Zhou Taichangand汪志鹏
70ef548448
Signed-off-by: Taichang Zhou <tzhouam@connect.ust.hk> Signed-off-by: tzhouam <tzhouam@connect.ust.hk> Signed-off-by: princepride <wangzhipeng628@gmail.com> Co-authored-by: 汪志鹏 <wangzhipeng628@gmail.com>
467 lines
19 KiB
Python
467 lines
19 KiB
Python
from ast import Dict
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from collections.abc import Callable
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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.logger import init_logger
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from vllm.outputs import PoolingRequestOutput
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from vllm.sampling_params import RequestOutputKind
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from vllm.tokenizers import TokenizerLike
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from vllm.v1.engine import EngineCoreOutput, EngineCoreRequest, FinishReason
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from vllm.v1.engine.output_processor import OutputProcessor as VLLMOutputProcessor
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from vllm.v1.engine.output_processor import (
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OutputProcessorOutput,
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RequestOutputCollector,
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RequestState,
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)
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from vllm.v1.engine.parallel_sampling import ParentRequest
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from vllm.v1.metrics.stats import IterationStats
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from vllm_omni.outputs import OmniRequestOutput
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logger = init_logger(__name__)
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class OmniRequestState(RequestState):
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"""Request state for omni models, tracking multimodal outputs.
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Extends the base RequestState with support for accumulating
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multimodal tensor outputs (e.g., images, audio, latents) that
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are produced incrementally during generation.
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"""
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def __init__(
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self,
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*args,
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**kwargs,
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):
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super().__init__(*args, **kwargs)
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self.mm_type: str | None = None
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self.mm_accumulated: Dict[str, Any] | None = None
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def add_multimodal_tensor(self, payload: Any | None, mm_type: str | None) -> None:
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if payload is None:
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return
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try:
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if mm_type:
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self.mm_type = (mm_type or "").lower()
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# Normalize incoming payload to dict on CPU
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def _to_cpu(x):
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if isinstance(x, torch.Tensor):
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try:
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return x.detach().to("cpu", non_blocking=True).contiguous()
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except Exception:
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return x
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return x
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if isinstance(payload, dict):
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incoming: Dict[str, Any] = {}
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target_key = self.mm_type or "hidden"
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# Iterate directly without unnecessary dict copy
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for k, v in payload.items():
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# Optional remap: if producer used "model_outputs" or "hidden", rename to mm_type
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if k == "model_outputs":
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k = target_key
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elif k == "hidden" and target_key != "hidden":
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k = target_key
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if isinstance(v, dict):
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incoming[k] = {str(sk): _to_cpu(sv) for sk, sv in v.items()}
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else:
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incoming[k] = _to_cpu(v)
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else:
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key = self.mm_type or "hidden"
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incoming = {key: _to_cpu(payload)}
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if self.mm_accumulated is None:
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self.mm_accumulated = incoming
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else:
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# Merge keys; accumulate tensors in lists for deferred concatenation
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for k, v in incoming.items():
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if k not in self.mm_accumulated:
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self.mm_accumulated[k] = v
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else:
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existing = self.mm_accumulated[k]
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if isinstance(v, torch.Tensor) and isinstance(existing, torch.Tensor):
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# Use list accumulation to avoid O(n²) repeated concatenation
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self.mm_accumulated[k] = [existing, v]
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elif isinstance(v, torch.Tensor) and isinstance(existing, list):
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# Append to existing list
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existing.append(v)
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elif isinstance(v, dict) and isinstance(existing, dict):
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# Merge nested dicts with list accumulation for tensors
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for sk, sv in v.items():
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if sk not in existing:
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existing[sk] = sv
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elif isinstance(sv, torch.Tensor) and isinstance(existing[sk], torch.Tensor):
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existing[sk] = [existing[sk], sv]
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elif isinstance(sv, torch.Tensor) and isinstance(existing[sk], list):
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existing[sk].append(sv)
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else:
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existing[sk] = sv
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else:
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self.mm_accumulated[k] = v
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except Exception:
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# Log and continue without crashing the output pipeline
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logger.exception("Error accumulating multimodal tensor")
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def _consolidate_multimodal_tensors(self) -> None:
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"""Consolidate accumulated tensor lists into single tensors via concatenation."""
