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revert lora_kwargs change

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Signed-off-by: bk-201 <joy25810@foxmail.com>
This commit is contained in:
bk-201 2025-12-21 04:14:11 +00:00
parent 20402090b8
commit 81b5ace128
3 changed files with 0 additions and 37 deletions
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12
vllm/inputs/preprocess.py
View File

@ -246,7 +246,6 @@ class InputPreprocessor:
tokenization_kwargs: dict[str, Any] | None = None, tokenization_kwargs: dict[str, Any] | None = None,
*, *,
mm_uuids: MultiModalUUIDDict | None = None, mm_uuids: MultiModalUUIDDict | None = None,
lora_kwargs: dict[str, Any] | None = None,
) -> MultiModalInputs: ) -> MultiModalInputs:
""" """
Apply the model's multi-modal processor to a multi-modal prompt, Apply the model's multi-modal processor to a multi-modal prompt,
@ -263,7 +262,6 @@ class InputPreprocessor:
hf_processor_mm_kwargs=mm_processor_kwargs, hf_processor_mm_kwargs=mm_processor_kwargs,
tokenization_kwargs=tokenization_kwargs, tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids, mm_uuids=mm_uuids,
lora_kwargs=lora_kwargs,
) )
mm_hashes = mm_input["mm_hashes"] mm_hashes = mm_input["mm_hashes"]
@ -361,7 +359,6 @@ class InputPreprocessor:
tokenization_kwargs: dict[str, Any] | None = None, tokenization_kwargs: dict[str, Any] | None = None,
*, *,
mm_uuids: MultiModalUUIDDict | None = None, mm_uuids: MultiModalUUIDDict | None = None,
lora_kwargs: dict[str, Any] | None = None,
) -> TokenInputs | MultiModalInputs: ) -> TokenInputs | MultiModalInputs:
prompt_text = parsed_content["prompt"] prompt_text = parsed_content["prompt"]
@ -373,7 +370,6 @@ class InputPreprocessor:
parsed_content.get("mm_processor_kwargs") or {}, parsed_content.get("mm_processor_kwargs") or {},
tokenization_kwargs=tokenization_kwargs, tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids, mm_uuids=mm_uuids,
lora_kwargs=lora_kwargs,
) )
else: else:
prompt_token_ids = self._tokenize_prompt( prompt_token_ids = self._tokenize_prompt(
@ -393,7 +389,6 @@ class InputPreprocessor:
tokenization_kwargs: dict[str, Any] | None = None, tokenization_kwargs: dict[str, Any] | None = None,
*, *,
mm_uuids: MultiModalUUIDDict | None = None, mm_uuids: MultiModalUUIDDict | None = None,
lora_kwargs: dict[str, Any] | None = None,
) -> SingletonInputs: ) -> SingletonInputs:
""" """
Extract the singleton inputs from a prompt. Extract the singleton inputs from a prompt.
@ -420,7 +415,6 @@ class InputPreprocessor:
parsed["content"], parsed["content"],
tokenization_kwargs=tokenization_kwargs, tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids, mm_uuids=mm_uuids,
lora_kwargs=lora_kwargs,
) )
if parsed["type"] == "str": if parsed["type"] == "str":
return self._process_text( return self._process_text(
@ -632,7 +626,6 @@ class InputPreprocessor:
tokenization_kwargs: dict[str, Any] | None = None, tokenization_kwargs: dict[str, Any] | None = None,
*, *,
mm_uuids: MultiModalUUIDDict | None = None, mm_uuids: MultiModalUUIDDict | None = None,
lora_kwargs: dict[str, Any] | None = None,
) -> DecoderOnlyInputs: ) -> DecoderOnlyInputs:
""" """
For decoder-only models: For decoder-only models:
@ -652,7 +645,6 @@ class InputPreprocessor:
prompt, prompt,
tokenization_kwargs=tokenization_kwargs, tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids, mm_uuids=mm_uuids,
lora_kwargs=lora_kwargs,
) )
return self._build_decoder_only_llm_inputs(prompt_comps) return self._build_decoder_only_llm_inputs(prompt_comps)
@ -663,7 +655,6 @@ class InputPreprocessor:
tokenization_kwargs: dict[str, Any] | None = None, tokenization_kwargs: dict[str, Any] | None = None,
*, *,
mm_uuids: MultiModalUUIDDict | None = None, mm_uuids: MultiModalUUIDDict | None = None,
lora_kwargs: dict[str, Any] | None = None,
) -> ProcessorInputs: ) -> ProcessorInputs:
if self.model_config.is_encoder_decoder: if self.model_config.is_encoder_decoder:
# Encoder-decoder model requires special mapping of # Encoder-decoder model requires special mapping of
@ -685,7 +676,6 @@ class InputPreprocessor:
cast(SingletonPrompt, prompt), cast(SingletonPrompt, prompt),
tokenization_kwargs=tokenization_kwargs, tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids, mm_uuids=mm_uuids,
lora_kwargs=lora_kwargs,
) )
def preprocess( def preprocess(
@ -694,14 +684,12 @@ class InputPreprocessor:
tokenization_kwargs: dict[str, Any] | None = None, tokenization_kwargs: dict[str, Any] | None = None,
*, *,
mm_uuids: MultiModalUUIDDict | None = None, mm_uuids: MultiModalUUIDDict | None = None,
lora_kwargs: dict[str, Any] | None = None,
) -> ProcessorInputs: ) -> ProcessorInputs:
"""Preprocess the input prompt.""" """Preprocess the input prompt."""
res = self._preprocess( res = self._preprocess(
prompt, prompt,
tokenization_kwargs, tokenization_kwargs,
mm_uuids=mm_uuids, mm_uuids=mm_uuids,
lora_kwargs=lora_kwargs,
) )
if self.mm_processor_cache and self.mm_cache_stats is not None: if self.mm_processor_cache and self.mm_cache_stats is not None:

