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[Renderer] Move MM Hash parsing into Renderer (#34711)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2026-02-18 11:18:55 +08:00
committed by GitHub
parent df3f537a66
commit a0d8d944e2
16 changed files with 254 additions and 217 deletions
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35
tests/renderers/test_process_multi_modal_uuids.py
View File

@@ -6,6 +6,7 @@ import pytest
from vllm.assets.image import ImageAsset
from vllm.assets.video import VideoAsset
from vllm.config import CacheConfig, ModelConfig, VllmConfig
from vllm.multimodal.parse import parse_mm_uuids
from vllm.renderers.hf import HfRenderer
from vllm.tokenizers.registry import tokenizer_args_from_config
@@ -45,10 +46,11 @@ def test_multi_modal_uuids_length_mismatch_raises():
mm_uuids = {"image": ["hash_cherry"]}
mm_processor = renderer.get_mm_processor()
mm_items = mm_processor.info.parse_mm_data(mm_data)
mm_data_items = mm_processor.info.parse_mm_data(mm_data)
mm_uuid_items = parse_mm_uuids(mm_uuids)
with pytest.raises(ValueError, match="must have same length as"):
renderer._process_mm_uuids(mm_data, mm_items, mm_uuids, "req-1")
renderer._process_mm_uuids(mm_data, mm_data_items, mm_uuid_items, "req-1")
def test_multi_modal_uuids_missing_modality_raises():
@@ -63,10 +65,11 @@ def test_multi_modal_uuids_missing_modality_raises():
mm_uuids = {"image": ["hash_cherry"]}
mm_processor = renderer.get_mm_processor()
mm_items = mm_processor.info.parse_mm_data(mm_data)
mm_data_items = mm_processor.info.parse_mm_data(mm_data)
mm_uuid_items = parse_mm_uuids(mm_uuids)
with pytest.raises(ValueError, match="is empty but .* is missing"):
renderer._process_mm_uuids(mm_data, mm_items, mm_uuids, "req-2")
renderer._process_mm_uuids(mm_data, mm_data_items, mm_uuid_items, "req-2")
@pytest.mark.parametrize(
@@ -78,7 +81,7 @@ def test_multi_modal_uuids_missing_modality_raises():
],
)
def test_multi_modal_uuids_accepts_none_and_passes_through(
monkeypatch, mm_cache_gb: float, enable_prefix_caching: bool
mm_cache_gb: float, enable_prefix_caching: bool
):
renderer = _build_renderer(
mm_cache_gb=mm_cache_gb,
@@ -94,9 +97,11 @@ def test_multi_modal_uuids_accepts_none_and_passes_through(
mm_uuids = {"image": [None, "hash_stop"], "video": None}
mm_processor = renderer.get_mm_processor()
mm_items = mm_processor.info.parse_mm_data(mm_data)
mm_data_items = mm_processor.info.parse_mm_data(mm_data)
mm_uuid_items = parse_mm_uuids(mm_uuids)
processed_mm_uuids = renderer._process_mm_uuids(
mm_data, mm_items, mm_uuids, "req-3"
mm_data, mm_data_items, mm_uuid_items, "req-3"
)
assert processed_mm_uuids == mm_uuids
@@ -111,7 +116,7 @@ def test_multi_modal_uuids_accepts_none_and_passes_through(
],
)
def test_multi_modal_uuids_accepts_empty(
monkeypatch, mm_cache_gb: float, enable_prefix_caching: bool
mm_cache_gb: float, enable_prefix_caching: bool
):
renderer = _build_renderer(
mm_cache_gb=mm_cache_gb,
@@ -124,15 +129,17 @@ def test_multi_modal_uuids_accepts_empty(
mm_uuids = {"image": [], "video": None} # type: ignore[var-annotated]
mm_processor = renderer.get_mm_processor()
mm_items = mm_processor.info.parse_mm_data(mm_data)
mm_data_items = mm_processor.info.parse_mm_data(mm_data)
mm_uuid_items = parse_mm_uuids(mm_uuids)
processed_mm_uuids = renderer._process_mm_uuids(
mm_data, mm_items, mm_uuids, "req-4"
mm_data, mm_data_items, mm_uuid_items, "req-4"
)
assert processed_mm_uuids == mm_uuids
def test_multi_modal_uuids_ignored_when_caching_disabled(monkeypatch):
def test_multi_modal_uuids_ignored_when_caching_disabled():
# When both processor cache is 0 and prefix caching disabled, the
# processor builds overrides from request id instead of using user UUIDs.
renderer = _build_renderer(mm_cache_gb=0.0, enable_prefix_caching=False)
@@ -145,9 +152,11 @@ def test_multi_modal_uuids_ignored_when_caching_disabled(monkeypatch):
mm_uuids = {"image": ["hash_cherry", "hash_stop"], "video": ["hash_video"]}
mm_processor = renderer.get_mm_processor()
mm_items = mm_processor.info.parse_mm_data(mm_data)
mm_data_items = mm_processor.info.parse_mm_data(mm_data)
mm_uuid_items = parse_mm_uuids(mm_uuids)
processed_mm_uuids = renderer._process_mm_uuids(
mm_data, mm_items, mm_uuids, request_id
mm_data, mm_data_items, mm_uuid_items, request_id
)
# Expect request-id-based overrides are passed through

