Raphaël RiallandandSimon Veitner
c32495d58f
Signed-off-by: Raphael Rialland <raphael.rialland@mistral.ai> Signed-off-by: Simon Veitner <sveitner@redhat.com> Co-authored-by: Simon Veitner <sveitner@redhat.com>
658 lines
28 KiB
Python
658 lines
28 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import time
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from collections.abc import Callable, Mapping
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from functools import partial
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from typing import Any, Literal
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import vllm.envs as envs
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from vllm.config import VllmConfig
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from vllm.exceptions import VLLMValidationError
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from vllm.inputs import (
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EngineInput,
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PromptType,
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SingletonInput,
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split_enc_dec_input,
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)
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from vllm.logger import init_logger
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from vllm.lora.request import LoRARequest
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from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
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from vllm.multimodal.encoder_budget import MultiModalBudget
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from vllm.multimodal.inputs import MultiModalFeatureSpec
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from vllm.multimodal.utils import argsort_mm_positions
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from vllm.platforms import current_platform
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from vllm.pooling_params import PoolingParams
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from vllm.renderers import BaseRenderer, renderer_from_config
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from vllm.renderers.inputs.preprocess import parse_model_prompt
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from vllm.sampling_params import BeamSearchParams, SamplingParams
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from vllm.tasks import GENERATION_TASKS, POOLING_TASKS, SupportedTask
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from vllm.tokenizers import TokenizerLike
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from vllm.utils import length_from_prompt_token_ids_or_embeds, random_uuid
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from vllm.utils.async_utils import make_async
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from vllm.utils.diffusion import validate_diffusion_sampling_params
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from vllm.utils.jsontree import json_iter_leaves
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from vllm.v1.engine import EngineCoreRequest
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from vllm.v1.kv_hints import KvHintsEnvelope
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logger = init_logger(__name__)
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class InputProcessor:
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def __init__(
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self,
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vllm_config: VllmConfig,
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renderer: BaseRenderer | None = None,
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*,
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mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
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) -> None:
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self.vllm_config = vllm_config
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self.model_config = model_config = vllm_config.model_config
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self.cache_config = vllm_config.cache_config
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self.lora_config = vllm_config.lora_config
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self.scheduler_config = vllm_config.scheduler_config
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self.speculative_config = vllm_config.speculative_config
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self.structured_outputs_config = vllm_config.structured_outputs_config
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self.observability_config = vllm_config.observability_config
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self.diffusion_config = vllm_config.diffusion_config
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# Load the custom logits processor classes once; the returned callable
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# runs their validate_params hooks per request at admission.
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self.validate_logits_processors_params = (
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self._build_logits_processors_params_validator()
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)
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self.generation_config_fields = model_config.try_get_generation_config()
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self.renderer = renderer or renderer_from_config(vllm_config)
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self.supports_mm_inputs = model_config.supports_multimodal_inputs
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self.mm_encoder_cache_size = 0
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self.skip_prompt_length_check = False
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if self.supports_mm_inputs:
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mm_budget = MultiModalBudget(vllm_config, mm_registry)
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self.mm_encoder_cache_size = mm_budget.encoder_cache_size
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self.skip_prompt_length_check = (
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mm_budget.processor.info.skip_prompt_length_check
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)
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mm_budget.reset_cache() # Not used anymore
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# Raw-prompt preprocessing (tokenization and multimodal processing)
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# is blocking, so async callers should run it on the renderer's
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# thread pool to keep their event loop responsive.
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self.process_inputs_async = make_async(
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self.process_inputs, executor=self.renderer._executor
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)
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@property
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def tokenizer(self) -> TokenizerLike | None:
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return self.renderer.tokenizer
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def get_tokenizer(self) -> TokenizerLike:
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return self.renderer.get_tokenizer()
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def _build_logits_processors_params_validator(
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self,
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) -> Callable[[SamplingParams], None]:
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"""Load the custom logits processor classes and return the per-request
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params validator for the active model runner."""
