| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| import tvm |
| import tvm.testing |
| from tvm import te |
| import numpy as np |
| |
| |
| @tvm.testing.requires_llvm |
| def test_dot(): |
| nn = 12 |
| n = tvm.runtime.convert(nn) |
| A = te.placeholder((n,), name="A") |
| B = te.placeholder((n,), name="B") |
| k = te.reduce_axis((0, n), "k") |
| C = te.compute((1,), lambda _: te.sum(A[k] * B[k], axis=k), name="C") |
| s = te.create_schedule(C.op) |
| |
| def verify(target): |
| f = tvm.driver.build(s, [A, B, C], target) |
| # verify |
| ctx = tvm.cpu(0) |
| a = tvm.nd.array(np.random.uniform(size=(nn,)).astype(A.dtype), ctx) |
| b = tvm.nd.array(np.random.uniform(size=(nn,)).astype(B.dtype), ctx) |
| c = tvm.nd.array(np.zeros((1,), dtype=C.dtype), ctx) |
| f(a, b, c) |
| tvm.testing.assert_allclose(c.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()), rtol=1e-4) |
| |
| verify("llvm") |
| |
| |
| if __name__ == "__main__": |
| test_dot() |