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Kokkos_SYCL_ParallelScan_Range.hpp
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Kokkos_SYCL_ParallelScan_Range.hpp
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//@HEADER
// ************************************************************************
//
// Kokkos v. 4.0
// Copyright (2022) National Technology & Engineering
// Solutions of Sandia, LLC (NTESS).
//
// Under the terms of Contract DE-NA0003525 with NTESS,
// the U.S. Government retains certain rights in this software.
//
// Part of Kokkos, under the Apache License v2.0 with LLVM Exceptions.
// See https://kokkos.org/LICENSE for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//@HEADER
#ifndef KOKKOS_SYCL_PARALLEL_SCAN_RANGE_HPP
#define KOKKOS_SYCL_PARALLEL_SCAN_RANGE_HPP
#include <Kokkos_Macros.hpp>
#include <memory>
#include <vector>
namespace Kokkos::Impl {
// Perform a scan over a workgroup.
// At the end of this function, the subgroup scans are stored in the local array
// such that the last value (at position n_active_subgroups-1) contains the
// total sum.
template <int dim, typename ValueType, typename FunctorType>
void workgroup_scan(sycl::nd_item<dim> item, const FunctorType& final_reducer,
sycl::local_accessor<ValueType> local_mem,
ValueType& local_value, int global_range) {
// subgroup scans
auto sg = item.get_sub_group();
const int sg_group_id = sg.get_group_id()[0];
const int id_in_sg = sg.get_local_id()[0];
const int local_range = std::min<int>(sg.get_local_range()[0], global_range);
#if defined(KOKKOS_ARCH_INTEL_GPU) || defined(KOKKOS_IMPL_ARCH_NVIDIA_GPU)
auto shuffle_combine = [&](int stride) {
if (stride < local_range) {
auto tmp = sg.shuffle_up(local_value, stride);
if (id_in_sg >= stride) final_reducer.join(&local_value, &tmp);
}
};
shuffle_combine(1);
shuffle_combine(2);
shuffle_combine(4);
shuffle_combine(8);
shuffle_combine(16);
KOKKOS_ASSERT(local_range <= 32);
#else
for (int stride = 1; stride < local_range; stride <<= 1) {
auto tmp = sg.shuffle_up(local_value, stride);
if (id_in_sg >= stride) final_reducer.join(&local_value, &tmp);
}
#endif
const int max_subgroup_size = sg.get_max_local_range()[0];
const int n_active_subgroups =
(global_range + max_subgroup_size - 1) / max_subgroup_size;
if (id_in_sg == local_range - 1 && sg_group_id < n_active_subgroups)
local_mem[sg_group_id] = local_value;
local_value = sg.shuffle_up(local_value, 1);
if (id_in_sg == 0) final_reducer.init(&local_value);
sycl::group_barrier(item.get_group());
// scan subgroup results using the first subgroup
if (n_active_subgroups > 1) {
if (sg_group_id == 0) {
const int n_rounds = (n_active_subgroups + local_range - 1) / local_range;
for (int round = 0; round < n_rounds; ++round) {
const int idx = id_in_sg + round * local_range;
const auto upper_bound =
std::min(local_range, n_active_subgroups - round * local_range);
auto local_sg_value = local_mem[idx < n_active_subgroups ? idx : 0];
#if defined(KOKKOS_ARCH_INTEL_GPU) || defined(KOKKOS_IMPL_ARCH_NVIDIA_GPU)
auto shuffle_combine_sg = [&](int stride) {
if (stride < upper_bound) {
auto tmp = sg.shuffle_up(local_sg_value, stride);
if (id_in_sg >= stride) {
if (idx < n_active_subgroups)
final_reducer.join(&local_sg_value, &tmp);
else
local_sg_value = tmp;
}
}
};
shuffle_combine_sg(1);
shuffle_combine_sg(2);
shuffle_combine_sg(4);
shuffle_combine_sg(8);
shuffle_combine_sg(16);
KOKKOS_ASSERT(upper_bound <= 32);
#else
for (int stride = 1; stride < upper_bound; stride <<= 1) {
auto tmp = sg.shuffle_up(local_sg_value, stride);
if (id_in_sg >= stride) {
if (idx < n_active_subgroups)
