anodyine/vllm-omni
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[CI] fix ci and format existing code (#71)

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Signed-off-by: zjy0516 <riverclouds.zhu@qq.com>
This commit is contained in:
Jiangyun Zhu authored and GitHub committed 2025-11-18 20:54:26 +08:00
1 parent 09fab2ecc9
commit 5707fc78d5
52 files changed
+813 -2155
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vllm_omni/engine/output_processor.py
+22 -63
View File
@@ -1,7 +1,6 @@
from typing import Any, Callable, Dict, Optional, Union
from typing import Any, Callable, Optional, Union
import torch
from vllm.logger import init_logger
from vllm.outputs import PoolingRequestOutput
from vllm.sampling_params import RequestOutputKind
@@ -10,20 +9,16 @@ from vllm.v1.engine import EngineCoreOutput, EngineCoreRequest, FinishReason
from vllm.v1.engine.detokenizer import IncrementalDetokenizer
from vllm.v1.engine.logprobs import LogprobsProcessor
from vllm.v1.engine.output_processor import OutputProcessor as VLLMOutputProcessor
from vllm.v1.engine.output_processor import (
OutputProcessorOutput,
RequestOutputCollector,
RequestState,
)
from vllm.v1.engine.output_processor import OutputProcessorOutput, RequestOutputCollector, RequestState
from vllm.v1.engine.parallel_sampling import ParentRequest
from vllm.v1.metrics.stats import IterationStats
from vllm_omni.outputs import OmniRequestOutput
logger = init_logger(__name__)
class OmniRequestState(RequestState):
def __init__(
self,
*args,
@@ -44,7 +39,6 @@ class OmniRequestState(RequestState):
queue: Optional[Any],
log_stats: bool,
) -> "OmniRequestState":
if sampling_params := request.sampling_params:
if not sampling_params.detokenize:
tokenizer = None
@@ -75,9 +69,7 @@ class OmniRequestState(RequestState):
request_id=request.request_id,
parent_req=parent_req,
request_index=request_index,
lora_name=(
request.lora_request.name if request.lora_request is not None else None
),
lora_name=(request.lora_request.name if request.lora_request is not None else None),
output_kind=output_kind,
prompt=prompt,
prompt_token_ids=request.prompt_token_ids,
@@ -93,9 +85,7 @@ class OmniRequestState(RequestState):
log_stats=log_stats,
)
def add_multimodal_tensor(
self, tensor: Optional[torch.Tensor], mm_type: Optional[str]
) -> None:
def add_multimodal_tensor(self, tensor: Optional[torch.Tensor], mm_type: Optional[str]) -> None:
if tensor is None:
return
try:
@@ -137,15 +127,11 @@ class OmniRequestState(RequestState):
if self.parent_req is None:
outputs = [output]
else:
request_id, outputs, finished = self.parent_req.get_outputs(
request_id, output
)
request_id, outputs, finished = self.parent_req.get_outputs(request_id, output)
if not outputs:
return None
return self._new_request_output(
request_id, outputs, finished, kv_transfer_params
)
return self._new_request_output(request_id, outputs, finished, kv_transfer_params)
def _new_completion_output(
self,
@@ -154,9 +140,7 @@ class OmniRequestState(RequestState):
stop_reason: Optional[Union[int, str]],
) -> Any:
