477 lines
19 KiB
Python
477 lines
19 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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import warnings
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from collections.abc import Mapping
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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.inputs.data import (
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ProcessorInputs,
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PromptType,
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SingletonInputs,
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)
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from vllm.inputs.parse import split_enc_dec_inputs
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from vllm.inputs.preprocess import InputPreprocessor
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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 (
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MultiModalFeatureSpec,
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)
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from vllm.multimodal.utils import argsort_mm_positions
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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.sampling_params import 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.func_utils import supports_kw
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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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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.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 = mm_registry.supports_multimodal_inputs(model_config)
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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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self.input_preprocessor = InputPreprocessor(
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vllm_config,
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renderer=renderer,
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mm_registry=mm_registry,
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)
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from vllm.platforms import current_platform
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platform_validate_request = current_platform.validate_request
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if supports_kw(platform_validate_request, "prompt"):
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logger.warning_once(
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"The signature of Platform.validate_request has changed from "
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"`(cls, prompt, params, processed_inputs) -> None` to "
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"`(cls, processed_inputs, params) -> None`. The old signature "
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"will no longer be supported starting from v0.18."
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)
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orig_validate_request = platform_validate_request
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def compat_validate_request(
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processed_inputs: ProcessorInputs,
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params: SamplingParams | PoolingParams,
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):
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return orig_validate_request(
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processed_inputs,
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params,
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processed_inputs, # type: ignore
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) # type: ignore
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platform_validate_request = compat_validate_request
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self._platform_validate_request = platform_validate_request
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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 _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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) -> None:
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"""Raise `ValueError` if SamplingParams or PoolingParams is not valid."""
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if params.truncate_prompt_tokens is not None:
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params_type = type(params).__name__
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warnings.warn(
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f"The `truncate_prompt_tokens` parameter in `{params_type}` "
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"is deprecated and will be removed in v0.17. "
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"Please pass it via `tokenization_kwargs` instead.",
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DeprecationWarning,
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stacklevel=2,
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)
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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 ValueError("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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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 ValueError("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 ValueError(
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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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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 ValueError(
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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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"""
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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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@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 uniquness.
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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 | ProcessorInputs,
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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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) -> EngineCoreRequest:
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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 ValueError(
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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 tokenization_kwargs:
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logger.warning_once(
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"Passing tokenization_kwargs to InputProcessor is deprecated "
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"and will be removed in v0.18. You should instead pass "
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"them to 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 = prompt.get("arrival_time", time.time()) # type: ignore[assignment]
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processed_inputs: ProcessorInputs = 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 v0.18. 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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processed_inputs = self.input_preprocessor.preprocess(
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prompt,
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tokenization_kwargs=tokenization_kwargs,
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)
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self._platform_validate_request(processed_inputs, params)
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encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)
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self._validate_model_inputs(encoder_inputs, decoder_inputs)
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# Mypy can be conservative for TypedDict unions; normalize access.
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if decoder_inputs["type"] == "embeds":
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prompt_token_ids = None
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prompt_embeds = decoder_inputs["prompt_embeds"]
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else:
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prompt_token_ids = decoder_inputs["prompt_token_ids"]
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prompt_embeds = 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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# If unset max tokens, then generate up to the max_model_len.
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if sampling_params.max_tokens is None:
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seq_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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sampling_params.max_tokens = self.model_config.max_model_len - seq_len
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sampling_params.update_from_generation_config(
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self.generation_config_fields,
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self.renderer.get_eos_token_id(),
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)
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if self.tokenizer is not None:
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sampling_params.update_from_tokenizer(self.tokenizer)
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else:
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pooling_params = params.clone()
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# Multimodal related.
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mm_features: list[MultiModalFeatureSpec] | None = None
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if decoder_inputs["type"] == "multimodal":
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decoder_mm_inputs = decoder_inputs["mm_kwargs"]
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decoder_mm_positions = decoder_inputs["mm_placeholders"]
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decoder_mm_hashes = decoder_inputs["mm_hashes"]
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if not all(
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isinstance(leaf, str) for leaf in json_iter_leaves(decoder_mm_hashes)
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):
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raise ValueError(
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f"mm_hashes must contain only strings, got: {decoder_mm_hashes}. "
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"This is likely due to an incorrect custom implementation of "
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"MultiModalProcessor.apply method."
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)
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# Merge and flatten multimodal placeholders, hashes and inputs
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# from dictionaries to lists, and sort them by each item's position
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# in the input sequence.
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sorted_mm_idxs = argsort_mm_positions(decoder_mm_positions)
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mm_features = []
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for modality, idx in sorted_mm_idxs:
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base_mm_hash = decoder_mm_hashes[modality][idx]
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mm_features.append(
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MultiModalFeatureSpec(
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data=decoder_mm_inputs[modality][idx],
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modality=modality,
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identifier=self._get_mm_identifier(
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base_mm_hash,
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lora_request,
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),
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mm_position=decoder_mm_positions[modality][idx],
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mm_hash=base_mm_hash,
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)
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)
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return EngineCoreRequest(
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request_id=request_id,
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prompt_token_ids=prompt_token_ids,
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prompt_embeds=prompt_embeds,
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mm_features=mm_features,
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sampling_params=sampling_params,
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pooling_params=pooling_params,
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arrival_time=arrival_time,
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lora_request=lora_request,
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cache_salt=decoder_inputs.get("cache_salt"),
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priority=priority,
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data_parallel_rank=data_parallel_rank,
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trace_headers=trace_headers,
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resumable=resumable,
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)
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def _validate_prompt_len(
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self,
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prompt_len: int,
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prompt_type: Literal["encoder", "decoder"],
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):
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if self.skip_prompt_length_check:
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return
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if prompt_len == 0 and prompt_type == "decoder":
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raise ValueError(f"The {prompt_type} prompt cannot be empty")
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model_config = self.model_config
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max_prompt_len = (
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model_config.max_model_len
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if prompt_type == "decoder"
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else self.mm_encoder_cache_size
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)
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if prompt_len > max_prompt_len:
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if self.supports_mm_inputs:
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suggestion = (
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"Make sure that `max_model_len` is no smaller than the "
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"number of text tokens plus multimodal tokens. For image "
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"inputs, the number of image tokens depends on the number "
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"of images, and possibly their aspect ratios as well."
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)
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else:
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suggestion = (
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"Make sure that `max_model_len` is no smaller than the "
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"number of text tokens."
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)
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raise ValueError(
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f"The {prompt_type} prompt (length {prompt_len}) is "
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f"longer than the maximum model length of {max_prompt_len}. "
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f"{suggestion}"
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)
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elif prompt_len == max_prompt_len and model_config.runner_type == "generate":
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suggestion = (
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"Make sure that `max_model_len` is no smaller than the "
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"number of text tokens (prompt + requested output tokens)."
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)
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raise ValueError(
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f"The {prompt_type} prompt (length {prompt_len}) plus the number of "
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f"requested output tokens (at least 1) is longer than the maximum "
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f"model length of {max_prompt_len}. {suggestion}"
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)
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def _validate_model_input(
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self,
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prompt_inputs: SingletonInputs,
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prompt_type: Literal["encoder", "decoder"],
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) -> None:
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model_config = self.model_config
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tokenizer = self.tokenizer
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prompt_ids = (
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None
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if prompt_inputs["type"] == "embeds"
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else prompt_inputs["prompt_token_ids"]
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)
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prompt_embeds = (
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prompt_inputs["prompt_embeds"]
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if prompt_inputs["type"] == "embeds"
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else None
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)
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prompt_len = length_from_prompt_token_ids_or_embeds(prompt_ids, prompt_embeds)
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self._validate_prompt_len(prompt_len, prompt_type)
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if prompt_inputs["type"] == "multimodal":
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decoder_mm_positions = prompt_inputs["mm_placeholders"]
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for modality, mm_positions in decoder_mm_positions.items():
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for mm_position in mm_positions:
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embed_length = mm_position.get_num_embeds()
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if embed_length > self.mm_encoder_cache_size:
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raise ValueError(
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f"The {prompt_type} prompt contains a(n) {modality} item "
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f"with length {embed_length}, which exceeds the "
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f"pre-allocated encoder cache size "
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f"{self.mm_encoder_cache_size}. Please reduce the input "
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f"size or increase the encoder cache size "
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f"by setting --limit-mm-per-prompt at startup."
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)
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if prompt_ids and tokenizer is not None:
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max_input_id = max(prompt_ids, default=0)
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# NOTE: tokenizer.max_token_id is the tokenizer’s vocab size while
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# self.model_config.get_vocab_size() is the model’s vocab size.
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# For Qwen3 models, the language model has extra tokens that do
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# not exist in the tokenizer, and vice versa for multimodal
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# placeholder tokens in some multimodal models.
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# See https://github.com/QwenLM/Qwen3/issues/29#issuecomment-1933720399 # noqa: E501
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# and https://github.com/vllm-project/vllm/pull/22471#discussion_r2312251421 # noqa: E501
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# Here we take the max of the two to determine if a token id is
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# truly out-of-vocabulary.
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model_vocab_size = model_config.get_vocab_size()
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if max_input_id > max(tokenizer.max_token_id, model_vocab_size - 1):
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raise ValueError(f"Token id {max_input_id} is out of vocabulary")
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def _validate_model_inputs(
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self,
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encoder_inputs: SingletonInputs | None,
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decoder_inputs: SingletonInputs,
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):
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if encoder_inputs is not None:
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self._validate_model_input(encoder_inputs, prompt_type="encoder")
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self._validate_model_input(decoder_inputs, prompt_type="decoder")
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