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Adding TensorIndexTuple and TensorTupleReduceOP backend (ArgMax/Min) for sycl; fixing the address space issue for const TensorMap; converting all discard_write to write due to data missmatch.

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This commit is contained in:
Mehdi Goli
2017-03-07 14:27:10 +00:00
parent 8296b87d7b
commit f84963ed95
19 changed files with 813 additions and 226 deletions
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1
unsupported/test/CMakeLists.txt
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@@ -173,6 +173,7 @@ if(EIGEN_TEST_CXX11)
ei_add_test_sycl(cxx11_tensor_patch_sycl "-std=c++11")
ei_add_test_sycl(cxx11_tensor_image_patchOP_sycl "-std=c++11")
ei_add_test_sycl(cxx11_tensor_volume_patchOP_sycl "-std=c++11")
ei_add_test_sycl(cxx11_tensor_argmax_sycl "-std=c++11")
ei_add_test_sycl(cxx11_tensor_custom_op_sycl "-std=c++11")
endif(EIGEN_TEST_SYCL)
# It should be safe to always run these tests as there is some fallback code for

248
unsupported/test/cxx11_tensor_argmax_sycl.cpp Normal file
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@@ -0,0 +1,248 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli Codeplay Software Ltd.
// Ralph Potter Codeplay Software Ltd.
// Luke Iwanski Codeplay Software Ltd.
// Contact: <eigen@codeplay.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_argmax_sycl
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;
template <typename DataType, int Layout, typename DenseIndex>
static void test_sycl_simple_argmax(const Eigen::SyclDevice &sycl_device){
Tensor<DataType, 3, Layout, DenseIndex> in(Eigen::array<DenseIndex, 3>{{2,2,2}});
Tensor<DenseIndex, 0, Layout, DenseIndex> out_max;
Tensor<DenseIndex, 0, Layout, DenseIndex> out_min;
in.setRandom();
in *= in.constant(100.0);
in(0, 0, 0) = -1000.0;
in(1, 1, 1) = 1000.0;
std::size_t in_bytes = in.size() * sizeof(DataType);
std::size_t out_bytes = out_max.size() * sizeof(DenseIndex);
DataType * d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
DenseIndex* d_out_max = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
DenseIndex* d_out_min = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
Eigen::TensorMap<Eigen::Tensor<DataType, 3, Layout, DenseIndex> > gpu_in(d_in, Eigen::array<DenseIndex, 3>{{2,2,2}});
Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout, DenseIndex> > gpu_out_max(d_out_max);
Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout, DenseIndex> > gpu_out_min(d_out_min);
sycl_device.memcpyHostToDevice(d_in, in.data(),in_bytes);
gpu_out_max.device(sycl_device) = gpu_in.argmax();
gpu_out_min.device(sycl_device) = gpu_in.argmin();
sycl_device.memcpyDeviceToHost(out_max.data(), d_out_max, out_bytes);
sycl_device.memcpyDeviceToHost(out_min.data(), d_out_min, out_bytes);
VERIFY_IS_EQUAL(out_max(), 2*2*2 - 1);
VERIFY_IS_EQUAL(out_min(), 0);
sycl_device.deallocate(d_in);
sycl_device.deallocate(d_out_max);
sycl_device.deallocate(d_out_min);
}
template <typename DataType, int DataLayout, typename DenseIndex>
static void test_sycl_argmax_dim(const Eigen::SyclDevice &sycl_device)
{
DenseIndex sizeDim0=9;
DenseIndex sizeDim1=3;
DenseIndex sizeDim2=5;
DenseIndex sizeDim3=7;
Tensor<DataType, 4, DataLayout, DenseIndex> tensor(sizeDim0,sizeDim1,sizeDim2,sizeDim3);
std::vector<DenseIndex> dims;
dims.push_back(sizeDim0); dims.push_back(sizeDim1); dims.push_back(sizeDim2); dims.push_back(sizeDim3);
for (DenseIndex dim = 0; dim < 4; ++dim) {
array<DenseIndex, 3> out_shape;
for (DenseIndex d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d+1];
Tensor<DenseIndex, 3, DataLayout, DenseIndex> tensor_arg(out_shape);
array<DenseIndex, 4> ix;
for (DenseIndex i = 0; i < sizeDim0; ++i) {
for (DenseIndex j = 0; j < sizeDim1; ++j) {
for (DenseIndex k = 0; k < sizeDim2; ++k) {
for (DenseIndex l = 0; l < sizeDim3; ++l) {
ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
// suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = 10.0
tensor(ix)=(ix[dim] != 0)?-1.0:10.0;
}
}
}
}
std::size_t in_bytes = tensor.size() * sizeof(DataType);
std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
DataType * d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
DenseIndex* d_out= static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, DenseIndex> > gpu_in(d_in, Eigen::array<DenseIndex, 4>{{sizeDim0,sizeDim1,sizeDim2,sizeDim3}});
Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout, DenseIndex> > gpu_out(d_out, out_shape);
sycl_device.memcpyHostToDevice(d_in, tensor.data(),in_bytes);
