667 lines
22 KiB
C++
667 lines
22 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 Implicit GEMM testbed
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*/
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#pragma once
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#include <fstream>
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#include "../../common/cutlass_unit_test.h"
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#include "cutlass/cutlass.h"
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#include "cutlass/conv/device/implicit_gemm_convolution.h"
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#include "cutlass/reduction/device/reduce_split_k.h"
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#include "cutlass/reduction/thread/reduction_operators.h"
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#include "conv2d_problems.h"
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#include "cutlass/util/host_tensor.h"
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#include "cutlass/util/reference/host/tensor_fill.h"
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#include "cutlass/util/reference/device/tensor_compare.h"
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#include "cutlass/util/reference/host/tensor_compare.h"
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#include "cutlass/util/host_reorder.h"
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#include "cutlass/util/reference/host/convolution.h"
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#include "cutlass/util/reference/device/convolution.h"
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#include "cutlass/core_io.h"
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#include "cutlass/util/tensor_view_io.h"
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#include "../cache_testbed_output.h"
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namespace test {
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namespace conv {
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namespace device {
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template <typename Conv2d, int InterleavedK>
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class InterleavedTestbedConv2d {
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public:
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using ElementA = typename Conv2d::ElementA;
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using LayoutA = typename Conv2d::LayoutA;
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using ElementB = typename Conv2d::ElementB;
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using LayoutB = typename Conv2d::LayoutB;
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using ElementC = typename Conv2d::ElementC;
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using LayoutC = typename Conv2d::LayoutC;
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using ElementAccumulator = typename Conv2d::ElementAccumulator;
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using ElementCompute = typename Conv2d::ElementCompute;
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using EpilogueOutputOp = typename Conv2d::EpilogueOutputOp;
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static cutlass::conv::Operator const kConvolutionalOperator = Conv2d::kConvolutionalOperator;
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/// Reduction kernel
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using ReductionOp = cutlass::reduction::thread::ReduceAdd<
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ElementAccumulator,
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typename EpilogueOutputOp::ElementAccumulator,
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EpilogueOutputOp::kCount
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>;
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using ReductionKernel = cutlass::reduction::kernel::ReduceSplitK<
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cutlass::MatrixShape<4, 32 * EpilogueOutputOp::kCount>,
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EpilogueOutputOp,
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ReductionOp
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>;
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using ReductionDevice = cutlass::reduction::device::ReduceSplitK<ReductionKernel>;
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using ReductionStrideIndex = typename ReductionDevice::StrideIndex;
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public:
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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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cutlass::HostTensor<ElementA, LayoutA> tensor_A;
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cutlass::HostTensor<ElementB, LayoutB> tensor_B;
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cutlass::HostTensor<ElementB, LayoutB> tensor_B_reordered;
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cutlass::HostTensor<ElementC, LayoutC> tensor_C;
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cutlass::HostTensor<ElementC, LayoutC> tensor_D_computed;
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cutlass::HostTensor<ElementC, LayoutC> tensor_D_reference;
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public:
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InterleavedTestbedConv2d(
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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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}
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/// Helper to initialize a tensor view
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template <typename Element, typename Layout>
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void 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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int scope;
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int bits = cutlass::sizeof_bits<Element>::value;
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if (bits <= 8) {
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scope = 2;
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}
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else if (bits == 16) {
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scope = 3;
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}
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else {
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scope = 8;
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}
