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eigen/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// 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/.
/*****************************************************************
* TensorReductionSycl.h
*
* \brief:
* This is the specialization of the reduction operation. Two phase reduction approach
* is used since the GPU does not have Global Synchronization for global memory among
* different work-group/thread block. To solve the problem, we need to create two kernels
* to reduce the data, where the first kernel reduce the data locally and each local
* workgroup/thread-block save the input data into global memory. In the second phase (global reduction)
* one work-group uses one work-group/thread-block to reduces the intermediate data into one single element.
* Here is an NVIDIA presentation explaining the optimized two phase reduction algorithm on GPU:
* https://developer.download.nvidia.com/assets/cuda/files/reduction.pdf
*
*****************************************************************/
#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
namespace Eigen {
namespace TensorSycl {
namespace internal {
template <typename Op, typename CoeffReturnType, typename Index, bool Vectorizable>
struct OpDefiner {
typedef typename Vectorise<CoeffReturnType, Eigen::SyclDevice, Vectorizable>::PacketReturnType PacketReturnType;
typedef Op type;
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE type get_op(Op &op) { return op; }
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType finalise_op(const PacketReturnType &accumulator,
const Index &) {
return accumulator;
}
};
template <typename CoeffReturnType, typename Index>
struct OpDefiner<Eigen::internal::MeanReducer<CoeffReturnType>, CoeffReturnType, Index, false> {
typedef Eigen::internal::SumReducer<CoeffReturnType> type;
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE type get_op(Eigen::internal::MeanReducer<CoeffReturnType> &) {
return type();
}
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType finalise_op(const CoeffReturnType &accumulator,
const Index &scale) {
::Eigen::internal::scalar_quotient_op<CoeffReturnType> quotient_op;
return quotient_op(accumulator, CoeffReturnType(scale));
}
};
template <typename CoeffReturnType, typename Index>
struct OpDefiner<Eigen::internal::MeanReducer<CoeffReturnType>, CoeffReturnType, Index, true> {
typedef typename Vectorise<CoeffReturnType, Eigen::SyclDevice, true>::PacketReturnType PacketReturnType;
typedef Eigen::internal::SumReducer<CoeffReturnType> type;
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE type get_op(Eigen::internal::MeanReducer<CoeffReturnType> &) {
return type();
}
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType finalise_op(const PacketReturnType &accumulator,
const Index &scale) {
return ::Eigen::internal::pdiv(accumulator, ::Eigen::internal::pset1<PacketReturnType>(CoeffReturnType(scale)));
}
};
template <typename CoeffReturnType, typename OpType, typename InputAccessor, typename OutputAccessor, typename Index,
Index local_range>
struct SecondStepFullReducer {
typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
LocalAccessor;
typedef OpDefiner<OpType, CoeffReturnType, Index, true> OpDef;
typedef typename OpDef::type Op;
LocalAccessor scratch;
InputAccessor aI;
OutputAccessor outAcc;
Op op;
SecondStepFullReducer(LocalAccessor scratch_, InputAccessor aI_, OutputAccessor outAcc_, OpType op_)
: scratch(scratch_), aI(aI_), outAcc(outAcc_), op(OpDef::get_op(op_)) {}
void operator()(cl::sycl::nd_item<1> itemID) {
// Our empirical research shows that the best performance will be achieved
// when there is only one element per thread to reduce in the second step.
// in this step the second step reduction time is almost negligible.
// Hence, in the second step of reduction the input size is fixed to the
// local size, thus, there is only one element read per thread. The
// algorithm must be changed if the number of reduce per thread in the
// second step is greater than 1. Otherwise, the result will be wrong.
