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Commit 0f1e8187 authored by Jing Zhang's avatar Jing Zhang
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Merge remote-tracking branch 'origin/develop' into simple_gemm_dl

parents e5863fd6 b0568b72
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include/ck/tensor_operation/gpu/device/impl/device_column_to_image_impl.hpp
View file @ 0f1e8187
...@@ -17,15 +17,18 @@ ...@@ -17,15 +17,18 @@
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp" #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/conv_tensor_rearrange_op.hpp" #include "ck/tensor_operation/gpu/device/conv_tensor_rearrange_op.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_utils.hpp"
#include "ck/host_utility/io.hpp" #include "ck/host_utility/io.hpp"
namespace ck { namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace device { namespace device {
// Image to column for input layout NDHWC: // Column to Image:
// input : image converted to the gemm problem [N * Do * Ho * Wo, Z * Y * X * C] // input : gemm form [G, N * Do * Ho * Wo, Z * Y * X * C]
// output : image [N, Di, Hi, Wi, C] // output : input image [G, N, Di, Hi, Wi, C]
// input : gemm form [N * Do * Ho * Wo, G, Z * Y * X * C]
// output : input image [N, Di, Hi, Wi, G, C]
template <index_t NDimSpatial, template <index_t NDimSpatial,
typename ImageLayout, typename ImageLayout,
typename InputDataType, typename InputDataType,
...@@ -43,6 +46,14 @@ struct DeviceColumnToImageImpl ...@@ -43,6 +46,14 @@ struct DeviceColumnToImageImpl
OutputDataType, OutputDataType,
conv_tensor_rearrange_op::ColumnToImage> conv_tensor_rearrange_op::ColumnToImage>
{ {
static constexpr bool is_NSpatialGC =
std::is_same_v<ImageLayout, tensor_layout::convolution::NWGC> ||
std::is_same_v<ImageLayout, tensor_layout::convolution::NHWGC> ||
std::is_same_v<ImageLayout, tensor_layout::convolution::NDHWGC>;
static constexpr bool is_GNSpatialC =
std::is_same_v<ImageLayout, tensor_layout::convolution::GNWC> ||
std::is_same_v<ImageLayout, tensor_layout::convolution::GNHWC> ||
std::is_same_v<ImageLayout, tensor_layout::convolution::GNDHWC>;
static constexpr auto I0 = Number<0>{}; static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{}; static constexpr auto I1 = Number<1>{};
...@@ -90,7 +101,7 @@ struct DeviceColumnToImageImpl ...@@ -90,7 +101,7 @@ struct DeviceColumnToImageImpl
const std::array<index_t, NDimSpatial>& filter_spatial_lengths, const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
const std::array<index_t, NDimSpatial>& output_spatial_lengths, const std::array<index_t, NDimSpatial>& output_spatial_lengths,
const std::array<index_t, NDimSpatial>& conv_filter_strides, const std::array<index_t, NDimSpatial>& conv_filter_strides,
const std::array<index_t, 2>& gemm_m_k_strides, const std::array<index_t, 3>& gemm_g_m_k_strides,
const std::array<index_t, NDimSpatial>& independent_filters, const std::array<index_t, NDimSpatial>& independent_filters,
const std::array<index_t, NDimSpatial>& effs) const std::array<index_t, NDimSpatial>& effs)
{ {
...@@ -100,23 +111,23 @@ struct DeviceColumnToImageImpl ...@@ -100,23 +111,23 @@ struct DeviceColumnToImageImpl
C * ck::accumulate_n<index_t>( C * ck::accumulate_n<index_t>(
filter_spatial_lengths.begin(), NDimSpatial, 1, std::multiplies<>()); filter_spatial_lengths.begin(), NDimSpatial, 1, std::multiplies<>());
const index_t NStride = DoHoWo * gemm_m_k_strides[I0] * gemm_m_k_strides[I1]; const index_t NStride = DoHoWo * gemm_g_m_k_strides[I1] * gemm_g_m_k_strides[I2];
// Calculate the appropriate stride for each set of independent filters // Calculate the appropriate stride for each set of independent filters
// in each dimension // in each dimension
const index_t WStride = const index_t WStride = math::integer_divide_ceil(effs[XIdx], conv_filter_strides[XIdx]) *
math::integer_divide_ceil(effs[XIdx], conv_filter_strides[XIdx]) * gemm_m_k_strides[I0]; gemm_g_m_k_strides[I1];
const index_t HStride = math::integer_divide_ceil(effs[YIdx], conv_filter_strides[YIdx]) * const index_t HStride = math::integer_divide_ceil(effs[YIdx], conv_filter_strides[YIdx]) *
output_spatial_lengths[XIdx] * gemm_m_k_strides[I0]; output_spatial_lengths[XIdx] * gemm_g_m_k_strides[I1];
const index_t DStride = math::integer_divide_ceil(effs[ZIdx], conv_filter_strides[ZIdx]) * const index_t DStride = math::integer_divide_ceil(effs[ZIdx], conv_filter_strides[ZIdx]) *
output_spatial_lengths[YIdx] * output_spatial_lengths[XIdx] * output_spatial_lengths[YIdx] * output_spatial_lengths[XIdx] *
gemm_m_k_strides[I0]; gemm_g_m_k_strides[I1];
// Create descriptor for independent filters in each dimension and // Create descriptor for independent filters in each dimension and
// then merge them into column form // then merge them into column form
if constexpr(NDimSpatial == 1) if constexpr(NDimSpatial == 1)
{ {
const auto desc_gemm_form = const auto desc_gemm_form =
make_naive_tensor_descriptor(make_tuple(N, independent_filters[XIdx], CZYX), make_naive_tensor_descriptor(make_tuple(N, independent_filters[XIdx], CZYX),
make_tuple(NStride, WStride, gemm_m_k_strides[I1])); make_tuple(NStride, WStride, gemm_g_m_k_strides[I2]));
const auto desc_gemm_form_merged_filters = transform_tensor_descriptor( const auto desc_gemm_form_merged_filters = transform_tensor_descriptor(
desc_gemm_form, desc_gemm_form,
make_tuple(make_merge_transform(make_tuple(N, independent_filters[XIdx])), make_tuple(make_merge_transform(make_tuple(N, independent_filters[XIdx])),
...@@ -130,7 +141,7 @@ struct DeviceColumnToImageImpl ...@@ -130,7 +141,7 @@ struct DeviceColumnToImageImpl
{ {
const auto desc_gemm_form = make_naive_tensor_descriptor( const auto desc_gemm_form = make_naive_tensor_descriptor(
make_tuple(N, independent_filters[YIdx], independent_filters[XIdx], CZYX), make_tuple(N, independent_filters[YIdx], independent_filters[XIdx], CZYX),
make_tuple(NStride, HStride, WStride, gemm_m_k_strides[I1])); make_tuple(NStride, HStride, WStride, gemm_g_m_k_strides[I2]));
const auto desc_gemm_form_merged_filters = transform_tensor_descriptor( const auto desc_gemm_form_merged_filters = transform_tensor_descriptor(
desc_gemm_form, desc_gemm_form,
make_tuple(make_merge_transform( make_tuple(make_merge_transform(
...@@ -149,7 +160,7 @@ struct DeviceColumnToImageImpl ...@@ -149,7 +160,7 @@ struct DeviceColumnToImageImpl
independent_filters[YIdx], independent_filters[YIdx],
independent_filters[XIdx], independent_filters[XIdx],
CZYX), CZYX),
make_tuple(NStride, DStride, HStride, WStride, gemm_m_k_strides[I1])); make_tuple(NStride, DStride, HStride, WStride, gemm_g_m_k_strides[I2]));
const auto desc_gemm_form_merged_filters = transform_tensor_descriptor( const auto desc_gemm_form_merged_filters = transform_tensor_descriptor(
desc_gemm_form, desc_gemm_form,
make_tuple(make_merge_transform(make_tuple(N, make_tuple(make_merge_transform(make_tuple(N,
...@@ -252,34 +263,38 @@ struct DeviceColumnToImageImpl ...@@ -252,34 +263,38 @@ struct DeviceColumnToImageImpl
decltype(BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, KPerBlock, InputGridDesc>( decltype(BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, KPerBlock, InputGridDesc>(
InputGridDesc{}))>; InputGridDesc{}))>;
using GridwiseTensorRearrangeKernel = GridwiseTensorRearrange<InputGridDesc, using GridwiseTensorRearrangeKernel =
InputDataType, GridwiseTensorRearrange<InputGridDesc,
OutputGridDesc, InputDataType,
OutputDataType, OutputGridDesc,
BlockSize, OutputDataType,
MPerBlock, BlockSize,
KPerBlock, MPerBlock,
ThreadClusterLengths, KPerBlock,
ScalarPerVector, ThreadClusterLengths,
InMemoryDataOperationEnum::Add, ScalarPerVector,
Block2ETileMap>; InMemoryDataOperationEnum::Add,
Block2ETileMap,
ComputePtrOffsetOfStridedBatch<I0>>;
struct Argument : public BaseArgument struct Argument : public BaseArgument
{ {
Argument(const void* p_in, // input image Argument(const void* p_in, // input image
void* p_out, // output image void* p_out, // output image
const ck::index_t G,
const ck::index_t N, const ck::index_t N,
const ck::index_t C, const ck::index_t C,
const std::array<index_t, NDimSpatial>& input_spatial_lengths, const std::array<index_t, NDimSpatial>& input_spatial_lengths,
const std::array<index_t, NDimSpatial>& filter_spatial_lengths, const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
const std::array<index_t, NDimSpatial>& output_spatial_lengths, const std::array<index_t, NDimSpatial>& output_spatial_lengths,
const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides, const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides,
const std::array<index_t, 2>& gemm_m_k_strides, const std::array<index_t, 3>& gemm_g_m_k_strides,
const std::array<index_t, NDimSpatial>& conv_filter_strides, const std::array<index_t, NDimSpatial>& conv_filter_strides,
const std::array<index_t, NDimSpatial>& conv_filter_dilations, const std::array<index_t, NDimSpatial>& conv_filter_dilations,
const std::array<index_t, NDimSpatial>& input_left_pads, const std::array<index_t, NDimSpatial>& input_left_pads,
const std::array<index_t, NDimSpatial>& input_right_pads) const std::array<index_t, NDimSpatial>& input_right_pads)
: C_(C), : G_(G),
C_(C),
X_(filter_spatial_lengths[NDimSpatial - I1]), X_(filter_spatial_lengths[NDimSpatial - I1]),
p_in_{static_cast<const InputDataType*>(p_in)}, p_in_{static_cast<const InputDataType*>(p_in)},
p_out_{static_cast<OutputDataType*>(p_out)}, p_out_{static_cast<OutputDataType*>(p_out)},
...@@ -289,6 +304,9 @@ struct DeviceColumnToImageImpl ...@@ -289,6 +304,9 @@ struct DeviceColumnToImageImpl
input_left_pads_{input_left_pads}, input_left_pads_{input_left_pads},
input_right_pads_{input_right_pads} input_right_pads_{input_right_pads}
{ {
compute_ptr_offset_of_batch_.BatchStrideA_ = gemm_g_m_k_strides[I0];
compute_ptr_offset_of_batch_.BatchStrideC_ = image_g_n_c_wis_strides[I0];
const index_t x_eff = const index_t x_eff =
(filter_spatial_lengths[XIdx] - 1) * conv_filter_dilations[XIdx] + 1; (filter_spatial_lengths[XIdx] - 1) * conv_filter_dilations[XIdx] + 1;
const index_t y_eff = const index_t y_eff =
...@@ -354,7 +372,7 @@ struct DeviceColumnToImageImpl ...@@ -354,7 +372,7 @@ struct DeviceColumnToImageImpl
filter_spatial_lengths, filter_spatial_lengths,
output_spatial_lengths, output_spatial_lengths,
conv_filter_strides, conv_filter_strides,
gemm_m_k_strides, gemm_g_m_k_strides,
independent_filters, independent_filters,
effs); effs);
const auto out_grid_desc_m_k = const auto out_grid_desc_m_k =
...@@ -387,10 +405,9 @@ struct DeviceColumnToImageImpl ...@@ -387,10 +405,9 @@ struct DeviceColumnToImageImpl
// Memory offsets to next set of independent filters, // Memory offsets to next set of independent filters,
// move to independent filters in each dimension // move to independent filters in each dimension
const index_t in_offset = const index_t in_offset =
x_idx * gemm_m_k_strides[0] + (x_idx + y_idx * output_spatial_lengths[XIdx] +
y_idx * gemm_m_k_strides[0] * output_spatial_lengths[XIdx] + z_idx * output_spatial_lengths[YIdx] * output_spatial_lengths[XIdx]) *
z_idx * gemm_m_k_strides[0] * output_spatial_lengths[YIdx] * gemm_g_m_k_strides[I1];
output_spatial_lengths[XIdx];
// Move to independent filters in appropriate dimensions // Move to independent filters in appropriate dimensions
const index_t out_offset = const index_t out_offset =
x_offset_with_pad * image_g_n_c_wis_strides[spatial_offset + XIdx] + x_offset_with_pad * image_g_n_c_wis_strides[spatial_offset + XIdx] +
...@@ -417,6 +434,7 @@ struct DeviceColumnToImageImpl ...@@ -417,6 +434,7 @@ struct DeviceColumnToImageImpl
} }
} }
const ck::index_t G_;
const ck::index_t C_; const ck::index_t C_;
const ck::index_t X_; const ck::index_t X_;
...@@ -434,6 +452,8 @@ struct DeviceColumnToImageImpl ...@@ -434,6 +452,8 @@ struct DeviceColumnToImageImpl
std::vector<const InputDataType*> p_in_container_; std::vector<const InputDataType*> p_in_container_;
std::vector<OutputDataType*> p_out_container_; std::vector<OutputDataType*> p_out_container_;
ComputePtrOffsetOfStridedBatch<I0> compute_ptr_offset_of_batch_;
}; };
struct Invoker : public BaseInvoker struct Invoker : public BaseInvoker
...@@ -451,6 +471,7 @@ struct DeviceColumnToImageImpl ...@@ -451,6 +471,7 @@ struct DeviceColumnToImageImpl
OutputGridDesc, OutputGridDesc,
OutputDataType, OutputDataType,
Block2ETileMap, Block2ETileMap,
ComputePtrOffsetOfStridedBatch<I0>,
GridwiseTensorRearrangeKernel>; GridwiseTensorRearrangeKernel>;
// Execute each set of independent filters // Execute each set of independent filters
...@@ -460,7 +481,7 @@ struct DeviceColumnToImageImpl ...@@ -460,7 +481,7 @@ struct DeviceColumnToImageImpl
BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, KPerBlock, InputGridDesc>( BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, KPerBlock, InputGridDesc>(
arg.out_grid_desc_m_k_container_[i]); arg.out_grid_desc_m_k_container_[i]);
const index_t grid_size = const index_t grid_size =
block_2_tile_map.CalculateGridSize(arg.in_grid_desc_m_k_container_[i]); block_2_tile_map.CalculateGridSize(arg.in_grid_desc_m_k_container_[i]) * arg.G_;
elapsed_time += launch_and_time_kernel(stream_config, elapsed_time += launch_and_time_kernel(stream_config,
kernel, kernel,
dim3(grid_size), dim3(grid_size),
...@@ -470,7 +491,9 @@ struct DeviceColumnToImageImpl ...@@ -470,7 +491,9 @@ struct DeviceColumnToImageImpl
arg.p_in_container_[i], arg.p_in_container_[i],
arg.out_grid_desc_m_k_container_[i], arg.out_grid_desc_m_k_container_[i],
arg.p_out_container_[i], arg.p_out_container_[i],
block_2_tile_map); arg.G_,
block_2_tile_map,
arg.compute_ptr_offset_of_batch_);
} }
return elapsed_time; return elapsed_time;
} }
...@@ -485,8 +508,7 @@ struct DeviceColumnToImageImpl ...@@ -485,8 +508,7 @@ struct DeviceColumnToImageImpl
bool IsSupportedArgument(const Argument& arg) bool IsSupportedArgument(const Argument& arg)
{ {
using namespace tensor_layout::convolution; using namespace tensor_layout::convolution;
if constexpr(!(std::is_same_v<ImageLayout, GNWC> || std::is_same_v<ImageLayout, GNHWC> || if constexpr(!(is_NSpatialGC || is_GNSpatialC))
std::is_same_v<ImageLayout, GNDHWC>))
{ {
return false; return false;
} }
...@@ -534,13 +556,14 @@ struct DeviceColumnToImageImpl ...@@ -534,13 +556,14 @@ struct DeviceColumnToImageImpl
static auto MakeArgument(const void* p_in, // input image static auto MakeArgument(const void* p_in, // input image
void* p_out, // output image void* p_out, // output image
const ck::index_t G,
const ck::index_t N, const ck::index_t N,
const ck::index_t C, const ck::index_t C,
const std::array<index_t, NDimSpatial>& input_spatial_lengths, const std::array<index_t, NDimSpatial>& input_spatial_lengths,
const std::array<index_t, NDimSpatial>& filter_spatial_lengths, const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
const std::array<index_t, NDimSpatial>& output_spatial_lengths, const std::array<index_t, NDimSpatial>& output_spatial_lengths,
const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides, const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides,
const std::array<index_t, 2>& gemm_m_k_strides, const std::array<index_t, 3>& gemm_g_m_k_strides,
const std::array<index_t, NDimSpatial>& conv_filter_strides, const std::array<index_t, NDimSpatial>& conv_filter_strides,
const std::array<index_t, NDimSpatial>& conv_filter_dilations, const std::array<index_t, NDimSpatial>& conv_filter_dilations,
const std::array<index_t, NDimSpatial>& input_left_pads, const std::array<index_t, NDimSpatial>& input_left_pads,
...@@ -548,13 +571,14 @@ struct DeviceColumnToImageImpl ...@@ -548,13 +571,14 @@ struct DeviceColumnToImageImpl
{ {
return Argument{static_cast<const InputDataType*>(p_in), return Argument{static_cast<const InputDataType*>(p_in),
static_cast<OutputDataType*>(p_out), static_cast<OutputDataType*>(p_out),
G,
N, N,
C, C,
input_spatial_lengths, input_spatial_lengths,
filter_spatial_lengths, filter_spatial_lengths,
output_spatial_lengths, output_spatial_lengths,
image_g_n_c_wis_strides, image_g_n_c_wis_strides,
gemm_m_k_strides, gemm_g_m_k_strides,
conv_filter_strides, conv_filter_strides,
conv_filter_dilations, conv_filter_dilations,
input_left_pads, input_left_pads,
...@@ -566,13 +590,14 @@ struct DeviceColumnToImageImpl ...@@ -566,13 +590,14 @@ struct DeviceColumnToImageImpl
std::unique_ptr<BaseArgument> std::unique_ptr<BaseArgument>
MakeArgumentPointer(const void* p_in, // input image MakeArgumentPointer(const void* p_in, // input image
void* p_out, // output image void* p_out, // output image
const ck::index_t G,
const ck::index_t N, const ck::index_t N,
const ck::index_t C, const ck::index_t C,
const std::array<index_t, NDimSpatial>& input_spatial_lengths, const std::array<index_t, NDimSpatial>& input_spatial_lengths,
const std::array<index_t, NDimSpatial>& filter_spatial_lengths, const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
const std::array<index_t, NDimSpatial>& output_spatial_lengths, const std::array<index_t, NDimSpatial>& output_spatial_lengths,
const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides, const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides,
const std::array<index_t, 2>& gemm_m_k_strides, const std::array<index_t, 3>& gemm_g_m_k_strides,
const std::array<index_t, NDimSpatial>& conv_filter_strides, const std::array<index_t, NDimSpatial>& conv_filter_strides,
const std::array<index_t, NDimSpatial>& conv_filter_dilations, const std::array<index_t, NDimSpatial>& conv_filter_dilations,
const std::array<index_t, NDimSpatial>& input_left_pads, const std::array<index_t, NDimSpatial>& input_left_pads,
...@@ -580,13 +605,14 @@ struct DeviceColumnToImageImpl ...@@ -580,13 +605,14 @@ struct DeviceColumnToImageImpl
{ {
return std::make_unique<Argument>(static_cast<const InputDataType*>(p_in), return std::make_unique<Argument>(static_cast<const InputDataType*>(p_in),
static_cast<OutputDataType*>(p_out), static_cast<OutputDataType*>(p_out),
G,
N, N,
C, C,
input_spatial_lengths, input_spatial_lengths,
filter_spatial_lengths, filter_spatial_lengths,
output_spatial_lengths, output_spatial_lengths,
image_g_n_c_wis_strides, image_g_n_c_wis_strides,
gemm_m_k_strides, gemm_g_m_k_strides,
conv_filter_strides, conv_filter_strides,
conv_filter_dilations, conv_filter_dilations,
input_left_pads, input_left_pads,
......
