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[Refactor] Remove InputPreprocessor (#53064)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2026-08-20 08:39:48 +00:00
committed by GitHub
parent 14617c2b6c
commit 4666a8ba9e
8 changed files with 29 additions and 367 deletions
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.buildkite/test-amd.yaml
-2
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@@ -172,7 +172,6 @@ steps:
source_file_dependencies:
- vllm/
- tests/test_envs.py
- tests/test_inputs.py
- tests/test_outputs.py
- tests/test_pooling_params.py
- tests/test_ray_env.py
@@ -189,7 +188,6 @@ steps:
commands:
- python3 standalone_tests/lazy_imports.py
- pytest -v -s test_envs.py
- pytest -v -s test_inputs.py
- pytest -v -s test_outputs.py
- pytest -v -s test_pooling_params.py
- pytest -v -s test_ray_env.py
.buildkite/test_areas/misc.yaml
-2
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@@ -358,7 +358,6 @@ steps:
- vllm/utils/
- vllm/v1/
- tests/test_envs.py
- tests/test_inputs.py
- tests/test_outputs.py
- tests/test_pooling_params.py
- tests/test_ray_env.py
@@ -376,7 +375,6 @@ steps:
commands:
- python3 standalone_tests/lazy_imports.py
- pytest -v -s test_envs.py
- pytest -v -s test_inputs.py
- pytest -v -s test_outputs.py
- pytest -v -s test_pooling_params.py
- pytest -v -s test_ray_env.py
.github/CODEOWNERS
-1
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@@ -107,7 +107,6 @@
/tests/multimodal @DarkLight1337 @ywang96 @NickLucche @shen-shanshan
/tests/multimodal/media @Isotr0py
/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256 @pavanimajety @zyongye @AndreasKaratzas
/tests/test_inputs.py @DarkLight1337 @ywang96
/tests/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
/tests/v1/structured_output @mgoin @russellb @aarnphm
/tests/v1/core @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @alexm-redhat @heheda12345 @ApostaC @orozery @ivanium
tests/renderers/test_process_multi_modal_uuids.py
+1 -2
View File
@@ -54,11 +54,10 @@ def test_text_only_model_mm_data_maps_to_bad_request():
with pytest.raises(ValueError, match="text-only") as exc_info:
renderer._process_multimodal(
prompt="What is in this image?",
prompt=[1],
mm_data={"image": [cherry_pil_image]},
mm_uuids=None,
mm_processor_kwargs=None,
tokenization_kwargs=None,
)
error_response = create_error_response(exc_info.value)
tests/test_inputs.py
-31
View File
@@ -1,31 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from vllm.config import ModelConfig, VllmConfig
from vllm.inputs.preprocess import InputPreprocessor
pytestmark = pytest.mark.cpu_test
@pytest.mark.parametrize("model_id", ["facebook/chameleon-7b"])
@pytest.mark.parametrize("prompt", ["", {"prompt_token_ids": []}])
@pytest.mark.skip(
reason=(
"Applying huggingface processor on text inputs results in "
"significant performance regression for multimodal models. "
"See https://github.com/vllm-project/vllm/issues/26320"
)
)
def test_preprocessor_always_mm_code_path(model_id, prompt):
model_config = ModelConfig(model=model_id)
vllm_config = VllmConfig(model_config=model_config)
input_preprocessor = InputPreprocessor(vllm_config)
# HF processor adds sep token
tokenizer = input_preprocessor.get_tokenizer()
sep_token_id = tokenizer.vocab[tokenizer.sep_token]
processed_inputs = input_preprocessor.preprocess(prompt)
assert sep_token_id in processed_inputs["prompt_token_ids"]
vllm/inputs/preprocess.py
-291
View File
@@ -1,291 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Mapping
from typing import Any, overload
from typing_extensions import assert_never
from vllm.config import VllmConfig
from vllm.inputs import build_enc_dec_input
from vllm.logger import init_logger
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
from vllm.renderers import BaseRenderer, renderer_from_config
from vllm.renderers.inputs import (
DecoderDictPrompt,
DecoderOnlyDictPrompt,
EncoderDecoderDictPrompt,
EncoderDictPrompt,
SingletonDictPrompt,
)
from vllm.renderers.inputs.preprocess import parse_dec_only_prompt, parse_enc_dec_prompt
from vllm.tokenizers import TokenizerLike
from .engine import (
DecoderEngineInput,
DecoderOnlyEngineInput,
EmbedsInput,
EncoderDecoderInput,
EncoderInput,
EngineInput,
MultiModalInput,
SingletonInput,
TokensInput,
tokens_input,
)
from .llm import (
EmbedsPrompt,
MultiModalDataDict,
MultiModalUUIDDict,
PromptType,
TextPrompt,
TokensPrompt,
)
logger = init_logger(__name__)
class InputPreprocessor:
def __init__(
self,
vllm_config: VllmConfig,
renderer: BaseRenderer | None = None,
mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
) -> None:
super().__init__()
self.model_config = vllm_config.model_config
self.renderer = renderer or renderer_from_config(vllm_config)
self.mm_registry = mm_registry
@property
def tokenizer(self) -> TokenizerLike | None:
return self.renderer.tokenizer
def get_tokenizer(self) -> TokenizerLike:
return self.renderer.get_tokenizer()
def _tokenize_prompt(
self,
prompt: str,
tokenization_kwargs: dict[str, Any] | None = None,
) -> list[int]:
"""
Apply the model's tokenizer to a text prompt, returning the
corresponding token IDs.
