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// =================================================================================================
// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max-
// width of 100 characters per line.
//
// Author(s):
// Cedric Nugteren <www.cedricnugteren.nl>
//
// This file uses the CLTune auto-tuner to tune the direct xgemm kernels. There are two variations:
// - V==1: This tests some limited set of tuning parameters exhaustively.
// - V==2: This tests a much larger set of tuning parameters by randomly sampling a subset.
//
// =================================================================================================
#include <string>
#include <vector>
#include "utilities/utilities.hpp"
#include "tuning/tuning.hpp"
namespace clblast {
// =================================================================================================
// See comment at top of file for a description of the class
template <typename T, int V>
class TuneXgemmDirect {
public:
// Settings for this kernel (default command-line arguments)
static TunerDefaults GetTunerDefaults() {
auto settings = TunerDefaults();
settings.options = {kArgM, kArgN, kArgK, kArgAlpha, kArgBeta, kArgFraction,
kArgHeuristicSelection, kArgPsoSwarmSize,
kArgPsoInfGlobal, kArgPsoInfLocal, kArgPsoInfRandom};
settings.default_m = 256;
settings.default_n = 256;
settings.default_k = 256;
settings.default_fraction = (V==1) ? 1.0 : 32.0; // test all or sample randomly
settings.default_num_runs = 4;
settings.default_heuristic = static_cast<size_t>(cltune::SearchMethod::RandomSearch);
return settings;
}
// Settings for this kernel (general)
static TunerSettings GetTunerSettings(const Arguments<T> &args) {
auto settings = TunerSettings();
// Identification of the kernel
settings.kernel_family = (V==1) ? "xgemm_direct_1" : "xgemm_direct_2";
settings.kernel_name = "XgemmDirectTN";
settings.sources =
#include "../src/kernels/common.opencl"
#include "../src/kernels/level3/xgemm_direct_part1.opencl"
#include "../src/kernels/level3/xgemm_direct_part2.opencl"
#include "../src/kernels/level3/xgemm_direct_part3.opencl"
;
// Buffer sizes
settings.size_a = args.m * args.k;
settings.size_b = args.n * args.k;
settings.size_c = args.m * args.n;
// Sets the base thread configuration
settings.global_size = {args.m, args.n};
settings.global_size_ref = settings.global_size;
settings.local_size = {1, 1};
settings.local_size_ref = {8, 8};
// Transforms the thread configuration based on the parameters
settings.mul_local = {{"MDIMCD", "NDIMCD"}};
settings.mul_global = {{"MDIMCD", "NDIMCD"}};
settings.div_global = {{"WGD", "WGD"}};
// Sets the tuning parameters and their possible values
if (V==1) { // limited subset of tuning parameters - but explorable exhaustively
settings.parameters = {
{"WGD", {8, 16, 32}},
{"MDIMCD", {8, 16, 32}},
{"NDIMCD", {8, 16, 32}},
{"MDIMAD", {8, 16, 32}},
{"NDIMBD", {8, 16, 32}},
{"KWID", {2}},
{"VWMD", {1, 2, 4, 8}},
{"VWND", {1, 2, 4, 8}},
{"PADA", {1}},
{"PADB", {1}},
};
}
else { // a lot more tuning parameters - has to be sampled randomly, too much to test all
settings.parameters = {
{"WGD", {8, 16, 32, 64, 128}},
{"MDIMCD", {8, 16, 32}},
{"NDIMCD", {8, 16, 32}},
{"MDIMAD", {8, 16, 32}},
{"NDIMBD", {8, 16, 32}},
{"KWID", {2, 8, 16}},
{"VWMD", {1, 2, 4, 8}},
{"VWND", {1, 2, 4, 8}},
{"PADA", {0, 1}},
{"PADB", {0, 1}},
};
}
// Describes how to compute the performance metrics
settings.metric_amount = 2 * args.m * args.n * args.k;
settings.performance_unit = "GFLOPS";
// Returns which search heuristic to use
if (V==1) { settings.heuristic = static_cast<size_t>(cltune::SearchMethod::FullSearch); }
else {
// Use full-search to explore all parameter combinations or another strategy to search only a
// part of the parameter values. The fraction is set as a command-line argument.
