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.
122 lines
4.2 KiB
Plaintext
122 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/thread/linear_combination.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(Epilogue_thread_linear_combination, device_side_f16_f32_value) {
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using Element = float;
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using ElementOutput = cutlass::half_t;
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int const kCount = 8;
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using LinearCombination = cutlass::epilogue::thread::LinearCombination<
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ElementOutput,
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kCount,
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Element,
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Element>;
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Element alpha = Element(2);
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Element beta = Element(1);
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typename LinearCombination::Params params(alpha, beta);
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LinearCombination linear_combination_op(params);
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cutlass::Array<ElementOutput, kCount> source;
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cutlass::Array<Element, kCount> accum;
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for (int i = 0; i < kCount; ++i) {
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accum[i] = Element(i * 2);
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source[i] = ElementOutput((i * 7 % 9) - 4);
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}
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cutlass::Array<ElementOutput, kCount> destination = linear_combination_op(accum, source);
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for (int i = 0; i < kCount; ++i) {
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ElementOutput expected = ElementOutput(
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alpha * accum[i] +
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beta * Element(ElementOutput(source[i]))
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);
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ElementOutput got = destination[i];
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EXPECT_TRUE(expected == got);
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}
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(Epilogue_thread_linear_combination, device_side_f16_f32_ptr) {
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using Element = float;
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using ElementOutput = cutlass::half_t;
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int const kCount = 8;
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using LinearCombination = cutlass::epilogue::thread::LinearCombination<
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ElementOutput,
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kCount,
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Element,
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Element>;
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Element alpha = Element(2);
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Element beta = Element(1);
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typename LinearCombination::Params params(&alpha, &beta);
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LinearCombination linear_combination_op(params);
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cutlass::Array<ElementOutput, kCount> source;
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cutlass::Array<Element, kCount> accum;
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for (int i = 0; i < kCount; ++i) {
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accum[i] = Element(i * 2);
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source[i] = ElementOutput((i * 7 % 9) - 4);
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}
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cutlass::Array<ElementOutput, kCount> destination = linear_combination_op(accum, source);
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for (int i = 0; i < kCount; ++i) {
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ElementOutput expected = ElementOutput(
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alpha * accum[i] +
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beta * Element(ElementOutput(source[i]))
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);
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ElementOutput got = destination[i];
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EXPECT_TRUE(expected == got);
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}
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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