"""
renderer = self.renderer
tok_params = renderer.default_cmpl_tok_params.with_kwargs(
**(tokenization_kwargs or {})
)
tok_prompt = renderer._tokenize_singleton_prompt(
TextPrompt(prompt=prompt),
tok_params,
)
return tok_prompt["prompt_token_ids"]
def _process_multimodal(
self,
prompt: str | list[int],
mm_data: MultiModalDataDict,
mm_processor_kwargs: Mapping[str, object] | None = None,
tokenization_kwargs: dict[str, Any] | None = None,
*,
mm_uuids: MultiModalUUIDDict | None = None,
) -> MultiModalInput:
"""
Apply the model's multi-modal processor to a multi-modal prompt,
returning the corresponding token IDs and metadata.
"""
return self.renderer._process_multimodal(
prompt,
mm_data,
mm_uuids=mm_uuids,
mm_processor_kwargs=mm_processor_kwargs,
tokenization_kwargs=tokenization_kwargs,
)
def _process_embeds(
self,
parsed_content: EmbedsPrompt,
) -> EmbedsInput:
return self.renderer._process_embeds(parsed_content)
def _truncate_inputs(
self, inputs: list[int], tokenization_kwargs: dict[str, Any] | None = None
) -> list[int]:
renderer = self.renderer
tok_params = renderer.default_cmpl_tok_params.with_kwargs(
**(tokenization_kwargs or {})
)
tok_prompt = renderer._tokenize_singleton_prompt(
TokensPrompt(prompt_token_ids=inputs),
tok_params,
)
return tok_prompt["prompt_token_ids"]
def _process_tokens(
self,
parsed_content: TokensPrompt,
tokenization_kwargs: dict[str, Any] | None = None,
) -> TokensInput | MultiModalInput:
prompt_token_ids = self._truncate_inputs(
parsed_content["prompt_token_ids"], tokenization_kwargs
)
inputs: TokensInput | MultiModalInput
if multi_modal_data := parsed_content.get("multi_modal_data"):
inputs = self._process_multimodal(
prompt_token_ids,
multi_modal_data,
parsed_content.get("mm_processor_kwargs"),
tokenization_kwargs=tokenization_kwargs,
mm_uuids=parsed_content.get("multi_modal_uuids"),
)
else:
inputs = tokens_input(prompt_token_ids)
if prompt_text := parsed_content.get("prompt"):
inputs["prompt"] = prompt_text
if cache_salt := parsed_content.get("cache_salt"):
inputs["cache_salt"] = cache_salt
return inputs
def _process_text(
self,
parsed_content: TextPrompt,
tokenization_kwargs: dict[str, Any] | None = None,
) -> TokensInput | MultiModalInput:
prompt_text = parsed_content["prompt"]
inputs: TokensInput | MultiModalInput
if multi_modal_data := parsed_content.get("multi_modal_data"):
inputs = self._process_multimodal(
prompt_text,
multi_modal_data,
parsed_content.get("mm_processor_kwargs") or {},
tokenization_kwargs=tokenization_kwargs,
)
else:
prompt_token_ids = self._tokenize_prompt(
prompt_text,
tokenization_kwargs=tokenization_kwargs,
)
inputs = tokens_input(prompt_token_ids)
inputs["prompt"] = prompt_text
if cache_salt := parsed_content.get("cache_salt"):
inputs["cache_salt"] = cache_salt
return inputs
@overload
def _prompt_to_llm_inputs(
self,
prompt: EncoderDictPrompt,
tokenization_kwargs: dict[str, Any] | None = None,
) -> EncoderInput: ...
@overload
def _prompt_to_llm_inputs( # type: ignore[misc]
self,
prompt: DecoderDictPrompt,
tokenization_kwargs: dict[str, Any] | None = None,
) -> DecoderEngineInput: ...
