# 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 from tvm import te def test_lower_rfactor(): n = te.size_var("n") m = te.size_var("m") A = te.placeholder((n, m), name='A') k = te.reduce_axis((0, m), "k") B = te.compute((n,), lambda i: te.sum(A[i, k], axis=k), name="B") s = te.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.op].bind(xo, te.thread_axis("blockIdx.x")) s[B.op].bind(xi, te.thread_axis("threadIdx.y")) s[B].bind(s[B].op.reduce_axis[0], te.thread_axis("threadIdx.x")) s[BF].compute_at(s[B], s[B].op.reduce_axis[0]) fapi = tvm.lower(s, [A, B]) def test_dependent_output_shape(): n, m, x = te.size_var('n'), te.size_var('m'), te.size_var('x') A = te.placeholder((n, m)) B = te.compute((m, n//x), lambda i, j: A[i,j] , name='B') s = te.create_schedule(B.op) mod = tvm.build(s, [A, B, x]) if __name__ == "__main__": test_lower_rfactor() test_dependent_output_shape()