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[Model] Use merge_by_field_config for MM models (G) (#26117)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2025-10-03 13:38:29 +08:00
committed by GitHub
parent 711f485643
commit 39b643dc1a
5 changed files with 56 additions and 108 deletions
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38
vllm/model_executor/models/glm4_1v.py
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@@ -1319,6 +1319,8 @@ class Glm4vMultiModalProcessor(BaseMultiModalProcessor[Glm4vProcessingInfo]):
)
class Glm4vForConditionalGeneration(nn.Module, SupportsMultiModal,
SupportsLoRA, SupportsPP):
merge_by_field_config = True
packed_modules_mapping = {
"qkv_proj": [
"q_proj",
@@ -1381,22 +1383,6 @@ class Glm4vForConditionalGeneration(nn.Module, SupportsMultiModal,
self.make_empty_intermediate_tensors = (
self.language_model.make_empty_intermediate_tensors)
def _validate_and_reshape_mm_tensor(self, mm_input: object,
name: str) -> torch.Tensor:
if not isinstance(mm_input, (torch.Tensor, list)):
raise ValueError(
f"Incorrect type of {name}. Got type: {type(mm_input)}")
if isinstance(mm_input, torch.Tensor):
if mm_input.ndim == 2:
return mm_input
if mm_input.ndim != 3:
raise ValueError(f"{name} should be 2D or batched 3D tensor. "
f"Got ndim: {mm_input.ndim} "
f"(shape={mm_input.shape})")
return mm_input.reshape(-1, mm_input.shape[-1])
else:
return torch.concat(mm_input)
def _parse_and_validate_image_input(
self, **kwargs: object) -> Optional[Glm4vImageInputs]:
pixel_values = kwargs.pop("pixel_values", None)
@@ -1407,11 +1393,6 @@ class Glm4vForConditionalGeneration(nn.Module, SupportsMultiModal,
return None
if pixel_values is not None:
pixel_values = self._validate_and_reshape_mm_tensor(
pixel_values, "image pixel values")
image_grid_thw = self._validate_and_reshape_mm_tensor(
image_grid_thw, "image grid_thw")
return Glm4vImagePixelInputs(
type="pixel_values",
pixel_values=pixel_values,
@@ -1419,11 +1400,6 @@ class Glm4vForConditionalGeneration(nn.Module, SupportsMultiModal,
)
if image_embeds is not None:
image_embeds = self._validate_and_reshape_mm_tensor(
image_embeds, "image embeds")
image_grid_thw = self._validate_and_reshape_mm_tensor(
image_grid_thw, "image grid_thw")
return Glm4vImageEmbeddingInputs(
type="image_embeds",
image_embeds=image_embeds,
@@ -1440,11 +1416,6 @@ class Glm4vForConditionalGeneration(nn.Module, SupportsMultiModal,
return None
if pixel_values_videos is not None:
pixel_values_videos = self._validate_and_reshape_mm_tensor(
pixel_values_videos, "video pixel values")
video_grid_thw = self._validate_and_reshape_mm_tensor(
video_grid_thw, "video grid_thw")
return Glm4vVideoPixelInputs(
type="pixel_values_videos",
pixel_values_videos=pixel_values_videos,
@@ -1452,11 +1423,6 @@ class Glm4vForConditionalGeneration(nn.Module, SupportsMultiModal,
)
if video_embeds is not None:
video_embeds = self._validate_and_reshape_mm_tensor(
video_embeds, "video embeds")
video_grid_thw = self._validate_and_reshape_mm_tensor(
video_grid_thw, "video grid_thw")
return Glm4vVideoEmbeddingInputs(
type="video_embeds",
video_embeds=video_embeds,