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Unverified Commit 987506bc authored by Cyrus Leung's avatar Cyrus Leung Committed by GitHub
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[Refactor] Simplify dummy data generation (#35025) 


Signed-off-by: default avatarDarkLight1337 <tlleungac@connect.ust.hk>
parent c645e9a2
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Showing with 88 additions and 129 deletions
vllm/model_executor/models/qwen2_5_omni_thinker.py
View file @ 987506bc
......@@ -357,15 +357,13 @@ class Qwen2_5OmniThinkerDummyInputsBuilder(
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> MultiModalDataDict:
num_audios = mm_counts.get("audio", 0)
num_images = mm_counts.get("image", 0)
num_videos = mm_counts.get("video", 0)
mm_processor_kwargs = mm_processor_kwargs or {}
feature_extractor = self.info.get_feature_extractor(**mm_processor_kwargs)
feature_extractor = self.info.get_feature_extractor()
target_audio_length = (
min(
......@@ -375,16 +373,14 @@ class Qwen2_5OmniThinkerDummyInputsBuilder(
* feature_extractor.sampling_rate
)
target_width, target_height = self.info.get_image_size_with_most_features(
max_pixels=mm_processor_kwargs.get("max_pixels", None),
)
target_width, target_height = self.info.get_image_size_with_most_features()
target_num_frames = self.info.get_num_frames_with_most_features(
seq_len, mm_counts
)
image_overrides = mm_options.get("image") if mm_options else None
video_overrides = mm_options.get("video") if mm_options else None
audio_overrides = mm_options.get("audio") if mm_options else None
image_overrides = mm_options.get("image")
video_overrides = mm_options.get("video")
audio_overrides = mm_options.get("audio")
mm_data = {
"audio": self._get_dummy_audios(
......
vllm/model_executor/models/qwen2_audio.py
View file @ 987506bc
......@@ -195,22 +195,21 @@ class Qwen2AudioDummyInputsBuilder(BaseDummyInputsBuilder[Qwen2AudioProcessingIn
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> MultiModalDataDict:
feature_extractor = self.info.get_feature_extractor(
**(mm_processor_kwargs or {})
)
feature_extractor = self.info.get_feature_extractor()
sampling_rate = feature_extractor.sampling_rate
audio_len = feature_extractor.chunk_length * sampling_rate
num_audios = mm_counts.get("audio", 0)
audio_overrides = mm_options.get("audio") if mm_options else None
audio_overrides = mm_options.get("audio")
return {
"audio": self._get_dummy_audios(
length=audio_len, num_audios=num_audios, overrides=audio_overrides
length=audio_len,
num_audios=num_audios,
overrides=audio_overrides,
)
}
......
vllm/model_executor/models/qwen2_vl.py
View file @ 987506bc
......@@ -925,9 +925,14 @@ class Qwen2VLProcessingInfo(BaseProcessingInfo):
vision_config = hf_config.vision_config
patch_size = vision_config.patch_size
merge_size = vision_config.spatial_merge_size
if max_pixels is None:
image_processor = self.get_image_processor()
max_pixels = image_processor.size["longest_edge"]
mm_kwargs = self.ctx.get_merged_mm_kwargs({})
size = mm_kwargs.get("size", image_processor.size)
max_pixels = size["longest_edge"]
unit = patch_size * merge_size
max_seq_len = max_pixels // (unit * unit)
......@@ -1027,22 +1032,18 @@ class Qwen2VLDummyInputsBuilder(BaseDummyInputsBuilder[Qwen2VLProcessingInfo]):
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> MultiModalDataDict:
num_images = mm_counts.get("image", 0)
num_videos = mm_counts.get("video", 0)
mm_processor_kwargs = mm_processor_kwargs or {}
target_width, target_height = self.info.get_image_size_with_most_features(
max_pixels=mm_processor_kwargs.get("max_pixels", None)
)
target_width, target_height = self.info.get_image_size_with_most_features()
target_num_frames = self.info.get_num_frames_with_most_features(
seq_len, mm_counts
)
image_overrides = mm_options.get("image") if mm_options else None
video_overrides = mm_options.get("video") if mm_options else None
image_overrides = mm_options.get("image")
video_overrides = mm_options.get("video")
return {
"image": self._get_dummy_images(
......
vllm/model_executor/models/qwen3_asr.py
View file @ 987506bc
......@@ -146,14 +146,11 @@ class Qwen3ASRDummyInputsBuilder(BaseDummyInputsBuilder[Qwen3ASRProcessingInfo])
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> MultiModalDataDict:
num_audios = mm_counts.get("audio", 0)
feature_extractor = self.info.get_feature_extractor(
**(mm_processor_kwargs or {})
)
feature_extractor = self.info.get_feature_extractor()
target_audio_length = (
min(
......@@ -163,7 +160,7 @@ class Qwen3ASRDummyInputsBuilder(BaseDummyInputsBuilder[Qwen3ASRProcessingInfo])
* feature_extractor.sampling_rate
)
audio_overrides = mm_options.get("audio") if mm_options else None
audio_overrides = mm_options.get("audio")
return {
"audio": self._get_dummy_audios(
......
