a73e183e36
Signed-off-by: sibi <85477603+t-sibiraj@users.noreply.github.com> Signed-off-by: Aaron Pham <contact@aarnphm.xyz> Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com> Co-authored-by: Aaron Pham <contact@aarnphm.xyz>
47 lines
1.9 KiB
Python
47 lines
1.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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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(not hasattr(torch.ops._C, "awq_dequantize"),
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reason="AWQ is not supported on this GPU type.")
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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(-2000000000,
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2000000000, (8192, 256),
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device='cuda',
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dtype=torch.int32)
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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(torch.ops._C.awq_dequantize,
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(qweight, scales, zeros, split_k_iters, thx, thy))
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@pytest.mark.skip(reason="Not working; needs investigation.")
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@pytest.mark.skipif(not hasattr(torch.ops._C, "awq_gemm"),
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reason="AWQ is not supported on this GPU type.")
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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(-2000000000,
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2000000000, (8192, 256),
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device='cuda',
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dtype=torch.int32)
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scales = torch.randint(-2000000000,
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2000000000, (64, 256),
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device='cuda',
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dtype=torch.int32)
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qzeros = torch.empty((64, 2048), device='cuda', dtype=torch.float16)
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split_k_iters = 8
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opcheck(torch.ops._C.awq_gemm,
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(input, qweight, qzeros, scales, split_k_iters))
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