final_reducer.join(&local_sg_value, &tmp);
else
local_sg_value = tmp;
}
}
#endif
if (idx < n_active_subgroups) {
local_mem[idx] = local_sg_value;
if (round > 0)
final_reducer.join(&local_mem[idx],
&local_mem[round * local_range - 1]);
}
if (round + 1 < n_rounds) sycl::group_barrier(sg);
}
}
sycl::group_barrier(item.get_group());
}
// add results to all subgroups
if (sg_group_id > 0)
final_reducer.join(&local_value, &local_mem[sg_group_id - 1]);
}
template <class FunctorType, class ValueType, class... Traits>
class ParallelScanSYCLBase {
public:
using Policy = Kokkos::RangePolicy<Traits...>;
protected:
using Member = typename Policy::member_type;
using WorkTag = typename Policy::work_tag;
using LaunchBounds = typename Policy::launch_bounds;
public:
using Analysis = FunctorAnalysis<FunctorPatternInterface::SCAN, Policy,
FunctorType, ValueType>;
using pointer_type = typename Analysis::pointer_type;
using value_type = typename Analysis::value_type;
using reference_type = typename Analysis::reference_type;
using functor_type = FunctorType;
using size_type = Kokkos::Experimental::SYCL::size_type;
using index_type = typename Policy::index_type;
protected:
const CombinedFunctorReducer<FunctorType, typename Analysis::Reducer>
m_functor_reducer;
const Policy m_policy;
Kokkos::Impl::SYCLTypes::host_ptr<value_type> m_scratch_host = nullptr;
pointer_type m_result_ptr;
const bool m_result_ptr_device_accessible;
// Only let one ParallelScan instance at a time use the host scratch memory.
// The constructor acquires the mutex which is released in the destructor.
std::scoped_lock<std::mutex> m_scratch_buffers_lock;
private:
template <typename FunctorWrapper>
sycl::event sycl_direct_launch(const FunctorWrapper& functor_wrapper,
sycl::event memcpy_event) {
// Convenience references
const Kokkos::Experimental::SYCL& space = m_policy.space();
Kokkos::Experimental::Impl::SYCLInternal& instance =
*space.impl_internal_space_instance();
sycl::queue& q = space.sycl_queue();
const auto size = m_policy.end() - m_policy.begin();
auto scratch_flags =
static_cast<Kokkos::Impl::SYCLTypes::device_ptr<unsigned int>>(
instance.scratch_flags(sizeof(unsigned int)));
const auto begin = m_policy.begin();
// Initialize global memory
auto scan_lambda_factory =
[&](sycl::local_accessor<value_type> local_mem,
sycl::local_accessor<unsigned int> num_teams_done,
Kokkos::Impl::SYCLTypes::device_ptr<value_type> global_mem_,
Kokkos::Impl::SYCLTypes::device_ptr<value_type> group_results_) {
auto lambda = [=](sycl::nd_item<1> item) {
auto global_mem = global_mem_;
auto group_results = group_results_;
const CombinedFunctorReducer<
FunctorType, typename Analysis::Reducer>& functor_reducer =
functor_wrapper.get_functor();
const FunctorType& functor = functor_reducer.get_functor();
const typename Analysis::Reducer& reducer =
functor_reducer.get_reducer();
const auto n_wgroups = item.get_group_range()[0];
const int wgroup_size = item.get_local_range()[0];
const int local_id = item.get_local_linear_id();
const index_type global_id = item.get_global_linear_id();
// Initialize local memory
value_type local_value;
reducer.init(&local_value);
if (global_id < size) {
if constexpr (std::is_void<WorkTag>::value)
functor(global_id + begin, local_value, false);
else
functor(WorkTag(), global_id + begin, local_value, false);
}
workgroup_scan<>(item, reducer, local_mem, local_value,
wgroup_size);
// Write results to global memory
if (global_id < size) global_mem[global_id] = local_value;
if (local_id == wgroup_size - 1) {
group_results[item.get_group_linear_id()] =