gpu_out.device(sycl_device) = gpu_in.argmax(dim);
sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
VERIFY_IS_EQUAL(static_cast<size_t>(tensor_arg.size()),
size_t(sizeDim0*sizeDim1*sizeDim2*sizeDim3 / tensor.dimension(dim)));
for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
// Expect max to be in the first index of the reduced dimension
VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
}
sycl_device.synchronize();
for (DenseIndex i = 0; i < sizeDim0; ++i) {
for (DenseIndex j = 0; j < sizeDim1; ++j) {
for (DenseIndex k = 0; k < sizeDim2; ++k) {
for (DenseIndex l = 0; l < sizeDim3; ++l) {
ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
// suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
tensor(ix)=(ix[dim] != tensor.dimension(dim) - 1)?-1.0:20.0;
}
}
}
}
sycl_device.memcpyHostToDevice(d_in, tensor.data(),in_bytes);
gpu_out.device(sycl_device) = gpu_in.argmax(dim);
sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
// Expect max to be in the last index of the reduced dimension
VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
}
sycl_device.deallocate(d_in);
sycl_device.deallocate(d_out);
}
}
template <typename DataType, int DataLayout, typename DenseIndex>
static void test_sycl_argmin_dim(const Eigen::SyclDevice &sycl_device)
{
DenseIndex sizeDim0=9;
DenseIndex sizeDim1=3;
DenseIndex sizeDim2=5;
DenseIndex sizeDim3=7;
Tensor<DataType, 4, DataLayout, DenseIndex> tensor(sizeDim0,sizeDim1,sizeDim2,sizeDim3);
std::vector<DenseIndex> dims;
dims.push_back(sizeDim0); dims.push_back(sizeDim1); dims.push_back(sizeDim2); dims.push_back(sizeDim3);
for (DenseIndex dim = 0; dim < 4; ++dim) {
array<DenseIndex, 3> out_shape;
for (DenseIndex d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d+1];
Tensor<DenseIndex, 3, DataLayout, DenseIndex> tensor_arg(out_shape);
array<DenseIndex, 4> ix;
for (DenseIndex i = 0; i < sizeDim0; ++i) {
for (DenseIndex j = 0; j < sizeDim1; ++j) {
for (DenseIndex k = 0; k < sizeDim2; ++k) {
for (DenseIndex l = 0; l < sizeDim3; ++l) {
ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
// suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = 10.0
tensor(ix)=(ix[dim] != 0)?1.0:-10.0;
}
}
}
}
std::size_t in_bytes = tensor.size() * sizeof(DataType);
std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
DataType * d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
DenseIndex* d_out= static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, DenseIndex> > gpu_in(d_in, Eigen::array<DenseIndex, 4>{{sizeDim0,sizeDim1,sizeDim2,sizeDim3}});
Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout, DenseIndex> > gpu_out(d_out, out_shape);
sycl_device.memcpyHostToDevice(d_in, tensor.data(),in_bytes);
gpu_out.device(sycl_device) = gpu_in.argmin(dim);
sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
VERIFY_IS_EQUAL(static_cast<size_t>(tensor_arg.size()),
size_t(sizeDim0*sizeDim1*sizeDim2*sizeDim3 / tensor.dimension(dim)));
for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
// Expect max to be in the first index of the reduced dimension
VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
}
sycl_device.synchronize();
for (DenseIndex i = 0; i < sizeDim0; ++i) {
for (DenseIndex j = 0; j < sizeDim1; ++j) {
for (DenseIndex k = 0; k < sizeDim2; ++k) {
for (DenseIndex l = 0; l < sizeDim3; ++l) {
ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
// suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
tensor(ix)=(ix[dim] != tensor.dimension(dim) - 1)?1.0:-20.0;
}
}
}
}
sycl_device.memcpyHostToDevice(d_in, tensor.data(),in_bytes);
gpu_out.device(sycl_device) = gpu_in.argmin(dim);
sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
// Expect max to be in the last index of the reduced dimension
VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
}
sycl_device.deallocate(d_in);
sycl_device.deallocate(d_out);
}
}
template<typename DataType, typename Device_Selector> void sycl_argmax_test_per_device(const Device_Selector& d){
QueueInterface queueInterface(d);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
test_sycl_simple_argmax<DataType, RowMajor, int64_t>(sycl_device);
test_sycl_simple_argmax<DataType, ColMajor, int64_t>(sycl_device);
test_sycl_argmax_dim<DataType, ColMajor, int64_t>(sycl_device);
test_sycl_argmax_dim<DataType, RowMajor, int64_t>(sycl_device);
test_sycl_argmin_dim<DataType, ColMajor, int64_t>(sycl_device);
test_sycl_argmin_dim<DataType, RowMajor, int64_t>(sycl_device);
}
void test_cxx11_tensor_argmax_sycl() {
for (const auto& device :Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_argmax_test_per_device<double>(device));
}
}