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custom_logitsprocs = self.model_config.logits_processors
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if self.vllm_config.use_v2_model_runner:
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if self.model_config.runner_type == "pooling" and not custom_logitsprocs:
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return lambda _: None
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from vllm.v1.worker.gpu.sample.logits_processor import (
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build_custom_logits_processors_params_validator,
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)
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return build_custom_logits_processors_params_validator(custom_logitsprocs)
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from vllm.v1.sample.logits_processor import (
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validate_logits_processors_parameters,
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)
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return partial(validate_logits_processors_parameters, custom_logitsprocs)
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def resolve_watermarking(self, params: SamplingParams | BeamSearchParams) -> bool:
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"""Return whether watermarking will be applied to this request.
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``None`` and ``True`` request watermarking, while ``False`` opts out.
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If watermarking is unavailable or incompatible with the request, emit
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a warning and return ``False``.
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"""
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return self.vllm_config._check_supports_watermarking(params)
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def apply_watermarking(self, params: SamplingParams, watermarking: bool) -> None:
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"""Store the resolved `watermarking` on request-local params.
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Also records whether watermarking was requested on a watermarking
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engine but could not be applied, as opposed to an explicit opt-out.
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"""
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# Sticky, so resolving an already resolved request keeps the flag.
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params._watermarking_skipped = params._watermarking_skipped or (
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self.vllm_config.watermark_config is not None
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and params.watermarking is not False
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and not watermarking
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)
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params.watermarking = watermarking
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def _validate_params(
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self,
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params: SamplingParams | PoolingParams,
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supported_tasks: tuple[SupportedTask, ...],
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) -> bool | None:
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"""Raise `ValueError` if SamplingParams or PoolingParams is not valid.
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Returns the resolved `watermarking` value for `SamplingParams` (`None`
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for `PoolingParams`). The caller applies it to its private copy of the
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params so that a caller-owned params object is never mutated.
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"""
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watermarking: bool | None = None
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if isinstance(params, SamplingParams):
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supported_generation_tasks = [
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task for task in supported_tasks if task in GENERATION_TASKS
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]
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if not supported_generation_tasks:
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raise VLLMValidationError("This model does not support generation")
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params.verify(
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self.model_config,
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self.speculative_config,
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self.structured_outputs_config,
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self.tokenizer,
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)
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if params.prompt_logprob_token_ids is not None:
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if not self.vllm_config.use_v2_model_runner:
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raise VLLMValidationError(
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"prompt_logprob_token_ids requires the V2 model runner "
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"(VLLM_USE_V2_MODEL_RUNNER=1).",
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parameter="prompt_logprob_token_ids",
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)
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if self.vllm_config.cache_config.kv_sharing_fast_prefill:
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raise VLLMValidationError(
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"prompt_logprob_token_ids is incorrect with "
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"--kv-sharing-fast-prefill; disable it for scoring.",
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parameter="prompt_logprob_token_ids",
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)
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self.validate_logits_processors_params(params)
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if self.model_config.is_diffusion:
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# Without --diffusion-config the served canvas is unknown here;
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# the ids and the read-only normalisation are still checked.
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validate_diffusion_sampling_params(
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params,
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canvas_length=(
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self.diffusion_config.canvas_length
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if self.diffusion_config is not None
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else None
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),
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vocab_size=self.model_config.get_vocab_size(),
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)
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if self.model_config.return_sampling_mask:
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if params.temperature <= 0:
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raise ValueError(
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"sampling distribution replay requires temperature > 0"
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)
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if params.top_k <= 0:
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raise ValueError(
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"sampling distribution replay requires top_k > 0 to "
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"bound sampling mask size, reduce transfer overhead, "
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"and avoid potential OOMs"
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)
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if params.thinking_token_budget is not None and (
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self.vllm_config.reasoning_config is None
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or not self.vllm_config.reasoning_config.enabled
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):
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raise VLLMValidationError(
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"thinking_token_budget is set but reasoning_config is "
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"not configured. Please set --reasoning-parser "
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"and/or --reasoning-config to use thinking_token_budget."
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)
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if (
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params.trace_decode_token_ids
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and not self.model_config.enable_trace_replay
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):
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raise VLLMValidationError(
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"trace_decode_token_ids is set but trace replay is not "
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"enabled. Start the engine with --enable-trace-replay "
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"to use it."
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)
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# Resolved last so that feature gates above report the missing
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# feature rather than its incompatibility with watermarking.