const Index localid = itemID.get_local_id(0);
auto aInPtr = aI.get_pointer() + localid;
auto aOutPtr = outAcc.get_pointer();
CoeffReturnType *scratchptr = scratch.get_pointer();
CoeffReturnType accumulator = *aInPtr;
scratchptr[localid] = op.finalize(accumulator);
for (Index offset = itemID.get_local_range(0) / 2; offset > 0; offset /= 2) {
itemID.barrier(cl::sycl::access::fence_space::local_space);
if (localid < offset) {
op.reduce(scratchptr[localid + offset], &accumulator);
scratchptr[localid] = op.finalize(accumulator);
}
}
if (localid == 0) *aOutPtr = op.finalize(accumulator);
}
};
// Full reduction first phase. In this version the vectorization is true and the reduction accept
// any generic reducerOp e.g( max, min, sum, mean, iamax, iamin, etc ).
template <typename Evaluator, typename OpType, typename Evaluator::Index local_range>
class FullReductionKernelFunctor {
public:
typedef typename Evaluator::CoeffReturnType CoeffReturnType;
typedef typename Evaluator::Index Index;
typedef OpDefiner<OpType, typename Evaluator::CoeffReturnType, Index,
(Evaluator::ReducerTraits::PacketAccess & Evaluator::InputPacketAccess)>
OpDef;
typedef typename OpDef::type Op;
typedef typename Evaluator::EvaluatorPointerType EvaluatorPointerType;
typedef typename Evaluator::PacketReturnType PacketReturnType;
typedef
typename ::Eigen::internal::conditional<(Evaluator::ReducerTraits::PacketAccess & Evaluator::InputPacketAccess),
PacketReturnType, CoeffReturnType>::type OutType;
typedef cl::sycl::accessor<OutType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
LocalAccessor;
LocalAccessor scratch;
Evaluator evaluator;
EvaluatorPointerType final_output;
Index rng;
Op op;
FullReductionKernelFunctor(LocalAccessor scratch_, Evaluator evaluator_, EvaluatorPointerType final_output_,
Index rng_, OpType op_)
: scratch(scratch_), evaluator(evaluator_), final_output(final_output_), rng(rng_), op(OpDef::get_op(op_)) {}
void operator()(cl::sycl::nd_item<1> itemID) { compute_reduction(itemID); }
template <bool Vect = (Evaluator::ReducerTraits::PacketAccess & Evaluator::InputPacketAccess)>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ::Eigen::internal::enable_if<Vect>::type compute_reduction(
const cl::sycl::nd_item<1> &itemID) {
auto output_ptr = final_output.get_pointer();
Index VectorizedRange = (rng / Evaluator::PacketSize) * Evaluator::PacketSize;
Index globalid = itemID.get_global_id(0);
Index localid = itemID.get_local_id(0);
Index step = Evaluator::PacketSize * itemID.get_global_range(0);
Index start = Evaluator::PacketSize * globalid;
// vectorizable parts
PacketReturnType packetAccumulator = op.template initializePacket<PacketReturnType>();
for (Index i = start; i < VectorizedRange; i += step) {
op.template reducePacket<PacketReturnType>(evaluator.impl().template packet<Unaligned>(i), &packetAccumulator);
}
globalid += VectorizedRange;
// non vectorizable parts
for (Index i = globalid; i < rng; i += itemID.get_global_range(0)) {
op.template reducePacket<PacketReturnType>(
::Eigen::TensorSycl::internal::PacketWrapper<PacketReturnType, Evaluator::PacketSize>::convert_to_packet_type(
evaluator.impl().coeff(i), op.initialize()),
&packetAccumulator);
}
scratch[localid] = packetAccumulator =
OpDef::finalise_op(op.template finalizePacket<PacketReturnType>(packetAccumulator), rng);
// reduction parts // Local size is always power of 2
EIGEN_UNROLL_LOOP
for (Index offset = local_range / 2; offset > 0; offset /= 2) {
itemID.barrier(cl::sycl::access::fence_space::local_space);
if (localid < offset) {
op.template reducePacket<PacketReturnType>(scratch[localid + offset], &packetAccumulator);
scratch[localid] = op.template finalizePacket<PacketReturnType>(packetAccumulator);
}
}
if (localid == 0) {
