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Update Optional[x] -> x | None and Union[x, y] to x | y (#26633)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-12 17:51:31 +01:00
committed by GitHub
parent 9bb38130cb
commit 8fcaaf6a16
944 changed files with 9490 additions and 10121 deletions
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76
vllm/model_executor/models/qwen2_vl.py
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@@ -25,9 +25,9 @@
# limitations under the License.
"""Inference-only Qwen2-VL model compatible with HuggingFace weights."""
from collections.abc import Iterable, Mapping, Sequence
from collections.abc import Callable, Iterable, Mapping, Sequence
from functools import partial
from typing import Annotated, Any, Callable, Literal, Optional, Union
from typing import Annotated, Any, Literal, TypeAlias
import torch
import torch.nn as nn
@@ -167,7 +167,7 @@ class Qwen2VLImageEmbeddingInputs(TensorSchema):
]
Qwen2VLImageInputs = Union[Qwen2VLImagePixelInputs, Qwen2VLImageEmbeddingInputs]
Qwen2VLImageInputs: TypeAlias = Qwen2VLImagePixelInputs | Qwen2VLImageEmbeddingInputs
class Qwen2VLVideoPixelInputs(TensorSchema):
@@ -228,7 +228,7 @@ class Qwen2VLVideoEmbeddingInputs(TensorSchema):
]
Qwen2VLVideoInputs = Union[Qwen2VLVideoPixelInputs, Qwen2VLVideoEmbeddingInputs]
Qwen2VLVideoInputs: TypeAlias = Qwen2VLVideoPixelInputs | Qwen2VLVideoEmbeddingInputs
# === Vision Encoder === #
@@ -239,7 +239,7 @@ class Qwen2VisionMLP(nn.Module):
in_features: int,
hidden_features: int,
act_layer: type[nn.Module] = QuickGELU,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
use_data_parallel: bool = False,
):
@@ -317,7 +317,7 @@ class Qwen2VisionAttention(nn.Module):
embed_dim: int,
num_heads: int,
projection_size: int,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
use_data_parallel: bool = False,
) -> None:
@@ -413,8 +413,8 @@ class Qwen2VisionAttention(nn.Module):
x: torch.Tensor,
cu_seqlens: torch.Tensor,
rotary_pos_emb: torch.Tensor,
max_seqlen: Optional[int] = None, # Only used for Flash Attention
seqlens: Optional[list[int]] = None, # Only used for xFormers
max_seqlen: int | None = None, # Only used for Flash Attention
seqlens: list[int] | None = None, # Only used for xFormers
) -> torch.Tensor:
# [s, b, c] --> [s, b, 3 * head * head_dim]
x, _ = self.qkv(x)
@@ -493,8 +493,8 @@ class Qwen2VisionBlock(nn.Module):
num_heads: int,
mlp_ratio: float,
act_layer: type[nn.Module] = QuickGELU,
norm_layer: Optional[Callable[[int], nn.Module]] = None,
quant_config: Optional[QuantizationConfig] = None,
norm_layer: Callable[[int], nn.Module] | None = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
use_data_parallel: bool = False,
) -> None:
@@ -527,8 +527,8 @@ class Qwen2VisionBlock(nn.Module):
x: torch.Tensor,
cu_seqlens: torch.Tensor,
rotary_pos_emb: torch.Tensor,
max_seqlen: Optional[int] = None, # Only used for Flash Attention
seqlens: Optional[list[int]] = None, # Only used for xFormers
max_seqlen: int | None = None, # Only used for Flash Attention
seqlens: list[int] | None = None, # Only used for xFormers
) -> torch.Tensor:
x = x + self.attn(
self.norm1(x),
@@ -576,9 +576,9 @@ class Qwen2VisionPatchMerger(nn.Module):
self,
d_model: int,
context_dim: int,
norm_layer: Optional[Callable[[int], nn.Module]] = None,
norm_layer: Callable[[int], nn.Module] | None = None,
spatial_merge_size: int = 2,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
use_data_parallel: bool = False,
) -> None:
@@ -659,7 +659,7 @@ class Qwen2VisionTransformer(nn.Module):
self,
vision_config: Qwen2VLVisionConfig,
norm_eps: float = 1e-6,
quant_config: Optional[QuantizationConfig] = None,
quant_config: QuantizationConfig | None = None,
prefix: str = "",
use_data_parallel: bool = False,
) -> None:
@@ -766,7 +766,7 @@ class Qwen2VisionTransformer(nn.Module):
def compute_attn_mask_seqlen(
self, cu_seqlens: torch.Tensor
) -> tuple[Optional[int], Optional[list[int]]]:
) -> tuple[int | None, list[int] | None]:
max_seqlen, seqlens = None, None
if (
self.attn_backend == _Backend.FLASH_ATTN
@@ -889,8 +889,8 @@ class Qwen2VLMultiModalDataParser(MultiModalDataParser):
def _parse_image_data(
self,
data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
) -> Optional[ModalityDataItems[Any, Any]]:
data: dict[str, torch.Tensor] | ModalityData[ImageItem],
) -> ModalityDataItems[Any, Any] | None:
if isinstance(data, dict):
return DictEmbeddingItems(
data,
@@ -903,8 +903,8 @@ class Qwen2VLMultiModalDataParser(MultiModalDataParser):
def _parse_video_data(
self,
data: Union[dict[str, torch.Tensor], ModalityData[VideoItem]],
) -> Optional[ModalityDataItems[Any, Any]]:
