252 lines
10 KiB
Python
252 lines
10 KiB
Python
import time
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from collections.abc import Mapping, Sequence
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from typing import Any, Optional, Union
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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.lora.request import LoRARequest
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from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs, MultiModalRegistry
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from vllm.multimodal.inputs import PlaceholderRange
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from vllm.multimodal.utils import merge_and_sort_multimodal_metadata
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from vllm.platforms import current_platform
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from vllm.pooling_params import PoolingParams
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from vllm.sampling_params import SamplingParams
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from vllm.transformers_utils.tokenizer_group import TokenizerGroup
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from vllm.v1.engine.processor import Processor
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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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class OmniProcessor(Processor):
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@staticmethod
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def _dtype_to_name(dtype: torch.dtype) -> str:
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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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tokenizer: TokenizerGroup,
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mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
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):
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super().__init__(vllm_config, tokenizer, mm_registry)
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self.input_preprocessor = OmniInputPreprocessor(
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self.model_config, self.tokenizer, 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,
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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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tokenization_kwargs: Optional[dict[str, Any]] = None,
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trace_headers: Optional[Mapping[str, str]] = None,
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priority: int = 0,
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data_parallel_rank: Optional[int] = None,
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) -> tuple[Optional[str], OmniEngineCoreRequest]:
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# TODO(woosuk): Support pooling models.
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# TODO(woosuk): Support encoder-decoder models.
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self._validate_lora(lora_request)
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self._validate_params(params, lora_request)
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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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data_parallel_size = self.vllm_config.parallel_config.data_parallel_size
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if data_parallel_rank is not None and not (
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0 <= data_parallel_rank < data_parallel_size
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):
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raise ValueError(
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f"data_parallel_rank {data_parallel_rank} "
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f"is out of range [0, {data_parallel_size})."
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)
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if arrival_time is None:
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arrival_time = time.time()
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# 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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return_mm_hashes = self.model_config.processor_return_mm_hashes or bool(
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self.cache_config.enable_prefix_caching
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)
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processed_inputs: ProcessorInputs = self.input_preprocessor.preprocess(
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prompt,
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tokenization_kwargs=tokenization_kwargs,
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lora_request=lora_request,
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return_mm_hashes=return_mm_hashes,
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)
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current_platform.validate_request(
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prompt=prompt,
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params=params,
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processed_inputs=processed_inputs,
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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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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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sampling_params.max_tokens = self.model_config.max_model_len - len(
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decoder_inputs["prompt_token_ids"]
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)
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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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)
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if self.tokenizer is not None:
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sampling_params.update_from_tokenizer(
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self.tokenizer.get_lora_tokenizer(lora_request)
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)
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else:
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pooling_params = params.clone()
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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 return_mm_hashes 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(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])
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for item in 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(
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orig_sorted_mm_inputs, sorted_mm_hashes
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)
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else:
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sorted_mm_inputs = orig_sorted_mm_inputs
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# Serialize prompt_embeds and additional_information if provided
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# (direct-transfer path)
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prompt_embeds_payload: Optional[PromptEmbedsPayload] = None
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additional_information_payload: Optional[AdditionalInformationPayload] = None
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if "prompt_embeds" in decoder_inputs: # type: ignore[operator]
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pe: torch.Tensor = decoder_inputs["prompt_embeds"] # type: ignore[index]
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if pe.ndim != 2:
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raise ValueError(
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"prompt_embeds must be of shape (seq_len, hidden_size)"
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)
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# Move to CPU and ensure contiguous memory for stable serialization
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pe_cpu = pe.detach().to("cpu").contiguous()
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seq_len, hidden_size = pe_cpu.shape
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dtype_str = self._dtype_to_name(pe_cpu.dtype)
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data_bytes = pe_cpu.numpy().tobytes()
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prompt_embeds_payload = PromptEmbedsPayload(
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data=data_bytes,
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shape=[int(seq_len), int(hidden_size)],
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dtype=dtype_str,
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)
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if "additional_information" in decoder_inputs: # type: ignore[operator]
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raw_info: dict[str, Any] = decoder_inputs["additional_information"] # type: ignore[index] # noqa: E501
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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(
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"additional_information values must be Tensor or list"
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)
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entries[key] = entry
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additional_information_payload = AdditionalInformationPayload(
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entries=entries
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)
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return decoder_inputs.get("prompt"), OmniEngineCoreRequest(
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request_id=request_id,
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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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pooling_params=pooling_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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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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prompt_embeds=prompt_embeds_payload,
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additional_information=additional_information_payload,
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)
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