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# Licensed to the Apache Software Foundation (ASF) under one
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# 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
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# 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()