output_ptr[itemID.get_group(0)] =
op.finalizeBoth(op.initialize(), op.template finalizePacket<PacketReturnType>(packetAccumulator));
}
}
template <bool Vect = (Evaluator::ReducerTraits::PacketAccess & Evaluator::InputPacketAccess)>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ::Eigen::internal::enable_if<!Vect>::type compute_reduction(
const cl::sycl::nd_item<1> &itemID) {
auto output_ptr = final_output.get_pointer();
Index globalid = itemID.get_global_id(0);
Index localid = itemID.get_local_id(0);
// vectorizable parts
CoeffReturnType accumulator = op.initialize();
// non vectorizable parts
for (Index i = globalid; i < rng; i += itemID.get_global_range(0)) {
op.reduce(evaluator.impl().coeff(i), &accumulator);
}
scratch[localid] = accumulator = OpDef::finalise_op(op.finalize(accumulator), rng);
// reduction parts. the local size is always power of 2
EIGEN_UNROLL_LOOP
for (Index offset = local_range / 2; offset > 0; offset /= 2) {
itemID.barrier(cl::sycl::access::fence_space::local_space);
if (localid < offset) {
op.reduce(scratch[localid + offset], &accumulator);
scratch[localid] = op.finalize(accumulator);
}
}
if (localid == 0) {
output_ptr[itemID.get_group(0)] = op.finalize(accumulator);
}
}
};
template <typename Evaluator, typename OpType>
class GenericNondeterministicReducer {
public:
typedef typename Evaluator::CoeffReturnType CoeffReturnType;
typedef typename Evaluator::EvaluatorPointerType EvaluatorPointerType;
typedef typename Evaluator::Index Index;
typedef OpDefiner<OpType, CoeffReturnType, Index, false> OpDef;
typedef typename OpDef::type Op;
template <typename Scratch>
GenericNondeterministicReducer(Scratch, Evaluator evaluator_, EvaluatorPointerType output_accessor_, OpType functor_,
Index range_, Index num_values_to_reduce_)
: evaluator(evaluator_),
output_accessor(output_accessor_),
functor(OpDef::get_op(functor_)),
range(range_),
num_values_to_reduce(num_values_to_reduce_) {}
void operator()(cl::sycl::nd_item<1> itemID) {
auto output_accessor_ptr = output_accessor.get_pointer();
/// const cast added as a naive solution to solve the qualifier drop error
Index globalid = static_cast<Index>(itemID.get_global_linear_id());
if (globalid < range) {
CoeffReturnType accum = functor.initialize();
Eigen::internal::GenericDimReducer<Evaluator::NumReducedDims - 1, Evaluator, Op>::reduce(
evaluator, evaluator.firstInput(globalid), functor, &accum);
output_accessor_ptr[globalid] = OpDef::finalise_op(functor.finalize(accum), num_values_to_reduce);
}
}
private:
Evaluator evaluator;
EvaluatorPointerType output_accessor;
Op functor;
Index range;
Index num_values_to_reduce;
};
enum class reduction_dim { inner_most, outer_most };
// default is preserver
template <typename Evaluator, typename OpType, typename PannelParameters, reduction_dim rt>
struct PartialReductionKernel {
typedef typename Evaluator::CoeffReturnType CoeffReturnType;
typedef typename Evaluator::EvaluatorPointerType EvaluatorPointerType;
typedef typename Evaluator::Index Index;
typedef OpDefiner<OpType, CoeffReturnType, Index, false> OpDef;
typedef typename OpDef::type Op;
typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
ScratchAcc;
ScratchAcc scratch;
Evaluator evaluator;
EvaluatorPointerType output_accessor;
Op op;
const Index preserve_elements_num_groups;
const Index reduce_elements_num_groups;
const Index num_coeffs_to_preserve;
const Index num_coeffs_to_reduce;
PartialReductionKernel(ScratchAcc scratch_, Evaluator evaluator_, EvaluatorPointerType output_accessor_, OpType op_,
const Index preserve_elements_num_groups_, const Index reduce_elements_num_groups_,
const Index num_coeffs_to_preserve_, const Index num_coeffs_to_reduce_)
: scratch(scratch_),
evaluator(evaluator_),