11
vllm/multimodal/processing.py
View File

@ -1672,7 +1672,6 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
tokenization_kwargs: Mapping[str, object], tokenization_kwargs: Mapping[str, object],
*, *,
mm_uuids: MultiModalUUIDDict | None = None, mm_uuids: MultiModalUUIDDict | None = None,
lora_kwargs: dict[str, Any] | None = None,
) -> MultiModalHashes: ) -> MultiModalHashes:
"""Create MM hashes to be returned. """Create MM hashes to be returned.
@ -1684,7 +1683,6 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
hashes: MultiModalHashes = {} hashes: MultiModalHashes = {}
mm_uuids = mm_uuids or {} mm_uuids = mm_uuids or {}
lora_kwargs = lora_kwargs or {}
for modality, items in mm_items.items(): for modality, items in mm_items.items():
if modality in mm_uuids: if modality in mm_uuids:
@ -1705,7 +1703,6 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
item_uuid is None item_uuid is None
or hf_processor_mm_kwargs or hf_processor_mm_kwargs
or tokenization_kwargs or tokenization_kwargs
or lora_kwargs
): ):
# NOTE: use provided hash string to hash with kwargs # NOTE: use provided hash string to hash with kwargs
# if available for better performance. # if available for better performance.
@ -1716,7 +1713,6 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
**{modality: item}, **{modality: item},
**hf_processor_mm_kwargs, **hf_processor_mm_kwargs,
**tokenization_kwargs, **tokenization_kwargs,
**lora_kwargs,
) )
) )
else: else:
@ -1729,7 +1725,6 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
**{modality: item}, **{modality: item},
**hf_processor_mm_kwargs, **hf_processor_mm_kwargs,
**tokenization_kwargs, **tokenization_kwargs,
**lora_kwargs,
) )
for item in items for item in items
] ]
@ -1888,7 +1883,6 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
tokenization_kwargs: Mapping[str, object], tokenization_kwargs: Mapping[str, object],
*, *,
mm_uuids: MultiModalUUIDDict | None = None, mm_uuids: MultiModalUUIDDict | None = None,
lora_kwargs: dict[str, Any] | None = None,
) -> tuple[list[int], MultiModalProcessingInfo, bool]: ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
""" """
Apply the HF processor on the full prompt text, Apply the HF processor on the full prompt text,
@ -1911,7 +1905,6 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
hf_processor_mm_kwargs, hf_processor_mm_kwargs,
tokenization_kwargs, tokenization_kwargs,
mm_uuids=mm_uuids, mm_uuids=mm_uuids,
lora_kwargs=lora_kwargs,
) )
mm_is_cached, mm_missing_data_items = self._get_cache_missing_items( mm_is_cached, mm_missing_data_items = self._get_cache_missing_items(
@ -2122,7 +2115,6 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
tokenization_kwargs: Mapping[str, object] | None = None, tokenization_kwargs: Mapping[str, object] | None = None,
*, *,
mm_uuids: MultiModalUUIDDict | None = None, mm_uuids: MultiModalUUIDDict | None = None,
lora_kwargs: dict[str, Any] | None = None,
) -> MultiModalInputs: ) -> MultiModalInputs:
""" """
