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https://github.com/ROCm/composable_kernel.git
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* Format * Format * Format * Remove const * Use the right template * Format * Format * add row/col instances * Add missing file * fixed * fixing block to etile error * Format * Updates * Format * fixed rrr layout * generating a sample JSON file: currently contains includes, prologue/epilogue and instances * version where the json is passed into the instances to generate a key * updated run function to just launch kernel * updated run function: only contains kernel object, json file is updated but still needs to be cleaned up, added front-end API to parse JSON into character buffer * adding in testing files * cleaned up comments, still need to work on including header files * removed unneeded files * removed/commented out JSON implementation * added fusion(prologue/epilogue) into instance generation * working on instance selection * added instance selection, need to fix instance validation * removed block2etile map validity check for testing purposes * test running: failing due to incorrect files/input * all grid descs/ptrs completed, but device file not found * Update test and embed modules * Restore older version * added convolution operation, written test, debugging generated code for compilation * attempting to include CK in host directory: _Float16 error * CK header file issues * slight fix * don't crash when hip can't report total memory * dump generated code to a file * changing sizes * creating tensor descriptors using CK methods: set up grid desc manually, also trying to set up an argument pointer - this needs to be fixed * some fixes to call the device code * separating test files for conv and gemm * completed arg ptr, now have linking errors * clang format fix * resolved linker issues in conv test * remove dependency on libutility from ck * resolved num dim error * properly passing arg ptr, errors with passing typenames: redefinition/redeclaration * undo the commenting of device function * hand created kernel code to find rtc issues * dump the full src to file * resolved redeclaration errors, cleaned up errors for Amber's kernel code * debugging purposes: redeclaration error * config files * resolved errors for NumTensor and redeclaration, formatted version.h * resolved most errors in manually added kernel and my own. error with calling kernel object: overloaded function type * WIP: close to getting kernel compiled * WIP: fixing rtc errors * fixed sequence errors, formatting, still one error with run fcn * yay: kernel compiles and runs * updated templated/generated version to run and compile * minor fixes * working generated example, resolved memory access error due to padding * adding in reference kernel, validation failing against reference * debugging: printing kernel argsz * reduced error in results * debugged reference kernel and output errors, added to generated version, currently debugging prologue function issues * working validation (using reference convolution) with prologue function for both hard-coded and generated version * WIP: create an alt version that creates Argument on the device * wip: added new duplicate files, fixed fusion templating errors from working example, setting up kernel arguments * wip: making necessary methods device code * added grid descs, working on grid pointers, errors with stl numerics * wip: updating kernel args - issue, replacing some std functions * replaced std::accumulate call with temp hardcoded version * wip: args causing memory issue * Construct Argument object inside the kernel and use it to call convolution device function. Code runs and verification passes * adding object file dump * temporary hardcoding of grid size, can remove device op inst + arg ptr * minor fix for grid size * added modified example where arg ptr is created on the device for generated version as well * removed device op instance and arg ptr from modified examples * moving device op file for testing purposes and to properly build CK * commenting out print-outs * adjust compiler args to produce a valid ELF file * temporary removal of validation * reverting compiler args back for working example * retrieve necessary arguments from generated template parameters in correct format * calculating grid size on host-side, still need to clean up process, pass parameters to host functions properly * scaled up factory functions/wrapper structs to implement host-side launch parameter calculations using CK host side functions - in hard-coded example * temporary change to generate ELF format binary object file * removed unecessary code, added comments * formatting fix * cleaned up code, added new tests, restructured library: move helper into CK * refactored launch parameter calculation to be more concise * renamed files and variables for more clarity/uniformity * more code cleaning, removed debug statements * moved majority of my files into codegen directory, running properly * updated Embed.cmake(string_view) in codegen directory * updated host directory to match Embed.cmake as well * added old tests in * updated instance generation methods to be more concise * removed layout from launch parameter calculation * working test * fixed issue with verification, all instances working * updated verification in other tests * removed duplicate matrix padder file, removed code dumps * removed old hard-coded tests * removed old host directory, all files in codegen directory now * fixed copyright in files * commenting out validation * renamed files * made changes for review: fixed copyright, renamed files for clarity, removed comments, refactored code * updated headers * removing duplicate file for fwd conv to gemm, merging with original file * fix building codegen with clang++ directly * resolving build error from conv_fwd_to_gemm * fix for previous error * renaming tests * created common test file * cleaned up code, added comments * renamed device op * fixed typos in comments * removed extra space * code cleanup: resolving Amber's comments * removed wrapper struct for matrix padder, fixed template * cleaned up if statements for better readability --------- Co-authored-by: Paul <pfultz2@yahoo.com> Co-authored-by: Jing Zhang <jizha@amd.com> Co-authored-by: M. Amber Hassaan <amber_474@yahoo.com> Co-authored-by: illsilin <Illia.Silin@amd.com> Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
191 lines
6.1 KiB
C++
191 lines
6.1 KiB
C++
#include "ck/host/device_gemm_multiple_d/problem.hpp"
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#include "ck/host/device_gemm_multiple_d/operation.hpp"
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#include "ck/host/headers.hpp"
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#include "ck/host/stringutils.hpp"
