108 lines
4.3 KiB
C++
108 lines
4.3 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2025 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (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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#pragma once
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#include <iostream>
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#include <cute/config.hpp>
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#include <cute/tensor_impl.hpp>
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#include <cute/algorithm/functional.hpp>
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#include <cute/algorithm/fill.hpp>
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namespace cute
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{
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// Reduce @src tensor using binary reduction operator @op and initial value @init and return a scalar.
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template <class SrcEngine, class SrcLayout, class T, class BinaryOp = cute::plus>
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CUTE_HOST_DEVICE constexpr
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T
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reduce(Tensor<SrcEngine,SrcLayout> const& src, T init, BinaryOp op = {})
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{
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for (auto i = 0; i < size(src); ++i) {
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init = op(init, src(i));
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}
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return init;
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}
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// Reduce @src tensor RedMode using binary reduction operator @op and store the result in @dst tensor
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// for each index in @dst/BatchMode.
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// @pre @src tensor has rank 2
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// @pre size of @src batch mode is equal to size of @dst batch mode
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template <class SrcEngine, class SrcLayout,
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class DstEngine, class DstLayout,
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class BinaryOp = cute::plus>
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CUTE_HOST_DEVICE constexpr
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void
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batch_reduce(Tensor<SrcEngine, SrcLayout> const& src, // (RedMode, BatchMode)
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Tensor<DstEngine, DstLayout> & dst, // (BatchMode)
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BinaryOp op = {})
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{
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// Precondition
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CUTE_STATIC_ASSERT_V(rank(src) == Int<2>{});
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assert(size<1>(src) == size(dst));
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for (int i = 0; i < size(dst); ++i) {
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dst(i) = reduce(src(_,i), dst(i), op);
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}
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}
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// Reduce @src tensor along selected modes specified in @target_profile using binary reduction operator @op
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// and store the result in @dst tensor. @target_profile is a tuple where '_' indicates modes to keep and
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// integers indicates modes to reduce.
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// @pre @target_profile is compatible with @src layout
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template <class SrcEngine, class SrcLayout,
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class DstEngine, class DstLayout,
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class TargetProfile,
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class BinaryOp = cute::plus>
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CUTE_HOST_DEVICE constexpr
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void
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logical_reduce(Tensor<SrcEngine, SrcLayout> const& src,
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Tensor<DstEngine, DstLayout> & dst,
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TargetProfile const& target_profile,
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BinaryOp op = {})
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{
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// Precondition
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assert(compatible(target_profile, shape(src)));
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auto diced_layout = dice(target_profile, src.layout());
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auto sliced_layout = slice(target_profile, src.layout());
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auto red_mode = conditional_return<rank(diced_layout) == Int<0>{}>(Layout<_1,_0>{}, diced_layout);
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auto batch_mode = conditional_return<rank(sliced_layout) == Int<0>{}>(Layout<_1,_0>{}, sliced_layout);
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auto src_tensor = make_tensor(src.data(), make_layout(red_mode, batch_mode));
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batch_reduce(src_tensor, dst, op);
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}
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} // end namespace cute
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