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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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264
vllm/model_executor/models/skyworkr1v.py
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@@ -21,17 +21,30 @@ from vllm.config.multimodal import BaseDummyOptions
from vllm.model_executor.layers.linear import ReplicatedLinear
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.quantization.awq import AWQConfig
from vllm.model_executor.models.intern_vit import (InternVisionModel,
InternVisionPatchModel)
from vllm.model_executor.models.intern_vit import (
InternVisionModel,
InternVisionPatchModel,
)
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.image import convert_image_mode
from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
MultiModalKwargsItems)
from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
ImageSize, MultiModalDataItems)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
BaseProcessingInfo, PromptReplacement,
PromptUpdate, PromptUpdateDetails)
from vllm.multimodal.inputs import (
MultiModalDataDict,
MultiModalFieldConfig,
MultiModalKwargsItems,
)
from vllm.multimodal.parse import (
ImageEmbeddingItems,
ImageProcessorItems,
ImageSize,
MultiModalDataItems,
)
from vllm.multimodal.processing import (
BaseMultiModalProcessor,
BaseProcessingInfo,
PromptReplacement,
PromptUpdate,
PromptUpdateDetails,
)
from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.tokenizer import AnyTokenizer
@@ -40,9 +53,9 @@ from vllm.utils.tensor_schema import TensorSchema, TensorShape
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix
IMG_START = '<img>'
IMG_END = '</img>'
IMG_CONTEXT = '<IMG_CONTEXT>'
IMG_START = "<img>"
IMG_END = "</img>"
IMG_CONTEXT = "<IMG_CONTEXT>"
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
@@ -57,6 +70,7 @@ class SkyworkR1VImagePixelInputs(TensorSchema):
- w: Width
- bn: Batch size * number of images
"""
type: Literal["pixel_values"] = "pixel_values"
pixel_values_flat: Annotated[
@@ -75,9 +89,10 @@ class SkyworkR1VImageEmbeddingInputs(TensorSchema):
Dimensions:
- ni: Number of images
- ifs: Image feature size
- hs: Hidden size (must match the hidden size of language model
- hs: Hidden size (must match the hidden size of language model
backbone)
"""
type: Literal["image_embeds"] = "image_embeds"
data: Annotated[
@@ -86,20 +101,24 @@ class SkyworkR1VImageEmbeddingInputs(TensorSchema):
]
SkyworkR1VImageInputs = Union[SkyworkR1VImagePixelInputs,
SkyworkR1VImageEmbeddingInputs]
SkyworkR1VImageInputs = Union[
SkyworkR1VImagePixelInputs, SkyworkR1VImageEmbeddingInputs
]
# adapted from https://huggingface.co/Skywork/Skywork-R1V-38B/
def build_transform(input_size: int):
MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
return T.Compose([
T.Lambda(lambda img: convert_image_mode(img, 'RGB')),
T.Resize((input_size, input_size),
interpolation=T.InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD)
])
return T.Compose(
[
T.Lambda(lambda img: convert_image_mode(img, "RGB")),
T.Resize(
(input_size, input_size), interpolation=T.InterpolationMode.BICUBIC
),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD),
]
)
# adapted from https://huggingface.co/Skywork/Skywork-R1V-38B/
@@ -111,7 +130,7 @@ def find_closest_aspect_ratio(
height: int,
image_size: int,
) -> tuple[int, int]:
best_ratio_diff = float('inf')
best_ratio_diff = float("inf")
best_ratio = (1, 1)
area = width * height
for ratio in target_ratios:
@@ -146,10 +165,13 @@ def get_skyworkr1v_target_ratios(
