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439 lines
15 KiB
Plaintext
439 lines
15 KiB
Plaintext
// 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) 2014 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_DEFAULT_DENSE_INDEX_TYPE int
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#define EIGEN_USE_GPU
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#include "main.h"
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#include "OffByOneScalar.h"
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#include <unsupported/Eigen/CXX11/Tensor>
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#include <unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h>
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using Eigen::RowMajor;
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using Eigen::Tensor;
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// Context for evaluation on cpu
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struct CPUContext {
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CPUContext(const Eigen::Tensor<float, 3>& in1, Eigen::Tensor<float, 3>& in2, Eigen::Tensor<float, 3>& out)
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: in1_(in1), in2_(in2), out_(out), kernel_1d_(2), kernel_2d_(2, 2), kernel_3d_(2, 2, 2) {
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kernel_1d_(0) = 3.14f;
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kernel_1d_(1) = 2.7f;
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kernel_2d_(0, 0) = 3.14f;
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kernel_2d_(1, 0) = 2.7f;
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kernel_2d_(0, 1) = 0.2f;
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kernel_2d_(1, 1) = 7.0f;
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kernel_3d_(0, 0, 0) = 3.14f;
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kernel_3d_(0, 1, 0) = 2.7f;
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kernel_3d_(0, 0, 1) = 0.2f;
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kernel_3d_(0, 1, 1) = 7.0f;
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kernel_3d_(1, 0, 0) = -1.0f;
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kernel_3d_(1, 1, 0) = -0.3f;
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kernel_3d_(1, 0, 1) = -0.7f;
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kernel_3d_(1, 1, 1) = -0.5f;
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}
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const Eigen::DefaultDevice& device() const { return cpu_device_; }
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const Eigen::Tensor<float, 3>& in1() const { return in1_; }
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const Eigen::Tensor<float, 3>& in2() const { return in2_; }
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Eigen::Tensor<float, 3>& out() { return out_; }
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const Eigen::Tensor<float, 1>& kernel1d() const { return kernel_1d_; }
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const Eigen::Tensor<float, 2>& kernel2d() const { return kernel_2d_; }
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const Eigen::Tensor<float, 3>& kernel3d() const { return kernel_3d_; }
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private:
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const Eigen::Tensor<float, 3>& in1_;
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const Eigen::Tensor<float, 3>& in2_;
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Eigen::Tensor<float, 3>& out_;
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Eigen::Tensor<float, 1> kernel_1d_;
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Eigen::Tensor<float, 2> kernel_2d_;
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Eigen::Tensor<float, 3> kernel_3d_;
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Eigen::DefaultDevice cpu_device_;
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};
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// Context for evaluation on GPU
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struct GPUContext {
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GPUContext(const Eigen::TensorMap<Eigen::Tensor<float, 3>>& in1, Eigen::TensorMap<Eigen::Tensor<float, 3>>& in2,
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Eigen::TensorMap<Eigen::Tensor<float, 3>>& out)
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: in1_(in1), in2_(in2), out_(out), gpu_device_(&stream_) {
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assert(gpuMalloc((void**)(&kernel_1d_), 2 * sizeof(float)) == gpuSuccess);
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float kernel_1d_val[] = {3.14f, 2.7f};
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assert(gpuMemcpy(kernel_1d_, kernel_1d_val, 2 * sizeof(float), gpuMemcpyHostToDevice) == gpuSuccess);
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assert(gpuMalloc((void**)(&kernel_2d_), 4 * sizeof(float)) == gpuSuccess);
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float kernel_2d_val[] = {3.14f, 2.7f, 0.2f, 7.0f};
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assert(gpuMemcpy(kernel_2d_, kernel_2d_val, 4 * sizeof(float), gpuMemcpyHostToDevice) == gpuSuccess);
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assert(gpuMalloc((void**)(&kernel_3d_), 8 * sizeof(float)) == gpuSuccess);
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float kernel_3d_val[] = {3.14f, -1.0f, 2.7f, -0.3f, 0.2f, -0.7f, 7.0f, -0.5f};
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assert(gpuMemcpy(kernel_3d_, kernel_3d_val, 8 * sizeof(float), gpuMemcpyHostToDevice) == gpuSuccess);
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}
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~GPUContext() {
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assert(gpuFree(kernel_1d_) == gpuSuccess);
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assert(gpuFree(kernel_2d_) == gpuSuccess);
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assert(gpuFree(kernel_3d_) == gpuSuccess);
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}
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const Eigen::GpuDevice& device() const { return gpu_device_; }
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const Eigen::TensorMap<Eigen::Tensor<float, 3>>& in1() const { return in1_; }
