cub/device/device_reduce.cuh
File members: cub/device/device_reduce.cuh
/******************************************************************************
* Copyright (c) 2011, Duane Merrill. All rights reserved.
* Copyright (c) 2011-2024, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
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* notice, this list of conditions and the following disclaimer.
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* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
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* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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#pragma once
#include <cub/config.cuh>
#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
# pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
# pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
# pragma system_header
#endif // no system header
#include <cub/detail/choose_offset.cuh>
#include <cub/detail/temporary_storage.cuh>
#include <cub/device/dispatch/dispatch_reduce.cuh>
#include <cub/device/dispatch/dispatch_reduce_by_key.cuh>
#include <cub/device/dispatch/dispatch_reduce_deterministic.cuh>
#include <cub/device/dispatch/dispatch_streaming_reduce.cuh>
#include <cub/util_type.cuh>
#include <thrust/iterator/tabulate_output_iterator.h>
#include <cuda/__execution/determinism.h>
#include <cuda/__execution/require.h>
#include <cuda/__execution/tune.h>
#include <cuda/__memory_resource/get_memory_resource.h>
#include <cuda/__stream/get_stream.h>
#include <cuda/std/__execution/env.h>
#include <cuda/std/limits>
CUB_NAMESPACE_BEGIN
namespace detail
{
namespace reduce
{
struct get_tuning_query_t
{};
template <class Derived>
struct tuning
{
[[nodiscard]] _CCCL_TRIVIAL_API constexpr auto query(const get_tuning_query_t&) const noexcept -> Derived
{
return static_cast<const Derived&>(*this);
}
};
struct default_tuning : tuning<default_tuning>
{
template <class AccumT, class Offset, class OpT>
using fn = policy_hub<AccumT, Offset, OpT>;
};
struct default_rfa_tuning : tuning<default_tuning>
{
template <class AccumT, class Offset, class OpT>
using fn = detail::rfa::policy_hub<AccumT, Offset, OpT>;
};
template <typename ExtremumOutIteratorT, typename IndexOutIteratorT>
struct unzip_and_write_arg_extremum_op
{
ExtremumOutIteratorT result_out_it;
IndexOutIteratorT index_out_it;
template <typename IndexT, typename KeyValuePairT>
_CCCL_DEVICE _CCCL_FORCEINLINE void operator()(IndexT, KeyValuePairT reduced_result)
{
*result_out_it = reduced_result.value;
*index_out_it = reduced_result.key;
}
};
} // namespace reduce
// TODO(gevtushenko): move cudax `device_memory_resource` to `cuda::__device_memory_resource` and use it here
struct device_memory_resource
{
void* allocate(size_t bytes, size_t /* alignment */)
{
void* ptr{nullptr};
_CCCL_TRY_CUDA_API(::cudaMalloc, "allocate failed to allocate with cudaMalloc", &ptr, bytes);
return ptr;
}
void deallocate(void* ptr, size_t /* bytes */)
{
_CCCL_ASSERT_CUDA_API(::cudaFree, "deallocate failed", ptr);
}
void* allocate_async(size_t bytes, size_t /* alignment */, ::cuda::stream_ref stream)
{
return allocate_async(bytes, stream);
}
void* allocate_async(size_t bytes, ::cuda::stream_ref stream)
{
void* ptr{nullptr};
_CCCL_TRY_CUDA_API(
::cudaMallocAsync, "allocate_async failed to allocate with cudaMallocAsync", &ptr, bytes, stream.get());
return ptr;
}
void deallocate_async(void* ptr, size_t /* bytes */, const ::cuda::stream_ref stream)
{
_CCCL_ASSERT_CUDA_API(::cudaFreeAsync, "deallocate_async failed", ptr, stream.get());
}
};
} // namespace detail
struct DeviceReduce
{
private:
// TODO(gevtushenko): dispatch to atomic reduce once merged
template <typename TuningEnvT,
typename InputIteratorT,
typename OutputIteratorT,
typename ReductionOpT,
typename T,
typename NumItemsT,
::cuda::execution::determinism::__determinism_t Determinism>
CUB_RUNTIME_FUNCTION static cudaError_t reduce_impl(