6
vllm/inputs/preprocess.py
View File

@@ -91,7 +91,7 @@ class InputPreprocessor:
self,
prompt: str | list[int],
mm_data: MultiModalDataDict,
mm_processor_kwargs: Mapping[str, object] | None,
mm_processor_kwargs: Mapping[str, object] | None = None,
tokenization_kwargs: dict[str, Any] | None = None,
*,
mm_uuids: MultiModalUUIDDict | None = None,
@@ -103,9 +103,9 @@ class InputPreprocessor:
return self.renderer._process_multimodal(
prompt,
mm_data,
mm_uuids=mm_uuids,
mm_processor_kwargs=mm_processor_kwargs,
tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids,
)
def _process_embeds(
@@ -144,7 +144,7 @@ class InputPreprocessor:
inputs = self._process_multimodal(
prompt_token_ids,
multi_modal_data,
parsed_content.get("mm_processor_kwargs") or {},
parsed_content.get("mm_processor_kwargs"),
tokenization_kwargs=tokenization_kwargs,
mm_uuids=parsed_content.get("multi_modal_uuids"),
)

15
vllm/model_executor/models/clip.py
View File

@@ -36,9 +36,13 @@ from vllm.multimodal.inputs import (
MultiModalFieldConfig,
MultiModalInputs,
MultiModalKwargsItems,
MultiModalUUIDDict,
)
from vllm.multimodal.parse import ImageProcessorItems, ImageSize, MultiModalDataItems
from vllm.multimodal.parse import (
ImageProcessorItems,
ImageSize,
MultiModalDataItems,
MultiModalUUIDItems,
)
from vllm.multimodal.processing import (
BaseDummyInputsBuilder,
BaseMultiModalProcessor,
@@ -203,10 +207,9 @@ class CLIPMultiModalProcessor(BaseMultiModalProcessor[CLIPProcessingInfo]):
self,
prompt: str | list[int],
mm_items: MultiModalDataItems,
hf_processor_mm_kwargs: Mapping[str, object],
mm_uuid_items: MultiModalUUIDItems | None = None,
hf_processor_mm_kwargs: Mapping[str, object] | None = None,
tokenization_kwargs: Mapping[str, object] | None = None,
*,
mm_uuids: MultiModalUUIDDict | None = None,
) -> MultiModalInputs:
if mm_items:
if isinstance(prompt, str):
@@ -235,9 +238,9 @@ class CLIPMultiModalProcessor(BaseMultiModalProcessor[CLIPProcessingInfo]):
return super().apply(
prompt=prompt,
mm_items=mm_items,
mm_uuid_items=mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids,
)
def _hf_processor_applies_updates(

8
vllm/model_executor/models/deepseek_vl2.py
View File

@@ -24,13 +24,13 @@ from vllm.multimodal.inputs import (
MultiModalDataDict,
MultiModalFieldConfig,
MultiModalKwargsItems,
MultiModalUUIDDict,
)
from vllm.multimodal.parse import (
ImageEmbeddingItems,
ImageProcessorItems,
ImageSize,
MultiModalDataItems,
MultiModalUUIDItems,
)
from vllm.multimodal.processing import BaseDummyInputsBuilder
from vllm.multimodal.processing.processor import (
@@ -313,9 +313,9 @@ class DeepseekVL2MultiModalProcessor(
self,
prompt: str | list[int],
mm_data_items: MultiModalDataItems,
mm_uuid_items: MultiModalUUIDItems | None,
hf_processor_mm_kwargs: Mapping[str, object],
tokenization_kwargs: Mapping[str, object],
mm_uuids: MultiModalUUIDDict | None = None,
) -> tuple[list[int], MultiModalProcessingInfo, bool]:
# The processor logic is different for len(images) <= 2 vs > 2
# Since the processing cache assumes that the processor output is
@@ -325,17 +325,17 @@ class DeepseekVL2MultiModalProcessor(
return self._apply_hf_processor(
prompt=prompt,
mm_data_items=mm_data_items,
mm_uuid_items=mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids,
)
return super()._cached_apply_hf_processor(
prompt=prompt,
mm_data_items=mm_data_items,
mm_uuid_items=mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids,
)

9
vllm/model_executor/models/h2ovl.py
View File

@@ -16,11 +16,12 @@ from transformers import PretrainedConfig
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import MultiModalKwargsItems, MultiModalUUIDDict
from vllm.multimodal.inputs import MultiModalKwargsItems
from vllm.multimodal.parse import (
ImageEmbeddingItems,
ImageProcessorItems,
MultiModalDataItems,
MultiModalUUIDItems,
)
from vllm.multimodal.processing.processor import (
MultiModalProcessingInfo,
@@ -491,9 +492,9 @@ class H2OVLMultiModalProcessor(BaseInternVLMultiModalProcessor[H2OVLProcessingIn
self,
prompt: str | list[int],
mm_data_items: MultiModalDataItems,
mm_uuid_items: MultiModalUUIDItems | None,
hf_processor_mm_kwargs: Mapping[str, object],
tokenization_kwargs: Mapping[str, object],
mm_uuids: MultiModalUUIDDict | None = None,
) -> tuple[list[int], MultiModalProcessingInfo, bool]:
# The processor logic is different for len(images) <= 1 vs > 1
# Since the processing cache assumes that the processor output is
@@ -503,17 +504,17 @@ class H2OVLMultiModalProcessor(BaseInternVLMultiModalProcessor[H2OVLProcessingIn
return self._apply_hf_processor(
prompt=prompt,
mm_data_items=mm_data_items,
mm_uuid_items=mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids,
)
return super()._cached_apply_hf_processor(
prompt=prompt,
mm_data_items=mm_data_items,
mm_uuid_items=mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids,
)