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if self.mm_accumulated is None:
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return
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try:
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for k, v in self.mm_accumulated.items():
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if isinstance(v, list) and v and isinstance(v[0], torch.Tensor):
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try:
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if k == "audio":
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# When the audio tensor shape is inconsistent, torch.cat will fail.
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# We need to use torch.cat in -1 dimension.
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continue
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else:
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self.mm_accumulated[k] = torch.cat(v, dim=0)
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except Exception:
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# Keep last tensor on failure
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logger.warning(f"Error concatenating tensor for key {k}; keeping last tensor")
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self.mm_accumulated[k] = v[-1]
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elif isinstance(v, dict):
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for sk, sv in v.items():
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if isinstance(sv, list) and sv and isinstance(sv[0], torch.Tensor):
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try:
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v[sk] = torch.cat(sv, dim=0)
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except Exception:
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v[sk] = sv[-1]
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except Exception:
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logger.exception("Error consolidating multimodal tensors")
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# Override: do not route to pooling-only path; always create completion
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# outputs, and attach pooling_result into the CompletionOutput.
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def make_request_output(
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self,
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new_token_ids: list[int],
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pooling_output: torch.Tensor | None,
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finish_reason: FinishReason | None,
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stop_reason: int | str | None,
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kv_transfer_params: dict[str, Any] | None = None,
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routed_experts: np.ndarray | None = None,
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) -> OmniRequestOutput | PoolingRequestOutput | None:
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"""Create a request output from generation results.
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Creates a RequestOutput or PoolingRequestOutput from the generated
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tokens and accumulated multimodal outputs. Attaches multimodal
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tensors to the completion output if available.
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Args:
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new_token_ids: List of newly generated token IDs
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pooling_output: Optional pooling output tensor
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finish_reason: Optional finish reason indicating why generation stopped
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stop_reason: Optional stop reason (token ID or stop string)
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kv_transfer_params: Optional KV cache transfer parameters
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Returns:
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OmniRequestOutput or PoolingRequestOutput if output should be
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emitted (based on finish status and output kind), None otherwise
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"""
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# Pooling-only requests should follow base behavior.
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if self.detokenizer is None and pooling_output is not None:
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return super().make_request_output(
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new_token_ids,
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pooling_output,
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finish_reason,
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stop_reason,
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kv_transfer_params,
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routed_experts,
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)
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finished = finish_reason is not None
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final_only = self.output_kind == RequestOutputKind.FINAL_ONLY
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if not finished and final_only:
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return None
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# Consolidate accumulated tensors when finishing.
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if finished:
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self._consolidate_multimodal_tensors()
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if self.stream_interval > 1:
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assert self.detokenizer is not None
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# Send output request only when
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# 1. It has finished, or
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# 2. It is the first token, or
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# 3. It has reached the stream interval number of tokens
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if not (
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finished
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or self.sent_tokens_offset == 0
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or len(self.detokenizer.output_token_ids) - self.sent_tokens_offset >= self.stream_interval
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):
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return None
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if self.output_kind == RequestOutputKind.DELTA:
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# Send tokens from the offset in DELTA mode, otherwise all
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# tokens are sent.
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new_token_ids = self.detokenizer.output_token_ids[self.sent_tokens_offset :]
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self.sent_tokens_offset = len(self.detokenizer.output_token_ids)
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request_id = self.request_id
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output = self._new_completion_output(new_token_ids, finish_reason, stop_reason, routed_experts)
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if self.parent_req is None:
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outputs = [output]
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else:
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request_id, outputs, finished = self.parent_req.get_outputs(request_id, output)
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if not outputs:
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return None
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return self._new_request_output(request_id, outputs, finished, kv_transfer_params)
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def _new_completion_output(
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self,
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token_ids: list[int],
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finish_reason: FinishReason | None,
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stop_reason: int | str | None,
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routed_experts: np.ndarray | None = None,
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) -> Any:
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# Reuse base text/logprobs logic, then annotate with pooling_result.
