CUTLASS 2.0 (#62)
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.
This commit is contained in:
Andrew Kerr
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/***************************************************************************************************
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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_complex.h"
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#include "testbed.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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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>
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struct TestbedComplex : public Testbed<Gemm> {
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using Base = Testbed<Gemm>;
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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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//
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// Methods
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//
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TestbedComplex(
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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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Base(init_A_, init_B_, init_C_, seed_) { }
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/// Verifies the result is a GEMM
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bool verify(
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cutlass::gemm::GemmCoord problem_size,
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ElementCompute alpha,
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ElementCompute beta) {
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//
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// Verify
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//
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cutlass::reference::host::GemmComplex(
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problem_size,
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alpha,
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this->tensor_A.host_ref(),
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Gemm::kTransformA,
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this->tensor_B.host_ref(),
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Gemm::kTransformB,
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beta,
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this->reference_D.host_ref(),
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ElementAccumulator(0)
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);
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return this->compare_reference(problem_size, alpha, beta);
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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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int split_k_slices = 1,
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ElementCompute alpha = ElementCompute(1),
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ElementCompute beta = ElementCompute(0)) {
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this->initialize(problem_size);
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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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this->tensor_A.device_ref(),
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this->tensor_B.device_ref(),
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this->tensor_C.device_ref(),
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this->tensor_D.device_ref(),
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{alpha, beta},
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split_k_slices
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};
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Gemm gemm_op;
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size_t workspace_size = Gemm::get_workspace_size(arguments);
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cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
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cutlass::Status status = gemm_op.initialize(arguments, workspace.get());
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EXPECT_TRUE(status == cutlass::Status::kSuccess) << to_string(status);
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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) << to_string(status);
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//
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// Verify
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//
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bool passed = this->verify(problem_size, alpha, beta);
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if (!passed) {
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std::cout << "Error with split_k_slices = " << split_k_slices << ", alpha: " << alpha << std::endl;
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}
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return passed;
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename Gemm>
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bool TestAllGemmComplex() {
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bool passed = true;
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using ElementCompute = typename Gemm::EpilogueOutputOp::ElementCompute;
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int const kMinimumOperandElementSize =
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std::min(
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int(cutlass::sizeof_bits<typename Gemm::ElementA>::value),
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int(cutlass::sizeof_bits<typename Gemm::ElementB>::value));
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int const kAlignment =
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cutlass::platform::is_same<
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typename Gemm::OperatorClass,
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cutlass::arch::OpClassSimt>::value ? 1 : 128 / kMinimumOperandElementSize;
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int problem_size_m[] = {
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kAlignment, 512 - 3*kAlignment
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};
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int problem_size_n[] = {
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kAlignment, 512 - 2*kAlignment
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};
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int problem_size_k[] = {
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kAlignment, 128 - kAlignment
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};
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int split_k_slices[] = {
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1, 2, 3
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};
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double problem_alpha[] = {
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1
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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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TestbedComplex<Gemm> testbed;
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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 (int split_k : split_k_slices) {
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if (!Gemm::kSplitKSerial && split_k > 1) {
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continue;
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}
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for (auto alpha : problem_alpha) {
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for (auto beta : problem_beta) {
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cutlass::gemm::GemmCoord problem_size(m, n, k);
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passed = testbed.run(
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problem_size,
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split_k,
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cutlass::from_real<ElementCompute>(alpha),
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cutlass::from_real<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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}
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return passed;
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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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