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[SYCL] Rebasing the SYCL support branch on top of the Einge upstream master branch.

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* Unifying all loadLocalTile from lhs and rhs to an extract_block function.
* Adding get_tensor operation which was missing in TensorContractionMapper.
* Adding the -D method missing from cmake for Disable_Skinny Contraction operation.
* Wrapping all the indices in TensorScanSycl into Scan parameter struct.
* Fixing typo in Device SYCL
* Unifying load to private register for tall/skinny no shared
* Unifying load to vector tile for tensor-vector/vector-tensor operation
* Removing all the LHS/RHS class for extracting data from global
* Removing Outputfunction from TensorContractionSkinnyNoshared.
* Combining the local memory version of tall/skinny and normal tensor contraction into one kernel.
* Combining the no-local memory version of tall/skinny and normal tensor contraction into one kernel.
* Combining General Tensor-Vector and VectorTensor contraction into one kernel.
* Making double buffering optional for Tensor contraction when local memory is version is used.
* Modifying benchmark to accept custom Reduction Sizes
* Disabling AVX optimization for SYCL backend on the host to allow SSE optimization to the host
* Adding Test for SYCL
* Modifying SYCL CMake
This commit is contained in:
Mehdi Goli
2019-11-28 10:08:54 +00:00
parent ea51a9eace
commit 00f32752f7
56 changed files with 7336 additions and 4826 deletions
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136
unsupported/test/cxx11_tensor_argmax_sycl.cpp
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@@ -18,6 +18,7 @@
#define EIGEN_USE_SYCL
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::array;
@@ -26,9 +27,8 @@ 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}});
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();
@@ -39,14 +39,15 @@ static void test_sycl_simple_argmax(const Eigen::SyclDevice &sycl_device){
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));
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<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);
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();
@@ -54,7 +55,7 @@ static void test_sycl_simple_argmax(const Eigen::SyclDevice &sycl_device){
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_max(), 2 * 2 * 2 - 1);
VERIFY_IS_EQUAL(out_min(), 0);
sycl_device.deallocate(d_in);
@@ -62,22 +63,22 @@ static void test_sycl_simple_argmax(const Eigen::SyclDevice &sycl_device){
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);
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);
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];
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);
@@ -86,9 +87,13 @@ static void test_sycl_argmax_dim(const Eigen::SyclDevice &sycl_device)
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;
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;
}
}
}
@@ -97,23 +102,23 @@ static void test_sycl_argmax_dim(const Eigen::SyclDevice &sycl_device)
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));
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<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);
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)));
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);
VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
}
sycl_device.synchronize();
@@ -122,15 +127,18 @@ static void test_sycl_argmax_dim(const Eigen::SyclDevice &sycl_device)
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;
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;
tensor(ix) = (ix[dim] != tensor.dimension(dim) - 1) ? -1.0 : 20.0;
}
}
}
}
sycl_device.memcpyHostToDevice(d_in, tensor.data(),in_bytes);
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);
@@ -144,20 +152,21 @@ static void test_sycl_argmax_dim(const Eigen::SyclDevice &sycl_device)
}
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);
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);
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];
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);
@@ -166,9 +175,12 @@ static void test_sycl_argmin_dim(const Eigen::SyclDevice &sycl_device)
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;
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;
}
}
}
@@ -177,23 +189,23 @@ static void test_sycl_argmin_dim(const Eigen::SyclDevice &sycl_device)
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));
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<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);
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)));
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);
VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
}
sycl_device.synchronize();
@@ -202,15 +214,18 @@ static void test_sycl_argmin_dim(const Eigen::SyclDevice &sycl_device)
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;
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);
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);
@@ -223,10 +238,8 @@ static void test_sycl_argmin_dim(const Eigen::SyclDevice &sycl_device)
}
}
template<typename DataType, typename Device_Selector> void sycl_argmax_test_per_device(const Device_Selector& d){
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);
@@ -238,8 +251,7 @@ template<typename DataType, typename Device_Selector> void sycl_argmax_test_per_
}
EIGEN_DECLARE_TEST(cxx11_tensor_argmax_sycl) {
for (const auto& device :Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_argmax_test_per_device<double>(device));
for (const auto& device : Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_argmax_test_per_device<float>(device));
}
}