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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
2026-02-17 19:18:55 -08:00
committed by GitHub
parent df3f537a66
commit a0d8d944e2
16 changed files with 254 additions and 217 deletions
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vllm/multimodal/processing/processor.py
+58 -67
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@@ -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(