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const Eigen::TensorMap<Eigen::Tensor<float, 3>>& in2() const { return in2_; }
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Eigen::TensorMap<Eigen::Tensor<float, 3>>& out() { return out_; }
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Eigen::TensorMap<Eigen::Tensor<float, 1>> kernel1d() const {
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return Eigen::TensorMap<Eigen::Tensor<float, 1>>(kernel_1d_, 2);
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}
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Eigen::TensorMap<Eigen::Tensor<float, 2>> kernel2d() const {
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return Eigen::TensorMap<Eigen::Tensor<float, 2>>(kernel_2d_, 2, 2);
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}
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Eigen::TensorMap<Eigen::Tensor<float, 3>> kernel3d() const {
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return Eigen::TensorMap<Eigen::Tensor<float, 3>>(kernel_3d_, 2, 2, 2);
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}
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private:
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const Eigen::TensorMap<Eigen::Tensor<float, 3>>& in1_;
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const Eigen::TensorMap<Eigen::Tensor<float, 3>>& in2_;
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Eigen::TensorMap<Eigen::Tensor<float, 3>>& out_;
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float* kernel_1d_;
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float* kernel_2d_;
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float* kernel_3d_;
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Eigen::GpuStreamDevice stream_;
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Eigen::GpuDevice gpu_device_;
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};
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// The actual expression to evaluate
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template <typename Context>
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void test_contextual_eval(Context* context) {
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context->out().device(context->device()) = context->in1() + context->in2() * 3.14f + context->in1().constant(2.718f);
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}
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template <typename Context>
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void test_forced_contextual_eval(Context* context) {
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context->out().device(context->device()) =
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(context->in1() + context->in2()).eval() * 3.14f + context->in1().constant(2.718f);
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}
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template <typename Context>
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void test_compound_assignment(Context* context) {
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context->out().device(context->device()) = context->in1().constant(2.718f);
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context->out().device(context->device()) += context->in1() + context->in2() * 3.14f;
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}
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template <typename Context>
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void test_contraction(Context* context) {
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Eigen::array<std::pair<int, int>, 2> dims;
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dims[0] = std::make_pair(1, 1);
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dims[1] = std::make_pair(2, 2);
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Eigen::array<int, 2> shape{40, 50 * 70};
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Eigen::DSizes<int, 2> indices(0, 0);
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Eigen::DSizes<int, 2> sizes(40, 40);
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context->out().reshape(shape).slice(indices, sizes).device(context->device()) =
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context->in1().contract(context->in2(), dims);
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}
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template <typename Context>
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void test_1d_convolution(Context* context) {
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Eigen::DSizes<int, 3> indices(0, 0, 0);
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Eigen::DSizes<int, 3> sizes(40, 49, 70);
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Eigen::array<int, 1> dims{1};
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context->out().slice(indices, sizes).device(context->device()) = context->in1().convolve(context->kernel1d(), dims);
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}
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template <typename Context>
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void test_2d_convolution(Context* context) {
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Eigen::DSizes<int, 3> indices(0, 0, 0);
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Eigen::DSizes<int, 3> sizes(40, 49, 69);
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Eigen::array<int, 2> dims{1, 2};
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context->out().slice(indices, sizes).device(context->device()) = context->in1().convolve(context->kernel2d(), dims);
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}
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template <typename Context>
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void test_3d_convolution(Context* context) {
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Eigen::DSizes<int, 3> indices(0, 0, 0);
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Eigen::DSizes<int, 3> sizes(39, 49, 69);
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Eigen::array<int, 3> dims{0, 1, 2};
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context->out().slice(indices, sizes).device(context->device()) = context->in1().convolve(context->kernel3d(), dims);
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}
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// Helper method to synchronize device.