vllm/model_executor/models/qwen3_vl.py
View file @ 987506bc
......@@ -703,11 +703,18 @@ class Qwen3VLProcessingInfo(Qwen2VLProcessingInfo):
mm_counts: Mapping[str, int],
) -> int:
video_processor = self.get_video_processor()
video_max_pixels = video_processor.size["longest_edge"]
mm_kwargs = self.ctx.get_merged_mm_kwargs({})
video_size = mm_kwargs.get("size", video_processor.size)
temporal_patch_size = mm_kwargs.get(
"temporal_patch_size", video_processor.temporal_patch_size
)
# video_max_pixels contains the temporal compression factor,
# so we divide by 2 to get the maximum number of image pixels.
video_max_pixels = video_size["longest_edge"]
target_width, target_height = self.get_image_size_with_most_features(
max_pixels=video_max_pixels // video_processor.temporal_patch_size
max_pixels=video_max_pixels // temporal_patch_size
)
num_video_soft_tokens = self.get_num_video_tokens(
image_width=target_width,
......@@ -789,19 +796,15 @@ class Qwen3VLDummyInputsBuilder(BaseDummyInputsBuilder[Qwen3VLProcessingInfo]):
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> MultiModalDataDict:
num_images = mm_counts.get("image", 0)
num_videos = mm_counts.get("video", 0)
image_overrides = mm_options.get("image") if mm_options else None
video_overrides = mm_options.get("video") if mm_options else None
image_overrides = mm_options.get("image")
video_overrides = mm_options.get("video")
mm_processor_kwargs = mm_processor_kwargs or {}
target_image_width, target_image_height = (
self.info.get_image_size_with_most_features(
max_pixels=mm_processor_kwargs.get("max_pixels", None),
)
self.info.get_image_size_with_most_features()
)
# treat videos as special images
......@@ -826,13 +829,20 @@ class Qwen3VLDummyInputsBuilder(BaseDummyInputsBuilder[Qwen3VLProcessingInfo]):
target_num_frames = min(target_num_frames, num_frames_override)
target_num_frames = max(target_num_frames, 2)
video_processor = self.info.get_video_processor(**(mm_processor_kwargs or {}))
video_max_pixels = video_processor.size["longest_edge"]
video_processor = self.info.get_video_processor()
mm_kwargs = self.info.ctx.get_merged_mm_kwargs({})
video_size = mm_kwargs.get("size", video_processor.size)
temporal_patch_size = mm_kwargs.get(
"temporal_patch_size", video_processor.temporal_patch_size
)
# video_max_pixels contains the temporal compression factor,
# so we divide by 2 to get the maximum number of image pixels.
video_max_pixels = video_size["longest_edge"]
target_video_width, target_video_height = (
self.info.get_image_size_with_most_features(
max_pixels=video_max_pixels // video_processor.temporal_patch_size
max_pixels=video_max_pixels // temporal_patch_size
)
)
target_video_size, _ = self.info._get_vision_info(
......
vllm/model_executor/models/qwen_vl.py
View file @ 987506bc
......@@ -617,8 +617,7 @@ class QwenVLDummyInputsBuilder(BaseDummyInputsBuilder[QwenVLProcessingInfo]):
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> MultiModalDataDict:
hf_config = self.info.get_hf_config()
vision_config = hf_config.visual
......@@ -626,7 +625,7 @@ class QwenVLDummyInputsBuilder(BaseDummyInputsBuilder[QwenVLProcessingInfo]):
target_width = target_height = vision_config["image_size"]
num_images = mm_counts.get("image", 0)
image_overrides = mm_options.get("image") if mm_options else None
image_overrides = mm_options.get("image")
return {
"image": self._get_dummy_images(
......
vllm/model_executor/models/rvl.py
View file @ 987506bc
......@@ -40,14 +40,13 @@ class RVLDummyInputsBuilder(LlavaDummyInputsBuilder[RVLProcessingInfo]):
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> MultiModalDataDict:
num_images = mm_counts.get("image", 0)
target_width, target_height = self.info.get_image_size_with_most_features()
image_overrides = mm_options.get("image") if mm_options else None
image_overrides = mm_options.get("image")
return {
"image": self._get_dummy_images(
......
vllm/model_executor/models/siglip.py
View file @ 987506bc
......@@ -158,14 +158,13 @@ class SiglipDummyInputsBuilder(BaseDummyInputsBuilder[SiglipProcessingInfo]):
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> MultiModalDataDict:
num_images = mm_counts.get("image", 0)
target_width, target_height = self.info.get_image_size_with_most_features()
image_overrides = mm_options.get("image") if mm_options else None
image_overrides = mm_options.get("image")
return {
"image": self._get_dummy_images(
......