@overload
def _prompt_to_llm_inputs( # type: ignore[misc]
self,
prompt: DecoderOnlyDictPrompt,
tokenization_kwargs: dict[str, Any] | None = None,
) -> DecoderOnlyEngineInput: ...
def _prompt_to_llm_inputs(
self,
prompt: SingletonDictPrompt,
tokenization_kwargs: dict[str, Any] | None = None,
) -> SingletonInput:
if "prompt_embeds" in prompt:
return self._process_embeds(prompt) # type: ignore[arg-type]
if "prompt_token_ids" in prompt:
return self._process_tokens(prompt) # type: ignore[arg-type]
if "prompt" in prompt:
return self._process_text(
prompt, # type: ignore[arg-type]
tokenization_kwargs=tokenization_kwargs,
)
assert_never(prompt) # type: ignore[arg-type]
def _process_encoder_decoder_prompt(
self,
prompt: EncoderDecoderDictPrompt,
tokenization_kwargs: dict[str, Any] | None = None,
) -> EncoderDecoderInput:
encoder_prompt = prompt["encoder_prompt"]
decoder_prompt = prompt["decoder_prompt"]
skip_decoder_start_token = False
if self.renderer.mm_processor is not None:
from vllm.multimodal.processing import EncDecMultiModalProcessor
if isinstance(self.renderer.mm_processor, EncDecMultiModalProcessor):
skip_decoder_start_token = (
self.renderer.mm_processor.skip_decoder_start_token
)
return build_enc_dec_input(
encoder_input=self._prompt_to_llm_inputs(
encoder_prompt,
tokenization_kwargs=tokenization_kwargs,
),
decoder_input=(
None
if decoder_prompt is None
else self._prompt_to_llm_inputs(
decoder_prompt,
tokenization_kwargs=tokenization_kwargs,
)
),
decoder_start_token_id=self.renderer.get_dec_start_token_id(),
skip_decoder_start_token=skip_decoder_start_token,
)
def _process_decoder_only_prompt(
self,
prompt: DecoderOnlyDictPrompt,
tokenization_kwargs: dict[str, Any] | None = None,
) -> DecoderOnlyEngineInput:
return self._prompt_to_llm_inputs(
prompt,
tokenization_kwargs=tokenization_kwargs,
)
def preprocess(
self,
prompt: PromptType,
tokenization_kwargs: dict[str, Any] | None = None,
) -> EngineInput:
"""Preprocess the input prompt."""
if self.model_config.is_encoder_decoder:
# Encoder-decoder model requires special mapping of
# input prompts to encoder & decoder.
return self._process_encoder_decoder_prompt(
parse_enc_dec_prompt(prompt),
tokenization_kwargs,
)
return self._process_decoder_only_prompt(
parse_dec_only_prompt(prompt),
tokenization_kwargs=tokenization_kwargs,
)
vllm/renderers/base.py
+1 -6
View File
@@ -725,14 +725,12 @@ class BaseRenderer(ABC, Generic[_T]):
return mm_uuid_items
# TODO: Remove str and tokenization_kwargs after deprecating InputPreprocessor
def _process_multimodal(
self,
prompt: list[int] | str,
prompt: list[int],
mm_data: MultiModalDataDict,
mm_uuids: MultiModalUUIDDict | None,
mm_processor_kwargs: Mapping[str, object] | None,
tokenization_kwargs: dict[str, Any] | None,
*,
skip_mm_cache: bool = False,
) -> "MultiModalInput":
@@ -755,7 +753,6 @@ class BaseRenderer(ABC, Generic[_T]):
mm_data_items,
mm_uuid_items,
hf_processor_mm_kwargs=mm_processor_kwargs or {},
tokenization_kwargs=tokenization_kwargs or {},
)
mm_timing_ctx = self._mm_timing_registry.get(mm_req_id)
@@ -783,7 +780,6 @@ class BaseRenderer(ABC, Generic[_T]):
prompt_token_ids,
multi_modal_data,
mm_processor_kwargs=prompt.get("mm_processor_kwargs"),
tokenization_kwargs=None, # Tokenization already done in Step 2
mm_uuids=prompt.get("multi_modal_uuids"),
skip_mm_cache=skip_mm_cache,
)
@@ -846,7 +842,6 @@ class BaseRenderer(ABC, Generic[_T]):
prompt_token_ids,
multi_modal_data,
mm_processor_kwargs=prompt.get("mm_processor_kwargs"),
tokenization_kwargs=None,
mm_uuids=prompt.get("multi_modal_uuids"),
skip_mm_cache=skip_mm_cache,
)
vllm/v1/engine/input_processor.py
+27 -32
View File
@@ -14,7 +14,6 @@ from vllm.inputs import (
SingletonInput,
split_enc_dec_input,
)
from vllm.inputs.preprocess import InputPreprocessor
from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
@@ -24,6 +23,7 @@ from vllm.multimodal.utils import argsort_mm_positions
from vllm.platforms import current_platform
from vllm.pooling_params import PoolingParams
from vllm.renderers import BaseRenderer, renderer_from_config
from vllm.renderers.inputs.preprocess import parse_model_prompt
from vllm.sampling_params import SamplingParams
from vllm.tasks import GENERATION_TASKS, POOLING_TASKS, SupportedTask
from vllm.tokenizers import TokenizerLike
@@ -67,12 +67,6 @@ class InputProcessor:
)
mm_budget.reset_cache() # Not used anymore
self.input_preprocessor = InputPreprocessor(
vllm_config,
renderer=renderer,
mm_registry=mm_registry,
)
