159 lines
5.1 KiB
C++
159 lines
5.1 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2023 - 2023 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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/** Common algorithms on (hierarchical) tensors */
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#pragma once
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#include <cute/config.hpp>
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#include <cute/tensor.hpp>
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namespace cute
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{
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//
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// for_each
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//
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template <class Engine, class Layout, class UnaryOp>
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CUTE_HOST_DEVICE constexpr
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void
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for_each(Tensor<Engine,Layout> const& tensor, UnaryOp&& op)
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{
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CUTE_UNROLL
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for (int i = 0; i < size(tensor); ++i) {
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static_cast<UnaryOp&&>(op)(tensor(i));
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}
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}
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template <class Engine, class Layout, class UnaryOp>
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CUTE_HOST_DEVICE constexpr
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void
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for_each(Tensor<Engine,Layout>& tensor, UnaryOp&& op)
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{
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CUTE_UNROLL
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for (int i = 0; i < size(tensor); ++i) {
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static_cast<UnaryOp&&>(op)(tensor(i));
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}
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}
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// Accept mutable temporaries
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template <class Engine, class Layout, class UnaryOp>
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CUTE_HOST_DEVICE constexpr
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void
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for_each(Tensor<Engine,Layout>&& tensor, UnaryOp&& op)
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{
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return for_each(tensor, static_cast<UnaryOp&&>(op));
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}
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//
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// transform
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//
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// Similar to std::transform but does not return number of elements affected
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template <class Engine, class Layout, class UnaryOp>
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CUTE_HOST_DEVICE constexpr
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void
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transform(Tensor<Engine,Layout>& tensor, UnaryOp&& op)
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{
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CUTE_UNROLL
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for (int i = 0; i < size(tensor); ++i) {
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tensor(i) = static_cast<UnaryOp&&>(op)(tensor(i));
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}
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}
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// Accept mutable temporaries
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template <class Engine, class Layout, class UnaryOp>
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CUTE_HOST_DEVICE constexpr
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void
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transform(Tensor<Engine,Layout>&& tensor, UnaryOp&& op)
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{
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return transform(tensor, std::forward<UnaryOp>(op));
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}
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// Similar to std::transform transforms one tensors and assigns it to another
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template <class EngineIn, class LayoutIn, class EngineOut, class LayoutOut, class UnaryOp>
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CUTE_HOST_DEVICE constexpr
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void
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transform(Tensor<EngineIn,LayoutIn>& tensor_in, Tensor<EngineOut,LayoutOut>& tensor_out, UnaryOp&& op)
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{
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CUTE_UNROLL
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for (int i = 0; i < size(tensor_in); ++i) {
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tensor_out(i) = static_cast<UnaryOp&&>(op)(tensor_in(i));
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}
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}
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// Accept mutable temporaries
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template <class EngineIn, class LayoutIn,
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class EngineOut, class LayoutOut, class UnaryOp>
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CUTE_HOST_DEVICE constexpr
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void
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transform(Tensor<EngineIn,LayoutIn>&& tensor_in, Tensor<EngineOut,LayoutOut>&& tensor_out, UnaryOp&& op)
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{
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return transform(tensor_in, tensor_out, op);
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}
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// Similar to std::transform with a binary operation
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// Takes two tensors as input and one tensor as output.
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// Applies the binary_op to tensor_in1 and and tensor_in2 and
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// assigns it to tensor_out
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template <class EngineIn1, class LayoutIn1,
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class EngineIn2, class LayoutIn2,
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class EngineOut, class LayoutOut, class BinaryOp>
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CUTE_HOST_DEVICE constexpr
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void
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transform(Tensor<EngineIn1,LayoutIn1>& tensor_in1,
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Tensor<EngineIn2,LayoutIn2>& tensor_in2,
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Tensor<EngineOut,LayoutOut>& tensor_out,
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BinaryOp&& op)
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{
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CUTE_UNROLL
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for (int i = 0; i < size(tensor_in1); ++i) {
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tensor_out(i) = static_cast<BinaryOp&&>(op)(tensor_in1(i), tensor_in2(i));
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}
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}
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// Accept mutable temporaries
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template <class EngineIn1, class LayoutIn1,
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class EngineIn2, class LayoutIn2,
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class EngineOut, class LayoutOut, class BinaryOp>
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CUTE_HOST_DEVICE constexpr
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void
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transform(Tensor<EngineIn1,LayoutIn1>&& tensor_in1,
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Tensor<EngineIn2,LayoutIn2>&& tensor_in2,
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Tensor<EngineOut,LayoutOut>&& tensor_out,
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BinaryOp&& op)
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{
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return transform(tensor_in1, tensor_in2, tensor_out, op);
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}
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} // end namespace cute
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