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cutlass::reference::host::TensorFillRandomUniform(
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view, seed, scope, -scope, 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(view.data(), view.capacity());
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}
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else {
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}
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}
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void initialize(
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cutlass::conv::Conv2dProblemSize const &problem_size, uint64_t seed = 2019) {
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tensor_A.resize(implicit_gemm_tensor_a_extent(kConvolutionalOperator, problem_size));
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tensor_B.resize(implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size));
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tensor_B_reordered.resize(implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size));
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tensor_C.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size));
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tensor_D_computed.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size));
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tensor_D_reference.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size));
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initialize_tensor(tensor_A.host_view(), init_A, seed);
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initialize_tensor(tensor_B.host_view(), init_B, seed * 17);
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initialize_tensor(tensor_C.host_view(), init_C, seed * 39);
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cutlass::reorder_convK<InterleavedK>(
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tensor_B_reordered.host_ref(), tensor_B.host_ref(), implicit_gemm_problem_size(kConvolutionalOperator, problem_size));
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tensor_A.sync_device();
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tensor_B.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_computed.sync_device();
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tensor_D_reference.sync_device();
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}
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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 Conv2d::UnderlyingKernel::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.sharedMemPerMultiprocessor < 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::conv::Conv2dProblemSize const &problem_size,
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cutlass::conv::SplitKMode const &split_k_mode = cutlass::conv::SplitKMode::kSerial,
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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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#if 0 //display conv2d problem size for debugging
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std::cout << problem_size << std::endl
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<< "alpha, beta: (" << float(alpha) << ", " << float(beta) << ")" << std::endl
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<< "split_k_mode: " << ((split_k_mode == cutlass::conv::SplitKMode::kSerial) ? "(serial)" : "(parallel)") << std::endl
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<< std::endl;
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#endif
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initialize(problem_size);
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// configure the operator
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Conv2d conv2d_op;
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typename Conv2d::Arguments conv2d_args(
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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_computed.device_ref(),
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{alpha, beta},
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split_k_mode
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);
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// find workspace requirement for parallel split-k reduction
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size_t workspace_size = Conv2d::get_workspace_size(conv2d_args);
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cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
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cutlass::Status status = conv2d_op.initialize(conv2d_args, workspace.get());
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// conv2d operation with parallel split-k-mode
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if (split_k_mode == cutlass::conv::SplitKMode::kParallel) {
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// conv2d output is written to workspace in global memory
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conv2d_args.ref_D.reset(reinterpret_cast<ElementC*>(workspace.get()));
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// accumulate mma for each cta in k-dimension (1.0 * A * B)
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conv2d_args.output_op = {ElementCompute(1), ElementCompute(0)};
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// update conv2d operator arguments
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status = conv2d_op.update(conv2d_args, workspace.get());
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}
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EXPECT_TRUE(status == cutlass::Status::kSuccess);
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if (status != cutlass::Status::kSuccess) {
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return false;
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}
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// run conv2d operator
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status = conv2d_op();
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EXPECT_TRUE(status == cutlass::Status::kSuccess);
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if (status != cutlass::Status::kSuccess) {
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return false;
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}
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if (split_k_mode == cutlass::conv::SplitKMode::kParallel) {
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// configure parallel reduction operator
