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Adding mean to TensorReductionSycl.h

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This commit is contained in:
Mehdi Goli 2017-02-07 15:43:17 +00:00
parent 42bd5c4e7b
commit 0ee97b60c2
3 changed files with 148 additions and 26 deletions
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23
unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h
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@ -25,8 +25,8 @@
namespace Eigen { namespace Eigen {
namespace internal { namespace internal {
template<typename CoeffReturnType> struct syclGenericBufferReducer{ template<typename OP, typename CoeffReturnType> struct syclGenericBufferReducer{
template<typename OP, typename BufferTOut, typename BufferTIn> template<typename BufferTOut, typename BufferTIn>
static void run(OP op, BufferTOut& bufOut, BufferTIn& bufI, const Eigen::SyclDevice& dev, size_t length, size_t local){ static void run(OP op, BufferTOut& bufOut, BufferTIn& bufI, const Eigen::SyclDevice& dev, size_t length, size_t local){
do { do {
auto f = [length, local, op, &bufOut, &bufI](cl::sycl::handler& h) mutable { auto f = [length, local, op, &bufOut, &bufI](cl::sycl::handler& h) mutable {
@ -54,13 +54,18 @@ static void run(OP op, BufferTOut& bufOut, BufferTIn& bufI, const Eigen::SyclDev
length = length / local; length = length / local;
} while (length > 1); } while (length > 1);
} }
}; };
template<typename CoeffReturnType> struct syclGenericBufferReducer<Eigen::internal::MeanReducer<CoeffReturnType>, CoeffReturnType>{
template<typename BufferTOut, typename BufferTIn>
static void run(Eigen::internal::MeanReducer<CoeffReturnType>, BufferTOut& bufOut, BufferTIn& bufI, const Eigen::SyclDevice& dev, size_t length, size_t local){
syclGenericBufferReducer<Eigen::internal::SumReducer<CoeffReturnType>, CoeffReturnType>::run(Eigen::internal::SumReducer<CoeffReturnType>(),
bufOut, bufI, dev, length, local);
}
};
/// Self is useless here because in expression construction we are going to treat reduction as a leafnode. /// Self is useless here because in expression construction we are going to treat reduction as a leafnode.
/// we want to take reduction child and then build a construction and apply the full reducer function on it. Fullreducre applies the /// we want to take reduction child and then build a construction and apply the full reducer function on it. Fullreducre applies the
/// reduction operation on the child of the reduction. once it is done the reduction is an empty shell and can be thrown away and treated as /// reduction operation on the child of the reduction. once it is done the reduction is an empty shell and can be thrown away and treated as
@ -123,7 +128,7 @@ struct FullReducer<Self, Op, const Eigen::SyclDevice, Vectorizable> {
// getting final out buffer at the moment the created buffer is true because there is no need for assign // getting final out buffer at the moment the created buffer is true because there is no need for assign
auto out_buffer =dev.get_sycl_buffer(output); auto out_buffer =dev.get_sycl_buffer(output);
/// This is used to recursively reduce the tmp value to an element of 1; /// This is used to recursively reduce the tmp value to an element of 1;
syclGenericBufferReducer<CoeffReturnType>::run(reducer, out_buffer, temp_global_buffer,dev, GRange, outTileSize); syclGenericBufferReducer<Op, CoeffReturnType>::run(reducer, out_buffer, temp_global_buffer,dev, GRange, outTileSize);
} }
}; };
@ -135,7 +140,7 @@ struct InnerReducer<Self, Op, const Eigen::SyclDevice> {
typedef typename Self::CoeffReturnType CoeffReturnType; typedef typename Self::CoeffReturnType CoeffReturnType;
static const bool HasOptimizedImplementation = false; static const bool HasOptimizedImplementation = false;
static bool run(const Self& self, Op& reducer, const Eigen::SyclDevice& dev, CoeffReturnType* output, typename Self::Index , typename Self::Index num_coeffs_to_preserve) { static bool run(const Self& self, Op& reducer, const Eigen::SyclDevice& dev, CoeffReturnType* output, typename Self::Index num_values_to_reduce, typename Self::Index num_coeffs_to_preserve) {
typedef const typename Self::ChildType HostExpr; /// this is the child of reduction typedef const typename Self::ChildType HostExpr; /// this is the child of reduction
typedef Eigen::TensorSycl::internal::FunctorExtractor<TensorEvaluator<HostExpr, const Eigen::SyclDevice> > FunctorExpr; typedef Eigen::TensorSycl::internal::FunctorExtractor<TensorEvaluator<HostExpr, const Eigen::SyclDevice> > FunctorExpr;