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watermarking = self.resolve_watermarking(params)
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elif isinstance(params, PoolingParams):
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supported_pooling_tasks = [
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task for task in supported_tasks if task in POOLING_TASKS
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]
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if not supported_pooling_tasks:
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raise VLLMValidationError("This model does not support pooling")
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if params.task is None:
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if "token_embed" in supported_pooling_tasks:
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params.task = "token_embed"
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elif "token_classify" in supported_pooling_tasks:
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params.task = "token_classify"
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elif "plugin" in supported_pooling_tasks:
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params.task = "plugin"
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if params.task not in supported_pooling_tasks:
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raise VLLMValidationError(
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f"Unsupported task: {params.task!r} "
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f"Supported tasks: {supported_pooling_tasks}"
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)
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params.verify(self.model_config)
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else:
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raise TypeError(
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f"params must be either SamplingParams or PoolingParams, "
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f"but got {type(params).__name__}"
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)
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return watermarking
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def _normalize_trace_replay_params(
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self, sampling_params: SamplingParams, prompt_len: int
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) -> None:
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"""Apply trace replay's generation semantics to request-local params."""
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trace_token_ids = sampling_params.trace_decode_token_ids
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assert trace_token_ids
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assert sampling_params.max_tokens is not None
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max_trace_len = max(self.model_config.max_model_len - prompt_len, 1)
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trace_token_ids = trace_token_ids[:max_trace_len]
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sampling_params.trace_decode_token_ids = trace_token_ids
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# Apply this after the generation config so its EOS token cannot stop
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# replay before the trace is exhausted.
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sampling_params.max_tokens = min(
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len(trace_token_ids), sampling_params.max_tokens
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)
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sampling_params.min_tokens = 0
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sampling_params.ignore_eos = True
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sampling_params._eos_token_id = None
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sampling_params.stop = []
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sampling_params.stop_token_ids = []
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sampling_params._all_stop_token_ids = set()
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def _validate_lora(self, lora_request: LoRARequest | None) -> None:
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if lora_request is None:
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return
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# LoRA request passed in while LoRA is not enabled
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if not self.lora_config:
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raise VLLMValidationError(
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f"Got lora_request {lora_request} but LoRA is not enabled!"
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)
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if self.tokenizer is not None:
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logger.warning_once(
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"vLLM has deprecated support for supporting different "
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"tokenizers for different LoRAs. By default, vLLM uses base "
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"model's tokenizer. If you are using a LoRA "
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"with its own tokenizer, consider specifying `--tokenizer "
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"[lora_path]` to use the LoRA tokenizer."
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)
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def _get_mm_identifier(
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self,
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mm_hash: str,
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lora_request: LoRARequest | None,
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) -> str:
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"""When enable_tower_connector_lora is True, multi-modal embeddings
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vary depending on the LoRA request. Therefore, the mm_hash must be
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generated based on the LoRA request to prevent incorrect cache hits.
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"""
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if (
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lora_request is None
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or self.lora_config is None
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or not self.lora_config.enable_tower_connector_lora
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):
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return mm_hash
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return f"{lora_request.lora_name}:{mm_hash}"
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def inject_into_mm_cache(
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self,
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mm_hashes: dict[str, list[str]],
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mm_kwargs: dict[str, list],
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) -> None:
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"""Inject pre-processed mm_kwargs into the processor cache.
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Call this when mm_kwargs have already been through the HF processor
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externally (e.g. by a frontend that transfers pre-processed tensors
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to the backend). This ensures MM cache hit rate metrics are reported
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accurately and avoids redundant processing on subsequent requests
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with the same images.
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Uses ``get_and_update_item()`` with an empty prompt_updates list,
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since token expansion has already been handled externally.
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"""
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cache = self.renderer.mm_processor_cache
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if cache is None:
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return
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try:
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for modality, hashes in mm_hashes.items():
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items = mm_kwargs.get(modality, [])
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for i, mm_hash in enumerate(hashes):
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if i < len(items) and items[i] is not None:
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# Insert into cache via get_and_update_item.
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# Use the returned item (may be an address for SHM
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# cache or the original item for LRU cache).
