Added first version of a tuner for the ConvGemm direct kernel
Cedric Nugteren
Koichi Akabe
parent
301dc280df
commit
1f0cd61824
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@ -213,6 +213,7 @@ endif()
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# Sets the supported routines and the used kernels. New routines and kernels should be added here.
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set(KERNELS copy_fast copy_pad transpose_fast transpose_pad xaxpy xdot xger
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xgemm xgemm_direct xgemv invert)
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set(KERNELS_EXTRA xconvgemm) # kernels for which not to include a tuner in 'all tuners' target
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set(DATABASES copy pad padtranspose transpose xaxpy xdot
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xgemm xgemm_direct xgemv xgemv_fast xgemv_fast_rot xger invert
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gemm_routine trsv_routine)
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@ -434,7 +435,8 @@ if(TUNERS)
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endif()
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# Adds tuning executables
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foreach(KERNEL ${KERNELS})
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set(ALLKERNELS ${KERNELS} ${KERNELS_EXTRA})
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foreach(KERNEL ${ALLKERNELS})
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add_executable(clblast_tuner_${KERNEL} ${TUNERS_COMMON} src/tuning/kernels/${KERNEL}.cpp)
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target_link_libraries(clblast_tuner_${KERNEL} ${API_LIBRARIES})
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target_include_directories(clblast_tuner_${KERNEL} PUBLIC $<TARGET_PROPERTY:clblast,INTERFACE_INCLUDE_DIRECTORIES> ${API_INCLUDE_DIRS})
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@ -0,0 +1,38 @@
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// =================================================================================================
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// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
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// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max-
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// width of 100 characters per line.
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//
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// Author(s):
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// Cedric Nugteren <www.cedricnugteren.nl>
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//
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// This file uses the auto-tuner to tune the convgemm kernels.
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//
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// =================================================================================================
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#include "tuning/kernels/xconvgemm.hpp"
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// Shortcuts to the clblast namespace
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using half = clblast::half;
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using float2 = clblast::float2;
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using double2 = clblast::double2;
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// Function to tune a specific variation V (not within the clblast namespace)
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template <int V>
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void StartVariation(int argc, char *argv[]) {
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const auto command_line_args = clblast::RetrieveCommandLineArguments(argc, argv);
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switch(clblast::GetPrecision(command_line_args)) {
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case clblast::Precision::kHalf: clblast::Tuner<half>(argc, argv, V, clblast::XConvGemmGetTunerDefaults, clblast::XConvGemmGetTunerSettings<half>, clblast::XConvGemmTestValidArguments<half>, clblast::XConvGemmSetConstraints, clblast::XConvGemmComputeLocalMemSize<half>, clblast::XConvGemmSetArguments<half>); break;
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case clblast::Precision::kSingle: clblast::Tuner<float>(argc, argv, V, clblast::XConvGemmGetTunerDefaults, clblast::XConvGemmGetTunerSettings<float>, clblast::XConvGemmTestValidArguments<float>, clblast::XConvGemmSetConstraints, clblast::XConvGemmComputeLocalMemSize<float>, clblast::XConvGemmSetArguments<float>); break;
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case clblast::Precision::kDouble: clblast::Tuner<double>(argc, argv, V, clblast::XConvGemmGetTunerDefaults, clblast::XConvGemmGetTunerSettings<double>, clblast::XConvGemmTestValidArguments<double>, clblast::XConvGemmSetConstraints, clblast::XConvGemmComputeLocalMemSize<double>, clblast::XConvGemmSetArguments<double>); break;
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}
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}
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// Main function (not within the clblast namespace)
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int main(int argc, char *argv[]) {
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StartVariation<1>(argc, argv);
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return 0;
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}
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// =================================================================================================
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@ -0,0 +1,186 @@
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// =================================================================================================
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// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
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// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max-
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// width of 100 characters per line.
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//
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// Author(s):
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// Cedric Nugteren <www.cedricnugteren.nl>
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//
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// This file uses the auto-tuner to tune the ConvGemm kernels. These kernels are based on the GEMM
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// direct kernel and will use those parameters, this tuner is just optional to use for advanced
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// users.