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base_output = super()._new_completion_output(token_ids, finish_reason, stop_reason, routed_experts)
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try:
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if self.mm_accumulated is not None:
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# Attach accumulated multimodal dict on the completion output
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if not hasattr(base_output, "multimodal_output"):
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setattr(base_output, "multimodal_output", {})
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mm_out = getattr(base_output, "multimodal_output")
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if isinstance(mm_out, dict):
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for k, v in self.mm_accumulated.items():
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mm_out[k] = v
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else:
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setattr(base_output, "multimodal_output", self.mm_accumulated)
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except Exception:
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logger.exception("Error in _new_completion_output")
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return base_output
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class MultimodalOutputProcessor(VLLMOutputProcessor):
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"""Handles multimodal output processing by normalizing EngineCoreOutput
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before delegating to the base vLLM OutputProcessor.
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Strategy:
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- Route by EngineCoreOutput.output_type when present
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("image", "text+image", "latents", "text").
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- Fallback to pooling/text heuristics when output_type is absent.
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- Mutate EngineCoreOutput in-place to ensure vLLM's base processor can
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produce the correct RequestOutput/PoolingRequestOutput.
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- Allow custom per-modality handlers via register_handler().
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"""
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def __init__(
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self,
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tokenizer: TokenizerLike,
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log_stats: bool,
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engine_core_output_type: str | None = None,
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):
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"""Initialize the multimodal output processor.
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Args:
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tokenizer: Tokenizer for detokenizing text outputs
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log_stats: Whether to log statistics
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engine_core_output_type: Optional output type specification
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(e.g., "image", "audio", "latents"). Used to route outputs
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to appropriate processors. If None, output type is inferred.
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"""
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super().__init__(tokenizer=tokenizer, log_stats=log_stats)
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self.output_handlers: dict[str, Callable[[EngineCoreOutput], None]] = {}
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self._reqid_to_mm_type: dict[str, str] = {}
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self.engine_core_output_type = engine_core_output_type
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def register_handler(self, modality: str, handler: Callable[[EngineCoreOutput], None]) -> None:
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"""Register a custom handler for a specific modality.
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Allows custom processing logic for specific output modalities.
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The handler is called before default processing for outputs
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matching the specified modality.
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Args:
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modality: Modality name (e.g., "image", "audio", "latents")
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handler: Callable that takes an EngineCoreOutput and processes it
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"""
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self.output_handlers[modality.lower()] = handler
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def add_request(
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self,
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request: EngineCoreRequest,
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prompt: str | None,
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parent_req: ParentRequest | None = None,
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request_index: int = 0,
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queue: RequestOutputCollector | None = None,
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) -> None:
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"""Add a new request to be processed.
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Creates an OmniRequestState for the request and registers it
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for output processing.
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Args:
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request: Engine core request to add
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prompt: Optional prompt string for the request
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parent_req: Optional parent request for parallel sampling
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request_index: Index of the request in the batch
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queue: Optional queue for collecting outputs
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Raises:
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ValueError: If the request ID is already registered
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"""
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request_id = request.request_id
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req_state = self.request_states.get(request_id)
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if req_state is not None:
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self._update_streaming_request_state(req_state, request, prompt)
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return
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req_state = OmniRequestState.from_new_request(
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tokenizer=self.tokenizer,
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request=request,
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prompt=prompt,
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parent_req=parent_req,
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request_index=request_index,
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queue=queue,
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log_stats=self.log_stats,
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stream_interval=self.stream_interval,
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)
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if self._requests_drained.is_set():
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self._requests_drained.clear()
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self.request_states[request_id] = req_state
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if parent_req:
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self.parent_requests[parent_req.request_id] = parent_req
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self.external_req_ids[req_state.external_req_id].append(request_id)
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def process_outputs(
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self,
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engine_core_outputs: list[EngineCoreOutput],
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engine_core_timestamp: float | None = None,
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iteration_stats: IterationStats | None = None,
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) -> OutputProcessorOutput:
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self._reqid_to_mm_type.clear()
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for eco in engine_core_outputs:
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mm_type = (self.engine_core_output_type or "").lower()
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if mm_type:
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self._reqid_to_mm_type[eco.request_id] = mm_type
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self._route_and_normalize(eco)
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req_state = self.request_states.get(eco.request_id)
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if req_state is None or not isinstance(req_state, OmniRequestState):
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continue
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if eco.pooling_output is not None and req_state.detokenizer is not None:
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req_state.add_multimodal_tensor(
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eco.pooling_output,
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(getattr(eco, "output_type", self.engine_core_output_type) or "").lower(),
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)
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# Force text path in base processor for multimodal outputs.