Process multi-modal inputs to be used in vLLM. Process multi-modal inputs to be used in vLLM.
@ -2152,7 +2144,6 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
hf_processor_mm_kwargs, hf_processor_mm_kwargs,
tokenization_kwargs=tokenization_kwargs, tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids, mm_uuids=mm_uuids,
lora_kwargs=lora_kwargs,
) )
# NOTE: tokenization_kwargs are not required to init processor # NOTE: tokenization_kwargs are not required to init processor
@ -2233,7 +2224,6 @@ class EncDecMultiModalProcessor(BaseMultiModalProcessor[_I]):
tokenization_kwargs: Mapping[str, object] | None = None, tokenization_kwargs: Mapping[str, object] | None = None,
*, *,
mm_uuids: MultiModalUUIDDict | None = None, mm_uuids: MultiModalUUIDDict | None = None,
lora_kwargs: dict[str, Any] | None = None,
) -> MultiModalEncDecInputs: ) -> MultiModalEncDecInputs:
""" """
Process multi-modal inputs to be used in vLLM. Process multi-modal inputs to be used in vLLM.
@ -2249,7 +2239,6 @@ class EncDecMultiModalProcessor(BaseMultiModalProcessor[_I]):
hf_processor_mm_kwargs, hf_processor_mm_kwargs,
tokenization_kwargs, tokenization_kwargs,
mm_uuids=mm_uuids, mm_uuids=mm_uuids,
lora_kwargs=lora_kwargs,
) )
return self._get_enc_dec_inputs( return self._get_enc_dec_inputs(

14
vllm/v1/engine/input_processor.py
View File

@ -5,8 +5,6 @@ import time
from collections.abc import Mapping from collections.abc import Mapping
from typing import Any, Literal, cast from typing import Any, Literal, cast
import msgspec
from vllm.config import VllmConfig from vllm.config import VllmConfig
from vllm.inputs import ProcessorInputs, PromptType, SingletonInputs from vllm.inputs import ProcessorInputs, PromptType, SingletonInputs
from vllm.inputs.parse import split_enc_dec_inputs from vllm.inputs.parse import split_enc_dec_inputs
@ -460,17 +458,6 @@ class InputProcessor:
else: else:
mm_uuids = None mm_uuids = None
# When enable_tower_connector_lora is True, multi-modal embeddings
# vary depending on the LoRA request. Therefore, the mm_hash must be
# generated based on the LoRA request to prevent incorrect cache hits.
lora_config = self.lora_config
lora_kwargs = (
msgspec.structs.asdict(lora_request)
if lora_request and lora_config and lora_config.enable_tower_connector_lora
else {}
)
lora_kwargs = {k: v for k, v in lora_kwargs.items() if v is not None}
# Process inputs, which includes: # Process inputs, which includes:
# 1. Tokenize text prompt, with LoRA request if one exists. # 1. Tokenize text prompt, with LoRA request if one exists.
# 2. For multimodal models with a merged preprocessor, preprocess # 2. For multimodal models with a merged preprocessor, preprocess
@ -479,7 +466,6 @@ class InputProcessor:
prompt, prompt,
tokenization_kwargs=tokenization_kwargs, tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids, mm_uuids=mm_uuids,
lora_kwargs=lora_kwargs,
) )
from vllm.platforms import current_platform from vllm.platforms import current_platform