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.
141 lines
4.8 KiB
Plaintext
141 lines
4.8 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 Tests for device-wide GEMM interface
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*/
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#include <iostream>
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#include "cutlass/cutlass.h"
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#include "cutlass/gemm/device/gemm_splitk_parallel.h"
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#include "../../common/cutlass_unit_test.h"
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#include "cutlass/util/host_tensor.h"
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#include "cutlass/util/tensor_view_io.h"
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#include "cutlass/util/reference/host/tensor_fill.h"
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#include "cutlass/util/reference/host/tensor_copy.h"
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#include "cutlass/util/reference/host/tensor_compare.h"
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#include "cutlass/util/reference/host/gemm.h"
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#include "testbed_splitk.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(SM50_Device_GemmSplitKParallel_f32n_f32t_f32t_simt_f32, 128x128x8) {
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using ElementOutput = float;
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using ElementAccumulator = float;
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using Gemm = cutlass::gemm::device::GemmSplitKParallel<
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float,
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cutlass::layout::ColumnMajor,
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float,
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cutlass::layout::RowMajor,
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ElementOutput,
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cutlass::layout::RowMajor,
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ElementAccumulator,
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cutlass::arch::OpClassSimt,
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cutlass::arch::Sm50,
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cutlass::gemm::GemmShape<128, 128, 8>,
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cutlass::gemm::GemmShape<32, 64, 8>,
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cutlass::gemm::GemmShape<1, 1, 1>
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>;
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test::gemm::device::TestAllGemmSplitK<Gemm>();
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}
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TEST(SM50_Device_GemmSplitKParallel_f32n_f32n_f32n_simt_f32, 128x128x8) {
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using ElementOutput = float;
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using ElementAccumulator = float;
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using Gemm = cutlass::gemm::device::GemmSplitKParallel<
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float,
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cutlass::layout::ColumnMajor,
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float,
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cutlass::layout::ColumnMajor,
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ElementOutput,
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cutlass::layout::ColumnMajor,
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ElementAccumulator,
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cutlass::arch::OpClassSimt,
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cutlass::arch::Sm50,
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cutlass::gemm::GemmShape<128, 128, 8>,
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cutlass::gemm::GemmShape<32, 64, 8>,
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cutlass::gemm::GemmShape<1, 1, 1>
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>;
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test::gemm::device::TestAllGemmSplitK<Gemm>();
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(SM50_Device_GemmSplitKParallel_f64n_f64n_f64t_simt_f64, 64x128x8) {
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using Element = double;
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using Gemm = cutlass::gemm::device::GemmSplitKParallel<
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Element,
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cutlass::layout::ColumnMajor,
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Element,
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cutlass::layout::ColumnMajor,
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Element,
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cutlass::layout::RowMajor,
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Element,
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cutlass::arch::OpClassSimt,
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cutlass::arch::Sm50,
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cutlass::gemm::GemmShape<64, 128, 8>,
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cutlass::gemm::GemmShape<32, 64, 8>,
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cutlass::gemm::GemmShape<1, 1, 1>
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>;
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test::gemm::device::TestAllGemmSplitK<Gemm>();
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}
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TEST(SM50_Device_GemmSplitKParallel_f64t_f64t_f64n_simt_f64, 64x64x8) {
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using Element = double;
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using Gemm = cutlass::gemm::device::GemmSplitKParallel<
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Element,
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cutlass::layout::RowMajor,
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Element,
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cutlass::layout::RowMajor,
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Element,
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cutlass::layout::ColumnMajor,
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Element,
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cutlass::arch::OpClassSimt,
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cutlass::arch::Sm50,
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cutlass::gemm::GemmShape<64, 64, 8>,
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cutlass::gemm::GemmShape<32, 64, 8>,
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cutlass::gemm::GemmShape<1, 1, 1>
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>;
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test::gemm::device::TestAllGemmSplitK<Gemm>();
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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