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template <typename Device>
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void synchronize(Device& device) { /*nothing*/
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}
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template <>
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void synchronize(Eigen::GpuDevice& device) {
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device.synchronize();
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}
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template <typename DataType, typename TensorDevice>
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void test_device_memory(const TensorDevice& device) {
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int count = 100;
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Eigen::array<int, 1> tensorRange{count};
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Eigen::Tensor<DataType, 1> host(tensorRange);
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Eigen::Tensor<DataType, 1> expected(tensorRange);
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DataType* device_data = static_cast<DataType*>(device.allocate(count * sizeof(DataType)));
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// memset
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const char byte_value = static_cast<char>(0xAB);
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device.memset(device_data, byte_value, count * sizeof(DataType));
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device.memcpyDeviceToHost(host.data(), device_data, count * sizeof(DataType));
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synchronize(device);
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memset(expected.data(), byte_value, count * sizeof(DataType));
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for (size_t i = 0; i < count; i++) {
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VERIFY_IS_EQUAL(host(i), expected(i));
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}
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// fill
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DataType fill_value = DataType(7);
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std::fill_n(expected.data(), count, fill_value);
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device.fill(device_data, device_data + count, fill_value);
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device.memcpyDeviceToHost(host.data(), device_data, count * sizeof(DataType));
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synchronize(device);
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for (int i = 0; i < count; i++) {
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VERIFY_IS_EQUAL(host(i), expected(i));
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}
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device.deallocate(device_data);
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}
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void test_cpu() {
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Eigen::Tensor<float, 3> in1(40, 50, 70);
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Eigen::Tensor<float, 3> in2(40, 50, 70);
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Eigen::Tensor<float, 3> out(40, 50, 70);
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in1 = in1.random() + in1.constant(10.0f);
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in2 = in2.random() + in2.constant(10.0f);
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CPUContext context(in1, in2, out);
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test_contextual_eval(&context);
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for (int i = 0; i < 40; ++i) {
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for (int j = 0; j < 50; ++j) {
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for (int k = 0; k < 70; ++k) {
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VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) + in2(i, j, k) * 3.14f + 2.718f);
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}
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}
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}
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test_forced_contextual_eval(&context);
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for (int i = 0; i < 40; ++i) {
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for (int j = 0; j < 50; ++j) {
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for (int k = 0; k < 70; ++k) {
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VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) + in2(i, j, k)) * 3.14f + 2.718f);
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}
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}
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}
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test_compound_assignment(&context);
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for (int i = 0; i < 40; ++i) {
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for (int j = 0; j < 50; ++j) {
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for (int k = 0; k < 70; ++k) {
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VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) + in2(i, j, k) * 3.14f + 2.718f);
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}
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}
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}
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test_contraction(&context);
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for (int i = 0; i < 40; ++i) {
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for (int j = 0; j < 40; ++j) {
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const float result = out(i, j, 0);
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float expected = 0;
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for (int k = 0; k < 50; ++k) {
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for (int l = 0; l < 70; ++l) {
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expected += in1(i, k, l) * in2(j, k, l);
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}
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}
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VERIFY_IS_APPROX(expected, result);
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}
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}
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test_1d_convolution(&context);
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for (int i = 0; i < 40; ++i) {
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for (int j = 0; j < 49; ++j) {
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for (int k = 0; k < 70; ++k) {
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VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) * 3.14f + in1(i, j + 1, k) * 2.7f));
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}
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}
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}
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test_2d_convolution(&context);
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for (int i = 0; i < 40; ++i) {
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for (int j = 0; j < 49; ++j) {
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for (int k = 0; k < 69; ++k) {
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const float result = out(i, j, k);
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const float expected =
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(in1(i, j, k) * 3.14f + in1(i, j + 1, k) * 2.7f) + (in1(i, j, k + 1) * 0.2f + in1(i, j + 1, k + 1) * 7.0f);
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if (fabs(expected) < 1e-4f && fabs(result) < 1e-4f) {
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continue;
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}
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VERIFY_IS_APPROX(expected, result);
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}
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}
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}
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test_3d_convolution(&context);
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for (int i = 0; i < 39; ++i) {
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for (int j = 0; j < 49; ++j) {
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for (int k = 0; k < 69; ++k) {
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const float result = out(i, j, k);
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const float expected =
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(in1(i, j, k) * 3.14f + in1(i, j + 1, k) * 2.7f + in1(i, j, k + 1) * 0.2f + in1(i, j + 1, k + 1) * 7.0f) +
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(in1(i + 1, j, k) * -1.0f + in1(i + 1, j + 1, k) * -0.3f + in1(i + 1, j, k + 1) * -0.7f +
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in1(i + 1, j + 1, k + 1) * -0.5f);