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eco.pooling_output = None
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return super().process_outputs(
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engine_core_outputs,
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engine_core_timestamp=engine_core_timestamp,
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iteration_stats=iteration_stats,
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)
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# ---- routing helpers ----
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def _route_and_normalize(self, eco: EngineCoreOutput) -> None:
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output_type = (getattr(eco, "output_type", self.engine_core_output_type) or "").lower()
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# Custom handler first (if registered)
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if output_type in self.output_handlers:
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try:
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self.output_handlers[output_type](eco)
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# Fall through to default fixups in case the handler left gaps
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except Exception:
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logger.exception("Error in custom output handler for %s", output_type)
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if output_type == "image":
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self._process_image_output(eco)
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elif output_type in ("text+image", "text,image", "image+text"):
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self._process_text_image_output(eco)
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elif output_type in ("latents", "latent"):
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self._process_latents_output(eco)
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elif output_type in ("audio", "speech"):
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self._process_audio_output(eco)
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elif output_type == "text":
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self._process_text_output(eco)
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else:
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# Fallback heuristic
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if eco.pooling_output is not None:
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self._process_pooling_output(eco)
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else:
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self._process_text_output(eco)
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# ---- modality processors ----
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def _process_image_output(self, eco: EngineCoreOutput) -> None:
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"""Ensure image tensors are surfaced via pooling_output for vLLM."""
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if eco.pooling_output is None:
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tensor = self._extract_from_multimodal_outputs(eco, keys=("image", "images", "pixel_values", "pixels"))
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if tensor is not None:
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eco.pooling_output = tensor
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def _process_text_image_output(self, eco: EngineCoreOutput) -> None:
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"""Allow text+image outputs. Text path stays as new_token_ids;
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image/latents route via pooling_output."""
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# Preserve text tokens as-is; ensure pooling_output carries image/latents
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if eco.pooling_output is None:
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tensor = self._extract_from_multimodal_outputs(
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eco,
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keys=(
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"image",
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"images",
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"pixel_values",
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"pixels",
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"latent",
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"latents",
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"z",
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),
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)
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if tensor is not None:
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eco.pooling_output = tensor
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def _process_latents_output(self, eco: EngineCoreOutput) -> None:
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"""Ensure latent tensors are surfaced via pooling_output."""
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if eco.pooling_output is None:
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tensor = self._extract_from_multimodal_outputs(eco, keys=("latent", "latents", "z", "posterior"))
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if tensor is not None:
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eco.pooling_output = tensor
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def _process_audio_output(self, eco: EngineCoreOutput) -> None:
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"""Ensure audio tensors are surfaced via pooling_output."""
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if eco.pooling_output is None:
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tensor = self._extract_from_multimodal_outputs(
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eco, keys=("audio", "audios", "wav", "waveform", "audio_pcm", "pcm")
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)
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if tensor is not None:
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eco.pooling_output = tensor
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def _process_text_output(self, eco: EngineCoreOutput) -> None:
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"""No-op; base processor will detokenize new_token_ids → text."""
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return
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def _process_pooling_output(self, eco: EngineCoreOutput) -> None:
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"""Optional sanity checks for pooling tensor."""
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if eco.pooling_output is None:
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return
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if not isinstance(eco.pooling_output, torch.Tensor):
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# Best-effort: convert to tensor if it's a list/ndarray-like
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try:
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eco.pooling_output = torch.as_tensor(eco.pooling_output)
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except Exception:
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pass
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def _extract_from_multimodal_outputs(self, eco: EngineCoreOutput, keys: tuple[str, ...]) -> torch.Tensor | None:
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mm = getattr(eco, "multimodal_outputs", None)
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if not isinstance(mm, dict):
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return None
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for k in keys:
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v = mm.get(k)
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if isinstance(v, torch.Tensor):
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return v
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# Try the first tensor in the dict as a fallback
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for v in mm.values():
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if isinstance(v, torch.Tensor):
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return v
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return None
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