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ReductionDevice reduction_op;
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typename ReductionDevice::Arguments reduction_args(
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cutlass::conv::implicit_gemm_problem_size(kConvolutionalOperator, problem_size).mn(),
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problem_size.split_k_slices,
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cutlass::conv::implicit_gemm_tensor_c_size(kConvolutionalOperator, problem_size),
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{
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reinterpret_cast<ElementAccumulator*> (workspace.get()),
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ReductionStrideIndex(tensor_C.stride()[Conv2d::UnderlyingKernel::kTensorCStrideIdx])
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},
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{
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tensor_D_computed.device_data(),
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ReductionStrideIndex(tensor_C.stride()[Conv2d::UnderlyingKernel::kTensorCStrideIdx])
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},
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{
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tensor_C.device_data(),
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ReductionStrideIndex(tensor_C.stride()[Conv2d::UnderlyingKernel::kTensorCStrideIdx])
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},
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// apply alpha, beta to obtain the following equation alpha * ReduceAdd(A * B) + beta * C
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{alpha, beta}
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);
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status = reduction_op.initialize(reduction_args, nullptr);
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EXPECT_TRUE(status == cutlass::Status::kSuccess);
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if (status != cutlass::Status::kSuccess) {
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return false;
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}
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// run prallel reduction kernel
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status = reduction_op();
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EXPECT_TRUE(status == cutlass::Status::kSuccess);
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if (status != cutlass::Status::kSuccess) {
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return false;
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}
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}
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bool passed = false;
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tensor_D_computed.sync_host();
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//
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// Reference check - support caching results
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//
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CachedTestKey cached_test_key = CreateCachedConv2dTestKey<
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ElementA, LayoutA,
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ElementB, LayoutB,
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ElementC, LayoutC,
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ElementAccumulator,
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ElementCompute
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>(
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kConvolutionalOperator,
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problem_size,
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alpha,
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beta,
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tensor_A.host_view(),
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tensor_B.host_view(),
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tensor_C.host_view()
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);
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//
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// Look for the cached key
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//
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bool cached_result_loaded = false;
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CachedTestResult cached_test_result;
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std::string conv2d_result_cache_name =
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std::string("cached_results_") + CUTLASS_TARGET_NAME + ".txt";
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if (CUTLASS_TEST_ENABLE_CACHED_RESULTS) {
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CachedTestResultListing cached_results(conv2d_result_cache_name);
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auto cached = cached_results.find(cached_test_key);
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cached_result_loaded = cached.first;
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if (cached_result_loaded) {
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cached_test_result = cached.second;
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}
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}
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if (!cached_result_loaded) {
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#if CUTLASS_CONV_TEST_UNIT_REFERENCE_DEVICE_ENABLED
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cutlass::reference::device::Conv2d<
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ElementA,
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LayoutA,
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ElementB,
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LayoutB,
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ElementC,
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LayoutC,
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ElementCompute,
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ElementAccumulator,
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cutlass::NumericConverterClamp<ElementC, ElementCompute>
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>(
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kConvolutionalOperator,
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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_reference.device_ref(),
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alpha,
