// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_TEST_FUNC cxx11_tensor_device
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_GPU
#if defined __CUDACC_VER__ && __CUDACC_VER__ >= 70500
#include <cuda_fp16.h>
#endif
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::Tensor;
using Eigen::RowMajor;
// Context for evaluation on cpu
struct CPUContext {
CPUContext(const Eigen::Tensor<float, 3>& in1, Eigen::Tensor<float, 3>& in2, Eigen::Tensor<float, 3>& out) : in1_(in1), in2_(in2), out_(out), kernel_1d_(2), kernel_2d_(2,2), kernel_3d_(2,2,2) {
kernel_1d_(0) = 3.14f;
kernel_1d_(1) = 2.7f;
kernel_2d_(0,0) = 3.14f;
kernel_2d_(1,0) = 2.7f;
kernel_2d_(0,1) = 0.2f;
kernel_2d_(1,1) = 7.0f;
kernel_3d_(0,0,0) = 3.14f;
kernel_3d_(0,1,0) = 2.7f;
kernel_3d_(0,0,1) = 0.2f;
kernel_3d_(0,1,1) = 7.0f;
kernel_3d_(1,0,0) = -1.0f;
kernel_3d_(1,1,0) = -0.3f;
kernel_3d_(1,0,1) = -0.7f;
kernel_3d_(1,1,1) = -0.5f;
}
const Eigen::DefaultDevice& device() const { return cpu_device_; }
const Eigen::Tensor<float, 3>& in1() const { return in1_; }
const Eigen::Tensor<float, 3>& in2() const { return in2_; }
Eigen::Tensor<float, 3>& out() { return out_; }
const Eigen::Tensor<float, 1>& kernel1d() const { return kernel_1d_; }
const Eigen::Tensor<float, 2>& kernel2d() const { return kernel_2d_; }
const Eigen::Tensor<float, 3>& kernel3d() const { return kernel_3d_; }
private:
const Eigen::Tensor<float, 3>& in1_;
const Eigen::Tensor<float, 3>& in2_;
Eigen::Tensor<float, 3>& out_;
Eigen::Tensor<float, 1> kernel_1d_;
Eigen::Tensor<float, 2> kernel_2d_;
Eigen::Tensor<float, 3> kernel_3d_;
Eigen::DefaultDevice cpu_device_;
};
// Context for evaluation on GPU
struct GPUContext {
GPUContext(const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in1, Eigen::TensorMap<Eigen::Tensor<float, 3> >& in2, Eigen::TensorMap<Eigen::Tensor<float, 3> >& out) : in1_(in1), in2_(in2), out_(out), gpu_device_(&stream_) {
assert(cudaMalloc((void**)(&kernel_1d_), 2*sizeof(float)) == cudaSuccess);
float kernel_1d_val[] = {3.14f, 2.7f};
assert(cudaMemcpy(kernel_1d_, kernel_1d_val, 2*sizeof(float), cudaMemcpyHostToDevice) == cudaSuccess);
assert(cudaMalloc((void**)(&kernel_2d_), 4*sizeof(float)) == cudaSuccess);
float kernel_2d_val[] = {3.14f, 2.7f, 0.2f, 7.0f};
assert(cudaMemcpy(kernel_2d_, kernel_2d_val, 4*sizeof(float), cudaMemcpyHostToDevice) == cudaSuccess);
assert(cudaMalloc((void**)(&kernel_3d_), 8*sizeof(float)) == cudaSuccess);
float kernel_3d_val[] = {3.14f, -1.0f, 2.7f, -0.3f, 0.2f, -0.7f, 7.0f, -0.5f};
assert(cudaMemcpy(kernel_3d_, kernel_3d_val, 8*sizeof(float), cudaMemcpyHostToDevice) == cudaSuccess);
}
~GPUContext() {
assert(cudaFree(kernel_1d_) == cudaSuccess);
assert(cudaFree(kernel_2d_) == cudaSuccess);
assert(cudaFree(kernel_3d_) == cudaSuccess);
}
const Eigen::GpuDevice& device() const { return gpu_device_; }
const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in1() const { return in1_; }
const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in2() const { return in2_; }
Eigen::TensorMap<Eigen::Tensor<float, 3> >& out() { return out_; }
Eigen::TensorMap<Eigen::Tensor<float, 1> > kernel1d() const { return Eigen::TensorMap<Eigen::Tensor<float, 1> >(kernel_1d_, 2); }
Eigen::TensorMap<Eigen::Tensor<float, 2> > kernel2d() const { return Eigen::TensorMap<Eigen::Tensor<float, 2> >(kernel_2d_, 2, 2); }
Eigen::TensorMap<Eigen::Tensor<float, 3> > kernel3d() const { return Eigen::TensorMap<Eigen::Tensor<float, 3> >(kernel_3d_, 2, 2, 2); }
private:
const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in1_;
const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in2_;
Eigen::TensorMap<Eigen::Tensor<float, 3> >& out_;
