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268 lines
17 KiB
C++
268 lines
17 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2016
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// Mehdi Goli Codeplay Software Ltd.
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// Ralph Potter Codeplay Software Ltd.
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// Luke Iwanski Codeplay Software Ltd.
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// Contact: <eigen@codeplay.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#define EIGEN_TEST_NO_LONGDOUBLE
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#define EIGEN_TEST_NO_COMPLEX
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#define EIGEN_TEST_FUNC cxx11_tensor_builtins_sycl
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#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
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#define EIGEN_USE_SYCL
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#include "main.h"
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#include <unsupported/Eigen/CXX11/Tensor>
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using Eigen::array;
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using Eigen::SyclDevice;
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using Eigen::Tensor;
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using Eigen::TensorMap;
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namespace std {
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template <typename T> T rsqrt(T x) { return 1 / std::sqrt(x); }
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template <typename T> T square(T x) { return x * x; }
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template <typename T> T cube(T x) { return x * x * x; }
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template <typename T> T inverse(T x) { return 1 / x; }
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}
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#define TEST_UNARY_BUILTINS_FOR_SCALAR(FUNC, SCALAR, OPERATOR, Layout) \
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{ \
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/* out OPERATOR in.FUNC() */ \
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Tensor<SCALAR, 3, Layout, int64_t> in(tensorRange); \
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Tensor<SCALAR, 3, Layout, int64_t> out(tensorRange); \
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in = in.random() + static_cast<SCALAR>(0.01); \
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out = out.random() + static_cast<SCALAR>(0.01); \
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Tensor<SCALAR, 3, Layout, int64_t> reference(out); \
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SCALAR *gpu_data = static_cast<SCALAR *>( \
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sycl_device.allocate(in.size() * sizeof(SCALAR))); \
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SCALAR *gpu_data_out = static_cast<SCALAR *>( \
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sycl_device.allocate(out.size() * sizeof(SCALAR))); \
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TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu(gpu_data, tensorRange); \
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TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_out(gpu_data_out, tensorRange); \
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sycl_device.memcpyHostToDevice(gpu_data, in.data(), \
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(in.size()) * sizeof(SCALAR)); \
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sycl_device.memcpyHostToDevice(gpu_data_out, out.data(), \
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(out.size()) * sizeof(SCALAR)); \
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gpu_out.device(sycl_device) OPERATOR gpu.FUNC(); \
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sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, \
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(out.size()) * sizeof(SCALAR)); \
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for (int64_t i = 0; i < out.size(); ++i) { \
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SCALAR ver = reference(i); \
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ver OPERATOR std::FUNC(in(i)); \
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VERIFY_IS_APPROX(out(i), ver); \
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} \
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sycl_device.deallocate(gpu_data); \
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sycl_device.deallocate(gpu_data_out); \
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} \
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{ \
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/* out OPERATOR out.FUNC() */ \
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Tensor<SCALAR, 3, Layout, int64_t> out(tensorRange); \
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out = out.random() + static_cast<SCALAR>(0.01); \
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Tensor<SCALAR, 3, Layout, int64_t> reference(out); \
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SCALAR *gpu_data_out = static_cast<SCALAR *>( \
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sycl_device.allocate(out.size() * sizeof(SCALAR))); \
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TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_out(gpu_data_out, tensorRange); \
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sycl_device.memcpyHostToDevice(gpu_data_out, out.data(), \
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(out.size()) * sizeof(SCALAR)); \
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gpu_out.device(sycl_device) OPERATOR gpu_out.FUNC(); \
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sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, \
