487 lines
16 KiB
C++
487 lines
16 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2024 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 Tests for device-wide GEMM interface
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Testbed for sparse operations not to be released for CUDA 11.0 GA. Expected release is 11.1.
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*/
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#pragma once
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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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#include "cutlass/util/host_uncompress.h"
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#include "testbed_utils.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>
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struct SparseTestbed {
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using ElementA = typename Gemm::ElementA;
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using ElementB = typename Gemm::ElementB;
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using ElementC = typename Gemm::ElementC;
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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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static int const kSparse = Gemm::GemmKernel::kSparse;
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static int const kMetaSizeInBits = Gemm::GemmKernel::kMetaSizeInBits;
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static int const kMaxID2 = Gemm::GemmKernel::kMaxID2;
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static int const kElementsPerElementE = Gemm::GemmKernel::kElementsPerElementE;
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using ElementE = typename Gemm::GemmKernel::ElementE;
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using LayoutE = cutlass::layout::RowMajor;
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using ReorderedLayoutE = typename Gemm::GemmKernel::LayoutE;
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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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cutlass::Distribution::Kind init_E;
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uint64_t seed;
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cutlass::HostTensor<typename Gemm::ElementA, typename Gemm::LayoutA> tensor_A;
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cutlass::HostTensor<typename Gemm::ElementA, typename Gemm::LayoutA> tensor_A_uncompressed;
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cutlass::HostTensor<typename Gemm::ElementB, typename Gemm::LayoutB> tensor_B;
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cutlass::HostTensor<typename Gemm::ElementC, typename Gemm::LayoutC> tensor_C;
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cutlass::HostTensor<typename Gemm::ElementC, typename Gemm::LayoutC> tensor_D;
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cutlass::HostTensor<typename Gemm::ElementC, typename Gemm::LayoutC> reference_D;
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cutlass::HostTensor<ElementE, LayoutE> tensor_E;
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cutlass::HostTensor<ElementE, ReorderedLayoutE> tensor_E_reordered;
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//
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// Methods
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//
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SparseTestbed(
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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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cutlass::Distribution::Kind init_E_ = cutlass::Distribution::Uniform,
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uint64_t seed_ = 2080)
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: init_A(init_A_),
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init_B(init_B_),
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init_C(init_C_),
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init_E(init_E_),
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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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double scope_max, scope_min;
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int bits_input = cutlass::sizeof_bits<Element>::value;
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int bits_output = cutlass::sizeof_bits<typename Gemm::ElementC>::value;
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if (bits_input == 1) {
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scope_max = 2;
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scope_min = 0;
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} else if (bits_input <= 8) {
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scope_max = 2;
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scope_min = -2;
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} else if (bits_output == 16) {
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scope_max = 5;
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scope_min = -5;
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} else {
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scope_max = 8;
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scope_min = -8;
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}
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cutlass::reference::host::TensorFillRandomUniform(
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view, seed, scope_max, scope_min, 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::Gaussian) {
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cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
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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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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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/// Initializes data structures
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void initialize(cutlass::gemm::GemmCoord problem_size) {
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//
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// Allocate the GEMM workspace
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//
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tensor_A.resize(cutlass::make_Coord(problem_size.m(), problem_size.k() / kSparse));
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tensor_A_uncompressed.resize(problem_size.mk());
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tensor_B.resize(problem_size.kn());
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tensor_C.resize(problem_size.mn());
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tensor_D.resize(problem_size.mn());
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reference_D.resize(problem_size.mn(), false);
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tensor_E.resize(cutlass::make_Coord(
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problem_size.m(), problem_size.k() / kSparse / kElementsPerElementE));
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tensor_E_reordered.resize(cutlass::make_Coord(
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problem_size.m(), problem_size.k() / kSparse / kElementsPerElementE));
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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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if (init_E == cutlass::Distribution::Uniform) {
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uint64_t seed = 7;
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cutlass::reference::host::TensorFillRandomSparseMeta(
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tensor_E.host_view(), seed, kMetaSizeInBits);
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} else if (init_E == cutlass::Distribution::Identity) {
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uint32_t content = (kMaxID2 == 1) ? 0x44444444 : 0x4444;
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cutlass::reference::host::TensorFill(tensor_E.host_view(),
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(ElementE)(content));
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} else {
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EXPECT_TRUE(false);
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}
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cutlass::reorder_meta(tensor_E_reordered.host_ref(), tensor_E.host_ref(),
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{problem_size.m(), problem_size.n(),
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problem_size.k() / kSparse / kElementsPerElementE});
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// It is possible to randomly initialize to all zeros, so override this with non-zeros
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// in the upper left corner of each operand.