include/ck/tensor_operation/gpu/device/impl/device_contraction_multiple_d_xdl_cshuffle.hpp
View file @ 0f1e8187
...@@ -145,7 +145,8 @@ template <index_t NumDimM, ...@@ -145,7 +145,8 @@ template <index_t NumDimM,
index_t CShuffleNXdlPerWavePerShuffle, index_t CShuffleNXdlPerWavePerShuffle,
typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock, typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
index_t CDEBlockTransferScalarPerVector_NPerBlock, index_t CDEBlockTransferScalarPerVector_NPerBlock,
LoopScheduler LoopSched = make_default_loop_scheduler()> typename ComputeDataType = ADataType,
LoopScheduler LoopSched = make_default_loop_scheduler()>
struct DeviceContractionMultipleD_Xdl_CShuffle struct DeviceContractionMultipleD_Xdl_CShuffle
: public DeviceContractionMultipleD<NumDimM, : public DeviceContractionMultipleD<NumDimM,
NumDimN, NumDimN,
...@@ -156,7 +157,8 @@ struct DeviceContractionMultipleD_Xdl_CShuffle ...@@ -156,7 +157,8 @@ struct DeviceContractionMultipleD_Xdl_CShuffle
EDataType, EDataType,
AElementwiseOperation, AElementwiseOperation,
BElementwiseOperation, BElementwiseOperation,
CDEElementwiseOperation> CDEElementwiseOperation,
ComputeDataType>
{ {
using DeviceOp = DeviceContractionMultipleD_Xdl_CShuffle; using DeviceOp = DeviceContractionMultipleD_Xdl_CShuffle;
...@@ -310,8 +312,6 @@ struct DeviceContractionMultipleD_Xdl_CShuffle ...@@ -310,8 +312,6 @@ struct DeviceContractionMultipleD_Xdl_CShuffle
using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({{}}, {{}}))>; using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({{}}, {{}}))>;
using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N({}, {})); using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N({}, {}));
using ComputeDataType = ADataType;
// GridwiseGemm // GridwiseGemm
using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle< using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle<
ADataType, // TODO: distinguish A/B datatype ADataType, // TODO: distinguish A/B datatype
......
include/ck/tensor_operation/gpu/device/impl/device_gemm_xdl.hpp
View file @ 0f1e8187
...@@ -184,7 +184,8 @@ struct DeviceGemmXdl : public DeviceGemm<ALayout, ...@@ -184,7 +184,8 @@ struct DeviceGemmXdl : public DeviceGemm<ALayout,
return false; return false;
} }
} }
else if(ck::get_device_name() == "gfx90a" || ck::get_device_name() == "gfx940") else if(ck::get_device_name() == "gfx90a" || ck::get_device_name() == "gfx940" ||
ck::get_device_name() == "gfx941" || ck::get_device_name() == "gfx942")
{ {
if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, float> || if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, float> ||
is_same_v<AccDataType, int32_t> || is_same_v<AccDataType, double>)) is_same_v<AccDataType, int32_t> || is_same_v<AccDataType, double>))
......
include/ck/tensor_operation/gpu/device/impl/device_image_to_column_impl.hpp
View file @ 0f1e8187
...@@ -15,15 +15,18 @@ ...@@ -15,15 +15,18 @@
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp" #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/conv_tensor_rearrange_op.hpp" #include "ck/tensor_operation/gpu/device/conv_tensor_rearrange_op.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_utils.hpp"
#include "ck/host_utility/io.hpp" #include "ck/host_utility/io.hpp"
namespace ck { namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace device { namespace device {
// Image to column for input layout NDHWC: // Image to column:
// input : input image [N, Di, Hi, Wi, C] // input : input image [G, N, Di, Hi, Wi, C]
// output : gemm form [N * Do * Ho * Wo, Z * Y * X * C] // output : gemm form [G * N * Do * Ho * Wo, Z * Y * X * C]
// input : input image [N, Di, Hi, Wi, G, C]
// output : gemm form [N * Do * Ho * Wo * G, Z * Y * X * C]
template <index_t NDimSpatial, template <index_t NDimSpatial,
typename ImageLayout, typename ImageLayout,
typename InputDataType, typename InputDataType,
...@@ -41,6 +44,14 @@ struct DeviceImageToColumnImpl ...@@ -41,6 +44,14 @@ struct DeviceImageToColumnImpl
OutputDataType, OutputDataType,
conv_tensor_rearrange_op::ImageToColumn> conv_tensor_rearrange_op::ImageToColumn>
{ {
static constexpr bool is_NSpatialGC =
std::is_same_v<ImageLayout, tensor_layout::convolution::NWGC> ||
std::is_same_v<ImageLayout, tensor_layout::convolution::NHWGC> ||
std::is_same_v<ImageLayout, tensor_layout::convolution::NDHWGC>;
static constexpr bool is_GNSpatialC =
std::is_same_v<ImageLayout, tensor_layout::convolution::GNWC> ||
std::is_same_v<ImageLayout, tensor_layout::convolution::GNHWC> ||
std::is_same_v<ImageLayout, tensor_layout::convolution::GNDHWC>;
static constexpr auto I0 = Number<0>{}; static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{}; static constexpr auto I1 = Number<1>{};
...@@ -109,7 +120,7 @@ struct DeviceImageToColumnImpl ...@@ -109,7 +120,7 @@ struct DeviceImageToColumnImpl
const ck::index_t C, const ck::index_t C,
const std::array<index_t, NDimSpatial>& filter_spatial_lengths, const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
const std::array<index_t, NDimSpatial>& output_spatial_lengths, const std::array<index_t, NDimSpatial>& output_spatial_lengths,
const std::array<index_t, 2>& gemm_m_k_strides) const std::array<index_t, 3>& gemm_g_m_k_strides)
{ {
const index_t NDoHoWo = const index_t NDoHoWo =
N * ck::accumulate_n<index_t>( N * ck::accumulate_n<index_t>(
...@@ -117,11 +128,10 @@ struct DeviceImageToColumnImpl ...@@ -117,11 +128,10 @@ struct DeviceImageToColumnImpl
const index_t CZYX = const index_t CZYX =
C * ck::accumulate_n<index_t>( C * ck::accumulate_n<index_t>(
filter_spatial_lengths.begin(), NDimSpatial, 1, std::multiplies<>()); filter_spatial_lengths.begin(), NDimSpatial, 1, std::multiplies<>());
const auto desc_mraw_kraw = make_naive_tensor_descriptor(
make_tuple(NDoHoWo, CZYX), make_tuple(gemm_m_k_strides[I0], gemm_m_k_strides[I1]));
const auto desc_m_k = matrix_padder.PadADescriptor_M_K(desc_mraw_kraw); const auto desc_mraw_kraw = make_naive_tensor_descriptor(
return desc_m_k; make_tuple(NDoHoWo, CZYX), make_tuple(gemm_g_m_k_strides[I1], gemm_g_m_k_strides[I2]));
return matrix_padder.PadADescriptor_M_K(desc_mraw_kraw);
} }
using InputGridDesc = using InputGridDesc =
...@@ -132,34 +142,38 @@ struct DeviceImageToColumnImpl ...@@ -132,34 +142,38 @@ struct DeviceImageToColumnImpl
decltype(BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, KPerBlock, OutputGridDesc>( decltype(BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, KPerBlock, OutputGridDesc>(
OutputGridDesc{}))>; OutputGridDesc{}))>;
using GridwiseTensorRearrangeKernel = GridwiseTensorRearrange<InputGridDesc, using GridwiseTensorRearrangeKernel =
InputDataType, GridwiseTensorRearrange<InputGridDesc,
OutputGridDesc, InputDataType,
OutputDataType, OutputGridDesc,
BlockSize, OutputDataType,
MPerBlock, BlockSize,
KPerBlock, MPerBlock,
ThreadClusterLengths, KPerBlock,
ScalarPerVector, ThreadClusterLengths,
InMemoryDataOperationEnum::Set, ScalarPerVector,
Block2ETileMap>; InMemoryDataOperationEnum::Set,
Block2ETileMap,
ComputePtrOffsetOfStridedBatch<I0>>;
struct Argument : public BaseArgument struct Argument : public BaseArgument
{ {
Argument(const void* p_in, // input image Argument(const void* p_in, // input image
void* p_out, // gemm form void* p_out, // gemm form
const ck::index_t G,
const ck::index_t N, const ck::index_t N,
const ck::index_t C, const ck::index_t C,
const std::array<index_t, NDimSpatial>& input_spatial_lengths, const std::array<index_t, NDimSpatial>& input_spatial_lengths,
const std::array<index_t, NDimSpatial>& filter_spatial_lengths, const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
const std::array<index_t, NDimSpatial>& output_spatial_lengths, const std::array<index_t, NDimSpatial>& output_spatial_lengths,
const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides, const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides,
const std::array<index_t, 2>& gemm_m_k_strides, const std::array<index_t, 3>& gemm_g_m_k_strides,
const std::array<index_t, NDimSpatial>& conv_filter_strides, const std::array<index_t, NDimSpatial>& conv_filter_strides,
const std::array<index_t, NDimSpatial>& conv_filter_dilations, const std::array<index_t, NDimSpatial>& conv_filter_dilations,
const std::array<index_t, NDimSpatial>& input_left_pads, const std::array<index_t, NDimSpatial>& input_left_pads,
const std::array<index_t, NDimSpatial>& input_right_pads) const std::array<index_t, NDimSpatial>& input_right_pads)
: C_(C), : G_(G),
C_(C),
X_(filter_spatial_lengths[NDimSpatial - I1]), X_(filter_spatial_lengths[NDimSpatial - I1]),
p_in_{static_cast<const InputDataType*>(p_in)}, p_in_{static_cast<const InputDataType*>(p_in)},
p_out_{static_cast<OutputDataType*>(p_out)}, p_out_{static_cast<OutputDataType*>(p_out)},
...@@ -176,14 +190,16 @@ struct DeviceImageToColumnImpl ...@@ -176,14 +190,16 @@ struct DeviceImageToColumnImpl
filter_spatial_lengths, filter_spatial_lengths,
output_spatial_lengths, output_spatial_lengths,
image_g_n_c_wis_strides, image_g_n_c_wis_strides,
conv_filter_strides, conv_filter_strides,
conv_filter_dilations, conv_filter_dilations,
input_left_pads, input_left_pads,
input_right_pads); input_right_pads);
out_grid_desc_m_k_ = MakeOutDescriptor_M_K( out_grid_desc_m_k_ = MakeOutDescriptor_M_K(
N, C, filter_spatial_lengths, output_spatial_lengths, gemm_m_k_strides); N, C, filter_spatial_lengths, output_spatial_lengths, gemm_g_m_k_strides);
compute_ptr_offset_of_batch_.BatchStrideA_ = image_g_n_c_wis_strides[I0];
compute_ptr_offset_of_batch_.BatchStrideC_ = gemm_g_m_k_strides[I0];
} }
void Print() const void Print() const
...@@ -192,6 +208,7 @@ struct DeviceImageToColumnImpl ...@@ -192,6 +208,7 @@ struct DeviceImageToColumnImpl
std::cout << out_grid_desc_m_k_ << std::endl; std::cout << out_grid_desc_m_k_ << std::endl;
} }
const ck::index_t G_;
const ck::index_t C_; const ck::index_t C_;
const ck::index_t X_; const ck::index_t X_;
...@@ -206,6 +223,8 @@ struct DeviceImageToColumnImpl ...@@ -206,6 +223,8 @@ struct DeviceImageToColumnImpl
InputGridDesc in_grid_desc_m_k_; InputGridDesc in_grid_desc_m_k_;
OutputGridDesc out_grid_desc_m_k_; OutputGridDesc out_grid_desc_m_k_;
ComputePtrOffsetOfStridedBatch<I0> compute_ptr_offset_of_batch_;
}; };
struct Invoker : public BaseInvoker struct Invoker : public BaseInvoker
...@@ -220,12 +239,14 @@ struct DeviceImageToColumnImpl ...@@ -220,12 +239,14 @@ struct DeviceImageToColumnImpl
const auto block_2_tile_map = const auto block_2_tile_map =
BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, KPerBlock, OutputGridDesc>( BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, KPerBlock, OutputGridDesc>(
arg.out_grid_desc_m_k_); arg.out_grid_desc_m_k_);
const index_t grid_size = block_2_tile_map.CalculateGridSize(arg.out_grid_desc_m_k_); const index_t grid_size =
const auto kernel = kernel_tensor_rearrange<InputGridDesc, block_2_tile_map.CalculateGridSize(arg.out_grid_desc_m_k_) * arg.G_;
const auto kernel = kernel_tensor_rearrange<InputGridDesc,
InputDataType, InputDataType,
OutputGridDesc, OutputGridDesc,
OutputDataType, OutputDataType,
Block2ETileMap, Block2ETileMap,
ComputePtrOffsetOfStridedBatch<I0>,
GridwiseTensorRearrangeKernel>; GridwiseTensorRearrangeKernel>;
float elapsed_time = launch_and_time_kernel(stream_config, float elapsed_time = launch_and_time_kernel(stream_config,
...@@ -237,7 +258,9 @@ struct DeviceImageToColumnImpl ...@@ -237,7 +258,9 @@ struct DeviceImageToColumnImpl
arg.p_in_, arg.p_in_,
arg.out_grid_desc_m_k_, arg.out_grid_desc_m_k_,
arg.p_out_, arg.p_out_,
block_2_tile_map); arg.G_,
block_2_tile_map,
arg.compute_ptr_offset_of_batch_);
return elapsed_time; return elapsed_time;
} }
...@@ -250,9 +273,7 @@ struct DeviceImageToColumnImpl ...@@ -250,9 +273,7 @@ struct DeviceImageToColumnImpl
bool IsSupportedArgument(const Argument& arg) bool IsSupportedArgument(const Argument& arg)
{ {
using namespace tensor_layout::convolution; if constexpr(!(is_NSpatialGC || is_GNSpatialC))
if constexpr(!(std::is_same_v<ImageLayout, GNWC> || std::is_same_v<ImageLayout, GNHWC> ||
std::is_same_v<ImageLayout, GNDHWC>))
{ {
return false; return false;
} }
...@@ -295,13 +316,14 @@ struct DeviceImageToColumnImpl ...@@ -295,13 +316,14 @@ struct DeviceImageToColumnImpl
static auto MakeArgument(const void* p_in, // input image static auto MakeArgument(const void* p_in, // input image
void* p_out, // gemm form void* p_out, // gemm form
const ck::index_t G,
const ck::index_t N, const ck::index_t N,
const ck::index_t C, const ck::index_t C,
const std::array<index_t, NDimSpatial>& input_spatial_lengths, const std::array<index_t, NDimSpatial>& input_spatial_lengths,
const std::array<index_t, NDimSpatial>& filter_spatial_lengths, const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
const std::array<index_t, NDimSpatial>& output_spatial_lengths, const std::array<index_t, NDimSpatial>& output_spatial_lengths,
const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides, const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides,
const std::array<index_t, 2>& gemm_m_k_strides, const std::array<index_t, 3>& gemm_g_m_k_strides,
const std::array<index_t, NDimSpatial>& conv_filter_strides, const std::array<index_t, NDimSpatial>& conv_filter_strides,
const std::array<index_t, NDimSpatial>& conv_filter_dilations, const std::array<index_t, NDimSpatial>& conv_filter_dilations,
const std::array<index_t, NDimSpatial>& input_left_pads, const std::array<index_t, NDimSpatial>& input_left_pads,
...@@ -309,13 +331,14 @@ struct DeviceImageToColumnImpl ...@@ -309,13 +331,14 @@ struct DeviceImageToColumnImpl
{ {
return Argument{static_cast<const InputDataType*>(p_in), return Argument{static_cast<const InputDataType*>(p_in),