local_mem[item.get_sub_group().get_group_range()[0] - 1];
sycl::atomic_ref<unsigned, sycl::memory_order::acq_rel,
sycl::memory_scope::device,
sycl::access::address_space::global_space>
scratch_flags_ref(*scratch_flags);
num_teams_done[0] = ++scratch_flags_ref;
}
item.barrier(sycl::access::fence_space::global_space);
if (num_teams_done[0] == n_wgroups) {
if (local_id == 0) *scratch_flags = 0;
value_type total;
reducer.init(&total);
for (unsigned int offset = 0; offset < n_wgroups;
offset += wgroup_size) {
index_type id = local_id + offset;
if (id < static_cast<index_type>(n_wgroups))
local_value = group_results[id];
else
reducer.init(&local_value);
workgroup_scan<>(
item, reducer, local_mem, local_value,
std::min<index_type>(n_wgroups - offset, wgroup_size));
if (id < static_cast<index_type>(n_wgroups)) {
reducer.join(&local_value, &total);
group_results[id] = local_value;
}
reducer.join(
&total,
&local_mem[item.get_sub_group().get_group_range()[0] - 1]);
if (offset + wgroup_size < n_wgroups)
item.barrier(sycl::access::fence_space::global_space);
}
}
};
return lambda;
};
size_t wgroup_size;
size_t n_wgroups;
Kokkos::Impl::SYCLTypes::device_ptr<value_type> global_mem;
Kokkos::Impl::SYCLTypes::device_ptr<value_type> group_results;
desul::ensure_sycl_lock_arrays_on_device(q);
auto perform_work_group_scans = q.submit([&](sycl::handler& cgh) {
sycl::local_accessor<unsigned int> num_teams_done(1, cgh);
auto dummy_scan_lambda =
scan_lambda_factory({1, cgh}, num_teams_done, nullptr, nullptr);
static sycl::kernel kernel = [&] {
sycl::kernel_id functor_kernel_id =
sycl::get_kernel_id<decltype(dummy_scan_lambda)>();
auto kernel_bundle =
sycl::get_kernel_bundle<sycl::bundle_state::executable>(
q.get_context(), std::vector{functor_kernel_id});
return kernel_bundle.get_kernel(functor_kernel_id);
}();
auto multiple = kernel.get_info<sycl::info::kernel_device_specific::
preferred_work_group_size_multiple>(
q.get_device());
auto max =
kernel.get_info<sycl::info::kernel_device_specific::work_group_size>(
q.get_device());
wgroup_size = static_cast<size_t>(max / multiple) * multiple;
n_wgroups = (size + wgroup_size - 1) / wgroup_size;
// Compute the total amount of memory we will need.
// We need to allocate memory for the whole range (rounded towards the
// next multiple of the workgroup size) and for one element per workgroup
// that will contain the sum of the previous workgroups totals.
// FIXME_SYCL consider only storing one value per block and recreate
// initial results in the end before doing the final pass
global_mem = static_cast<Kokkos::Impl::SYCLTypes::device_ptr<value_type>>(
instance.scratch_space(n_wgroups * (wgroup_size + 1) *
sizeof(value_type)));
m_scratch_host =
static_cast<Kokkos::Impl::SYCLTypes::host_ptr<value_type>>(
instance.scratch_host(sizeof(value_type)));
group_results = global_mem + n_wgroups * wgroup_size;
// Store subgroup totals in local space
const auto min_subgroup_size =
q.get_device()
.template get_info<sycl::info::device::sub_group_sizes>()
.front();
sycl::local_accessor<value_type> local_mem(
sycl::range<1>((wgroup_size + min_subgroup_size - 1) /
min_subgroup_size),
cgh);
#ifndef KOKKOS_IMPL_SYCL_USE_IN_ORDER_QUEUES
cgh.depends_on(memcpy_event);
#else
(void)memcpy_event;
#endif
auto scan_lambda = scan_lambda_factory(local_mem, num_teams_done,
global_mem, group_results);
cgh.parallel_for(sycl::nd_range<1>(n_wgroups * wgroup_size, wgroup_size),
scan_lambda);
});
// Write results to global memory
auto update_global_results = q.submit([&](sycl::handler& cgh) {
// The compiler failed with CL_INVALID_ARG_VALUE if using m_result_ptr
// directly.
