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Introduce CUB transform reduce (#1091)
* Introduce CUB transform reduce * Enable graph capture for transform reduce * Typo in transform reduce docs
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| /****************************************************************************** | ||
| * Copyright (c) 2011-2023, 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: | ||
| * * Redistributions of source code must retain the above copyright | ||
| * notice, this list of conditions and the following disclaimer. | ||
| * * Redistributions in binary form must reproduce the above copyright | ||
| * notice, this list of conditions and the following disclaimer in the | ||
| * documentation and/or other materials provided with the distribution. | ||
| * * Neither the name of the NVIDIA CORPORATION nor the | ||
| * 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" AND | ||
| * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED | ||
| * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE | ||
| * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY | ||
| * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES | ||
| * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; | ||
| * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND | ||
| * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT | ||
| * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS | ||
| * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | ||
| * | ||
| ******************************************************************************/ | ||
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| #include "thrust/iterator/transform_iterator.h" | ||
| #include <nvbench_helper.cuh> | ||
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| // %RANGE% TUNE_ITEMS_PER_THREAD ipt 7:24:1 | ||
| // %RANGE% TUNE_THREADS_PER_BLOCK tpb 128:1024:32 | ||
| // %RANGE% TUNE_ITEMS_PER_VEC_LOAD_POW2 ipv 1:2:1 | ||
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| #ifndef TUNE_BASE | ||
| #define TUNE_ITEMS_PER_VEC_LOAD (1 << TUNE_ITEMS_PER_VEC_LOAD_POW2) | ||
| #endif | ||
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| #if !TUNE_BASE | ||
| template <typename AccumT, typename OffsetT> | ||
| struct policy_hub_t | ||
| { | ||
| struct policy_t : cub::ChainedPolicy<300, policy_t, policy_t> | ||
| { | ||
| static constexpr int threads_per_block = TUNE_THREADS_PER_BLOCK; | ||
| static constexpr int items_per_thread = TUNE_ITEMS_PER_THREAD; | ||
| static constexpr int items_per_vec_load = TUNE_ITEMS_PER_VEC_LOAD; | ||
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| using ReducePolicy = cub::AgentReducePolicy<threads_per_block, | ||
| items_per_thread, | ||
| AccumT, | ||
| items_per_vec_load, | ||
| cub::BLOCK_REDUCE_WARP_REDUCTIONS, | ||
| cub::LOAD_DEFAULT>; | ||
|
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| // SingleTilePolicy | ||
| using SingleTilePolicy = ReducePolicy; | ||
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| // SegmentedReducePolicy | ||
| using SegmentedReducePolicy = ReducePolicy; | ||
| }; | ||
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| using MaxPolicy = policy_t; | ||
| }; | ||
| #endif // !TUNE_BASE | ||
|
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| template <class T> | ||
| struct square_t | ||
| { | ||
| __host__ __device__ T operator()(const T& x) const | ||
| { | ||
| return x * x; | ||
| } | ||
| }; | ||
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| #define USE_TRANSPOSE_ITERATOR 0 | ||
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| #if USE_TRANSPOSE_ITERATOR | ||
| template <typename T, typename OffsetT> | ||
| void reduce(nvbench::state &state, nvbench::type_list<T, OffsetT>) | ||
| { | ||
| using accum_t = T; | ||
| using input_it_t = thrust::transform_iterator<square_t<T>, typename thrust::device_vector<T>::iterator>; | ||
| using output_it_t = T*; | ||
| using offset_t = typename cub::detail::ChooseOffsetT<OffsetT>::Type; | ||
| using output_t = T; | ||
| using init_t = T; | ||
| using reduction_op_t = cub::Sum; | ||
| using transform_op_t = square_t<T>; | ||
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| #if !TUNE_BASE | ||
| using policy_t = policy_hub_t<accum_t, offset_t>; | ||
| using dispatch_t = cub:: | ||
| DispatchReduce<input_it_t, output_it_t, offset_t, reduction_op_t, init_t, accum_t, policy_t>; | ||
| #else // TUNE_BASE | ||
| using dispatch_t = | ||
| cub::DispatchReduce<input_it_t, output_it_t, offset_t, reduction_op_t, init_t, accum_t>; | ||