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
NumItemsT num_items,
ReductionOpT reduction_op,
T init,
::cuda::execution::determinism::__determinism_holder_t<Determinism>,
cudaStream_t stream)
{
using offset_t = detail::choose_offset_t<NumItemsT>;
using accum_t = ::cuda::std::__accumulator_t<ReductionOpT, detail::it_value_t<InputIteratorT>, T>;
using transform_t = ::cuda::std::identity;
using reduce_tuning_t = ::cuda::std::execution::
__query_result_or_t<TuningEnvT, detail::reduce::get_tuning_query_t, detail::reduce::default_tuning>;
using policy_t = typename reduce_tuning_t::template fn<accum_t, offset_t, ReductionOpT>;
using dispatch_t =
DispatchReduce<InputIteratorT, OutputIteratorT, offset_t, ReductionOpT, T, accum_t, transform_t, policy_t>;
return dispatch_t::Dispatch(
d_temp_storage, temp_storage_bytes, d_in, d_out, static_cast<offset_t>(num_items), reduction_op, init, stream);
}
template <typename TuningEnvT,
typename InputIteratorT,
typename OutputIteratorT,
typename ReductionOpT,
typename T,
typename NumItemsT>
CUB_RUNTIME_FUNCTION static cudaError_t reduce_impl(
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
NumItemsT num_items,
ReductionOpT,
T init,
::cuda::execution::determinism::gpu_to_gpu_t,
cudaStream_t stream)
{
using offset_t = detail::choose_offset_t<NumItemsT>;
using accum_t = ::cuda::std::__accumulator_t<ReductionOpT, detail::it_value_t<InputIteratorT>, T>;
// RFA is only supported for float and double accumulators
constexpr bool is_float_or_double = detail::is_one_of_v<accum_t, float, double>;
constexpr bool is_sum = detail::reduce::is_plus<ReductionOpT>::value;
constexpr bool is_supported = is_float_or_double && is_sum;
static_assert(is_supported, "gpu-to-gpu deterministic reduction supports only float and double sum.");
if constexpr (is_supported)
{
using transform_t = ::cuda::std::identity;
using reduce_tuning_t = ::cuda::std::execution::
__query_result_or_t<TuningEnvT, detail::reduce::get_tuning_query_t, detail::reduce::default_rfa_tuning>;
using policy_t = typename reduce_tuning_t::template fn<accum_t, offset_t, ReductionOpT>;
using dispatch_t =
detail::DispatchReduceDeterministic<InputIteratorT, OutputIteratorT, offset_t, T, transform_t, accum_t, policy_t>;
return dispatch_t::Dispatch(
d_temp_storage, temp_storage_bytes, d_in, d_out, static_cast<offset_t>(num_items), init, stream);
}
else
{
return cudaErrorNotSupported;
}
}
public:
template <typename InputIteratorT, typename OutputIteratorT, typename ReductionOpT, typename T, typename NumItemsT>
CUB_RUNTIME_FUNCTION static cudaError_t Reduce(
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
NumItemsT num_items,
ReductionOpT reduction_op,
T init,
cudaStream_t stream = 0)
{
_CCCL_NVTX_RANGE_SCOPE_IF(d_temp_storage, "cub::DeviceReduce::Reduce");
// Signed integer type for global offsets
using OffsetT = detail::choose_offset_t<NumItemsT>;
return DispatchReduce<InputIteratorT, OutputIteratorT, OffsetT, ReductionOpT, T>::Dispatch(
d_temp_storage, temp_storage_bytes, d_in, d_out, static_cast<OffsetT>(num_items), reduction_op, init, stream);
}
template <typename InputIteratorT,
typename OutputIteratorT,
typename ReductionOpT,
typename T,
typename NumItemsT,
typename EnvT = ::cuda::std::execution::env<>>
CUB_RUNTIME_FUNCTION static cudaError_t Reduce(
InputIteratorT d_in, OutputIteratorT d_out, NumItemsT num_items, ReductionOpT reduction_op, T init, EnvT env = {})
{
_CCCL_NVTX_RANGE_SCOPE("cub::DeviceReduce::Reduce");
static_assert(!_CUDA_STD_EXEC::__queryable_with<EnvT, _CUDA_EXEC::determinism::__get_determinism_t>,
"Determinism should be used inside requires to have an effect.");
using requirements_t =
_CUDA_STD_EXEC::__query_result_or_t<EnvT, _CUDA_EXEC::__get_requirements_t, _CUDA_STD_EXEC::env<>>;
using determinism_t =
_CUDA_STD_EXEC::__query_result_or_t<requirements_t, //
_CUDA_EXEC::determinism::__get_determinism_t,
_CUDA_EXEC::determinism::run_to_run_t>;
// Query relevant properties from the environment