12
vllm/model_executor/models/llava.py
View File

@@ -30,7 +30,6 @@ from vllm.multimodal.inputs import (
MultiModalFieldConfig,
MultiModalInputs,
MultiModalKwargsItems,
MultiModalUUIDDict,
mm_inputs,
)
from vllm.multimodal.parse import (
@@ -38,6 +37,7 @@ from vllm.multimodal.parse import (
ImageProcessorItems,
ImageSize,
MultiModalDataItems,
MultiModalUUIDItems,
)
from vllm.multimodal.processing import (
BaseDummyInputsBuilder,
@@ -773,9 +773,9 @@ class MantisMultiModalProcessor(LlavaMultiModalProcessor):
self,
prompt: str | list[int],
mm_items: MultiModalDataItems,
hf_processor_mm_kwargs: Mapping[str, object],
mm_uuid_items: MultiModalUUIDItems | None = None,
hf_processor_mm_kwargs: Mapping[str, object] | None = None,
tokenization_kwargs: Mapping[str, object] | None = None,
mm_uuids: MultiModalUUIDDict | None = None,
) -> MultiModalInputs:
hf_config = self.info.get_hf_config()
image_token_id = hf_config.image_token_index
@@ -789,9 +789,9 @@ class MantisMultiModalProcessor(LlavaMultiModalProcessor):
result = super().apply(
prompt,
mm_items,
hf_processor_mm_kwargs,
tokenization_kwargs,
mm_uuids=mm_uuids,
mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
tokenization_kwargs=tokenization_kwargs,
)
mm_item_counts = mm_items.get_all_counts()

12
vllm/model_executor/models/paligemma.py
View File

@@ -16,12 +16,12 @@ from vllm.multimodal.inputs import (
MultiModalFieldConfig,
MultiModalInputs,
MultiModalKwargsItems,
MultiModalUUIDDict,
)
from vllm.multimodal.parse import (
ImageEmbeddingItems,
ImageProcessorItems,
MultiModalDataItems,
MultiModalUUIDItems,
)
from vllm.multimodal.processing import (
BaseDummyInputsBuilder,
@@ -231,16 +231,16 @@ class PaliGemmaMultiModalProcessor(BaseMultiModalProcessor[PaliGemmaProcessingIn
self,
prompt: str | list[int],
mm_items: MultiModalDataItems,
hf_processor_mm_kwargs: Mapping[str, object],
mm_uuid_items: MultiModalUUIDItems | None = None,
hf_processor_mm_kwargs: Mapping[str, object] | None = None,
tokenization_kwargs: Mapping[str, object] | None = None,
mm_uuids: MultiModalUUIDDict | None = None,
) -> MultiModalInputs:
mm_inputs = super().apply(
prompt,
mm_items,
hf_processor_mm_kwargs,
tokenization_kwargs,
mm_uuids=mm_uuids,
mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
tokenization_kwargs=tokenization_kwargs,
)
prompt_token_ids = mm_inputs["prompt_token_ids"]

12
vllm/model_executor/models/pixtral.py
View File

@@ -44,10 +44,14 @@ from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargsItems
from vllm.multimodal.inputs import (
MultiModalDataDict,
MultiModalFieldConfig,
MultiModalUUIDDict,
NestedTensors,
)
from vllm.multimodal.parse import ImageProcessorItems, ImageSize, MultiModalDataItems
from vllm.multimodal.parse import (
ImageProcessorItems,
ImageSize,
MultiModalDataItems,
MultiModalUUIDItems,
)
from vllm.multimodal.processing import BaseDummyInputsBuilder, ProcessorInputs
from vllm.multimodal.processing.processor import (
BaseMultiModalProcessor,
@@ -344,16 +348,16 @@ class PixtralMultiModalProcessor(BaseMultiModalProcessor[PixtralProcessingInfo])
self,
prompt: str | list[int],
mm_data_items: MultiModalDataItems,
mm_uuid_items: MultiModalUUIDItems | None,
hf_processor_mm_kwargs: Mapping[str, object],
tokenization_kwargs: Mapping[str, object],
mm_uuids: MultiModalUUIDDict | None = None,
) -> tuple[list[int], MultiModalProcessingInfo, bool]:
prompt_ids, mm_info, _ = super()._cached_apply_hf_processor(
prompt=prompt,
mm_data_items=mm_data_items,
mm_uuid_items=mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids,
)
# NOTE: The tokens are already inserted by the chat template

15
vllm/model_executor/models/siglip.py
View File

@@ -42,9 +42,13 @@ from vllm.multimodal.inputs import (
MultiModalFieldConfig,
MultiModalInputs,
MultiModalKwargsItems,
MultiModalUUIDDict,
)
from vllm.multimodal.parse import ImageProcessorItems, ImageSize, MultiModalDataItems
from vllm.multimodal.parse import (
ImageProcessorItems,
ImageSize,
MultiModalDataItems,
MultiModalUUIDItems,
)
from vllm.multimodal.processing import (
BaseDummyInputsBuilder,
BaseMultiModalProcessor,
@@ -189,10 +193,9 @@ class SiglipMultiModalProcessor(BaseMultiModalProcessor[SiglipProcessingInfo]):
self,
prompt: str | list[int],
mm_items: MultiModalDataItems,
hf_processor_mm_kwargs: Mapping[str, object],
mm_uuid_items: MultiModalUUIDItems | None = None,
hf_processor_mm_kwargs: Mapping[str, object] | None = None,
tokenization_kwargs: Mapping[str, object] | None = None,
*,
mm_uuids: MultiModalUUIDDict | None = None,
) -> MultiModalInputs:
if mm_items:
if isinstance(prompt, str):
@@ -221,9 +224,9 @@ class SiglipMultiModalProcessor(BaseMultiModalProcessor[SiglipProcessingInfo]):
return super().apply(
prompt=prompt,
mm_items=mm_items,
mm_uuid_items=mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids,
)
def _hf_processor_applies_updates(