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beta);
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cudaError_t result = cudaDeviceSynchronize();
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EXPECT_EQ(result, cudaSuccess) << " device reference error: "
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<< cudaGetErrorString(result);
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// sync host (copy device data to host) for dumping error output in case of mismatches
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tensor_D_reference.sync_host();
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#else
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cutlass::reference::host::Conv2d<
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ElementA,
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LayoutA,
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ElementB,
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LayoutB,
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ElementC,
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LayoutC,
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ElementCompute,
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ElementAccumulator,
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ElementC,
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cutlass::NumericConverterClamp<ElementC, ElementCompute>
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>(
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kConvolutionalOperator,
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problem_size,
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tensor_A.host_ref(),
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tensor_B.host_ref(),
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tensor_C.host_ref(),
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tensor_D_reference.host_ref(),
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alpha,
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beta);
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#endif
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if (CUTLASS_TEST_ENABLE_CACHED_RESULTS) {
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cached_test_result.D = TensorHash(tensor_D_reference.host_view());
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CachedTestResultListing cached_results(conv2d_result_cache_name);
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cached_results.append(cached_test_key, cached_test_result);
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cached_results.write(conv2d_result_cache_name);
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}
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} // if (!cached_result_loaded)
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uint32_t tensor_D_hash = TensorHash(tensor_D_computed.host_view());
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if (CUTLASS_TEST_ENABLE_CACHED_RESULTS) {
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passed = (tensor_D_hash == cached_test_result.D);
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EXPECT_EQ(tensor_D_hash, cached_test_result.D)
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<< "Hash-based comparison failed for key:" << "\n" << cached_test_key << "\n";
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}
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else {
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passed = cutlass::reference::host::TensorEquals(
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tensor_D_computed.host_view(),
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tensor_D_reference.host_view());
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}
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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_Conv2d_ImplicitGemm_device_"
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<< (split_k_mode == cutlass::conv::SplitKMode::kSerial ? "serial_reduction_" : "parallel_reduction_")
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<< (Conv2d::kConvolutionalOperator == cutlass::conv::Operator::kFprop ? "fprop_" :
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(Conv2d::kConvolutionalOperator == cutlass::conv::Operator::kDgrad ? "dgrad_" : "wgrad_"))
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<< "ncxhwx_"
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<< problem_size.N << "x"
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<< problem_size.H << "x"
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<< problem_size.W << "x"
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<< problem_size.C
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<< "_cxrskx_"
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<< problem_size.K << "x"
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<< problem_size.R << "x"
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<< problem_size.S << "x"
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<< problem_size.C
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<< "_padding_"
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<< problem_size.pad_h << "x"
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<< problem_size.pad_w
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<< "_stride_"
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<< problem_size.stride_h << "x"
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<< problem_size.stride_w
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<< "_dilation_"
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<< problem_size.dilation_h << "x"
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<< problem_size.dilation_w << "_"
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<< (problem_size.mode == cutlass::conv::Mode::kCrossCorrelation ? "xcorr_" : "conv_")
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<< Conv2d::ThreadblockShape::kM << "x"
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<< Conv2d::ThreadblockShape::kN << "x"
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<< Conv2d::ThreadblockShape::kK << "_"
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<< Conv2d::WarpShape::kM << "x"
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<< Conv2d::WarpShape::kN << "x"
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<< Conv2d::WarpShape::kK << ".txt";
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std::cout << fname.str() << std::endl;
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std::ofstream results(fname.str());
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results << problem_size << std::endl;
