Commit a44ac185 by Thomas Viehmann Committed by masahi

add rocm codegen unittest for cross thread reduction (#4423)

parent dabde40f
# Licensed to the Apache Software Foundation (ASF) under one
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# to you under the Apache License, Version 2.0 (the
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import tvm
import numpy as np
def test_rocm_cross_thread_reduction():
if not tvm.rocm(0).exist or not tvm.module.enabled("rocm"):
print("skip because rocm is not enabled..")
return
# based on the reduction tutorial
n = tvm.var("n")
m = tvm.var("m")
A = tvm.placeholder((n, m), name='A')
k = tvm.reduce_axis((0, m), "k")
B = tvm.compute((n,), lambda i: tvm.sum(A[i, k], axis=k), name="B")
s = tvm.create_schedule(B.op)
ko, ki = s[B].split(B.op.reduce_axis[0], factor=16)
BF = s.rfactor(B, ki)
xo, xi = s[B].split(s[B].op.axis[0], factor=32)
s[B].bind(xo, tvm.thread_axis("blockIdx.x"))
s[B].bind(xi, tvm.thread_axis("threadIdx.y"))
tx = tvm.thread_axis("threadIdx.x")
s[B].bind(s[B].op.reduce_axis[0], tx)
s[BF].compute_at(s[B], s[B].op.reduce_axis[0])
s[B].set_store_predicate(tx.var.equal(0))
frocm = tvm.build(s, [A, B], "rocm")
nn = 128
ctx = tvm.rocm(0)
a = tvm.nd.array(np.random.uniform(size=(nn, nn)).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(nn, dtype=B.dtype), ctx)
frocm(a, b)
tvm.testing.assert_allclose(
b.asnumpy(), np.sum(a.asnumpy(), axis=1), rtol=1e-4)
if __name__ == "__main__":
test_rocm_cross_thread_reduction()
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