12
vllm/model_executor/models/terratorch.py
View File

@@ -46,7 +46,6 @@ from vllm.multimodal.inputs import (
MultiModalFieldConfig,
MultiModalInputs,
MultiModalKwargsItems,
MultiModalUUIDDict,
PlaceholderRange,
mm_inputs,
)
@@ -55,6 +54,7 @@ from vllm.multimodal.parse import (
ModalityDataItems,
MultiModalDataItems,
MultiModalDataParser,
MultiModalUUIDItems,
)
from vllm.multimodal.processing import (
BaseDummyInputsBuilder,
@@ -196,15 +196,19 @@ class TerratorchMultiModalProcessor(BaseMultiModalProcessor[TerratorchProcessing
self,
prompt: str | list[int],
mm_items: MultiModalDataItems,
hf_processor_mm_kwargs: Mapping[str, object],
mm_uuid_items: MultiModalUUIDItems | None = None,
hf_processor_mm_kwargs: Mapping[str, object] | None = None,
tokenization_kwargs: Mapping[str, object] | None = None,
mm_uuids: MultiModalUUIDDict | None = None,
) -> MultiModalInputs:
if hf_processor_mm_kwargs is None:
hf_processor_mm_kwargs = {}
if tokenization_kwargs is None:
tokenization_kwargs = {}
mm_hashes = self._hash_mm_items(
mm_items, hf_processor_mm_kwargs, tokenization_kwargs, mm_uuids=mm_uuids
mm_items,
mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
)
_, passthrough_data = self._get_hf_mm_data(mm_items)

17
vllm/model_executor/models/transformers/multimodal.py
View File

@@ -31,11 +31,14 @@ from vllm.multimodal.inputs import (
MultiModalFeatureSpec,
MultiModalFieldConfig,
MultiModalInputs,
MultiModalUUIDDict,
PlaceholderRange,
mm_inputs,
)
from vllm.multimodal.parse import ImageProcessorItems, MultiModalDataItems
from vllm.multimodal.parse import (
ImageProcessorItems,
MultiModalDataItems,
MultiModalUUIDItems,
)
from vllm.multimodal.processing import (
BaseDummyInputsBuilder,
BaseMultiModalProcessor,
@@ -177,9 +180,9 @@ class MultiModalProcessor(BaseMultiModalProcessor[MultiModalProcessingInfo]):
self,
prompt: str | list[int],
mm_items: MultiModalDataItems,
hf_processor_mm_kwargs: Mapping[str, object],
mm_uuid_items: MultiModalUUIDItems | None = None,
hf_processor_mm_kwargs: Mapping[str, object] | None = None,
tokenization_kwargs: Mapping[str, object] | None = None,
mm_uuids: MultiModalUUIDDict | None = None,
) -> MultiModalInputs:
"""
Process multi-modal inputs to be used in vLLM.
@@ -187,6 +190,8 @@ class MultiModalProcessor(BaseMultiModalProcessor[MultiModalProcessingInfo]):
Apply HF Processor on prompt text and multi-modal data together,
outputting token IDs and processed tensors.
"""
if hf_processor_mm_kwargs is None:
hf_processor_mm_kwargs = {}
if tokenization_kwargs is None:
tokenization_kwargs = {}
@@ -258,7 +263,9 @@ class MultiModalProcessor(BaseMultiModalProcessor[MultiModalProcessingInfo]):
# Use overrides if provided; fallback to data-dependent hashing.
mm_hashes = self._hash_mm_items(
mm_items, hf_processor_mm_kwargs, tokenization_kwargs, mm_uuids=mm_uuids
mm_items,
mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
)
return mm_inputs(

6
vllm/model_executor/models/voxtral.py
View File

@@ -41,13 +41,13 @@ from vllm.multimodal.inputs import (
MultiModalDataDict,
MultiModalFieldConfig,
MultiModalKwargsItems,
MultiModalUUIDDict,
NestedTensors,
)
from vllm.multimodal.parse import (
AudioProcessorItems,
MultiModalDataItems,
MultiModalDataParser,
MultiModalUUIDItems,
)
from vllm.multimodal.processing import BaseDummyInputsBuilder, ProcessorInputs
from vllm.multimodal.processing.processor import (
@@ -363,16 +363,16 @@ class VoxtralMultiModalProcessor(BaseMultiModalProcessor[VoxtralProcessingInfo])
self,
prompt: str | list[int],
mm_data_items: MultiModalDataItems,
mm_uuid_items: MultiModalUUIDItems | None,
hf_processor_mm_kwargs: Mapping[str, object],
tokenization_kwargs: Mapping[str, object],
mm_uuids: MultiModalUUIDDict | None = None,
) -> tuple[list[int], MultiModalProcessingInfo, bool]:
prompt_ids, mm_info, _ = super()._cached_apply_hf_processor(
prompt=prompt,
mm_data_items=mm_data_items,
mm_uuid_items=mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids,
)
# NOTE: The tokens are already inserted by the chat template

