo,
dummy_inputs=DeepseekVL2DummyInputsBuilder)
dummy_inputs=DeepseekVL2DummyInputsBuilder,
)
class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
merge_by_field_config = True
hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
"language.": "language_model.",
})
hf_to_vllm_mapper = WeightsMapper(
orig_to_new_prefix={
"language.": "language_model.",
}
)
@classmethod
def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
@@ -351,9 +376,9 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
tokenizer = cached_tokenizer_from_config(model_config)
self.image_token_id: int = tokenizer.vocab[_IMAGE_TOKEN]
self.vision = self._init_vision_module(self.vision_config,
quant_config,
maybe_prefix(prefix, "vision"))
self.vision = self._init_vision_module(
self.vision_config, quant_config, maybe_prefix(prefix, "vision")
)
self.projector = MlpProjector(self.projector_config)
self.tile_tag = config.tile_tag
@@ -361,14 +386,17 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
# special token for image token sequence format
embed_std = 1 / torch.sqrt(
torch.tensor(self.projector_config.n_embed, dtype=torch.float32))
torch.tensor(self.projector_config.n_embed, dtype=torch.float32)
)
if self.tile_tag == "2D":
# <|view_seperator|>, <|\n|>
self.image_newline = nn.Parameter(
torch.randn(self.projector_config.n_embed) * embed_std)
torch.randn(self.projector_config.n_embed) * embed_std
)
# This is a typo in original implementation
self.view_seperator = nn.Parameter(
torch.randn(self.projector_config.n_embed) * embed_std)
torch.randn(self.projector_config.n_embed) * embed_std
)
else:
raise ValueError(
f"Only 2D tile_tag is supported currently, got: {self.tile_tag}"
@@ -389,19 +417,19 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
)
self.make_empty_intermediate_tensors = (
self.language_model.make_empty_intermediate_tensors)
self.language_model.make_empty_intermediate_tensors
)
def _get_parent_and_attr(self, root: torch.nn.Module, dotted_name: str):
"""Return (parent_module, final_attr_name) for a dotted module path."""
names = dotted_name.split('.')
names = dotted_name.split(".")
parent = root
for n in names[:-1]:
parent = getattr(parent, n)
return parent, names[-1]
#patch for timm ViT instance to support tensor parallel
def patch_vit_for_tp(self, vit: torch.nn.Module,
quant_config: QuantizationConfig):
# patch for timm ViT instance to support tensor parallel
def patch_vit_for_tp(self, vit: torch.nn.Module, quant_config: QuantizationConfig):
try:
import timm
except ImportError as e:
@@ -411,17 +439,14 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
if isinstance(module, nn.Linear):
parent, attr_name = self._get_parent_and_attr(vit, name)
if isinstance(parent, timm.layers.Mlp) and attr_name == "fc1":
new_linear = replace_linear_class(module,
"colwise",
quant_config,
prefix=name)
new_linear = replace_linear_class(
module, "colwise", quant_config, prefix=name
)
setattr(parent, attr_name, new_linear)
elif isinstance(parent,
timm.layers.Mlp) and attr_name == "fc2":
new_linear = replace_linear_class(module,
"rowwise",
quant_config,
prefix=name)
elif isinstance(parent, timm.layers.Mlp) and attr_name == "fc2":
new_linear = replace_linear_class(
module, "rowwise", quant_config, prefix=name
)
setattr(parent, attr_name, new_linear)
return vit
@@ -454,7 +479,8 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
return model
def _parse_and_validate_image_input(
self, **kwargs: object) -> Optional[DeepseekVL2ImageInputs]:
self, **kwargs: object
) -> Optional[DeepseekVL2ImageInputs]:
pixel_values = kwargs.pop("pixel_values", None)
images_spatial_crop = kwargs.pop("images_spatial_crop", None)
image_embeds = kwargs.pop("image_embeds", None)
@@ -471,7 +497,8 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
resolve_bindings={
"h": expected_h,
"w": expected_w,
})
},
)
if image_embeds is not None:
return DeepseekVL2VImageEmbeddingInputs(
@@ -509,8 +536,9 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
global_features = images_embeds[tile_index]
