tzhouam
4cd3b36fa0
- Removed redundant imports of `torch` and `numpy` in `OmniProcessor` class. - Improved comments in `parse_singleton_prompt_omni` function for clarity on token handling. - Ensured `current_platform` is imported only once for better code organization.
213 lines
9.8 KiB
Python
213 lines
9.8 KiB
Python
import time
|
|
from collections.abc import Mapping, Sequence
|
|
from typing import Any, Optional, Union
|
|
|
|
from vllm.inputs import ProcessorInputs, PromptType
|
|
from vllm.inputs.parse import split_enc_dec_inputs
|
|
from vllm.lora.request import LoRARequest
|
|
from vllm.multimodal import MultiModalKwargs
|
|
from vllm.multimodal.inputs import PlaceholderRange
|
|
from vllm.multimodal.utils import merge_and_sort_multimodal_metadata
|
|
from vllm.pooling_params import PoolingParams
|
|
from vllm.sampling_params import SamplingParams
|
|
from vllm.config import VllmConfig
|
|
from vllm.transformers_utils.tokenizer_group import TokenizerGroup
|
|
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
|
|
from vllm_omni.inputs.preprocess import OmniInputPreprocessor
|
|
from vllm.platforms import current_platform
|
|
|
|
from vllm.v1.engine.processor import Processor
|
|
from vllm_omni.engine import PromptEmbedsPayload, AdditionalInformationPayload, AdditionalInformationEntry, OmniEngineCoreRequest
|
|
import torch
|
|
|
|
class OmniProcessor(Processor):
|
|
def __init__(self,
|
|
vllm_config: VllmConfig,
|
|
tokenizer: TokenizerGroup,
|
|
mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
|
|
):
|
|
super().__init__(vllm_config, tokenizer, mm_registry)
|
|
self.input_preprocessor = OmniInputPreprocessor(self.model_config,
|
|
self.tokenizer,
|
|
mm_registry)
|
|
|
|
def process_inputs(
|
|
self,
|
|
request_id: str,
|
|
prompt: PromptType,
|
|
params: Union[SamplingParams, PoolingParams],
|
|
arrival_time: Optional[float] = None,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
tokenization_kwargs: Optional[dict[str, Any]] = None,
|
|
trace_headers: Optional[Mapping[str, str]] = None,
|
|
priority: int = 0,
|
|
data_parallel_rank: Optional[int] = None,
|
|
) -> tuple[Optional[str], OmniEngineCoreRequest]:
|
|
|
|
# TODO(woosuk): Support pooling models.
|
|
# TODO(woosuk): Support encoder-decoder models.
|
|
self._validate_lora(lora_request)
|
|
self._validate_params(params, lora_request)
|
|
if trace_headers is not None:
|
|
raise ValueError("V1 does not support tracing yet.")
|
|
|
|
data_parallel_size = self.vllm_config.parallel_config.data_parallel_size
|
|
if data_parallel_rank is not None and not (0 <= data_parallel_rank <
|
|
data_parallel_size):
|
|
raise ValueError(f"data_parallel_rank {data_parallel_rank} "
|
|
f"is out of range [0, {data_parallel_size}).")
|
|
|
|
if arrival_time is None:
|
|
arrival_time = time.time()
|
|
|
|
# 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.
|
|
return_mm_hashes = (self.model_config.processor_return_mm_hashes
|
|
or bool(self.cache_config.enable_prefix_caching))
|
|
processed_inputs: ProcessorInputs = self.input_preprocessor.preprocess(
|
|
prompt,
|
|
tokenization_kwargs=tokenization_kwargs,
|
|
lora_request=lora_request,
|
|
return_mm_hashes=return_mm_hashes,
|
|
)
|
|
current_platform.validate_request(
|
|
prompt=prompt,
|
|
params=params,
|
|
processed_inputs=processed_inputs,
|
|
)
|
|
eos_token_id = self.input_preprocessor.get_eos_token_id(lora_request)
|
|
|
|
self._validate_model_inputs(processed_inputs, lora_request)
|
|
|
|
encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)
|
|
|
|
# TODO: Impl encoder-decoder
|
|
if encoder_inputs is not None:
|
|
raise NotImplementedError
|
|
|
|
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:
|
|
sampling_params.max_tokens = (
|
|
self.model_config.max_model_len -
|
|
len(decoder_inputs["prompt_token_ids"]))
|
|
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.get_lora_tokenizer(lora_request))
|
|
else:
|
|
pooling_params = params.clone()