static_cast<OutputDataType*>(p_out), static_cast<OutputDataType*>(p_out),
G,
N, N,
C, C,
input_spatial_lengths, input_spatial_lengths,
filter_spatial_lengths, filter_spatial_lengths,
output_spatial_lengths, output_spatial_lengths,
image_g_n_c_wis_strides, image_g_n_c_wis_strides,
gemm_m_k_strides, gemm_g_m_k_strides,
conv_filter_strides, conv_filter_strides,
conv_filter_dilations, conv_filter_dilations,
input_left_pads, input_left_pads,
...@@ -327,13 +350,14 @@ struct DeviceImageToColumnImpl ...@@ -327,13 +350,14 @@ struct DeviceImageToColumnImpl
std::unique_ptr<BaseArgument> std::unique_ptr<BaseArgument>
MakeArgumentPointer(const void* p_in, // input image MakeArgumentPointer(const void* p_in, // input image
void* p_out, // gemm form void* p_out, // gemm form
const ck::index_t G,
const ck::index_t N, const ck::index_t N,
const ck::index_t C, const ck::index_t C,
const std::array<index_t, NDimSpatial>& input_spatial_lengths, const std::array<index_t, NDimSpatial>& input_spatial_lengths,
const std::array<index_t, NDimSpatial>& filter_spatial_lengths, const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
const std::array<index_t, NDimSpatial>& output_spatial_lengths, const std::array<index_t, NDimSpatial>& output_spatial_lengths,
const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides, const std::array<index_t, NDimSpatial + 3>& image_g_n_c_wis_strides,
const std::array<index_t, 2>& gemm_m_k_strides, const std::array<index_t, 3>& gemm_g_m_k_strides,
const std::array<index_t, NDimSpatial>& conv_filter_strides, const std::array<index_t, NDimSpatial>& conv_filter_strides,
const std::array<index_t, NDimSpatial>& conv_filter_dilations, const std::array<index_t, NDimSpatial>& conv_filter_dilations,
const std::array<index_t, NDimSpatial>& input_left_pads, const std::array<index_t, NDimSpatial>& input_left_pads,
...@@ -341,13 +365,14 @@ struct DeviceImageToColumnImpl ...@@ -341,13 +365,14 @@ struct DeviceImageToColumnImpl
{ {
return std::make_unique<Argument>(static_cast<const InputDataType*>(p_in), return std::make_unique<Argument>(static_cast<const InputDataType*>(p_in),
static_cast<OutputDataType*>(p_out), static_cast<OutputDataType*>(p_out),
G,
N, N,
C, C,
input_spatial_lengths, input_spatial_lengths,
filter_spatial_lengths, filter_spatial_lengths,
output_spatial_lengths, output_spatial_lengths,
image_g_n_c_wis_strides, image_g_n_c_wis_strides,
gemm_m_k_strides, gemm_g_m_k_strides,
conv_filter_strides, conv_filter_strides,
conv_filter_dilations, conv_filter_dilations,
input_left_pads, input_left_pads,
......
include/ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp
View file @ 0f1e8187
...@@ -186,6 +186,25 @@ struct Bilinear ...@@ -186,6 +186,25 @@ struct Bilinear
y = type_convert<half_t>(alpha_ * x0 + beta_ * ck::type_convert<float>(x1)); y = type_convert<half_t>(alpha_ * x0 + beta_ * ck::type_convert<float>(x1));
}; };
template <>
__host__ __device__ constexpr void
operator()<bhalf_t, bhalf_t, bhalf_t>(bhalf_t& y, const bhalf_t& x0, const bhalf_t& x1) const
{
const float x0_tmp = type_convert<float>(x0);
const float x1_tmp = type_convert<float>(x1);
const float y_tmp = alpha_ * x0_tmp + beta_ * x1_tmp;
y = type_convert<bhalf_t>(y_tmp);
};
template <>
__host__ __device__ constexpr void
operator()<bhalf_t, float, bhalf_t>(bhalf_t& y, const float& x0, const bhalf_t& x1) const
{
const float x1_tmp = ck::type_convert<float>(x1);
const float y_tmp = alpha_ * x0 + beta_ * x1_tmp;
y = y_tmp;
};
template <> template <>
__host__ __device__ constexpr void operator()<std::int8_t, std::int32_t, std::int8_t>( __host__ __device__ constexpr void operator()<std::int8_t, std::int32_t, std::int8_t>(
std::int8_t& y, const std::int32_t& x0, const std::int8_t& x1) const std::int8_t& y, const std::int32_t& x0, const std::int8_t& x1) const
......
include/ck/tensor_operation/gpu/element/element_wise_operation.hpp
View file @ 0f1e8187
...@@ -311,6 +311,71 @@ struct AddAddFastGelu ...@@ -311,6 +311,71 @@ struct AddAddFastGelu
} }
}; };
// E = Relu(alpha1 * C + alpha2 * D0 + D1)
struct ScaleAddScaleAddRelu
{
ScaleAddScaleAddRelu(const float alpha1 = 1.f, const float alpha2 = 1.f)
: alpha1_(alpha1), alpha2_(alpha2)
{
}
template <typename E, typename C, typename D0, typename D1>
__host__ __device__ constexpr void
operator()(E& e, const C& c, const D0& d0, const D1& d1) const;
template <>
__host__ __device__ constexpr void operator()<float, float, float, float>(float& e,
const float& c,
const float& d0,
const float& d1) const
{
const float x = c * alpha1_ + alpha2_ * d0 + d1;
Relu{}.template operator()<float>(e, x);
}
template <>
__host__ __device__ constexpr void operator()<half_t, half_t, half_t, half_t>(
half_t& e, const half_t& c, const half_t& d0, const half_t& d1) const
{
const float x = type_convert<float>(c) * alpha1_ + alpha2_ * type_convert<float>(d0) +
type_convert<float>(d1);
float result = 0;
Relu{}.template operator()<float>(result, x);
e = type_convert<half_t>(result);
}
template <>
__host__ __device__ constexpr void operator()<bhalf_t, bhalf_t, bhalf_t, bhalf_t>(
bhalf_t& e, const bhalf_t& c, const bhalf_t& d0, const bhalf_t& d1) const
{
const float x = type_convert<float>(c) * alpha1_ + alpha2_ * type_convert<float>(d0) +
type_convert<float>(d1);
float result = 0;
Relu{}.template operator()<float>(result, x);
e = type_convert<bhalf_t>(result);
}
template <>
__host__ __device__ constexpr void operator()<int8_t, int8_t, float, float>(
int8_t& e, const int8_t& c, const float& d0, const float& d1) const
{
const float x = type_convert<float>(c) * alpha1_ + alpha2_ * d0 + d1;
float result = 0;
Relu{}.template operator()<float>(result, x);
e = type_convert<int8_t>(result);
}
const float alpha1_;
const float alpha2_;
};
struct Normalize struct Normalize
{ {
// FIXME: is double absolutely necessary? // FIXME: is double absolutely necessary?
......
include/ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp
View file @ 0f1e8187
...@@ -33,6 +33,12 @@ struct PassThrough ...@@ -33,6 +33,12 @@ struct PassThrough
y = type_convert<float>(x); y = type_convert<float>(x);
} }
template <>
__host__ __device__ void operator()<double, float>(double& y, const float& x) const
{
y = type_convert<double>(x);
}
template <> template <>
__host__ __device__ void operator()<float, float>(float& y, const float& x) const __host__ __device__ void operator()<float, float>(float& y, const float& x) const
{ {
...@@ -69,6 +75,12 @@ struct PassThrough ...@@ -69,6 +75,12 @@ struct PassThrough
y = type_convert<bhalf_t>(x); y = type_convert<bhalf_t>(x);
} }
template <>
__host__ __device__ void operator()<float, bhalf_t>(float& y, const bhalf_t& x) const
{
y = type_convert<float>(x);
}
template <> template <>
__host__ __device__ void operator()<bhalf_t, half_t>(bhalf_t& y, const half_t& x) const __host__ __device__ void operator()<bhalf_t, half_t>(bhalf_t& y, const half_t& x) const
{ {
...@@ -225,6 +237,20 @@ struct Scale ...@@ -225,6 +237,20 @@ struct Scale
template <typename Y, typename X> template <typename Y, typename X>
__host__ __device__ void operator()(Y& y, const X& x) const; __host__ __device__ void operator()(Y& y, const X& x) const;
template <>
__host__ __device__ void operator()<half_t, half_t>(half_t& y, const half_t& x) const
{
y = ck::type_convert<half_t>(scale_) * x;
};
template <>
__host__ __device__ void operator()<bhalf_t, bhalf_t>(bhalf_t& y, const bhalf_t& x) const
{
const float x_tmp = ck::type_convert<float>(x);
const float y_tmp = scale_ * x_tmp;
y = ck::type_convert<bhalf_t>(y_tmp);
};
template <> template <>
__host__ __device__ void operator()<float, float>(float& y, const float& x) const __host__ __device__ void operator()<float, float>(float& y, const float& x) const
{ {
......
include/ck/tensor_operation/gpu/grid/gridwise_tensor_rearrange.hpp
View file @ 0f1e8187
...@@ -21,6 +21,7 @@ template <typename InputGridDesc, ...@@ -21,6 +21,7 @@ template <typename InputGridDesc,
typename OutputGridDesc, typename OutputGridDesc,
typename OutputDataType, typename OutputDataType,
typename Block2ETileMap, typename Block2ETileMap,
typename ComputePtrOffsetOfStridedBatch,
typename GridwiseTensorRearrangeKernel> typename GridwiseTensorRearrangeKernel>
__global__ void __global__ void
#if CK_USE_LAUNCH_BOUNDS #if CK_USE_LAUNCH_BOUNDS
...@@ -30,13 +31,20 @@ __global__ void ...@@ -30,13 +31,20 @@ __global__ void
const InputDataType* __restrict__ p_in_global, const InputDataType* __restrict__ p_in_global,
const OutputGridDesc out_grid_desc, const OutputGridDesc out_grid_desc,
OutputDataType* __restrict__ p_out_global, OutputDataType* __restrict__ p_out_global,
const Block2ETileMap block_2_tile_map) const index_t batch_count,
const Block2ETileMap block_2_tile_map,
const ComputePtrOffsetOfStridedBatch compute_ptr_offset_of_batch)
{ {
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx906__) || defined(__gfx908__) || \ #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx906__) || defined(__gfx908__) || \
defined(__gfx90a__) || defined(__gfx940__) || defined(__gfx1030__) || defined(__gfx1100__) || \ defined(__gfx90a__) || defined(__gfx940__) || defined(__gfx1030__) || defined(__gfx1100__) || \
defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx941__) || defined(__gfx942__)) defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx941__) || defined(__gfx942__))
GridwiseTensorRearrangeKernel::Run( GridwiseTensorRearrangeKernel::Run(in_grid_desc,
in_grid_desc, p_in_global, out_grid_desc, p_out_global, block_2_tile_map); p_in_global,
out_grid_desc,
p_out_global,
batch_count,
block_2_tile_map,
compute_ptr_offset_of_batch);
#else #else
ignore = in_grid_desc; ignore = in_grid_desc;
ignore = p_in_global; ignore = p_in_global;
...@@ -56,7 +64,8 @@ template <typename InputGridDesc, ...@@ -56,7 +64,8 @@ template <typename InputGridDesc,
typename ThreadClusterLengths, typename ThreadClusterLengths,
index_t ScalarPerVector, index_t ScalarPerVector,
InMemoryDataOperationEnum DstInMemOp, InMemoryDataOperationEnum DstInMemOp,
typename Block2ETileMap> typename Block2ETileMap,
typename ComputePtrOffsetOfStridedBatch>
struct GridwiseTensorRearrange struct GridwiseTensorRearrange
{ {
...@@ -69,7 +78,9 @@ struct GridwiseTensorRearrange ...@@ -69,7 +78,9 @@ struct GridwiseTensorRearrange
const InputDataType* __restrict__ p_in_global, const InputDataType* __restrict__ p_in_global,
const OutputGridDesc& out_grid_desc, const OutputGridDesc& out_grid_desc,
OutputDataType* __restrict__ p_out_global, OutputDataType* __restrict__ p_out_global,
const Block2ETileMap& block_2_tile_map) const index_t batch_count,
const Block2ETileMap& block_2_tile_map,
const ComputePtrOffsetOfStridedBatch& compute_ptr_offset_of_batch)
{ {
const auto block_work_idx = const auto block_work_idx =
block_2_tile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id())); block_2_tile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
...@@ -80,12 +91,6 @@ struct GridwiseTensorRearrange ...@@ -80,12 +91,6 @@ struct GridwiseTensorRearrange
const index_t k_block_data_idx_on_grid = const index_t k_block_data_idx_on_grid =
__builtin_amdgcn_readfirstlane(block_work_idx[I1] * KPerBlock); __builtin_amdgcn_readfirstlane(block_work_idx[I1] * KPerBlock);
// Global Memory
const auto in_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_in_global, in_grid_desc.GetElementSpaceSize());
auto out_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_out_global, out_grid_desc.GetElementSpaceSize());
auto copy_global_to_global = auto copy_global_to_global =
ThreadGroupTensorSliceTransfer_v7<ThisThreadBlock, ThreadGroupTensorSliceTransfer_v7<ThisThreadBlock,
Tuple<InputDataType>, Tuple<InputDataType>,
...@@ -108,6 +113,22 @@ struct GridwiseTensorRearrange ...@@ -108,6 +113,22 @@ struct GridwiseTensorRearrange
make_tuple(make_multi_index(m_block_data_idx_on_grid, k_block_data_idx_on_grid)), make_tuple(make_multi_index(m_block_data_idx_on_grid, k_block_data_idx_on_grid)),
tensor_operation::element_wise::PassThrough{}}; tensor_operation::element_wise::PassThrough{}};
const index_t num_blocks_per_batch =
__builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
const index_t g_idx =
__builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
// Global Memory
const index_t a_batch_offset =
__builtin_amdgcn_readfirstlane(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx));
const index_t c_batch_offset =
__builtin_amdgcn_readfirstlane(compute_ptr_offset_of_batch.GetCPtrOffset(g_idx));
const auto in_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_in_global + a_batch_offset, in_grid_desc.GetElementSpaceSize());
auto out_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_out_global + c_batch_offset, out_grid_desc.GetElementSpaceSize());
copy_global_to_global.Run( copy_global_to_global.Run(
tie(in_grid_desc), tie(in_global_buf), tie(out_grid_desc), tie(out_global_buf)); tie(in_grid_desc), tie(in_global_buf), tie(out_grid_desc), tie(out_global_buf));
} }
......
include/ck/utility/type_convert.hpp
View file @ 0f1e8187
...@@ -100,6 +100,8 @@ template <> ...@@ -100,6 +100,8 @@ template <>
inline __host__ __device__ f8_t type_convert<f8_t, float>(float x) inline __host__ __device__ f8_t type_convert<f8_t, float>(float x)
{ {
#if defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__) #if defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__)
float max_fp8 = 240.0f;
x = x > max_fp8 ? max_fp8 : (x < -max_fp8 ? -max_fp8 : x);
union union
{ {
float fval; float fval;
......
library/include/ck/library/reference_tensor_operation/cpu/reference_column_to_image.hpp
View file @ 0f1e8187
...@@ -19,9 +19,7 @@ namespace host { ...@@ -19,9 +19,7 @@ namespace host {
* \brief Reference implementation for column to image. * \brief Reference implementation for column to image.
