# 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 import numpy as np from tvm.contrib.dlpack import to_pytorch_func def test(): a = np.random.randn(1337) tvm_a = tvm.nd.array(a) np.testing.assert_equal(tvm.nd.from_dlpack(tvm_a.to_dlpack()).asnumpy(), a) try: import torch import torch.utils.dlpack x = torch.rand(56, 56) tvm_x = tvm.nd.from_dlpack(torch.utils.dlpack.to_dlpack(x)) np.testing.assert_equal(x.numpy(), tvm_x.asnumpy()) y = tvm.nd.from_dlpack(tvm_x.to_dlpack()) np.testing.assert_equal(y.asnumpy(), tvm_x.asnumpy()) np.testing.assert_equal(torch.utils.dlpack.from_dlpack(y.to_dlpack()).numpy(), tvm_x.asnumpy()) n = tvm.runtime.convert(137) xx = torch.rand(137,137) yy = torch.rand(137,137) zz2 = torch.empty(137,137) zz = xx.mm(yy) XX = te.placeholder((n,n), name='X') YY = te.placeholder((n,n), name='Y') k = te.reduce_axis((0, n), name='k') ZZ = te.compute((n,n), lambda i,j : te.sum(XX[i,k]*YY[k,j], axis=k)) s = te.create_schedule(ZZ.op) f = tvm.build(s, [XX, YY, ZZ], target_host='llvm', name='f') f_pytorch = to_pytorch_func(f) zz2 = torch.empty(137,137) f_pytorch(xx, yy, zz2) tvm.testing.assert_allclose(zz.numpy(), zz2.numpy(), rtol=1e-6) except ImportError: pass if __name__ == '__main__': test()