float* kernel_1d_;
float* kernel_2d_;
float* kernel_3d_;
Eigen::CudaStreamDevice stream_;
Eigen::GpuDevice gpu_device_;
};
// The actual expression to evaluate
template <typename Context>
void test_contextual_eval(Context* context)
{
context->out().device(context->device()) = context->in1() + context->in2() * 3.14f + context->in1().constant(2.718f);
}
template <typename Context>
void test_forced_contextual_eval(Context* context)
{
context->out().device(context->device()) = (context->in1() + context->in2()).eval() * 3.14f + context->in1().constant(2.718f);
}
template <typename Context>
void test_compound_assignment(Context* context)
{
context->out().device(context->device()) = context->in1().constant(2.718f);
context->out().device(context->device()) += context->in1() + context->in2() * 3.14f;
}
template <typename Context>
void test_contraction(Context* context)
{
Eigen::array<std::pair<int, int>, 2> dims;
dims[0] = std::make_pair(1, 1);
dims[1] = std::make_pair(2, 2);
Eigen::array<int, 2> shape(40, 50*70);
Eigen::DSizes<int, 2> indices(0,0);
Eigen::DSizes<int, 2> sizes(40,40);
context->out().reshape(shape).slice(indices, sizes).device(context->device()) = context->in1().contract(context->in2(), dims);
}
template <typename Context>
void test_1d_convolution(Context* context)
{
Eigen::DSizes<int, 3> indices(0,0,0);
Eigen::DSizes<int, 3> sizes(40,49,70);
Eigen::array<int, 1> dims(1);
context->out().slice(indices, sizes).device(context->device()) = context->in1().convolve(context->kernel1d(), dims);
}
template <typename Context>
void test_2d_convolution(Context* context)
{
Eigen::DSizes<int, 3> indices(0,0,0);
Eigen::DSizes<int, 3> sizes(40,49,69);
Eigen::array<int, 2> dims(1,2);
context->out().slice(indices, sizes).device(context->device()) = context->in1().convolve(context->kernel2d(), dims);
}
template <typename Context>
void test_3d_convolution(Context* context)
{
Eigen::DSizes<int, 3> indices(0,0,0);
Eigen::DSizes<int, 3> sizes(39,49,69);
Eigen::array<int, 3> dims(0,1,2);
context->out().slice(indices, sizes).device(context->device()) = context->in1().convolve(context->kernel3d(), dims);
}
void test_cpu() {
Eigen::Tensor<float, 3> in1(40,50,70);
Eigen::Tensor<float, 3> in2(40,50,70);
Eigen::Tensor<float, 3> out(40,50,70);
in1 = in1.random() + in1.constant(10.0f);
in2 = in2.random() + in2.constant(10.0f);
CPUContext context(in1, in2, out);
test_contextual_eval(&context);
for (int i = 0; i < 40; ++i) {
for (int j = 0; j < 50; ++j) {
for (int k = 0; k < 70; ++k) {
VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) + in2(i,j,k) * 3.14f + 2.718f);
}
}
}
test_forced_contextual_eval(&context);
for (int i = 0; i < 40; ++i) {
for (int j = 0; j < 50; ++j) {
for (int k = 0; k < 70; ++k) {
VERIFY_IS_APPROX(out(i,j,k), (in1(i,j,k) + in2(i,j,k)) * 3.14f + 2.718f);
}
}
}
test_compound_assignment(&context);
for (int i = 0; i < 40; ++i) {
for (int j = 0; j < 50; ++j) {
for (int k = 0; k < 70; ++k) {
VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) + in2(i,j,k) * 3.14f + 2.718f);
}
}
}
test_contraction(&context);
for (int i = 0; i < 40; ++i) {
for (int j = 0; j < 40; ++j) {
const float result = out(i,j,0);
float expected = 0;
for (int k = 0; k < 50; ++k) {
for (int l = 0; l < 70; ++l) {
expected += in1(i, k, l) * in2(j, k, l);
}
}
VERIFY_IS_APPROX(expected, result);
}
}
test_1d_convolution(&context);
for (int i = 0; i < 40; ++i) {
for (int j = 0; j < 49; ++j) {
for (int k = 0; k < 70; ++k) {
VERIFY_IS_APPROX(out(i,j,k), (in1(i,j,k) * 3.14f + in1(i,j+1,k) * 2.7f));
}
}
}
test_2d_convolution(&context);
for (int i = 0; i < 40; ++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);