* *
* Input tensor descriptor has [N * Do * Ho * Wo, Z * Y * X * C] data layout. * Input tensor descriptor has [N * Do * Ho * Wo, Z * Y * X * C] data layout.
* Memory layout is the same.
* Output tensor descriptor has [G, N, C, Di, Hi, Wi] data layout. * Output tensor descriptor has [G, N, C, Di, Hi, Wi] data layout.
* G must be equal to 1. Memory layout is [G, N, Di, Hi, Wi, C].
* *
* \tparam NDimSpatial Number of spatial dimensions. * \tparam NDimSpatial Number of spatial dimensions.
* \tparam ImageLayout Image Layout. * \tparam ImageLayout Image Layout.
...@@ -95,18 +93,19 @@ struct ReferenceColumnToImage : public device::BaseOperator ...@@ -95,18 +93,19 @@ struct ReferenceColumnToImage : public device::BaseOperator
float Run(const Argument& arg) float Run(const Argument& arg)
{ {
if(!(arg.output_.GetNumOfDimension() == NDimSpatial + 3 && if(!(arg.output_.GetNumOfDimension() == NDimSpatial + 3 &&
arg.input_.GetNumOfDimension() == 2)) arg.input_.GetNumOfDimension() == 3))
{ {
throw std::runtime_error("wrong! inconsistent dimension"); throw std::runtime_error("wrong! inconsistent dimension");
} }
const index_t G = arg.output_.GetLengths()[0];
const index_t N = arg.output_.GetLengths()[1]; const index_t N = arg.output_.GetLengths()[1];
const index_t C = arg.output_.GetLengths()[2]; const index_t C = arg.output_.GetLengths()[2];
if constexpr(NDimSpatial == 1) if constexpr(NDimSpatial == 1)
{ {
const index_t Wo = arg.output_spatial_lengths_[0]; const index_t Wo = arg.output_spatial_lengths_[0];
auto func = [&](auto n) { auto func = [&](auto g, auto n) {
for(index_t wo = 0; wo < Wo; ++wo) for(index_t wo = 0; wo < Wo; ++wo)
{ {
index_t row = n * Wo + wo; index_t row = n * Wo + wo;
...@@ -123,9 +122,10 @@ struct ReferenceColumnToImage : public device::BaseOperator ...@@ -123,9 +122,10 @@ struct ReferenceColumnToImage : public device::BaseOperator
if(wi >= 0 && if(wi >= 0 &&
ck::type_convert<std::size_t>(wi) < arg.output_.GetLengths()[3]) ck::type_convert<std::size_t>(wi) < arg.output_.GetLengths()[3])
{ {
float v_in = ck::type_convert<float>(arg.input_(row, column)); float v_in =
float v_out = ck::type_convert<float>(arg.output_(0, n, c, wi)); ck::type_convert<float>(arg.input_(g, row, column));
arg.output_(0, n, c, wi) = float v_out = ck::type_convert<float>(arg.output_(g, n, c, wi));
arg.output_(g, n, c, wi) =
ck::type_convert<OutDataType>(v_in + v_out); ck::type_convert<OutDataType>(v_in + v_out);
} }
column++; column++;
...@@ -134,7 +134,7 @@ struct ReferenceColumnToImage : public device::BaseOperator ...@@ -134,7 +134,7 @@ struct ReferenceColumnToImage : public device::BaseOperator
} }
}; };
make_ParallelTensorFunctor(func, N)(std::thread::hardware_concurrency()); make_ParallelTensorFunctor(func, G, N)(std::thread::hardware_concurrency());
return 0; return 0;
} }
...@@ -143,7 +143,7 @@ struct ReferenceColumnToImage : public device::BaseOperator ...@@ -143,7 +143,7 @@ struct ReferenceColumnToImage : public device::BaseOperator
const index_t Ho = arg.output_spatial_lengths_[0]; const index_t Ho = arg.output_spatial_lengths_[0];
const index_t Wo = arg.output_spatial_lengths_[1]; const index_t Wo = arg.output_spatial_lengths_[1];
auto func = [&](auto n) { auto func = [&](auto g, auto n) {
for(index_t ho = 0; ho < Ho; ++ho) for(index_t ho = 0; ho < Ho; ++ho)
{ {
for(index_t wo = 0; wo < Wo; ++wo) for(index_t wo = 0; wo < Wo; ++wo)
...@@ -176,10 +176,10 @@ struct ReferenceColumnToImage : public device::BaseOperator ...@@ -176,10 +176,10 @@ struct ReferenceColumnToImage : public device::BaseOperator
arg.output_.GetLengths()[4]) arg.output_.GetLengths()[4])
{ {
float v_in = float v_in =
ck::type_convert<float>(arg.input_(row, column)); ck::type_convert<float>(arg.input_(g, row, column));
float v_out = ck::type_convert<float>( float v_out = ck::type_convert<float>(
arg.output_(0, n, c, hi, wi)); arg.output_(g, n, c, hi, wi));
arg.output_(0, n, c, hi, wi) = arg.output_(g, n, c, hi, wi) =
ck::type_convert<OutDataType>(v_in + v_out); ck::type_convert<OutDataType>(v_in + v_out);
} }
column++; column++;
...@@ -190,7 +190,7 @@ struct ReferenceColumnToImage : public device::BaseOperator ...@@ -190,7 +190,7 @@ struct ReferenceColumnToImage : public device::BaseOperator
} }
}; };
make_ParallelTensorFunctor(func, N)(std::thread::hardware_concurrency()); make_ParallelTensorFunctor(func, G, N)(std::thread::hardware_concurrency());
return 0; return 0;
} }
...@@ -200,7 +200,7 @@ struct ReferenceColumnToImage : public device::BaseOperator ...@@ -200,7 +200,7 @@ struct ReferenceColumnToImage : public device::BaseOperator
const index_t Ho = arg.output_spatial_lengths_[1]; const index_t Ho = arg.output_spatial_lengths_[1];
const index_t Wo = arg.output_spatial_lengths_[2]; const index_t Wo = arg.output_spatial_lengths_[2];
auto func = [&](auto n) { auto func = [&](auto g, auto n) {
for(index_t d_o = 0; d_o < Do; ++d_o) for(index_t d_o = 0; d_o < Do; ++d_o)
{ {
for(index_t ho = 0; ho < Ho; ++ho) for(index_t ho = 0; ho < Ho; ++ho)
...@@ -245,10 +245,10 @@ struct ReferenceColumnToImage : public device::BaseOperator ...@@ -245,10 +245,10 @@ struct ReferenceColumnToImage : public device::BaseOperator
arg.output_.GetLengths()[5]) arg.output_.GetLengths()[5])
{ {
float v_in = ck::type_convert<float>( float v_in = ck::type_convert<float>(
arg.input_(row, column)); arg.input_(g, row, column));
float v_out = ck::type_convert<float>( float v_out = ck::type_convert<float>(
arg.output_(0, n, c, di, hi, wi)); arg.output_(g, n, c, di, hi, wi));
arg.output_(0, n, c, di, hi, wi) = arg.output_(g, n, c, di, hi, wi) =
ck::type_convert<OutDataType>(v_in + v_out); ck::type_convert<OutDataType>(v_in + v_out);
} }
column++; column++;
...@@ -261,7 +261,7 @@ struct ReferenceColumnToImage : public device::BaseOperator ...@@ -261,7 +261,7 @@ struct ReferenceColumnToImage : public device::BaseOperator
} }
}; };
make_ParallelTensorFunctor(func, N)(std::thread::hardware_concurrency()); make_ParallelTensorFunctor(func, G, N)(std::thread::hardware_concurrency());
return 0; return 0;
} }
...@@ -303,8 +303,9 @@ struct ReferenceColumnToImage : public device::BaseOperator ...@@ -303,8 +303,9 @@ struct ReferenceColumnToImage : public device::BaseOperator
C * ck::accumulate_n<index_t>( C * ck::accumulate_n<index_t>(
arg.filter_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>()); arg.filter_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>());
if(!(arg.input_.GetLengths()[0] == static_cast<std::size_t>(NDoHoWo) && if(!(arg.input_.GetLengths()[0] == static_cast<std::size_t>(G) &&
arg.input_.GetLengths()[1] == static_cast<std::size_t>(CZYX))) arg.input_.GetLengths()[1] == static_cast<std::size_t>(NDoHoWo) &&
arg.input_.GetLengths()[2] == static_cast<std::size_t>(CZYX)))
{ {
return false; return false;
} }
......
library/include/ck/library/reference_tensor_operation/cpu/reference_contraction.hpp
View file @ 0f1e8187
...@@ -23,6 +23,7 @@ template <ck::index_t NumDimM, ...@@ -23,6 +23,7 @@ template <ck::index_t NumDimM,
typename BDataType, typename BDataType,
typename CDataType, typename CDataType,
typename AccDataType, typename AccDataType,
typename ComputeDataType,
typename AElementwiseOperation, typename AElementwiseOperation,
typename BElementwiseOperation, typename BElementwiseOperation,
ck::enable_if_t<NumDimM == 2 && NumDimN == 2 && NumDimK == 2, bool> = false> ck::enable_if_t<NumDimM == 2 && NumDimN == 2 && NumDimK == 2, bool> = false>
...@@ -69,19 +70,24 @@ struct ReferenceContraction_M2_N2_K2 : public ck::tensor_operation::device::Base ...@@ -69,19 +70,24 @@ struct ReferenceContraction_M2_N2_K2 : public ck::tensor_operation::device::Base
{ {
for(ck::index_t k1 = 0; k1 < K1; ++k1) for(ck::index_t k1 = 0; k1 < K1; ++k1)
{ {
// Simulate the possible casting when ComputeDataType is different than the
// A/B data types
ComputeDataType v_a_compute_input =
ck::type_convert<ComputeDataType>(arg.a_ms_ks_(m0, m1, k0, k1));
ComputeDataType v_b_compute_input =
ck::type_convert<ComputeDataType>(arg.b_ns_ks_(n0, n1, k0, k1));
AccDataType v_a; AccDataType v_a;
AccDataType v_b; AccDataType v_b;
arg.a_element_op_( arg.a_element_op_(v_a, ck::type_convert<AccDataType>(v_a_compute_input));
v_a, ck::type_convert<const AccDataType>(arg.a_ms_ks_(m0, m1, k0, k1))); arg.b_element_op_(v_b, ck::type_convert<AccDataType>(v_b_compute_input));
arg.b_element_op_(
v_b, ck::type_convert<const AccDataType>(arg.b_ns_ks_(n0, n1, k0, k1)));
v_acc += v_a * v_b; v_acc += v_a * v_b;
} }
} }
arg.c_ms_ns_(m0, m1, n0, n1) = v_acc; arg.c_ms_ns_(m0, m1, n0, n1) = ck::type_convert<CDataType>(v_acc);
}; };
make_ParallelTensorFunctor(f_ms_ns, make_ParallelTensorFunctor(f_ms_ns,
......
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp
View file @ 0f1e8187
...@@ -42,6 +42,7 @@ template <ck::index_t NDimSpatial, ...@@ -42,6 +42,7 @@ template <ck::index_t NDimSpatial,
typename InElementwiseOperation, typename InElementwiseOperation,
typename WeiElementwiseOperation, typename WeiElementwiseOperation,
typename OutElementwiseOperation, typename OutElementwiseOperation,
ck::index_t NumDTensor = 0,
typename std::enable_if<NDimSpatial >= 1 && NDimSpatial <= 3, bool>::type = false> typename std::enable_if<NDimSpatial >= 1 && NDimSpatial <= 3, bool>::type = false>
struct ReferenceConvFwd : public device::BaseOperator struct ReferenceConvFwd : public device::BaseOperator
{ {
...@@ -57,10 +58,12 @@ struct ReferenceConvFwd : public device::BaseOperator ...@@ -57,10 +58,12 @@ struct ReferenceConvFwd : public device::BaseOperator
std::vector<ck::index_t> input_right_pads, std::vector<ck::index_t> input_right_pads,
InElementwiseOperation in_element_op, InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op, WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op) OutElementwiseOperation out_element_op,
const std::array<Tensor<OutDataType>, NumDTensor>& d_tensors)
: input_{input}, : input_{input},
weight_{weight}, weight_{weight},
output_{output}, output_{output},
d_tensors_{d_tensors},
conv_strides_{conv_filter_strides}, conv_strides_{conv_filter_strides},
conv_dilations_{conv_filter_dilations}, conv_dilations_{conv_filter_dilations},
in_left_pads_{input_left_pads}, in_left_pads_{input_left_pads},
...@@ -75,6 +78,8 @@ struct ReferenceConvFwd : public device::BaseOperator ...@@ -75,6 +78,8 @@ struct ReferenceConvFwd : public device::BaseOperator
const Tensor<WeiDataType>& weight_; const Tensor<WeiDataType>& weight_;
Tensor<OutDataType>& output_; Tensor<OutDataType>& output_;
const std::array<Tensor<OutDataType>, NumDTensor>& d_tensors_;
std::vector<index_t> conv_strides_; std::vector<index_t> conv_strides_;
std::vector<index_t> conv_dilations_; std::vector<index_t> conv_dilations_;
std::vector<index_t> in_left_pads_; std::vector<index_t> in_left_pads_;
...@@ -129,7 +134,26 @@ struct ReferenceConvFwd : public device::BaseOperator ...@@ -129,7 +134,26 @@ struct ReferenceConvFwd : public device::BaseOperator
} }
OutDataType v_out; OutDataType v_out;
arg.out_element_op_(v_out, ck::type_convert<OutDataType>(v_acc)); OutDataType v_acc_converted = ck::type_convert<OutDataType>(v_acc);
if constexpr(NumDTensor == 0)
{
arg.out_element_op_(v_out, v_acc_converted);
}
else if constexpr(NumDTensor == 1)
{
arg.out_element_op_(v_out, v_acc_converted, arg.d_tensors_[0](g, n, k, wo));
}
else if constexpr(NumDTensor == 2)
{
arg.out_element_op_(v_out,
v_acc_converted,
arg.d_tensors_[0](g, n, k, wo),
arg.d_tensors_[1](g, n, k, wo));
}
else
{
throw std::runtime_error("Output ElementOp not supported in reference.");
}
arg.output_(g, n, k, wo) = v_out; arg.output_(g, n, k, wo) = v_out;
}; };
...@@ -183,7 +207,27 @@ struct ReferenceConvFwd : public device::BaseOperator ...@@ -183,7 +207,27 @@ struct ReferenceConvFwd : public device::BaseOperator
} }
OutDataType v_out; OutDataType v_out;
arg.out_element_op_(v_out, ck::type_convert<OutDataType>(v_acc)); OutDataType v_acc_converted = ck::type_convert<OutDataType>(v_acc);
if constexpr(NumDTensor == 0)
{
arg.out_element_op_(v_out, v_acc_converted);
}
else if constexpr(NumDTensor == 1)
{
arg.out_element_op_(
v_out, v_acc_converted, arg.d_tensors_[0](g, n, k, ho, wo));
}
else if constexpr(NumDTensor == 2)
{
arg.out_element_op_(v_out,
v_acc_converted,
arg.d_tensors_[0](g, n, k, ho, wo),
arg.d_tensors_[1](g, n, k, ho, wo));
}
else
{
throw std::runtime_error("Output ElementOp not supported in reference.");
}
arg.output_(g, n, k, ho, wo) = v_out; arg.output_(g, n, k, ho, wo) = v_out;
}; };
...@@ -250,7 +294,27 @@ struct ReferenceConvFwd : public device::BaseOperator ...@@ -250,7 +294,27 @@ struct ReferenceConvFwd : public device::BaseOperator
} }
OutDataType v_out; OutDataType v_out;
arg.out_element_op_(v_out, ck::type_convert<OutDataType>(v_acc)); OutDataType v_acc_converted = ck::type_convert<OutDataType>(v_acc);
if constexpr(NumDTensor == 0)
{
arg.out_element_op_(v_out, v_acc_converted);
}
else if constexpr(NumDTensor == 1)
{
arg.out_element_op_(
v_out, v_acc_converted, arg.d_tensors_[0](g, n, k, d_o, ho, wo));
}
else if constexpr(NumDTensor == 2)
{
arg.out_element_op_(v_out,
v_acc_converted,
arg.d_tensors_[0](g, n, k, d_o, ho, wo),
arg.d_tensors_[1](g, n, k, d_o, ho, wo));
}
else
{
throw std::runtime_error("Output ElementOp not supported in reference.");
}
arg.output_(g, n, k, d_o, ho, wo) = v_out; arg.output_(g, n, k, d_o, ho, wo) = v_out;
}; };
...@@ -294,7 +358,8 @@ struct ReferenceConvFwd : public device::BaseOperator ...@@ -294,7 +358,8 @@ struct ReferenceConvFwd : public device::BaseOperator
std::vector<ck::index_t> input_right_pads, std::vector<ck::index_t> input_right_pads,
InElementwiseOperation in_element_op, InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op, WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op) OutElementwiseOperation out_element_op,
const std::array<Tensor<OutDataType>, NumDTensor>& d_tensors = {})
{ {
return Argument{input, return Argument{input,
weight, weight,
...@@ -305,7 +370,8 @@ struct ReferenceConvFwd : public device::BaseOperator ...@@ -305,7 +370,8 @@ struct ReferenceConvFwd : public device::BaseOperator
input_right_pads, input_right_pads,
in_element_op, in_element_op,
wei_element_op, wei_element_op,
out_element_op}; out_element_op,
d_tensors};
} }
static auto MakeInvoker() { return Invoker{}; } static auto MakeInvoker() { return Invoker{}; }
......
library/include/ck/library/reference_tensor_operation/cpu/reference_image_to_column.hpp
View file @ 0f1e8187
...@@ -19,9 +19,7 @@ namespace host { ...@@ -19,9 +19,7 @@ namespace host {
* \brief Reference implementation for image to column. * \brief Reference implementation for image to column.
