anodyine/vllm-omni
1
0 Fork 0
Code Issues 4 Pull Requests 2 Actions Packages Projects Releases Wiki Activity

Dev/rebase v0170 (#1639)

Browse Source 
Signed-off-by: Taichang Zhou <tzhouam@connect.ust.hk>
Signed-off-by: tzhouam <tzhouam@connect.ust.hk>
Signed-off-by: princepride <wangzhipeng628@gmail.com>
Signed-off-by: Zhou Taichang <tzhouam@connect.ust.hk>
Co-authored-by: 汪志鹏 <wangzhipeng628@gmail.com>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
This commit is contained in:
Zhou Taichang汪志鹏Hongsheng LiuCyrus Leung
 authored and GitHub committed 2026-03-09 17:29:38 +08:00
1 parent 4db10d6dec
commit 89b527e533
38 files changed
+828 -365
Download Patch File Download Diff File Expand all files Collapse all files

No files matched your search

vllm_omni/engine/input_processor.py
+67 -68
View File
@@ -1,6 +1,6 @@
import time
from collections.abc import Mapping
from typing import Any, cast
from typing import Any
import numpy as np
import torch
@@ -9,26 +9,14 @@ from vllm.inputs import ProcessorInputs, PromptType
from vllm.inputs.parse import split_enc_dec_inputs
from vllm.logger import init_logger
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
from vllm.multimodal.inputs import MultiModalFeatureSpec, MultiModalUUIDDict
try:
from vllm.multimodal.processing import set_request_id
except ImportError: # vllm without set_request_id (older releases)
from contextlib import contextmanager
@contextmanager
def set_request_id(_request_id: str):
yield
from vllm.multimodal.inputs import MultiModalFeatureSpec
from vllm.multimodal.utils import argsort_mm_positions
from vllm.platforms import current_platform
from vllm.pooling_params import PoolingParams
from vllm.renderers.inputs import DictPrompt, TokPrompt
from vllm.renderers import BaseRenderer
from vllm.sampling_params import SamplingParams
from vllm.tasks import SupportedTask
from vllm.utils import length_from_prompt_token_ids_or_embeds
from vllm.utils.torch_utils import set_default_torch_num_threads
from vllm.utils.jsontree import json_iter_leaves
from vllm.v1.engine.input_processor import InputProcessor
from vllm_omni.engine import (
@@ -42,6 +30,35 @@ from vllm_omni.lora.request import LoRARequest
logger = init_logger(__name__)
_OMNI_EXTRA_KEYS = (
"additional_information",
"prompt_embeds",
"negative_prompt",
"negative_prompt_embeds",
)
def reinject_omni_fields(
results: list[ProcessorInputs],
original_prompts: list[dict],
) -> None:
"""Re-inject omni-specific fields that the upstream renderer discards.
The upstream renderer's ``process_for_engine`` creates new dicts that only
copy standard vLLM fields (prompt_token_ids, multi_modal_data, …).
Omni-specific fields such as ``additional_information`` and
``prompt_embeds`` are silently dropped. This helper copies them back from
the *original* parsed prompts into the renderer outputs so they survive
into ``OmniInputProcessor.process_inputs()``.
"""
for result, orig in zip(results, original_prompts):
if not isinstance(orig, dict):
continue
for key in _OMNI_EXTRA_KEYS:
val = orig.get(key)
if val is not None and key not in result:
result[key] = val
class OmniInputProcessor(InputProcessor):
"""Processor for omni models, handling multimodal inputs and embeddings.
@@ -88,28 +105,29 @@ class OmniInputProcessor(InputProcessor):
def __init__(
self,
vllm_config: VllmConfig,
renderer: BaseRenderer | None = None,
*,
mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
):
super().__init__(vllm_config, mm_registry)
super().__init__(vllm_config, renderer=renderer, mm_registry=mm_registry)
self.input_preprocessor = OmniInputPreprocessor(
self.model_config,
vllm_config.observability_config,
mm_registry,
mm_processor_cache=self.mm_processor_cache,
vllm_config=vllm_config,
renderer=self.renderer,
mm_registry=mm_registry,
)
def process_inputs(
self,
request_id: str,
prompt: PromptType | DictPrompt | TokPrompt,
prompt: PromptType | ProcessorInputs,
params: SamplingParams | PoolingParams,
supported_tasks: tuple[SupportedTask, ...] = ("generate",),
arrival_time: float | None = None,
lora_request: LoRARequest | None = None,
tokenization_kwargs: dict[str, Any] | None = None,
trace_headers: Mapping[str, str] | None = None,
priority: int = 0,
data_parallel_rank: int | None = None,
supported_tasks: tuple[SupportedTask, ...] | None = None,
resumable: bool = False,
) -> OmniEngineCoreRequest:
"""Process input prompt into an engine core request.
@@ -123,6 +141,7 @@ class OmniInputProcessor(InputProcessor):
request_id: Unique identifier for this request
prompt: Input prompt (text, token IDs, embeddings, or multimodal)
params: Sampling or pooling parameters for generation