output_accessor(output_accessor_),
op(OpDef::get_op(op_)),
preserve_elements_num_groups(preserve_elements_num_groups_),
reduce_elements_num_groups(reduce_elements_num_groups_),
num_coeffs_to_preserve(num_coeffs_to_preserve_),
num_coeffs_to_reduce(num_coeffs_to_reduce_) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void element_wise_reduce(Index globalRId, Index globalPId,
CoeffReturnType &accumulator) {
if (globalPId >= num_coeffs_to_preserve) {
return;
}
Index global_offset = rt == reduction_dim::outer_most ? globalPId + (globalRId * num_coeffs_to_preserve)
: globalRId + (globalPId * num_coeffs_to_reduce);
Index localOffset = globalRId;
const Index per_thread_local_stride = PannelParameters::LocalThreadSizeR * reduce_elements_num_groups;
const Index per_thread_global_stride =
rt == reduction_dim::outer_most ? num_coeffs_to_preserve * per_thread_local_stride : per_thread_local_stride;
for (Index i = globalRId; i < num_coeffs_to_reduce; i += per_thread_local_stride) {
op.reduce(evaluator.impl().coeff(global_offset), &accumulator);
localOffset += per_thread_local_stride;
global_offset += per_thread_global_stride;
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) {
const Index linearLocalThreadId = itemID.get_local_id(0);
Index pLocalThreadId = rt == reduction_dim::outer_most ? linearLocalThreadId % PannelParameters::LocalThreadSizeP
: linearLocalThreadId / PannelParameters::LocalThreadSizeR;
Index rLocalThreadId = rt == reduction_dim::outer_most ? linearLocalThreadId / PannelParameters::LocalThreadSizeP
: linearLocalThreadId % PannelParameters::LocalThreadSizeR;
const Index pGroupId = rt == reduction_dim::outer_most ? itemID.get_group(0) % preserve_elements_num_groups
: itemID.get_group(0) / reduce_elements_num_groups;
const Index rGroupId = rt == reduction_dim::outer_most ? itemID.get_group(0) / preserve_elements_num_groups
: itemID.get_group(0) % reduce_elements_num_groups;
Index globalPId = pGroupId * PannelParameters::LocalThreadSizeP + pLocalThreadId;
const Index globalRId = rGroupId * PannelParameters::LocalThreadSizeR + rLocalThreadId;
auto scratchPtr = scratch.get_pointer().get();
auto outPtr =
output_accessor.get_pointer() + (reduce_elements_num_groups > 1 ? rGroupId * num_coeffs_to_preserve : 0);
CoeffReturnType accumulator = op.initialize();
element_wise_reduce(globalRId, globalPId, accumulator);
accumulator = OpDef::finalise_op(op.finalize(accumulator), num_coeffs_to_reduce);
scratchPtr[pLocalThreadId + rLocalThreadId * (PannelParameters::LocalThreadSizeP + PannelParameters::BC)] =
accumulator;
if (rt == reduction_dim::inner_most) {
pLocalThreadId = linearLocalThreadId % PannelParameters::LocalThreadSizeP;
rLocalThreadId = linearLocalThreadId / PannelParameters::LocalThreadSizeP;
globalPId = pGroupId * PannelParameters::LocalThreadSizeP + pLocalThreadId;
}
/* Apply the reduction operation between the current local
* id and the one on the other half of the vector. */
auto out_scratch_ptr =
scratchPtr + (pLocalThreadId + (rLocalThreadId * (PannelParameters::LocalThreadSizeP + PannelParameters::BC)));
itemID.barrier(cl::sycl::access::fence_space::local_space);
if (rt == reduction_dim::inner_most) {
accumulator = *out_scratch_ptr;
}
// The Local LocalThreadSizeR is always power of 2
EIGEN_UNROLL_LOOP
for (Index offset = PannelParameters::LocalThreadSizeR >> 1; offset > 0; offset >>= 1) {
if (rLocalThreadId < offset) {
op.reduce(out_scratch_ptr[(PannelParameters::LocalThreadSizeP + PannelParameters::BC) * offset], &accumulator);
// The result has already been divided for mean reducer in the
// previous reduction so no need to divide furthermore
*out_scratch_ptr = op.finalize(accumulator);
}
/* All threads collectively read from global memory into local.