FunctorExpr functors = TensorSycl::internal::extractFunctors(self.impl()); FunctorExpr functors = TensorSycl::internal::extractFunctors(self.impl());
@ -153,10 +158,10 @@ struct InnerReducer<Self, Op, const Eigen::SyclDevice> {
// create a tuple of accessors from Evaluator // create a tuple of accessors from Evaluator
Tuple_of_Acc tuple_of_accessors = TensorSycl::internal::createTupleOfAccessors(cgh, self.impl()); Tuple_of_Acc tuple_of_accessors = TensorSycl::internal::createTupleOfAccessors(cgh, self.impl());
auto output_accessor = dev.template get_sycl_accessor<cl::sycl::access::mode::discard_write>(cgh, output); auto output_accessor = dev.template get_sycl_accessor<cl::sycl::access::mode::discard_write>(cgh, output);
Index red_size = (num_values_to_reduce!=0)? num_values_to_reduce : static_cast<Index>(1);
cgh.parallel_for( cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(tileSize)), cgh.parallel_for( cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(tileSize)),
TensorSycl::internal::ReductionFunctor<HostExpr, FunctorExpr, Tuple_of_Acc, Dims, Op, typename Self::Index> TensorSycl::internal::ReductionFunctor<HostExpr, FunctorExpr, Tuple_of_Acc, Dims, Op, typename Self::Index>
(output_accessor, functors, tuple_of_accessors, self.xprDims(), reducer, range)); (output_accessor, functors, tuple_of_accessors, self.xprDims(), reducer, range, red_size));
}); });
dev.asynchronousExec(); dev.asynchronousExec();

98
unsupported/Eigen/CXX11/src/Tensor/TensorSyclFunctors.h
View File

@ -72,7 +72,7 @@ template < typename HostExpr, typename FunctorExpr, typename Tuple_of_Acc, typen
public: public:
typedef typename TensorSycl::internal::createPlaceHolderExpression<HostExpr>::Type PlaceHolderExpr; typedef typename TensorSycl::internal::createPlaceHolderExpression<HostExpr>::Type PlaceHolderExpr;
typedef cl::sycl::accessor<uint8_t, 1, cl::sycl::access::mode::discard_write, cl::sycl::access::target::global_buffer> write_accessor; typedef cl::sycl::accessor<uint8_t, 1, cl::sycl::access::mode::discard_write, cl::sycl::access::target::global_buffer> write_accessor;
ReductionFunctor(write_accessor output_accessor_, FunctorExpr functors_, Tuple_of_Acc tuple_of_accessors_,Dims dims_, Op functor_, Index range_) ReductionFunctor(write_accessor output_accessor_, FunctorExpr functors_, Tuple_of_Acc tuple_of_accessors_,Dims dims_, Op functor_, Index range_, Index)
:output_accessor(output_accessor_), functors(functors_), tuple_of_accessors(tuple_of_accessors_), dims(dims_), functor(functor_), range(range_) {} :output_accessor(output_accessor_), functors(functors_), tuple_of_accessors(tuple_of_accessors_), dims(dims_), functor(functor_), range(range_) {}
void operator()(cl::sycl::nd_item<1> itemID) { void operator()(cl::sycl::nd_item<1> itemID) {
@ -105,6 +105,46 @@ template < typename HostExpr, typename FunctorExpr, typename Tuple_of_Acc, typen
Index range; Index range;
}; };
template < typename HostExpr, typename FunctorExpr, typename Tuple_of_Acc, typename Dims, typename Index>
class ReductionFunctor<HostExpr, FunctorExpr, Tuple_of_Acc, Dims, Eigen::internal::MeanReducer<typename HostExpr::CoeffReturnType>, Index> {
public:
typedef typename TensorSycl::internal::createPlaceHolderExpression<HostExpr>::Type PlaceHolderExpr;
typedef cl::sycl::accessor<uint8_t, 1, cl::sycl::access::mode::discard_write, cl::sycl::access::target::global_buffer> write_accessor;
typedef Eigen::internal::SumReducer<typename HostExpr::CoeffReturnType> Op;
ReductionFunctor(write_accessor output_accessor_, FunctorExpr functors_, Tuple_of_Acc tuple_of_accessors_,Dims dims_,
Eigen::internal::MeanReducer<typename HostExpr::CoeffReturnType>, Index range_, Index num_values_to_reduce_)
:output_accessor(output_accessor_), functors(functors_), tuple_of_accessors(tuple_of_accessors_), dims(dims_), functor(Op()), range(range_), num_values_to_reduce(num_values_to_reduce_) {}
void operator()(cl::sycl::nd_item<1> itemID) {
typedef typename ConvertToDeviceExpression<const HostExpr>::Type DevExpr;
auto device_expr = createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors);
/// reduction cannot be captured automatically through our device conversion recursion. The reason is that reduction has two behaviour
/// the first behaviour is when it is used as a root to lauch the sub-kernel. The second one is when it is treated as a leafnode to pass the
/// calculated result to its parent kernel. While the latter is automatically detected through our device expression generator. The former is created here.