pointer_type result_ptr = m_result_ptr_device_accessible
? m_result_ptr
: static_cast<pointer_type>(m_scratch_host);
#ifndef KOKKOS_IMPL_SYCL_USE_IN_ORDER_QUEUES
cgh.depends_on(perform_work_group_scans);
#endif
cgh.parallel_for(
sycl::nd_range<1>(n_wgroups * wgroup_size, wgroup_size),
[=](sycl::nd_item<1> item) {
const index_type global_id = item.get_global_linear_id();
const CombinedFunctorReducer<
FunctorType, typename Analysis::Reducer>& functor_reducer =
functor_wrapper.get_functor();
const FunctorType& functor = functor_reducer.get_functor();
const typename Analysis::Reducer& reducer =
functor_reducer.get_reducer();
if (global_id < size) {
value_type update = global_mem[global_id];
reducer.join(&update, &group_results[item.get_group_linear_id()]);
if constexpr (std::is_void<WorkTag>::value)
functor(global_id + begin, update, true);
else
functor(WorkTag(), global_id + begin, update, true);
if (global_id == size - 1) *result_ptr = update;
}
});
});
#ifndef KOKKOS_IMPL_SYCL_USE_IN_ORDER_QUEUES
q.ext_oneapi_submit_barrier(
std::vector<sycl::event>{update_global_results});
#endif
return update_global_results;
}
public:
template <typename PostFunctor>
void impl_execute(const PostFunctor& post_functor) {
if (m_policy.begin() == m_policy.end()) return;
auto& instance = *m_policy.space().impl_internal_space_instance();
Kokkos::Experimental::Impl::SYCLInternal::IndirectKernelMem&
indirectKernelMem = instance.get_indirect_kernel_mem();
auto functor_wrapper = Experimental::Impl::make_sycl_function_wrapper(
m_functor_reducer, indirectKernelMem);
sycl::event event =
sycl_direct_launch(functor_wrapper, functor_wrapper.get_copy_event());
functor_wrapper.register_event(event);
post_functor();
}
ParallelScanSYCLBase(const FunctorType& arg_functor, const Policy& arg_policy,
pointer_type arg_result_ptr,
bool arg_result_ptr_device_accessible)
: m_functor_reducer(arg_functor, typename Analysis::Reducer{arg_functor}),
m_policy(arg_policy),
m_result_ptr(arg_result_ptr),
m_result_ptr_device_accessible(arg_result_ptr_device_accessible),
m_scratch_buffers_lock(m_policy.space()
.impl_internal_space_instance()
->m_mutexScratchSpace) {}
};
} // namespace Kokkos::Impl
template <class FunctorType, class... Traits>
class Kokkos::Impl::ParallelScan<FunctorType, Kokkos::RangePolicy<Traits...>,
Kokkos::Experimental::SYCL>
: private ParallelScanSYCLBase<FunctorType, void, Traits...> {
public:
using Base = ParallelScanSYCLBase<FunctorType, void, Traits...>;
inline void execute() {
Base::impl_execute([]() {});
}
ParallelScan(const FunctorType& arg_functor,
const typename Base::Policy& arg_policy)
: Base(arg_functor, arg_policy, nullptr, false) {}
};
//----------------------------------------------------------------------------
template <class FunctorType, class ReturnType, class... Traits>
class Kokkos::Impl::ParallelScanWithTotal<
FunctorType, Kokkos::RangePolicy<Traits...>, ReturnType,
Kokkos::Experimental::SYCL>
: public ParallelScanSYCLBase<FunctorType, ReturnType, Traits...> {
public:
using Base = ParallelScanSYCLBase<FunctorType, ReturnType, Traits...>;
const Kokkos::Experimental::SYCL& m_exec;
inline void execute() {
Base::impl_execute([&]() {
const long long nwork = Base::m_policy.end() - Base::m_policy.begin();
if (nwork > 0 && !Base::m_result_ptr_device_accessible) {
// Using DeepCopy instead of fence+memcpy turned out to be up to 2x
// slower.
m_exec.fence(
"Kokkos::Impl::ParallelReduce<SYCL, MDRangePolicy>::execute: "
"result not device-accessible");
const int size = Base::m_functor_reducer.get_reducer().value_size();
std::memcpy(Base::m_result_ptr, Base::m_scratch_host, size);
}
});
}
template <class ViewType>
ParallelScanWithTotal(const FunctorType& arg_functor,
const typename Base::Policy& arg_policy,
const ViewType& arg_result_view)
: Base(arg_functor, arg_policy, arg_result_view.data(),
MemorySpaceAccess<Experimental::SYCLDeviceUSMSpace,
typename ViewType::memory_space>::accessible),
m_exec(arg_policy.space()) {}
};
#endif