* The barrier ensures all threads' IO is resolved before
* execution continues (strictly speaking, all threads within
* a single work-group - there is no co-ordination between
* work-groups, only work-items). */
itemID.barrier(cl::sycl::access::fence_space::local_space);
}
if (rLocalThreadId == 0 && (globalPId < num_coeffs_to_preserve)) {
outPtr[globalPId] = op.finalize(accumulator);
}
}
};
template <typename OutScalar, typename Index, typename InputAccessor, typename OutputAccessor, typename OpType>
struct SecondStepPartialReduction {
typedef OpDefiner<OpType, OutScalar, Index, false> OpDef;
typedef typename OpDef::type Op;
typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
ScratchAccessor;
InputAccessor input_accessor;
OutputAccessor output_accessor;
Op op;
const Index num_coeffs_to_preserve;
const Index num_coeffs_to_reduce;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE SecondStepPartialReduction(ScratchAccessor, InputAccessor input_accessor_,
OutputAccessor output_accessor_, OpType op_,
const Index num_coeffs_to_preserve_,
const Index num_coeffs_to_reduce_)
: input_accessor(input_accessor_),
output_accessor(output_accessor_),
op(OpDef::get_op(op_)),
num_coeffs_to_preserve(num_coeffs_to_preserve_),
num_coeffs_to_reduce(num_coeffs_to_reduce_) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) {
const Index globalId = itemID.get_global_id(0);
if (globalId >= num_coeffs_to_preserve) return;
auto in_ptr = input_accessor.get_pointer() + globalId;
OutScalar accumulator = op.initialize();
// num_coeffs_to_reduce is not bigger that 256
for (Index i = 0; i < num_coeffs_to_reduce; i++) {
op.reduce(*in_ptr, &accumulator);
in_ptr += num_coeffs_to_preserve;
}
output_accessor.get_pointer()[globalId] = op.finalize(accumulator);
}
}; // namespace internal
template <typename Index, Index LTP, Index LTR, bool BC_>
struct ReductionPannel {
static EIGEN_CONSTEXPR Index LocalThreadSizeP = LTP;
static EIGEN_CONSTEXPR Index LocalThreadSizeR = LTR;
static EIGEN_CONSTEXPR bool BC = BC_;
};
template <typename Self, typename Op, TensorSycl::internal::reduction_dim rt>
struct PartialReducerLauncher {
typedef typename Self::EvaluatorPointerType EvaluatorPointerType;
typedef typename Self::CoeffReturnType CoeffReturnType;
typedef typename Self::Storage Storage;
typedef typename Self::Index Index;
typedef ReductionPannel<typename Self::Index, EIGEN_SYCL_LOCAL_THREAD_DIM0, EIGEN_SYCL_LOCAL_THREAD_DIM1, true>
PannelParameters;
typedef PartialReductionKernel<Self, Op, PannelParameters, rt> SyclReducerKerneType;
static bool run(const Self &self, const Op &reducer, const Eigen::SyclDevice &dev, EvaluatorPointerType output,
Index num_coeffs_to_reduce, Index num_coeffs_to_preserve) {
Index roundUpP = roundUp(num_coeffs_to_preserve, PannelParameters::LocalThreadSizeP);
// getPowerOfTwo makes sure local range is power of 2 and <=
// maxSyclThreadPerBlock this will help us to avoid extra check on the
// kernel
static_assert(!((PannelParameters::LocalThreadSizeP * PannelParameters::LocalThreadSizeR) &
(PannelParameters::LocalThreadSizeP * PannelParameters::LocalThreadSizeR - 1)),
"The Local thread size must be a power of 2 for the reduction "
"operation");
EIGEN_CONSTEXPR Index localRange = PannelParameters::LocalThreadSizeP * PannelParameters::LocalThreadSizeR;
// In this step, we force the code not to be more than 2-step reduction:
// Our empirical research shows that if each thread reduces at least 64
// elemnts individually, we get better performance. However, this can change
// on different platforms. In this step we force the code not to be
// morthan step reduction: Our empirical research shows that for inner_most
// dim reducer, it is better to have 8 group in a reduce dimension for sizes
// > 1024 to achieve the best performance.