const auto device_self_expr= Eigen::TensorReductionOp<Op, Dims, decltype(device_expr.expr) ,MakeGlobalPointer>(device_expr.expr, dims, functor);
/// This is the evaluator for device_self_expr. This is exactly similar to the self which has been passed to run function. The difference is
/// the device_evaluator is detectable and recognisable on the device.
typedef Eigen::TensorEvaluator<decltype(device_self_expr), Eigen::DefaultDevice> DeviceSelf;
auto device_self_evaluator = Eigen::TensorEvaluator<decltype(device_self_expr), Eigen::DefaultDevice>(device_self_expr, Eigen::DefaultDevice());
auto output_accessor_ptr =ConvertToActualTypeSycl(typename DeviceSelf::CoeffReturnType, output_accessor);
/// const cast added as a naive solution to solve the qualifier drop error
auto globalid=static_cast<Index>(itemID.get_global_linear_id());
if (globalid< range) {
typename DeviceSelf::CoeffReturnType accum = functor.initialize();
Eigen::internal::GenericDimReducer<DeviceSelf::NumReducedDims-1, DeviceSelf, Op>::reduce(device_self_evaluator, device_self_evaluator.firstInput(static_cast<typename DevExpr::Index>(globalid)),const_cast<Op&>(functor), &accum);
functor.finalize(accum);
output_accessor_ptr[globalid]= accum/num_values_to_reduce;
}
}
private:
write_accessor output_accessor;
FunctorExpr functors;
Tuple_of_Acc tuple_of_accessors;
Dims dims;
Op functor;
Index range;
Index num_values_to_reduce;
};
template<typename CoeffReturnType ,typename OutAccessor, typename HostExpr, typename FunctorExpr, typename Op, typename Dims, typename Index, typename TupleType> template<typename CoeffReturnType ,typename OutAccessor, typename HostExpr, typename FunctorExpr, typename Op, typename Dims, typename Index, typename TupleType>
class FullReductionKernelFunctor{ class FullReductionKernelFunctor{
@ -134,14 +174,11 @@ public:
/// const cast added as a naive solution to solve the qualifier drop error /// const cast added as a naive solution to solve the qualifier drop error
auto globalid=itemID.get_global_linear_id(); auto globalid=itemID.get_global_linear_id();
if(globalid<rng) tmp_global_accessor.get_pointer()[globalid]=(globalid<rng) ? Eigen::internal::InnerMostDimReducer<decltype(device_self_evaluator), Op, false>::reduce(device_self_evaluator, static_cast<typename DevExpr::Index>(red_factor*globalid), red_factor, const_cast<Op&>(op))
tmp_global_accessor.get_pointer()[globalid]=Eigen::internal::InnerMostDimReducer<decltype(device_self_evaluator), Op, false>::reduce(device_self_evaluator, static_cast<typename DevExpr::Index>(red_factor*globalid), red_factor, const_cast<Op&>(op)); : static_cast<CoeffReturnType>(op.initialize());
else
tmp_global_accessor.get_pointer()[globalid]=static_cast<CoeffReturnType>(op.initialize());
if(remaining!=0 && globalid==0 ){ if(remaining!=0 && globalid==0 ){
// this will add the rest of input buffer when the input size is not devidable to red_factor. // this will add the rest of input buffer when the input size is not devidable to red_factor.