min_num: int,
max_num: int,
) -> list[tuple[int, int]]:
target_ratios = {(i, j)
for n in range(min_num, max_num + 1)
for i in range(1, n + 1)
for j in range(1, n + 1) if min_num <= i * j <= max_num}
target_ratios = {
(i, j)
for n in range(min_num, max_num + 1)
for i in range(1, n + 1)
for j in range(1, n + 1)
if min_num <= i * j <= max_num
}
return sorted(target_ratios, key=lambda x: x[0] * x[1])
@@ -206,10 +228,12 @@ def dynamic_preprocess_skyworkr1v(
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = ((i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size)
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size,
)
# split the image
split_img = resized_img.crop(box)
processed_images.append(split_img)
@@ -285,7 +309,8 @@ class SkyworkR1VProcessor:
assert isinstance(dynamic_image_size, bool)
self.num_image_token = int(
(image_size // patch_size)**2 * (config.downsample_ratio**2))
(image_size // patch_size) ** 2 * (config.downsample_ratio**2)
)
self.image_size = image_size
self.min_dynamic_patch = min_dynamic_patch
self.max_dynamic_patch = max_dynamic_patch
@@ -314,14 +339,18 @@ class SkyworkR1VProcessor:
dynamic_image_size: Optional[bool] = None,
use_thumbnail: Optional[bool] = None,
) -> tuple[int, int]:
min_dynamic_patch = (self.min_dynamic_patch if min_dynamic_patch
is None else min_dynamic_patch)
max_dynamic_patch = (self.max_dynamic_patch if max_dynamic_patch
is None else max_dynamic_patch)
dynamic_image_size = (self.dynamic_image_size if dynamic_image_size
is None else dynamic_image_size)
use_thumbnail = (self.use_thumbnail
if use_thumbnail is None else use_thumbnail)
min_dynamic_patch = (
self.min_dynamic_patch if min_dynamic_patch is None else min_dynamic_patch
)
max_dynamic_patch = (
self.max_dynamic_patch if max_dynamic_patch is None else max_dynamic_patch
)
dynamic_image_size = (
self.dynamic_image_size
if dynamic_image_size is None
else dynamic_image_size
)
use_thumbnail = self.use_thumbnail if use_thumbnail is None else use_thumbnail
return resolve_skyworkr1v_min_max_num(
min_dynamic_patch=min_dynamic_patch,
@@ -388,7 +417,8 @@ class SkyworkR1VProcessor:
min_num=min_num,
max_num=max_num,
use_thumbnail=self.use_thumbnail,
) for image in images
)
for image in images
]
def __call__(
@@ -419,10 +449,10 @@ class SkyworkR1VProcessor:
dynamic_image_size=dynamic_image_size,
)
image_inputs = {
"pixel_values_flat":
torch.cat(pixel_values_lst),
"image_num_patches":
torch.tensor([len(item) for item in pixel_values_lst]),
"pixel_values_flat": torch.cat(pixel_values_lst),
"image_num_patches": torch.tensor(
[len(item) for item in pixel_values_lst]
),
}
for pixel_values in pixel_values_lst:
@@ -431,7 +461,7 @@ class SkyworkR1VProcessor:
image_repl = self.get_image_repl(feature_size, num_patches)
text = [t.replace('<image>', image_repl.full, 1) for t in text]
text = [t.replace("<image>", image_repl.full, 1) for t in text]
text_inputs = self.tokenizer(text)
@@ -441,7 +471,6 @@ class SkyworkR1VProcessor:
class SkyworkR1VProcessingInfo(BaseProcessingInfo):
def get_hf_processor(self, **kwargs: object) -> SkyworkR1VProcessor:
return self.ctx.init_processor(
SkyworkR1VProcessor,
@@ -485,8 +514,7 @@ class SkyworkR1VProcessingInfo(BaseProcessingInfo):
)
if feat_size > largest_feature_size:
largest_feature_size = feat_size
largest_feature_pinpoint = ImageSize(width=width,
height=height)
largest_feature_pinpoint = ImageSize(width=width, height=height)
if largest_feature_size == 0 or largest_feature_pinpoint is None:
raise ValueError("Cannot have a largest feature size of 0!")