vllm/model_executor/models/skyworkr1v.py
View file @ 987506bc
......@@ -529,13 +529,12 @@ class SkyworkR1VDummyInputsBuilder(BaseDummyInputsBuilder[SkyworkR1VProcessingIn
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> MultiModalDataDict:
target_width, target_height = self.info.get_image_size_with_most_features()
num_images = mm_counts.get("image", 0)
image_overrides = mm_options.get("image") if mm_options else None
image_overrides = mm_options.get("image")
return {
"image": self._get_dummy_images(
......
vllm/model_executor/models/step3_vl.py
View file @ 987506bc
......@@ -564,13 +564,12 @@ class Step3VLDummyInputsBuilder(BaseDummyInputsBuilder[Step3VLProcessingInfo]):
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> MultiModalDataDict:
target_width, target_height = self.info.get_image_size_with_most_features()
num_images = mm_counts.get("image", 0)
image_overrides = mm_options.get("image") if mm_options else None
image_overrides = mm_options.get("image")
return {
"image": self._get_dummy_images(
......
vllm/model_executor/models/terratorch.py
View file @ 987506bc
......@@ -154,8 +154,7 @@ class TerratorchInputBuilder(BaseDummyInputsBuilder[TerratorchProcessingInfo]):
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> MultiModalDataDict:
# Dummy data is generated based on the 'input' section
# defined in the HF configuration file
......
vllm/model_executor/models/transformers/multimodal.py
View file @ 987506bc
......@@ -101,14 +101,13 @@ class MultiModalDummyInputsBuilder(BaseDummyInputsBuilder[MultiModalProcessingIn
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, "BaseDummyOptions"] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, "BaseDummyOptions"],
) -> MultiModalDataDict:
num_images = mm_counts.get("image", 0)
target_width, target_height = self.info.get_max_image_size()
image_overrides = mm_options.get("image") if mm_options else None
image_overrides = mm_options.get("image")
return {
"image": self._get_dummy_images(
......
vllm/model_executor/models/ultravox.py
View file @ 987506bc
......@@ -164,12 +164,9 @@ class UltravoxDummyInputsBuilder(BaseDummyInputsBuilder[UltravoxProcessingInfo])
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> MultiModalDataDict:
feature_extractor = self.info.get_feature_extractor(
**(mm_processor_kwargs or {})
)
feature_extractor = self.info.get_feature_extractor()
sampling_rate = feature_extractor.sampling_rate
audio_len = (
......@@ -177,11 +174,13 @@ class UltravoxDummyInputsBuilder(BaseDummyInputsBuilder[UltravoxProcessingInfo])
)
num_audios = mm_counts.get("audio", 0)
audio_overrides = mm_options.get("audio") if mm_options else None
audio_overrides = mm_options.get("audio")
return {
"audio": self._get_dummy_audios(
length=audio_len, num_audios=num_audios, overrides=audio_overrides
length=audio_len,
num_audios=num_audios,
overrides=audio_overrides,
)
}
......
vllm/model_executor/models/voxtral.py
View file @ 987506bc
......@@ -218,18 +218,19 @@ class VoxtralDummyInputsBuilder(BaseDummyInputsBuilder[VoxtralProcessingInfo]):
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> MultiModalDataDict:
num_audios = mm_counts.get("audio", 0)
target_length = self.info.get_max_audio_array_len()
audio_overrides = mm_options.get("audio") if mm_options else None
audio_overrides = mm_options.get("audio")
return {
"audio": self._get_dummy_audios(
length=target_length, num_audios=num_audios, overrides=audio_overrides
length=target_length,
num_audios=num_audios,
overrides=audio_overrides,
)
}
......@@ -237,8 +238,7 @@ class VoxtralDummyInputsBuilder(BaseDummyInputsBuilder[VoxtralProcessingInfo]):
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> ProcessorInputs:
tokenizer = self.info.get_tokenizer()
......
vllm/model_executor/models/whisper.py
View file @ 987506bc
......@@ -695,22 +695,21 @@ class WhisperDummyInputsBuilder(BaseDummyInputsBuilder[WhisperProcessingInfo]):
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> MultiModalDataDict:
feature_extractor = self.info.get_feature_extractor(
**(mm_processor_kwargs or {})
)
feature_extractor = self.info.get_feature_extractor()
sampling_rate = feature_extractor.sampling_rate
audio_len = feature_extractor.chunk_length * sampling_rate
num_audios = mm_counts.get("audio", 0)
audio_overrides = mm_options.get("audio") if mm_options else None
audio_overrides = mm_options.get("audio")
return {
"audio": self._get_dummy_audios(
length=audio_len, num_audios=num_audios, overrides=audio_overrides
length=audio_len,
num_audios=num_audios,
overrides=audio_overrides,
)
}
......
