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Commit cc8df39e authored by carlushuang's avatar carlushuang
Browse files

Merge remote-tracking branch 'origin/develop' into cpu_avx2

parents 0b9fe840 98e1e2d0
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Showing with 2725 additions and 253 deletions
example/01_gemm/gemm_xdl_int8.cpp
View file @ cc8df39e
......@@ -53,9 +53,9 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl_C_Shuffle
256, // BlockSize
256, // MPerBlock
128, // NPerBlock
32, // KPerBlock
8, // AK1
8, // BK1
64, // KPerBlock
16, // AK1
16, // BK1
32, // MPerXDL
32, // NPerXDL
4, // MXdlPerWave
......@@ -64,15 +64,15 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl_C_Shuffle
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // ABlockTransferSrcAccessOrder
2, // ABlockTransferSrcVectorDim
8, // ABlockTransferSrcScalarPerVector
8, // ABlockTransferDstScalarPerVector_K1
16, // ABlockTransferSrcScalarPerVector
16, // ABlockTransferDstScalarPerVector_K1
true, // ABlockLdsAddExtraM
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_K0_N_K1
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // BBlockTransferSrcAccessOrder
2, // BBlockTransferSrcVectorDim
8, // BBlockTransferSrcScalarPerVector
8, // BBlockTransferDstScalarPerVector_K1
16, // BBlockTransferSrcScalarPerVector
16, // BBlockTransferDstScalarPerVector_K1
true, // BBlockLdsAddExtraN
1, // CShuffleMXdlPerWavePerShuffle
1, // CShuffleNXdlPerWavePerShuffle
......
example/10_conv2d_bwd_data/conv2d_bwd_data_xdl.cpp
View file @ cc8df39e
......@@ -68,6 +68,7 @@ using DeviceConvBwdDataInstance = ck::tensor_operation::device::
using ReferenceConvBwdInstance = ck::tensor_operation::host::ReferenceConvBwdData<InDataType,
WeiDataType,
OutDataType,
AccDataType,
InElementOp,
WeiElementOp,
OutElementOp>;
......
example/14_gemm_xdl_requant_relu_requant/gemm_xdl_requant_relu_requant_int8.cpp
View file @ cc8df39e
......@@ -28,11 +28,11 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using RequantReluRequant = ck::tensor_operation::element_wise::RequantReluRequant;
using ADataType = int8_t;
using BDataType = int8_t;
using CDataType = int8_t;
using AccDataType = int32_t;
using ShuffleDataType = int32_t;
using ADataType = int8_t;
using BDataType = int8_t;
using CDataType = int8_t;
using AccDataType = int32_t;
using CShuffleDataType = int32_t;
using ALayout = ck::tensor_layout::gemm::RowMajor;
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
......@@ -44,7 +44,7 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl_C_Shuffle
BDataType, // BDataType
CDataType, // CDataType
AccDataType, // AccDataType
ShuffleDataType, // ShuffleDataType
CShuffleDataType, // CShuffleDataType
ALayout, // ALayout
BLayout, // BLayout
CLayout, // CLayout
......@@ -54,9 +54,9 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl_C_Shuffle
256, // BlockSize
256, // MPerBlock
128, // NPerBlock
32, // KPerBlock
8, // AK1
8, // BK1
64, // KPerBlock
16, // AK1
16, // BK1
32, // MPerXDL
32, // NPerXDL
4, // MXdlPerWave
......@@ -65,20 +65,20 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl_C_Shuffle
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // ABlockTransferSrcAccessOrder
2, // ABlockTransferSrcVectorDim
8, // ABlockTransferSrcScalarPerVector
8, // ABlockTransferDstScalarPerVector_K1
16, // ABlockTransferSrcScalarPerVector
16, // ABlockTransferDstScalarPerVector_K1
true, // ABlockLdsAddExtraM
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_K0_N_K1
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // BBlockTransferSrcAccessOrder
2, // BBlockTransferSrcVectorDim
8, // BBlockTransferSrcScalarPerVector
8, // BBlockTransferDstScalarPerVector_K1
16, // BBlockTransferSrcScalarPerVector
16, // BBlockTransferDstScalarPerVector_K1
true, // BBlockLdsAddExtraN
1, // CShuffleMXdlPerWavePerShuffle
1, // CShuffleNXdlPerWavePerShuffle
S<1, 1, 32, 1, 1, 8>, // CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
8>; // CBlockTransferScalarPerVector_NWaveNPerXdl
S<1, 1, 64, 1, 1, 4>, // CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
16>; // CBlockTransferScalarPerVector_NWaveNPerXdl
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::
......
