2015-06-30 05:10:32 +08:00
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// 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) 2015 Benoit Steiner <benoit.steiner.goog@gmail.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_reduction_cuda
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#define EIGEN_USE_GPU
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2016-09-05 23:14:20 +08:00
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#if defined __CUDACC_VER__ && __CUDACC_VER__ >= 70500
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2016-09-01 19:39:33 +08:00
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#include <cuda_fp16.h>
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2016-09-05 23:14:20 +08:00
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#endif
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2015-06-30 05:10:32 +08:00
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#include "main.h"
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#include <unsupported/Eigen/CXX11/Tensor>
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2016-07-02 02:08:26 +08:00
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template<typename Type, int DataLayout>
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2015-06-30 05:10:32 +08:00
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static void test_full_reductions() {
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2015-07-16 03:39:26 +08:00
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Eigen::CudaStreamDevice stream;
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Eigen::GpuDevice gpu_device(&stream);
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2015-06-30 05:10:32 +08:00
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const int num_rows = internal::random<int>(1024, 5*1024);
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const int num_cols = internal::random<int>(1024, 5*1024);
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2016-07-02 02:08:26 +08:00
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Tensor<Type, 2, DataLayout> in(num_rows, num_cols);
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2015-06-30 05:10:32 +08:00
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in.setRandom();
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2016-07-02 02:08:26 +08:00
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Tensor<Type, 0, DataLayout> full_redux;
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2015-06-30 05:10:32 +08:00
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full_redux = in.sum();
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2016-07-02 02:08:26 +08:00
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std::size_t in_bytes = in.size() * sizeof(Type);
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std::size_t out_bytes = full_redux.size() * sizeof(Type);
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Type* gpu_in_ptr = static_cast<Type*>(gpu_device.allocate(in_bytes));
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Type* gpu_out_ptr = static_cast<Type*>(gpu_device.allocate(out_bytes));
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2015-06-30 05:10:32 +08:00
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gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes);
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2016-07-02 02:08:26 +08:00
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TensorMap<Tensor<Type, 2, DataLayout> > in_gpu(gpu_in_ptr, num_rows, num_cols);
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TensorMap<Tensor<Type, 0, DataLayout> > out_gpu(gpu_out_ptr);
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2015-06-30 05:10:32 +08:00
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out_gpu.device(gpu_device) = in_gpu.sum();
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2016-07-02 02:08:26 +08:00
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Tensor<Type, 0, DataLayout> full_redux_gpu;
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2015-06-30 05:10:32 +08:00
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gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes);
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gpu_device.synchronize();
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// Check that the CPU and GPU reductions return the same result.
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2015-11-06 06:22:30 +08:00
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VERIFY_IS_APPROX(full_redux(), full_redux_gpu());
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2016-01-31 03:59:22 +08:00
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gpu_device.deallocate(gpu_in_ptr);
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gpu_device.deallocate(gpu_out_ptr);
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2015-06-30 05:10:32 +08:00
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}
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2016-09-13 09:36:52 +08:00
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template<typename Type, int DataLayout>
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static void test_first_dim_reductions() {
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int dim_x = 33;
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int dim_y = 1;
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int dim_z = 128;
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Tensor<Type, 3, DataLayout> in(dim_x, dim_y, dim_z);
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in.setRandom();
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Eigen::array<int, 1> red_axis;
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red_axis[0] = 0;
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Tensor<Type, 2, DataLayout> redux = in.sum(red_axis);
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// Create device
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Eigen::CudaStreamDevice stream;
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Eigen::GpuDevice dev(&stream);
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// Create data(T)
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Type* in_data = (Type*)dev.allocate(dim_x*dim_y*dim_z*sizeof(Type));
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Type* out_data = (Type*)dev.allocate(dim_z*dim_y*sizeof(Type));
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Eigen::TensorMap<Eigen::Tensor<Type, 3, DataLayout> > gpu_in(in_data, dim_x, dim_y, dim_z);
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Eigen::TensorMap<Eigen::Tensor<Type, 2, DataLayout> > gpu_out(out_data, dim_y, dim_z);
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// Perform operation
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dev.memcpyHostToDevice(in_data, in.data(), in.size()*sizeof(Type));
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gpu_out.device(dev) = gpu_in.sum(red_axis);
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gpu_out.device(dev) += gpu_in.sum(red_axis);
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Tensor<Type, 2, DataLayout> redux_gpu(dim_y, dim_z);
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dev.memcpyDeviceToHost(redux_gpu.data(), out_data, gpu_out.size()*sizeof(Type));
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dev.synchronize();
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// Check that the CPU and GPU reductions return the same result.