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tensor_A.host_view().at({0, 0}) = typename Gemm::ElementA(1);
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tensor_B.host_view().at({0, 0}) = typename Gemm::ElementB(1);
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tensor_C.host_view().at({0, 0}) = typename Gemm::ElementC(1);
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cutlass::reference::host::TensorCopy(reference_D.host_view(), tensor_C.host_view());
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tensor_A.sync_device();
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tensor_B.sync_device();
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tensor_C.sync_device();
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tensor_D.sync_device();
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tensor_E_reordered.sync_device();
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}
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/// Compares computed reference with device reference and outputs to a file if incorrect
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bool compare_reference(
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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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tensor_D.sync_host();
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EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_A.host_view()), 0);
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EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_B.host_view()), 0);
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EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_C.host_view()), 0);
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if (tensor_D.size() > 1)
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EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_D.host_view()), 0);
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if (reference_D.size() > 1)
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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(reference_D.host_view(), 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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<< "\nC =\n" << tensor_C.host_view()
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<< "\nE =\n" << tensor_E.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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/// 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::uncompress(tensor_A_uncompressed.host_ref(), tensor_A.host_ref(),
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tensor_E.host_ref(), problem_size.m(), problem_size.k());
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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,
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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_uncompressed.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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return compare_reference(problem_size, alpha, beta);
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}
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/// Returns true if the CUDA device is sufficient to execute the kernel.
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bool sufficient() const {
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//
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// Determine SMEM requirements and waive if not satisfied
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//
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size_t smem_size = sizeof(typename Gemm::GemmKernel::SharedStorage);
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cudaDeviceProp properties;
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int device_idx;
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cudaError_t result = cudaGetDevice(&device_idx);
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if (result != cudaSuccess) {
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throw std::runtime_error("cudaGetDevice() API call failed.");
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}
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result = cudaGetDeviceProperties(&properties, device_idx);
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if (result != cudaSuccess) {
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throw std::runtime_error("cudaGetDeviceProperties() failed");
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}
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if (properties.sharedMemPerBlockOptin < smem_size) {
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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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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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// Waive test if insufficient CUDA device
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if (!sufficient()) {
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if (CUTLASS_TEST_UNIT_ENABLE_WARNINGS) {
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std::cerr << "Test waived due to insufficient CUDA device." << std::endl;
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}
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return true;
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}
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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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tensor_A.device_ref(),
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tensor_B.device_ref(),
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tensor_C.device_ref(),
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tensor_D.device_ref(),
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tensor_E_reordered.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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// This failure is likely due to insufficient device capabilities. Waive the test.
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if (status != cutlass::Status::kSuccess) {
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return true;
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}
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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 TestAllSparseGemm() {
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bool passed = true;
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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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// M dimension has to be multiple of 32 (sparse float) or 16 (sparse int)
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// because of the reordering of operand E
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int const kAlignmentM = std::max(((sizeof(typename Gemm::ElementE) == 2) ? 32 : 16),
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kMinimumOperandElementSize);
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int const kAlignmentN = 128 / kMinimumOperandElementSize;
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int problem_size_m[] = {kAlignmentM, 512 - 3 * kAlignmentM};
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int problem_size_n[] = {kAlignmentN, 512 - 2 * kAlignmentN};
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int problem_size_k[] = {Gemm::ThreadblockShape::kK,
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Gemm::ThreadblockShape::kK * (Gemm::kStages + 1)};
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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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SparseTestbed<Gemm> testbed;
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using ElementCompute = typename Gemm::EpilogueOutputOp::ElementCompute;
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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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if (split_k > 1 && k / Gemm::ThreadblockShape::kK < split_k) {
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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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