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#include "ck/host/utils.hpp"
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#include <algorithm>
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#include <cmath>
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#include <iterator>
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#include <random>
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#include <test.hpp>
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#include <rtc/compile_kernel.hpp>
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#include <rtc/hip.hpp>
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#include <fstream>
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using half = _Float16;
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// using half = __fp16;
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std::vector<rtc::src_file> get_headers_for_test()
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{
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std::vector<rtc::src_file> result;
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auto hs = ck::host::GetHeaders();
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std::transform(
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hs.begin(), hs.end(), std::back_inserter(result), [&](const auto& p) -> rtc::src_file {
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return {p.first, p.second};
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});
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return result;
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}
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template <class T>
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rtc::buffer<T> generate_buffer(std::size_t n, std::size_t seed = 0)
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{
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rtc::buffer<T> result(n);
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std::mt19937 gen(seed);
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std::uniform_real_distribution<double> dis(-1.0);
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std::generate(result.begin(), result.end(), [&] { return dis(gen); });
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return result;
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}
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template <class T, class U>
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bool allclose(const T& a, const U& b, double atol = 0.01, double rtol = 0.01)
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{
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return std::equal(a.begin(), a.end(), b.begin(), b.end(), [&](double x, double y) {
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return fabs(x - y) < atol + rtol * fabs(y);
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});
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}
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std::string classify(double x)
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{
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switch(std::fpclassify(x))
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{
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case FP_INFINITE: return "inf";
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case FP_NAN: return "nan";
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case FP_NORMAL: return "normal";
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case FP_SUBNORMAL: return "subnormal";
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case FP_ZERO: return "zero";
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default: return "unknown";
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}
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}
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template <class Buffer>
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void print_classification(const Buffer& x)
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{
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std::unordered_set<std::string> result;
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for(const auto& i : x)
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result.insert(classify(i));
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for(const auto& c : result)
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std::cout << c << ", ";
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std::cout << std::endl;
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}
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template <class Buffer>
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void print_statistics(const Buffer& x)
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{
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std::cout << "Min value: " << *std::min_element(x.begin(), x.end()) << ", ";
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std::cout << "Max value: " << *std::max_element(x.begin(), x.end()) << ", ";
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double num_elements = x.size();
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auto mean =
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std::accumulate(x.begin(), x.end(), double{0.0}, std::plus<double>{}) / num_elements;
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auto stddev = std::sqrt(
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std::accumulate(x.begin(),
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x.end(),
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double{0.0},
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[&](double r, double v) { return r + std::pow((v - mean), 2.0); }) /
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num_elements);
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std::cout << "Mean: " << mean << ", ";
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std::cout << "StdDev: " << stddev << "\n";
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}
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template <class Buffer>
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void print_preview(const Buffer& x)
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{
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if(x.size() <= 10)
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{
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std::for_each(x.begin(), x.end(), [&](double i) { std::cout << i << ", "; });
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}
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else
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{
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std::for_each(x.begin(), x.begin() + 5, [&](double i) { std::cout << i << ", "; });
