ROCm/composable_kernel
1
0
Fork 0
mirror of https://github.com/ROCm/composable_kernel.git synced 2026-04-19 22:39:03 +00:00
Code Issues Packages Projects Releases Wiki Activity

[CK_TILE] fix formatting of pooling in ckTileEngine with clang-format

Browse Source
This commit is contained in:
Aleksander Dudek
2025-12-16 10:40:41 +00:00
parent d5cef00770
commit 1bccd37e06
3 changed files with 102 additions and 114 deletions
Download Patch File Download Diff File Expand all files Collapse all files

87
tile_engine/ops/pooling/pool_benchmark_single.cpp
View File

@@ -24,8 +24,7 @@
inline auto create_args(int argc, char* argv[])
{
ck_tile::ArgParser arg_parser;
arg_parser
.insert("N", "2", "Batch size N dimension. Default is 2.")
arg_parser.insert("N", "2", "Batch size N dimension. Default is 2.")
.insert("D", "30", "Depth D dimension (for 3D pooling). Default is 30.")
.insert("H", "30", "Height H dimension. Default is 30.")
.insert("W", "30", "Width W dimension. Default is 30.")
@@ -49,15 +48,15 @@ inline auto create_args(int argc, char* argv[])
"0",
"The type of validation. Set to 0 for no validation, 1 for validation on CPU. "
"Default is 0.")
.insert("log",
"false",
"Whether output kernel instance information or not. Default is false")
.insert(
"log", "false", "Whether output kernel instance information or not. Default is false")
.insert("warmup", "20", "The number of warmup iterations. Default is 20.")
.insert("repeat", "100", "The number of benchmark iterations. Default is 100.")
.insert("timer", "true", "Whether to use GPU timer. Default is true.")
.insert("init",
"0",
"The method of tensor initialization. 0=random, 1=linear, 2=constant(1). Default is 0.")
.insert(
"init",
"0",
"The method of tensor initialization. 0=random, 1=linear, 2=constant(1). Default is 0.")
.insert("json_output",
"false",
"Whether to output results in JSON format only. Default is false");
@@ -127,18 +126,20 @@ void run_benchmark(const ck_tile::ArgParser& arg_parser)
}
// Create shapes using ck_tile::make_tuple
const auto input_shape = ck_tile::make_tuple(N, D, H, W, C);
const auto output_shape = ck_tile::make_tuple(N, Do, Ho, Wo, C);
const auto input_strides = ck_tile::make_tuple(D * H * W * C, H * W * C, W * C, C, 1);
const auto output_strides = ck_tile::make_tuple(Do * Ho * Wo * C, Ho * Wo * C, Wo * C, C, 1);
const auto window_lengths = ck_tile::make_tuple(Z, Y, X);
const auto window_strides = ck_tile::make_tuple(Sz, Sy, Sx);
const auto input_shape = ck_tile::make_tuple(N, D, H, W, C);
const auto output_shape = ck_tile::make_tuple(N, Do, Ho, Wo, C);
const auto input_strides = ck_tile::make_tuple(D * H * W * C, H * W * C, W * C, C, 1);
const auto output_strides =
ck_tile::make_tuple(Do * Ho * Wo * C, Ho * Wo * C, Wo * C, C, 1);
const auto window_lengths = ck_tile::make_tuple(Z, Y, X);
const auto window_strides = ck_tile::make_tuple(Sz, Sy, Sx);
const auto window_dilations = ck_tile::make_tuple(Dz, Dy, Dx);
const auto input_left_pads = ck_tile::make_tuple(LeftPz, LeftPy, LeftPx);
const auto input_right_pads = ck_tile::make_tuple(RightPz, RightPy, RightPx);
// Allocate host tensors
ck_tile::HostTensor<InDataType> in({N, D, H, W, C}, {D * H * W * C, H * W * C, W * C, C, 1});
ck_tile::HostTensor<InDataType> in({N, D, H, W, C},
{D * H * W * C, H * W * C, W * C, C, 1});
ck_tile::HostTensor<OutDataType> out({N, Do, Ho, Wo, C},
{Do * Ho * Wo * C, Ho * Wo * C, Wo * C, C, 1});
ck_tile::HostTensor<IndexDataType> out_index(
@@ -172,21 +173,19 @@ void run_benchmark(const ck_tile::ArgParser& arg_parser)
in_buf.ToDevice(in.data());
// Create host arguments
auto host_args =
ck_tile::PoolHostArgs<decltype(input_shape), decltype(window_lengths)>{
static_cast<InDataType*>(in_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_buf.GetDeviceBuffer()),
OUTPUT_INDEX ? static_cast<IndexDataType*>(out_index_buf.GetDeviceBuffer())
: nullptr,
input_shape,
output_shape,
input_strides,
output_strides,
window_lengths,
window_strides,
window_dilations,
input_left_pads,
input_right_pads};
auto host_args = ck_tile::PoolHostArgs<decltype(input_shape), decltype(window_lengths)>{
static_cast<InDataType*>(in_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_buf.GetDeviceBuffer()),
OUTPUT_INDEX ? static_cast<IndexDataType*>(out_index_buf.GetDeviceBuffer()) : nullptr,
input_shape,
output_shape,
input_strides,
output_strides,
window_lengths,
window_strides,
window_dilations,
input_left_pads,
input_right_pads};
auto kernel_args = Kernel::MakeKernelArgs(host_args);
@@ -335,21 +334,19 @@ void run_benchmark(const ck_tile::ArgParser& arg_parser)
in_buf.ToDevice(in.data());
auto host_args =
ck_tile::PoolHostArgs<decltype(input_shape), decltype(window_lengths)>{
static_cast<InDataType*>(in_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_buf.GetDeviceBuffer()),
OUTPUT_INDEX ? static_cast<IndexDataType*>(out_index_buf.GetDeviceBuffer())
: nullptr,
input_shape,
output_shape,
input_strides,
output_strides,
window_lengths,
window_strides,
window_dilations,
input_left_pads,
input_right_pads};
auto host_args = ck_tile::PoolHostArgs<decltype(input_shape), decltype(window_lengths)>{
static_cast<InDataType*>(in_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_buf.GetDeviceBuffer()),
OUTPUT_INDEX ? static_cast<IndexDataType*>(out_index_buf.GetDeviceBuffer()) : nullptr,
input_shape,
output_shape,
input_strides,
output_strides,
window_lengths,
window_strides,
window_dilations,
input_left_pads,
input_right_pads};
auto kernel_args = Kernel::MakeKernelArgs(host_args);