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(out.size()) * sizeof(SCALAR)); \
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for (int64_t i = 0; i < out.size(); ++i) { \
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SCALAR ver = reference(i); \
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ver OPERATOR std::FUNC(reference(i)); \
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VERIFY_IS_APPROX(out(i), ver); \
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} \
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sycl_device.deallocate(gpu_data_out); \
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}
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#define TEST_UNARY_BUILTINS_OPERATOR(SCALAR, OPERATOR , Layout) \
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TEST_UNARY_BUILTINS_FOR_SCALAR(abs, SCALAR, OPERATOR , Layout) \
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TEST_UNARY_BUILTINS_FOR_SCALAR(sqrt, SCALAR, OPERATOR , Layout) \
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TEST_UNARY_BUILTINS_FOR_SCALAR(rsqrt, SCALAR, OPERATOR , Layout) \
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TEST_UNARY_BUILTINS_FOR_SCALAR(square, SCALAR, OPERATOR , Layout) \
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TEST_UNARY_BUILTINS_FOR_SCALAR(cube, SCALAR, OPERATOR , Layout) \
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TEST_UNARY_BUILTINS_FOR_SCALAR(inverse, SCALAR, OPERATOR , Layout) \
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TEST_UNARY_BUILTINS_FOR_SCALAR(tanh, SCALAR, OPERATOR , Layout) \
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TEST_UNARY_BUILTINS_FOR_SCALAR(exp, SCALAR, OPERATOR , Layout) \
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TEST_UNARY_BUILTINS_FOR_SCALAR(expm1, SCALAR, OPERATOR , Layout) \
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TEST_UNARY_BUILTINS_FOR_SCALAR(log, SCALAR, OPERATOR , Layout) \
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TEST_UNARY_BUILTINS_FOR_SCALAR(abs, SCALAR, OPERATOR , Layout) \
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TEST_UNARY_BUILTINS_FOR_SCALAR(ceil, SCALAR, OPERATOR , Layout) \
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TEST_UNARY_BUILTINS_FOR_SCALAR(floor, SCALAR, OPERATOR , Layout) \
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TEST_UNARY_BUILTINS_FOR_SCALAR(round, SCALAR, OPERATOR , Layout) \
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TEST_UNARY_BUILTINS_FOR_SCALAR(log1p, SCALAR, OPERATOR , Layout)
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#define TEST_IS_THAT_RETURNS_BOOL(SCALAR, FUNC, Layout) \
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{ \
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/* out = in.FUNC() */ \
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Tensor<SCALAR, 3, Layout, int64_t> in(tensorRange); \
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Tensor<bool, 3, Layout, int64_t> out(tensorRange); \
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in = in.random() + static_cast<SCALAR>(0.01); \
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SCALAR *gpu_data = static_cast<SCALAR *>( \
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sycl_device.allocate(in.size() * sizeof(SCALAR))); \
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bool *gpu_data_out = \
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static_cast<bool *>(sycl_device.allocate(out.size() * sizeof(bool))); \
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TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu(gpu_data, tensorRange); \
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TensorMap<Tensor<bool, 3, Layout, int64_t>> gpu_out(gpu_data_out, tensorRange); \
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sycl_device.memcpyHostToDevice(gpu_data, in.data(), \
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(in.size()) * sizeof(SCALAR)); \
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gpu_out.device(sycl_device) = gpu.FUNC(); \
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sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, \
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(out.size()) * sizeof(bool)); \
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for (int64_t i = 0; i < out.size(); ++i) { \
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VERIFY_IS_EQUAL(out(i), std::FUNC(in(i))); \
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} \
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sycl_device.deallocate(gpu_data); \
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sycl_device.deallocate(gpu_data_out); \
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}
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#define TEST_UNARY_BUILTINS(SCALAR, Layout) \
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TEST_UNARY_BUILTINS_OPERATOR(SCALAR, +=, Layout) \
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TEST_UNARY_BUILTINS_OPERATOR(SCALAR, =, Layout) \
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TEST_IS_THAT_RETURNS_BOOL(SCALAR, isnan, Layout) \
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TEST_IS_THAT_RETURNS_BOOL(SCALAR, isfinite, Layout) \
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TEST_IS_THAT_RETURNS_BOOL(SCALAR, isinf, Layout)
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static void test_builtin_unary_sycl(const Eigen::SyclDevice &sycl_device) {
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int64_t sizeDim1 = 10;
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int64_t sizeDim2 = 10;
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int64_t sizeDim3 = 10;
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array<int64_t, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
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TEST_UNARY_BUILTINS(float, RowMajor)