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for (int i = 0; i < gpu_out.size(); ++i) {
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VERIFY_IS_APPROX(2*redux(i), redux_gpu(i));
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}
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dev.deallocate(in_data);
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dev.deallocate(out_data);
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}
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template<typename Type, int DataLayout>
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static void test_last_dim_reductions() {
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int dim_x = 128;
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int dim_y = 1;
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int dim_z = 33;
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Tensor<Type, 3, DataLayout> in(dim_x, dim_y, dim_z);
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in.setRandom();
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Eigen::array<int, 1> red_axis;
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red_axis[0] = 2;
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Tensor<Type, 2, DataLayout> redux = in.sum(red_axis);
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// Create device
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Eigen::CudaStreamDevice stream;
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Eigen::GpuDevice dev(&stream);
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// Create data
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Type* in_data = (Type*)dev.allocate(dim_x*dim_y*dim_z*sizeof(Type));
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Type* out_data = (Type*)dev.allocate(dim_x*dim_y*sizeof(Type));
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Eigen::TensorMap<Eigen::Tensor<Type, 3, DataLayout> > gpu_in(in_data, dim_x, dim_y, dim_z);
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Eigen::TensorMap<Eigen::Tensor<Type, 2, DataLayout> > gpu_out(out_data, dim_x, dim_y);
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// Perform operation
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dev.memcpyHostToDevice(in_data, in.data(), in.size()*sizeof(Type));
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gpu_out.device(dev) = gpu_in.sum(red_axis);
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gpu_out.device(dev) += gpu_in.sum(red_axis);
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Tensor<Type, 2, DataLayout> redux_gpu(dim_x, dim_y);
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dev.memcpyDeviceToHost(redux_gpu.data(), out_data, gpu_out.size()*sizeof(Type));
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dev.synchronize();
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// Check that the CPU and GPU reductions return the same result.
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for (int i = 0; i < gpu_out.size(); ++i) {
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VERIFY_IS_APPROX(2*redux(i), redux_gpu(i));
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}
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dev.deallocate(in_data);
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dev.deallocate(out_data);
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}
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2015-06-30 05:10:32 +08:00
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void test_cxx11_tensor_reduction_cuda() {
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2016-07-02 02:08:26 +08:00
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CALL_SUBTEST_1((test_full_reductions<float, ColMajor>()));
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CALL_SUBTEST_1((test_full_reductions<double, ColMajor>()));
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CALL_SUBTEST_2((test_full_reductions<float, RowMajor>()));
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CALL_SUBTEST_2((test_full_reductions<double, RowMajor>()));
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2016-09-13 09:36:52 +08:00
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CALL_SUBTEST_3((test_first_dim_reductions<float, ColMajor>()));
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CALL_SUBTEST_3((test_first_dim_reductions<double, ColMajor>()));
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CALL_SUBTEST_4((test_first_dim_reductions<float, RowMajor>()));
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// Outer reductions of doubles aren't supported just yet.
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// CALL_SUBTEST_4((test_first_dim_reductions<double, RowMajor>()))
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CALL_SUBTEST_5((test_last_dim_reductions<float, ColMajor>()));
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// Outer reductions of doubles aren't supported just yet.
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// CALL_SUBTEST_5((test_last_dim_reductions<double, ColMajor>()));
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CALL_SUBTEST_6((test_last_dim_reductions<float, RowMajor>()));
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CALL_SUBTEST_6((test_last_dim_reductions<double, RowMajor>()));
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2015-06-30 05:10:32 +08:00
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}
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