* *
* Input tensor descriptor has [G, N, C, Di, Hi, Wi] data layout. * Input tensor descriptor has [G, N, C, Di, Hi, Wi] data layout.
* G must be equal to 1. Memory layout is [G, N, Di, Hi, Wi, C]. * Output tensor descriptor has [G * N * Do * Ho * Wo, Z * Y * X * C] data layout.
* Output tensor descriptor has [N * Do * Ho * Wo, Z * Y * X * C] data layout.
* Memory layout is the same.
* *
* \tparam NDimSpatial Number of spatial dimensions. * \tparam NDimSpatial Number of spatial dimensions.
* \tparam ImageLayout Image Layout. * \tparam ImageLayout Image Layout.
...@@ -95,18 +93,19 @@ struct ReferenceImageToColumn : public device::BaseOperator ...@@ -95,18 +93,19 @@ struct ReferenceImageToColumn : public device::BaseOperator
float Run(const Argument& arg) float Run(const Argument& arg)
{ {
if(!(arg.input_.GetNumOfDimension() == NDimSpatial + 3 && if(!(arg.input_.GetNumOfDimension() == NDimSpatial + 3 &&
arg.output_.GetNumOfDimension() == 2)) arg.output_.GetNumOfDimension() == 3))
{ {
throw std::runtime_error("wrong! inconsistent dimension"); throw std::runtime_error("wrong! inconsistent dimension");
} }
const index_t G = arg.input_.GetLengths()[0];
const index_t N = arg.input_.GetLengths()[1]; const index_t N = arg.input_.GetLengths()[1];
const index_t C = arg.input_.GetLengths()[2]; const index_t C = arg.input_.GetLengths()[2];
if constexpr(NDimSpatial == 1) if constexpr(NDimSpatial == 1)
{ {
const index_t Wo = arg.output_spatial_lengths_[0]; const index_t Wo = arg.output_spatial_lengths_[0];
auto func = [&](auto n, auto wo) { auto func = [&](auto g, auto n, auto wo) {
index_t row = n * Wo + wo; index_t row = n * Wo + wo;
index_t column = 0; index_t column = 0;
...@@ -121,15 +120,15 @@ struct ReferenceImageToColumn : public device::BaseOperator ...@@ -121,15 +120,15 @@ struct ReferenceImageToColumn : public device::BaseOperator
if(wi >= 0 && if(wi >= 0 &&
ck::type_convert<std::size_t>(wi) < arg.input_.GetLengths()[3]) ck::type_convert<std::size_t>(wi) < arg.input_.GetLengths()[3])
{ {
InDataType v_in = arg.input_(0, n, c, wi); InDataType v_in = arg.input_(g, n, c, wi);
arg.output_(row, column) = ck::type_convert<OutDataType>(v_in); arg.output_(g, row, column) = ck::type_convert<OutDataType>(v_in);
} }
column++; column++;
} }
} }
}; };
make_ParallelTensorFunctor(func, N, Wo)(std::thread::hardware_concurrency()); make_ParallelTensorFunctor(func, G, N, Wo)(std::thread::hardware_concurrency());
return 0; return 0;
} }
...@@ -138,7 +137,7 @@ struct ReferenceImageToColumn : public device::BaseOperator ...@@ -138,7 +137,7 @@ struct ReferenceImageToColumn : public device::BaseOperator
const index_t Ho = arg.output_spatial_lengths_[0]; const index_t Ho = arg.output_spatial_lengths_[0];
const index_t Wo = arg.output_spatial_lengths_[1]; const index_t Wo = arg.output_spatial_lengths_[1];
auto func = [&](auto n, auto ho, auto wo) { auto func = [&](auto g, auto n, auto ho, auto wo) {
index_t row = n * Ho * Wo + ho * Wo + wo; index_t row = n * Ho * Wo + ho * Wo + wo;
index_t column = 0; index_t column = 0;
...@@ -162,8 +161,9 @@ struct ReferenceImageToColumn : public device::BaseOperator ...@@ -162,8 +161,9 @@ struct ReferenceImageToColumn : public device::BaseOperator
wi >= 0 && wi >= 0 &&
ck::type_convert<std::size_t>(wi) < arg.input_.GetLengths()[4]) ck::type_convert<std::size_t>(wi) < arg.input_.GetLengths()[4])
{ {
InDataType v_in = arg.input_(0, n, c, hi, wi); InDataType v_in = arg.input_(g, n, c, hi, wi);
arg.output_(row, column) = ck::type_convert<OutDataType>(v_in); arg.output_(g, row, column) =
ck::type_convert<OutDataType>(v_in);
} }
column++; column++;
} }
...@@ -171,7 +171,7 @@ struct ReferenceImageToColumn : public device::BaseOperator ...@@ -171,7 +171,7 @@ struct ReferenceImageToColumn : public device::BaseOperator
} }
}; };
make_ParallelTensorFunctor(func, N, Ho, Wo)(std::thread::hardware_concurrency()); make_ParallelTensorFunctor(func, G, N, Ho, Wo)(std::thread::hardware_concurrency());
return 0; return 0;
} }
...@@ -181,7 +181,7 @@ struct ReferenceImageToColumn : public device::BaseOperator ...@@ -181,7 +181,7 @@ struct ReferenceImageToColumn : public device::BaseOperator
const index_t Ho = arg.output_spatial_lengths_[1]; const index_t Ho = arg.output_spatial_lengths_[1];
const index_t Wo = arg.output_spatial_lengths_[2]; const index_t Wo = arg.output_spatial_lengths_[2];
auto func = [&](auto n, auto d_o, auto ho, auto wo) { auto func = [&](auto g, auto n, auto d_o, auto ho, auto wo) {
index_t row = n * Do * Ho * Wo + d_o * Ho * Wo + ho * Wo + wo; index_t row = n * Do * Ho * Wo + d_o * Ho * Wo + ho * Wo + wo;
index_t column = 0; index_t column = 0;
...@@ -213,8 +213,8 @@ struct ReferenceImageToColumn : public device::BaseOperator ...@@ -213,8 +213,8 @@ struct ReferenceImageToColumn : public device::BaseOperator
ck::type_convert<std::size_t>(wi) < ck::type_convert<std::size_t>(wi) <
arg.input_.GetLengths()[5]) arg.input_.GetLengths()[5])
{ {
InDataType v_in = arg.input_(0, n, c, di, hi, wi); InDataType v_in = arg.input_(g, n, c, di, hi, wi);
arg.output_(row, column) = arg.output_(g, row, column) =
ck::type_convert<OutDataType>(v_in); ck::type_convert<OutDataType>(v_in);
} }
column++; column++;
...@@ -224,7 +224,7 @@ struct ReferenceImageToColumn : public device::BaseOperator ...@@ -224,7 +224,7 @@ struct ReferenceImageToColumn : public device::BaseOperator
} }
}; };
make_ParallelTensorFunctor(func, N, Do, Ho, Wo)( make_ParallelTensorFunctor(func, G, N, Do, Ho, Wo)(
std::thread::hardware_concurrency()); std::thread::hardware_concurrency());
return 0; return 0;
...@@ -267,8 +267,9 @@ struct ReferenceImageToColumn : public device::BaseOperator ...@@ -267,8 +267,9 @@ struct ReferenceImageToColumn : public device::BaseOperator
C * ck::accumulate_n<index_t>( C * ck::accumulate_n<index_t>(
arg.filter_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>()); arg.filter_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>());
if(!(arg.output_.GetLengths()[0] == static_cast<std::size_t>(NDoHoWo) && if(!(arg.output_.GetLengths()[0] == static_cast<std::size_t>(G) &&
arg.output_.GetLengths()[1] == static_cast<std::size_t>(CZYX))) arg.output_.GetLengths()[1] == static_cast<std::size_t>(NDoHoWo) &&
arg.output_.GetLengths()[2] == static_cast<std::size_t>(CZYX)))
{ {
return false; return false;
} }
......
library/include/ck/library/tensor_operation_instance/device_operation_instance_factory.hpp
View file @ 0f1e8187
...@@ -25,6 +25,8 @@ using BF8 = ck::bf8_t; ...@@ -25,6 +25,8 @@ using BF8 = ck::bf8_t;
using Empty_Tuple = ck::Tuple<>; using Empty_Tuple = ck::Tuple<>;
using BF16_Tuple = ck::Tuple<BF16>;
using F16_Tuple = ck::Tuple<F16>; using F16_Tuple = ck::Tuple<F16>;
using F16_F16_Tuple = ck::Tuple<F16, F16>; using F16_F16_Tuple = ck::Tuple<F16, F16>;
......
library/include/ck/library/tensor_operation_instance/gpu/contraction/device_contraction_instance.hpp 0 → 100644
View file @ 0f1e8187
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_contraction_multiple_d_xdl_cshuffle.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using F32 = float;
using F64 = double;
using F16_Tuple = ck::Tuple<F16>;
using BF16_Tuple = ck::Tuple<BF16>;
using F32_Tuple = ck::Tuple<F32>;
using F64_Tuple = ck::Tuple<F64>;
using Empty_Tuple = ck::Tuple<>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Bilinear = ck::tensor_operation::element_wise::Bilinear;
using Scale = ck::tensor_operation::element_wise::Scale;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
template <typename ADataType,
typename BDataType,
typename AccDataType,
typename CShuffleDataType,
typename DsDataType,
typename EDataType,
typename ComputeDataType,
typename AElementwiseOp,
typename BElementwiseOp,
typename CDEElementwiseOp>
using device_contraction_kk_instance = std::tuple<
// clang-format off
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| Compute|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| Data|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| Type|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 256, 16, 4, 4, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 64, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 64, 64, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 8>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 64, 16, 4, 4, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 64, 128, 16, 4, 4, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 32, 16, 4, 4, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 32, 128, 16, 4, 4, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 64, 64, 32, 16, 4, 4, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 8>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 64, 32, 64, 16, 4, 4, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 8>, 4, ComputeDataType>
// clang-format on
>;
template <typename ADataType,
typename BDataType,
typename AccDataType,
typename CShuffleDataType,
typename DsDataType,
typename EDataType,
typename ComputeDataType,
typename AElementwiseOp,
typename BElementwiseOp,
typename CDEElementwiseOp>
using device_contraction_kn_instance = std::tuple<
// clang-format off
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| Compute|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| Data|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| Type|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 256, 128, 16, 4, 1, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 256, 16, 4, 1, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 256, 16, 4, 4, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 128, 16, 4, 1, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 8, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 128, 16, 4, 1, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 64, 16, 4, 1, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 8>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 1, S<1, 16, 1, 8>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 64, 128, 16, 4, 1, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 8, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 64, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 64, 16, 4, 1, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 64, 16, 4, 4, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 64, 128, 16, 4, 1, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 64, 128, 16, 4, 4, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>
// clang-format on
>;
template <typename ADataType,
typename BDataType,
typename AccDataType,
typename CShuffleDataType,
typename DsDataType,
typename EDataType,
typename ComputeDataType,
typename AElementwiseOp,
typename BElementwiseOp,
typename CDEElementwiseOp>
using device_contraction_mk_instance = std::tuple<
// clang-format off
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| Compute|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| Data|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| Type|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 256, 128, 16, 1, 4, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 256, 16, 1, 4, 32, 32, 2, 4, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 256, 16, 4, 4, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 128, 16, 1, 4, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 128, 16, 1, 4, 32, 32, 2, 2, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 64, 16, 1, 4, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 8>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 64, 128, 16, 1, 4, 32, 32, 2, 2, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 64, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 64, 16, 1, 4, 32, 32, 2, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 64, 16, 4, 4, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 64, 128, 16, 1, 4, 32, 32, 1, 2, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 64, 128, 16, 4, 4, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>
// clang-format on
>;
template <typename ADataType,
typename BDataType,
typename AccDataType,
typename CShuffleDataType,
typename DsDataType,
typename EDataType,
typename ComputeDataType,
typename AElementwiseOp,
typename BElementwiseOp,
typename CDEElementwiseOp>
using device_contraction_mn_instance = std::tuple<
// clang-format off
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| Compute|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| Data|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| Type|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 256, 128, 16, 1, 1, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 256, 16, 1, 1, 32, 32, 2, 4, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 256, 16, 4, 4, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 128, 16, 1, 1, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 8, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 128, 16, 1, 1, 32, 32, 2, 2, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 64, 16, 1, 1, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 8>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 1, S<1, 16, 1, 8>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 64, 128, 16, 1, 1, 32, 32, 2, 2, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 8, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 64, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 8, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 64, 16, 1, 1, 32, 32, 2, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 64, 16, 4, 4, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 64, 128, 16, 1, 1, 32, 32, 1, 2, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, 0, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 64, 128, 16, 4, 4, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>
// clang-format on
>;
template <typename ADataType,
typename BDataType,
typename AccDataType,
typename CShuffleDataType,
typename DsDataType,
typename EDataType,
typename ComputeDataType,
typename AElementwiseOp,
typename BElementwiseOp,
typename CDEElementwiseOp>
using device_contraction_f64_kk_instance = std::tuple<
// clang-format off
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| Compute|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| Data|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| Type|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 128, 16, 2, 2, 16, 16, 4, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 64, 16, 2, 2, 16, 16, 4, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 8>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 64, 128, 16, 2, 2, 16, 16, 4, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 8, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 64, 64, 64, 16, 2, 2, 16, 16, 4, 4, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 8, 1, 8>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 64, 16, 2, 2, 16, 16, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 64, 128, 16, 2, 2, 16, 16, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 32, 16, 2, 2, 16, 16, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 8>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 32, 128, 16, 2, 2, 16, 16, 2, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 8, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 64, 64, 32, 16, 2, 2, 16, 16, 4, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 8, 1, 8>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 64, 32, 64, 16, 2, 2, 16, 16, 2, 4, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 8, 1, 8>, 1, ComputeDataType>
// clang-format on
>;
template <typename ADataType,
typename BDataType,
typename AccDataType,
typename CShuffleDataType,
typename DsDataType,
typename EDataType,
typename ComputeDataType,
typename AElementwiseOp,
typename BElementwiseOp,
typename CDEElementwiseOp>
using device_contraction_f64_kn_instance = std::tuple<
// clang-format off
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| Compute|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| Data|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| Type|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 128, 16, 2, 1, 16, 16, 4, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 128, 16, 2, 2, 16, 16, 4, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 64, 16, 2, 1, 16, 16, 4, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, 1, 1, S<1, 16, 1, 8>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 64, 16, 2, 2, 16, 16, 4, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 16, 1, 8>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 64, 128, 16, 2, 1, 16, 16, 4, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, 1, 1, S<1, 8, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 64, 128, 16, 2, 2, 16, 16, 4, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 8, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 64, 16, 2, 1, 16, 16, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 64, 16, 2, 2, 16, 16, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 64, 128, 16, 2, 1, 16, 16, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 64, 128, 16, 2, 2, 16, 16, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>
// clang-format on
>;
template <typename ADataType,
typename BDataType,
typename AccDataType,
typename CShuffleDataType,
typename DsDataType,
typename EDataType,
typename ComputeDataType,
typename AElementwiseOp,
typename BElementwiseOp,
typename CDEElementwiseOp>
using device_contraction_f64_mk_instance = std::tuple<
// clang-format off
