187 lines
8.2 KiB
Plaintext
187 lines
8.2 KiB
Plaintext
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/***************************************************************************************************
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* Copyright (c) 2017 - 2022 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 Unit tests for CTA-level GEMM specifically for sliced-k kernels (SM_61 and SM_75)
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*/
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#include "mma_pipelined_testbed_slicedk.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// igemm_NT DP4A
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/////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(SM61_igemm_sliced_k, igemm_int8_nt_32x32x128_32x32x4) {
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using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
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cutlass::gemm::GemmShape<32, 32, 128>, // ThreadblockShape,
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cutlass::gemm::GemmShape<32, 32, 32>, // WarpShape,
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cutlass::gemm::GemmShape<1, 1, 4>, // InstructionShape,
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int8_t, // ElementA,
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cutlass::layout::ColumnMajor, // LayoutA,
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int8_t, // ElementB,
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cutlass::layout::RowMajor, // LayoutB,
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int, // ElementC,
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cutlass::layout::RowMajor, // LayoutC,
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cutlass::arch::OpClassSimt, // OpClass
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2>; // Stages,
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cutlass::gemm::GemmCoord problem_size(32, 32, 128);
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float alpha = 1.f;
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float beta = 0.0f;
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dim3 grid(1, 1);
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dim3 block(32, 4, 1);
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test::gemm::threadblock::Testbed<MmaCore>(
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problem_size.m(), problem_size.n(), problem_size.k(), alpha, beta)
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.run(grid, block, cutlass::Distribution::Uniform, cutlass::Distribution::Uniform);
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}
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TEST(SM61_igemm_sliced_k_big, igemm_int8_nt_32x32x128_32x32x4_bigk) {
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using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
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cutlass::gemm::GemmShape<32, 32, 128>, // ThreadblockShape,
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cutlass::gemm::GemmShape<32, 32, 32>, // WarpShape,
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cutlass::gemm::GemmShape<1, 1, 4>, // InstructionShape,
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int8_t, // ElementA,
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cutlass::layout::ColumnMajor, // LayoutA,
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int8_t, // ElementB,
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cutlass::layout::RowMajor, // LayoutB,
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int, // ElementC,
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cutlass::layout::RowMajor, // LayoutC,
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cutlass::arch::OpClassSimt, // OpClass
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2>; // Stages,
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cutlass::gemm::GemmCoord problem_size(32, 32, 1024);
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float alpha = 1.f;
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float beta = 0.0f;
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dim3 grid(1, 1);
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dim3 block(32, 4, 1);
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test::gemm::threadblock::Testbed<MmaCore>(
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problem_size.m(), problem_size.n(), problem_size.k(), alpha, beta)
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.run(grid, block, cutlass::Distribution::Uniform, cutlass::Distribution::Uniform);
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}
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TEST(SM61_igemm_sliced_k, igemm_int8_nt_32x64x128_32x32x4) {
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using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
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cutlass::gemm::GemmShape<32, 64, 128>, // ThreadblockShape,
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cutlass::gemm::GemmShape<32, 32, 64>, // WarpShape,
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cutlass::gemm::GemmShape<1, 1, 4>, // InstructionShape,
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int8_t, // ElementA,
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cutlass::layout::ColumnMajor, // LayoutA,
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int8_t, // ElementB,
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cutlass::layout::RowMajor, // LayoutB,
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int, // ElementC,
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cutlass::layout::RowMajor, // LayoutC,
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cutlass::arch::OpClassSimt, // OpClass
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2>; // Stages,
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cutlass::gemm::GemmCoord problem_size(32, 64, 256);
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float alpha = 1.f;
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float beta = 0.0f;
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dim3 grid(1, 1);
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dim3 block(32, 4, 1);
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test::gemm::threadblock::Testbed<MmaCore>(
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problem_size.m(), problem_size.n(), problem_size.k(), alpha, beta)
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.run(grid, block, cutlass::Distribution::Uniform, cutlass::Distribution::Uniform);
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}
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#if defined(CUTLASS_ARCH_MMA_SM75_SUPPORTED)
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// Tensor Op GEMM for SM_75
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/////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(SM75_gemm_threadblock_congruous_sliced, tensor_op_64x64x256_tb64x64x64_warp64x32x32_16x8x8) {
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using ElementA = cutlass::half_t;
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using LayoutA = cutlass::layout::ColumnMajor;
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using ElementB = cutlass::half_t;
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using LayoutB = cutlass::layout::RowMajor;
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using ElementC = float;
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using LayoutC = cutlass::layout::ColumnMajor;
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cutlass::gemm::GemmCoord problem_size(64, 64, 256);
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using ThreadblockShape = cutlass::gemm::GemmShape<64, 64, 64>;
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using WarpShape = cutlass::gemm::GemmShape<64, 32, 32>;
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using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
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float alpha = 1.f;
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float beta = 0.0f;
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// Define the MmaCore components
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using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
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ThreadblockShape, WarpShape, InstructionShape, ElementA, LayoutA,
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ElementB, LayoutB, ElementC, LayoutC, cutlass::arch::OpClassTensorOp, 2,
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cutlass::arch::OpMultiplyAdd>;
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dim3 grid(1, 1);
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dim3 block(32, 4, 1);
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test::gemm::threadblock::Testbed<MmaCore>(problem_size.m(), problem_size.n(),
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problem_size.k(), alpha, beta)
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.run(grid, block);
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}
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TEST(SM75_gemm_threadblock_crosswise_sliced, tensor_op_64x64x256_tb64x64x64_warp64x32x32_16x8x8) {
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using ElementA = cutlass::half_t;
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using LayoutA = cutlass::layout::RowMajor;
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using ElementB = cutlass::half_t;
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using LayoutB = cutlass::layout::ColumnMajor;
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using ElementC = float;
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using LayoutC = cutlass::layout::ColumnMajor;
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cutlass::gemm::GemmCoord problem_size(64, 64, 256);
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using ThreadblockShape = cutlass::gemm::GemmShape<64, 64, 64>;
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using WarpShape = cutlass::gemm::GemmShape<64, 32, 32>;
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using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
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float alpha = 1.f;
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float beta = 0.0f;
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// Define the MmaCore components
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using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
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ThreadblockShape, WarpShape, InstructionShape, ElementA, LayoutA,
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ElementB, LayoutB, ElementC, LayoutC, cutlass::arch::OpClassTensorOp, 2,
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cutlass::arch::OpMultiplyAdd>;
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dim3 grid(1, 1);
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dim3 block(32, 4, 1);
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test::gemm::threadblock::Testbed<MmaCore>(problem_size.m(), problem_size.n(),
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problem_size.k(), alpha, beta)
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.run(grid, block);
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}
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////////////////////////////////////////////////////////////////////////////////
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#endif
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