362 lines
15 KiB
Python
362 lines
15 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import time
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from collections.abc import Mapping, Sequence
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from typing import Literal, Optional, Union
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from vllm.config import VllmConfig
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from vllm.inputs import ProcessorInputs, PromptType, SingletonInputs
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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.lora.request import LoRARequest
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from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalKwargs,
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MultiModalRegistry)
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from vllm.multimodal.inputs import PlaceholderRange
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from vllm.multimodal.processing import EncDecMultiModalProcessor
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from vllm.multimodal.utils import merge_and_sort_multimodal_metadata
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from vllm.pooling_params import PoolingParams
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from vllm.prompt_adapter.request import PromptAdapterRequest
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from vllm.sampling_params import SamplingParams
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from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup
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from vllm.v1.engine import EngineCoreRequest
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from vllm.v1.engine.mm_input_cache import MirroredProcessingCache
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from vllm.v1.structured_output.backend_guidance import (
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validate_guidance_grammar)
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from vllm.v1.structured_output.utils import (
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validate_structured_output_request_xgrammar)
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class Processor:
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def __init__(
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self,
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vllm_config: VllmConfig,
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tokenizer: BaseTokenizerGroup,
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mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
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):
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self.vllm_config = vllm_config
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self.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.decoding_config = vllm_config.decoding_config
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self.tokenizer = tokenizer
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self.generation_config_fields = (
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self.model_config.try_get_generation_config())
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self.input_preprocessor = InputPreprocessor(self.model_config,
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self.tokenizer,
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mm_registry)
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self.mm_input_cache_client = MirroredProcessingCache(self.model_config)
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# Multi-modal hasher (for images)
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self.use_hash = (
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not self.model_config.disable_mm_preprocessor_cache) or \
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self.cache_config.enable_prefix_caching
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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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# Validate sample logprobs.
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if params.logprobs and params.logprobs > max_logprobs:
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raise ValueError(
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f"Requested sample logprobs of {params.logprobs}, "
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f"which is greater than max allowed: {max_logprobs}")
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# Validate prompt logprobs.
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if params.prompt_logprobs and params.prompt_logprobs > max_logprobs:
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raise ValueError(
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f"Requested prompt logprobs of {params.prompt_logprobs}, "
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f"which is greater than max allowed: {max_logprobs}")
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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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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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vocab_size = self.model_config.get_vocab_size()
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if not all(0 <= tid < vocab_size for tid in params.allowed_token_ids):
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raise ValueError(
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"allowed_token_ids contains out-of-vocab token id!")
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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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# Best of not yet supported.
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if params.best_of is not None and params.best_of > 1:
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raise ValueError("vLLM V1 does not yet support best_of.")
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# Logits processors not supported.
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if params.logits_processors:
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raise ValueError("vLLM V1 does not support per request "
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"user provided logits processors.")
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def _validate_params(
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self,
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params: Union[SamplingParams, PoolingParams],
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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 not isinstance(params, SamplingParams):
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raise ValueError("V1 does not yet support Pooling models.")
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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 _validate_lora(self, lora_request: Optional[LoRARequest]) -> None:
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if lora_request is not None and not self.lora_config:
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raise ValueError(f"Got lora_request {lora_request} but LoRA is "
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"not enabled!")
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def _validate_structured_output(self, params: SamplingParams) -> None:
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if not params.guided_decoding or not self.decoding_config:
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return
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supported_backends = [
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"xgrammar", "xgrammar:disable-any-whitespace", "guidance",
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"guidance:disable-any-whitespace", "auto"
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]
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engine_level_backend = self.decoding_config.guided_decoding_backend
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if engine_level_backend not in supported_backends:
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raise ValueError(f"Only {supported_backends} structured output is "
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"supported in V1.")
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if params.guided_decoding.backend:
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if params.guided_decoding.backend != engine_level_backend:
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raise ValueError("Request-level structured output backend "
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"must match engine-level backend. "
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f"{params.guided_decoding.backend}"
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f" != {engine_level_backend}")
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else:
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params.guided_decoding.backend = engine_level_backend
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# Request content validation
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if engine_level_backend.startswith("xgrammar"):
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# xgrammar with no fallback
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validate_structured_output_request_xgrammar(params)
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params.guided_decoding.backend = engine_level_backend
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elif engine_level_backend == "auto":
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# "auto" is an opt-in to opinionated behavior where we try to
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# choose a backend based on request contents. This is not the
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# default as it is less predictable and subject to change
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# between releases as feature support changes.
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try:
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validate_structured_output_request_xgrammar(params)
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params.guided_decoding.backend = "xgrammar"
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except ValueError:
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# The request includes some jsonschema feature(s) that
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# are not supported in xgrammar. Fall back to guidance.
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params.guided_decoding.backend = "guidance"
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if engine_level_backend.startswith("guidance"):
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# TODO ideally we would have the LLTokenizer here as Lark syntax
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# allows <|special_token|> and similar, see
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# https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#special-tokens
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# Without tokenizer these are disallowed in grammars.
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validate_guidance_grammar(params, tokenizer=None)
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params.guided_decoding.backend = engine_level_backend
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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,
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params: Union[SamplingParams, PoolingParams],
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arrival_time: Optional[float] = None,
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lora_request: Optional[LoRARequest] = None,
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trace_headers: Optional[Mapping[str, str]] = None,
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prompt_adapter_request: Optional[PromptAdapterRequest] = None,
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priority: int = 0,
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) -> EngineCoreRequest:
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# TODO(woosuk): Support pooling models.
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# TODO(woosuk): Support encoder-decoder models.