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items[i], _ = cache.get_and_update_item(
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(items[i], []),
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mm_hash,
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)
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# Update cache stats to reflect the externally processed items
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self.renderer.update_mm_cache_stats()
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except Exception:
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logger.warning(
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"Failed to inject mm_kwargs into processor cache",
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exc_info=True,
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)
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@staticmethod
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def assign_request_id(request: EngineCoreRequest):
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"""Replace the externally supplied request ID with an internal request ID
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that adds 8 random characters in order to ensure uniqueness.
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"""
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if request.external_req_id is not None:
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raise ValueError(
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"The external_req_id field should not be set on EngineCoreRequests"
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" passed to vLLM; use the request_id field."
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)
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request.external_req_id = request.request_id
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if envs.VLLM_DISABLE_REQUEST_ID_RANDOMIZATION:
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logger.warning_once(
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"VLLM_DISABLE_REQUEST_ID_RANDOMIZATION is set and will be "
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"removed in a future release. Duplicate externally-provided "
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"request IDs may cause failures and/or subtle correctness errors."
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)
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else:
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request.request_id = f"{request.external_req_id}-{random_uuid():.8}"
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def process_inputs(
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self,
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request_id: str,
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prompt: PromptType | EngineInput,
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params: SamplingParams | PoolingParams,
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supported_tasks: tuple[SupportedTask, ...],
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arrival_time: float | None = None,
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lora_request: LoRARequest | None = None,
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tokenization_kwargs: dict[str, Any] | None = None,
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trace_headers: Mapping[str, str] | None = None,
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priority: int = 0,
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data_parallel_rank: int | None = None,
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resumable: bool = False,
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session_id: str | None = None,
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kv_hints: KvHintsEnvelope | None = None,
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) -> EngineCoreRequest:
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watermarking = self._validate_params(params, supported_tasks)
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self._validate_lora(lora_request)
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parallel_config = self.vllm_config.parallel_config
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dp_size = parallel_config.data_parallel_size
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dp_local_size = parallel_config.data_parallel_size_local
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num_ranks = dp_local_size if parallel_config.local_engines_only else dp_size
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if data_parallel_rank is not None and not (0 <= data_parallel_rank < num_ranks):
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raise VLLMValidationError(
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f"data_parallel_rank {data_parallel_rank} "
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f"is out of range [0, {num_ranks})."
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)
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if isinstance(prompt, dict) and "type" in prompt:
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if arrival_time is None:
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arrival_time = prompt.get("arrival_time", time.time()) # type: ignore[assignment]
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engine_input: EngineInput = prompt # type: ignore[assignment]
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else:
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logger.warning_once(
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"Passing raw prompts to InputProcessor is deprecated "
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"and will be removed in the future. You should instead pass "
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"the outputs of Renderer.render_cmpl() or Renderer.render_chat()."
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)
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if arrival_time is None:
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arrival_time = time.time()
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renderer = self.renderer
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model_config = self.model_config
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parsed_prompt = parse_model_prompt(model_config, prompt)
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tok_params = renderer.default_cmpl_tok_params.with_kwargs(
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**(tokenization_kwargs or {})
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)
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(engine_input,) = renderer.render_cmpl(
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[parsed_prompt],
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tok_params,
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)
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current_platform.validate_request(engine_input, params)
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encoder_input, decoder_input = split_enc_dec_input(engine_input)
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self._validate_model_inputs(encoder_input, decoder_input)
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# Mypy can be conservative for TypedDict unions; normalize access.
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if decoder_input["type"] == "embeds":
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prompt_embeds = decoder_input["prompt_embeds"]
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prompt_token_ids = decoder_input.get("prompt_token_ids")
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prompt_is_token_ids = decoder_input.get("is_token_ids")
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else:
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prompt_token_ids = decoder_input["prompt_token_ids"]
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prompt_embeds = None
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prompt_is_token_ids = None
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sampling_params = None
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pooling_params = None
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if isinstance(params, SamplingParams):
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# TODO: can we avoid cloning here in multiproc case?
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sampling_params = params.clone()
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# Resolve on the request-local copy: `params` may be shared across
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# prompts, requests and even engines by the caller.