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| Compute|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| Data|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| Type|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 128, 16, 1, 2, 16, 16, 4, 4, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 128, 16, 2, 2, 16, 16, 4, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 64, 16, 1, 2, 16, 16, 4, 4, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 8>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 64, 16, 2, 2, 16, 16, 4, 4, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 8>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 64, 128, 16, 1, 2, 16, 16, 4, 4, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 8, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 64, 128, 16, 2, 2, 16, 16, 4, 4, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 8, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 64, 16, 1, 2, 16, 16, 4, 2, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 64, 16, 2, 2, 16, 16, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 64, 128, 16, 1, 2, 16, 16, 2, 4, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 64, 128, 16, 2, 2, 16, 16, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>
// clang-format on
>;
template <typename ADataType,
typename BDataType,
typename AccDataType,
typename CShuffleDataType,
typename DsDataType,
typename EDataType,
typename ComputeDataType,
typename AElementwiseOp,
typename BElementwiseOp,
typename CDEElementwiseOp>
using device_contraction_f64_mn_instance = std::tuple<
// clang-format off
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| Compute|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| Data|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| Type|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 128, 16, 1, 1, 16, 16, 4, 4, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 128, 16, 2, 2, 16, 16, 4, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 64, 16, 1, 1, 16, 16, 4, 4, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, 1, 1, S<1, 16, 1, 8>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 128, 64, 16, 2, 2, 16, 16, 4, 4, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 16, 1, 8>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 64, 128, 16, 1, 1, 16, 16, 4, 4, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, 1, 1, S<1, 8, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 128, 64, 128, 16, 2, 2, 16, 16, 4, 4, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 8, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 64, 16, 1, 1, 16, 16, 4, 2, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 128, 64, 16, 2, 2, 16, 16, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 64, 128, 16, 1, 1, 16, 16, 2, 4, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 0, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>,
DeviceContractionMultipleD_Xdl_CShuffle< 2, 2, 2, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementwiseOp, BElementwiseOp, CDEElementwiseOp, GemmMNKPadding, 1, 256, 64, 128, 16, 2, 2, 16, 16, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, 1, 1, 1, S<1, 16, 1, 16>, 1, ComputeDataType>
// clang-format on
>;
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/tensor_operation_instance/gpu/contraction_bilinear.hpp
View file @ 0f1e8187
...@@ -17,7 +17,6 @@ namespace tensor_operation { ...@@ -17,7 +17,6 @@ namespace tensor_operation {
namespace device { namespace device {
namespace instance { namespace instance {
#ifdef CK_ENABLE_FP32 #ifdef CK_ENABLE_FP32
// float
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_kknn_instance( void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_kknn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2, std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2, 2,
...@@ -28,7 +27,8 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_kknn ...@@ -28,7 +27,8 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_kknn
F32, F32,
PassThrough, PassThrough,
PassThrough, PassThrough,
Bilinear>>>& instances); Bilinear,
F32>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_knnn_instance( void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_knnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2, std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
...@@ -40,7 +40,8 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_knnn ...@@ -40,7 +40,8 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_knnn
F32, F32,
PassThrough, PassThrough,
PassThrough, PassThrough,
Bilinear>>>& instances); Bilinear,
F32>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_mknn_instance( void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_mknn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2, std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
...@@ -52,7 +53,8 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_mknn ...@@ -52,7 +53,8 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_mknn
F32, F32,
PassThrough, PassThrough,
PassThrough, PassThrough,
Bilinear>>>& instances); Bilinear,
F32>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_mnnn_instance( void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_mnnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2, std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
...@@ -64,10 +66,115 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_mnnn ...@@ -64,10 +66,115 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_mnnn
F32, F32,
PassThrough, PassThrough,
PassThrough, PassThrough,
Bilinear>>>& instances); Bilinear,
#endif F32>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_compute_f16_kknn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F32,
F32,
F32_Tuple,
F32,
PassThrough,
PassThrough,
Bilinear,
F16>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_compute_f16_knnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F32,
F32,
F32_Tuple,
F32,
PassThrough,
PassThrough,
Bilinear,
F16>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_compute_f16_mknn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F32,
F32,
F32_Tuple,
F32,
PassThrough,
PassThrough,
Bilinear,
F16>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_compute_f16_mnnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F32,
F32,
F32_Tuple,
F32,
PassThrough,
PassThrough,
Bilinear,
F16>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_compute_bf16_kknn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F32,
F32,
F32_Tuple,
F32,
PassThrough,
PassThrough,
Bilinear,
BF16>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_compute_bf16_knnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F32,
F32,
F32_Tuple,
F32,
PassThrough,
PassThrough,
Bilinear,
BF16>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_compute_bf16_mknn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F32,
F32,
F32_Tuple,
F32,
PassThrough,
PassThrough,
Bilinear,
BF16>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_compute_bf16_mnnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F32,
F32,
F32_Tuple,
F32,
PassThrough,
PassThrough,
Bilinear,
BF16>>>& instances);
#endif // CK_ENABLE_FP32
#ifdef CK_ENABLE_FP64 #ifdef CK_ENABLE_FP64
// double
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_kknn_instance( void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_kknn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2, std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2, 2,
...@@ -78,7 +185,8 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_kknn ...@@ -78,7 +185,8 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_kknn
F64, F64,
PassThrough, PassThrough,
PassThrough, PassThrough,
Bilinear>>>& instances); Bilinear,
F64>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_knnn_instance( void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_knnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2, std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
...@@ -90,7 +198,8 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_knnn ...@@ -90,7 +198,8 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_knnn
F64, F64,
PassThrough, PassThrough,
PassThrough, PassThrough,
Bilinear>>>& instances); Bilinear,
F64>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_mknn_instance( void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_mknn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2, std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
...@@ -102,7 +211,8 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_mknn ...@@ -102,7 +211,8 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_mknn
F64, F64,
PassThrough, PassThrough,
PassThrough, PassThrough,
Bilinear>>>& instances); Bilinear,
F64>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_mnnn_instance( void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_mnnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2, std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
...@@ -114,8 +224,170 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_mnnn ...@@ -114,8 +224,170 @@ void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_mnnn
F64, F64,
PassThrough, PassThrough,
PassThrough, PassThrough,
Bilinear>>>& instances); Bilinear,
#endif F64>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_compute_f32_kknn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F64,
F64,
F64_Tuple,
F64,
PassThrough,
PassThrough,
Bilinear,
F32>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_compute_f32_knnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F64,
F64,
F64_Tuple,
F64,
PassThrough,
PassThrough,
Bilinear,
F32>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_compute_f32_mknn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F64,
F64,
F64_Tuple,
F64,
PassThrough,
PassThrough,
Bilinear,
F32>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_compute_f32_mnnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F64,
F64,
F64_Tuple,
F64,
PassThrough,
PassThrough,
Bilinear,
F32>>>& instances);
#endif // CK_ENABLE_FP64
#ifdef CK_ENABLE_FP16
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f16_f16_f16_f16_compute_f32_kknn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F16,
F16,
F16_Tuple,
F16,
PassThrough,
PassThrough,
Bilinear,
F32>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f16_f16_f16_f16_compute_f32_knnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F16,
F16,
F16_Tuple,
F16,
PassThrough,
PassThrough,
Bilinear,
F32>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f16_f16_f16_f16_compute_f32_mknn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F16,
F16,
F16_Tuple,
F16,
PassThrough,
PassThrough,
Bilinear,
F32>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f16_f16_f16_f16_compute_f32_mnnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F16,
F16,
F16_Tuple,
F16,
PassThrough,
PassThrough,
Bilinear,
F32>>>& instances);
#endif // CK_ENABLE_FP16
#ifdef CK_ENABLE_BF16
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_bf16_bf16_bf16_bf16_compute_f32_kknn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
BF16,
BF16,
BF16_Tuple,
BF16,
PassThrough,
PassThrough,
Bilinear,
F32>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_bf16_bf16_bf16_bf16_compute_f32_knnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
BF16,
BF16,
BF16_Tuple,
BF16,
PassThrough,
PassThrough,
Bilinear,
F32>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_bf16_bf16_bf16_bf16_compute_f32_mknn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
BF16,
BF16,
BF16_Tuple,
BF16,
PassThrough,
PassThrough,
Bilinear,
F32>>>& instances);
void add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_bf16_bf16_bf16_bf16_compute_f32_mnnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
BF16,
BF16,
BF16_Tuple,
BF16,
PassThrough,
PassThrough,
Bilinear,
F32>>>& instances);
#endif // CK_ENABLE_FP16
// Contraction + Bilinear // Contraction + Bilinear
template <index_t NumDimM, template <index_t NumDimM,
index_t NumDimN, index_t NumDimN,
...@@ -123,7 +395,8 @@ template <index_t NumDimM, ...@@ -123,7 +395,8 @@ template <index_t NumDimM,
typename ADataType, typename ADataType,
typename BDataType, typename BDataType,
typename DDataType, typename DDataType,
typename EDataType> typename EDataType,
typename ComputeDataType>
struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContractionMultipleD< struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContractionMultipleD<
NumDimM, NumDimM,
NumDimN, NumDimN,
...@@ -134,7 +407,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra ...@@ -134,7 +407,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra
EDataType, EDataType,
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::Bilinear>> ck::tensor_operation::element_wise::Bilinear,
ComputeDataType>>
{ {
using DeviceOp = DeviceContractionMultipleD<NumDimM, using DeviceOp = DeviceContractionMultipleD<NumDimM,
NumDimN, NumDimN,
...@@ -145,45 +419,125 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra ...@@ -145,45 +419,125 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra
EDataType, EDataType,
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::Bilinear>; ck::tensor_operation::element_wise::Bilinear,
ComputeDataType>;
static auto GetInstances() static auto GetInstances()
{ {
std::vector<std::unique_ptr<DeviceOp>> op_ptrs; std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
#ifdef CK_ENABLE_FP32 #ifdef CK_ENABLE_FP32
if constexpr(is_same_v<ADataType, float> && is_same_v<BDataType, float> && if constexpr(is_same_v<ADataType, float> && is_same_v<BDataType, float> &&
is_same_v<DDataType, float> && is_same_v<EDataType, float>) is_same_v<EDataType, float>)
{ {
if constexpr(NumDimM == 2 && NumDimN == 2 && NumDimK == 2) if constexpr(NumDimM == 2 && NumDimN == 2 && NumDimK == 2)
{ {
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_knnn_instance( if constexpr(is_same_v<ComputeDataType, float>)
op_ptrs); {
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_kknn_instance( add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_kknn_instance(
op_ptrs); op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_mnnn_instance( add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_knnn_instance(
op_ptrs); op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_mknn_instance( add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_mknn_instance(
op_ptrs); op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_mnnn_instance(
op_ptrs);
}
else if constexpr(is_same_v<ComputeDataType, ck::half_t>)
{
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_compute_f16_kknn_instance(
op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_compute_f16_knnn_instance(
op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_compute_f16_mknn_instance(
op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_compute_f16_mnnn_instance(
op_ptrs);
}
else if constexpr(is_same_v<ComputeDataType, ck::bhalf_t>)
{
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_compute_bf16_kknn_instance(
op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_compute_bf16_knnn_instance(
op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_compute_bf16_mknn_instance(
op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_f32_compute_bf16_mnnn_instance(
op_ptrs);
}
} }
} }
#endif #endif // CK_ENABLE_FP32
#ifdef CK_ENABLE_FP64 #ifdef CK_ENABLE_FP64
if constexpr(is_same_v<ADataType, double> && is_same_v<BDataType, double> && if constexpr(is_same_v<ADataType, double> && is_same_v<BDataType, double> &&
is_same_v<DDataType, double> && is_same_v<EDataType, double>) is_same_v<EDataType, double>)
{
if constexpr(NumDimM == 2 && NumDimN == 2 && NumDimK == 2)
{
if constexpr(is_same_v<ComputeDataType, double>)
{
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_kknn_instance(
op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_knnn_instance(
op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_mknn_instance(
op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_mnnn_instance(
op_ptrs);
}
else if constexpr(is_same_v<ComputeDataType, float>)
{
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_compute_f32_kknn_instance(
op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_compute_f32_knnn_instance(
op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_compute_f32_mknn_instance(
op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_compute_f32_mnnn_instance(
op_ptrs);
}
}
}
#endif // CK_ENABLE_FP64
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<ADataType, ck::half_t> && is_same_v<BDataType, ck::half_t> &&
is_same_v<EDataType, ck::half_t>)
{
if constexpr(NumDimM == 2 && NumDimN == 2 && NumDimK == 2)
{
if constexpr(is_same_v<ComputeDataType, float>)
{
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f16_f16_f16_f16_compute_f32_kknn_instance(
op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f16_f16_f16_f16_compute_f32_knnn_instance(
op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f16_f16_f16_f16_compute_f32_mknn_instance(
op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f16_f16_f16_f16_compute_f32_mnnn_instance(
op_ptrs);
}
}
}
#endif // CK_ENABLE_FP16
#ifdef CK_ENABLE_BF16
if constexpr(is_same_v<ADataType, ck::bhalf_t> && is_same_v<BDataType, ck::bhalf_t> &&
is_same_v<EDataType, ck::bhalf_t>)
{ {
if constexpr(NumDimM == 2 && NumDimN == 2 && NumDimK == 2) if constexpr(NumDimM == 2 && NumDimN == 2 && NumDimK == 2)
{ {
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_kknn_instance( if constexpr(is_same_v<ComputeDataType, float>)
op_ptrs); {
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_knnn_instance( add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_bf16_bf16_bf16_bf16_compute_f32_kknn_instance(
op_ptrs); op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_mnnn_instance( add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_bf16_bf16_bf16_bf16_compute_f32_knnn_instance(
op_ptrs); op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_f64_mknn_instance( add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_bf16_bf16_bf16_bf16_compute_f32_mknn_instance(
op_ptrs); op_ptrs);
add_device_contraction_bilinear_m2_n2_k2_xdl_c_shuffle_bf16_bf16_bf16_bf16_compute_f32_mnnn_instance(
op_ptrs);
}
} }
} }
#endif #endif // CK_ENABLE_BF16
return op_ptrs; return op_ptrs;
} }
}; };
......