if (args.fraction == 1.0 || args.fraction == 0.0) {
settings.heuristic = static_cast<size_t>(cltune::SearchMethod::FullSearch);
} else {
settings.heuristic = args.heuristic_selection;
}
}
return settings;
}
// Tests for valid arguments
static void TestValidArguments(const Arguments<T> &) { }
// Sets the constraints
static void SetConstraints(cltune::Tuner &tuner, const size_t id) {
auto MultipleOfX = [] (std::vector<size_t> v) { return IsMultiple(v[0], v[1]); };
auto MultipleOfXMulY = [] (std::vector<size_t> v) { return IsMultiple(v[0], v[1]*v[2]); };
auto MultipleOfXMulYDivZ = [] (std::vector<size_t> v) { return IsMultiple(v[0], (v[1]*v[2])/v[3]); };
// Requirement for unrolling the WGD loop
tuner.AddConstraint(id, MultipleOfX, {"WGD", "KWID"});
// Required for integer MWID and NWID
tuner.AddConstraint(id, MultipleOfXMulY, {"WGD", "MDIMCD", "VWMD"});
tuner.AddConstraint(id, MultipleOfXMulY, {"WGD", "NDIMCD", "VWND"});
// Required for integer MWIAD and NWIBD
tuner.AddConstraint(id, MultipleOfXMulY, {"WGD", "MDIMAD", "VWMD"});
tuner.AddConstraint(id, MultipleOfXMulY, {"WGD", "NDIMBD", "VWND"});
// WGD has to be a multiple of KDIMAD = ((MDIMCD*NDIMCD)/(MDIMAD)) and KDIMBD = (...)
tuner.AddConstraint(id, MultipleOfXMulYDivZ, {"WGD", "MDIMCD", "NDIMCD", "MDIMAD"});
tuner.AddConstraint(id, MultipleOfXMulYDivZ, {"WGD", "MDIMCD", "NDIMCD", "NDIMBD"});
// Extra constraints for variation 1 to limit the set of options significantly
if (V==1) {
auto IsEqual = [] (std::vector<size_t> v) { return v[0] == v[1]; };
tuner.AddConstraint(id, IsEqual, {"MDIMCD", "MDIMAD"});
tuner.AddConstraint(id, IsEqual, {"NDIMCD", "NDIMBD"});
}
}
// Sets the local memory size
static void SetLocalMemorySize(cltune::Tuner &tuner, const size_t id, const Arguments<T> &args) {
auto LocalMemorySize = [args] (std::vector<size_t> v) {
return ((v[0]*(v[0] + v[1]) + v[0]*(v[0] + v[2]))*GetBytes(args.precision));
};
tuner.SetLocalMemoryUsage(id, LocalMemorySize, {"WGD", "PADA", "PADB"});
}
// Sets the kernel's arguments
static void SetArguments(cltune::Tuner &tuner, const Arguments<T> &args,
std::vector<T> &, std::vector<T> &,
std::vector<T> &a_mat, std::vector<T> &b_mat, std::vector<T> &c_mat,
std::vector<T> &) {
tuner.AddArgumentScalar(static_cast<int>(args.m));
tuner.AddArgumentScalar(static_cast<int>(args.n));
tuner.AddArgumentScalar(static_cast<int>(args.k));
tuner.AddArgumentScalar(GetRealArg(args.alpha));
tuner.AddArgumentScalar(GetRealArg(args.beta));
tuner.AddArgumentInput(a_mat);
tuner.AddArgumentScalar(0); // a_offset
tuner.AddArgumentScalar(static_cast<int>(args.k)); // a_ld
tuner.AddArgumentInput(b_mat);
tuner.AddArgumentScalar(0); // b_offset
tuner.AddArgumentScalar(static_cast<int>(args.n)); // b_ld
tuner.AddArgumentOutput(c_mat);
tuner.AddArgumentScalar(0); // c_offset
tuner.AddArgumentScalar(static_cast<int>(args.n)); // c_ld
tuner.AddArgumentScalar(1); // c_do_transpose
tuner.AddArgumentScalar(0); // a_conjugate
tuner.AddArgumentScalar(0); // b_conjugate
}
};
// =================================================================================================
} // namespace clblast
// Shortcuts to the clblast namespace
using half = clblast::half;
using float2 = clblast::float2;
using double2 = clblast::double2;
// Function to tune a specific variation V (not within the clblast namespace)
template <int V>
void StartVariation(int argc, char *argv[]) {
const auto command_line_args = clblast::RetrieveCommandLineArguments(argc, argv);
switch(clblast::GetPrecision(command_line_args)) {
case clblast::Precision::kHalf: clblast::Tuner<clblast::TuneXgemmDirect<half,V>, half>(argc, argv); break;
case clblast::Precision::kSingle: clblast::Tuner<clblast::TuneXgemmDirect<float,V>, float>(argc, argv); break;
case clblast::Precision::kDouble: clblast::Tuner<clblast::TuneXgemmDirect<double,V>, double>(argc, argv); break;
case clblast::Precision::kComplexSingle: clblast::Tuner<clblast::TuneXgemmDirect<float2,V>, float2>(argc, argv); break;
case clblast::Precision::kComplexDouble: clblast::Tuner<clblast::TuneXgemmDirect<double2,V>, double2>(argc, argv); break;
}
}
// Main function (not within the clblast namespace)
int main(int argc, char *argv[]) {
StartVariation<1>(argc, argv);
StartVariation<2>(argc, argv);
return 0;
}
// =================================================================================================
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