const Index reductionPerThread = 64;
Index cu = dev.getPowerOfTwo(dev.getNumSyclMultiProcessors(), true);
const Index pNumGroups = roundUpP / PannelParameters::LocalThreadSizeP;
Index rGroups = (cu + pNumGroups - 1) / pNumGroups;
const Index rNumGroups = num_coeffs_to_reduce > reductionPerThread * localRange ? std::min(rGroups, localRange) : 1;
const Index globalRange = pNumGroups * rNumGroups * localRange;
EIGEN_CONSTEXPR Index scratchSize =
PannelParameters::LocalThreadSizeR * (PannelParameters::LocalThreadSizeP + PannelParameters::BC);
auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(globalRange), cl::sycl::range<1>(localRange));
if (rNumGroups > 1) {
CoeffReturnType *temp_pointer = static_cast<CoeffReturnType *>(
dev.allocate_temp(num_coeffs_to_preserve * rNumGroups * sizeof(CoeffReturnType)));
EvaluatorPointerType temp_accessor = dev.get(temp_pointer);
dev.template unary_kernel_launcher<CoeffReturnType, SyclReducerKerneType>(
self, temp_accessor, thread_range, scratchSize, reducer, pNumGroups, rNumGroups, num_coeffs_to_preserve,
num_coeffs_to_reduce);
typedef SecondStepPartialReduction<CoeffReturnType, Index, EvaluatorPointerType, EvaluatorPointerType, Op>
SecondStepPartialReductionKernel;
dev.template unary_kernel_launcher<CoeffReturnType, SecondStepPartialReductionKernel>(
temp_accessor, output,
cl::sycl::nd_range<1>(cl::sycl::range<1>(pNumGroups * localRange), cl::sycl::range<1>(localRange)), Index(1),
reducer, num_coeffs_to_preserve, rNumGroups);
self.device().deallocate_temp(temp_pointer);
} else {
dev.template unary_kernel_launcher<CoeffReturnType, SyclReducerKerneType>(
self, output, thread_range, scratchSize, reducer, pNumGroups, rNumGroups, num_coeffs_to_preserve,
num_coeffs_to_reduce);
}
return false;
}
};
} // namespace internal
} // namespace TensorSycl
namespace internal {
template <typename Self, typename Op, bool Vectorizable>
struct FullReducer<Self, Op, Eigen::SyclDevice, Vectorizable> {
typedef typename Self::CoeffReturnType CoeffReturnType;
typedef typename Self::EvaluatorPointerType EvaluatorPointerType;
static EIGEN_CONSTEXPR bool HasOptimizedImplementation = true;
static EIGEN_CONSTEXPR int PacketSize = Self::PacketAccess ? Self::PacketSize : 1;
static void run(const Self &self, Op &reducer, const Eigen::SyclDevice &dev, EvaluatorPointerType data) {
typedef typename conditional<Self::PacketAccess, typename Self::PacketReturnType, CoeffReturnType>::type OutType;
static_assert(!((EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1) &
(EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1 - 1)),
"The Local thread size must be a power of 2 for the reduction "
"operation");
EIGEN_CONSTEXPR Index local_range = EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1;
typename Self::Index inputSize = self.impl().dimensions().TotalSize();
// In this step we force the code not to be more than 2-step reduction:
// Our empirical research shows that if each thread reduces at least 512
// elemnts individually, we get better performance.