# [num_height_tiles * num_width_tiles, hw, D]
local_features = images_embeds[tile_index + 1:tile_index + 1 +
num_tiles_in_image]
local_features = images_embeds[
tile_index + 1 : tile_index + 1 + num_tiles_in_image
]
tile_index += num_tiles_in_image + 1
# format global and local features
@@ -522,8 +550,7 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
new_lines_in_global = repeat(self.image_newline, "d -> h 1 d", h=h)
# cat([h, w, D], [h, 1, D], dim=1) -> [h, w + 1, D]
global_features = torch.cat([global_features, new_lines_in_global],
dim=1)
global_features = torch.cat([global_features, new_lines_in_global], dim=1)
# [h, w + 1, D] -> [h * (w + 1), D]
global_features = global_features.view(-1, n_dim)
@@ -531,22 +558,22 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
# ----------------- local view add newline -----------------
# [num_height_tiles * num_width_tiles, h * w, D] ->
# [num_height_tiles * h, num_width_tiles * w, D]
local_features = rearrange(local_features,
"(th tw) (h w) d -> (th h) (tw w) d",
th=num_height_tiles,
tw=num_width_tiles,
h=h,
w=w)
local_features = rearrange(
local_features,
"(th tw) (h w) d -> (th h) (tw w) d",
th=num_height_tiles,
tw=num_width_tiles,
h=h,
w=w,
)
# [D] -> [num_height_tiles * h, 1, D]
new_lines_in_local = repeat(self.image_newline,
"d -> (th h) 1 d",
th=num_height_tiles,
h=h)
new_lines_in_local = repeat(
self.image_newline, "d -> (th h) 1 d", th=num_height_tiles, h=h
)
# [num_height_tiles * h, num_width_tiles * w + 1, D]
local_features = torch.cat([local_features, new_lines_in_local],
dim=1)
local_features = torch.cat([local_features, new_lines_in_local], dim=1)
# [num_height_tiles * h, num_width_tiles * w + 1, D]
# --> [(num_height_tiles * h) * (num_width_tiles * w + 1), D]
@@ -554,23 +581,28 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
# merge global and local tiles
if self.global_view_pos == "head":
global_local_features = torch.cat([
global_features,
self.view_seperator[None, :],
local_features,
])
global_local_features = torch.cat(
[
global_features,
self.view_seperator[None, :],
local_features,
]
)
else:
global_local_features = torch.cat([
local_features,
self.view_seperator[None, :],
global_features,
])
global_local_features = torch.cat(
[
local_features,
self.view_seperator[None, :],
global_features,
]
)
vision_embeddings.append(global_local_features)
return vision_embeddings
def _process_image_input(
self, image_input: DeepseekVL2ImageInputs) -> list[torch.Tensor]:
self, image_input: DeepseekVL2ImageInputs
) -> list[torch.Tensor]:
if image_input["type"] == "image_embeds":
image_data = image_input["data"]
if is_list_of(image_data, torch.Tensor):
@@ -588,33 +620,33 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
images_spatial_crop = image_input["images_spatial_crop"]
return self._pixel_values_to_embedding(
pixel_values=pixel_values, images_spatial_crop=images_spatial_crop)
pixel_values=pixel_values, images_spatial_crop=images_spatial_crop
)
def get_language_model(self) -> torch.nn.Module:
return self.language_model
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 []
vision_embeddings = self._process_image_input(image_input)
return vision_embeddings
def forward(self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
**kwargs: object):
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
**kwargs: object,
):
if intermediate_tensors is not None:
inputs_embeds = None
hidden_states = self.language_model(input_ids,
positions,
intermediate_tensors,
inputs_embeds=inputs_embeds)
hidden_states = self.language_model(
input_ids, positions, intermediate_tensors, inputs_embeds=inputs_embeds
)
return hidden_states
@@ -624,10 +656,7 @@ class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
) -> Optional[torch.Tensor]:
return self.language_model.compute_logits(hidden_states)
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
loader = AutoWeightsLoader(self)
autoloaded_weights = loader.load_weights(weights,
mapper=self.hf_to_vllm_mapper)
autoloaded_weights = loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
return autoloaded_weights