307 lines
12 KiB
Python
307 lines
12 KiB
Python
import time
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from collections.abc import Mapping
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from typing import Any
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import numpy as np
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import torch
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from vllm.config import VllmConfig
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from vllm.inputs import ProcessorInputs, PromptType
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from vllm.inputs.parse import split_enc_dec_inputs
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from vllm.logger import init_logger
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from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
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from vllm.multimodal.inputs import MultiModalFeatureSpec
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try:
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from vllm.multimodal.processing import set_request_id
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except ImportError: # vllm without set_request_id (older releases)
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from contextlib import contextmanager
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@contextmanager
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def set_request_id(_request_id: str):
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yield
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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 SamplingParams
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from vllm.tasks import SupportedTask
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from vllm.utils import length_from_prompt_token_ids_or_embeds
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from vllm.utils.torch_utils import set_default_torch_num_threads
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from vllm.v1.engine.input_processor import InputProcessor
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from vllm_omni.engine import (
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AdditionalInformationEntry,
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AdditionalInformationPayload,
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OmniEngineCoreRequest,
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PromptEmbedsPayload,
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)
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from vllm_omni.inputs.preprocess import OmniInputPreprocessor
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from vllm_omni.lora.request import LoRARequest
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logger = init_logger(__name__)
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_OMNI_EXTRA_KEYS = (
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"additional_information",
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"prompt_embeds",
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"negative_prompt",
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"negative_prompt_embeds",
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)
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def reinject_omni_fields(
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results: list[ProcessorInputs],
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original_prompts: list[dict],
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) -> None:
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"""Re-inject omni-specific fields that the upstream renderer discards.
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The upstream renderer's ``process_for_engine`` creates new dicts that only
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copy standard vLLM fields (prompt_token_ids, multi_modal_data, …).
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Omni-specific fields such as ``additional_information`` and
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``prompt_embeds`` are silently dropped. This helper copies them back from
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the *original* parsed prompts into the renderer outputs so they survive
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into ``OmniInputProcessor.process_inputs()``.
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"""
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for result, orig in zip(results, original_prompts):
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if not isinstance(orig, dict):
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continue
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for key in _OMNI_EXTRA_KEYS:
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val = orig.get(key)
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if val is not None and key not in result:
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result[key] = val
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class OmniInputProcessor(InputProcessor):
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"""Processor for omni models, handling multimodal inputs and embeddings.
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Extends the base vLLM Processor with support for processing prompt
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embeddings and additional information payloads, enabling direct transfer
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of pre-computed embeddings between pipeline stages.
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Args:
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vllm_config: Global vLLM configuration
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mm_registry: Multi-modal registry for processing multimodal inputs
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"""
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@staticmethod
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def _dtype_to_name(dtype: torch.dtype) -> str:
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"""Convert torch dtype to string representation.
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Args:
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dtype: PyTorch dtype to convert
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Returns:
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String representation of the dtype (e.g., "float32", "int64")
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"""
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mapping = {
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torch.float32: "float32",
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torch.float: "float32",
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torch.float16: "float16",
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torch.half: "float16",
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torch.bfloat16: "bfloat16",
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torch.float64: "float64",
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torch.double: "float64",
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torch.int64: "int64",
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torch.long: "int64",
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torch.int32: "int32",
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torch.int: "int32",
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torch.int16: "int16",
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torch.short: "int16",
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torch.int8: "int8",
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torch.uint8: "uint8",
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torch.bool: "bool",
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}
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return mapping.get(dtype, str(dtype).replace("torch.", ""))
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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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):
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super().__init__(vllm_config, renderer=renderer, mm_registry=mm_registry)
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self.input_preprocessor = OmniInputPreprocessor(
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vllm_config=vllm_config,
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renderer=self.renderer,
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mm_registry=mm_registry,
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)
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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 | DictPrompt | TokPrompt,
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params: SamplingParams | PoolingParams,
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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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supported_tasks: tuple[SupportedTask, ...] | None = None,
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resumable: bool = False,
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) -> OmniEngineCoreRequest:
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"""Process input prompt into an engine core request.