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results
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<< "\nA:\n" << tensor_A.host_view() << "\n"
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<< "\nB:\n" << tensor_B.host_view() << "\n"
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<< "\nC:\n" << tensor_C.host_view() << "\n";
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results << "\nD reference (hash: " << cached_test_result.D << ")\n";
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if (!cached_result_loaded) {
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results
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<< tensor_D_reference.host_view() << "\n";
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}
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results
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<< "\nD computed (hash: " << tensor_D_hash << ")\n"
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<< tensor_D_computed.host_view() << "\n";
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|
|
|
}
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|
|
|
return passed;
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|
}
|
|
|
|
};
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|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////
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|
// TestAllConv: Runs cutlass::conv::device::ImplicitGemmConvolution operator and compares it with reference
|
|
// TestAllConv runs conv operator on default conv problem sizes from test::conv::device::TestbedConv2dProblemSizes
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|
// Additionally, each conv2d test can provide conv problem sizes (conv_test_sizes) and blacklist of sizes
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|
// (conv_blacklist_sizes)
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|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////
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|
template <typename ImplicitGemm, int InterleavedK>
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|
bool TestAllInterleavedConv2d(
|
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const Conv2dProblemVector & conv_test_sizes = Conv2dProblemVector(),
|
|
const Conv2dProblemVector & conv_blacklist_sizes = Conv2dProblemVector()) {
|
|
|
|
bool passed = true;
|
|
|
|
//
|
|
// Testbed object
|
|
//
|
|
|
|
InterleavedTestbedConv2d<ImplicitGemm, InterleavedK> testbed;
|
|
|
|
//
|
|
// Get conv problem sizes to run conv operator
|
|
//
|
|
TestbedConv2dProblemSizes conv_problems(InterleavedK); // minimum channel size must be multiple of InterleavedK for interleaved layout
|
|
|
|
// Vector of conv2d problem sizes to avoid duplicate runs
|
|
Conv2dProblemVector conv_tested_sizes;
|
|
|
|
Conv2dProblemVector const *problem_vectors[] = {
|
|
&conv_test_sizes, // run user specified sizes
|
|
&conv_problems.conv2d_default_sizes, // run default and cudnn bug sizes
|
|
&conv_problems.conv2d_resnet50_sizes, // run resnet50 sizes
|
|
#if CUTLASS_CONV_UNIT_TEST_RIGOROUS_SIZE_ENABLED
|
|
&conv_problems.conv2d_rigorous_sizes, // run large and rigorous sizes if enabled
|
|
#endif
|
|
};
|
|
|
|
// Sweep conv2d problem sizes (split-k-mode=kSerial, split-k-slice=1, alpha=1.0, beta=0.0)
|
|
for (Conv2dProblemVector const * problem_vector : problem_vectors) {
|
|
|
|
ChannelDivisibilitySpecification channel_spec(InterleavedK); //input and output channels must be multiple of InterleavedK
|
|
auto pruned_problem_vector = prune(*problem_vector, channel_spec);
|
|
|
|
// Run conv testbed on default convolution sizes
|
|
for(auto conv_problem : pruned_problem_vector) {
|
|
|
|
// Skip blacklist and avoid duplicate problem sizes
|
|
if (std::find(conv_blacklist_sizes.begin(), conv_blacklist_sizes.end(), conv_problem) != conv_blacklist_sizes.end() ||
|
|
std::find(conv_tested_sizes.begin(), conv_tested_sizes.end(), conv_problem) != conv_tested_sizes.end()) {
|
|
continue;
|
|
}
|
|
|
|
//
|
|
// Procedurally disable certain cases
|
|
//
|
|
|
|
// CUTLASS DGRAD's unity stride specialization only support stride {1, 1}
|
|
if ((ImplicitGemm::kConvolutionalOperator ==
|
|
cutlass::conv::Operator::kDgrad) &&
|
|
(ImplicitGemm::UnderlyingKernel::Mma::IteratorA::kStrideSupport ==
|
|
cutlass::conv::StrideSupport::kUnity)) {
|
|
if (!((conv_problem.stride_h == 1) && (conv_problem.stride_w == 1))) {
|
|
continue;
|
|
}
|
|
}
|
|
|
|
//
|
|
// Test
|
|
//
|
|
// push back tested problem size to avoid re-running duplicates
|
|
conv_tested_sizes.push_back(conv_problem);
|
|
|
|
// test mode = xcross
|
|
passed = testbed.run(
|
|
conv_problem,
|
|
cutlass::conv::SplitKMode::kSerial);
|
|
|
|
if (!passed) {
|
|
return false;
|
|
}
|
|
|
|
// test mode = convolution
|
|
passed = testbed.run(
|
|
conv_problem.reset_mode(cutlass::conv::Mode::kConvolution),
|
|
cutlass::conv::SplitKMode::kSerial);
|
|
|
|
if (!passed) {
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
|
|
#if 0
|
|
// Sweep split-k-slice using serial and prallel reduction with non-unity alpha and non-zero beta for
|
|
// a single conv2d problem size. Convolution unit tests take a long time to run so only sweep parameters
|
|
// which are abolutely necessary to catch functional bugs. The below code does provide option to sweep
|
|
// alpha and beta for local testing, but only runs one value for alpha and beta.
|
|
cutlass::conv::Conv2dProblemSize conv2d_split_k_test_size (
|
|
{1, 17, 11, 288}, // input size (NHWC)
|
|
{160, 3, 3, 288}, // filter size (KRSC)
|
|
{1, 1, 1, 1}, // padding (pad_h, _, pad_w, _)
|
|
{1, 1}, // stride (stride_h, stride_w)
|
|
{1, 1} // dilation (dilation_h, dilation_w)
|
|
);
|
|
|
|
cutlass::conv::SplitKMode split_k_modes [] = {
|
|
cutlass::conv::SplitKMode::kSerial,
|
|
cutlass::conv::SplitKMode::kParallel,
|
|
};
|
|
|
|
int split_k_slices[] = {
|
|
1, 2, 3, 4, 201
|
|
};
|
|
|
|
double problem_alpha[] = {
|
|
2.0
|
|
};
|
|
|
|
double problem_beta[] = {
|
|
2.0
|
|
};
|
|
|
|
for (auto split_k_mode : split_k_modes) {
|
|
for (auto split_k_slice : split_k_slices) {
|
|
for (auto alpha : problem_alpha) {
|
|
for (auto beta : problem_beta) {
|
|
|
|
passed = testbed.run(
|
|
conv2d_split_k_test_size.reset_split_k_slices(split_k_slice),
|
|
split_k_mode,
|
|
cutlass::from_real<typename ImplicitGemm::ElementCompute>(alpha),
|
|
cutlass::from_real<typename ImplicitGemm::ElementCompute>(beta));
|
|
|
|
if (!passed) {
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
#endif
|
|
|
|
return passed;
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace device
|
|
} // namespace conv
|
|
} // namespace test
|