197 lines
7.8 KiB
Python
197 lines
7.8 KiB
Python
import time
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from typing import Any, Dict, Mapping, Optional, Tuple, Union
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from vllm.config import CacheConfig, LoRAConfig, ModelConfig
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from vllm.inputs import (INPUT_REGISTRY, InputRegistry, ProcessorInputs,
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PromptType, SingletonInputsAdapter)
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from vllm.inputs.parse import is_encoder_decoder_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.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.config import try_get_generation_config
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from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup
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from vllm.v1.engine import DetokenizerRequest, EngineCoreRequest
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from vllm.v1.engine.mm_input_mapper import MMHasher, MMInputMapperClient
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class Processor:
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def __init__(
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self,
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model_config: ModelConfig,
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cache_config: CacheConfig,
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lora_config: Optional[LoRAConfig],
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tokenizer: BaseTokenizerGroup,
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input_registry: InputRegistry = INPUT_REGISTRY,
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mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
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):
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self.model_config = model_config
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self.lora_config = lora_config
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self.tokenizer = tokenizer
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self.generation_config_fields = _load_generation_config_dict(
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model_config)
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self.input_preprocessor = InputPreprocessor(model_config,
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self.tokenizer,
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mm_registry)
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self.input_processor = input_registry.create_input_processor(
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model_config)
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# Multi-modal (huggingface) input mapper
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self.mm_input_mapper_client = MMInputMapperClient(model_config)
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# Multi-modal hasher (for images)
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self.use_hash = (not model_config.disable_mm_preprocessor_cache) or \
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cache_config.enable_prefix_caching
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self.mm_hasher = MMHasher()
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# TODO: run in an ThreadpoolExecutor or BackgroundProcess.
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# This ideally should releases the GIL, so we should not block the
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# asyncio loop while this is running.
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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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) -> Tuple[DetokenizerRequest, EngineCoreRequest]:
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# TODO(woosuk): Support pooling models.
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# TODO(woosuk): Check max_logprobs
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# TODO(woosuk): Support encoder-decoder models.
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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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if arrival_time is None:
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arrival_time = time.time()
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assert priority == 0, "vLLM V1 does not support priority at the moment."
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assert trace_headers is None, "vLLM V1 does not support tracing yet."
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# Compute MM hashes (if enabled)
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mm_hashes = None
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if self.use_hash:
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mm_hashes = self.mm_hasher.hash_prompt(prompt)
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# Process inputs.
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preprocessed_inputs = self.input_preprocessor.preprocess(
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prompt,
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request_id=request_id,
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lora_request=lora_request,
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prompt_adapter_request=prompt_adapter_request,
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)
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processed_inputs = self.input_processor(preprocessed_inputs)
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self._validate_model_inputs(processed_inputs)
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eos_token_id = self.input_preprocessor.get_eos_token_id(lora_request)
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if is_encoder_decoder_inputs(processed_inputs):
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decoder_inputs = SingletonInputsAdapter(
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processed_inputs["decoder"])
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encoder_inputs = SingletonInputsAdapter(
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processed_inputs["encoder"])
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else:
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decoder_inputs = SingletonInputsAdapter(processed_inputs)
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encoder_inputs = None
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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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sampling_params.update_from_generation_config(
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self.generation_config_fields, eos_token_id)
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# For merged preprocessor, mm_data is already mm_inputs
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precomputed_mm_inputs = None
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if isinstance(decoder_inputs.multi_modal_data, MultiModalKwargs):
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precomputed_mm_inputs = [decoder_inputs.multi_modal_data]
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# Apply MM mapper
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mm_inputs = None
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if len(decoder_inputs.multi_modal_data) > 0:
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mm_inputs = self.mm_input_mapper_client.process_inputs(
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decoder_inputs.multi_modal_data, mm_hashes,
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decoder_inputs.mm_processor_kwargs, precomputed_mm_inputs)
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# Make Request for Detokenizer.
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detokenizer_request = DetokenizerRequest(
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request_id,
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decoder_inputs.prompt,
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decoder_inputs.prompt_token_ids,
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sampling_params.skip_special_tokens,
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sampling_params.spaces_between_special_tokens,
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sampling_params.output_kind,
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sampling_params.stop,
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sampling_params.include_stop_str_in_output,
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)
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# Make Request for EngineCore.
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engine_core_request = EngineCoreRequest(
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request_id,
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decoder_inputs.prompt,
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decoder_inputs.prompt_token_ids,
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mm_inputs,
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mm_hashes,
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decoder_inputs.multi_modal_placeholders,
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sampling_params,
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eos_token_id,
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arrival_time,
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lora_request,
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)
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return detokenizer_request, engine_core_request
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def _validate_model_inputs(self, inputs: ProcessorInputs):
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if is_encoder_decoder_inputs(inputs):
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# For encoder-decoder multimodal models, the max_prompt_len
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# restricts the decoder prompt length
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prompt_inputs = inputs["decoder" if self.model_config.
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is_multimodal_model else "encoder"]
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else:
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prompt_inputs = inputs
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prompt_ids = SingletonInputsAdapter(prompt_inputs).prompt_token_ids
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if prompt_ids is None or len(prompt_ids) == 0:
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raise ValueError("Prompt cannot be empty")
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if self.model_config.is_multimodal_model:
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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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raise ValueError(
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f"The prompt (total length {len(prompt_ids)}) is too long "
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f"to fit into the model (context length {max_prompt_len}). "
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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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# 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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def _load_generation_config_dict(model_config: ModelConfig) -> Dict[str, Any]:
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config = try_get_generation_config(
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model_config.model,
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trust_remote_code=model_config.trust_remote_code,
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revision=model_config.revision,
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)
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if config is None:
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return {}
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return config.to_diff_dict()
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