example/17_convnd_bwd_data_xdl/CMakeLists.txt 0 → 100644
View file @ cc8df39e
add_example_executable(example_convnd_bwd_data_xdl convnd_bwd_data_xdl.cpp)
example/17_convnd_bwd_data_xdl/README.md 0 → 100644
View file @ cc8df39e
# Instructions for ```convnd_bwd_data_xdl``` Example
## Docker script
```bash
docker run \
-it \
--rm \
--privileged \
--group-add sudo \
-w /root/workspace \
-v ${PATH_TO_LOCAL_WORKSPACE}:/root/workspace \
rocm/tensorflow:rocm4.3.1-tf2.6-dev \
/bin/bash
```
## Build ```convnd_bwd_data_xdl```
```bash
mkdir build && cd build
```
```bash
# Need to specify target ID, example below is gfx908
cmake \
-D BUILD_DEV=OFF \
-D CMAKE_BUILD_TYPE=Release \
-D CMAKE_CXX_FLAGS="-DCK_AMD_GPU_GFX908 --amdgpu-target=gfx908 -O3 " \
-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
-D CMAKE_PREFIX_PATH=/opt/rocm \
..
```
```bash
make -j convnd_bwd_data_xdl
```
## Run ```example_convnd_bwd_data_xdl```
```bash
#arg1: verification (0=no, 1=yes)
#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
#arg3: run kernel # of times (>1)
#arg4: num_dim_spatial(1|2|3)
#arg5 to ...: N, K, C, [Z,] [Y,] X, [Di,] [Hi,] Wi, S[z,] [Sy,] Sx, [Dz,] [Dy,] Dx, [LeftPz,] [LeftPy,] LeftPx, [RightPy,] [RightPy,] RightPx
./bin/convnd_bwd_data_xdl 0 1 5
```
Result
```
in_n_c_hi_wi: dim 4, lengths {128, 128, 71, 71}, strides {645248, 1, 9088, 128}
wei_k_c_y_x: dim 4, lengths {256, 128, 3, 3}, strides {1152, 1, 384, 128}
out_n_k_ho_wo: dim 4, lengths {128, 256, 36, 36}, strides {331776, 1, 9216, 256}
arg.a_grid_desc_k0_m_k1_container_{128, 175232, 8}
arg.b_grid_desc_k0_n_k1_container_{128, 128, 8}
arg.c_grid_desc_m_n_container_{ 175232, 128}
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( 2738, 2, 2, 2, 4, 2 )
launch_and_time_kernel: grid_dim {1369, 1, 1}, block_dim {256, 1, 1}
Warm up
Start running 1 times...
arg.a_grid_desc_k0_m_k1_container_{64, 175232, 8}
arg.b_grid_desc_k0_n_k1_container_{64, 128, 8}
arg.c_grid_desc_m_n_container_{ 175232, 128}
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( 2738, 2, 2, 2, 4, 2 )
launch_and_time_kernel: grid_dim {1369, 1, 1}, block_dim {256, 1, 1}
Warm up
Start running 1 times...
arg.a_grid_desc_k0_m_k1_container_{64, 175232, 8}
arg.b_grid_desc_k0_n_k1_container_{64, 128, 8}
arg.c_grid_desc_m_n_container_{ 175232, 128}
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( 2738, 2, 2, 2, 4, 2 )
launch_and_time_kernel: grid_dim {1369, 1, 1}, block_dim {256, 1, 1}
Warm up
Start running 1 times...
arg.a_grid_desc_k0_m_k1_container_{32, 175232, 8}
arg.b_grid_desc_k0_n_k1_container_{32, 128, 8}
arg.c_grid_desc_m_n_container_{ 175232, 128}
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( 2738, 2, 2, 2, 4, 2 )
launch_and_time_kernel: grid_dim {1369, 1, 1}, block_dim {256, 1, 1}
Warm up
Start running 1 times...
