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merging the CUDA and HIP implementation for the Tensor directory and the unit tests

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This commit is contained in:
Deven Desai
2018-06-20 16:44:58 -04:00
parent cfdabbcc8f
commit 1bb6fa99a3
22 changed files with 1208 additions and 804 deletions
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58
unsupported/test/cxx11_tensor_device.cu
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@@ -16,6 +16,7 @@
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
#include <unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h>
using Eigen::Tensor;
using Eigen::RowMajor;
@@ -66,22 +67,22 @@ struct CPUContext {
// 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);
assert(gpuMalloc((void**)(&kernel_1d_), 2*sizeof(float)) == gpuSuccess);
float kernel_1d_val[] = {3.14f, 2.7f};
assert(cudaMemcpy(kernel_1d_, kernel_1d_val, 2*sizeof(float), cudaMemcpyHostToDevice) == cudaSuccess);
assert(gpuMemcpy(kernel_1d_, kernel_1d_val, 2*sizeof(float), gpuMemcpyHostToDevice) == gpuSuccess);
assert(cudaMalloc((void**)(&kernel_2d_), 4*sizeof(float)) == cudaSuccess);
assert(gpuMalloc((void**)(&kernel_2d_), 4*sizeof(float)) == gpuSuccess);
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(gpuMemcpy(kernel_2d_, kernel_2d_val, 4*sizeof(float), gpuMemcpyHostToDevice) == gpuSuccess);
assert(cudaMalloc((void**)(&kernel_3d_), 8*sizeof(float)) == cudaSuccess);
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(cudaMemcpy(kernel_3d_, kernel_3d_val, 8*sizeof(float), cudaMemcpyHostToDevice) == cudaSuccess);
assert(gpuMemcpy(kernel_3d_, kernel_3d_val, 8*sizeof(float), gpuMemcpyHostToDevice) == gpuSuccess);
}
~GPUContext() {
assert(cudaFree(kernel_1d_) == cudaSuccess);
assert(cudaFree(kernel_2d_) == cudaSuccess);
assert(cudaFree(kernel_3d_) == cudaSuccess);
assert(gpuFree(kernel_1d_) == gpuSuccess);
assert(gpuFree(kernel_2d_) == gpuSuccess);
assert(gpuFree(kernel_3d_) == gpuSuccess);
}
const Eigen::GpuDevice& device() const { return gpu_device_; }
@@ -102,7 +103,7 @@ struct GPUContext {
float* kernel_2d_;
float* kernel_3d_;
Eigen::CudaStreamDevice stream_;
Eigen::GpuStreamDevice stream_;
Eigen::GpuDevice gpu_device_;
};
@@ -281,12 +282,12 @@ void test_gpu() {
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);
gpuMalloc((void**)(&d_in1), in1_bytes);
gpuMalloc((void**)(&d_in2), in2_bytes);
gpuMalloc((void**)(&d_out), out_bytes);
cudaMemcpy(d_in1, in1.data(), in1_bytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_in2, in2.data(), in2_bytes, cudaMemcpyHostToDevice);
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);
@@ -294,7 +295,7 @@ void test_gpu() {
GPUContext context(gpu_in1, gpu_in2, gpu_out);
test_contextual_eval(&context);
assert(cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost) == cudaSuccess);
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) {
@@ -304,7 +305,7 @@ void test_gpu() {
}
test_forced_contextual_eval(&context);
assert(cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost) == cudaSuccess);
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) {
@@ -314,7 +315,7 @@ void test_gpu() {
}
test_compound_assignment(&context);
assert(cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost) == cudaSuccess);
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) {
@@ -324,7 +325,7 @@ void test_gpu() {
}
test_contraction(&context);
assert(cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost) == cudaSuccess);
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);
@@ -339,8 +340,8 @@ void test_gpu() {
}
test_1d_convolution(&context);
assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, context.device().stream()) == cudaSuccess);
assert(cudaStreamSynchronize(context.device().stream()) == cudaSuccess);
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) {
@@ -350,8 +351,8 @@ void test_gpu() {
}
test_2d_convolution(&context);
assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, context.device().stream()) == cudaSuccess);
assert(cudaStreamSynchronize(context.device().stream()) == cudaSuccess);
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) {
@@ -363,9 +364,13 @@ void test_gpu() {
}
}
#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(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, context.device().stream()) == cudaSuccess);
assert(cudaStreamSynchronize(context.device().stream()) == cudaSuccess);
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) {
@@ -378,6 +383,9 @@ void test_gpu() {
}
}
}
#endif
}