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from vllm.platforms import current_platform
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current_platform.validate_request(
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prompt=prompt,
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params=params,
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)
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self._validate_lora(lora_request)
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self._validate_params(params)
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if priority != 0:
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raise ValueError("V1 does not support priority yet.")
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if trace_headers is not None:
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raise ValueError("V1 does not support tracing yet.")
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if prompt_adapter_request is not None:
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raise ValueError("V1 does not support prompt_adapter_request.")
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if arrival_time is None:
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arrival_time = time.time()
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# Process inputs, which includes:
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# 1. Tokenize text prompt, with LoRA request if one exists.
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# 2. For multimodal models with a merged preprocessor, preprocess
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# multimodal data and expand prompt token ids accordingly.
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# 3. Apply prompt adapter to prompt token ids if one exists.
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processed_inputs: ProcessorInputs = self.input_preprocessor.preprocess(
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prompt,
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lora_request=lora_request,
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prompt_adapter_request=prompt_adapter_request,
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return_mm_hashes=self.use_hash,
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)
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eos_token_id = self.input_preprocessor.get_eos_token_id(lora_request)
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self._validate_model_inputs(processed_inputs, lora_request)
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encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)
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# TODO: Impl encoder-decoder
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if encoder_inputs is not None:
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raise NotImplementedError
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assert 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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sampling_params.max_tokens = (
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self.model_config.max_model_len -
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len(decoder_inputs["prompt_token_ids"]))
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sampling_params.update_from_generation_config(
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self.generation_config_fields, eos_token_id)
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sampling_params.update_from_tokenizer(
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self.tokenizer.get_lora_tokenizer(lora_request))
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# Multimodal related.
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sorted_mm_inputs: Optional[Sequence[Optional[MultiModalKwargs]]] = None
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sorted_mm_positions: Optional[list[PlaceholderRange]] = None
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sorted_mm_hashes: Optional[list[str]] = 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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# 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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(
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sorted_item_modalities,
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sorted_mm_positions,
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sorted_mm_hashes,
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) = merge_and_sort_multimodal_metadata(
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decoder_inputs["mm_placeholders"],
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decoder_inputs["mm_hashes"] if self.use_hash else None,
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)
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# The output of merged multi-modal processor (`decoder_mm_inputs`)
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# is a single MultiModalKwargs for all items from all modalities.
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# This code flattens kwargs for individual items in a list and
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# sorts them by each item's position in the input sequence if there
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# are multiple modalities.
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unique_modalities = set(sorted_item_modalities)
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if len(unique_modalities) > 1:
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orig_sorted_mm_inputs = []
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used_indices = {modality: 0 for modality in unique_modalities}
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for modality in sorted_item_modalities:
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items = decoder_mm_inputs.get_items(modality)
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item = items[used_indices[modality]]
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orig_sorted_mm_inputs.append(
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MultiModalKwargs.from_items([item]))
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used_indices[modality] += 1
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else:
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orig_sorted_mm_inputs = [
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MultiModalKwargs.from_items([item]) for item in
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decoder_mm_inputs.get_items(sorted_item_modalities[0])
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]
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if sorted_mm_hashes is not None:
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sorted_mm_inputs = self.mm_input_cache_client.get_and_update_p0(
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orig_sorted_mm_inputs, sorted_mm_hashes)
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else:
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sorted_mm_inputs = orig_sorted_mm_inputs
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return EngineCoreRequest(
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request_id=request_id,
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prompt=decoder_inputs.get("prompt"),
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prompt_token_ids=decoder_inputs["prompt_token_ids"],
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mm_inputs=sorted_mm_inputs,
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mm_hashes=sorted_mm_hashes,
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mm_placeholders=sorted_mm_positions,
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sampling_params=sampling_params,
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eos_token_id=eos_token_id,
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arrival_time=arrival_time,
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lora_request=lora_request,
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)
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def _validate_model_inputs(self,
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inputs: ProcessorInputs,
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lora_request: Optional[LoRARequest] = None):
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encoder_inputs, decoder_inputs = split_enc_dec_inputs(inputs)
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if encoder_inputs is not None:
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self._validate_model_input(encoder_inputs,
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lora_request,
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prompt_type="encoder")
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self._validate_model_input(decoder_inputs,
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lora_request,
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prompt_type="decoder")
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def _validate_model_input(
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self,
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prompt_inputs: SingletonInputs,
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lora_request: Optional[LoRARequest],
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*,
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prompt_type: Literal["encoder", "decoder"],
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):
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tokenizer = self.tokenizer.get_lora_tokenizer(lora_request)
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if prompt_type == "encoder":
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model_config = self.model_config
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if model_config.is_multimodal_model:
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mm_registry = self.input_preprocessor.mm_registry
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mm_processor = mm_registry.create_processor(
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model_config, tokenizer=tokenizer)
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assert isinstance(mm_processor, EncDecMultiModalProcessor)
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if mm_processor.pad_dummy_encoder_prompt:
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return # Skip encoder length check for Whisper
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prompt_ids = prompt_inputs["prompt_token_ids"]
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if not prompt_ids:
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raise ValueError(f"The {prompt_type} prompt cannot be empty")
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max_input_id = max(prompt_ids)
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if max_input_id > tokenizer.max_token_id:
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raise ValueError(f"Token id {max_input_id} is out of vocabulary")
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max_prompt_len = self.model_config.max_model_len
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if len(prompt_ids) >= max_prompt_len:
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if self.model_config.is_multimodal_model:
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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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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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raise ValueError(
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f"The {prompt_type} prompt (length {len(prompt_ids)}) 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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# TODO: Find out how many placeholder tokens are there so we can
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# check that chunked prefill does not truncate them
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# max_batch_len = self.scheduler_config.max_num_batched_tokens
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