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82f0ce2726
This provide several advantages: - more flexibility in designing unit tests - unit tests can be glued to speed up compilation - unit tests are compiled with same predefined macros, which is a requirement for zapcc
246 lines
9.4 KiB
C++
246 lines
9.4 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_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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template <typename DataType, int Layout, typename DenseIndex>
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static void test_sycl_simple_argmax(const Eigen::SyclDevice &sycl_device){
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Tensor<DataType, 3, Layout, DenseIndex> in(Eigen::array<DenseIndex, 3>{{2,2,2}});
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Tensor<DenseIndex, 0, Layout, DenseIndex> out_max;
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Tensor<DenseIndex, 0, Layout, DenseIndex> out_min;
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in.setRandom();
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in *= in.constant(100.0);
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in(0, 0, 0) = -1000.0;
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in(1, 1, 1) = 1000.0;
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std::size_t in_bytes = in.size() * sizeof(DataType);
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std::size_t out_bytes = out_max.size() * sizeof(DenseIndex);
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DataType * d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
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DenseIndex* d_out_max = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
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DenseIndex* d_out_min = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
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Eigen::TensorMap<Eigen::Tensor<DataType, 3, Layout, DenseIndex> > gpu_in(d_in, Eigen::array<DenseIndex, 3>{{2,2,2}});
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Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout, DenseIndex> > gpu_out_max(d_out_max);
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Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout, DenseIndex> > gpu_out_min(d_out_min);
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sycl_device.memcpyHostToDevice(d_in, in.data(),in_bytes);
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gpu_out_max.device(sycl_device) = gpu_in.argmax();
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gpu_out_min.device(sycl_device) = gpu_in.argmin();
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sycl_device.memcpyDeviceToHost(out_max.data(), d_out_max, out_bytes);
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sycl_device.memcpyDeviceToHost(out_min.data(), d_out_min, out_bytes);
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VERIFY_IS_EQUAL(out_max(), 2*2*2 - 1);
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VERIFY_IS_EQUAL(out_min(), 0);
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sycl_device.deallocate(d_in);
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sycl_device.deallocate(d_out_max);
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sycl_device.deallocate(d_out_min);
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}
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template <typename DataType, int DataLayout, typename DenseIndex>
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static void test_sycl_argmax_dim(const Eigen::SyclDevice &sycl_device)
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{
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DenseIndex sizeDim0=9;
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DenseIndex sizeDim1=3;
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DenseIndex sizeDim2=5;
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DenseIndex sizeDim3=7;
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Tensor<DataType, 4, DataLayout, DenseIndex> tensor(sizeDim0,sizeDim1,sizeDim2,sizeDim3);
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std::vector<DenseIndex> dims;
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dims.push_back(sizeDim0); dims.push_back(sizeDim1); dims.push_back(sizeDim2); dims.push_back(sizeDim3);
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for (DenseIndex dim = 0; dim < 4; ++dim) {
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array<DenseIndex, 3> out_shape;
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for (DenseIndex d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d+1];
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Tensor<DenseIndex, 3, DataLayout, DenseIndex> tensor_arg(out_shape);
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array<DenseIndex, 4> ix;
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for (DenseIndex i = 0; i < sizeDim0; ++i) {
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for (DenseIndex j = 0; j < sizeDim1; ++j) {
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for (DenseIndex k = 0; k < sizeDim2; ++k) {
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for (DenseIndex l = 0; l < sizeDim3; ++l) {
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ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
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// suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = 10.0
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tensor(ix)=(ix[dim] != 0)?-1.0:10.0;
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}
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}
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}
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}
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std::size_t in_bytes = tensor.size() * sizeof(DataType);
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std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
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DataType * d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
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DenseIndex* d_out= static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
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Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, DenseIndex> > gpu_in(d_in, Eigen::array<DenseIndex, 4>{{sizeDim0,sizeDim1,sizeDim2,sizeDim3}});
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Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout, DenseIndex> > gpu_out(d_out, out_shape);
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sycl_device.memcpyHostToDevice(d_in, tensor.data(),in_bytes);
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gpu_out.device(sycl_device) = gpu_in.argmax(dim);
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sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
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VERIFY_IS_EQUAL(static_cast<size_t>(tensor_arg.size()),
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size_t(sizeDim0*sizeDim1*sizeDim2*sizeDim3 / tensor.dimension(dim)));
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for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
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// Expect max to be in the first index of the reduced dimension
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VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
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}
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sycl_device.synchronize();
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for (DenseIndex i = 0; i < sizeDim0; ++i) {
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for (DenseIndex j = 0; j < sizeDim1; ++j) {
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for (DenseIndex k = 0; k < sizeDim2; ++k) {
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for (DenseIndex l = 0; l < sizeDim3; ++l) {
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ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
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// suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
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tensor(ix)=(ix[dim] != tensor.dimension(dim) - 1)?-1.0:20.0;
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}
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}
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}
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}
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sycl_device.memcpyHostToDevice(d_in, tensor.data(),in_bytes);
