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70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: BSD-3-Clause
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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# 2. Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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# 3. Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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import pytest
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import torch
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import cutlass_api
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from cutlass_api.utils import device_cc
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@pytest.mark.parametrize(
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"M, N",
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[
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(256, 512),
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(1024, 8192),
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],
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)
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@pytest.mark.parametrize(
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"dtype",
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[
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torch.float32,
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torch.float16,
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],
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)
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def test_elementwise_add(
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M: int,
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N: int,
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dtype: torch.dtype,
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):
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A = torch.randint(-1, 2, (M, N), device="cuda", dtype=dtype)
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B = torch.randint(-1, 2, (M, N), device="cuda", dtype=dtype)
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D = torch.empty((M, N), device="cuda", dtype=dtype)
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args = cutlass_api.arguments.ElementwiseArguments(A=A, B=B, out=D)
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kernels = cutlass_api.get_kernels(args)
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assert len(kernels) > 0
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kernel = kernels[0]
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kernel.run(args)
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reference = A + B
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assert torch.allclose(D, reference)
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