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if (fabs(expected) < 1e-4f && fabs(result) < 1e-4f) {
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continue;
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}
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VERIFY_IS_APPROX(expected, result);
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}
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}
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}
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test_device_memory<float>(context.device());
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test_device_memory<OffByOneScalar<int>>(context.device());
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}
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void test_gpu() {
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Eigen::Tensor<float, 3> in1(40, 50, 70);
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Eigen::Tensor<float, 3> in2(40, 50, 70);
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Eigen::Tensor<float, 3> out(40, 50, 70);
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in1 = in1.random() + in1.constant(10.0f);
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in2 = in2.random() + in2.constant(10.0f);
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std::size_t in1_bytes = in1.size() * sizeof(float);
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std::size_t in2_bytes = in2.size() * sizeof(float);
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std::size_t out_bytes = out.size() * sizeof(float);
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float* d_in1;
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float* d_in2;
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float* d_out;
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gpuMalloc((void**)(&d_in1), in1_bytes);
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gpuMalloc((void**)(&d_in2), in2_bytes);
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gpuMalloc((void**)(&d_out), out_bytes);
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gpuMemcpy(d_in1, in1.data(), in1_bytes, gpuMemcpyHostToDevice);
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gpuMemcpy(d_in2, in2.data(), in2_bytes, gpuMemcpyHostToDevice);
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Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in1(d_in1, 40, 50, 70);
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Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in2(d_in2, 40, 50, 70);
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Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_out(d_out, 40, 50, 70);
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GPUContext context(gpu_in1, gpu_in2, gpu_out);
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test_contextual_eval(&context);
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assert(gpuMemcpy(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost) == gpuSuccess);
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for (int i = 0; i < 40; ++i) {
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for (int j = 0; j < 50; ++j) {
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for (int k = 0; k < 70; ++k) {
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VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) + in2(i, j, k) * 3.14f + 2.718f);
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}
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}
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}
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test_forced_contextual_eval(&context);
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assert(gpuMemcpy(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost) == gpuSuccess);
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for (int i = 0; i < 40; ++i) {
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for (int j = 0; j < 50; ++j) {
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for (int k = 0; k < 70; ++k) {
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VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) + in2(i, j, k)) * 3.14f + 2.718f);
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}
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}
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}
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test_compound_assignment(&context);
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assert(gpuMemcpy(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost) == gpuSuccess);
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for (int i = 0; i < 40; ++i) {
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for (int j = 0; j < 50; ++j) {
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for (int k = 0; k < 70; ++k) {
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VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) + in2(i, j, k) * 3.14f + 2.718f);
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}
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}
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}
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test_contraction(&context);
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assert(gpuMemcpy(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost) == gpuSuccess);
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for (int i = 0; i < 40; ++i) {
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for (int j = 0; j < 40; ++j) {
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const float result = out(i, j, 0);
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float expected = 0;
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for (int k = 0; k < 50; ++k) {
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for (int l = 0; l < 70; ++l) {
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expected += in1(i, k, l) * in2(j, k, l);
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}
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}
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VERIFY_IS_APPROX(expected, result);
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}
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}
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test_1d_convolution(&context);
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assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, context.device().stream()) == gpuSuccess);
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assert(gpuStreamSynchronize(context.device().stream()) == gpuSuccess);
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for (int i = 0; i < 40; ++i) {
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for (int j = 0; j < 49; ++j) {
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for (int k = 0; k < 70; ++k) {
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VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) * 3.14f + in1(i, j + 1, k) * 2.7f));
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}
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}
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}
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test_2d_convolution(&context);
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assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, context.device().stream()) == gpuSuccess);
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assert(gpuStreamSynchronize(context.device().stream()) == gpuSuccess);
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for (int i = 0; i < 40; ++i) {
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for (int j = 0; j < 49; ++j) {
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for (int k = 0; k < 69; ++k) {
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const float result = out(i, j, k);
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const float expected =
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(in1(i, j, k) * 3.14f + in1(i, j + 1, k) * 2.7f + in1(i, j, k + 1) * 0.2f + in1(i, j + 1, k + 1) * 7.0f);
|
|
VERIFY_IS_APPROX(expected, result);
|
|
}
|
|
}
|
|
}
|
|
|
|
#if !defined(EIGEN_USE_HIP)
|
|
// disable this test on the HIP platform
|
|
// 3D tensor convolutions seem to hang on the HIP platform
|
|
|
|
test_3d_convolution(&context);
|
|
assert(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, context.device().stream()) == gpuSuccess);
|
|
assert(gpuStreamSynchronize(context.device().stream()) == gpuSuccess);
|
|
for (int i = 0; i < 39; ++i) {
|
|
for (int j = 0; j < 49; ++j) {
|
|
for (int k = 0; k < 69; ++k) {
|
|
const float result = out(i, j, k);
|
|
const float expected = (in1(i, j, k) * 3.14f + in1(i, j + 1, k) * 2.7f + in1(i, j, k + 1) * 0.2f +
|
|
in1(i, j + 1, k + 1) * 7.0f + in1(i + 1, j, k) * -1.0f + in1(i + 1, j + 1, k) * -0.3f +
|
|
in1(i + 1, j, k + 1) * -0.7f + in1(i + 1, j + 1, k + 1) * -0.5f);
|
|
VERIFY_IS_APPROX(expected, result);
|
|
}
|
|
}
|
|
}
|
|
|
|
#endif
|
|
|
|
test_device_memory<float>(context.device());
|
|
test_device_memory<OffByOneScalar<int>>(context.device());
|
|
}
|
|
|
|
EIGEN_DECLARE_TEST(cxx11_tensor_device) {
|
|
CALL_SUBTEST_1(test_cpu());
|
|
CALL_SUBTEST_2(test_gpu());
|
|
}
|