2
vllm/multimodal/inputs.py
View File

@@ -155,7 +155,7 @@ The built-in modalities are defined by
[`MultiModalDataBuiltins`][vllm.multimodal.inputs.MultiModalDataBuiltins].
"""
MultiModalUUIDDict: TypeAlias = Mapping[str, list[str | None] | str]
MultiModalUUIDDict: TypeAlias = Mapping[str, Sequence[str | None] | str]
"""
A dictionary containing user-provided UUIDs for items in each modality.
If a UUID for an item is not provided, its entry will be `None` and

111
vllm/multimodal/parse.py
View File

@@ -3,7 +3,7 @@
from abc import ABC, abstractmethod
from collections import UserDict
from collections.abc import Callable, Iterator, Mapping, Sequence
from collections.abc import Callable, Iterator, Mapping, Sequence, Set
from typing import (
TYPE_CHECKING,
Any,
@@ -33,6 +33,7 @@ from .inputs import (
MultiModalDataDict,
MultiModalFieldConfig,
MultiModalKwargsItems,
MultiModalUUIDDict,
VideoItem,
)
from .media import MediaWithBytes
@@ -297,14 +298,15 @@ class DictEmbeddingItems(
return self.data
class AudioProcessorItems(ProcessorBatchItems[HfAudioItem]):
def __init__(self, data: Sequence[HfAudioItem] | None) -> None:
if data is None:
data = [None]
class AudioProcessorItems(ProcessorBatchItems[HfAudioItem | None]):
def __init__(self, data: Sequence[HfAudioItem | None]) -> None:
super().__init__(data, "audio")
def get_audio_length(self, item_idx: int) -> int:
audio = self.get(item_idx)
if audio is None:
raise ValueError(f"Cannot get length of cached audio at {item_idx}")
return len(audio)
@@ -322,14 +324,14 @@ class ImageSize(NamedTuple):
height: int
class ImageProcessorItems(ProcessorBatchItems[HfImageItem]):
def __init__(self, data: Sequence[HfImageItem] | None) -> None:
if data is None:
data = [None]
class ImageProcessorItems(ProcessorBatchItems[HfImageItem | None]):
def __init__(self, data: Sequence[HfImageItem | None]) -> None:
super().__init__(data, "image")
def get_image_size(self, item_idx: int) -> ImageSize:
image = self.get(item_idx)
if image is None:
raise ValueError(f"Cannot get size of cached image at {item_idx}")
if isinstance(image, PILImage.Image):
return ImageSize(*image.size)
@@ -349,22 +351,31 @@ class ImageEmbeddingItems(EmbeddingItems):
super().__init__(data, "image", expected_hidden_size)
class VideoProcessorItems(ProcessorBatchItems[HfVideoItem]):
class VideoProcessorItems(ProcessorBatchItems[HfVideoItem | None]):
def __init__(
self,
data: Sequence[HfVideoItem] | None,
data: Sequence[HfVideoItem | None],
metadata: dict[str, Any] | list[dict[str, Any] | None] | None = None,
) -> None:
if data is None:
data = [None]
super().__init__(data, "video")
self.metadata = metadata
def get_num_frames(self, item_idx: int) -> int:
return len(self.get(item_idx))
video = self.get(item_idx)
if video is None:
raise ValueError(f"Cannot get length of cached video at {item_idx}")
return len(video)
def get_frame_size(self, item_idx: int) -> ImageSize:
image = self.get(item_idx)[0] # Assume that the video isn't empty
video = self.get(item_idx)
if video is None:
raise ValueError(f"Cannot get size of cached video at {item_idx}")
if len(video) == 0:
raise ValueError(f"Cannot get size of empty video at {item_idx}")
image = video[0]
if isinstance(image, PILImage.Image):
return ImageSize(*image.size)
@@ -400,6 +411,15 @@ class MultiModalDataItems(UserDict[str, ModalityDataItems[Any, Any]]):
normalized such that each entry corresponds to a list.
"""
def select(self, modalities: Set[str]):
"""
Construct a new `MultiModalDataItems` instance containing only the
selected modalities.
"""
return MultiModalDataItems(
{modality: self[modality] for modality in modalities}
)
def get_count(self, modality: str, *, strict: bool = True) -> int:
"""
Get the number of data items belonging to a modality.
@@ -497,19 +517,11 @@ class MultiModalDataParser:
) -> TypeGuard[torch.Tensor | list[torch.Tensor]]:
if isinstance(data, torch.Tensor):
return data.ndim == 3
if is_list_of(data, torch.Tensor):
if is_list_of(data, torch.Tensor) and len(data) > 0:
return data[0].ndim == 2 # type: ignore[index]
return False
def _is_empty(self, data: object) -> TypeGuard[None]:
if isinstance(data, list):
return len(data) == 0
if isinstance(data, (np.ndarray, torch.Tensor)):
return data.size == 0
return False
def _get_audio_with_sr(
self,
audio: AudioItem,
@@ -545,12 +557,6 @@ class MultiModalDataParser:
data: ModalityData[AudioItem],
) -> ModalityDataItems[Any, Any] | None:
if data is None:
return AudioProcessorItems(None)
# also check single audio item with sampling rate
if self._is_empty(data) or (
isinstance(data, tuple) and self._is_empty(data[0])
):
return None
if self.is_embeddings(data):
@@ -558,9 +564,8 @@ class MultiModalDataParser:
data_items: list[AudioItem]
if (
is_list_of(data, float)
or isinstance(data, (np.ndarray, torch.Tensor))
and data.ndim == 1
(is_list_of(data, float) and len(data) > 0)
or (isinstance(data, (np.ndarray, torch.Tensor)) and data.ndim == 1)
or isinstance(data, tuple)
):
data_items = [data]
@@ -591,18 +596,13 @@ class MultiModalDataParser:
data: ModalityData[ImageItem],
) -> ModalityDataItems[Any, Any] | None:
if data is None:
return ImageProcessorItems(None)
if self._is_empty(data):
return None
if self.is_embeddings(data):
return ImageEmbeddingItems(data, self.expected_hidden_size)
if (
isinstance(data, (PILImage.Image, MediaWithBytes))
or isinstance(data, (np.ndarray, torch.Tensor))
and data.ndim == 3
if isinstance(data, (PILImage.Image, MediaWithBytes)) or (
isinstance(data, (np.ndarray, torch.Tensor)) and data.ndim == 3
):
data_items = [data]
elif isinstance(data, (np.ndarray, torch.Tensor)):
@@ -617,19 +617,14 @@ class MultiModalDataParser:
data: ModalityData[VideoItem],
) -> ModalityDataItems[Any, Any] | None:
if data is None:
return VideoProcessorItems(None)
if self._is_empty(data):
return None
if self.is_embeddings(data):
return VideoEmbeddingItems(data, self.expected_hidden_size)
data_items: list[VideoItem]
if (
is_list_of(data, PILImage.Image)
or isinstance(data, (np.ndarray, torch.Tensor))
and data.ndim == 4
if (is_list_of(data, PILImage.Image) and len(data) > 0) or (
isinstance(data, (np.ndarray, torch.Tensor)) and data.ndim == 4
):
data_items = [data]
elif isinstance(data, (np.ndarray, torch.Tensor)):
@@ -664,12 +659,15 @@ class MultiModalDataParser:
data: ModalityData[Any],
) -> ModalityDataItems[Any, Any] | None:
"""Parse vision chunk data (unified image and video chunks)."""
if data is None or self._is_empty(data):
if data is None:
return None
if self.is_embeddings(data):
raise ValueError("Do not support embedding data for vision_chunk right now")
if isinstance(data, dict):
data = [data]
return VisionChunkProcessorItems(data)
def _get_subparsers(self) -> Mapping[str, ModalityDataParser]:
@@ -693,3 +691,20 @@ class MultiModalDataParser:
mm_items[k] = parsed_data
return mm_items
MultiModalUUIDItems: TypeAlias = dict[str, Sequence[str | None]]
"""
As [`MultiModalUUIDDict`][vllm.multimodal.inputs.MultiModalUUIDDict], but
normalized such that each entry corresponds to a list.
"""
def parse_mm_uuids(mm_uuids: MultiModalUUIDDict | None) -> MultiModalUUIDItems:
if mm_uuids is None:
return {}
return {
modality: [uuids] if isinstance(uuids, str) else uuids
for modality, uuids in mm_uuids.items()
}