Perf: 1.40031 ms, 69.8734 TFlops, 179.037 GB/s
```
example/17_convnd_bwd_data_xdl/convnd_bwd_data_xdl.cpp 0 → 100644
View file @ cc8df39e
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>
#include <half.hpp>
#include "config.hpp"
#include "conv_utils.hpp"
#include "print.hpp"
#include "device.hpp"
#include "host_tensor.hpp"
#include "host_tensor_generator.hpp"
#include "device_tensor.hpp"
#include "tensor_layout.hpp"
#include "element_wise_operation.hpp"
#include "device_convnd_bwd_data_xdl_ndhwc_kzyxc_ndhwk.hpp"
#include "reference_conv_bwd_data.hpp"
using InDataType = ck::half_t;
using WeiDataType = ck::half_t;
using OutDataType = ck::half_t;
using AccDataType = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using InElementOp = ck::tensor_operation::element_wise::PassThrough;
using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdDefault =
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization_t::Default;
using DeviceConvBwdDataBasePtr =
ck::tensor_operation::device::DeviceConvBwdDataPtr<InElementOp, WeiElementOp, OutElementOp>;
template <ck::index_t NumDimSpatial>
using DeviceConvNDBwdDataInstance = ck::tensor_operation::device::
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<
InDataType, // InDataType
WeiDataType, // WeiDataType
OutDataType, // OutDataType
AccDataType, // AccDataType
InElementOp, // InElementwiseOperation
WeiElementOp, // WeiElementwiseOperation
OutElementOp, // OutElementwiseOperation
ConvBwdDefault, // ConvolutionBackwardDataSpecialization_t
NumDimSpatial, // NumDimSpatial
256, // BlockSize
128, // MPerBlock
128, // NPerBlock
4, // K0PerBlock
8, // K1
32, // MPerXdl
32, // NPerXdl
2, // MXdlPerWave
2, // NXdlPerWave
S<4, 64, 1>, // ABlockTransferThreadClusterLengths_K0_M_K1
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // ABlockTransferSrcAccessOrder
2, // ABlockTransferSrcVectorDim
8, // ABlockTransferSrcScalarPerVector
8, // ABlockTransferDstScalarPerVector_K1
true, // ABlockLdsAddExtraM
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_K0_N_K1
S<2, 0, 1>, // BBlockTransferThreadClusterArrangeOrder
S<0, 2, 1>, // BBlockTransferSrcAccessOrder
1, // BBlockTransferSrcVectorDim
2, // BBlockTransferSrcScalarPerVector
8, // BBlockTransferDstScalarPerVector_K1
true, // BBlockLdsAddExtraN
7,
1>; // GemmCThreadTransferDstScalarPerVector
template <ck::index_t NumDimSpatial>
using ReferenceConvBwdDataInstance =
ck::tensor_operation::host::ReferenceConvBwdData<InDataType,
WeiDataType,
OutDataType,
AccDataType,
InElementOp,
WeiElementOp,
OutElementOp,
NumDimSpatial>;
void PrintUseMsg()
{
std::cout << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=random value, 2= init to 1 )\n"
<< "arg3: run kernel # of times (>1)\n"
<< "arg4: N spatial dimensions (default 2)\n"
<< "Following arguments (depending on number of spatial dims):\n"
<< " N, K, C, \n"
<< " <filter spatial dimensions>, (ie Y, X for 2D)\n"
<< " <input image spatial dimensions>, (ie Hi, Wi for 2D)\n"
<< " <strides>, (ie Sy, Sx for 2D)\n"
<< " <dilations>, (ie Dy, Dx for 2D)\n"
<< " <left padding>, (ie LeftPy, LeftPx for 2D)\n"
<< " <right padding>, (ie RightPy, RightPx for 2D)\n"
<< std::endl;
}
ck::conv_util::ConvParams ParseConvParams(int num_dim_spatial, char* argv[])
{
// (N, K, C) + num_dim_spatial * 6 (filter, input, strides, dilations, pad left, pad right)
ck::conv_util::ConvParams params;
int arg_idx = 5;
params.num_dim_spatial = num_dim_spatial;
params.N = std::stoi(argv[arg_idx++]);
params.K = std::stoi(argv[arg_idx++]);
params.C = std::stoi(argv[arg_idx++]);
params.filter_spatial_lengths.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.filter_spatial_lengths[i] = std::stoi(argv[arg_idx++]);
}
params.input_spatial_lengths.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.input_spatial_lengths[i] = std::stoi(argv[arg_idx++]);
}
params.conv_filter_strides.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.conv_filter_strides[i] = std::stoi(argv[arg_idx++]);
}
params.conv_filter_dilations.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.conv_filter_dilations[i] = std::stoi(argv[arg_idx++]);
}