@@ -494,9 +522,7 @@ class SkyworkR1VProcessingInfo(BaseProcessingInfo):
return largest_feature_pinpoint
class SkyworkR1VDummyInputsBuilder(
BaseDummyInputsBuilder[SkyworkR1VProcessingInfo]):
class SkyworkR1VDummyInputsBuilder(BaseDummyInputsBuilder[SkyworkR1VProcessingInfo]):
def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
num_images = mm_counts.get("image", 0)
@@ -508,24 +534,22 @@ class SkyworkR1VDummyInputsBuilder(
mm_counts: Mapping[str, int],
mm_options: Optional[Mapping[str, BaseDummyOptions]] = None,
) -> MultiModalDataDict:
target_width, target_height = \
self.info.get_image_size_with_most_features()
target_width, target_height = self.info.get_image_size_with_most_features()
num_images = mm_counts.get("image", 0)
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 SkyworkR1VMultiModalProcessor(
BaseMultiModalProcessor[SkyworkR1VProcessingInfo]):
class SkyworkR1VMultiModalProcessor(BaseMultiModalProcessor[SkyworkR1VProcessingInfo]):
def _call_hf_processor(
self,
prompt: str,
@@ -560,7 +584,8 @@ class SkyworkR1VMultiModalProcessor(
return dict(
pixel_values_flat=MultiModalFieldConfig.flat_from_sizes(
"image", image_num_patches),
"image", image_num_patches
),
image_num_patches=MultiModalFieldConfig.batched("image"),
image_embeds=MultiModalFieldConfig.batched("image"),
image_token_id=MultiModalFieldConfig.shared("image", num_images),
@@ -588,7 +613,8 @@ class SkyworkR1VMultiModalProcessor(
def get_replacement_skyworkr1v(item_idx: int):
images = mm_items.get_items(
"image", (ImageEmbeddingItems, ImageProcessorItems))
"image", (ImageEmbeddingItems, ImageProcessorItems)
)
if isinstance(images, ImageEmbeddingItems):
feature_size = images.get_feature_size(item_idx)
@@ -618,7 +644,8 @@ class SkyworkR1VMultiModalProcessor(
@MULTIMODAL_REGISTRY.register_processor(
SkyworkR1VMultiModalProcessor,
info=SkyworkR1VProcessingInfo,
dummy_inputs=SkyworkR1VDummyInputsBuilder)
dummy_inputs=SkyworkR1VDummyInputsBuilder,
)
class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
merge_by_field_config = True
@@ -644,12 +671,13 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
patch_size = config.vision_config.patch_size
self.patch_size = patch_size
self.num_image_token = int(
(image_size // patch_size)**2 * (config.downsample_ratio**2))
(image_size // patch_size) ** 2 * (config.downsample_ratio**2)
)
self.downsample_ratio = config.downsample_ratio
self.ps_version = config.ps_version
self.llm_arch_name = config.text_config.architectures[0]
self.is_mono = self.llm_arch_name == 'SkyworkLM2VEForCausalLM'
self.is_mono = self.llm_arch_name == "SkyworkLM2VEForCausalLM"
self.vision_model = self._init_vision_model(
config,
quant_config=quant_config,
@@ -668,18 +696,20 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
self.img_context_token_id = None
self.visual_token_mask = None
self.make_empty_intermediate_tensors = (
self.language_model.make_empty_intermediate_tensors)
self.language_model.make_empty_intermediate_tensors
)
def _patch_quant_config(self, config: PretrainedConfig,
quant_config: QuantizationConfig):
def _patch_quant_config(
self, config: PretrainedConfig, quant_config: QuantizationConfig
):
# the awq models from OpenGVLab missing `modules_to_not_convert`
# patch the quant_config to add `modules_to_not_convert` back
if isinstance(quant_config, AWQConfig):
text_config = config.text_config
llm_quant_config = getattr(text_config, "quantization_config",
None)
if (not quant_config.modules_to_not_convert) and \
(llm_quant_config is not None):
llm_quant_config = getattr(text_config, "quantization_config", None)
if (not quant_config.modules_to_not_convert) and (
llm_quant_config is not None
):
quant_config.modules_to_not_convert.append("vision_model")
def _init_vision_model(
@@ -693,8 +723,9 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
if not is_mono:
vision_feature_layer = config.select_layer
if vision_feature_layer < 0:
num_hidden_layers = config.vision_config.num_hidden_layers \
+ vision_feature_layer + 1
num_hidden_layers = (
config.vision_config.num_hidden_layers + vision_feature_layer + 1
)
else:
num_hidden_layers = vision_feature_layer + 1
@@ -712,15 +743,14 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
llm_hidden_size = config.text_config.hidden_size
return nn.Sequential(
nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio)**2),
ReplicatedLinear(vit_hidden_size *
int(1 / self.downsample_ratio)**2,
llm_hidden_size,
return_bias=False),
nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
ReplicatedLinear(
vit_hidden_size * int(1 / self.downsample_ratio) ** 2,