125
vllm/multimodal/processing/processor.py
View File

@@ -32,7 +32,6 @@ from ..inputs import (
MultiModalKwargsItem,
MultiModalKwargsItems,
MultiModalKwargsOptionalItems,
MultiModalUUIDDict,
PlaceholderRange,
mm_enc_dec_inputs,
mm_inputs,
@@ -41,6 +40,7 @@ from ..parse import (
DictEmbeddingItems,
EmbeddingItems,
MultiModalDataItems,
MultiModalUUIDItems,
)
from .context import (
BaseProcessingInfo,
@@ -1014,11 +1014,15 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
self,
prompt: str,
mm_items: MultiModalDataItems,
hf_processor_mm_kwargs: Mapping[str, object],
*,
mm_uuids: MultiModalUUIDDict | None = None,
mm_uuid_items: MultiModalUUIDItems | None = None,
hf_processor_mm_kwargs: Mapping[str, object] | None = None,
) -> MultiModalInputs:
return self.apply(prompt, mm_items, hf_processor_mm_kwargs, mm_uuids=mm_uuids)
return self.apply(
prompt,
mm_items,
mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
)
@abstractmethod
def _get_mm_fields_config(
@@ -1174,7 +1178,10 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
In addition, return whether prompt updates have been applied.
"""
processor_data, passthrough_data = self._get_hf_mm_data(mm_items)
valid_mm_items = mm_items.select(
{k for k, c in mm_items.get_all_counts().items() if c > 0}
)
processor_data, passthrough_data = self._get_hf_mm_data(valid_mm_items)
processed_data = self._call_hf_processor(
prompt=prompt_text,
@@ -1301,69 +1308,57 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
def _hash_mm_items(
self,
mm_items: MultiModalDataItems,
mm_data_items: MultiModalDataItems,
mm_uuid_items: MultiModalUUIDItems | None,
hf_processor_mm_kwargs: Mapping[str, object],
tokenization_kwargs: Mapping[str, object],
*,
mm_uuids: MultiModalUUIDDict | None = None,
) -> MultiModalHashes:
"""Create MM hashes to be returned.
Note: When overrides are provided via callers of `apply`,
`_hash_mm_items` will be bypassed and the overrides will be used.
"""
model_id = self.info.model_id
hashes: MultiModalHashes = {}
mm_uuids = mm_uuids or {}
if mm_uuid_items is None:
mm_uuid_items = {}
for modality, items in mm_items.items():
if modality in mm_uuids:
mm_uuids_per_modality = mm_uuids[modality]
if isinstance(mm_uuids_per_modality, str):
mm_uuids_per_modality = [mm_uuids_per_modality]
mm_hashes: MultiModalHashes = {}
hasher = MultiModalHasher
for modality, data_items in mm_data_items.items():
if modality in mm_uuid_items:
uuid_items = mm_uuid_items[modality]
# For None entries, compute a hash; otherwise, use provided ID.
computed: list[str] = []
for i, item in enumerate(items.get_all_items_for_hash()):
item_uuid = mm_uuids_per_modality[i]
hashes: list[str] = []
for i, item in enumerate(data_items.get_all_items_for_hash()):
uuid_item = uuid_items[i]
# NOTE: Even if a item_uuid is provided, we still compute a
# hash if `hf_processor_mm_kwargs` or `tokenization_kwargs`
# are provided. This is because the processed multimodal
# inputs can be different depending on the processor kwargs.
if (
item_uuid is None
or hf_processor_mm_kwargs
or tokenization_kwargs
):
# NOTE: Even if a uuid_item is provided, we still compute a hash
# if `hf_processor_mm_kwargs` is provided.
# This is because the processed multimodal inputs can be different
# depending on the processor kwargs.
if uuid_item is None or hf_processor_mm_kwargs:
# NOTE: use provided hash string to hash with kwargs
# if available for better performance.
item = item_uuid if item_uuid is not None else item
computed.append(
MultiModalHasher.hash_kwargs(
item = uuid_item if uuid_item is not None else item
hashes.append(
hasher.hash_kwargs(
model_id=model_id,
**{modality: item},
**hf_processor_mm_kwargs,
**tokenization_kwargs,
)
)
else:
computed.append(item_uuid)
hashes[modality] = computed
hashes.append(uuid_item)
mm_hashes[modality] = hashes
else:
hashes[modality] = [