|
|
|
|
# Multimodal related.
|
|
sorted_mm_inputs: Optional[Sequence[Optional[MultiModalKwargs]]] = None
|
|
sorted_mm_positions: Optional[list[PlaceholderRange]] = None
|
|
sorted_mm_hashes: Optional[list[str]] = None
|
|
if decoder_inputs["type"] == "multimodal":
|
|
decoder_mm_inputs = decoder_inputs["mm_kwargs"]
|
|
|
|
# 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_item_modalities,
|
|
sorted_mm_positions,
|
|
sorted_mm_hashes,
|
|
) = merge_and_sort_multimodal_metadata(
|
|
decoder_inputs["mm_placeholders"],
|
|
decoder_inputs["mm_hashes"] if return_mm_hashes else None,
|
|
)
|
|
|
|
# The output of merged multi-modal processor (`decoder_mm_inputs`)
|
|
# is a single MultiModalKwargs for all items from all modalities.
|
|
# This code flattens kwargs for individual items in a list and
|
|
# sorts them by each item's position in the input sequence if there
|
|
# are multiple modalities.
|
|
unique_modalities = set(sorted_item_modalities)
|
|
if len(unique_modalities) > 1:
|
|
orig_sorted_mm_inputs = []
|
|
used_indices = {modality: 0 for modality in unique_modalities}
|
|
|
|
for modality in sorted_item_modalities:
|
|
items = decoder_mm_inputs.get_items(modality)
|
|
item = items[used_indices[modality]]
|
|
|
|
orig_sorted_mm_inputs.append(
|
|
MultiModalKwargs.from_items([item]))
|
|
used_indices[modality] += 1
|
|
else:
|
|
orig_sorted_mm_inputs = [
|
|
MultiModalKwargs.from_items([item]) for item in
|
|
decoder_mm_inputs.get_items(sorted_item_modalities[0])
|
|
]
|
|
|
|
if sorted_mm_hashes is not None:
|
|
sorted_mm_inputs = self.mm_input_cache_client.get_and_update(
|
|
orig_sorted_mm_inputs, sorted_mm_hashes)
|
|
else:
|
|
sorted_mm_inputs = orig_sorted_mm_inputs
|
|
|
|
# Serialize prompt_embeds and additional_information if provided (direct-transfer path)
|
|
prompt_embeds_payload: Optional[PromptEmbedsPayload] = None
|
|
additional_information_payload: Optional[AdditionalInformationPayload] = None
|
|
if "prompt_embeds" in decoder_inputs: # type: ignore[operator]
|
|
|
|
pe: torch.Tensor = decoder_inputs["prompt_embeds"] # type: ignore[index]
|
|
if pe.ndim != 2:
|
|
raise ValueError(
|
|
"prompt_embeds must be of shape (seq_len, hidden_size)")
|
|
# Move to CPU and ensure contiguous memory for stable serialization
|
|
pe_cpu = pe.detach().to("cpu").contiguous()
|
|
seq_len, hidden_size = pe_cpu.shape
|
|
dtype_str = str(pe_cpu.dtype).replace("torch.", "")
|
|
data_bytes = pe_cpu.numpy().tobytes()
|
|
prompt_embeds_payload = PromptEmbedsPayload(
|
|
data=data_bytes,
|
|
shape=[int(seq_len), int(hidden_size)],
|
|
dtype=dtype_str,
|
|
)
|
|
if "additional_information" in decoder_inputs: # type: ignore[operator]
|
|
raw_info: dict[str, Any] = decoder_inputs["additional_information"] # type: ignore[index]
|
|
entries: dict[str, AdditionalInformationEntry] = {}
|
|
for key, value in raw_info.items():
|
|
if isinstance(value, torch.Tensor):
|
|
v_cpu = value.detach().to("cpu").contiguous()
|
|
dtype_str = str(v_cpu.dtype).replace("torch.", "")
|
|
data_bytes = v_cpu.numpy().tobytes()
|
|
entry = AdditionalInformationEntry(
|
|
tensor_data=data_bytes,
|
|
tensor_shape=[int(x) for x in list(v_cpu.shape)],
|
|
tensor_dtype=dtype_str,
|
|
)
|
|
elif isinstance(value, list):
|
|
entry = AdditionalInformationEntry(list_data=value)
|
|
else:
|
|
raise ValueError(
|
|
"additional_information values must be Tensor or list")
|
|
entries[key] = entry
|
|
additional_information_payload = AdditionalInformationPayload(
|
|
entries=entries)
|
|
|
|
return decoder_inputs.get("prompt"), OmniEngineCoreRequest(
|
|
request_id=request_id,
|
|
prompt_token_ids=decoder_inputs["prompt_token_ids"],
|
|
mm_inputs=sorted_mm_inputs,
|
|
mm_hashes=sorted_mm_hashes,
|
|
mm_placeholders=sorted_mm_positions,
|
|
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,
|
|
prompt_embeds=prompt_embeds_payload,
|
|
additional_information=additional_information_payload,
|
|
) |