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>
50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import torch
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from vllm.sequence import IntermediateTensors
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def test_sequence_intermediate_tensors_equal():
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class AnotherIntermediateTensors(IntermediateTensors):
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pass
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intermediate_tensors = IntermediateTensors({})
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another_intermediate_tensors = AnotherIntermediateTensors({})
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assert intermediate_tensors != another_intermediate_tensors
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empty_intermediate_tensors_1 = IntermediateTensors({})
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empty_intermediate_tensors_2 = IntermediateTensors({})
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assert empty_intermediate_tensors_1 == empty_intermediate_tensors_2
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different_key_intermediate_tensors_1 = IntermediateTensors(
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{"1": torch.zeros([2, 4], dtype=torch.int32)}
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)
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difference_key_intermediate_tensors_2 = IntermediateTensors(
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{"2": torch.zeros([2, 4], dtype=torch.int32)}
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)
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assert different_key_intermediate_tensors_1 != difference_key_intermediate_tensors_2
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same_key_different_value_intermediate_tensors_1 = IntermediateTensors(
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{"1": torch.zeros([2, 4], dtype=torch.int32)}
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)
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same_key_different_value_intermediate_tensors_2 = IntermediateTensors(
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{"1": torch.zeros([2, 5], dtype=torch.int32)}
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)
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assert (
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same_key_different_value_intermediate_tensors_1
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!= same_key_different_value_intermediate_tensors_2
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)
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same_key_same_value_intermediate_tensors_1 = IntermediateTensors(
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{"1": torch.zeros([2, 4], dtype=torch.int32)}
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)
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same_key_same_value_intermediate_tensors_2 = IntermediateTensors(
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{"1": torch.zeros([2, 4], dtype=torch.int32)}
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)
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assert (
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same_key_same_value_intermediate_tensors_1
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== same_key_same_value_intermediate_tensors_2
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)
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