ROCm/composable_kernel
1
0
Fork 0
mirror of https://github.com/ROCm/composable_kernel.git synced 2026-05-13 01:36:06 +00:00
Code Issues Packages Projects Releases Wiki Activity

sync with upstream

Browse Source
This commit is contained in:
carlushuang
2024-03-26 16:05:54 +00:00
parent 04ee01191a
commit 1c92c5d83d
268 changed files with 16113 additions and 2241 deletions
Download Patch File Download Diff File Expand all files Collapse all files

42
client_example/25_wrapper/wrapper_img2col.cpp
View File

@@ -15,6 +15,7 @@
#include "ck/wrapper/layout.hpp"
#include "ck/wrapper/tensor.hpp"
#include "ck/wrapper/operations/copy.hpp"
#include "ck/wrapper/utils/kernel_utils.hpp"
static constexpr ck::index_t NumDimSpatial = 3;
using DataType = float;
@@ -36,21 +37,20 @@ struct SimpleDeviceMem
void* p_mem_;
};
// Test copy from Global to Global through LDS and VGPR
template <typename InputTensor,
typename OutputTensor,
typename BlockShape,
typename ThreadLayoutShape>
__global__ void DeviceImageToColumnPad0(InputTensor input_tensor,
OutputTensor output_tensor,
const BlockShape tile_shape,
const ThreadLayoutShape thread_layout)
template <typename InputTensor, typename OutputTensor, typename BlockShape, typename ThreadLayout>
__global__ void __CK_WRAPPER_LAUNCH_BOUNDS__
DeviceImageToColumnPad0(InputTensor input_tensor,
OutputTensor output_tensor,
const BlockShape tile_shape,
const ThreadLayout thread_layout)
{
const ck::index_t block_idx = static_cast<ck::index_t>(blockIdx.x);
// grid layout (dim1, dim0)
const auto block_idxs =
ck::make_tuple(static_cast<ck::index_t>(blockIdx.y), static_cast<ck::index_t>(blockIdx.x));
// Get local tiles for global memory
auto input_local_tile = ck::wrapper::make_local_tile(input_tensor, tile_shape, block_idx);
auto output_local_tile = ck::wrapper::make_local_tile(output_tensor, tile_shape, block_idx);
auto input_local_tile = ck::wrapper::make_local_tile(input_tensor, tile_shape, block_idxs);
auto output_local_tile = ck::wrapper::make_local_tile(output_tensor, tile_shape, block_idxs);
// Get partition per thread
const auto input_local_partition =
@@ -112,9 +112,11 @@ void PerformImageToColumnPad0(const ck::index_t G,
SimpleDeviceMem out_buf(ck::wrapper::size(out_layout) * sizeof(DataType));
// User can choose appropriate number of threads and sizes per block
const auto thread_layout = ck::make_tuple(ck::Number<8>{}, ck::Number<16>{});
const auto thread_layout =
ck::wrapper::make_layout(ck::make_tuple(ck::Number<8>{}, ck::Number<16>{}),
ck::make_tuple(ck::Number<16>{}, ck::Number<1>{}));
// This example doesn't support padding, user should select tile sizes
// which divides the shape completely
// which are divisible by the shape.
const auto tile_shape = ck::make_tuple(ck::Number<32>{}, ck::Number<64>{});
// Create buffers for global memory
@@ -123,10 +125,11 @@ void PerformImageToColumnPad0(const ck::index_t G,
auto output_tensor_global = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
static_cast<DataType*>(out_buf.GetDeviceBuffer()), out_layout);
const ck::index_t grid_size = ck::math::integer_divide_ceil(ck::wrapper::size<0>(in_layout),
ck::wrapper::size<0>(tile_shape)) *
ck::math::integer_divide_ceil(ck::wrapper::size<1>(in_layout),
ck::wrapper::size<1>(tile_shape));
// grid layout (dim1, dim0)
const ck::index_t grid_size_x = ck::math::integer_divide_ceil(ck::wrapper::size<1>(in_layout),
ck::wrapper::size<1>(tile_shape));
const ck::index_t grid_size_y = ck::math::integer_divide_ceil(ck::wrapper::size<0>(in_layout),
ck::wrapper::size<0>(tile_shape));
const auto kernel = DeviceImageToColumnPad0<decltype(input_tensor_global),
decltype(output_tensor_global),
@@ -134,7 +137,7 @@ void PerformImageToColumnPad0(const ck::index_t G,
decltype(thread_layout)>;
const float avg_time = launch_and_time_kernel(StreamConfig{nullptr, true},
kernel,
dim3(grid_size),
dim3(grid_size_x, grid_size_y, 1),
dim3(ck::wrapper::size(thread_layout)),
0,
input_tensor_global,
@@ -178,3 +181,4 @@ int main(int argc, char* argv[])
{1, 1, 1} /*filter_dilations*/);
return 0;
}
// MI100 Perf: 0.255178 ms, 1698.9 GB/s,