library/include/ck/library/tensor_operation_instance/gpu/contraction_scale.hpp
View file @ 0f1e8187
...@@ -17,7 +17,6 @@ namespace tensor_operation { ...@@ -17,7 +17,6 @@ namespace tensor_operation {
namespace device { namespace device {
namespace instance { namespace instance {
#ifdef CK_ENABLE_FP32 #ifdef CK_ENABLE_FP32
// float
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_kkn_instance( void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_kkn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2, std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2, 2,
...@@ -28,7 +27,8 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_kkn_instanc ...@@ -28,7 +27,8 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_kkn_instanc
F32, F32,
PassThrough, PassThrough,
PassThrough, PassThrough,
Scale>>>& instances); Scale,
F32>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_knn_instance( void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_knn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2, std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
...@@ -40,7 +40,8 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_knn_instanc ...@@ -40,7 +40,8 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_knn_instanc
F32, F32,
PassThrough, PassThrough,
PassThrough, PassThrough,
Scale>>>& instances); Scale,
F32>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_mkn_instance( void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_mkn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2, std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
...@@ -52,7 +53,8 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_mkn_instanc ...@@ -52,7 +53,8 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_mkn_instanc
F32, F32,
PassThrough, PassThrough,
PassThrough, PassThrough,
Scale>>>& instances); Scale,
F32>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_mnn_instance( void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_mnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2, std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
...@@ -64,10 +66,115 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_mnn_instanc ...@@ -64,10 +66,115 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_mnn_instanc
F32, F32,
PassThrough, PassThrough,
PassThrough, PassThrough,
Scale>>>& instances); Scale,
#endif F32>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_compute_f16_kkn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
Scale,
F16>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_compute_f16_knn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
Scale,
F16>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_compute_f16_mkn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
Scale,
F16>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_compute_f16_mnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
Scale,
F16>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_compute_bf16_kkn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
Scale,
BF16>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_compute_bf16_knn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
Scale,
BF16>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_compute_bf16_mkn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
Scale,
BF16>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_compute_bf16_mnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F32,
F32,
Empty_Tuple,
F32,
PassThrough,
PassThrough,
Scale,
BF16>>>& instances);
#endif // CK_ENABLE_FP32
#ifdef CK_ENABLE_FP64 #ifdef CK_ENABLE_FP64
// double
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_kkn_instance( void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_kkn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2, std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2, 2,
...@@ -78,7 +185,8 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_kkn_instanc ...@@ -78,7 +185,8 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_kkn_instanc
F64, F64,
PassThrough, PassThrough,
PassThrough, PassThrough,
Scale>>>& instances); Scale,
F64>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_knn_instance( void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_knn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2, std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
...@@ -90,7 +198,8 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_knn_instanc ...@@ -90,7 +198,8 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_knn_instanc
F64, F64,
PassThrough, PassThrough,
PassThrough, PassThrough,
Scale>>>& instances); Scale,
F64>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_mkn_instance( void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_mkn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2, std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
...@@ -102,7 +211,8 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_mkn_instanc ...@@ -102,7 +211,8 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_mkn_instanc
F64, F64,
PassThrough, PassThrough,
PassThrough, PassThrough,
Scale>>>& instances); Scale,
F64>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_mnn_instance( void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_mnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2, std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
...@@ -114,15 +224,178 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_mnn_instanc ...@@ -114,15 +224,178 @@ void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_mnn_instanc
F64, F64,
PassThrough, PassThrough,
PassThrough, PassThrough,
Scale>>>& instances); Scale,
#endif F64>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_compute_f32_kkn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F64,
F64,
Empty_Tuple,
F64,
PassThrough,
PassThrough,
Scale,
F32>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_compute_f32_knn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F64,
F64,
Empty_Tuple,
F64,
PassThrough,
PassThrough,
Scale,
F32>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_compute_f32_mkn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F64,
F64,
Empty_Tuple,
F64,
PassThrough,
PassThrough,
Scale,
F32>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_compute_f32_mnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F64,
F64,
Empty_Tuple,
F64,
PassThrough,
PassThrough,
Scale,
F32>>>& instances);
#endif // CK_ENABLE_FP64
#ifdef CK_ENABLE_FP16
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f16_f16_f16_compute_f32_kkn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
Scale,
F32>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f16_f16_f16_compute_f32_knn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
Scale,
F32>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f16_f16_f16_compute_f32_mkn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
Scale,
F32>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f16_f16_f16_compute_f32_mnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
Scale,
F32>>>& instances);
#endif // CK_ENABLE_FP16
#ifdef CK_ENABLE_BF16
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_bf16_bf16_bf16_compute_f32_kkn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
BF16,
BF16,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
Scale,
F32>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_bf16_bf16_bf16_compute_f32_knn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
BF16,
BF16,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
Scale,
F32>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_bf16_bf16_bf16_compute_f32_mkn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
BF16,
BF16,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
Scale,
F32>>>& instances);
void add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_bf16_bf16_bf16_compute_f32_mnn_instance(
std::vector<std::unique_ptr<DeviceContractionMultipleD<2,
2,
2,
BF16,
BF16,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
Scale,
F32>>>& instances);
#endif // CK_ENABLE_FP16
// Contraction + Scale // Contraction + Scale
template <index_t NumDimM, template <index_t NumDimM,
index_t NumDimN, index_t NumDimN,
index_t NumDimK, index_t NumDimK,
typename ADataType, typename ADataType,
typename BDataType, typename BDataType,
typename EDataType> typename EDataType,
typename ComputeDataType>
struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContractionMultipleD< struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContractionMultipleD<
NumDimM, NumDimM,
NumDimN, NumDimN,
...@@ -133,7 +406,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra ...@@ -133,7 +406,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra
EDataType, EDataType,
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::Scale>> ck::tensor_operation::element_wise::Scale,
ComputeDataType>>
{ {
using DeviceOp = DeviceContractionMultipleD<NumDimM, using DeviceOp = DeviceContractionMultipleD<NumDimM,
NumDimN, NumDimN,
...@@ -144,7 +418,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra ...@@ -144,7 +418,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra
EDataType, EDataType,
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough, ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::Scale>; ck::tensor_operation::element_wise::Scale,
ComputeDataType>;
static auto GetInstances() static auto GetInstances()
{ {
...@@ -155,34 +430,113 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra ...@@ -155,34 +430,113 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceContra
{ {
if constexpr(NumDimM == 2 && NumDimN == 2 && NumDimK == 2) if constexpr(NumDimM == 2 && NumDimN == 2 && NumDimK == 2)
{ {
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_kkn_instance( if constexpr(is_same_v<ComputeDataType, float>)
op_ptrs); {
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_knn_instance( add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_kkn_instance(
op_ptrs); op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_mkn_instance( add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_knn_instance(
op_ptrs); op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_mnn_instance( add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_mkn_instance(
op_ptrs); op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_mnn_instance(
op_ptrs);
}
else if constexpr(is_same_v<ComputeDataType, ck::half_t>)
{
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_compute_f16_kkn_instance(
op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_compute_f16_knn_instance(
op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_compute_f16_mkn_instance(
op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_compute_f16_mnn_instance(
op_ptrs);
}
else if constexpr(is_same_v<ComputeDataType, ck::bhalf_t>)
{
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_compute_bf16_kkn_instance(
op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_compute_bf16_knn_instance(
op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_compute_bf16_mkn_instance(
op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f32_f32_f32_compute_bf16_mnn_instance(
op_ptrs);
}
} }
} }
#endif #endif // CK_ENABLE_FP32
#ifdef CK_ENABLE_FP64 #ifdef CK_ENABLE_FP64
if constexpr(is_same_v<ADataType, double> && is_same_v<BDataType, double> && if constexpr(is_same_v<ADataType, double> && is_same_v<BDataType, double> &&
is_same_v<EDataType, double>) is_same_v<EDataType, double>)
{ {
if constexpr(NumDimM == 2 && NumDimN == 2 && NumDimK == 2) if constexpr(NumDimM == 2 && NumDimN == 2 && NumDimK == 2)
{ {
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_kkn_instance( if constexpr(is_same_v<ComputeDataType, double>)
op_ptrs); {
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_knn_instance( add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_kkn_instance(
op_ptrs); op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_mkn_instance( add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_knn_instance(
op_ptrs); op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_mnn_instance( add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_mkn_instance(
op_ptrs); op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_mnn_instance(
op_ptrs);
}
else if constexpr(is_same_v<ComputeDataType, float>)
{
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_compute_f32_kkn_instance(
op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_compute_f32_knn_instance(
op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_compute_f32_mkn_instance(
op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f64_f64_f64_compute_f32_mnn_instance(
op_ptrs);
}
}
}
#endif // CK_ENABLE_FP64
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<ADataType, ck::half_t> && is_same_v<BDataType, ck::half_t> &&
is_same_v<EDataType, ck::half_t>)
{
if constexpr(NumDimM == 2 && NumDimN == 2 && NumDimK == 2)
{
if constexpr(is_same_v<ComputeDataType, float>)
{
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f16_f16_f16_compute_f32_kkn_instance(
op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f16_f16_f16_compute_f32_knn_instance(
op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f16_f16_f16_compute_f32_mkn_instance(
op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_f16_f16_f16_compute_f32_mnn_instance(
op_ptrs);
}
}
}
#endif // CK_ENABLE_FP16
#ifdef CK_ENABLE_BF16
if constexpr(is_same_v<ADataType, ck::bhalf_t> && is_same_v<BDataType, ck::bhalf_t> &&
is_same_v<EDataType, ck::bhalf_t>)
{
if constexpr(NumDimM == 2 && NumDimN == 2 && NumDimK == 2)
{
if constexpr(is_same_v<ComputeDataType, float>)
{
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_bf16_bf16_bf16_compute_f32_kkn_instance(
op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_bf16_bf16_bf16_compute_f32_knn_instance(
op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_bf16_bf16_bf16_compute_f32_mkn_instance(
op_ptrs);
add_device_contraction_scale_m2_n2_k2_xdl_c_shuffle_bf16_bf16_bf16_compute_f32_mnn_instance(
op_ptrs);
}
} }
} }
#endif #endif // CK_ENABLE_BF16
return op_ptrs; return op_ptrs;
} }
}; };
......
library/include/ck/library/tensor_operation_instance/gpu/conv_tensor_rearrange.hpp
View file @ 0f1e8187
...@@ -19,109 +19,214 @@ namespace instance { ...@@ -19,109 +19,214 @@ namespace instance {
using namespace ck::conv_tensor_rearrange_op; using namespace ck::conv_tensor_rearrange_op;
// GNWC/GNHWC/GNDHWC
// Image to Column // Image to Column
// nhwc, 1d // GNWC, 1d
void add_device_image_to_column_nwc_1d_bf16_instances( void add_device_image_to_column_gnwc_1d_bf16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, BF16, BF16, ImageToColumn>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, BF16, BF16, ImageToColumn>>>&
instances); instances);
void add_device_image_to_column_nwc_1d_f16_instances( void add_device_image_to_column_gnwc_1d_f16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, F16, F16, ImageToColumn>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, F16, F16, ImageToColumn>>>&
instances); instances);
void add_device_image_to_column_nwc_1d_f32_instances( void add_device_image_to_column_gnwc_1d_f32_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, F32, F32, ImageToColumn>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, F32, F32, ImageToColumn>>>&
instances); instances);
void add_device_image_to_column_nwc_1d_i8_instances( void add_device_image_to_column_gnwc_1d_i8_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, int8_t, int8_t, ImageToColumn>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, int8_t, int8_t, ImageToColumn>>>&
instances); instances);
// nhwc, 2d // GNHWC, 2d
void add_device_image_to_column_nhwc_2d_bf16_instances( void add_device_image_to_column_gnhwc_2d_bf16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, BF16, BF16, ImageToColumn>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, BF16, BF16, ImageToColumn>>>&
instances); instances);
void add_device_image_to_column_nhwc_2d_f16_instances( void add_device_image_to_column_gnhwc_2d_f16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, F16, F16, ImageToColumn>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, F16, F16, ImageToColumn>>>&
instances); instances);
void add_device_image_to_column_nhwc_2d_f32_instances( void add_device_image_to_column_gnhwc_2d_f32_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, F32, F32, ImageToColumn>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, F32, F32, ImageToColumn>>>&
instances); instances);
void add_device_image_to_column_nhwc_2d_i8_instances( void add_device_image_to_column_gnhwc_2d_i8_instances(
std::vector< std::vector<
std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, int8_t, int8_t, ImageToColumn>>>& std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, int8_t, int8_t, ImageToColumn>>>&
instances); instances);
// nhwc, 3d // GNDHWC, 3d
void add_device_image_to_column_ndhwc_3d_bf16_instances( void add_device_image_to_column_gndhwc_3d_bf16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, BF16, BF16, ImageToColumn>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, BF16, BF16, ImageToColumn>>>&
instances); instances);
void add_device_image_to_column_ndhwc_3d_f16_instances( void add_device_image_to_column_gndhwc_3d_f16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, F16, F16, ImageToColumn>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, F16, F16, ImageToColumn>>>&
instances); instances);
void add_device_image_to_column_ndhwc_3d_f32_instances( void add_device_image_to_column_gndhwc_3d_f32_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, F32, F32, ImageToColumn>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, F32, F32, ImageToColumn>>>&
instances); instances);
void add_device_image_to_column_ndhwc_3d_i8_instances( void add_device_image_to_column_gndhwc_3d_i8_instances(
std::vector< std::vector<
std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, int8_t, int8_t, ImageToColumn>>>& std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, int8_t, int8_t, ImageToColumn>>>&
instances); instances);
// Column to Image // Column to Image
// nhwc, 1d // GNWC, 1d
void add_device_column_to_image_nwc_1d_bf16_instances( void add_device_column_to_image_gnwc_1d_bf16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, BF16, BF16, ColumnToImage>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, BF16, BF16, ColumnToImage>>>&
instances); instances);
void add_device_column_to_image_nwc_1d_f16_instances( void add_device_column_to_image_gnwc_1d_f16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, F16, F16, ColumnToImage>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, F16, F16, ColumnToImage>>>&
instances); instances);
void add_device_column_to_image_nwc_1d_f32_instances( void add_device_column_to_image_gnwc_1d_f32_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, F32, F32, ColumnToImage>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, F32, F32, ColumnToImage>>>&
instances); instances);
void add_device_column_to_image_nwc_1d_i8_instances( void add_device_column_to_image_gnwc_1d_i8_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, int8_t, int8_t, ColumnToImage>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, GNWC, int8_t, int8_t, ColumnToImage>>>&
instances); instances);
// nhwc, 2d // GNHWC, 2d
void add_device_column_to_image_nhwc_2d_bf16_instances( void add_device_column_to_image_gnhwc_2d_bf16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, BF16, BF16, ColumnToImage>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, BF16, BF16, ColumnToImage>>>&
instances); instances);
void add_device_column_to_image_nhwc_2d_f16_instances( void add_device_column_to_image_gnhwc_2d_f16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, F16, F16, ColumnToImage>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, F16, F16, ColumnToImage>>>&
instances); instances);
void add_device_column_to_image_nhwc_2d_f32_instances( void add_device_column_to_image_gnhwc_2d_f32_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, F32, F32, ColumnToImage>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, F32, F32, ColumnToImage>>>&
instances); instances);
void add_device_column_to_image_nhwc_2d_i8_instances( void add_device_column_to_image_gnhwc_2d_i8_instances(
std::vector< std::vector<
std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, int8_t, int8_t, ColumnToImage>>>& std::unique_ptr<DeviceConvTensorRearrange<2, GNHWC, int8_t, int8_t, ColumnToImage>>>&
instances); instances);
// nhwc, 3d // GNDHWC, 3d
void add_device_column_to_image_ndhwc_3d_bf16_instances( void add_device_column_to_image_gndhwc_3d_bf16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, BF16, BF16, ColumnToImage>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, BF16, BF16, ColumnToImage>>>&
instances); instances);
void add_device_column_to_image_ndhwc_3d_f16_instances( void add_device_column_to_image_gndhwc_3d_f16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, F16, F16, ColumnToImage>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, F16, F16, ColumnToImage>>>&
instances); instances);
void add_device_column_to_image_ndhwc_3d_f32_instances( void add_device_column_to_image_gndhwc_3d_f32_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, F32, F32, ColumnToImage>>>& std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, F32, F32, ColumnToImage>>>&
instances); instances);
void add_device_column_to_image_ndhwc_3d_i8_instances( void add_device_column_to_image_gndhwc_3d_i8_instances(
std::vector< std::vector<
std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, int8_t, int8_t, ColumnToImage>>>& std::unique_ptr<DeviceConvTensorRearrange<3, GNDHWC, int8_t, int8_t, ColumnToImage>>>&
instances); instances);
// NWGC/NHWGC/NDHWGC