const Index reductionPerThread = 2048;
// const Index num_work_group =
Index reductionGroup = dev.getPowerOfTwo(
(inputSize + (reductionPerThread * local_range - 1)) / (reductionPerThread * local_range), true);
const Index num_work_group = std::min(reductionGroup, local_range);
// 1
// ? local_range
// : 1);
const Index global_range = num_work_group * local_range;
auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(global_range), cl::sycl::range<1>(local_range));
typedef TensorSycl::internal::FullReductionKernelFunctor<Self, Op, local_range> reduction_kernel_t;
if (num_work_group > 1) {
CoeffReturnType *temp_pointer =
static_cast<CoeffReturnType *>(dev.allocate_temp(num_work_group * sizeof(CoeffReturnType)));
typename Self::EvaluatorPointerType tmp_global_accessor = dev.get(temp_pointer);
dev.template unary_kernel_launcher<OutType, reduction_kernel_t>(self, tmp_global_accessor, thread_range,
local_range, inputSize, reducer);
typedef TensorSycl::internal::SecondStepFullReducer<CoeffReturnType, Op, EvaluatorPointerType,
EvaluatorPointerType, Index, local_range>
GenericRKernel;
dev.template unary_kernel_launcher<CoeffReturnType, GenericRKernel>(
tmp_global_accessor, data,
cl::sycl::nd_range<1>(cl::sycl::range<1>(num_work_group), cl::sycl::range<1>(num_work_group)), num_work_group,
reducer);
dev.deallocate_temp(temp_pointer);
} else {
dev.template unary_kernel_launcher<OutType, reduction_kernel_t>(self, data, thread_range, local_range, inputSize,
reducer);
}
}
};
// vectorizable inner_most most dim preserver
// col reduction
template <typename Self, typename Op>
struct OuterReducer<Self, Op, Eigen::SyclDevice> {
static EIGEN_CONSTEXPR bool HasOptimizedImplementation = true;
static bool run(const Self &self, const Op &reducer, const Eigen::SyclDevice &dev,
typename Self::EvaluatorPointerType output, typename Self::Index num_coeffs_to_reduce,
typename Self::Index num_coeffs_to_preserve) {
return ::Eigen::TensorSycl::internal::PartialReducerLauncher<
Self, Op, ::Eigen::TensorSycl::internal::reduction_dim::outer_most>::run(self, reducer, dev, output,
num_coeffs_to_reduce,
num_coeffs_to_preserve);
}
};
// row reduction
template <typename Self, typename Op>
struct InnerReducer<Self, Op, Eigen::SyclDevice> {
static EIGEN_CONSTEXPR bool HasOptimizedImplementation = true;
static bool run(const Self &self, const Op &reducer, const Eigen::SyclDevice &dev,
typename Self::EvaluatorPointerType output, typename Self::Index num_coeffs_to_reduce,
typename Self::Index num_coeffs_to_preserve) {
return ::Eigen::TensorSycl::internal::PartialReducerLauncher<
Self, Op, ::Eigen::TensorSycl::internal::reduction_dim::inner_most>::run(self, reducer, dev, output,
num_coeffs_to_reduce,
num_coeffs_to_preserve);
}
};
// ArmgMax uses this kernel for partial reduction//
// TODO(@mehdi.goli) come up with a better kernel
// generic partial reduction
template <typename Self, typename Op>
struct GenericReducer<Self, Op, Eigen::SyclDevice> {
static EIGEN_CONSTEXPR bool HasOptimizedImplementation = false;
static bool run(const Self &self, const Op &reducer, const Eigen::SyclDevice &dev,
typename Self::EvaluatorPointerType output, typename Self::Index num_values_to_reduce,
typename Self::Index num_coeffs_to_preserve) {
typename Self::Index range, GRange, tileSize;
dev.parallel_for_setup(num_coeffs_to_preserve, tileSize, range, GRange);
dev.template unary_kernel_launcher<typename Self::CoeffReturnType,
TensorSycl::internal::GenericNondeterministicReducer<Self, Op>>(
self, output, cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(tileSize)), Index(1),
reducer, range, (num_values_to_reduce != 0) ? num_values_to_reduce : static_cast<Index>(1));
return false;
}
};
} // namespace internal
} // namespace Eigen
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
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