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Converts a prompt (text, tokens, or multimodal) into an
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OmniEngineCoreRequest that can be processed by the engine.
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Handles prompt embeddings and additional information payloads
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for direct transfer between stages.
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Args:
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request_id: Unique identifier for this request
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prompt: Input prompt (text, token IDs, embeddings, or multimodal)
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params: Sampling or pooling parameters for generation
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arrival_time: Optional arrival timestamp (defaults to current time)
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lora_request: Optional LoRA adapter request
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tokenization_kwargs: Optional additional tokenization arguments
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trace_headers: Optional tracing headers for observability
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priority: Request priority (higher values processed first)
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data_parallel_rank: Optional data parallel rank for distributed
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inference
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Returns:
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Tuple of (prompt_string, OmniEngineCoreRequest) where:
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- prompt_string: The original prompt as a string, or None if
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using embeddings
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- OmniEngineCoreRequest: Processed request ready for the engine
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Raises:
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ValueError: If data_parallel_rank is out of range or prompt_embeds
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has incorrect shape
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"""
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self._validate_lora(lora_request)
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self._validate_params(params, supported_tasks)
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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(f"data_parallel_rank {data_parallel_rank} is out of range [0, {num_ranks}).")
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# Short-circuit for prompts already processed by the renderer
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# (they carry a "type" key). Raw prompts must still go through the
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# omni preprocessor which preserves additional_information, etc.
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if isinstance(prompt, dict) and "type" in prompt:
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if arrival_time is None:
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arrival_time = prompt.get("arrival_time", time.time())
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processed_inputs: ProcessorInputs = prompt # type: ignore[assignment]
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else:
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if arrival_time is None:
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arrival_time = time.time()
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with set_request_id(request_id), set_default_torch_num_threads():
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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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eos_token_id = self.renderer.get_eos_token_id()
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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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# Normalize decoder prompt access across TypedDict variants.
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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 = decoder_inputs.get("prompt_embeds")
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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(prompt_token_ids, prompt_embeds)
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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(self.generation_config_fields, eos_token_id)
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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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# 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(base_mm_hash, lora_request),
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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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# Compatibility: decode serialized prompt embeds if provided.
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if isinstance(prompt_embeds, PromptEmbedsPayload):
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prompt_embeds = self._decode_prompt_embeds(prompt_embeds)
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additional_information_payload: AdditionalInformationPayload | None = None
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raw_info: dict[str, Any] | AdditionalInformationPayload | None = decoder_inputs.get("additional_information")
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if isinstance(raw_info, AdditionalInformationPayload):
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additional_information_payload = raw_info
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elif raw_info is not None:
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entries: dict[str, AdditionalInformationEntry] = {}
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for key, value in raw_info.items():
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if isinstance(value, torch.Tensor):
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v_cpu = value.detach().to("cpu").contiguous()
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dtype_str = self._dtype_to_name(v_cpu.dtype)
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data_bytes = v_cpu.numpy().tobytes()
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entry = AdditionalInformationEntry(
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tensor_data=data_bytes,
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tensor_shape=[int(x) for x in list(v_cpu.shape)],
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tensor_dtype=dtype_str,
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)
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elif isinstance(value, list):
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entry = AdditionalInformationEntry(list_data=value)
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else:
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raise ValueError("additional_information values must be Tensor or list")
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entries[key] = entry
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additional_information_payload = AdditionalInformationPayload(entries=entries)
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return OmniEngineCoreRequest(
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request_id=request_id,
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prompt_token_ids=prompt_token_ids,
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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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prompt_embeds=prompt_embeds,
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additional_information=additional_information_payload,
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resumable=resumable,
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)
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@staticmethod
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def _decode_prompt_embeds(payload: PromptEmbedsPayload) -> torch.Tensor:
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dtype = getattr(np, payload.dtype)
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arr = np.frombuffer(payload.data, dtype=dtype)
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arr = arr.reshape(payload.shape)
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return torch.from_numpy(arr)
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