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[Misc] Remove redundant num_embeds (#15443)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2025-03-25 18:27:57 +08:00
committed by GitHub
parent a9e879b316
commit 5994430b84
5 changed files with 25 additions and 64 deletions
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16
vllm/model_executor/models/gemma3_mm.py
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@@ -63,9 +63,6 @@ class Gemma3ImagePixelInputs(TypedDict):
Shape: `(batch_size, num_images, num_embeds)`
"""
num_embeds: Union[torch.Tensor, list[torch.Tensor]]
"""Shape: `(batch_size, num_images)`"""
Gemma3ImageInputs = Gemma3ImagePixelInputs
@@ -317,11 +314,6 @@ class Gemma3MultiModalProcessor(BaseMultiModalProcessor[Gemma3ProcessingInfo]):
tokenizer.encode(image_repl, add_special_tokens=False)
for image_repl in image_repl_features
]
num_embeds = [
len(image_repl_feature_tokens)
for image_repl_feature_tokens in image_repls_feature_tokens
]
processed_outputs["num_embeds"] = torch.tensor(num_embeds)
vocab = tokenizer.get_vocab()
image_token_id = vocab[tokenizer.image_token]
@@ -354,7 +346,6 @@ class Gemma3MultiModalProcessor(BaseMultiModalProcessor[Gemma3ProcessingInfo]):
"image", num_crops + 1),
num_crops=MultiModalFieldConfig.batched("image"),
embed_is_patch=MultiModalFieldConfig.batched("image"),
num_embeds=MultiModalFieldConfig.batched("image"),
)
def _get_prompt_updates(
@@ -583,7 +574,6 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
pixel_values = kwargs.pop("pixel_values", None)
num_crops = kwargs.pop("num_crops", None)
embed_is_patch = kwargs.pop("embed_is_patch", None)
num_embeds = kwargs.pop("num_embeds", None)
image_embeds = kwargs.pop("image_embeds", None)
assert image_embeds is None, "Gemma3 does not support image_embeds."
if pixel_values is None:
@@ -601,10 +591,6 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
raise ValueError("Incorrect type of embed_is_patch. "
f"Got type: {type(embed_is_patch)}")
if not isinstance(num_embeds, (torch.Tensor, list)):
raise ValueError("Incorrect type of num_embeds. "
f"Got type: {type(num_embeds)}")
pixel_values = flatten_bn(pixel_values, concat=True)
num_crops = flatten_bn(num_crops, concat=True)
@@ -613,7 +599,6 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
pixel_values=self._validate_pixel_values(pixel_values),
num_patches=num_crops + 1,
embed_is_patch=embed_is_patch,
num_embeds=num_embeds,
)
def _image_pixels_to_features(
@@ -656,7 +641,6 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
return flatten_2d_lists(
scatter_patch_features(*args) for args in zip(
image_features,
image_input["num_embeds"],
image_input["embed_is_patch"],
))