Gahow Wang
445e491123
third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:
vllm/v1/core/sched/scheduler.py:
Replace fatal assert with graceful skip when KV transfer callback
arrives for an already-aborted request during PD disaggregated serving.
Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Sequence and its related classes."""
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Any
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import torch
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if TYPE_CHECKING:
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from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorOutput
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else:
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KVConnectorOutput = Any
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# cannot use msgspec.Struct here because Dynamo does not support it
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@dataclass
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class IntermediateTensors:
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"""For all pipeline stages except the last, we need to return the hidden
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states and residuals to be sent to the next stage. This data structure
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contains the hidden states and residuals for a request.
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Each stage also needs to handle its own kv_connector_output.
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"""
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tensors: dict[str, torch.Tensor]
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kv_connector_output: KVConnectorOutput | None
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def __init__(
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self,
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tensors: dict[str, torch.Tensor],
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kv_connector_output: KVConnectorOutput | None = None,
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) -> None:
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# manually define this function, so that
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# Dynamo knows `IntermediateTensors()` comes from this file.
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# Otherwise, dataclass will generate this function by evaluating
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# a string, and we will lose the information about the source file.
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self.tensors = tensors
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self.kv_connector_output = kv_connector_output
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def __getitem__(self, key: str | slice):
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if isinstance(key, str):
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return self.tensors[key]
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elif isinstance(key, slice):
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return self.__class__({k: v[key] for k, v in self.tensors.items()})
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def __setitem__(self, key: str, value: torch.Tensor):
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self.tensors[key] = value
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def items(self):
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return self.tensors.items()
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def __len__(self):
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return len(self.tensors)
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def __eq__(self, other: object):
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if not isinstance(other, self.__class__):
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return False
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if self.tensors.keys() != other.tensors.keys():
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return False
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return all(torch.equal(self.tensors[k], other.tensors[k]) for k in self.tensors)
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def __repr__(self) -> str:
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return f"IntermediateTensors(tensors={self.tensors})"
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