| #endif // TUNE_BASE | ||
|
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| // Retrieve axis parameters | ||
| const auto elements = static_cast<std::size_t>(state.get_int64("Elements{io}")); | ||
| thrust::device_vector<T> in = generate(elements); | ||
| thrust::device_vector<T> out(1); | ||
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| input_it_t d_in = thrust::make_transform_iterator(in.begin(), square_t<T>{}); | ||
| output_it_t d_out = thrust::raw_pointer_cast(out.data()); | ||
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| // Enable throughput calculations and add "Size" column to results. | ||
| state.add_element_count(elements); | ||
| state.add_global_memory_reads<T>(elements, "Size"); | ||
| state.add_global_memory_writes<T>(1); | ||
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| // Allocate temporary storage: | ||
| std::size_t temp_size; | ||
| dispatch_t::Dispatch(nullptr, | ||
| temp_size, | ||
| d_in, | ||
| d_out, | ||
| static_cast<offset_t>(elements), | ||
| reduction_op_t{}, | ||
| init_t{}, | ||
| 0 /* stream */); | ||
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| thrust::device_vector<nvbench::uint8_t> temp(temp_size); | ||
| auto *temp_storage = thrust::raw_pointer_cast(temp.data()); | ||
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| state.exec(nvbench::exec_tag::no_batch, [&](nvbench::launch &launch) { | ||
| dispatch_t::Dispatch(temp_storage, | ||
| temp_size, | ||
| d_in, | ||
| d_out, | ||
| static_cast<offset_t>(elements), | ||
| reduction_op_t{}, | ||
| init_t{}, | ||
| launch.get_stream()); | ||
| }); | ||
| } | ||
| #else | ||
| template <typename T, typename OffsetT> | ||
| void reduce(nvbench::state &state, nvbench::type_list<T, OffsetT>) | ||
| { | ||
| using accum_t = T; | ||
| using input_it_t = const T*; | ||
| using output_it_t = T*; | ||
| using offset_t = typename cub::detail::ChooseOffsetT<OffsetT>::Type; | ||
| using output_t = T; | ||
| using init_t = T; | ||
| using reduction_op_t = cub::Sum; | ||
| using transform_op_t = square_t<T>; | ||
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| #if !TUNE_BASE | ||
| using policy_t = policy_hub_t<accum_t, offset_t>; | ||
| using dispatch_t = cub:: | ||
| DispatchTransformReduce<input_it_t, output_it_t, offset_t, reduction_op_t, transform_op_t, init_t, accum_t, policy_t>; | ||
| #else // TUNE_BASE | ||
| using dispatch_t = | ||
| cub::DispatchTransformReduce<input_it_t, output_it_t, offset_t, reduction_op_t, transform_op_t, init_t, accum_t>; | ||
| #endif // TUNE_BASE | ||
|
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| // Retrieve axis parameters | ||
| const auto elements = static_cast<std::size_t>(state.get_int64("Elements{io}")); | ||
| thrust::device_vector<T> in = generate(elements); | ||
| thrust::device_vector<T> out(1); | ||
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| input_it_t d_in = thrust::raw_pointer_cast(in.data()); | ||
| output_it_t d_out = thrust::raw_pointer_cast(out.data()); | ||
|
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| // Enable throughput calculations and add "Size" column to results. | ||
| state.add_element_count(elements); | ||
| state.add_global_memory_reads<T>(elements, "Size"); | ||
| state.add_global_memory_writes<T>(1); | ||
|
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| // Allocate temporary storage: | ||
| std::size_t temp_size; | ||
| dispatch_t::Dispatch(nullptr, | ||
| temp_size, | ||
| d_in, | ||
| d_out, | ||
| static_cast<offset_t>(elements), | ||
| reduction_op_t{}, | ||
| init_t{}, | ||
| 0 /* stream */, | ||
| transform_op_t{}); | ||
|
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| thrust::device_vector<nvbench::uint8_t> temp(temp_size); | ||
| auto *temp_storage = thrust::raw_pointer_cast(temp.data()); | ||
|
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| state.exec(nvbench::exec_tag::no_batch, [&](nvbench::launch &launch) { | ||
| dispatch_t::Dispatch(temp_storage, | ||
| temp_size, | ||
| d_in, | ||
| d_out, | ||
| static_cast<offset_t>(elements), | ||
| reduction_op_t{}, | ||
| init_t{}, | ||
| launch.get_stream(), | ||
| transform_op_t{}); | ||
| }); | ||
| } | ||
| #endif | ||
|
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| NVBENCH_BENCH_TYPES(reduce, NVBENCH_TYPE_AXES(all_types, offset_types)) | ||
| .set_name("base") | ||
| .set_type_axes_names({"T{ct}", "OffsetT{ct}"}) | ||
| .add_int64_power_of_two_axis("Elements{io}", nvbench::range(16, 28, 4)); |
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