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//
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// =================================================================================================
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#include <string>
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#include <vector>
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#include "utilities/utilities.hpp"
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#include "tuning/tuning.hpp"
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namespace clblast {
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// =================================================================================================
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// Helper functions
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template <typename T>
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size_t OutputHeight(const Arguments<T> &args) {
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const auto size = args.height + 2 * args.pad_h;
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const auto padding = args.dilation_h * (args.kernel_h - 1) + 1;
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if (size >= padding) { return (size - padding) / args.stride_h + 1; }
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return 1;
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}
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template <typename T>
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size_t OutputWidth(const Arguments<T> &args) {
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const auto size = args.width + 2 * args.pad_w;
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const auto padding = args.dilation_w * (args.kernel_w - 1) + 1;
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if (size >= padding) { return (size - padding) / args.stride_w + 1; }
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return 1;
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}
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// Settings for this kernel (default command-line arguments)
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TunerDefaults XConvGemmGetTunerDefaults(const int) {
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auto settings = TunerDefaults();
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settings.options = {kArgChannels, kArgHeight, kArgWidth, kArgKernelH, kArgKernelW,
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kArgNumKernels, kArgBatchCount, kArgFraction};
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settings.channels = 32;
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settings.height = 64;
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settings.width = 64;
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settings.kernel_h = 3;
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settings.kernel_w = 3;
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settings.num_kernels = 32;
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settings.default_batch_count = 16;
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settings.default_fraction = 1.0;
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settings.default_num_runs = 2;
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return settings;
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}
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// Settings for this kernel (general)
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template <typename T>
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TunerSettings XConvGemmGetTunerSettings(const int, const Arguments<T> &args) {
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auto settings = TunerSettings();
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// Identification of the kernel
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settings.kernel_family = "xconvgemm";
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settings.kernel_name = "Xconvgemm";
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settings.sources =
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"#define ROUTINE_CONVGEMM"
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#include "../src/kernels/level3/xgemm_direct_part1.opencl"
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#include "../src/kernels/level3/xgemm_direct_part2.opencl"
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#include "../src/kernels/level3/xgemm_direct_part3.opencl"
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#include "../src/kernels/levelx/xconvgemm_part1.opencl"
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#include "../src/kernels/levelx/xconvgemm_part2.opencl"
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;
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// Helper variables
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const auto patch_size = args.kernel_h * args.kernel_w * args.channels;
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const auto num_patches = OutputHeight(args) * OutputWidth(args);
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// Buffer sizes
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settings.size_a = args.batch_count * args.channels * args.height * args.width;
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settings.size_b = args.num_kernels * args.channels * args.kernel_h * args.kernel_w;
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settings.size_a = args.batch_count * args.num_kernels * OutputHeight(args) * OutputWidth(args);
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// Inputs and outputs IDs (X:0, Y:1, A:2, B:3, C:4, temp:5)
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settings.inputs = {2, 3, 4};
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settings.outputs = {4};
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// Sets the base thread configuration
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settings.global_size = {num_patches, args.num_kernels};
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settings.global_size_ref = settings.global_size;
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settings.local_size = {1, 1};
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settings.local_size_ref = {8, 8};
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// Transforms the thread configuration based on the parameters
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settings.mul_local = {{"MDIMCD", "NDIMCD"}};
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settings.mul_global = {{"MDIMCD", "NDIMCD"}};
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settings.div_global = {{"WGD", "WGD"}};
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// Sets the tuning parameters and their possible values
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settings.parameters = {
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{"WGD", {8, 16, 32}},
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{"MDIMCD", {8, 16, 32}},
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{"NDIMCD", {8, 16, 32}},
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{"MDIMAD", {8, 16, 32}},
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{"NDIMBD", {8, 16, 32}},
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{"KWID", {1}},
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{"VWMD", {1, 2, 4, 8}},
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{"VWND", {1, 2, 4, 8}},
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{"PADA", {0}},
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{"PADB", {0}},
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};
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// Describes how to compute the performance metrics
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settings.metric_amount = args.batch_count * 2 * num_patches * args.num_kernels * patch_size;
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settings.performance_unit = "GFLOPS";
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return settings;
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}
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// Tests for valid arguments
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template <typename T>
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void XConvGemmTestValidArguments(const int, const Arguments<T> &) { }
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std::vector<Constraint> XConvGemmSetConstraints(const int) {
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auto constraints = std::vector<Constraint>();
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auto MultipleOfX = [] (std::vector<size_t> v) { return IsMultiple(v[0], v[1]); };
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auto MultipleOfXMulY = [] (std::vector<size_t> v) { return IsMultiple(v[0], v[1]*v[2]); };
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auto MultipleOfXMulYDivZ = [] (std::vector<size_t> v) { return IsMultiple(v[0], (v[1]*v[2])/v[3]); };
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// Requirement for unrolling the WGD loop
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constraints.push_back({MultipleOfX, {"WGD", "KWID"}});
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// Required for integer MWID and NWID
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constraints.push_back({MultipleOfXMulY, {"WGD", "MDIMCD", "VWMD"}});
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constraints.push_back({MultipleOfXMulY, {"WGD", "NDIMCD", "VWND"}});
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// Required for integer MWIAD and NWIBD
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constraints.push_back({MultipleOfXMulY, {"WGD", "MDIMAD", "VWMD"}});
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constraints.push_back({MultipleOfXMulY, {"WGD", "NDIMBD", "VWND"}});
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// WGD has to be a multiple of KDIMAD = ((MDIMCD*NDIMCD)/(MDIMAD)) and KDIMBD = (...)
