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Apply Ck-tile argument parser for vectors [I/O] (#1758)

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* Parser for a vector was added. Additionaly we valid correctnes of numbers

* Remove unnecessary comments

* Review part 1

* Review part 2

* Add const to variadic lambda

* Rename C->K
This commit is contained in:
Mateusz Ozga
2024-12-19 17:55:35 +01:00
committed by GitHub
parent 453ca37347
commit e758d006a5
3 changed files with 78 additions and 22 deletions
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20
example/ck_tile/17_grouped_gemm/grouped_gemm.hpp
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@@ -34,13 +34,19 @@ using grouped_gemm_kargs = ck_tile::GroupedGemmHostArgs;
auto create_args(int argc, char* argv[])
{
ck_tile::ArgParser arg_parser;
arg_parser.insert("a_layout", "R", "A tensor data layout - Row by default")
.insert("b_layout", "R", "B tensor data layout - Row by default")
.insert("c_layout", "R", "C tensor data layout - Row by default")
.insert("validate", "1", "0. No validation, 1. Validation on CPU")
.insert("warmup", "10", "number of iterations before benchmark the kernel")
.insert("repeat", "100", "number of iterations to benchmark the kernel")
.insert("group_count", "16", "group count");
arg_parser.insert("Ms", "", "M dimensions - empty by default.")
.insert("Ns", "", "N dimensions - empty by default.")
.insert("Ks", "", "K dimensions - empty by default.")
.insert("stride_As", "", "Tensor A strides - it is empty by default.")
.insert("stride_Bs", "", "Tensor B strides - it is empty by default.")
.insert("stride_Cs", "", "Tensor C strides - it is empty by default.")
.insert("a_layout", "R", "A tensor data layout - Row by default.")
.insert("b_layout", "R", "B tensor data layout - Row by default.")
.insert("c_layout", "R", "C tensor data layout - Row by default.")
.insert("validate", "1", "0. No validation, 1. Validation on CPU.")
.insert("warmup", "10", "number of iterations before benchmark the kernel.")
.insert("repeat", "100", "number of iterations to benchmark the kernel.")
.insert("group_count", "16", "group count.");
bool result = arg_parser.parse(argc, argv);
return std::make_tuple(result, arg_parser);

34
example/ck_tile/17_grouped_gemm/run_grouped_gemm_example.inc
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@@ -53,26 +53,34 @@ int run_grouped_gemm_example_with_layouts(int argc,
return -1;
};
auto valid_input_data = [&](int group_count, const auto&... args) {
return !(args.empty() || ...) && group_count == (args.size() == ...);
};
const int group_count = arg_parser.get_int("group_count");
const int repeat = arg_parser.get_int("repeat");
const int warmup = arg_parser.get_int("warmup");
std::vector<ck_tile::index_t> Ms;
std::vector<ck_tile::index_t> Ns;
std::vector<ck_tile::index_t> Ks;
std::vector<ck_tile::index_t> stride_As;
std::vector<ck_tile::index_t> stride_Bs;
std::vector<ck_tile::index_t> stride_Cs;
std::vector<ck_tile::index_t> Ms = arg_parser.get_int_vec("Ms");
std::vector<ck_tile::index_t> Ns = arg_parser.get_int_vec("Ns");
std::vector<ck_tile::index_t> Ks = arg_parser.get_int_vec("Ks");
std::vector<ck_tile::index_t> stride_As = arg_parser.get_int_vec("stride_As");
std::vector<ck_tile::index_t> stride_Bs = arg_parser.get_int_vec("stride_Bs");
std::vector<ck_tile::index_t> stride_Cs = arg_parser.get_int_vec("stride_Cs");
for(int i = 0; i < group_count; i++)
if(!valid_input_data(group_count, Ms, Ns, Ks, stride_As, stride_Bs, stride_Cs))
{
Ms.push_back(256 + 256 * i);
Ns.push_back(128 + 128 * i);
Ks.push_back(128 + 64 * i);
std::cout << "Please check the input data. Default values will be used." << std::endl;
for(int i = 0; i < group_count; i++)
{
Ms.push_back(256 + 256 * i);
Ns.push_back(128 + 128 * i);
Ks.push_back(128 + 64 * i);
stride_As.push_back(Ks[i]);
stride_Bs.push_back(Ks[i]);
stride_Cs.push_back(Ns[i]);
stride_As.push_back(Ks[i]);
stride_Bs.push_back(Ks[i]);
stride_Cs.push_back(Ns[i]);
}
}
std::vector<ck_tile::HostTensor<ADataType>> a_m_k_tensors;