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TEST_UNARY_BUILTINS(float, ColMajor)
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}
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namespace std {
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template <typename T> T cwiseMax(T x, T y) { return std::max(x, y); }
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template <typename T> T cwiseMin(T x, T y) { return std::min(x, y); }
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}
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#define TEST_BINARY_BUILTINS_FUNC(SCALAR, FUNC, Layout) \
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{ \
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/* out = in_1.FUNC(in_2) */ \
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Tensor<SCALAR, 3, Layout, int64_t> in_1(tensorRange); \
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Tensor<SCALAR, 3, Layout, int64_t> in_2(tensorRange); \
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Tensor<SCALAR, 3, Layout, int64_t> out(tensorRange); \
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in_1 = in_1.random() + static_cast<SCALAR>(0.01); \
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in_2 = in_2.random() + static_cast<SCALAR>(0.01); \
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Tensor<SCALAR, 3, Layout, int64_t> reference(out); \
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SCALAR *gpu_data_1 = static_cast<SCALAR *>( \
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sycl_device.allocate(in_1.size() * sizeof(SCALAR))); \
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SCALAR *gpu_data_2 = static_cast<SCALAR *>( \
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sycl_device.allocate(in_2.size() * sizeof(SCALAR))); \
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SCALAR *gpu_data_out = static_cast<SCALAR *>( \
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sycl_device.allocate(out.size() * sizeof(SCALAR))); \
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TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_1(gpu_data_1, tensorRange); \
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TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_2(gpu_data_2, tensorRange); \
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TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_out(gpu_data_out, tensorRange); \
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sycl_device.memcpyHostToDevice(gpu_data_1, in_1.data(), \
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(in_1.size()) * sizeof(SCALAR)); \
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sycl_device.memcpyHostToDevice(gpu_data_2, in_2.data(), \
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(in_2.size()) * sizeof(SCALAR)); \
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gpu_out.device(sycl_device) = gpu_1.FUNC(gpu_2); \
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sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, \
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(out.size()) * sizeof(SCALAR)); \
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for (int64_t i = 0; i < out.size(); ++i) { \
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SCALAR ver = reference(i); \
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ver = std::FUNC(in_1(i), in_2(i)); \
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VERIFY_IS_APPROX(out(i), ver); \
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} \
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sycl_device.deallocate(gpu_data_1); \
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sycl_device.deallocate(gpu_data_2); \
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sycl_device.deallocate(gpu_data_out); \
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}
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#define TEST_BINARY_BUILTINS_OPERATORS(SCALAR, OPERATOR, Layout) \
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{ \
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/* out = in_1 OPERATOR in_2 */ \
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Tensor<SCALAR, 3, Layout, int64_t> in_1(tensorRange); \
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Tensor<SCALAR, 3, Layout, int64_t> in_2(tensorRange); \
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Tensor<SCALAR, 3, Layout, int64_t> out(tensorRange); \
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in_1 = in_1.random() + static_cast<SCALAR>(0.01); \
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in_2 = in_2.random() + static_cast<SCALAR>(0.01); \
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Tensor<SCALAR, 3, Layout, int64_t> reference(out); \
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SCALAR *gpu_data_1 = static_cast<SCALAR *>( \
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sycl_device.allocate(in_1.size() * sizeof(SCALAR))); \
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SCALAR *gpu_data_2 = static_cast<SCALAR *>( \
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sycl_device.allocate(in_2.size() * sizeof(SCALAR))); \
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SCALAR *gpu_data_out = static_cast<SCALAR *>( \
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sycl_device.allocate(out.size() * sizeof(SCALAR))); \
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TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_1(gpu_data_1, tensorRange); \
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TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_2(gpu_data_2, tensorRange); \
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TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_out(gpu_data_out, tensorRange); \
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sycl_device.memcpyHostToDevice(gpu_data_1, in_1.data(), \
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(in_1.size()) * sizeof(SCALAR)); \