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gpu_out.device(sycl_device) = gpu_in.argmax(dim);
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sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
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for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
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// Expect max to be in the last index of the reduced dimension
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VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
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}
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sycl_device.deallocate(d_in);
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sycl_device.deallocate(d_out);
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}
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}
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template <typename DataType, int DataLayout, typename DenseIndex>
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static void test_sycl_argmin_dim(const Eigen::SyclDevice &sycl_device)
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{
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DenseIndex sizeDim0=9;
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DenseIndex sizeDim1=3;
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DenseIndex sizeDim2=5;
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DenseIndex sizeDim3=7;
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Tensor<DataType, 4, DataLayout, DenseIndex> tensor(sizeDim0,sizeDim1,sizeDim2,sizeDim3);
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std::vector<DenseIndex> dims;
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dims.push_back(sizeDim0); dims.push_back(sizeDim1); dims.push_back(sizeDim2); dims.push_back(sizeDim3);
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for (DenseIndex dim = 0; dim < 4; ++dim) {
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array<DenseIndex, 3> out_shape;
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for (DenseIndex d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d+1];
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Tensor<DenseIndex, 3, DataLayout, DenseIndex> tensor_arg(out_shape);
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array<DenseIndex, 4> ix;
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for (DenseIndex i = 0; i < sizeDim0; ++i) {
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for (DenseIndex j = 0; j < sizeDim1; ++j) {
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for (DenseIndex k = 0; k < sizeDim2; ++k) {
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for (DenseIndex l = 0; l < sizeDim3; ++l) {
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ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
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// suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = 10.0
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tensor(ix)=(ix[dim] != 0)?1.0:-10.0;
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}
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}
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}
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}
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std::size_t in_bytes = tensor.size() * sizeof(DataType);
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std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
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DataType * d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
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DenseIndex* d_out= static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
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Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, DenseIndex> > gpu_in(d_in, Eigen::array<DenseIndex, 4>{{sizeDim0,sizeDim1,sizeDim2,sizeDim3}});
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Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout, DenseIndex> > gpu_out(d_out, out_shape);
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sycl_device.memcpyHostToDevice(d_in, tensor.data(),in_bytes);
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gpu_out.device(sycl_device) = gpu_in.argmin(dim);
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sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
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VERIFY_IS_EQUAL(static_cast<size_t>(tensor_arg.size()),
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size_t(sizeDim0*sizeDim1*sizeDim2*sizeDim3 / tensor.dimension(dim)));
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for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
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// Expect max to be in the first index of the reduced dimension
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VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
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}
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sycl_device.synchronize();
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for (DenseIndex i = 0; i < sizeDim0; ++i) {
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for (DenseIndex j = 0; j < sizeDim1; ++j) {
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for (DenseIndex k = 0; k < sizeDim2; ++k) {
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for (DenseIndex l = 0; l < sizeDim3; ++l) {
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ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
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// suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
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tensor(ix)=(ix[dim] != tensor.dimension(dim) - 1)?1.0:-20.0;
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}
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}
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}
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}
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sycl_device.memcpyHostToDevice(d_in, tensor.data(),in_bytes);
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gpu_out.device(sycl_device) = gpu_in.argmin(dim);
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sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
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for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
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// Expect max to be in the last index of the reduced dimension
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VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
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}
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sycl_device.deallocate(d_in);
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sycl_device.deallocate(d_out);
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}
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}
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template<typename DataType, typename Device_Selector> void sycl_argmax_test_per_device(const Device_Selector& d){
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QueueInterface queueInterface(d);
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auto sycl_device = Eigen::SyclDevice(&queueInterface);
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test_sycl_simple_argmax<DataType, RowMajor, int64_t>(sycl_device);
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test_sycl_simple_argmax<DataType, ColMajor, int64_t>(sycl_device);
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test_sycl_argmax_dim<DataType, ColMajor, int64_t>(sycl_device);
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test_sycl_argmax_dim<DataType, RowMajor, int64_t>(sycl_device);
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test_sycl_argmin_dim<DataType, ColMajor, int64_t>(sycl_device);
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test_sycl_argmin_dim<DataType, RowMajor, int64_t>(sycl_device);
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}
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EIGEN_DECLARE_TEST(cxx11_tensor_argmax_sycl) {
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for (const auto& device :Eigen::get_sycl_supported_devices()) {
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CALL_SUBTEST(sycl_argmax_test_per_device<double>(device));
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}
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}
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