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assert watermarking is not None
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self.apply_watermarking(sampling_params, watermarking)
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prompt_len = length_from_prompt_token_ids_or_embeds(
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prompt_token_ids, prompt_embeds
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)
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if not 0 <= sampling_params.routed_experts_prompt_start <= prompt_len:
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raise VLLMValidationError(
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f"routed_experts_prompt_start must be between 0 and "
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f"the prompt length ({prompt_len}), inclusive.",
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parameter="routed_experts_prompt_start",
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value=sampling_params.routed_experts_prompt_start,
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)
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rows = sampling_params.prompt_logprob_token_ids
|
|
if rows is not None:
|
|
start = sampling_params.prompt_logprob_start or 0
|
|
num_rows = max(prompt_len - 1 - start, 0)
|
|
if len(rows) != num_rows:
|
|
raise VLLMValidationError(
|
|
f"prompt_logprob_token_ids has {len(rows)} rows, but the "
|
|
f"prompt has {num_rows} scored rows "
|
|
f"(prompt_len - 1 - prompt_logprob_start).",
|
|
parameter="prompt_logprob_token_ids",
|
|
value=len(rows),
|
|
)
|
|
# If unset max tokens, then generate up to the max_model_len.
|
|
if sampling_params.max_tokens is None:
|
|
sampling_params.max_tokens = (
|
|
self.model_config.max_model_len - prompt_len
|
|
)
|
|
# min_tokens is not checked while max_tokens is unset.
|
|
if sampling_params.min_tokens > sampling_params.max_tokens:
|
|
raise VLLMValidationError(
|
|
f"min_tokens must be less than or equal to "
|
|
f"max_tokens={sampling_params.max_tokens}, got "
|
|
f"{sampling_params.min_tokens}."
|
|
)
|
|
|
|
sampling_params.update_from_generation_config(
|
|
self.generation_config_fields,
|
|
self.renderer.get_eos_token_id(),
|
|
)
|
|
if self.tokenizer is not None:
|
|
sampling_params.update_from_tokenizer(self.tokenizer)
|
|
if sampling_params.trace_decode_token_ids:
|
|
self._normalize_trace_replay_params(sampling_params, prompt_len)
|
|
else:
|
|
pooling_params = params.clone()
|
|
|
|
# Multimodal related.
|
|
mm_features: list[MultiModalFeatureSpec] | None = None
|
|
|
|
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)
|
|
):
|
|
raise ValueError(
|
|
f"mm_hashes must contain only strings, got: {decoder_mm_hashes}. "
|
|
"This is likely due to an incorrect custom implementation of "
|
|
"MultiModalProcessor.apply method."
|
|
)
|
|
|
|
# Merge and flatten multimodal placeholders, hashes and inputs
|
|
# from dictionaries to lists, and sort them by each item's position
|
|
# in the input sequence.
|
|
sorted_mm_idxs = argsort_mm_positions(decoder_mm_positions)
|
|
|
|
mm_features = []
|
|
for modality, idx in sorted_mm_idxs:
|
|
base_mm_hash = decoder_mm_hashes[modality][idx]
|
|
mm_features.append(
|
|
MultiModalFeatureSpec(
|
|
data=decoder_mm_inputs[modality][idx],
|
|
modality=modality,
|
|
identifier=self._get_mm_identifier(
|
|
base_mm_hash,
|
|
lora_request,
|
|
),
|
|
mm_position=decoder_mm_positions[modality][idx],
|
|
mm_hash=base_mm_hash,
|
|
)
|
|
)
|
|
|
|
return EngineCoreRequest(
|
|
request_id=request_id,
|
|
prompt_token_ids=prompt_token_ids,
|
|
prompt_embeds=prompt_embeds,
|
|
prompt_is_token_ids=prompt_is_token_ids,
|
|
mm_features=mm_features,
|
|
sampling_params=sampling_params,
|
|
pooling_params=pooling_params,
|
|
arrival_time=arrival_time,
|
|
lora_request=lora_request,
|
|
cache_salt=decoder_input.get("cache_salt"),
|
|
priority=priority,
|
|
data_parallel_rank=data_parallel_rank,
|
|
trace_headers=trace_headers,
|
|
resumable=resumable,
|
|
session_id=session_id,
|
|
kv_hints=kv_hints,
|
|
)
|
|
|
|
def _validate_prompt_len(
|
|
self,
|
|
prompt_len: int,
|
|
prompt_type: Literal["encoder", "decoder"],
|
|
):
|
|
if self.skip_prompt_length_check and prompt_type == "encoder":
|
|
return
|
|
|
|
if prompt_len == 0 and prompt_type == "decoder":
|
|
raise VLLMValidationError(f"The {prompt_type} prompt cannot be empty")
|
|
|
|
model_config = self.model_config
|
|
max_prompt_len = (
|
|
model_config.max_model_len
|
|
if prompt_type == "decoder"
|
|
else self.mm_encoder_cache_size
|
|
)
|
|
if prompt_len > max_prompt_len:
|
|
if self.supports_mm_inputs:
|
|
suggestion = (
|
|
"Make sure that `max_model_len` is no smaller than the "
|
|
"number of text tokens plus multimodal tokens. For image "
|
|
"inputs, the number of image tokens depends on the number "
|
|
"of images, and possibly their aspect ratios as well."