params.input_left_pads.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.input_left_pads[i] = std::stoi(argv[arg_idx++]);
}
params.input_right_pads.resize(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
params.input_right_pads[i] = std::stoi(argv[arg_idx++]);
}
return params;
}
HostTensorDescriptor GetInputHostTensorDescriptor(const std::vector<std::size_t>& dims,
int num_dim_spatial = 2)
{
namespace tl = ck::tensor_layout::convolution;
switch(num_dim_spatial)
{
case 3: {
return ck::conv_util::GetHostTensorDescriptor(dims, tl::NDHWC{});
}
case 2: {
return ck::conv_util::GetHostTensorDescriptor(dims, tl::NHWC{});
}
case 1: {
return ck::conv_util::GetHostTensorDescriptor(dims, tl::NWC{});
}
default: {
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
}
}
}
HostTensorDescriptor GetFiltersHostTensorDescriptor(const std::vector<std::size_t>& dims,
int num_dim_spatial = 2)
{
namespace tl = ck::tensor_layout::convolution;
switch(num_dim_spatial)
{
case 3: {
return ck::conv_util::GetHostTensorDescriptor(dims, tl::KZYXC{});
}
case 2: {
return ck::conv_util::GetHostTensorDescriptor(dims, tl::KYXC{});
}
case 1: {
return ck::conv_util::GetHostTensorDescriptor(dims, tl::KXC{});
}
default: {
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
}
}
}
HostTensorDescriptor GetOutputHostTensorDescriptor(const std::vector<std::size_t>& dims,
int num_dim_spatial = 2)
{
namespace tl = ck::tensor_layout::convolution;
switch(num_dim_spatial)
{
case 3: {
return ck::conv_util::GetHostTensorDescriptor(dims, tl::NDHWK{});
}
case 2: {
return ck::conv_util::GetHostTensorDescriptor(dims, tl::NHWK{});
}
case 1: {
return ck::conv_util::GetHostTensorDescriptor(dims, tl::NWK{});
}
default: {
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
}
}
}
DeviceConvBwdDataBasePtr GetConvInstance(int num_dim_spatial)
{
switch(num_dim_spatial)
{
case 3: {
return std::make_unique<DeviceConvNDBwdDataInstance<3>>();
}
case 2: {
return std::make_unique<DeviceConvNDBwdDataInstance<2>>();
}
case 1: {
return std::make_unique<DeviceConvNDBwdDataInstance<1>>();
}
default: {
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
}
}
}
int main(int argc, char* argv[])
{
bool do_verification = 0;
int init_method = 0;
int nrepeat = 5;
int num_dim_spatial = 2;
ck::conv_util::ConvParams params;
params.C = 128;
if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
}
else if(argc > 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
num_dim_spatial = std::stoi(argv[4]);
// check args number
int conv_args = 3 + num_dim_spatial * 6;
int cmdline_nargs = conv_args + 5;
if(cmdline_nargs != argc)
{
PrintUseMsg();
exit(1);
}
params = ParseConvParams(num_dim_spatial, argv);
}
else if(argc != 1)
{
PrintUseMsg();
exit(1);
}
std::vector<std::size_t> input_dims{static_cast<std::size_t>(params.N),
static_cast<std::size_t>(params.C)};
input_dims.insert(std::end(input_dims),
std::begin(params.input_spatial_lengths),
std::end(params.input_spatial_lengths));
std::vector<std::size_t> filter_dims{static_cast<std::size_t>(params.K),
static_cast<std::size_t>(params.C)};
filter_dims.insert(std::end(filter_dims),
std::begin(params.filter_spatial_lengths),
std::end(params.filter_spatial_lengths));
const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths();
std::vector<std::size_t> output_dims{static_cast<std::size_t>(params.N),
static_cast<std::size_t>(params.K)};
output_dims.insert(std::end(output_dims),
std::begin(output_spatial_lengths),
std::end(output_spatial_lengths));
Tensor<InDataType> in_n_c_hi_wi_host_result(
GetInputHostTensorDescriptor(input_dims, num_dim_spatial));
Tensor<InDataType> in_n_c_hi_wi_device_result(
GetInputHostTensorDescriptor(input_dims, num_dim_spatial));
Tensor<WeiDataType> wei_k_c_y_x(GetFiltersHostTensorDescriptor(filter_dims, num_dim_spatial));
Tensor<OutDataType> out_n_k_ho_wo(GetOutputHostTensorDescriptor(output_dims, num_dim_spatial));
std::cout << "in_n_c_hi_wi: " << in_n_c_hi_wi_host_result.mDesc << std::endl;
std::cout << "wei_k_c_y_x: " << wei_k_c_y_x.mDesc << std::endl;
std::cout << "out_n_k_ho_wo: " << out_n_k_ho_wo.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