auto stream = _CUDA_STD_EXEC::__query_or(env, ::cuda::get_stream, ::cuda::stream_ref{});
auto mr = _CUDA_STD_EXEC::__query_or(env, ::cuda::mr::__get_memory_resource, detail::device_memory_resource{});
void* d_temp_storage = nullptr;
size_t temp_storage_bytes = 0;
using tuning_t = _CUDA_STD_EXEC::__query_result_or_t<EnvT, _CUDA_EXEC::__get_tuning_t, _CUDA_STD_EXEC::env<>>;
// Query the required temporary storage size
cudaError_t error = reduce_impl<tuning_t>(
d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, reduction_op, init, determinism_t{}, stream.get());
if (error != cudaSuccess)
{
return error;
}
// TODO(gevtushenko): use uninitialized buffer whenit's available
error = CubDebug(detail::temporary_storage::allocate_async(d_temp_storage, temp_storage_bytes, mr, stream));
if (error != cudaSuccess)
{
return error;
}
// Run the algorithm
error = reduce_impl<tuning_t>(
d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, reduction_op, init, determinism_t{}, stream.get());
// Try to deallocate regardless of the error to avoid memory leaks
cudaError_t deallocate_error =
CubDebug(detail::temporary_storage::deallocate_async(d_temp_storage, temp_storage_bytes, mr, stream));
if (error != cudaSuccess)
{
// Reduction error takes precedence over deallocation error since it happens first
return error;
}
return deallocate_error;
}
template <typename InputIteratorT, typename OutputIteratorT, typename NumItemsT>
CUB_RUNTIME_FUNCTION static cudaError_t
Sum(void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
NumItemsT num_items,
cudaStream_t stream = 0)
{
_CCCL_NVTX_RANGE_SCOPE_IF(d_temp_storage, "cub::DeviceReduce::Sum");
// Signed integer type for global offsets
using OffsetT = detail::choose_offset_t<NumItemsT>;
// The output value type
using OutputT = cub::detail::non_void_value_t<OutputIteratorT, cub::detail::it_value_t<InputIteratorT>>;
using InitT = OutputT;
return DispatchReduce<InputIteratorT, OutputIteratorT, OffsetT, ::cuda::std::plus<>, InitT>::Dispatch(
d_temp_storage,
temp_storage_bytes,
d_in,
d_out,
static_cast<OffsetT>(num_items),
::cuda::std::plus<>{},
InitT{}, // zero-initialize
stream);
}
template <typename InputIteratorT, typename OutputIteratorT, typename NumItemsT>
CUB_RUNTIME_FUNCTION static cudaError_t
Min(void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
NumItemsT num_items,
cudaStream_t stream = 0)
{
_CCCL_NVTX_RANGE_SCOPE_IF(d_temp_storage, "cub::DeviceReduce::Min");
// Signed integer type for global offsets
using OffsetT = detail::choose_offset_t<NumItemsT>;
// The input value type
using InputT = cub::detail::it_value_t<InputIteratorT>;
using InitT = InputT;
return DispatchReduce<InputIteratorT, OutputIteratorT, OffsetT, ::cuda::minimum<>, InitT>::Dispatch(
d_temp_storage,
temp_storage_bytes,
d_in,
d_out,
static_cast<OffsetT>(num_items),
::cuda::minimum<>{},
::cuda::std::numeric_limits<InitT>::max(),
stream);
}
template <typename InputIteratorT, typename ExtremumOutIteratorT, typename IndexOutIteratorT>
CUB_RUNTIME_FUNCTION static cudaError_t ArgMin(
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
ExtremumOutIteratorT d_min_out,
IndexOutIteratorT d_index_out,
::cuda::std::int64_t num_items,
cudaStream_t stream = 0)
{
_CCCL_NVTX_RANGE_SCOPE_IF(d_temp_storage, "cub::DeviceReduce::ArgMin");
// The input type
using InputValueT = cub::detail::it_value_t<InputIteratorT>;
// Offset type used within the kernel and to index within one partition
using PerPartitionOffsetT = int;
// Offset type used to index within the total input in the range [d_in, d_in + num_items)
using GlobalOffsetT = ::cuda::std::int64_t;
// The value type used for the extremum
using OutputExtremumT = detail::non_void_value_t<ExtremumOutIteratorT, InputValueT>;
using InitT = OutputExtremumT;
// Reduction operation
using ReduceOpT = cub::ArgMin;
// Initial value
OutputExtremumT initial_value{::cuda::std::numeric_limits<InputValueT>::max()};