llm_hidden_size,
return_bias=False,
),
nn.GELU(),
ReplicatedLinear(llm_hidden_size,
llm_hidden_size,
return_bias=False),
ReplicatedLinear(llm_hidden_size, llm_hidden_size, return_bias=False),
)
def pixel_shuffle(self, x, scale_factor=0.5):
@@ -729,9 +759,13 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
# N, W, H * scale, C // scale --> N, H * scale, W, C // scale
x = x.permute(0, 2, 1, 3).contiguous()
x = x.view(n, int(h * scale_factor), int(w * scale_factor),
int(c / (scale_factor * scale_factor)))
if self.ps_version == 'v1':
x = x.view(
n,
int(h * scale_factor),
int(w * scale_factor),
int(c / (scale_factor * scale_factor)),
)
if self.ps_version == "v1":
pass
else:
x = x.permute(0, 2, 1, 3).contiguous()
@@ -741,17 +775,16 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
vit_embeds = self.vision_model(pixel_values=pixel_values)
vit_embeds = vit_embeds[:, 1:, :]
h = w = int(vit_embeds.shape[1]**0.5)
h = w = int(vit_embeds.shape[1] ** 0.5)
vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
vit_embeds = self.pixel_shuffle(vit_embeds,
scale_factor=self.downsample_ratio)
vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1,
vit_embeds.shape[-1])
vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
vit_embeds = self.mlp1(vit_embeds)
return vit_embeds
def _parse_and_validate_image_input(
self, **kwargs: object) -> Optional[SkyworkR1VImageInputs]:
self, **kwargs: object
) -> Optional[SkyworkR1VImageInputs]:
pixel_values_flat = kwargs.pop("pixel_values_flat", None)
image_num_patches = kwargs.pop("image_num_patches", None)
image_embeds = kwargs.pop("image_embeds", None)
@@ -777,7 +810,8 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
resolve_bindings={
"h": self.config.vision_config.image_size,
"w": self.config.vision_config.image_size,
})
},
)
raise AssertionError("This line should be unreachable.")
@@ -796,14 +830,14 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
# Only one image in the current batch
if len(num_patches) == 1:
return image_embeds.view(
-1, self.config.text_config.hidden_size).unsqueeze(0)
return image_embeds.view(-1, self.config.text_config.hidden_size).unsqueeze(
0
)
# NOTE: Image embeddings are split into separate tensors for each image
# by the size of each embedding.
feature_size = image_embeds.shape[1]
image_embeds = image_embeds.view(-1,
self.config.text_config.hidden_size)
image_embeds = image_embeds.view(-1, self.config.text_config.hidden_size)
image_feature_sizes = [
num_patches * feature_size for num_patches in num_patches
]
@@ -811,16 +845,16 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
def _set_visual_token_mask(self, input_ids: torch.Tensor) -> None:
if self.is_mono:
self.visual_token_mask = (
input_ids == self.img_context_token_id).reshape(-1, 1)
self.visual_token_mask = (input_ids == self.img_context_token_id).reshape(
-1, 1
)
else:
self.visual_token_mask = None
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 []
@@ -835,8 +869,7 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
is_multimodal: Optional[torch.Tensor] = None,
handle_oov_mm_token: bool = False,
) -> torch.Tensor:
if multimodal_embeddings is not None and len(
multimodal_embeddings) > 0:
if multimodal_embeddings is not None and len(multimodal_embeddings) > 0:
self._set_visual_token_mask(input_ids)
# This is to satisfy the type checker for each overload
@@ -858,7 +891,6 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
inputs_embeds: Optional[torch.Tensor] = None,
**kwargs: object,
) -> IntermediateTensors:
if intermediate_tensors is not None:
input_ids = None
inputs_embeds = None
@@ -872,8 +904,7 @@ class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
# Only required if the model is mono-architecture
if self.visual_token_mask is not None:
forward_kwargs.update(
{"visual_token_mask": self.visual_token_mask})
forward_kwargs.update({"visual_token_mask": self.visual_token_mask})
self.visual_token_mask = None
hidden_states = self.language_model.model(**forward_kwargs)
@@ -885,13 +916,20 @@ class SkyworkR1VChatModel(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]:
skip_prefixes = [
"action_embed", "temporal_embed", "track_embed",
"track_embed_decoder", "box_token", "cg_criterion", "cg_model",
"loc_encoder", "loc_decoder", "sam", "temporal_token",
"track_token"
"action_embed",
"temporal_embed",
"track_embed",
"track_embed_decoder",
"box_token",
"cg_criterion",
"cg_model",
"loc_encoder",
"loc_decoder",
"sam",
"temporal_token",
"track_token",
]
loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
return loader.load_weights(weights)