|
|
)
|
|
else:
|
|
suggestion = (
|
|
"Make sure that `max_model_len` is no smaller than the "
|
|
"number of text tokens."
|
|
)
|
|
|
|
raise VLLMValidationError(
|
|
f"The {prompt_type} prompt (length {prompt_len}) is "
|
|
f"longer than the maximum model length of {max_prompt_len}. "
|
|
f"{suggestion}"
|
|
)
|
|
elif prompt_len == max_prompt_len and model_config.runner_type == "generate":
|
|
suggestion = (
|
|
"Make sure that `max_model_len` is no smaller than the "
|
|
"number of text tokens (prompt + requested output tokens)."
|
|
)
|
|
raise VLLMValidationError(
|
|
f"The {prompt_type} prompt (length {prompt_len}) plus the number of "
|
|
f"requested output tokens (at least 1) is longer than the maximum "
|
|
f"model length of {max_prompt_len}. {suggestion}"
|
|
)
|
|
|
|
def _validate_model_input(
|
|
self,
|
|
prompt_input: SingletonInput,
|
|
prompt_type: Literal["encoder", "decoder"],
|
|
) -> None:
|
|
prompt_ids = (
|
|
None
|
|
if prompt_input["type"] == "embeds"
|
|
else prompt_input["prompt_token_ids"]
|
|
)
|
|
prompt_embeds = (
|
|
prompt_input["prompt_embeds"] if prompt_input["type"] == "embeds" else None
|
|
)
|
|
|
|
prompt_len = length_from_prompt_token_ids_or_embeds(prompt_ids, prompt_embeds)
|
|
self._validate_prompt_len(prompt_len, prompt_type)
|
|
|
|
if prompt_input["type"] == "embeds":
|
|
is_token_ids = prompt_input.get("is_token_ids")
|
|
if is_token_ids is not None and len(is_token_ids) != prompt_len:
|
|
raise VLLMValidationError(
|
|
"prompt_is_token_ids must have the same length as prompt_embeds "
|
|
f"(expected {prompt_len}, got {len(is_token_ids)}).",
|
|
parameter="prompt_is_token_ids",
|
|
)
|
|
|
|
if prompt_input["type"] == "multimodal":
|
|
decoder_mm_positions = prompt_input["mm_placeholders"]
|
|
for modality, mm_positions in decoder_mm_positions.items():
|
|
for mm_position in mm_positions:
|
|
num_embeds = mm_position.get_num_embeds()
|
|
if num_embeds > self.mm_encoder_cache_size:
|
|
raise VLLMValidationError(
|
|
f"The {prompt_type} prompt contains a(n) {modality} item "
|
|
f"with {num_embeds} embedding tokens, which exceeds the "
|
|
f"pre-allocated encoder cache size "
|
|
f"{self.mm_encoder_cache_size}. Please reduce the input "
|
|
f"size or increase the encoder cache size "
|
|
f"by setting --limit-mm-per-prompt at startup."
|
|
)
|
|
|
|
# Shared by generate, embedding and pooling requests.
|
|
if prompt_ids:
|
|
self.renderer.validate_token_ids(prompt_ids)
|
|
|
|
def _validate_model_inputs(
|
|
self,
|
|
encoder_input: SingletonInput | None,
|
|
decoder_input: SingletonInput,
|
|
):
|
|
if encoder_input is not None:
|
|
self._validate_model_input(encoder_input, prompt_type="encoder")
|
|
|
|
self._validate_model_input(decoder_input, prompt_type="decoder")
|