// Tabulate output iterator that unzips the result and writes it to the user-provided output iterators
auto out_it = THRUST_NS_QUALIFIER::make_tabulate_output_iterator(
detail::reduce::unzip_and_write_arg_extremum_op<ExtremumOutIteratorT, IndexOutIteratorT>{d_min_out, d_index_out});
return detail::reduce::dispatch_streaming_arg_reduce_t<
InputIteratorT,
decltype(out_it),
PerPartitionOffsetT,
GlobalOffsetT,
ReduceOpT,
InitT>::Dispatch(d_temp_storage,
temp_storage_bytes,
d_in,
out_it,
static_cast<GlobalOffsetT>(num_items),
ReduceOpT{},
initial_value,
stream);
}
template <typename InputIteratorT, typename OutputIteratorT>
CCCL_DEPRECATED_BECAUSE("CUB has superseded this interface in favor of the ArgMin interface that takes two separate "
"iterators: one iterator to which the extremum is written and another iterator to which the "
"index of the found extremum is written. ")
CUB_RUNTIME_FUNCTION static cudaError_t
ArgMin(void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
cudaStream_t stream = 0)
{
_CCCL_NVTX_RANGE_SCOPE_IF(d_temp_storage, "cub::DeviceReduce::ArgMin");
// Signed integer type for global offsets
using OffsetT = int;
// The input type
using InputValueT = cub::detail::it_value_t<InputIteratorT>;
// The output tuple type
using OutputTupleT = cub::detail::non_void_value_t<OutputIteratorT, KeyValuePair<OffsetT, InputValueT>>;
using AccumT = OutputTupleT;
using InitT = detail::reduce::empty_problem_init_t<AccumT>;
// The output value type
using OutputValueT = typename OutputTupleT::Value;
// Wrapped input iterator to produce index-value <OffsetT, InputT> tuples
using ArgIndexInputIteratorT = ArgIndexInputIterator<InputIteratorT, OffsetT, OutputValueT>;
ArgIndexInputIteratorT d_indexed_in(d_in);
// Initial value
InitT initial_value{AccumT(1, ::cuda::std::numeric_limits<InputValueT>::max())};
return DispatchReduce<ArgIndexInputIteratorT, OutputIteratorT, OffsetT, cub::ArgMin, InitT, AccumT>::Dispatch(
d_temp_storage, temp_storage_bytes, d_indexed_in, d_out, num_items, cub::ArgMin(), initial_value, stream);
}
template <typename InputIteratorT, typename OutputIteratorT, typename NumItemsT>
CUB_RUNTIME_FUNCTION static cudaError_t
Max(void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
NumItemsT num_items,
cudaStream_t stream = 0)
{
_CCCL_NVTX_RANGE_SCOPE_IF(d_temp_storage, "cub::DeviceReduce::Max");
// Signed integer type for global offsets
using OffsetT = detail::choose_offset_t<NumItemsT>;
// The input value type
using InputT = cub::detail::it_value_t<InputIteratorT>;
using InitT = InputT;
return DispatchReduce<InputIteratorT, OutputIteratorT, OffsetT, ::cuda::maximum<>, InitT>::Dispatch(
d_temp_storage,
temp_storage_bytes,
d_in,
d_out,
static_cast<OffsetT>(num_items),
::cuda::maximum<>{},
::cuda::std::numeric_limits<InitT>::lowest(),
stream);
}
template <typename InputIteratorT, typename ExtremumOutIteratorT, typename IndexOutIteratorT>
CUB_RUNTIME_FUNCTION static cudaError_t ArgMax(
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
ExtremumOutIteratorT d_max_out,
IndexOutIteratorT d_index_out,
::cuda::std::int64_t num_items,
cudaStream_t stream = 0)
{
_CCCL_NVTX_RANGE_SCOPE_IF(d_temp_storage, "cub::DeviceReduce::ArgMax");
// The input type
using InputValueT = cub::detail::it_value_t<InputIteratorT>;
// Offset type used within the kernel and to index within one partition
using PerPartitionOffsetT = int;
// Offset type used to index within the total input in the range [d_in, d_in + num_items)
using GlobalOffsetT = ::cuda::std::int64_t;
// The value type used for the extremum
using OutputExtremumT = detail::non_void_value_t<ExtremumOutIteratorT, InputValueT>;
using InitT = OutputExtremumT;
// Reduction operation
using ReduceOpT = cub::ArgMax;
// Initial value
OutputExtremumT initial_value{::cuda::std::numeric_limits<InputValueT>::lowest()};
// Tabulate output iterator that unzips the result and writes it to the user-provided output iterators
auto out_it = THRUST_NS_QUALIFIER::make_tabulate_output_iterator(
