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[Bugfix] Merge MM embeddings by index instead of token IDs (#16229)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Signed-off-by: NickLucche <nlucches@redhat.com>
Signed-off-by: Roger Wang <hey@rogerw.io>
Co-authored-by: NickLucche <nlucches@redhat.com>
Co-authored-by: Roger Wang <hey@rogerw.io>
This commit is contained in:
Cyrus Leung
2025-09-27 16:15:12 +08:00
committed by GitHub
parent 176173989a
commit 27d7638b94
80 changed files with 966 additions and 1139 deletions
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52
vllm/model_executor/models/step3_vl.py
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@@ -25,7 +25,7 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
MultiModalKwargsItems, NestedTensors)
MultiModalKwargsItems)
from vllm.multimodal.parse import ImageSize, MultiModalDataItems
from vllm.multimodal.processing import (BaseMultiModalProcessor,
BaseProcessingInfo, PromptReplacement,
@@ -37,8 +37,7 @@ from vllm.transformers_utils.tokenizer import AnyTokenizer
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
init_vllm_registered_model, maybe_prefix,
merge_multimodal_embeddings)
init_vllm_registered_model, maybe_prefix)
from .vision import run_dp_sharded_vision_model
@@ -996,10 +995,13 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal,
1 else cur_feature[0])
return merged_image_features
def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
def get_language_model(self) -> torch.nn.Module:
return self.language_model
def get_multimodal_embeddings(self, **kwargs) -> MultiModalEmbeddings:
image_input = self._parse_and_validate_image_input(**kwargs)
if image_input is None:
return None
return []
vision_embeddings = self._process_image_input(image_input)
return vision_embeddings
@@ -1007,24 +1009,21 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal,
self,
input_ids: torch.Tensor,
multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
*,
is_multimodal: Optional[torch.Tensor] = None,
# Multi-modal token ID may exceed vocab size
handle_oov_mm_token: bool = True,
) -> torch.Tensor:
if multimodal_embeddings is None:
inputs_embeds = self.language_model.model.get_input_embeddings(
input_ids)
else:
is_text = input_ids != self.config.image_token_id
text_ids = input_ids[is_text]
text_embeds = self.language_model.model.get_input_embeddings(
text_ids)
inputs_embeds = torch.empty(input_ids.shape[0],
text_embeds.shape[-1],
dtype=text_embeds.dtype,
device=text_embeds.device)
inputs_embeds[is_text] = text_embeds
inputs_embeds = merge_multimodal_embeddings(
input_ids, inputs_embeds, multimodal_embeddings,
self.config.image_token_id)
return inputs_embeds
# This is to satisfy the type checker for each overload
if multimodal_embeddings is None or is_multimodal is None:
return super().get_input_embeddings(input_ids)
return super().get_input_embeddings(
input_ids,
multimodal_embeddings=multimodal_embeddings,
is_multimodal=is_multimodal,
handle_oov_mm_token=handle_oov_mm_token,
)
def forward(
self,
@@ -1038,10 +1037,11 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal,
inputs_embeds = None
elif inputs_embeds is None:
vision_embeddings = self.get_multimodal_embeddings(**kwargs)
# always pass the input via `inputs_embeds`
# to make sure the computation graph is consistent
inputs_embeds = self.get_input_embeddings(input_ids,
vision_embeddings)
inputs_embeds = self.get_input_embeddings(
input_ids,
vision_embeddings,
is_multimodal=input_ids == self.config.image_token_id,
)
input_ids = None
hidden_states = self.language_model(input_ids,