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Vectorized the evaluation of tensor expression (using SSE, AVX, NEON, ...)

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Added the ability to parallelize the evaluation of a tensor expression over multiple cpu cores.
Added the ability to offload the evaluation of a tensor expression to a GPU.
This commit is contained in:
Benoit Steiner
2014-05-16 15:08:05 -07:00
parent 0320f7e3a7
commit 7402fea0a8
17 changed files with 719 additions and 65 deletions
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3
unsupported/test/CMakeLists.txt
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@@ -104,4 +104,7 @@ if(EIGEN_TEST_CXX11)
ei_add_test(cxx11_tensor_assign "-std=c++0x")
ei_add_test(cxx11_tensor_expr "-std=c++0x")
ei_add_test(cxx11_tensor_map "-std=c++0x")
ei_add_test(cxx11_tensor_device "-std=c++0x")
# ei_add_test(cxx11_tensor_fixed_size "-std=c++0x")
ei_add_test(cxx11_tensor_thread_pool "-std=c++0x")
endif()

126
unsupported/test/cxx11_tensor_device.cpp Normal file
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@@ -0,0 +1,126 @@
// 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
#include "main.h"
#include <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) { }
const Eigen::Tensor<float, 3>& in1() const { return in1_; }
const Eigen::Tensor<float, 3>& in2() const { return in2_; }
Eigen::TensorDevice<Eigen::Tensor<float, 3>, Eigen::DefaultDevice> out() { return TensorDevice<Eigen::Tensor<float, 3>, Eigen::DefaultDevice>(cpu_device_, out_); }
private:
const Eigen::Tensor<float, 3>& in1_;
const Eigen::Tensor<float, 3>& in2_;
Eigen::Tensor<float, 3>& out_;
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) { }
const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in1() const { return in1_; }
const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in2() const { return in2_; }
Eigen::TensorDevice<Eigen::TensorMap<Eigen::Tensor<float, 3> >, Eigen::GpuDevice> out() { return TensorDevice<Eigen::TensorMap<Eigen::Tensor<float, 3> >, Eigen::GpuDevice>(gpu_device_, out_); }
private:
const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in1_;
const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in2_;
Eigen::TensorMap<Eigen::Tensor<float, 3> >& out_;
Eigen::GpuDevice gpu_device_;
};
// The actual expression to evaluate
template <typename Context>
static void test_contextual_eval(Context* context)
{
context->out() = context->in1() + context->in2() * 3.14f;
}
static void test_cpu() {
Eigen::Tensor<float, 3> in1(Eigen::array<int, 3>(2,3,7));
Eigen::Tensor<float, 3> in2(Eigen::array<int, 3>(2,3,7));
Eigen::Tensor<float, 3> out(Eigen::array<int, 3>(2,3,7));
in1.setRandom();
in2.setRandom();
CPUContext context(in1, in2, out);
test_contextual_eval(&context);
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
for (int k = 0; k < 7; ++k) {
VERIFY_IS_APPROX(out(Eigen::array<int, 3>(i,j,k)), in1(Eigen::array<int, 3>(i,j,k)) + in2(Eigen::array<int, 3>(i,j,k)) * 3.14f);
}
}
}
}
static void test_gpu() {
Eigen::Tensor<float, 3> in1(Eigen::array<int, 3>(2,3,7));
Eigen::Tensor<float, 3> in2(Eigen::array<int, 3>(2,3,7));
Eigen::Tensor<float, 3> out(Eigen::array<int, 3>(2,3,7));
in1.setRandom();
in2.setRandom();
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, Eigen::array<int, 3>(2,3,7));
Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in2(d_in2, Eigen::array<int, 3>(2,3,7));
Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_out(d_out, Eigen::array<int, 3>(2,3,7));
GPUContext context(gpu_in1, gpu_in2, gpu_out);
test_contextual_eval(&context);
cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost);
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
for (int k = 0; k < 7; ++k) {
VERIFY_IS_APPROX(out(Eigen::array<int, 3>(i,j,k)), in1(Eigen::array<int, 3>(i,j,k)) + in2(Eigen::array<int, 3>(i,j,k)) * 3.14f);
}
}
}
}
void test_cxx11_tensor_device()
{
CALL_SUBTEST(test_cpu());
CALL_SUBTEST(test_gpu());
}

28
unsupported/test/cxx11_tensor_fixed_size.cpp
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@@ -159,9 +159,37 @@ static void test_3d()
}
static void test_array()
{
TensorFixedSize<float, Sizes<2, 3, 7> > mat1;
float val = 0.0;
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
for (int k = 0; k < 7; ++k) {
mat1(array<ptrdiff_t, 3>(i,j,k)) = val;
val += 1.0;
}
}
}
TensorFixedSize<float, Sizes<2, 3, 7> > mat3;
mat3 = mat1.cwisePow(3.5f);
val = 0.0;
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
for (int k = 0; k < 7; ++k) {
VERIFY_IS_APPROX(mat3(array<ptrdiff_t, 3>(i,j,k)), powf(val, 3.5f));
val += 1.0;
}
}
}
}
void test_cxx11_tensor_fixed_size()
{
CALL_SUBTEST(test_1d());
CALL_SUBTEST(test_2d());
CALL_SUBTEST(test_3d());
CALL_SUBTEST(test_array());
}

37
unsupported/test/cxx11_tensor_thread_pool.cpp Normal file
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@@ -0,0 +1,37 @@
// 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_USE_THREADS
#include "main.h"
#include <Eigen/CXX11/Tensor>
using Eigen::Tensor;
void test_cxx11_tensor_thread_pool()
{
Eigen::Tensor<float, 3> in1(Eigen::array<ptrdiff_t, 3>(2,3,7));
Eigen::Tensor<float, 3> in2(Eigen::array<ptrdiff_t, 3>(2,3,7));
Eigen::Tensor<float, 3> out(Eigen::array<ptrdiff_t, 3>(2,3,7));
in1.setRandom();
in2.setRandom();
Eigen::ThreadPoolDevice thread_pool_device(3);
out.device(thread_pool_device) = in1 + in2 * 3.14;
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
for (int k = 0; k < 7; ++k) {
VERIFY_IS_APPROX(out(Eigen::array<ptrdiff_t, 3>(i,j,k)), in1(Eigen::array<ptrdiff_t, 3>(i,j,k)) + in2(Eigen::array<ptrdiff_t, 3>(i,j,k)) * 3.14f);
}
}
}
}