// Image to Column
// NWGC, 1d
void add_device_image_to_column_nwgc_1d_bf16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, NWGC, BF16, BF16, ImageToColumn>>>&
instances);
void add_device_image_to_column_nwgc_1d_f16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, NWGC, F16, F16, ImageToColumn>>>&
instances);
void add_device_image_to_column_nwgc_1d_f32_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, NWGC, F32, F32, ImageToColumn>>>&
instances);
void add_device_image_to_column_nwgc_1d_i8_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, NWGC, int8_t, int8_t, ImageToColumn>>>&
instances);
// NHWGC, 2d
void add_device_image_to_column_nhwgc_2d_bf16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, NHWGC, BF16, BF16, ImageToColumn>>>&
instances);
void add_device_image_to_column_nhwgc_2d_f16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, NHWGC, F16, F16, ImageToColumn>>>&
instances);
void add_device_image_to_column_nhwgc_2d_f32_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, NHWGC, F32, F32, ImageToColumn>>>&
instances);
void add_device_image_to_column_nhwgc_2d_i8_instances(
std::vector<
std::unique_ptr<DeviceConvTensorRearrange<2, NHWGC, int8_t, int8_t, ImageToColumn>>>&
instances);
// NDHWGC, 3d
void add_device_image_to_column_ndhwgc_3d_bf16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, NDHWGC, BF16, BF16, ImageToColumn>>>&
instances);
void add_device_image_to_column_ndhwgc_3d_f16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, NDHWGC, F16, F16, ImageToColumn>>>&
instances);
void add_device_image_to_column_ndhwgc_3d_f32_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, NDHWGC, F32, F32, ImageToColumn>>>&
instances);
void add_device_image_to_column_ndhwgc_3d_i8_instances(
std::vector<
std::unique_ptr<DeviceConvTensorRearrange<3, NDHWGC, int8_t, int8_t, ImageToColumn>>>&
instances);
// Column to Image
// NWGC, 1d
void add_device_column_to_image_nwgc_1d_bf16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, NWGC, BF16, BF16, ColumnToImage>>>&
instances);
void add_device_column_to_image_nwgc_1d_f16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, NWGC, F16, F16, ColumnToImage>>>&
instances);
void add_device_column_to_image_nwgc_1d_f32_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, NWGC, F32, F32, ColumnToImage>>>&
instances);
void add_device_column_to_image_nwgc_1d_i8_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, NWGC, int8_t, int8_t, ColumnToImage>>>&
instances);
// NHWGC, 2d
void add_device_column_to_image_nhwgc_2d_bf16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, NHWGC, BF16, BF16, ColumnToImage>>>&
instances);
void add_device_column_to_image_nhwgc_2d_f16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, NHWGC, F16, F16, ColumnToImage>>>&
instances);
void add_device_column_to_image_nhwgc_2d_f32_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, NHWGC, F32, F32, ColumnToImage>>>&
instances);
void add_device_column_to_image_nhwgc_2d_i8_instances(
std::vector<
std::unique_ptr<DeviceConvTensorRearrange<2, NHWGC, int8_t, int8_t, ColumnToImage>>>&
instances);
// NDHWGC, 3d
void add_device_column_to_image_ndhwgc_3d_bf16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, NDHWGC, BF16, BF16, ColumnToImage>>>&
instances);
void add_device_column_to_image_ndhwgc_3d_f16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, NDHWGC, F16, F16, ColumnToImage>>>&
instances);
void add_device_column_to_image_ndhwgc_3d_f32_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, NDHWGC, F32, F32, ColumnToImage>>>&
instances);
void add_device_column_to_image_ndhwgc_3d_i8_instances(
std::vector<
std::unique_ptr<DeviceConvTensorRearrange<3, NDHWGC, int8_t, int8_t, ColumnToImage>>>&
instances);
template <ck::index_t NumDimSpatial, template <ck::index_t NumDimSpatial,
typename ImageLayout, typename ImageLayout,
...@@ -151,60 +256,120 @@ struct DeviceOperationInstanceFactory< ...@@ -151,60 +256,120 @@ struct DeviceOperationInstanceFactory<
{ {
if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>) if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>)
{ {
add_device_image_to_column_nwc_1d_f32_instances(op_ptrs); add_device_image_to_column_gnwc_1d_f32_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>) else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>)
{ {
add_device_image_to_column_nwc_1d_f16_instances(op_ptrs); add_device_image_to_column_gnwc_1d_f16_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, ck::bhalf_t> && else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<OutDataType, ck::bhalf_t>) is_same_v<OutDataType, ck::bhalf_t>)
{ {
add_device_image_to_column_nwc_1d_bf16_instances(op_ptrs); add_device_image_to_column_gnwc_1d_bf16_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>) else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>)
{ {
add_device_image_to_column_nwc_1d_i8_instances(op_ptrs); add_device_image_to_column_gnwc_1d_i8_instances(op_ptrs);
} }
} }
else if constexpr(NumDimSpatial == 2 && is_same_v<ImageLayout, GNHWC>) else if constexpr(NumDimSpatial == 2 && is_same_v<ImageLayout, GNHWC>)
{ {
if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>) if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>)
{ {
add_device_image_to_column_nhwc_2d_f32_instances(op_ptrs); add_device_image_to_column_gnhwc_2d_f32_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>) else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>)
{ {
add_device_image_to_column_nhwc_2d_f16_instances(op_ptrs); add_device_image_to_column_gnhwc_2d_f16_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, ck::bhalf_t> && else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<OutDataType, ck::bhalf_t>) is_same_v<OutDataType, ck::bhalf_t>)
{ {
add_device_image_to_column_nhwc_2d_bf16_instances(op_ptrs); add_device_image_to_column_gnhwc_2d_bf16_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>) else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>)
{ {
add_device_image_to_column_nhwc_2d_i8_instances(op_ptrs); add_device_image_to_column_gnhwc_2d_i8_instances(op_ptrs);
} }
} }
else if constexpr(NumDimSpatial == 3 && is_same_v<ImageLayout, GNDHWC>) else if constexpr(NumDimSpatial == 3 && is_same_v<ImageLayout, GNDHWC>)
{ {
if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>) if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>)
{ {
add_device_image_to_column_ndhwc_3d_f32_instances(op_ptrs); add_device_image_to_column_gndhwc_3d_f32_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>) else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>)
{ {
add_device_image_to_column_ndhwc_3d_f16_instances(op_ptrs); add_device_image_to_column_gndhwc_3d_f16_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, ck::bhalf_t> && else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<OutDataType, ck::bhalf_t>) is_same_v<OutDataType, ck::bhalf_t>)
{ {
add_device_image_to_column_ndhwc_3d_bf16_instances(op_ptrs); add_device_image_to_column_gndhwc_3d_bf16_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>) else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>)
{ {
add_device_image_to_column_ndhwc_3d_i8_instances(op_ptrs); add_device_image_to_column_gndhwc_3d_i8_instances(op_ptrs);
}
}
else if constexpr(NumDimSpatial == 1 && is_same_v<ImageLayout, NWGC>)
{
if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>)
{
add_device_image_to_column_nwgc_1d_f32_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>)
{
add_device_image_to_column_nwgc_1d_f16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_image_to_column_nwgc_1d_bf16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>)
{
add_device_image_to_column_nwgc_1d_i8_instances(op_ptrs);
}
}
else if constexpr(NumDimSpatial == 2 && is_same_v<ImageLayout, NHWGC>)
{
if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>)
{
add_device_image_to_column_nhwgc_2d_f32_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>)
{
add_device_image_to_column_nhwgc_2d_f16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_image_to_column_nhwgc_2d_bf16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>)
{
add_device_image_to_column_nhwgc_2d_i8_instances(op_ptrs);
}
}
else if constexpr(NumDimSpatial == 3 && is_same_v<ImageLayout, NDHWGC>)
{
if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>)
{
add_device_image_to_column_ndhwgc_3d_f32_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>)
{
add_device_image_to_column_ndhwgc_3d_f16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_image_to_column_ndhwgc_3d_bf16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>)
{
add_device_image_to_column_ndhwgc_3d_i8_instances(op_ptrs);
} }
} }
} }
...@@ -214,60 +379,120 @@ struct DeviceOperationInstanceFactory< ...@@ -214,60 +379,120 @@ struct DeviceOperationInstanceFactory<
{ {
if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>) if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>)
{ {
add_device_column_to_image_nwc_1d_f32_instances(op_ptrs); add_device_column_to_image_gnwc_1d_f32_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>) else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>)
{ {
add_device_column_to_image_nwc_1d_f16_instances(op_ptrs); add_device_column_to_image_gnwc_1d_f16_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, ck::bhalf_t> && else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<OutDataType, ck::bhalf_t>) is_same_v<OutDataType, ck::bhalf_t>)
{ {
add_device_column_to_image_nwc_1d_bf16_instances(op_ptrs); add_device_column_to_image_gnwc_1d_bf16_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>) else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>)
{ {
add_device_column_to_image_nwc_1d_i8_instances(op_ptrs); add_device_column_to_image_gnwc_1d_i8_instances(op_ptrs);
} }
} }
else if constexpr(NumDimSpatial == 2 && is_same_v<ImageLayout, GNHWC>) else if constexpr(NumDimSpatial == 2 && is_same_v<ImageLayout, GNHWC>)
{ {
if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>) if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>)
{ {
add_device_column_to_image_nhwc_2d_f32_instances(op_ptrs); add_device_column_to_image_gnhwc_2d_f32_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>) else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>)
{ {
add_device_column_to_image_nhwc_2d_f16_instances(op_ptrs); add_device_column_to_image_gnhwc_2d_f16_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, ck::bhalf_t> && else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<OutDataType, ck::bhalf_t>) is_same_v<OutDataType, ck::bhalf_t>)
{ {
add_device_column_to_image_nhwc_2d_bf16_instances(op_ptrs); add_device_column_to_image_gnhwc_2d_bf16_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>) else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>)
{ {
add_device_column_to_image_nhwc_2d_i8_instances(op_ptrs); add_device_column_to_image_gnhwc_2d_i8_instances(op_ptrs);
} }
} }
else if constexpr(NumDimSpatial == 3 && is_same_v<ImageLayout, GNDHWC>) else if constexpr(NumDimSpatial == 3 && is_same_v<ImageLayout, GNDHWC>)
{ {
if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>) if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>)
{ {
add_device_column_to_image_ndhwc_3d_f32_instances(op_ptrs); add_device_column_to_image_gndhwc_3d_f32_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>)
{
add_device_column_to_image_gndhwc_3d_f16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_column_to_image_gndhwc_3d_bf16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>)
{
add_device_column_to_image_gndhwc_3d_i8_instances(op_ptrs);
}
}
else if constexpr(NumDimSpatial == 1 && is_same_v<ImageLayout, NWGC>)
{
if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>)
{
add_device_column_to_image_nwgc_1d_f32_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>)
{
add_device_column_to_image_nwgc_1d_f16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_column_to_image_nwgc_1d_bf16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>)
{
add_device_column_to_image_nwgc_1d_i8_instances(op_ptrs);
}
}
else if constexpr(NumDimSpatial == 2 && is_same_v<ImageLayout, NHWGC>)
{
if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>)
{
add_device_column_to_image_nhwgc_2d_f32_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>)
{
add_device_column_to_image_nhwgc_2d_f16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_column_to_image_nhwgc_2d_bf16_instances(op_ptrs);
}
else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>)
{
add_device_column_to_image_nhwgc_2d_i8_instances(op_ptrs);
}
}
else if constexpr(NumDimSpatial == 3 && is_same_v<ImageLayout, NDHWGC>)
{
if constexpr(is_same_v<InDataType, float> && is_same_v<OutDataType, float>)
{
add_device_column_to_image_ndhwgc_3d_f32_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>) else if constexpr(is_same_v<InDataType, half_t> && is_same_v<OutDataType, half_t>)
{ {
add_device_column_to_image_ndhwc_3d_f16_instances(op_ptrs); add_device_column_to_image_ndhwgc_3d_f16_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, ck::bhalf_t> && else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<OutDataType, ck::bhalf_t>) is_same_v<OutDataType, ck::bhalf_t>)
{ {
add_device_column_to_image_ndhwc_3d_bf16_instances(op_ptrs); add_device_column_to_image_ndhwgc_3d_bf16_instances(op_ptrs);
} }
else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>) else if constexpr(is_same_v<InDataType, int8_t> && is_same_v<OutDataType, int8_t>)
{ {
add_device_column_to_image_ndhwc_3d_i8_instances(op_ptrs); add_device_column_to_image_ndhwgc_3d_i8_instances(op_ptrs);
} }
} }
} }
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_scaleadd_scaleadd_relu_instance.hpp 0 → 100644
View file @ 0f1e8187
// SPDX-License-Identifier: MIT
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/convolution_forward_specialization.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using namespace ck::tensor_layout::convolution;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ScaleAddScaleAddRelu = ck::tensor_operation::element_wise::ScaleAddScaleAddRelu;
static constexpr auto ConvFwdDefault =
ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
static constexpr auto ConvFwd1x1P0 = ConvolutionForwardSpecialization::Filter1x1Pad0;
static constexpr auto ConvFwd1x1S1P0 = ConvolutionForwardSpecialization::Filter1x1Stride1Pad0;
static constexpr auto ConvFwdOddC =
ck::tensor_operation::device::ConvolutionForwardSpecialization::OddC;
static constexpr auto GemmMNKPadding = GemmSpecialization::MNKPadding;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_scaleadd_scaleadd_relu_bf16_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, BF16, BF16, F32, BF16, ck::Tuple<BF16, BF16>, BF16, PassThrough, PassThrough, ScaleAddScaleAddRelu, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
// instances for small conv.K and conv.C
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, BF16, BF16, F32, BF16, ck::Tuple<BF16, BF16>, BF16, PassThrough, PassThrough, ScaleAddScaleAddRelu, ConvSpec, GemmMNKPadding, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, BF16, BF16, F32, BF16, ck::Tuple<BF16, BF16>, BF16, PassThrough, PassThrough, ScaleAddScaleAddRelu, ConvSpec, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, BF16, BF16, F32, BF16, ck::Tuple<BF16, BF16>, BF16, PassThrough, PassThrough, ScaleAddScaleAddRelu, ConvSpec, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_scaleadd_scaleadd_relu_f16_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F16, F16, F32, F16, ck::Tuple<F16, F16>, F16, PassThrough, PassThrough, ScaleAddScaleAddRelu, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
// instances for small conv.K and conv.C
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F16, F16, F32, F16, ck::Tuple<F16, F16>, F16, PassThrough, PassThrough, ScaleAddScaleAddRelu, ConvSpec, GemmMNKPadding, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F16, F16, F32, F16, ck::Tuple<F16, F16>, F16, PassThrough, PassThrough, ScaleAddScaleAddRelu, ConvSpec, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F16, F16, F32, F16, ck::Tuple<F16, F16>, F16, PassThrough, PassThrough, ScaleAddScaleAddRelu, ConvSpec, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_scaleadd_scaleadd_relu_f32_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, ck::Tuple<F32, F32>, F32, PassThrough, PassThrough, ScaleAddScaleAddRelu, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 8, 1, 8>, 1>,
// instances for small conv.K and conv.C
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, ck::Tuple<F32, F32>, F32, PassThrough, PassThrough, ScaleAddScaleAddRelu, ConvSpec, GemmMNKPadding, 1, 64, 64, 32, 16, 4, 4, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 8>, 1>,
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, ck::Tuple<F32, F32>, F32, PassThrough, PassThrough, ScaleAddScaleAddRelu, ConvSpec, GemmMNKPadding, 1, 256, 128, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 16, 1, 16>, 4>,
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, ck::Tuple<F32, F32>, F32, PassThrough, PassThrough, ScaleAddScaleAddRelu, ConvSpec, GemmMNKPadding, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_scaleadd_scaleadd_relu_int8_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, int8_t, int8_t, int32_t, int8_t, ck::Tuple<F32, F32>, int8_t, PassThrough, PassThrough, ScaleAddScaleAddRelu, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
// instances for small conv.K and conv.C
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, int8_t, int8_t, int32_t, int8_t, ck::Tuple<F32, F32>, int8_t, PassThrough, PassThrough, ScaleAddScaleAddRelu, ConvSpec, GemmMNKPadding, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, int8_t, int8_t, int32_t, int8_t, ck::Tuple<F32, F32>, int8_t, PassThrough, PassThrough, ScaleAddScaleAddRelu, ConvSpec, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvFwdMultipleD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, int8_t, int8_t, int32_t, int8_t, ck::Tuple<F32, F32>, int8_t, PassThrough, PassThrough, ScaleAddScaleAddRelu, ConvSpec, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>
// clang-format on
>;
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_scaleadd_scaleadd_relu.hpp 0 → 100644
View file @ 0f1e8187
// SPDX-License-Identifier: MIT
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <vector>
#include <memory>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_d.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ScaleAddScaleAddRelu = ck::tensor_operation::element_wise::ScaleAddScaleAddRelu;
#ifdef CK_ENABLE_BF16
// grouped conv3d forward, NDHWGC/GKZYXC/NDHWGK
void add_device_grouped_conv3d_fwd_xdl_scaleadd_scaleadd_relu_ndhwgc_gkzyxc_ndhwgk_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleD<3,
NDHWGC,
GKZYXC,
ck::Tuple<NDHWGK, NDHWGK>,
NDHWGK,
BF16,
BF16,
ck::Tuple<BF16, BF16>,
BF16,
PassThrough,
PassThrough,
ScaleAddScaleAddRelu>>>& instances);
#endif
#ifdef CK_ENABLE_FP16
void add_device_grouped_conv3d_fwd_xdl_scaleadd_scaleadd_relu_ndhwgc_gkzyxc_ndhwgk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleD<3,
NDHWGC,
GKZYXC,
ck::Tuple<NDHWGK, NDHWGK>,
NDHWGK,
F16,
F16,
ck::Tuple<F16, F16>,
F16,
PassThrough,
PassThrough,
ScaleAddScaleAddRelu>>>& instances);
#endif
#ifdef CK_ENABLE_FP32
void add_device_grouped_conv3d_fwd_xdl_scaleadd_scaleadd_relu_ndhwgc_gkzyxc_ndhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleD<3,
NDHWGC,
GKZYXC,
ck::Tuple<NDHWGK, NDHWGK>,
NDHWGK,
F32,
F32,
ck::Tuple<F32, F32>,
F32,
PassThrough,
PassThrough,
ScaleAddScaleAddRelu>>>& instances);
#endif
#ifdef CK_ENABLE_INT8
void add_device_grouped_conv3d_fwd_xdl_scaleadd_scaleadd_relu_ndhwgc_gkzyxc_ndhwgk_int8_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleD<3,
NDHWGC,
GKZYXC,
ck::Tuple<NDHWGK, NDHWGK>,
NDHWGK,
int8_t,
int8_t,
ck::Tuple<F32, F32>,
int8_t,
PassThrough,
PassThrough,
ScaleAddScaleAddRelu>>>& instances);
#endif
template <ck::index_t NumDimSpatial,
typename InLayout,
typename WeiLayout,
typename DLayouts,
typename OutLayout,
typename InDataType,
typename WeiDataType,
typename DDataTypes,
typename OutDataType,
typename ComputeType>
struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<
NumDimSpatial,
InLayout,
WeiLayout,
DLayouts,
OutLayout,
InDataType,
WeiDataType,
DDataTypes,
OutDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::ScaleAddScaleAddRelu,
ComputeType>>
{
using DeviceOp =
DeviceGroupedConvFwdMultipleD<NumDimSpatial,
InLayout,
WeiLayout,
DLayouts,
OutLayout,
InDataType,
WeiDataType,
DDataTypes,
OutDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::ScaleAddScaleAddRelu,
ComputeType>;
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
if constexpr(NumDimSpatial == 3 && is_same_v<InLayout, NDHWGC> &&
is_same_v<WeiLayout, GKZYXC> && is_same_v<OutLayout, NDHWGK>)
{
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float>)
{
add_device_grouped_conv3d_fwd_xdl_scaleadd_scaleadd_relu_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
}
#endif
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t> && is_same_v<ComputeType, half_t>)
{
add_device_grouped_conv3d_fwd_xdl_scaleadd_scaleadd_relu_ndhwgc_gkzyxc_ndhwgk_f16_instances(
op_ptrs);
}
#endif
#ifdef CK_ENABLE_BF16
if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<WeiDataType, ck::bhalf_t> && is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_grouped_conv3d_fwd_xdl_scaleadd_scaleadd_relu_ndhwgc_gkzyxc_ndhwgk_bf16_instances(
op_ptrs);
}
#endif
#ifdef CK_ENABLE_INT8
if constexpr(is_same_v<InDataType, int8_t> && is_same_v<WeiDataType, int8_t> &&
is_same_v<OutDataType, int8_t>)
{
add_device_grouped_conv3d_fwd_xdl_scaleadd_scaleadd_relu_ndhwgc_gkzyxc_ndhwgk_int8_instances(
op_ptrs);
}
#endif
}
return op_ptrs;
}
};
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
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