# Reuse base text/logprobs logic, then annotate with pooling_result.
base_output = super()._new_completion_output(
token_ids, finish_reason, stop_reason
)
base_output = super()._new_completion_output(token_ids, finish_reason, stop_reason)
try:
if self.mm_accumulated is not None:
tensor = self.mm_accumulated
@@ -196,14 +180,12 @@ class MultimodalOutputProcessor(VLLMOutputProcessor):
engine_core_output_type: Optional[str] = None,
):
super().__init__(tokenizer=tokenizer, log_stats=log_stats)
self.output_handlers: Dict[str, Callable[[EngineCoreOutput], None]] = {}
self._reqid_to_mm_type: Dict[str, str] = {}
self.output_handlers: dict[str, Callable[[EngineCoreOutput], None]] = {}
self._reqid_to_mm_type: dict[str, str] = {}
self.request_states: dict[str, OmniRequestState] = {}
self.engine_core_output_type = engine_core_output_type
def register_handler(
self, modality: str, handler: Callable[[EngineCoreOutput], None]
) -> None:
def register_handler(self, modality: str, handler: Callable[[EngineCoreOutput], None]) -> None:
self.output_handlers[modality.lower()] = handler
def add_request(
@@ -255,9 +237,7 @@ class MultimodalOutputProcessor(VLLMOutputProcessor):
continue
# 1) Stats
self._update_stats_from_output(
req_state, eco, engine_core_timestamp, iteration_stats
)
self._update_stats_from_output(req_state, eco, engine_core_timestamp, iteration_stats)
new_token_ids = eco.new_token_ids
pooling_output = eco.pooling_output
@@ -270,9 +250,7 @@ class MultimodalOutputProcessor(VLLMOutputProcessor):
# 2) Detokenize and logprobs when text path
assert req_state.detokenizer is not None
assert req_state.logprobs_processor is not None
stop_string = req_state.detokenizer.update(
new_token_ids, finish_reason == FinishReason.STOP
)
stop_string = req_state.detokenizer.update(new_token_ids, finish_reason == FinishReason.STOP)
if stop_string:
finish_reason = FinishReason.STOP
stop_reason = stop_string
@@ -280,12 +258,8 @@ class MultimodalOutputProcessor(VLLMOutputProcessor):
# 2.5) Accumulate multimodal tensors in RequestState
try:
mm_type = (
getattr(eco, "output_type", self.engine_core_output_type) or ""
).lower()
if pooling_output is not None and isinstance(
req_state, OmniRequestState
):
mm_type = (getattr(eco, "output_type", self.engine_core_output_type) or "").lower()
if pooling_output is not None and isinstance(req_state, OmniRequestState):
req_state.add_multimodal_tensor(pooling_output, mm_type)
except Exception:
logger.debug(
@@ -310,18 +284,13 @@ class MultimodalOutputProcessor(VLLMOutputProcessor):
if ro:
# Attach accumulated multimodal payload if any
try:
if (
isinstance(req_state, OmniRequestState)
and req_state.mm_accumulated is not None
):
if isinstance(req_state, OmniRequestState) and req_state.mm_accumulated is not None:
mm_key = req_state.mm_type or "latents"
if not hasattr(ro, "multimodal_output"):
setattr(ro, "multimodal_output", {})
ro.multimodal_output[mm_key] = req_state.mm_accumulated
except Exception:
logger.exception(
"Error attaching multimodal payload in process_outputs"
)
logger.exception("Error attaching multimodal payload in process_outputs")
if req_state.queue is not None:
req_state.queue.put(ro)
else:
@@ -335,9 +304,7 @@ class MultimodalOutputProcessor(VLLMOutputProcessor):
self.parent_requests.pop(parent_req.request_id, None)
if not eco.finished:
reqs_to_abort.append(req_id)
self._update_stats_from_finished(
req_state, finish_reason, iteration_stats
)
self._update_stats_from_finished(req_state, finish_reason, iteration_stats)
if self.tracer:
self.do_tracing(eco, req_state, iteration_stats)
# Cleanup per-request mm state
@@ -352,9 +319,7 @@ class MultimodalOutputProcessor(VLLMOutputProcessor):
# ---- routing helpers ----
def _route_and_normalize(self, eco: EngineCoreOutput) -> None:
output_type = (
getattr(eco, "output_type", self.engine_core_output_type) or ""
).lower()
output_type = (getattr(eco, "output_type", self.engine_core_output_type) or "").lower()
# Custom handler first (if registered)
if output_type in self.output_handlers:
@@ -385,9 +350,7 @@ class MultimodalOutputProcessor(VLLMOutputProcessor):
def _process_image_output(self, eco: EngineCoreOutput) -> None:
"""Ensure image tensors are surfaced via pooling_output for vLLM."""
if eco.pooling_output is None:
tensor = self._extract_from_multimodal_outputs(
eco, keys=("image", "images", "pixel_values", "pixels")
)
tensor = self._extract_from_multimodal_outputs(eco, keys=("image", "images", "pixel_values", "pixels"))
if tensor is not None:
eco.pooling_output = tensor
@@ -414,9 +377,7 @@ class MultimodalOutputProcessor(VLLMOutputProcessor):
def _process_latents_output(self, eco: EngineCoreOutput) -> None:
"""Ensure latent tensors are surfaced via pooling_output."""
if eco.pooling_output is None:
tensor = self._extract_from_multimodal_outputs(
eco, keys=("latent", "latents", "z", "posterior")
)
tensor = self._extract_from_multimodal_outputs(eco, keys=("latent", "latents", "z", "posterior"))
if tensor is not None:
eco.pooling_output = tensor
@@ -444,9 +405,7 @@ class MultimodalOutputProcessor(VLLMOutputProcessor):
except Exception:
pass
def _extract_from_multimodal_outputs(
self, eco: EngineCoreOutput, keys: tuple[str, ...]
) -> Optional[torch.Tensor]:
def _extract_from_multimodal_outputs(self, eco: EngineCoreOutput, keys: tuple[str, ...]) -> Optional[torch.Tensor]:
mm = getattr(eco, "multimodal_outputs", None)
if not isinstance(mm, dict):
return None