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sycl_device.memcpyHostToDevice(gpu_data_2, in_2.data(), \
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(in_2.size()) * sizeof(SCALAR)); \
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gpu_out.device(sycl_device) = gpu_1 OPERATOR gpu_2; \
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sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, \
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(out.size()) * sizeof(SCALAR)); \
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for (int64_t i = 0; i < out.size(); ++i) { \
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VERIFY_IS_APPROX(out(i), in_1(i) OPERATOR in_2(i)); \
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} \
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sycl_device.deallocate(gpu_data_1); \
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sycl_device.deallocate(gpu_data_2); \
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sycl_device.deallocate(gpu_data_out); \
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}
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#define TEST_BINARY_BUILTINS_OPERATORS_THAT_TAKES_SCALAR(SCALAR, OPERATOR, Layout) \
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{ \
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/* out = in_1 OPERATOR 2 */ \
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Tensor<SCALAR, 3, Layout, int64_t> in_1(tensorRange); \
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Tensor<SCALAR, 3, Layout, int64_t> out(tensorRange); \
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in_1 = in_1.random() + static_cast<SCALAR>(0.01); \
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Tensor<SCALAR, 3, Layout, int64_t> reference(out); \
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SCALAR *gpu_data_1 = static_cast<SCALAR *>( \
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sycl_device.allocate(in_1.size() * sizeof(SCALAR))); \
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SCALAR *gpu_data_out = static_cast<SCALAR *>( \
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sycl_device.allocate(out.size() * sizeof(SCALAR))); \
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TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_1(gpu_data_1, tensorRange); \
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TensorMap<Tensor<SCALAR, 3, Layout, int64_t>> gpu_out(gpu_data_out, tensorRange); \
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sycl_device.memcpyHostToDevice(gpu_data_1, in_1.data(), \
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(in_1.size()) * sizeof(SCALAR)); \
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gpu_out.device(sycl_device) = gpu_1 OPERATOR 2; \
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sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, \
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(out.size()) * sizeof(SCALAR)); \
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for (int64_t i = 0; i < out.size(); ++i) { \
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VERIFY_IS_APPROX(out(i), in_1(i) OPERATOR 2); \
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} \
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sycl_device.deallocate(gpu_data_1); \
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sycl_device.deallocate(gpu_data_out); \
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}
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#define TEST_BINARY_BUILTINS(SCALAR, Layout) \
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TEST_BINARY_BUILTINS_FUNC(SCALAR, cwiseMax , Layout) \
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TEST_BINARY_BUILTINS_FUNC(SCALAR, cwiseMin , Layout) \
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TEST_BINARY_BUILTINS_OPERATORS(SCALAR, + , Layout) \
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TEST_BINARY_BUILTINS_OPERATORS(SCALAR, - , Layout) \
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TEST_BINARY_BUILTINS_OPERATORS(SCALAR, * , Layout) \
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TEST_BINARY_BUILTINS_OPERATORS(SCALAR, / , Layout)
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static void test_builtin_binary_sycl(const Eigen::SyclDevice &sycl_device) {
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int64_t sizeDim1 = 10;
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int64_t sizeDim2 = 10;
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int64_t sizeDim3 = 10;
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array<int64_t, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
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TEST_BINARY_BUILTINS(float, RowMajor)
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TEST_BINARY_BUILTINS_OPERATORS_THAT_TAKES_SCALAR(int, %, RowMajor)
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TEST_BINARY_BUILTINS(float, ColMajor)
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TEST_BINARY_BUILTINS_OPERATORS_THAT_TAKES_SCALAR(int, %, ColMajor)
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}
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void test_cxx11_tensor_builtins_sycl() {
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for (const auto& device :Eigen::get_sycl_supported_devices()) {
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QueueInterface queueInterface(device);
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Eigen::SyclDevice sycl_device(&queueInterface);
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CALL_SUBTEST(test_builtin_unary_sycl(sycl_device));
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CALL_SUBTEST(test_builtin_binary_sycl(sycl_device));
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}
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}
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