MultiModalHasher.hash_kwargs(
mm_hashes[modality] = [
hasher.hash_kwargs(
model_id=model_id,
**{modality: item},
**hf_processor_mm_kwargs,
**tokenization_kwargs,
)
for item in items
for item in data_items
]
return hashes
return mm_hashes
def _get_cache_missing_items(
self,
@@ -1468,10 +1463,9 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
self,
prompt: str | list[int],
mm_data_items: MultiModalDataItems,
mm_uuid_items: MultiModalUUIDItems | None,
hf_processor_mm_kwargs: Mapping[str, object],
tokenization_kwargs: Mapping[str, object],
*,
mm_uuids: MultiModalUUIDDict | None = None,
) -> tuple[list[int], MultiModalProcessingInfo, bool]:
(
prompt_ids,
@@ -1494,9 +1488,8 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
with timed_preprocessor_operation(self.info.ctx, "hashing"):
mm_hashes = self._hash_mm_items(
mm_data_items,
hf_processor_mm_kwargs,
tokenization_kwargs,
mm_uuids=mm_uuids,
mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
)
mm_prompt_updates = self._get_mm_prompt_updates(
@@ -1517,10 +1510,9 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
self,
prompt: str | list[int],
mm_data_items: MultiModalDataItems,
mm_uuid_items: MultiModalUUIDItems | None,
hf_processor_mm_kwargs: Mapping[str, object],
tokenization_kwargs: Mapping[str, object],
*,
mm_uuids: MultiModalUUIDDict | None = None,
) -> tuple[list[int], MultiModalProcessingInfo, bool]:
"""
Apply the HF processor on the full prompt text,
@@ -1533,17 +1525,16 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
return self._apply_hf_processor(
prompt=prompt,
mm_data_items=mm_data_items,
mm_uuid_items=mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids,
)
with timed_preprocessor_operation(self.info.ctx, "hashing"):
mm_hashes = self._hash_mm_items(
mm_data_items,
hf_processor_mm_kwargs,
tokenization_kwargs,
mm_uuids=mm_uuids,
mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
)
with timed_preprocessor_operation(self.info.ctx, "cache_lookup"):
@@ -1753,10 +1744,9 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
self,
prompt: str | list[int],
mm_items: MultiModalDataItems,
hf_processor_mm_kwargs: Mapping[str, object],
mm_uuid_items: MultiModalUUIDItems | None = None,
hf_processor_mm_kwargs: Mapping[str, object] | None = None,
tokenization_kwargs: Mapping[str, object] | None = None,
*,
mm_uuids: MultiModalUUIDDict | None = None,
) -> MultiModalInputs:
"""
Process multi-modal inputs to be used in vLLM.
@@ -1775,6 +1765,8 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
if request_id is not None:
self.info.ctx.create_timing_stats(request_id)
if hf_processor_mm_kwargs is None:
hf_processor_mm_kwargs = {}
if tokenization_kwargs is None:
tokenization_kwargs = {}
@@ -1785,9 +1777,9 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
) = self._cached_apply_hf_processor(
prompt,
mm_items,
mm_uuid_items,
hf_processor_mm_kwargs,
tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids,
)
# NOTE: tokenization_kwargs are not required to init processor
@@ -1861,10 +1853,9 @@ class EncDecMultiModalProcessor(BaseMultiModalProcessor[_I]):
self,
prompt: str | list[int],
mm_items: MultiModalDataItems,
hf_processor_mm_kwargs: Mapping[str, object],
mm_uuid_items: MultiModalUUIDItems | None = None,
hf_processor_mm_kwargs: Mapping[str, object] | None = None,
tokenization_kwargs: Mapping[str, object] | None = None,
*,
mm_uuids: MultiModalUUIDDict | None = None,
) -> MultiModalEncDecInputs:
"""
Process multi-modal inputs to be used in vLLM.
@@ -1877,9 +1868,9 @@ class EncDecMultiModalProcessor(BaseMultiModalProcessor[_I]):
encoder_inputs = super().apply(
encoder_prompt,
mm_items,
hf_processor_mm_kwargs,
tokenization_kwargs,
mm_uuids=mm_uuids,
mm_uuid_items,
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
tokenization_kwargs=tokenization_kwargs,
)
return self._get_enc_dec_inputs(