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constraints.push_back({MultipleOfXMulYDivZ, {"WGD", "MDIMCD", "NDIMCD", "MDIMAD"}});
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constraints.push_back({MultipleOfXMulYDivZ, {"WGD", "MDIMCD", "NDIMCD", "NDIMBD"}});
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return constraints;
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}
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template <typename T>
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LocalMemSizeInfo XConvGemmComputeLocalMemSize(const int) {
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return {
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[] (std::vector<size_t> v) -> size_t {
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return GetBytes(PrecisionValue<T>()) * ((v[0]*(v[0] + v[1]) + v[0]*(v[0] + v[2])));
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},
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{"WGD", "PADA", "PADB"}
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};
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}
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// Sets the kernel's arguments
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template <typename T>
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void XConvGemmSetArguments(const int, Kernel &kernel, const Arguments<T> &args, std::vector<Buffer<T>>& buffers) {
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const auto output_h = OutputHeight(args);
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const auto output_w = OutputWidth(args);
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const auto patch_size = args.kernel_h * args.kernel_w * args.channels;
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const auto num_patches = output_h * output_w;
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const auto result_stride = args.num_kernels * output_h * output_w;
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kernel.SetArgument(0, static_cast<int>(num_patches));
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kernel.SetArgument(1, static_cast<int>(args.num_kernels));
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kernel.SetArgument(2, static_cast<int>(patch_size));
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kernel.SetArgument(3, buffers[3]()); // 3 == B matrix ==> kernel buffer
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kernel.SetArgument(4, 0); // c_offset
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kernel.SetArgument(5, buffers[4]()); // 4 == C matrix ==> result buffer
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kernel.SetArgument(6, 0); // c_offset
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kernel.SetArgument(7, static_cast<int>(result_stride));
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kernel.SetArgument(8, buffers[2]()); // 2 == A matrix ==> image buffer
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kernel.SetArgument(9, 0); // c_offset
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kernel.SetArgument(10, static_cast<int>(args.height));
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kernel.SetArgument(11, static_cast<int>(args.width));
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kernel.SetArgument(12, static_cast<int>(args.channels));
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kernel.SetArgument(13, static_cast<int>(args.kernel_h));
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kernel.SetArgument(14, static_cast<int>(args.kernel_w));
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kernel.SetArgument(15, 0); // pad_h
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kernel.SetArgument(16, 0); // pad_w
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kernel.SetArgument(17, 1); // stride_h
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kernel.SetArgument(18, 1); // stride_w
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kernel.SetArgument(19, 1); // dilation_h
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kernel.SetArgument(20, 1); // dilation_w
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kernel.SetArgument(21, static_cast<int>(output_h));
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kernel.SetArgument(22, static_cast<int>(output_w));
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}
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// =================================================================================================
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} // namespace clblast
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@ -122,8 +122,14 @@ void Tuner(int argc, char* argv[], const int V,
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if (o == kArgM) { args.m = GetArgument(command_line_args, help, kArgM, defaults.default_m); }
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if (o == kArgN) { args.n = GetArgument(command_line_args, help, kArgN, defaults.default_n); }
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if (o == kArgK) { args.k = GetArgument(command_line_args, help, kArgK, defaults.default_k); }
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if (o == kArgAlpha) { args.alpha = GetArgument(command_line_args, help, kArgAlpha, GetScalar<T>()); }
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if (o == kArgBeta) { args.beta = GetArgument(command_line_args, help, kArgBeta, GetScalar<T>()); }
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if (o == kArgChannels) { args.channels = GetArgument(command_line_args, help, kArgChannels, defaults.channels); }
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if (o == kArgHeight) { args.height = GetArgument(command_line_args, help, kArgHeight, defaults.height); }
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if (o == kArgWidth) { args.width = GetArgument(command_line_args, help, kArgWidth, defaults.width); }
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if (o == kArgKernelH) { args.kernel_h = GetArgument(command_line_args, help, kArgKernelH, defaults.kernel_h); }
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if (o == kArgKernelW) { args.kernel_w = GetArgument(command_line_args, help, kArgKernelW, defaults.kernel_w); }
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if (o == kArgNumKernels) { args.num_kernels = GetArgument(command_line_args, help, kArgNumKernels, defaults.num_kernels); }
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if (o == kArgAlpha) { args.alpha = GetArgument(command_line_args, help, kArgAlpha, GetScalar<T>()); }
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if (o == kArgBeta) { args.beta = GetArgument(command_line_args, help, kArgBeta, GetScalar<T>()); }
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if (o == kArgBatchCount) { args.batch_count = GetArgument(command_line_args, help, kArgBatchCount, defaults.default_batch_count); }
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}
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args.fraction = GetArgument(command_line_args, help, kArgFraction, defaults.default_fraction);
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@ -41,6 +41,13 @@ struct TunerDefaults {
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size_t default_m = 1;
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size_t default_n = 1;
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size_t default_k = 1;
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size_t channels = 1;
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size_t height = 1;
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size_t width = 1;
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size_t kernel_h = 3;
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size_t kernel_w = 3;
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size_t num_kernels = 1;
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size_t batch_count = 1;
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// Other defaults
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size_t default_batch_count = 1;
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