fb335f6a5f
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.
94 lines
4.5 KiB
Plaintext
94 lines
4.5 KiB
Plaintext
/***************************************************************************************************
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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 CUTLASS layout visualization example
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*/
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#include <map>
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#include <memory>
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#include "cutlass/layout/matrix.h"
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#include "cutlass/layout/pitch_linear.h"
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#include "cutlass/layout/tensor_op_multiplicand_sm70.h"
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#include "cutlass/layout/tensor_op_multiplicand_sm75.h"
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#include "visualize_layout.h"
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#include "register_layout.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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void RegisterLayouts(std::map<std::string, std::unique_ptr<VisualizeLayoutBase> > &layouts) {
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struct {
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char const *name;
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VisualizeLayoutBase *ptr;
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} layout_pairs[] = {
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{"PitchLinear", new VisualizeLayout<cutlass::layout::PitchLinear>},
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{"ColumnMajor", new VisualizeLayout<cutlass::layout::ColumnMajor>},
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{"RowMajor", new VisualizeLayout<cutlass::layout::RowMajor>},
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{"ColumnMajorInterleaved<4>",
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new VisualizeLayout<cutlass::layout::ColumnMajorInterleaved<4>>},
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{"RowMajorInterleaved<4>",
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new VisualizeLayout<cutlass::layout::RowMajorInterleaved<4>>},
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// Integer matrix multiply.int4 8832 Interleaved-64
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{"TensorOpMultiplicand<4,64>",
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new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<4, 64>>},
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// Integer matrix multiply.int4 8832 TN kblock128
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{"TensorOpMultiplicand<4,128>",
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new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<4, 128>>},
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// Integer matrix multiply 8816 Interleaved-32
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{"TensorOpMultiplicand<8,32>",
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new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<8, 32>>},
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// Integer matrix multiply 8816 TN kblock64
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{"TensorOpMultiplicand<8,64>",
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new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<8, 64>>},
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// Matrix Multiply 1688 TN kblock32
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{"TensorOpMultiplicand<16,32>",
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new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<16, 32>>},
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// Matrix multiply 1688 NT
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{"TensorOpMultiplicand<16,64>",
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new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<16, 64>>},
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{"TensorOpMultiplicandCongruous<128,4>",
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new VisualizeLayout<
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cutlass::layout::TensorOpMultiplicandCongruous<128, 4>>},
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{"TensorOpMultiplicandCrosswise<128,4>",
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new VisualizeLayout<
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cutlass::layout::TensorOpMultiplicandCrosswise<128, 4>>},
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{"VoltaTensorOpMultiplicandCongruous<16>",
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new VisualizeLayout<
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cutlass::layout::VoltaTensorOpMultiplicandCongruous<16>>},
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{"VoltaTensorOpMultiplicandCrosswise<16,32>",
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new VisualizeLayout<
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cutlass::layout::VoltaTensorOpMultiplicandCrosswise<16, 32>>},
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};
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for (auto layout : layout_pairs) {
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layouts.emplace(std::string(layout.name), std::unique_ptr<VisualizeLayoutBase>(layout.ptr));
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}
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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