# Raw-prompt preprocessing (tokenization and multimodal processing)
# is blocking, so async callers should run it on the renderer's
# thread pool to keep their event loop responsive.
@@ -313,44 +307,45 @@ class InputProcessor:
)
if isinstance(prompt, dict) and "type" in prompt:
if tokenization_kwargs:
logger.warning_once(
"Passing tokenization_kwargs to InputProcessor is deprecated "
"and will be removed in v0.18. You should instead pass "
"them to Renderer.render_cmpl() or Renderer.render_chat()."
)
if arrival_time is None:
arrival_time = prompt.get("arrival_time", time.time()) # type: ignore[assignment]
processed_inputs: EngineInput = prompt # type: ignore[assignment]
engine_input: EngineInput = prompt # type: ignore[assignment]
else:
logger.warning_once(
"Passing raw prompts to InputProcessor is deprecated "
"and will be removed in v0.18. You should instead pass "
"and will be removed in the future. You should instead pass "
"the outputs of Renderer.render_cmpl() or Renderer.render_chat()."
)
if arrival_time is None:
arrival_time = time.time()
processed_inputs = self.input_preprocessor.preprocess(
prompt,
tokenization_kwargs=tokenization_kwargs,
renderer = self.renderer
model_config = self.model_config
parsed_prompt = parse_model_prompt(model_config, prompt)
tok_params = renderer.default_cmpl_tok_params.with_kwargs(
**(tokenization_kwargs or {})
)
current_platform.validate_request(processed_inputs, params)
(engine_input,) = renderer.render_cmpl(
[parsed_prompt],
tok_params,
)
encoder_inputs, decoder_inputs = split_enc_dec_input(processed_inputs)
self._validate_model_inputs(encoder_inputs, decoder_inputs)
current_platform.validate_request(engine_input, params)
encoder_input, decoder_input = split_enc_dec_input(engine_input)
self._validate_model_inputs(encoder_input, decoder_input)
# Mypy can be conservative for TypedDict unions; normalize access.
if decoder_inputs["type"] == "embeds":
prompt_embeds = decoder_inputs["prompt_embeds"]
prompt_token_ids = decoder_inputs.get("prompt_token_ids")
prompt_is_token_ids = decoder_inputs.get("is_token_ids")
if decoder_input["type"] == "embeds":
prompt_embeds = decoder_input["prompt_embeds"]
prompt_token_ids = decoder_input.get("prompt_token_ids")
prompt_is_token_ids = decoder_input.get("is_token_ids")
else:
prompt_token_ids = decoder_inputs["prompt_token_ids"]
prompt_token_ids = decoder_input["prompt_token_ids"]
prompt_embeds = None
prompt_is_token_ids = None
@@ -385,10 +380,10 @@ class InputProcessor:
# Multimodal related.
mm_features: list[MultiModalFeatureSpec] | None = None
if decoder_inputs["type"] == "multimodal":
decoder_mm_inputs = decoder_inputs["mm_kwargs"]
decoder_mm_positions = decoder_inputs["mm_placeholders"]
decoder_mm_hashes = decoder_inputs["mm_hashes"]
if decoder_input["type"] == "multimodal":
decoder_mm_inputs = decoder_input["mm_kwargs"]
decoder_mm_positions = decoder_input["mm_placeholders"]
decoder_mm_hashes = decoder_input["mm_hashes"]
if not all(
isinstance(leaf, str) for leaf in json_iter_leaves(decoder_mm_hashes)
@@ -430,7 +425,7 @@ class InputProcessor:
pooling_params=pooling_params,
arrival_time=arrival_time,
lora_request=lora_request,
cache_salt=decoder_inputs.get("cache_salt"),
cache_salt=decoder_input.get("cache_salt"),
priority=priority,
data_parallel_rank=data_parallel_rank,
trace_headers=trace_headers,