Files
cutlass/include/cute/algorithm/tensor_reduce.hpp
2025-06-06 02:39:20 -04:00

108 lines
4.3 KiB
C++

/***************************************************************************************************
* Copyright (c) 2025 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* 2. 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.
*
* 3. Neither the name of the copyright holder 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 THE COPYRIGHT HOLDER OR CONTRIBUTORS 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.
*
**************************************************************************************************/
#pragma once
#include <iostream>
#include <cute/config.hpp>
#include <cute/tensor_impl.hpp>
#include <cute/algorithm/functional.hpp>
#include <cute/algorithm/fill.hpp>
namespace cute
{
// Reduce @src tensor using binary reduction operator @op and initial value @init and return a scalar.
template <class SrcEngine, class SrcLayout, class T, class BinaryOp = cute::plus>
CUTE_HOST_DEVICE constexpr
T
reduce(Tensor<SrcEngine,SrcLayout> const& src, T init, BinaryOp op = {})
{
for (auto i = 0; i < size(src); ++i) {
init = op(init, src(i));
}
return init;
}
// Reduce @src tensor RedMode using binary reduction operator @op and store the result in @dst tensor
// for each index in @dst/BatchMode.
// @pre @src tensor has rank 2
// @pre size of @src batch mode is equal to size of @dst batch mode
template <class SrcEngine, class SrcLayout,
class DstEngine, class DstLayout,
class BinaryOp = cute::plus>
CUTE_HOST_DEVICE constexpr
void
batch_reduce(Tensor<SrcEngine, SrcLayout> const& src, // (RedMode, BatchMode)
Tensor<DstEngine, DstLayout> & dst, // (BatchMode)
BinaryOp op = {})
{
// Precondition
CUTE_STATIC_ASSERT_V(rank(src) == Int<2>{});
assert(size<1>(src) == size(dst));
for (int i = 0; i < size(dst); ++i) {
dst(i) = reduce(src(_,i), dst(i), op);
}
}
// Reduce @src tensor along selected modes specified in @target_profile using binary reduction operator @op
// and store the result in @dst tensor. @target_profile is a tuple where '_' indicates modes to keep and
// integers indicates modes to reduce.
// @pre @target_profile is compatible with @src layout
template <class SrcEngine, class SrcLayout,
class DstEngine, class DstLayout,
class TargetProfile,
class BinaryOp = cute::plus>
CUTE_HOST_DEVICE constexpr
void
logical_reduce(Tensor<SrcEngine, SrcLayout> const& src,
Tensor<DstEngine, DstLayout> & dst,
TargetProfile const& target_profile,
BinaryOp op = {})
{
// Precondition
assert(compatible(target_profile, shape(src)));
auto diced_layout = dice(target_profile, src.layout());
auto sliced_layout = slice(target_profile, src.layout());
auto red_mode = conditional_return<rank(diced_layout) == Int<0>{}>(Layout<_1,_0>{}, diced_layout);
auto batch_mode = conditional_return<rank(sliced_layout) == Int<0>{}>(Layout<_1,_0>{}, sliced_layout);
auto src_tensor = make_tensor(src.data(), make_layout(red_mode, batch_mode));
batch_reduce(src_tensor, dst, op);
}
} // end namespace cute