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fix(vllm): support mixed multimodal payloads #10225

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Merged
hiyouga merged 1 commit into from
Feb 28, 2026
Merged

fix(vllm): support mixed multimodal payloads #10225

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42 changes: 20 additions & 22 deletions scripts/vllm_infer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -154,25 +154,24 @@ def vllm_infer(
batch = train_dataset[i : min(i + batch_size, len(train_dataset))]

for j in range(len(batch["input_ids"])):
multi_modal_data = {}
video_metadata_kwargs = None

if batch["images"][j] is not None:
image = batch["images"][j]
multi_modal_data = {
"image": template_obj.mm_plugin._regularize_images(
image, image_max_pixels=image_max_pixels, image_min_pixels=image_min_pixels
)["images"]
}
elif batch["videos"][j] is not None:
video_metadata, video_metadata_kwargs = None, None
multi_modal_data["image"] = template_obj.mm_plugin._regularize_images(
image, image_max_pixels=image_max_pixels, image_min_pixels=image_min_pixels
)["images"]

if batch["videos"][j] is not None:
video = batch["videos"][j]
multi_modal_data = {
"video": template_obj.mm_plugin._regularize_videos(
video,
image_max_pixels=image_max_pixels,
image_min_pixels=image_min_pixels,
video_fps=video_fps,
video_maxlen=video_maxlen,
)["videos"]
}
multi_modal_data["video"] = template_obj.mm_plugin._regularize_videos(
video,
image_max_pixels=image_max_pixels,
image_min_pixels=image_min_pixels,
video_fps=video_fps,
video_maxlen=video_maxlen,
)["videos"]
if need_video_kwargs:
container = av.open(video[0], "r")
video_stream = next(stream for stream in container.streams if stream.type == "video")
Expand All @@ -192,18 +191,17 @@ def vllm_infer(
video_backend="opencv",
)
multi_modal_data["video"] = (multi_modal_data["video"], video_metadata)
elif batch["audios"][j] is not None:

if batch["audios"][j] is not None:
audio = batch["audios"][j]
audio_data = template_obj.mm_plugin._regularize_audios(
audio,
sampling_rate=16000,
)
multi_modal_data = {"audio": zip(audio_data["audios"], audio_data["sampling_rates"])}
else:
multi_modal_data = None
multi_modal_data["audio"] = zip(audio_data["audios"], audio_data["sampling_rates"])

vllm_input_data = {"prompt_token_ids": batch["input_ids"][j], "multi_modal_data": multi_modal_data}
if "video_metadata_kwargs" in locals() and video_metadata_kwargs is not None:
vllm_input_data = {"prompt_token_ids": batch["input_ids"][j], "multi_modal_data": multi_modal_data or None}
if video_metadata_kwargs is not None:
vllm_input_data["mm_processor_kwargs"] = video_metadata_kwargs

vllm_inputs.append(vllm_input_data)
Expand Down
41 changes: 19 additions & 22 deletions src/llamafactory/chat/vllm_engine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -180,35 +180,32 @@ async def _generate(
else self.generating_args["skip_special_tokens"],
)

multi_modal_data = {}
if images is not None: # add image features
multi_modal_data = {
"image": self.template.mm_plugin._regularize_images(
images,
image_max_pixels=self.model_args.image_max_pixels,
image_min_pixels=self.model_args.image_min_pixels,
)["images"]
}
elif videos is not None:
multi_modal_data = {
"video": self.template.mm_plugin._regularize_videos(
videos,
image_max_pixels=self.model_args.video_max_pixels,
image_min_pixels=self.model_args.video_min_pixels,
video_fps=self.model_args.video_fps,
video_maxlen=self.model_args.video_maxlen,
)["videos"]
}
elif audios is not None:
multi_modal_data["image"] = self.template.mm_plugin._regularize_images(
images,
image_max_pixels=self.model_args.image_max_pixels,
image_min_pixels=self.model_args.image_min_pixels,
)["images"]

if videos is not None:
multi_modal_data["video"] = self.template.mm_plugin._regularize_videos(
videos,
image_max_pixels=self.model_args.video_max_pixels,
image_min_pixels=self.model_args.video_min_pixels,
video_fps=self.model_args.video_fps,
video_maxlen=self.model_args.video_maxlen,
)["videos"]

if audios is not None:
audio_data = self.template.mm_plugin._regularize_audios(
audios,
sampling_rate=self.model_args.audio_sampling_rate,
)
multi_modal_data = {"audio": zip(audio_data["audios"], audio_data["sampling_rates"])}
else:
multi_modal_data = None
multi_modal_data["audio"] = zip(audio_data["audios"], audio_data["sampling_rates"])

result_generator = self.model.generate(
{"prompt_token_ids": prompt_ids, "multi_modal_data": multi_modal_data},
{"prompt_token_ids": prompt_ids, "multi_modal_data": multi_modal_data or None},
sampling_params=sampling_params,
request_id=request_id,
lora_request=self.lora_request,
Expand Down