vllm/multimodal/processing/context.py
View file @ 987506bc
......@@ -266,11 +266,14 @@ class InputProcessingContext:
if isinstance(tokenizer, MistralTokenizer):
tokenizer = tokenizer.transformers_tokenizer
merged_kwargs = self.get_merged_mm_kwargs(kwargs)
merged_kwargs.pop("tokenizer", None)
return cached_processor_from_config(
self.model_config,
processor_cls=typ,
tokenizer=tokenizer,
**kwargs,
**merged_kwargs,
)
def init_processor(
......@@ -283,12 +286,7 @@ class InputProcessingContext:
Initialize a HuggingFace-like processor class, merging the
keyword arguments with those in the model's configuration.
"""
mm_config = self.model_config.get_multimodal_config()
base_kwargs = mm_config.mm_processor_kwargs
if base_kwargs is None:
base_kwargs = {}
merged_kwargs = {**base_kwargs, **kwargs}
merged_kwargs = self.get_merged_mm_kwargs(kwargs)
return typ(**merged_kwargs)
......
vllm/multimodal/processing/dummy_inputs.py
View file @ 987506bc
......@@ -62,8 +62,7 @@ class BaseDummyInputsBuilder(ABC, Generic[_I]):
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> MultiModalDataDict:
"""
Build the multimodal input which, after processing, results in
......@@ -83,8 +82,7 @@ class BaseDummyInputsBuilder(ABC, Generic[_I]):
self,
seq_len: int,
mm_counts: Mapping[str, int],
mm_options: Mapping[str, BaseDummyOptions] | None = None,
mm_processor_kwargs: Mapping[str, object] | None = None,
mm_options: Mapping[str, BaseDummyOptions],
) -> ProcessorInputs:
"""
Build the input which, after processing, results in
......@@ -94,16 +92,9 @@ class BaseDummyInputsBuilder(ABC, Generic[_I]):
seq_len: Sequence length
mm_counts: Count of items per modality
mm_options: Configurable options per modality (optional)
mm_processor_kwargs: Additional keyword arguments
for hf_processor (optional)
"""
dummy_text = self.get_dummy_text(mm_counts)
dummy_mm_data = self.get_dummy_mm_data(
seq_len,
mm_counts,
mm_options,
mm_processor_kwargs=mm_processor_kwargs,
)
dummy_mm_data = self.get_dummy_mm_data(seq_len, mm_counts, mm_options)
dummy_mm_items = self.info.parse_mm_data(dummy_mm_data, validate=False)
tokenization_kwargs = {"truncation": False}
......@@ -111,7 +102,6 @@ class BaseDummyInputsBuilder(ABC, Generic[_I]):
return ProcessorInputs(
prompt=dummy_text,
mm_items=dummy_mm_items,
hf_processor_mm_kwargs=mm_processor_kwargs or {},
tokenization_kwargs=tokenization_kwargs,
)
......
vllm/multimodal/registry.py
View file @ 987506bc
......@@ -5,7 +5,6 @@ from dataclasses import dataclass
from multiprocessing.synchronize import Lock as LockType
from typing import TYPE_CHECKING, Generic, Literal, Protocol, TypeVar, cast
from vllm.config.multimodal import BaseDummyOptions
from vllm.config.observability import ObservabilityConfig
from vllm.logger import init_logger
from vllm.tokenizers import TokenizerLike, cached_tokenizer_from_config
......@@ -99,27 +98,6 @@ class MultiModalRegistry:
A registry that dispatches data processing according to the model.
"""
def _extract_mm_options(
self,
model_config: "ModelConfig",
) -> Mapping[str, BaseDummyOptions] | None:
"""
Extract multimodal dummy options from model config.
Returns None if no configurable options are found, otherwise returns
a mapping of modality names to their dummy options.
"""
if not model_config.multimodal_config:
return None
mm_options = {
m: opt
for m in model_config.multimodal_config.limit_per_prompt
if (opt := model_config.multimodal_config.get_dummy_options(m)) is not None
}
return mm_options if len(mm_options) > 0 else None
def supports_multimodal_inputs(self, model_config: "ModelConfig") -> bool:
"""
Checks if the model supports multimodal inputs.
......@@ -261,8 +239,7 @@ class MultiModalRegistry:
processor_inputs = processor.dummy_inputs.get_dummy_processor_inputs(
seq_len=seq_len,
mm_counts=mm_counts,
mm_options=self._extract_mm_options(model_config),
mm_processor_kwargs=mm_config.mm_processor_kwargs,
mm_options=mm_config.limit_per_prompt,
)
mm_inputs = processor.apply(
prompt=processor_inputs.prompt,
......
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