// tmp_global_accessor.get_pointer()[0]+=
auto remaining_reduce =Eigen::internal::InnerMostDimReducer<decltype(device_self_evaluator), Op, false>:: auto remaining_reduce =Eigen::internal::InnerMostDimReducer<decltype(device_self_evaluator), Op, false>::
reduce(device_self_evaluator, static_cast<typename DevExpr::Index>(red_factor*(rng)), static_cast<typename DevExpr::Index>(remaining), const_cast<Op&>(op)); reduce(device_self_evaluator, static_cast<typename DevExpr::Index>(red_factor*(rng)), static_cast<typename DevExpr::Index>(remaining), const_cast<Op&>(op));
auto accum = op.initialize(); auto accum = op.initialize();
@ -150,13 +187,58 @@ public:
op.finalize(accum); op.finalize(accum);
tmp_global_accessor.get_pointer()[0]=accum; tmp_global_accessor.get_pointer()[0]=accum;
}
}
};
template<typename CoeffReturnType ,typename OutAccessor, typename HostExpr, typename FunctorExpr, typename Dims, typename Index, typename TupleType>
class FullReductionKernelFunctor<CoeffReturnType, OutAccessor, HostExpr, FunctorExpr, Eigen::internal::MeanReducer<CoeffReturnType>, Dims, Index, TupleType>{
public:
typedef typename TensorSycl::internal::createPlaceHolderExpression<HostExpr>::Type PlaceHolderExpr;
typedef Eigen::internal::SumReducer<CoeffReturnType> Op;
OutAccessor tmp_global_accessor;
Index rng , remaining, red_factor;
Op op;
Dims dims;
FunctorExpr functors;
TupleType tuple_of_accessors;
FullReductionKernelFunctor(OutAccessor acc, Index rng_, Index remaining_, Index red_factor_, Eigen::internal::MeanReducer<CoeffReturnType>, Dims dims_, FunctorExpr functors_, TupleType t_acc)
:tmp_global_accessor(acc), rng(rng_), remaining(remaining_), red_factor(red_factor_),op(Op()), dims(dims_), functors(functors_), tuple_of_accessors(t_acc){}
void operator()(cl::sycl::nd_item<1> itemID) {
typedef typename TensorSycl::internal::ConvertToDeviceExpression<const HostExpr>::Type DevExpr;
auto device_expr = TensorSycl::internal::createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors);
/// reduction cannot be captured automatically through our device conversion recursion. The reason is that reduction has two behaviour
/// the first behaviour is when it is used as a root to lauch the sub-kernel. The second one is when it is treated as a leafnode to pass the
/// calculated result to its parent kernel. While the latter is automatically detected through our device expression generator. The former is created here.
const auto device_self_expr= Eigen::TensorReductionOp<Op, Dims, decltype(device_expr.expr) ,MakeGlobalPointer>(device_expr.expr, dims, op);
/// This is the evaluator for device_self_expr. This is exactly similar to the self which has been passed to run function. The difference is
/// the device_evaluator is detectable and recognisable on the device.
auto device_self_evaluator = Eigen::TensorEvaluator<decltype(device_self_expr), Eigen::DefaultDevice>(device_self_expr, Eigen::DefaultDevice());
/// const cast added as a naive solution to solve the qualifier drop error
auto globalid=itemID.get_global_linear_id();
auto scale = (rng*red_factor) + remaining;
tmp_global_accessor.get_pointer()[globalid]= (globalid<rng)? ((Eigen::internal::InnerMostDimReducer<decltype(device_self_evaluator), Op, false>::reduce(device_self_evaluator, static_cast<typename DevExpr::Index>(red_factor*globalid), red_factor, const_cast<Op&>(op)))/scale)
:static_cast<CoeffReturnType>(op.initialize())/scale;
if(remaining!=0 && globalid==0 ){
// this will add the rest of input buffer when the input size is not devidable to red_factor.
auto remaining_reduce =Eigen::internal::InnerMostDimReducer<decltype(device_self_evaluator), Op, false>::reduce(device_self_evaluator, static_cast<typename DevExpr::Index>(red_factor*(rng)), static_cast<typename DevExpr::Index>(remaining), const_cast<Op&>(op));
auto accum = op.initialize();
tmp_global_accessor.get_pointer()[0]= tmp_global_accessor.get_pointer()[0]*scale;
op.reduce(tmp_global_accessor.get_pointer()[0], &accum);
op.reduce(remaining_reduce, &accum);
op.finalize(accum);
tmp_global_accessor.get_pointer()[0]=accum/scale;
} }
} }
}; };
} }
} }
} }

53
unsupported/test/cxx11_tensor_reduction_sycl.cpp
View File

@ -22,7 +22,7 @@
template <typename DataType, int DataLayout, typename IndexType> template <typename DataType, int DataLayout, typename IndexType>
static void test_full_reductions_sycl(const Eigen::SyclDevice& sycl_device) { static void test_full_reductions_mean_sycl(const Eigen::SyclDevice& sycl_device) {
const IndexType num_rows = 452; const IndexType num_rows = 452;
const IndexType num_cols = 765; const IndexType num_cols = 765;
@ -34,6 +34,37 @@ static void test_full_reductions_sycl(const Eigen::SyclDevice& sycl_device) {
in.setRandom(); in.setRandom();
full_redux = in.mean();
DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(DataType)));
DataType* gpu_out_data =(DataType*)sycl_device.allocate(sizeof(DataType));
TensorMap<Tensor<DataType, 2, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange);
TensorMap<Tensor<DataType, 0, DataLayout, IndexType> > out_gpu(gpu_out_data);
sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(DataType));
out_gpu.device(sycl_device) = in_gpu.mean();
sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(DataType));
// Check that the CPU and GPU reductions return the same result.
VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
sycl_device.deallocate(gpu_in_data);
sycl_device.deallocate(gpu_out_data);
}
template <typename DataType, int DataLayout, typename IndexType>
static void test_full_reductions_min_sycl(const Eigen::SyclDevice& sycl_device) {
const IndexType num_rows = 876;
const IndexType num_cols = 953;
array<IndexType, 2> tensorRange = {{num_rows, num_cols}};
Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange);
Tensor<DataType, 0, DataLayout, IndexType> full_redux;
Tensor<DataType, 0, DataLayout, IndexType> full_redux_gpu;
in.setRandom();
full_redux = in.minimum(); full_redux = in.minimum();
DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(DataType))); DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(DataType)));
@ -50,8 +81,10 @@ static void test_full_reductions_sycl(const Eigen::SyclDevice& sycl_device) {
sycl_device.deallocate(gpu_in_data); sycl_device.deallocate(gpu_in_data);
sycl_device.deallocate(gpu_out_data); sycl_device.deallocate(gpu_out_data);
} }
template <typename DataType, int DataLayout, typename IndexType> template <typename DataType, int DataLayout, typename IndexType>
static void test_first_dim_reductions_sycl(const Eigen::SyclDevice& sycl_device) { static void test_first_dim_reductions_max_sycl(const Eigen::SyclDevice& sycl_device) {
IndexType dim_x = 145; IndexType dim_x = 145;
IndexType dim_y = 1; IndexType dim_y = 1;
@ -90,7 +123,7 @@ static void test_first_dim_reductions_sycl(const Eigen::SyclDevice& sycl_device)
} }
template <typename DataType, int DataLayout, typename IndexType> template <typename DataType, int DataLayout, typename IndexType>
static void test_last_dim_reductions_sycl(const Eigen::SyclDevice &sycl_device) { static void test_last_dim_reductions_sum_sycl(const Eigen::SyclDevice &sycl_device) {
IndexType dim_x = 567; IndexType dim_x = 567;
IndexType dim_y = 1; IndexType dim_y = 1;
@ -132,12 +165,14 @@ template<typename DataType> void sycl_reduction_test_per_device(const cl::sycl::
QueueInterface queueInterface(d); QueueInterface queueInterface(d);
auto sycl_device = Eigen::SyclDevice(&queueInterface); auto sycl_device = Eigen::SyclDevice(&queueInterface);
test_full_reductions_sycl<DataType, RowMajor, int64_t>(sycl_device); test_full_reductions_mean_sycl<DataType, RowMajor, int64_t>(sycl_device);
test_first_dim_reductions_sycl<DataType, RowMajor, int64_t>(sycl_device); test_full_reductions_min_sycl<DataType, RowMajor, int64_t>(sycl_device);
test_last_dim_reductions_sycl<DataType, RowMajor, int64_t>(sycl_device); test_first_dim_reductions_max_sycl<DataType, RowMajor, int64_t>(sycl_device);
test_full_reductions_sycl<DataType, ColMajor, int64_t>(sycl_device); test_last_dim_reductions_sum_sycl<DataType, RowMajor, int64_t>(sycl_device);
test_first_dim_reductions_sycl<DataType, ColMajor, int64_t>(sycl_device); test_full_reductions_mean_sycl<DataType, ColMajor, int64_t>(sycl_device);
test_last_dim_reductions_sycl<DataType, ColMajor, int64_t>(sycl_device); test_full_reductions_min_sycl<DataType, ColMajor, int64_t>(sycl_device);
test_first_dim_reductions_max_sycl<DataType, ColMajor, int64_t>(sycl_device);
test_last_dim_reductions_sum_sycl<DataType, ColMajor, int64_t>(sycl_device);
} }
void test_cxx11_tensor_reduction_sycl() { void test_cxx11_tensor_reduction_sycl() {
for (const auto& device :Eigen::get_sycl_supported_devices()) { for (const auto& device :Eigen::get_sycl_supported_devices()) {