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std::cout << "..., ";
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std::for_each(x.end() - 5, x.end(), [&](double i) { std::cout << i << ", "; });
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}
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std::cout << std::endl;
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}
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template <class T>
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struct check_all
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{
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rtc::buffer<T> data{};
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bool operator()(const rtc::buffer<T>& x)
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{
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if(data.empty())
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{
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data = x;
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return true;
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}
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if(std::any_of(x.begin(), x.end(), [](double y) { return std::isnan(y); }))
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return false;
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return allclose(data, x);
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}
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};
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template <class Solution>
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auto report(const Solution& solution, bool pass)
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{
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return test::make_predicate(solution.ToTemplateString(), [=] { return pass; });
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}
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const std::string gemm_compile_check = R"__ck__(
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#include <${include}>
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extern "C" __global__ void f(const ck::half_t* a, const ck::half_t* b, ck::half_t* c) {
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using G = ${template};
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constexpr auto desc = ${template}::make_descriptor(ck::make_naive_tensor_descriptor_packed(ck::make_tuple(${m}, ${k})),
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ck::make_naive_tensor_descriptor(ck::make_tuple(${n}, ${k}), ck::make_tuple(1, ${n})),
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ck::make_tuple(),
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ck::make_naive_tensor_descriptor_packed(ck::make_tuple(${m}, ${n})));
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static_assert(desc.IsValid(), "Invalid ck gemm.");
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if constexpr(desc.IsValid())
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{
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${template}::Run(desc,
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a,
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b,
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ck::make_tuple(),
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c);
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}
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}
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)__ck__";
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TEST_CASE(test_problem_kernel)
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{
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ck::host::device_gemm_multiple_d::Problem prob;
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prob.M = 1024;
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prob.N = 1024;
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prob.K = 1024;
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check_all<half> check;
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auto a = to_gpu(generate_buffer<half>(1024 * 1024, 0));
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auto b = to_gpu(generate_buffer<half>(1024 * 1024, 1));
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auto c = to_gpu(generate_buffer<half>(1024 * 1024, 2));
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std::string epilogue = "";
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std::string prologue = "";
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for(auto solution : prob.GetSolutions("gfx90a", prologue, epilogue))
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{
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auto src = ck::host::InterpolateString(gemm_compile_check,
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{{"include", prob.GetIncludeHeader()},
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{"template", solution.ToTemplateString()},
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{"m", std::to_string(prob.M)},
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{"n", std::to_string(prob.N)},
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{"k", std::to_string(prob.K)}});
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auto srcs = get_headers_for_test();
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srcs.push_back({"main.cpp", src});
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rtc::compile_options options;
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options.kernel_name = "f";
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auto k = rtc::compile_kernel(srcs, options);
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auto block_size = solution.GetTemplateParameter<std::size_t>("BlockSize");
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auto m_per_block = solution.GetTemplateParameter<std::size_t>("MPerBlock");
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auto n_per_block = solution.GetTemplateParameter<std::size_t>("NPerBlock");
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auto grid_size = ck::host::integer_divide_ceil(prob.M, m_per_block) *
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ck::host::integer_divide_ceil(prob.N, n_per_block);
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k.launch(nullptr, grid_size * block_size, block_size)(a.data(), b.data(), c.data());
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CHECK(report(solution, check(rtc::from_gpu(c))));
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}
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}
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int main(int argc, const char* argv[]) { test::run(argc, argv); }
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