out_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_3<OutDataType>{-0.2, 0.2});
wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.2, 0.2});
break;
default:
out_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_1<OutDataType>{1});
wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{1});
}
DeviceMem in_device_buf(sizeof(InDataType) *
in_n_c_hi_wi_device_result.mDesc.GetElementSpace());
DeviceMem wei_device_buf(sizeof(WeiDataType) * wei_k_c_y_x.mDesc.GetElementSpace());
DeviceMem out_device_buf(sizeof(OutDataType) * out_n_k_ho_wo.mDesc.GetElementSpace());
out_device_buf.ToDevice(out_n_k_ho_wo.mData.data());
wei_device_buf.ToDevice(wei_k_c_y_x.mData.data());
// reset input to zero
in_n_c_hi_wi_device_result.GenerateTensorValue(GeneratorTensor_1<InDataType>{0});
in_device_buf.ToDevice(in_n_c_hi_wi_device_result.mData.data());
// do GEMM
auto conv = GetConvInstance(num_dim_spatial);
auto invoker = conv->MakeInvokerPointer();
auto argument =
conv->MakeArgumentPointer(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
params.N,
params.K,
params.C,
params.input_spatial_lengths,
params.filter_spatial_lengths,
output_spatial_lengths,
params.conv_filter_strides,
params.conv_filter_dilations,
params.input_left_pads,
params.input_right_pads,
InElementOp{},
WeiElementOp{},
OutElementOp{});
if(!conv->IsSupportedArgument(argument.get()))
{
throw std::runtime_error(
"wrong! device_conv with the specified compilation parameters does "
"not support this Conv problem");
}
float ave_time = invoker->Run(argument.get(), nrepeat);
std::size_t flop = ck::conv_util::GetFlops(
params.N, params.C, params.K, params.filter_spatial_lengths, output_spatial_lengths);
std::size_t num_btype =
ck::conv_util::GetBtype<InDataType, WeiDataType, OutDataType>(params.N,
params.C,
params.K,
params.input_spatial_lengths,
params.filter_spatial_lengths,
output_spatial_lengths);
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
<< std::endl;
if(do_verification)
{
auto verify_f = [&](const auto& ref_conv) {
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_argument = ref_conv.MakeArgument(in_n_c_hi_wi_host_result,
wei_k_c_y_x,
out_n_k_ho_wo,
params.conv_filter_strides,
params.conv_filter_dilations,
params.input_left_pads,
params.input_right_pads,
InElementOp{},
WeiElementOp{},
OutElementOp{});
ref_invoker.Run(ref_argument);
in_device_buf.FromDevice(in_n_c_hi_wi_device_result.mData.data());
check_error(in_n_c_hi_wi_host_result, in_n_c_hi_wi_device_result);
};
switch(num_dim_spatial)
{
case 3: {
auto ref_conv = ReferenceConvBwdDataInstance<3>();
verify_f(ref_conv);
break;
}
case 2: {
auto ref_conv = ReferenceConvBwdDataInstance<2>();
verify_f(ref_conv);
break;
}
case 1: {
auto ref_conv = ReferenceConvBwdDataInstance<1>();
verify_f(ref_conv);
break;
}
default: {
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
}
}
}
}
example/CMakeLists.txt
View file @ cc8df39e
......@@ -39,5 +39,6 @@ add_subdirectory(11_conv2d_bwd_wgt)
add_subdirectory(12_reduce)
add_subdirectory(13_pool2d_fwd)
add_subdirectory(14_gemm_xdl_requant_relu_requant)
add_subdirectory(17_convnd_bwd_data_xdl)
add_subdirectory(15_grouped_gemm)
add_subdirectory(16_gemm_reduce)
include/ck/problem_transform/transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk.hpp
View file @ cc8df39e
......@@ -7,9 +7,9 @@
namespace ck {
// Number of GEMMs = YTilda * XTilda
// Number of GEMMs = YTilde * XTilde
// GemmM = C
// GemmN = N * HTildaSlice * WTildaSlice
// GemmN = N * HTildeSlice * WTildeSlice
// GemmK = K * YDotSlice * XDotSlice
template <typename... Wei,
typename... In,
......@@ -18,8 +18,8 @@ template <typename... Wei,
typename ConvDilations,
typename InLeftPads,
typename InRightPads,
index_t IYTildaValue,
index_t IXTildaValue,
index_t IYTildeValue,
index_t IXTildeValue,
index_t GemmK1Value>
__host__ __device__ constexpr auto
transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
......@@ -30,8 +30,8 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
Number<IYTildaValue>,
Number<IXTildaValue>,
Number<IYTildeValue>,
Number<IXTildeValue>,
Number<GemmK1Value>)
{
constexpr auto I0 = Number<0>{};
......@@ -40,8 +40,8 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
