CUTLASS 2.0 (#62)
CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
This commit is contained in:
Andrew Kerr
GitHub
228
include/cutlass/reduction/thread/reduce.h
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228
include/cutlass/reduction/thread/reduce.h
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/***************************************************************************************************
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* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief Defines basic thread level reduction with specializations for Array<T, N>.
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/numeric_types.h"
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#include "cutlass/array.h"
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#include "cutlass/half.h"
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#include "cutlass/functional.h"
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namespace cutlass {
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namespace reduction {
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namespace thread {
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/// Structure to compute the thread level reduction
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template <typename Op, typename T>
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struct Reduce;
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Partial Specialization of Reduce for "plus" (a functional operator)
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template <typename T>
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struct Reduce< plus<T>, T > {
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CUTLASS_HOST_DEVICE
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T operator()(T lhs, T const &rhs) const {
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plus<T> _op;
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return _op(lhs, rhs);
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Partial specialization of Reduce for Array<T, N>
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template <typename T, int N>
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struct Reduce < plus<T>, Array<T, N>> {
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CUTLASS_HOST_DEVICE
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Array<T, 1> operator()(Array<T, N> const &in) const {
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Array<T, 1> result;
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Reduce< plus<T>, T > scalar_reduce;
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result.clear();
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CUTLASS_PRAGMA_UNROLL
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for (auto i = 0; i < N; ++i) {
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result[0] = scalar_reduce(result[0], in[i]);
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}
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return result;
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Partial specializations of Reduce for Array<half_t, N>
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template <int N>
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struct Reduce < plus<half_t>, Array<half_t, N> > {
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CUTLASS_HOST_DEVICE
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Array<half_t, 1> operator()(Array<half_t, N> const &input) {
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Array<half_t, 1> result;
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// If there is only 1 element - there is nothing to reduce
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if( N ==1 ){
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result[0] = input.front();
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} else {
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#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 600)
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__half result_d;
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Array<half_t, 1> const *in_ptr_half = reinterpret_cast<Array<half_t, 1> const *>(&input);
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Array<half_t, 2> const *in_ptr_half2 = reinterpret_cast<Array<half_t, 2> const *>(&input);
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__half2 const *x_in_half2 = reinterpret_cast<__half2 const *>(in_ptr_half2);
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// Set initial result = first half2, in case N==2
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__half2 tmp_result = x_in_half2[0];
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CUTLASS_PRAGMA_UNROLL
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for (int i = 1; i < N/2; ++i) {
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tmp_result = __hadd2(x_in_half2[i], tmp_result);
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}
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result_d = __hadd(__low2half(tmp_result), __high2half(tmp_result));
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// One final step is needed for odd "N" (to add the (N-1)th element)
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if( N%2 ){
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__half last_element;
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Array<half_t, 1> tmp_last;
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Array<half_t, 1> *tmp_last_ptr = &tmp_last;
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tmp_last_ptr[0] = in_ptr_half[N-1];
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last_element = reinterpret_cast<__half const &>(tmp_last);
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result_d = __hadd(result_d, last_element);
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}
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Array<half_t, 1> *result_ptr = &result;
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*result_ptr = reinterpret_cast<Array<half_t, 1> &>(result_d);
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#else
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Reduce< plus<half_t>, half_t > scalar_reduce;
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result.clear();
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CUTLASS_PRAGMA_UNROLL
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for (auto i = 0; i < N; ++i) {
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result[0] = scalar_reduce(result[0], input[i]);
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}
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#endif
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}
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return result;
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Partial specializations of Reduce for AlignedArray<half_t, N>
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template <int N>
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struct Reduce < plus<half_t>, AlignedArray<half_t, N> > {
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CUTLASS_HOST_DEVICE
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Array<half_t, 1> operator()(AlignedArray<half_t, N> const &input) {
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Array<half_t, 1> result;
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// If there is only 1 element - there is nothing to reduce
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if( N ==1 ){
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result[0] = input.front();
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} else {
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#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 600)
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__half result_d;
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AlignedArray<half_t, 1> const *in_ptr_half = reinterpret_cast<AlignedArray<half_t, 1> const *>(&input);
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AlignedArray<half_t, 2> const *in_ptr_half2 = reinterpret_cast<AlignedArray<half_t, 2> const *>(&input);
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__half2 const *x_in_half2 = reinterpret_cast<__half2 const *>(in_ptr_half2);
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// Set initial result = first half2, in case N==2
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__half2 tmp_result = x_in_half2[0];
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CUTLASS_PRAGMA_UNROLL
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for (int i = 1; i < N/2; ++i) {
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tmp_result = __hadd2(x_in_half2[i], tmp_result);
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}
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result_d = __hadd(__low2half(tmp_result), __high2half(tmp_result));
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// One final step is needed for odd "N" (to add the (N-1)th element)
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if( N%2 ){
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__half last_element;
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AlignedArray<half_t, 1> tmp_last;
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AlignedArray<half_t, 1> *tmp_last_ptr = &tmp_last;
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tmp_last_ptr[0] = in_ptr_half[N-1];
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last_element = reinterpret_cast<__half const &>(tmp_last);
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result_d = __hadd(result_d, last_element);
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}
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Array<half_t, 1> *result_ptr = &result;
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*result_ptr = reinterpret_cast<Array<half_t, 1> &>(result_d);
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#else
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Reduce< plus<half_t>, half_t > scalar_reduce;
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result.clear();
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CUTLASS_PRAGMA_UNROLL
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for (auto i = 0; i < N; ++i) {
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result[0] = scalar_reduce(result[0], input[i]);
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}
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#endif
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}
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return result;
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}
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};
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}
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}
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}
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96
include/cutlass/reduction/thread/reduction_operators.h
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96
include/cutlass/reduction/thread/reduction_operators.h
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/***************************************************************************************************
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* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief Kernel performing a reduction over densely packed tensors in global memory
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/tensor_ref.h"
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#include "cutlass/numeric_types.h"
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#include "cutlass/array.h"
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#include "cutlass/functional.h"
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#include "cutlass/numeric_conversion.h"
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namespace cutlass {
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namespace reduction {
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namespace thread {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Mixed-precision reduction
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template <
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typename ElementAccumulator_,
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typename Element_,
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int Count = 1
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>
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struct ReduceAdd {
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//
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// Type definitions
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//
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using ElementAccumulator = ElementAccumulator_;
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using Element = Element_;
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static int const kCount = Count;
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using FragmentAccumulator = cutlass::Array<ElementAccumulator, kCount>;
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using FragmentElement = cutlass::Array<Element, kCount>;
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struct Params { };
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//
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// Data members
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//
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/// Parameters object
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Params params;
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//
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// Methods
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//
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/// Constructor
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CUTLASS_HOST_DEVICE
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ReduceAdd(Params params_ = Params()): params(params_) { }
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/// Operator
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CUTLASS_HOST_DEVICE
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FragmentAccumulator operator()(
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FragmentAccumulator accumulator,
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FragmentElement element) const {
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plus<FragmentAccumulator> op;
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return op(accumulator, element);
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace thread
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} // namespace reduction
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} // namespace cutlass
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