Initial commit

python/cutlass_api/test/integration/test_elementwise_add.py

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