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.
125 lines
4.2 KiB
Plaintext
125 lines
4.2 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 Unit tests for thread-level GEMM
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*/
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#include "../../common/cutlass_unit_test.h"
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#include "cutlass/epilogue/epilogue_workspace.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace test {
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namespace gemm {
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namespace threadblock {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Kernel computes accumulator data and stores it out
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template <typename Epilogue>
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__global__ void kernel_epilogue_workspace(typename Epilogue::Params params) {
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__shared__ typename Epilogue::SharedStorage shared_storage;
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int warp_id = threadIdx.y;
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int lane_id = threadIdx.x;
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Epilogue epilogue(params, shared_storage, warp_id, lane_id);
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//
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// Initialize accumulator tile
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//
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typename Epilogue::FragmentC accum;
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < Epilogue::FragmentC::kElements; ++i) {
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accum[i] = Element(warp_id * blockDim.x + lane_id);
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}
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//
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// Efficient epilogue
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//
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cutlass::GemmCoord tb_tile_coord{blockIdx.x, blockIdx.y, 0};
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cutlass::GemmCoord problem_size =
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tb_tile_coord *
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cutlass::GemmCoord{Epilogue::Shape::kM, Epilogue::Shape::kN, 1};
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// Store accumulators
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epilogue(
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problem_size,
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tb_tile_coord,
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accum);
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace threadblock
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} // namespace gemm
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} // namespace test
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/////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(SM75_gemm_threadblock_epilogue_workspace, tensor_op_128x128_64x64) {
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//
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// Define an instance of the epilogue and see if it works
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//
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static int const kWarpCount = 4;
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static int const kWarpSize = 32;
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using Shape = cutlass::MatrixShape<128, 128>;
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using FragmentC = cutlass::Array<int, Shape::kCount / (kWarpCount * kWarpSize)>;
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using Epilogue = cutlass::gemm::threadblock::EpilogueWorkspace<
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Shape,
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kWarpCount,
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FragmentC
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>;
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typename Epilogue::Params params(
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);
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// Launch the kernel
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dim3 grid(1,1);
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dim3 block(kWarpSize, kWarpCount);
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test::gemm::threadblock::kernel_epilogue_workspace<Epilogue><<< grid, block >>>(
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params
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);
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cudaError_t result = cudaDeviceSynchronize();
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EXPECT_EQ(result, cudaSuccess) << "Kernel launch error - " << cudaGetErrorString(result);
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//
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//
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//
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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