supported_tasks: Tuple of supported tasks for validation
arrival_time: Optional arrival timestamp (defaults to current time)
lora_request: Optional LoRA adapter request
tokenization_kwargs: Optional additional tokenization arguments
@@ -130,19 +149,17 @@ class OmniInputProcessor(InputProcessor):
priority: Request priority (higher values processed first)
data_parallel_rank: Optional data parallel rank for distributed
inference
resumable: Whether the request supports streaming input
Returns:
Tuple of (prompt_string, OmniEngineCoreRequest) where:
- prompt_string: The original prompt as a string, or None if
using embeddings
- OmniEngineCoreRequest: Processed request ready for the engine
OmniEngineCoreRequest ready for the engine
Raises:
ValueError: If data_parallel_rank is out of range or prompt_embeds
has incorrect shape
"""
self._validate_lora(lora_request)
self._validate_params(params, supported_tasks)
self._validate_lora(lora_request)
parallel_config = self.vllm_config.parallel_config
dp_size = parallel_config.data_parallel_size
@@ -151,50 +168,23 @@ class OmniInputProcessor(InputProcessor):
if data_parallel_rank is not None and not (0 <= data_parallel_rank < num_ranks):
raise ValueError(f"data_parallel_rank {data_parallel_rank} is out of range [0, {num_ranks}).")
if arrival_time is None:
arrival_time = time.time()
# Optionally generate multimodal hash overrides to avoid hashing
# multimodal data items by their content as their identifiers.
# NOTE: when users explicitly turn off BOTH prefix caching and input
# processing caching, no multimodal features or embeddings will be
# reused across requests, therefore identifying multimodal data items
# by their content is no longer necessary, and we create uuids with
# request id-modality-index as multimodal hash overrides.
if (
self.model_config.multimodal_config
and self.model_config.multimodal_config.mm_processor_cache_gb == 0
and not self.cache_config.enable_prefix_caching
):
mm_uuids = self._maybe_build_mm_uuids(request_id, prompt)
# Short-circuit for prompts already processed by the renderer
# (they carry a "type" key). Raw prompts must still go through the
# omni preprocessor which preserves additional_information, etc.
if isinstance(prompt, dict) and "type" in prompt:
if arrival_time is None:
arrival_time = prompt.get("arrival_time", time.time())
processed_inputs: ProcessorInputs = prompt # type: ignore[assignment]
else:
# Otherwise, use user-provided uuids as multimodal hash overrides
# if provided.
self._validate_mm_uuids(prompt)
if isinstance(prompt, dict):
mm_uuids = cast(MultiModalUUIDDict | None, prompt.get("multi_modal_uuids"))
else:
mm_uuids = None
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.
with set_request_id(request_id), set_default_torch_num_threads():
processed_inputs: ProcessorInputs = self.input_preprocessor.preprocess(
processed_inputs = self.input_preprocessor.preprocess(
prompt,
tokenization_kwargs=tokenization_kwargs,
mm_uuids=mm_uuids,
)
current_platform.validate_request(
prompt=prompt,
params=params,
processed_inputs=processed_inputs,
)
eos_token_id = self.input_preprocessor.get_eos_token_id()
self._platform_validate_request(processed_inputs, params)
encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)
self._validate_model_inputs(encoder_inputs, decoder_inputs)
@@ -216,7 +206,10 @@ class OmniInputProcessor(InputProcessor):
if sampling_params.max_tokens is None:
seq_len = length_from_prompt_token_ids_or_embeds(prompt_token_ids, prompt_embeds)
sampling_params.max_tokens = self.model_config.max_model_len - seq_len
sampling_params.update_from_generation_config(self.generation_config_fields, eos_token_id)
sampling_params.update_from_generation_config(
self.generation_config_fields,
self.renderer.get_eos_token_id(),
)
if self.tokenizer is not None:
sampling_params.update_from_tokenizer(self.tokenizer)
else:
@@ -230,6 +223,13 @@ class OmniInputProcessor(InputProcessor):
decoder_mm_positions = decoder_inputs["mm_placeholders"]
decoder_mm_hashes = decoder_inputs["mm_hashes"]
if not all(isinstance(leaf, str) for leaf in json_iter_leaves(decoder_mm_hashes)):
raise ValueError(
f"mm_hashes must contain only strings, got: {decoder_mm_hashes}. "
"This is likely due to an incorrect custom implementation of "
"MultiModalProcessor.apply method."
)
# Merge and flatten multimodal placeholders, hashes and inputs
# from dictionaries to lists, and sort them by each item's position
# in the input sequence.
@@ -281,7 +281,6 @@ class OmniInputProcessor(InputProcessor):
mm_features=mm_features,
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"),