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eigen/unsupported/test/cxx11_tensor_device.cu
Deven Desai f124f07965 applying EIGEN_DECLARE_TEST to *gpu* tests 
Also, a few minor fixes for GPU tests running in HIP mode.

1. Adding an include for hip/hip_runtime.h in the Macros.h file
   For HIP __host__ and __device__ are macros which are defined in hip headers.
   Their definitions need to be included before their use in the file.

2. Fixing the compile failure in TensorContractionGpu introduced by the commit to
   "Fuse computations into the Tensor contractions using output kernel"

3. Fixing a HIP/clang specific compile error by making the struct-member assignment explicit
2018-07-17 14:16:48 -04:00

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// 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_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_GPU
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
#include <unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h>
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(gpuMalloc((void**)(&kernel_1d_), 2*sizeof(float)) == gpuSuccess);
float kernel_1d_val[] = {3.14f, 2.7f};
assert(gpuMemcpy(kernel_1d_, kernel_1d_val, 2*sizeof(float), gpuMemcpyHostToDevice) == gpuSuccess);
assert(gpuMalloc((void**)(&kernel_2d_), 4*sizeof(float)) == gpuSuccess);
float kernel_2d_val[] = {3.14f, 2.7f, 0.2f, 7.0f};
assert(gpuMemcpy(kernel_2d_, kernel_2d_val, 4*sizeof(float), gpuMemcpyHostToDevice) == gpuSuccess);
assert(gpuMalloc((void**)(&kernel_3d_), 8*sizeof(float)) == gpuSuccess);
float kernel_3d_val[] = {3.14f, -1.0f, 2.7f, -0.3f, 0.2f, -0.7f, 7.0f, -0.5f};
assert(gpuMemcpy(kernel_3d_, kernel_3d_val, 8*sizeof(float), gpuMemcpyHostToDevice) == gpuSuccess);
}
~GPUContext() {
assert(gpuFree(kernel_1d_) == gpuSuccess);
assert(gpuFree(kernel_2d_) == gpuSuccess);
assert(gpuFree(kernel_3d_) == gpuSuccess);
}
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::GpuStreamDevice 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;
gpuMalloc((void**)(&d_in1), in1_bytes);
gpuMalloc((void**)(&d_in2), in2_bytes);
gpuMalloc((void**)(&d_out), out_bytes);
gpuMemcpy(d_in1, in1.data(), in1_bytes, gpuMemcpyHostToDevice);
gpuMemcpy(d_in2, in2.data(), in2_bytes, gpuMemcpyHostToDevice);
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(gpuMemcpy(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost) == gpuSuccess);
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(gpuMemcpy(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost) == gpuSuccess);
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(gpuMemcpy(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost) == gpuSuccess);
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(gpuMemcpy(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost) == gpuSuccess);
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(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, context.device().stream()) == gpuSuccess);
assert(gpuStreamSynchronize(context.device().stream()) == gpuSuccess);
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(gpuMemcpyAsync(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost, context.device().stream()) == gpuSuccess);
assert(gpuStreamSynchronize(context.device().stream()) == gpuSuccess);
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);
}
}
}
#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
}
EIGEN_DECLARE_TEST(cxx11_tensor_device)
{
CALL_SUBTEST_1(test_cpu());
CALL_SUBTEST_2(test_gpu());
}
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