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 pytest
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import torch
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from tests.kernels.utils import opcheck
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from vllm import _custom_ops as ops # noqa: F401
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@pytest.mark.skipif(
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not hasattr(torch.ops._C, "awq_dequantize"),
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reason="AWQ is not supported on this GPU type.",
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)
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def test_awq_dequantize_opcheck(monkeypatch: pytest.MonkeyPatch):
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with monkeypatch.context() as m:
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m.setenv("VLLM_USE_TRITON_AWQ", "0")
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qweight = torch.randint(
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-2000000000, 2000000000, (8192, 256), device="cuda", dtype=torch.int32
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)
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scales = torch.rand((64, 2048), device="cuda", dtype=torch.float16)
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zeros = torch.empty((64, 256), device="cuda", dtype=torch.int32)
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split_k_iters = 0
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thx = 0
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thy = 0
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opcheck(
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torch.ops._C.awq_dequantize,
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(qweight, scales, zeros, split_k_iters, thx, thy),
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)
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@pytest.mark.skip(reason="Not working; needs investigation.")
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@pytest.mark.skipif(
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not hasattr(torch.ops._C, "awq_gemm"),
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reason="AWQ is not supported on this GPU type.",
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)
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def test_awq_gemm_opcheck(monkeypatch: pytest.MonkeyPatch):
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with monkeypatch.context() as m:
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m.setenv("VLLM_USE_TRITON_AWQ", "0")
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input = torch.rand((2, 8192), device="cuda", dtype=torch.float16)
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qweight = torch.randint(
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-2000000000, 2000000000, (8192, 256), device="cuda", dtype=torch.int32
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)
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scales = torch.empty((64, 2048), device="cuda", dtype=torch.float16)
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qzeros = torch.randint(
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-2000000000, 2000000000, (64, 256), device="cuda", dtype=torch.int32
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)
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split_k_iters = 8
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opcheck(torch.ops._C.awq_gemm, (input, qweight, scales, qzeros, split_k_iters))
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