detail::reduce::unzip_and_write_arg_extremum_op<ExtremumOutIteratorT, IndexOutIteratorT>{d_max_out, d_index_out});
return detail::reduce::dispatch_streaming_arg_reduce_t<
InputIteratorT,
decltype(out_it),
PerPartitionOffsetT,
GlobalOffsetT,
ReduceOpT,
InitT>::Dispatch(d_temp_storage,
temp_storage_bytes,
d_in,
out_it,
static_cast<GlobalOffsetT>(num_items),
ReduceOpT{},
initial_value,
stream);
}
template <typename InputIteratorT, typename OutputIteratorT>
CCCL_DEPRECATED_BECAUSE("CUB has superseded this interface in favor of the ArgMax interface that takes two separate "
"iterators: one iterator to which the extremum is written and another iterator to which the "
"index of the found extremum is written. ")
CUB_RUNTIME_FUNCTION static cudaError_t
ArgMax(void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
int num_items,
cudaStream_t stream = 0)
{
_CCCL_NVTX_RANGE_SCOPE_IF(d_temp_storage, "cub::DeviceReduce::ArgMax");
// Signed integer type for global offsets
using OffsetT = int;
// The input type
using InputValueT = cub::detail::it_value_t<InputIteratorT>;
// The output tuple type
using OutputTupleT = cub::detail::non_void_value_t<OutputIteratorT, KeyValuePair<OffsetT, InputValueT>>;
using AccumT = OutputTupleT;
// The output value type
using OutputValueT = typename OutputTupleT::Value;
using InitT = detail::reduce::empty_problem_init_t<AccumT>;
// Wrapped input iterator to produce index-value <OffsetT, InputT> tuples
using ArgIndexInputIteratorT = ArgIndexInputIterator<InputIteratorT, OffsetT, OutputValueT>;
ArgIndexInputIteratorT d_indexed_in(d_in);
// Initial value
InitT initial_value{AccumT(1, ::cuda::std::numeric_limits<InputValueT>::lowest())};
return DispatchReduce<ArgIndexInputIteratorT, OutputIteratorT, OffsetT, cub::ArgMax, InitT, AccumT>::Dispatch(
d_temp_storage, temp_storage_bytes, d_indexed_in, d_out, num_items, cub::ArgMax(), initial_value, stream);
}
template <typename InputIteratorT,
typename OutputIteratorT,
typename ReductionOpT,
typename TransformOpT,
typename T,
typename NumItemsT>
CUB_RUNTIME_FUNCTION static cudaError_t TransformReduce(
void* d_temp_storage,
size_t& temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
NumItemsT num_items,
ReductionOpT reduction_op,
TransformOpT transform_op,
T init,
cudaStream_t stream = 0)
{
_CCCL_NVTX_RANGE_SCOPE_IF(d_temp_storage, "cub::DeviceReduce::TransformReduce");
using OffsetT = detail::choose_offset_t<NumItemsT>;
return DispatchTransformReduce<InputIteratorT, OutputIteratorT, OffsetT, ReductionOpT, TransformOpT, T>::Dispatch(
d_temp_storage,
temp_storage_bytes,
d_in,
d_out,
static_cast<OffsetT>(num_items),
reduction_op,
init,
stream,
transform_op);
}
template <typename KeysInputIteratorT,
typename UniqueOutputIteratorT,
typename ValuesInputIteratorT,
typename AggregatesOutputIteratorT,
typename NumRunsOutputIteratorT,
typename ReductionOpT,
typename NumItemsT>
CUB_RUNTIME_FUNCTION _CCCL_FORCEINLINE static cudaError_t ReduceByKey(
void* d_temp_storage,
size_t& temp_storage_bytes,
KeysInputIteratorT d_keys_in,
UniqueOutputIteratorT d_unique_out,
ValuesInputIteratorT d_values_in,
AggregatesOutputIteratorT d_aggregates_out,
NumRunsOutputIteratorT d_num_runs_out,
ReductionOpT reduction_op,
NumItemsT num_items,
cudaStream_t stream = 0)
{
_CCCL_NVTX_RANGE_SCOPE_IF(d_temp_storage, "cub::DeviceReduce::ReduceByKey");
// Signed integer type for global offsets
using OffsetT = detail::choose_offset_t<NumItemsT>;
// FlagT iterator type (not used)
// Selection op (not used)
// Default == operator
using EqualityOp = ::cuda::std::equal_to<>;
return DispatchReduceByKey<
KeysInputIteratorT,
UniqueOutputIteratorT,
ValuesInputIteratorT,
AggregatesOutputIteratorT,
NumRunsOutputIteratorT,
EqualityOp,
ReductionOpT,
OffsetT>::Dispatch(d_temp_storage,
temp_storage_bytes,
d_keys_in,
d_unique_out,
d_values_in,
d_aggregates_out,
d_num_runs_out,
EqualityOp(),
reduction_op,
static_cast<OffsetT>(num_items),
stream);
}
};
CUB_NAMESPACE_END