if (fabs(expected) < 1e-4f && fabs(result) < 1e-4f) {
continue;
}
VERIFY_IS_APPROX(expected, result);
}
}
}
test_3d_convolution(&context);
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);
if (fabs(expected) < 1e-4f && fabs(result) < 1e-4f) {
continue;
}
VERIFY_IS_APPROX(expected, result);
}
}
}
}
void test_gpu() {
Eigen::Tensor<float, 3> in1(40,50,70);
Eigen::Tensor<float, 3> in2(40,50,70);
Eigen::Tensor<float, 3> out(40,50,70);
in1 = in1.random() + in1.constant(10.0f);
in2 = in2.random() + in2.constant(10.0f);
std::size_t in1_bytes = in1.size() * sizeof(float);
std::size_t in2_bytes = in2.size() * sizeof(float);
std::size_t out_bytes = out.size() * sizeof(float);
float* d_in1;
float* d_in2;
float* d_out;
cudaMalloc((void**)(&d_in1), in1_bytes);
cudaMalloc((void**)(&d_in2), in2_bytes);
cudaMalloc((void**)(&d_out), out_bytes);
cudaMemcpy(d_in1, in1.data(), in1_bytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_in2, in2.data(), in2_bytes, cudaMemcpyHostToDevice);
Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in1(d_in1, 40,50,70);
Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in2(d_in2, 40,50,70);
Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_out(d_out, 40,50,70);
GPUContext context(gpu_in1, gpu_in2, gpu_out);
test_contextual_eval(&context);
assert(cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost) == cudaSuccess);
for (int i = 0; i < 40; ++i) {
for (int j = 0; j < 50; ++j) {
for (int k = 0; k < 70; ++k) {
VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) + in2(i,j,k) * 3.14f + 2.718f);
}
}
}
test_forced_contextual_eval(&context);
assert(cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost) == cudaSuccess);
for (int i = 0; i < 40; ++i) {
for (int j = 0; j < 50; ++j) {
for (int k = 0; k < 70; ++k) {
VERIFY_IS_APPROX(out(i,j,k), (in1(i,j,k) + in2(i,j,k)) * 3.14f + 2.718f);
}
}
}
test_compound_assignment(&context);
assert(cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost) == cudaSuccess);
for (int i = 0; i < 40; ++i) {
for (int j = 0; j < 50; ++j) {
for (int k = 0; k < 70; ++k) {
VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) + in2(i,j,k) * 3.14f + 2.718f);
}
}
}
test_contraction(&context);
assert(cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost) == cudaSuccess);
for (int i = 0; i < 40; ++i) {
for (int j = 0; j < 40; ++j) {
const float result = out(i,j,0);
float expected = 0;
for (int k = 0; k < 50; ++k) {
for (int l = 0; l < 70; ++l) {
expected += in1(i, k, l) * in2(j, k, l);
}
}
VERIFY_IS_APPROX(expected, result);
}
}
test_1d_convolution(&context);
assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, context.device().stream()) == cudaSuccess);
assert(cudaStreamSynchronize(context.device().stream()) == cudaSuccess);
for (int i = 0; i < 40; ++i) {
for (int j = 0; j < 49; ++j) {
for (int k = 0; k < 70; ++k) {
VERIFY_IS_APPROX(out(i,j,k), (in1(i,j,k) * 3.14f + in1(i,j+1,k) * 2.7f));
}
}
}
test_2d_convolution(&context);
assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, context.device().stream()) == cudaSuccess);
assert(cudaStreamSynchronize(context.device().stream()) == cudaSuccess);
for (int i = 0; i < 40; ++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);
VERIFY_IS_APPROX(expected, result);
}
}
}
test_3d_convolution(&context);
assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, context.device().stream()) == cudaSuccess);
assert(cudaStreamSynchronize(context.device().stream()) == cudaSuccess);
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);
}
}
}
}
void test_cxx11_tensor_device()
{
CALL_SUBTEST_1(test_cpu());
CALL_SUBTEST_2(test_gpu());
}