74
vllm/renderers/base.py
View File

@@ -51,7 +51,7 @@ if TYPE_CHECKING:
MultiModalInputs,
MultiModalUUIDDict,
)
from vllm.multimodal.parse import MultiModalDataItems
from vllm.multimodal.parse import MultiModalDataItems, MultiModalUUIDItems
from vllm.multimodal.processing import BaseMultiModalProcessor
logger = init_logger(__name__)
@@ -463,23 +463,25 @@ class BaseRenderer(ABC, Generic[_T]):
def _validate_mm_uuids(
self,
mm_data: "MultiModalDataDict",
mm_items: "MultiModalDataItems",
mm_uuids: "MultiModalUUIDDict | None",
mm_data_items: "MultiModalDataItems",
mm_uuid_items: "MultiModalUUIDItems",
) -> None:
if mm_uuids is None:
mm_uuids = {}
# NOTE: Keys corresponding to `None` in `mm_data` don't appear in `mm_items`
modalities = mm_data.keys() | mm_uuids.keys()
# NOTE: Keys corresponding to `None` in `mm_data` don't appear in
# `mm_data_items`
modalities = mm_data.keys() | mm_uuid_items.keys()
for modality in modalities:
data_items = mm_items.get(modality) or list[Any]()
data_items = mm_data_items.get(modality)
uuid_items = mm_uuid_items.get(modality)
uuid_items = mm_uuids.get(modality) or list[str | None]()
if isinstance(uuid_items, str):
uuid_items = [uuid_items]
if data_items is None:
if uuid_items is None:
raise ValueError(
f"multi_modal_data[{modality!r}] is empty but "
f"multi_modal_uuids[{modality!r}] is missing."
)
if len(data_items) > 0:
elif uuid_items is not None:
if len(uuid_items) > 0 and len(data_items) != len(uuid_items):
raise ValueError(
f"If given, multi_modal_uuids[{modality!r}] must have "
@@ -488,24 +490,17 @@ class BaseRenderer(ABC, Generic[_T]):
)
for i, item in enumerate(data_items):
if item is None:
if not uuid_items:
raise ValueError(
f"multi_modal_data[{modality!r}][{i}] is empty but "
f"multi_modal_uuids[{modality!r}] is missing."
)
if uuid_items[i] is None:
raise ValueError(
f"multi_modal_data[{modality!r}][{i}] is empty but "
f"multi_modal_uuids[{modality!r}][{i}] is missing."
)
if item is None and uuid_items[i] is None:
raise ValueError(
f"multi_modal_data[{modality!r}][{i}] is empty but "
f"multi_modal_uuids[{modality!r}][{i}] is missing."
)
def _process_mm_uuids(
self,
mm_data: "MultiModalDataDict",
mm_items: "MultiModalDataItems",
mm_uuids: "MultiModalUUIDDict | None",
mm_data_items: "MultiModalDataItems",
mm_uuid_items: "MultiModalUUIDItems",
mm_req_id: str,
):
model_config = self.model_config
@@ -520,40 +515,45 @@ class BaseRenderer(ABC, Generic[_T]):
and model_config.multimodal_config.mm_processor_cache_gb == 0
and not self.config.cache_config.enable_prefix_caching
):
mm_uuids = {
mm_uuid_items = {
modality: [f"{mm_req_id}-{modality}-{i}" for i in range(data_count)]
for modality, data_count in mm_items.get_all_counts().items()
for modality, data_count in mm_data_items.get_all_counts().items()
}
self._validate_mm_uuids(mm_data, mm_items, mm_uuids)
self._validate_mm_uuids(mm_data, mm_data_items, mm_uuid_items)
return mm_uuids
return mm_uuid_items
# TODO: Remove str and tokenization_kwargs after deprecating InputPreprocessor
def _process_multimodal(
self,
prompt: list[int] | str,
mm_data: "MultiModalDataDict",
mm_uuids: "MultiModalUUIDDict | None",
mm_processor_kwargs: Mapping[str, object] | None,
tokenization_kwargs: dict[str, Any] | None,
mm_uuids: "MultiModalUUIDDict | None",
) -> "MultiModalInputs":
from vllm.multimodal.parse import parse_mm_uuids
from vllm.multimodal.processing.context import set_request_id
mm_req_id = f"renderer-mm-{self._mm_req_counter.inc(1)}"
mm_processor = self.get_mm_processor()
mm_items = mm_processor.info.parse_mm_data(mm_data)
mm_uuids = self._process_mm_uuids(mm_data, mm_items, mm_uuids, mm_req_id)
mm_data_items = mm_processor.info.parse_mm_data(mm_data)
mm_uuid_items = parse_mm_uuids(mm_uuids)
mm_uuids = self._process_mm_uuids(
mm_data, mm_data_items, mm_uuid_items, mm_req_id
)
with set_request_id(mm_req_id), set_default_torch_num_threads():
mm_inputs = mm_processor.apply(
prompt,
mm_items,
hf_processor_mm_kwargs=mm_processor_kwargs or {},
mm_data_items,
mm_uuid_items,
hf_processor_mm_kwargs=mm_processor_kwargs,
tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids,
)
self.update_mm_cache_stats()