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.
302 lines
8.9 KiB
C++
302 lines
8.9 KiB
C++
/***************************************************************************************************
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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 <fstream>
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#include <sstream>
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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/distribution.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/tensor_norm.h"
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#include "cutlass/util/reference/host/gemm.h"
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#include "cutlass/util/host_reorder.h"
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namespace test {
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namespace gemm {
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namespace device {
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////////////////////////////////////////////////////////////////////////////////
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template <typename Gemm, int InterleavedK>
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struct InterleavedTestbed {
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using ElementAccumulator = typename Gemm::ElementAccumulator;
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using ElementCompute = typename Gemm::GemmKernel::Epilogue::OutputOp::ElementCompute;
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/// Initialization
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cutlass::Distribution::Kind init_A;
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cutlass::Distribution::Kind init_B;
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cutlass::Distribution::Kind init_C;
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uint64_t seed;
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//
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// Methods
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//
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InterleavedTestbed(
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cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform,
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cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform,
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cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform,
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uint64_t seed_ = 2080
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):
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init_A(init_A_), init_B(init_B_), init_C(init_C_), seed(seed_) { }
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/// Helper to initialize a tensor view
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template <typename Element, typename Layout>
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bool initialize_tensor(
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cutlass::TensorView<Element, Layout> view,
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cutlass::Distribution::Kind dist_kind,
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uint64_t seed) {
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if (dist_kind == cutlass::Distribution::Uniform) {
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cutlass::reference::host::TensorFillRandomUniform(
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view, seed, 2, -2, 0);
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}
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else if (dist_kind == cutlass::Distribution::Identity) {
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cutlass::reference::host::TensorFillIdentity(view);
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}
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else if (dist_kind == cutlass::Distribution::Sequential) {
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cutlass::reference::host::BlockFillSequential(
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view.data(), view.capacity());
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}
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else {
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// TODO: Implement the rest
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EXPECT_TRUE(false) << "Not implemented";
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return false;
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}
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return true;
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}
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/// Executes one test
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bool run(
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cutlass::gemm::GemmCoord problem_size,
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ElementCompute alpha = ElementCompute(1),
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ElementCompute beta = ElementCompute(0)) {
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//
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// Allocate the GEMM workspace
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//
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cutlass::HostTensor<
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typename Gemm::ElementA,
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typename Gemm::LayoutA> tensor_A(problem_size.mk());
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cutlass::HostTensor<
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typename Gemm::ElementB,
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typename Gemm::LayoutB> tensor_B(problem_size.kn());
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cutlass::HostTensor<
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typename Gemm::ElementB,
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typename Gemm::LayoutB> tensor_B_reordered(problem_size.kn());
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cutlass::HostTensor<
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typename Gemm::ElementC,
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typename Gemm::LayoutC> tensor_C(problem_size.mn());
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cutlass::HostTensor<
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typename Gemm::ElementC,
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typename Gemm::LayoutC> tensor_D(problem_size.mn());
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cutlass::HostTensor<
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typename Gemm::ElementC,
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typename Gemm::LayoutC> reference_D(problem_size.mn(), false);
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EXPECT_TRUE(initialize_tensor(tensor_A.host_view(), init_A, seed + 2019));
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EXPECT_TRUE(initialize_tensor(tensor_B.host_view(), init_B, seed + 2018));
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EXPECT_TRUE(initialize_tensor(tensor_C.host_view(), init_C, seed + 2017));
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cutlass::reorder_column<InterleavedK>(
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tensor_B_reordered.host_ref(), tensor_B.host_ref(), problem_size);
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cutlass::reference::host::TensorCopy(
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reference_D.host_view(),
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tensor_C.host_view());
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tensor_A.sync_device();
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tensor_B_reordered.sync_device();
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tensor_C.sync_device();
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tensor_D.sync_device();
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//
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// Initialize the GEMM operator
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//
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typename Gemm::Arguments arguments{
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problem_size,
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tensor_A.device_ref(),
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tensor_B_reordered.device_ref(),
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tensor_C.device_ref(),
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tensor_D.device_ref(),
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{alpha, beta}
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};
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Gemm gemm_op;
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cutlass::Status status = gemm_op.initialize(arguments);
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EXPECT_TRUE(status == cutlass::Status::kSuccess);
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//
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// Run the GEMM
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//
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status = gemm_op();
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EXPECT_TRUE(status == cutlass::Status::kSuccess);
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//
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// Verify
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//
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cutlass::reference::host::Gemm<
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typename Gemm::ElementA, typename Gemm::LayoutA,
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typename Gemm::ElementB, typename Gemm::LayoutB,
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typename Gemm::ElementC, typename Gemm::LayoutC, ElementCompute,
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ElementAccumulator, typename Gemm::Operator>
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reference_gemm;
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reference_gemm(
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problem_size,
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alpha,
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tensor_A.host_ref(),
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tensor_B.host_ref(),
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beta,
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reference_D.host_ref(),
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ElementAccumulator(0)
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);
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tensor_D.sync_host();
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EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_D.host_view()), 0);
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EXPECT_GT(cutlass::reference::host::TensorNorm(reference_D.host_view()), 0);
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bool passed = cutlass::reference::host::TensorEquals(
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reference_D.host_view(),
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tensor_D.host_view());
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EXPECT_TRUE(passed);
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if (!passed) {
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std::stringstream fname;
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fname << "error_Gemm_device_"
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<< problem_size.m() << "x"
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<< problem_size.n() << "x"
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<< problem_size.k() << "_"
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<< Gemm::ThreadblockShape::kM << "x"
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<< Gemm::ThreadblockShape::kN << "x"
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<< Gemm::ThreadblockShape::kK << "_"
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<< Gemm::WarpShape::kM << "x"
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<< Gemm::WarpShape::kN << "x"
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<< Gemm::WarpShape::kK << ".txt";
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std::ofstream file(fname.str());
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file
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<< "problem: " << problem_size
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<< ", alpha: " << alpha << ", beta: " << beta << "\n\n";
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file
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<< "A =\n" << tensor_A.host_view()
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<< "\nB =\n" << tensor_B.host_view()
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<< "\nB_reordered =\n" << tensor_B_reordered.host_view()
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<< "\nC =\n" << tensor_C.host_view()
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<< "\n\nReference =\n" << reference_D.host_view()
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<< "\nComputed =\n" << tensor_D.host_view();
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}
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return passed;
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}
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/// Runs a set of problem sizes
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bool run_all() {
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bool passed = true;
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int problem_size_m[] = {
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InterleavedK, 256 + InterleavedK, 512 + InterleavedK
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};
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int problem_size_n[] = {
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InterleavedK, 256 + InterleavedK, 512 + InterleavedK
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};
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int problem_size_k[] = {
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InterleavedK, 256 + InterleavedK, 512 + InterleavedK
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};
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double problem_alpha[] = {
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1.0
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};
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double problem_beta[] = {
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2.0
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};
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for (int m : problem_size_m) {
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for (int n : problem_size_n) {
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for (int k : problem_size_k) {
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for (double alpha : problem_alpha) {
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for (double beta : problem_beta) {
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passed = run(
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{m, n, k},
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ElementCompute(alpha),
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ElementCompute(beta)
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);
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if (!passed) {
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return false;
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}
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}
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}
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}
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}
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}
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return true;
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}
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};
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////////////////////////////////////////////////////////////////////////////////
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} // namespace device
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} // namespace gemm
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} // namespace test
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////////////////////////////////////////////////////////////////////////////////
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