data: dict[str, torch.Tensor] | ModalityData[VideoItem],
) -> ModalityDataItems[Any, Any] | None:
if isinstance(data, dict):
return DictEmbeddingItems(
data,
@@ -930,7 +930,7 @@ class Qwen2VLProcessingInfo(BaseProcessingInfo):
def get_image_processor(self, **kwargs: object) -> Qwen2VLImageProcessor:
return self.get_hf_processor(**kwargs).image_processor
def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
def get_supported_mm_limits(self) -> Mapping[str, int | None]:
return {"image": None, "video": None}
def get_mm_max_tokens_per_item(
@@ -949,7 +949,7 @@ class Qwen2VLProcessingInfo(BaseProcessingInfo):
image_height: int,
num_frames: int = 1,
do_resize: bool = True,
image_processor: Optional[Qwen2VLImageProcessor],
image_processor: Qwen2VLImageProcessor | None,
) -> tuple[ImageSize, int]:
if image_processor is None:
image_processor = self.get_image_processor()
@@ -990,7 +990,7 @@ class Qwen2VLProcessingInfo(BaseProcessingInfo):
*,
image_width: int,
image_height: int,
image_processor: Optional[Qwen2VLImageProcessor],
image_processor: Qwen2VLImageProcessor | None,
) -> int:
_, num_image_tokens = self._get_vision_info(
image_width=image_width,
@@ -1006,7 +1006,7 @@ class Qwen2VLProcessingInfo(BaseProcessingInfo):
image_width: int,
image_height: int,
num_frames: int,
image_processor: Optional[Qwen2VLImageProcessor],
image_processor: Qwen2VLImageProcessor | None,
) -> int:
_, num_video_tokens = self._get_vision_info(
image_width=image_width,
@@ -1100,7 +1100,7 @@ class Qwen2VLDummyInputsBuilder(BaseDummyInputsBuilder[Qwen2VLProcessingInfo]):
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Optional[Mapping[str, BaseDummyOptions]] = None,
mm_options: Mapping[str, BaseDummyOptions] | None = None,
) -> MultiModalDataDict:
num_images = mm_counts.get("image", 0)
num_videos = mm_counts.get("video", 0)
@@ -1207,12 +1207,12 @@ class Qwen2VLForConditionalGeneration(
self,
input_tokens: list[int],
hf_config: PretrainedConfig,
image_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
video_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
second_per_grid_ts: Optional[list[float]] = None,
image_grid_thw: list[list[int]] | torch.Tensor | None,
video_grid_thw: list[list[int]] | torch.Tensor | None,
second_per_grid_ts: list[float] | None = None,
context_len: int = 0,
seq_len: Optional[int] = None,
audio_feature_lengths: Optional[torch.Tensor] = None,
seq_len: int | None = None,
audio_feature_lengths: torch.Tensor | None = None,
use_audio_in_video: bool = False,
) -> tuple[torch.Tensor, int]:
"""Get M-RoPE input positions for Qwen2-VL model."""
@@ -1335,7 +1335,7 @@ class Qwen2VLForConditionalGeneration(
return llm_positions, mrope_position_delta
@classmethod
def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
def get_placeholder_str(cls, modality: str, i: int) -> str | None:
if modality.startswith("image"):
return "<|vision_start|><|image_pad|><|vision_end|>"
if modality.startswith("video"):
@@ -1396,7 +1396,7 @@ class Qwen2VLForConditionalGeneration(
def _parse_and_validate_image_input(
self, **kwargs: object
) -> Optional[Qwen2VLImageInputs]:
) -> Qwen2VLImageInputs | None:
pixel_values = kwargs.pop("pixel_values", None)
image_embeds = kwargs.pop("image_embeds", None)
image_grid_thw = kwargs.pop("image_grid_thw", None)
@@ -1434,7 +1434,7 @@ class Qwen2VLForConditionalGeneration(
def _parse_and_validate_video_input(
self, **kwargs: object
) -> Optional[Qwen2VLVideoInputs]:
) -> Qwen2VLVideoInputs | None:
pixel_values_videos = kwargs.pop("pixel_values_videos", None)
video_embeds = kwargs.pop("video_embeds", None)
video_grid_thw = kwargs.pop("video_grid_thw", None)
@@ -1574,10 +1574,10 @@ class Qwen2VLForConditionalGeneration(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
intermediate_tensors: IntermediateTensors | None = None,
inputs_embeds: torch.Tensor | None = None,
**kwargs: object,
) -> Union[torch.Tensor, IntermediateTensors]:
) -> torch.Tensor | IntermediateTensors:
"""Run forward pass for Qwen2-VL.
Args:
@@ -1606,7 +1606,7 @@ class Qwen2VLForConditionalGeneration(
def compute_logits(
self,
hidden_states: torch.Tensor,
) -> Optional[torch.Tensor]:
) -> torch.Tensor | None:
return self.language_model.compute_logits(hidden_states)
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
@@ -1634,7 +1634,7 @@ class Tarsier2MultiModalProcessor(Qwen2VLMultiModalProcessor):
class Tarsier2ImageProcessor(Qwen2VLImageProcessor):
def __init__(
self,
size: Optional[dict[str, int]] = None,
size: dict[str, int] | None = None,
**kwargs,
) -> None:
if size is not None and "min_pixels" in size and "max_pixels" in size: