795 lines
33 KiB
Python
795 lines
33 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 Mapping
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from typing import Any, Literal, cast
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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.data import (
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ProcessorInputs,
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PromptType,
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SingletonInputs,
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SingletonPrompt,
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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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MultiModalDataDict,
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MultiModalFeatureSpec,
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MultiModalUUIDDict,
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)
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from vllm.multimodal.parse import ModalityDataItems, MultiModalDataItems
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from vllm.multimodal.processing.context import set_request_id
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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
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from vllm.renderers.inputs import DictPrompt, TokPrompt
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from vllm.sampling_params import _SAMPLING_EPS, SamplingParams
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from vllm.tasks import POOLING_TASKS, SupportedTask
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from vllm.tokenizers import TokenizerLike
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from vllm.tokenizers.mistral import MistralTokenizer
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from vllm.utils import length_from_prompt_token_ids_or_embeds, random_uuid
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from vllm.utils.torch_utils import set_default_torch_num_threads
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from vllm.v1.engine import EngineCoreRequest
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from vllm.v1.metrics.stats import MultiModalCacheStats
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from vllm.v1.structured_output.backend_guidance import (
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has_guidance_unsupported_json_features,
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validate_guidance_grammar,
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)
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from vllm.v1.structured_output.backend_lm_format_enforcer import (
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validate_structured_output_request_lm_format_enforcer,
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)
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from vllm.v1.structured_output.backend_outlines import (
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validate_structured_output_request_outlines,
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)
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from vllm.v1.structured_output.backend_xgrammar import validate_xgrammar_grammar
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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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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.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.mm_registry = mm_registry
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self.mm_processor_cache = mm_registry.processor_cache_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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model_config,
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self.observability_config,
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mm_registry,
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mm_processor_cache=self.mm_processor_cache,
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)
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@property
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def tokenizer(self) -> TokenizerLike | None:
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return self.input_preprocessor.tokenizer
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def get_tokenizer(self) -> TokenizerLike:
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return self.input_preprocessor.get_tokenizer()
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@property
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def renderer(self) -> BaseRenderer:
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return self.input_preprocessor.renderer
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def _validate_logprobs(
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self,
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params: SamplingParams,
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) -> None:
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max_logprobs = self.model_config.max_logprobs
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if max_logprobs == -1:
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max_logprobs = self.model_config.get_vocab_size()
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# Validate sample logprobs.
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if params.logprobs:
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num_logprobs = params.logprobs
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if num_logprobs == -1:
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num_logprobs = self.model_config.get_vocab_size()
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if num_logprobs > max_logprobs:
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raise VLLMValidationError(
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f"Requested sample logprobs of {num_logprobs}, "
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f"which is greater than max allowed: {max_logprobs}",
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parameter="logprobs",
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value=num_logprobs,
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)
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# Validate prompt logprobs.
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if params.prompt_logprobs:
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num_prompt_logprobs = params.prompt_logprobs
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if num_prompt_logprobs == -1:
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num_prompt_logprobs = self.model_config.get_vocab_size()
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if num_prompt_logprobs > max_logprobs:
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raise VLLMValidationError(
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f"Requested prompt logprobs of {num_prompt_logprobs}, "
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f"which is greater than max allowed: {max_logprobs}",
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parameter="prompt_logprobs",
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value=num_prompt_logprobs,
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)
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def _validate_sampling_params(
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self,
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params: SamplingParams,
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) -> None:
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self._validate_structured_output(params)
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self._validate_logit_bias(params)
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if params.allowed_token_ids is None:
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return
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if not params.allowed_token_ids:
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raise ValueError("allowed_token_ids is not None and empty!")
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if self.tokenizer is None:
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# When skip_tokenizer_init=True, we can't validate token IDs
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# Skip validation and let the model handle invalid tokens
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return
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vocab_size = len(self.tokenizer)
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if not all(0 <= tid < vocab_size for tid in params.allowed_token_ids):
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raise ValueError("allowed_token_ids contains out-of-vocab token id!")
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def _validate_logit_bias(
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self,
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params: SamplingParams,
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) -> None:
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"""Validate logit_bias token IDs are within vocabulary range."""
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if not params.logit_bias:
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return
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vocab_size = self.model_config.get_vocab_size()
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invalid_token_ids = []
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for token_id in params.logit_bias:
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if token_id < 0 or token_id >= vocab_size:
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invalid_token_ids.append(token_id)
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if invalid_token_ids:
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raise VLLMValidationError(
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f"token_id(s) {invalid_token_ids} in logit_bias contain "
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f"out-of-vocab token ids. Vocabulary size: {vocab_size}",
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parameter="logit_bias",
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value=invalid_token_ids,
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)
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def _validate_supported_sampling_params(
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self,
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params: SamplingParams,
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) -> None:
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# Logits processors not supported.
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if params.logits_processors:
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raise ValueError(
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"vLLM V1 does not support per request user-provided logits processors."
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)
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# Some sampling parameters are not yet compatible with spec decoding.
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if self.vllm_config.speculative_config is not None and (
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params.min_tokens > 1 or params.min_p > _SAMPLING_EPS or params.logit_bias
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):
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raise ValueError(
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"The min_tokens, min_p, and logit_bias sampling parameters "
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"are not yet supported with speculative decoding."
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)
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def _validate_params(
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self,
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params: SamplingParams | PoolingParams,
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# TODO: Validate generation tasks as well once `supported_tasks`
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# is passed to all `process_inputs` calls
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supported_tasks: tuple[SupportedTask, ...] | None,
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):
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"""
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Validate supported SamplingParam.
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Should raise ValueError if unsupported for API Server.
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"""
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if isinstance(params, PoolingParams):
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if supported_tasks is None:
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raise RuntimeError("`supported_tasks` must be passed for pooling")
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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 params.task is None:
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if not supported_pooling_tasks:
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raise ValueError("Pooling tasks are not supported")
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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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return
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self._validate_logprobs(params)
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self._validate_sampling_params(params)
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self._validate_supported_sampling_params(params)
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def _parse_mm_items(self, mm_data: MultiModalDataDict) -> MultiModalDataItems:
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mm_processor = self.input_preprocessor._get_mm_processor()
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return mm_processor.info.parse_mm_data(mm_data)
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def _validate_singleton_mm_uuids(self, prompt: SingletonPrompt) -> None:
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if not isinstance(prompt, dict):
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return
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mm_data = cast(MultiModalDataDict, prompt.get("multi_modal_data") or {})
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mm_uuids = cast(MultiModalUUIDDict, prompt.get("multi_modal_uuids") or {})
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if not mm_data and not mm_uuids:
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return
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mm_data_parsed = self._parse_mm_items(
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{k: v for k, v in mm_data.items() if v is not None}
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)
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mm_uuids_parsed = {
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k: [v] if isinstance(v, str) else v
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for k, v in mm_uuids.items()
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if v is not None
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}
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# NOTE: Include the keys corresponding to `None`
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modalities = mm_data.keys() | mm_uuids.keys()
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for modality in modalities:
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data_items = cast(
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ModalityDataItems | list[Any], mm_data_parsed.get(modality, [])
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)
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uuid_items = cast(list[str | None], mm_uuids_parsed.get(modality, []))
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if len(data_items) > 0:
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if len(uuid_items) > 0 and len(data_items) != len(uuid_items):
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raise ValueError(
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f"If given, multi_modal_uuids[{modality!r}] must have "
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f"same length as multi_modal_data[{modality!r}], but "
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f"got {len(uuid_items)} vs {len(data_items)}."
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)
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for i, item in enumerate(data_items):
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if item is None:
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if not uuid_items:
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raise ValueError(
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f"multi_modal_data[{modality!r}][{i}] is empty but "
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f"multi_modal_uuids[{modality!r}] is missing."
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)
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if uuid_items[i] is None:
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raise ValueError(
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f"multi_modal_data[{modality!r}][{i}] is empty but "
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f"multi_modal_uuids[{modality!r}][{i}] is missing."
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)
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else:
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if len(uuid_items) == 0:
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raise ValueError(
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f"multi_modal_data[{modality!r}] is empty but "
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f"multi_modal_uuids[{modality!r}] is missing."
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)
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def _validate_mm_uuids(self, prompt: PromptType | DictPrompt | TokPrompt) -> None:
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"""
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Validate that user-provided multi_modal_uuids align with
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multi_modal_data in the incoming request prompt(s).
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Only checks lengths; `None` entries are allowed and will be
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auto-hashed downstream.
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"""
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if isinstance(prompt, dict) and "encoder_prompt" in prompt:
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self._validate_singleton_mm_uuids(prompt["encoder_prompt"]) # type: ignore[typeddict-item]
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if (dec_prompt := prompt["decoder_prompt"]) is not None: # type: ignore[typeddict-item]
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self._validate_singleton_mm_uuids(dec_prompt)
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else:
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self._validate_singleton_mm_uuids(prompt)
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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 _validate_structured_output(self, params: SamplingParams) -> None:
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if not params.structured_outputs or not self.structured_outputs_config:
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return
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if self.model_config.skip_tokenizer_init and params.structured_outputs:
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raise ValueError(
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"Structured outputs requires a tokenizer so it can't be used with 'skip_tokenizer_init'" # noqa: E501
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)
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backend = self.structured_outputs_config.backend
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if _backend := params.structured_outputs._backend:
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# Request-level backend selection is not supported.
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# The values may differ if `params` is reused and was set
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# to a specific backend based on `auto` behavior in a previous
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# request. We remember that it was set as a result of `auto`
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# using the `_backend_was_auto` field set in the params.
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if backend != _backend and not (
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backend == "auto" and params.structured_outputs._backend_was_auto
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):
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raise ValueError(
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"Request-level structured output backend selection is not "
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f"supported. The request specified '{_backend}', but vLLM "
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f"was initialised with '{backend}'. This error can be "
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||
"resolved by removing '_backend' from the request."
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)
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else:
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params.structured_outputs._backend = backend
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|
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# Request content validation
|
||
if (
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isinstance(params.structured_outputs.choice, list)
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and not params.structured_outputs.choice
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):
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# It is invalid for choice to be an empty list
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raise ValueError(
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f"Choice '{params.structured_outputs.choice}' cannot be an empty list" # noqa: E501
|
||
)
|
||
# Reject empty string grammar early to avoid engine-side crashes
|
||
if (
|
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isinstance(params.structured_outputs.grammar, str)
|
||
and params.structured_outputs.grammar.strip() == ""
|
||
):
|
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raise ValueError("structured_outputs.grammar cannot be an empty string")
|
||
|
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if backend.startswith("xgrammar"):
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# xgrammar with no fallback
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validate_xgrammar_grammar(params)
|
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elif backend.startswith("guidance"):
|
||
# TODO: ideally we would have the LLTokenizer here as Lark syntax
|
||
# allows <|special_token|> and similar, see
|
||
# https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#special-tokens
|
||
# Without tokenizer these are disallowed in grammars.
|
||
if isinstance(self.tokenizer, MistralTokenizer):
|
||
raise ValueError(
|
||
"Mistral tokenizer is not supported for the 'guidance' "
|
||
"structured output backend. Please use ['xgrammar', 'outlines'] "
|
||
"backends or tokenizer_mode='hf' instead."
|
||
)
|
||
validate_guidance_grammar(params, tokenizer=None)
|
||
elif backend == "outlines":
|
||
# outlines backend
|
||
validate_structured_output_request_outlines(params)
|
||
elif backend == "lm-format-enforcer":
|
||
# lm format enforcer backend
|
||
if isinstance(self.tokenizer, MistralTokenizer):
|
||
raise ValueError(
|
||
"Mistral tokenizer is not supported for the 'lm-format-enforcer' "
|
||
"structured output backend. Please use ['xgrammar', 'outlines'] "
|
||
"backends or tokenizer_mode='hf' instead."
|
||
)
|
||
validate_structured_output_request_lm_format_enforcer(params)
|
||
else:
|
||
# NOTE: backend must be "auto" here, because we have
|
||
# checked supported_backends above.
|
||
# In this mode, we set opinionated defaults based on what we think
|
||
# will satisfy the most use cases without having to worry about
|
||
# this setting. We include fallback behavior here, but not with any
|
||
# other setting where a specific backend was specified.
|
||
try:
|
||
validate_xgrammar_grammar(params)
|
||
params.structured_outputs._backend = "xgrammar"
|
||
except ValueError:
|
||
# The request either failed validation
|
||
# or includes some jsonschema feature(s) that
|
||
# are not supported in xgrammar.
|
||
|
||
# Check if schema has features unsupported by guidance
|
||
so_params = params.structured_outputs
|
||
skip_guidance = False
|
||
if so_params.json:
|
||
if isinstance(so_params.json, str):
|
||
import json
|
||
|
||
schema = json.loads(so_params.json)
|
||
else:
|
||
schema = so_params.json
|
||
skip_guidance = has_guidance_unsupported_json_features(schema)
|
||
|
||
if isinstance(self.tokenizer, MistralTokenizer) or skip_guidance:
|
||
# Fall back to outlines if the tokenizer is Mistral
|
||
# or if schema contains features unsupported by guidance
|
||
validate_structured_output_request_outlines(params)
|
||
params.structured_outputs._backend = "outlines"
|
||
else:
|
||
# Fall back to guidance by default.
|
||
validate_guidance_grammar(params, tokenizer=None)
|
||
params.structured_outputs._backend = "guidance"
|
||
# Remember that this backend was set automatically
|
||
params.structured_outputs._backend_was_auto = True
|
||
|
||
# Run post-init validation. This is also important to ensure subsequent
|
||
# roundtrip serialization/deserialization won't fail.
|
||
params.structured_outputs.__post_init__()
|
||
|
||
def _extract_singleton_mm_data(
|
||
self, prompt: SingletonPrompt
|
||
) -> MultiModalDataDict | None:
|
||
if not isinstance(prompt, dict):
|
||
return None
|
||
|
||
return prompt.get("multi_modal_data")
|
||
|
||
def _extract_mm_data(
|
||
self, prompt: PromptType | DictPrompt | TokPrompt
|
||
) -> MultiModalDataDict | None:
|
||
if isinstance(prompt, dict) and "encoder_prompt" in prompt:
|
||
return self._extract_singleton_mm_data(prompt["encoder_prompt"]) # type: ignore[typeddict-item]
|
||
else:
|
||
return self._extract_singleton_mm_data(prompt)
|
||
|
||
def _maybe_build_mm_uuids(
|
||
self,
|
||
request_id: str,
|
||
prompt: PromptType | DictPrompt | TokPrompt,
|
||
) -> MultiModalUUIDDict | None:
|
||
"""Build per-item multimodal hash overrides when enabled. In this case,
|
||
multimodal data items are identified by their request id, modality and
|
||
index rather than their content.
|
||
|
||
Returns a dictionary of modality -> list[str] of overrides, or None if
|
||
disabled or no multimodal data is present.
|
||
"""
|
||
mm_data = self._extract_mm_data(prompt)
|
||
if not mm_data:
|
||
return None
|
||
|
||
mm_items = self._parse_mm_items(
|
||
{k: v for k, v in mm_data.items() if v is not None}
|
||
)
|
||
|
||
return {
|
||
modality: [f"{request_id}-{modality}-{i}" for i in range(data_count)]
|
||
for modality, data_count in mm_items.get_all_counts().items()
|
||
}
|
||
|
||
def _get_mm_identifier(
|
||
self,
|
||
mm_hash: str,
|
||
lora_request: LoRARequest | None,
|
||
) -> str:
|
||
"""
|
||
When enable_tower_connector_lora is True, multi-modal embeddings
|
||
vary depending on the LoRA request. Therefore, the mm_hash must be
|
||
generated based on the LoRA request to prevent incorrect cache hits.
|
||
"""
|
||
if (
|
||
lora_request is None
|
||
or self.lora_config is None
|
||
or not self.lora_config.enable_tower_connector_lora
|
||
):
|
||
return mm_hash
|
||
return f"{lora_request.lora_name}:{mm_hash}"
|
||
|
||
@staticmethod
|
||
def assign_request_id(request: EngineCoreRequest):
|
||
"""Replace the externally supplied request ID with an internal request ID
|
||
that adds 8 random characters in order to ensure uniquness.
|
||
"""
|
||
if request.external_req_id is not None:
|
||
raise ValueError(
|
||
"The external_req_id field should not be set on EngineCoreRequests"
|
||
" passed to vLLM; use the request_id field."
|
||
)
|
||
request.external_req_id = request.request_id
|
||
request.request_id = f"{request.external_req_id}-{random_uuid():.8}"
|
||
|
||
def process_inputs(
|
||
self,
|
||
request_id: str,
|
||
prompt: PromptType | DictPrompt | TokPrompt,
|
||
params: SamplingParams | PoolingParams,
|
||
arrival_time: float | None = None,
|
||
lora_request: LoRARequest | None = None,
|
||
tokenization_kwargs: dict[str, Any] | None = None,
|
||
trace_headers: Mapping[str, str] | None = None,
|
||
priority: int = 0,
|
||
data_parallel_rank: int | None = None,
|
||
supported_tasks: tuple[SupportedTask, ...] | None = None,
|
||
resumable: bool = False,
|
||
) -> EngineCoreRequest:
|
||
self._validate_lora(lora_request)
|
||
self._validate_params(params, supported_tasks)
|
||
|
||
parallel_config = self.vllm_config.parallel_config
|
||
dp_size = parallel_config.data_parallel_size
|
||
dp_local_size = parallel_config.data_parallel_size_local
|
||
num_ranks = dp_local_size if parallel_config.local_engines_only else dp_size
|
||
if data_parallel_rank is not None and not (0 <= data_parallel_rank < num_ranks):
|
||
raise ValueError(
|
||
f"data_parallel_rank {data_parallel_rank} "
|
||
f"is out of range [0, {num_ranks})."
|
||
)
|
||
|
||
if arrival_time is None:
|
||
arrival_time = time.time()
|
||
|
||
# Optionally generate multimodal hash overrides to avoid hashing
|
||
# multimodal data items by their content as their identifiers.
|
||
|
||
# NOTE: when users explicitly turn off BOTH prefix caching and input
|
||
# processing caching, no multimodal features or embeddings will be
|
||
# reused across requests, therefore identifying multimodal data items
|
||
# by their content is no longer necessary, and we create uuids with
|
||
# request id-modality-index as multimodal hash overrides.
|
||
if (
|
||
self.model_config.multimodal_config
|
||
and self.model_config.multimodal_config.mm_processor_cache_gb == 0
|
||
and not self.cache_config.enable_prefix_caching
|
||
):
|
||
mm_uuids = self._maybe_build_mm_uuids(request_id, prompt)
|
||
else:
|
||
# Otherwise, use user-provided uuids as multimodal hash overrides
|
||
# if provided.
|
||
self._validate_mm_uuids(prompt)
|
||
if isinstance(prompt, dict):
|
||
mm_uuids = cast(
|
||
MultiModalUUIDDict | None, prompt.get("multi_modal_uuids")
|
||
)
|
||
else:
|
||
mm_uuids = None
|
||
|
||
# Process inputs, which includes:
|
||
# 1. Tokenize text prompt, with LoRA request if one exists.
|
||
# 2. For multimodal models with a merged preprocessor, preprocess
|
||
# multimodal data and expand prompt token ids accordingly.
|
||
with set_request_id(request_id), set_default_torch_num_threads():
|
||
processed_inputs: ProcessorInputs = self.input_preprocessor.preprocess(
|
||
prompt,
|
||
tokenization_kwargs=tokenization_kwargs,
|
||
mm_uuids=mm_uuids,
|
||
)
|
||
|
||
from vllm.platforms import current_platform
|
||
|
||
current_platform.validate_request(
|
||
prompt=prompt,
|
||
params=params,
|
||
processed_inputs=processed_inputs,
|
||
)
|
||
|
||
eos_token_id = self.input_preprocessor.get_eos_token_id()
|
||
|
||
encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)
|
||
self._validate_model_inputs(encoder_inputs, decoder_inputs)
|
||
|
||
# Mypy can be conservative for TypedDict unions; normalize access.
|
||
if decoder_inputs["type"] == "embeds":
|
||
prompt_token_ids = None
|
||
prompt_embeds = decoder_inputs["prompt_embeds"]
|
||
else:
|
||
prompt_token_ids = decoder_inputs["prompt_token_ids"]
|
||
prompt_embeds = None
|
||
|
||
sampling_params = None
|
||
pooling_params = None
|
||
if isinstance(params, SamplingParams):
|
||
# TODO: can we avoid cloning here in multiproc case?
|
||
sampling_params = params.clone()
|
||
# If unset max tokens, then generate up to the max_model_len.
|
||
if sampling_params.max_tokens is None:
|
||
seq_len = length_from_prompt_token_ids_or_embeds(
|
||
prompt_token_ids, prompt_embeds
|
||
)
|
||
sampling_params.max_tokens = self.model_config.max_model_len - seq_len
|
||
sampling_params.update_from_generation_config(
|
||
self.generation_config_fields, eos_token_id
|
||
)
|
||
if self.tokenizer is not None:
|
||
sampling_params.update_from_tokenizer(self.tokenizer)
|
||
else:
|
||
pooling_params = params.clone()
|
||
|
||
# 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"]
|
||
|
||
# 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,
|
||
mm_features=mm_features,
|
||
sampling_params=sampling_params,
|
||
pooling_params=pooling_params,
|
||
eos_token_id=eos_token_id,
|
||
arrival_time=arrival_time,
|
||
lora_request=lora_request,
|
||
cache_salt=decoder_inputs.get("cache_salt"),
|
||
priority=priority,
|
||
data_parallel_rank=data_parallel_rank,
|
||
trace_headers=trace_headers,
|
||
resumable=resumable,
|
||
)
|
||
|
||
def _validate_prompt_len(
|
||
self,
|
||
prompt_len: int,
|
||
prompt_type: Literal["encoder", "decoder"],
|
||
):
|
||
if self.skip_prompt_length_check:
|
||
return
|
||
|
||
if prompt_len == 0 and prompt_type == "decoder":
|
||
raise ValueError(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 ValueError(
|
||
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 ValueError(
|
||
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_inputs: SingletonInputs,
|
||
prompt_type: Literal["encoder", "decoder"],
|
||
) -> None:
|
||
model_config = self.model_config
|
||
tokenizer = self.tokenizer
|
||
|
||
prompt_ids = (
|
||
None
|
||
if prompt_inputs["type"] == "embeds"
|
||
else prompt_inputs["prompt_token_ids"]
|
||
)
|
||
prompt_embeds = (
|
||
prompt_inputs["prompt_embeds"]
|
||
if prompt_inputs["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_inputs["type"] == "multimodal":
|
||
decoder_mm_positions = prompt_inputs["mm_placeholders"]
|
||
for modality, mm_positions in decoder_mm_positions.items():
|
||
for mm_position in mm_positions:
|
||
embed_length = mm_position.get_num_embeds()
|
||
if embed_length > self.mm_encoder_cache_size:
|
||
raise ValueError(
|
||
f"The {prompt_type} prompt contains a(n) {modality} item "
|
||
f"with length {embed_length}, 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."
|
||
)
|
||
|
||
if prompt_ids and tokenizer is not None:
|
||
max_input_id = max(prompt_ids, default=0)
|
||
|
||
# NOTE: tokenizer.max_token_id is the tokenizer’s vocab size while
|
||
# self.model_config.get_vocab_size() is the model’s vocab size.
|
||
# For Qwen3 models, the language model has extra tokens that do
|
||
# not exist in the tokenizer, and vice versa for multimodal
|
||
# placeholder tokens in some multimodal models.
|
||
# See https://github.com/QwenLM/Qwen3/issues/29#issuecomment-1933720399 # noqa: E501
|
||
# and https://github.com/vllm-project/vllm/pull/22471#discussion_r2312251421 # noqa: E501
|
||
|
||
# Here we take the max of the two to determine if a token id is
|
||
# truly out-of-vocabulary.
|
||
model_vocab_size = model_config.get_vocab_size()
|
||
if max_input_id > max(tokenizer.max_token_id, model_vocab_size - 1):
|
||
raise ValueError(f"Token id {max_input_id} is out of vocabulary")
|
||
|
||
def _validate_model_inputs(
|
||
self,
|
||
encoder_inputs: SingletonInputs | None,
|
||
decoder_inputs: SingletonInputs,
|
||
):
|
||
if encoder_inputs is not None:
|
||
self._validate_model_input(encoder_inputs, prompt_type="encoder")
|
||
|
||
self._validate_model_input(decoder_inputs, prompt_type="decoder")
|
||
|
||
def stat_mm_cache(self) -> MultiModalCacheStats | None:
|
||
return self.input_preprocessor.stat_mm_cache()
|
||
|
||
def clear_mm_cache(self) -> None:
|
||
self.input_preprocessor.clear_mm_cache()
|
||
|
||
def close(self) -> None:
|
||
if self.mm_processor_cache is not None:
|
||
self.mm_processor_cache.close()
|