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116 lines
3.8 KiB
C++
116 lines
3.8 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2024 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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/*! \file
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\brief Base functionality for common types of sparse GEMM kernel parameters
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace gemm {
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namespace kernel {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Parameters structure
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template <
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typename ThreadblockSwizzle,
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typename ParamsA,
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typename TensorRefA,
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typename ParamsB,
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typename TensorRefB,
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typename ParamsE,
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typename TensorRefE>
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struct SparseParamsBase
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{
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//
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// Data members
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//
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cutlass::gemm::GemmCoord problem_size{};
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cutlass::gemm::GemmCoord grid_tiled_shape{};
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int swizzle_log_tile;
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ParamsA params_A{};
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TensorRefA ref_A{};
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ParamsB params_B{};
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TensorRefB ref_B{};
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ParamsE params_E{};
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TensorRefE ref_E{};
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int gemm_k_iterations{0};
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int gemm_k_size{0};
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//
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// Host dispatch API
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//
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/// Default constructor
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SparseParamsBase() = default;
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/// Constructor
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CUTLASS_HOST_DEVICE
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SparseParamsBase(
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cutlass::gemm::GemmCoord const & problem_size,
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cutlass::gemm::GemmCoord const & grid_tiled_shape,
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TensorRefA ref_A,
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TensorRefB ref_B,
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TensorRefE ref_E,
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int const mma_shape_k)
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:
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problem_size(problem_size),
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grid_tiled_shape(grid_tiled_shape),
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swizzle_log_tile(ThreadblockSwizzle().get_log_tile(grid_tiled_shape)),
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params_A(ref_A.layout()),
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ref_A(ref_A),
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params_B(ref_B.layout()),
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ref_B(ref_B),
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params_E(ref_E.layout()),
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ref_E(ref_E)
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{
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int total_gemm_k_iterations = (problem_size.k() + mma_shape_k - 1) / mma_shape_k;
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int gemm_k_iterations = (total_gemm_k_iterations + grid_tiled_shape.k() - 1) / grid_tiled_shape.k();
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gemm_k_size = gemm_k_iterations * mma_shape_k;
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace kernel
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} // namespace gemm
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} // namespace cutlass
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/////////////////////////////////////////////////////////////////////////////////////////////////
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