constexpr auto I3 = Number<3>{};
constexpr auto GemmK1 = Number<GemmK1Value>{};
constexpr auto IYTilda = Number<IYTildaValue>{};
constexpr auto IXTilda = Number<IXTildaValue>{};
constexpr auto IYTilde = Number<IYTildeValue>{};
constexpr auto IXTilde = Number<IXTildeValue>{};
const auto N = in_n_hi_wi_c_grid_desc.GetLength(I0);
const auto C = in_n_hi_wi_c_grid_desc.GetLength(I3);
......@@ -71,55 +71,55 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
const auto YTilda = ConvStrideH / GcdStrideDilationH;
const auto XTilda = ConvStrideW / GcdStrideDilationW;
const auto YTilde = ConvStrideH / GcdStrideDilationH;
const auto XTilde = ConvStrideW / GcdStrideDilationW;
const auto YDot = math::integer_divide_ceil(Y, YTilda);
const auto XDot = math::integer_divide_ceil(X, XTilda);
const auto YDot = math::integer_divide_ceil(Y, YTilde);
const auto XDot = math::integer_divide_ceil(X, XTilde);
const auto HTilda = Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
const auto WTilda = Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
const auto HTilde = Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
const auto WTilde = Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
// only work on HTilda and WTilda that contribute to non-padding area of input tensor
const auto IHTildaSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadH - ConvDilationH * (YTilda - I1)), ConvStrideH);
const auto IWTildaSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadW - ConvDilationW * (XTilda - I1)), ConvStrideW);
// only work on HTilde and WTilde that contribute to non-padding area of input tensor
const auto IHTildeSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadH - ConvDilationH * (YTilde - I1)), ConvStrideH);
const auto IWTildeSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadW - ConvDilationW * (XTilde - I1)), ConvStrideW);
const auto IHTildaSliceEnd =
math::min(HTilda, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
const auto IWTildaSliceEnd =
math::min(WTilda, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
const auto IHTildeSliceEnd =
math::min(HTilde, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
const auto IWTildeSliceEnd =
math::min(WTilde, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
const auto HTildaSlice = IHTildaSliceEnd - IHTildaSliceBegin;
const auto WTildaSlice = IWTildaSliceEnd - IWTildaSliceBegin;
const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
// GemmK is different for each GEMM
const auto YDotSlice = math::integer_divide_ceil(Y - IYTilda, YTilda);
const auto XDotSlice = math::integer_divide_ceil(X - IXTilda, XTilda);
const auto YDotSlice = math::integer_divide_ceil(Y - IYTilde, YTilde);
const auto XDotSlice = math::integer_divide_ceil(X - IXTilde, XTilde);
const auto K1 = GemmK1;
const auto K0 = K / K1;
// weight tensor
const auto wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc = transform_tensor_descriptor(
const auto wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc = transform_tensor_descriptor(
wei_k_y_x_c_grid_desc,
make_tuple(make_pass_through_transform(K),
make_embed_transform(make_tuple(YDot, YTilda),
make_embed_transform(make_tuple(YDot, YTilde),
make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
make_embed_transform(make_tuple(XDot, XTilda),
make_embed_transform(make_tuple(XDot, XTilde),
make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto wei_k0_k1_ydotslice_xdotslice_c_grid_desc =
transform_tensor_descriptor(wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc,
transform_tensor_descriptor(wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_freeze_transform(IYTilda),
make_freeze_transform(IXTilda),
make_freeze_transform(IYTilde),
make_freeze_transform(IXTilde),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
......@@ -163,25 +163,25 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto out_n_ydot_htilda_xdot_wtilda_k_grid_desc = transform_tensor_descriptor(
const auto out_n_ydot_htilde_xdot_wtilde_k_grid_desc = transform_tensor_descriptor(
out_n_hop_wop_k_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(make_tuple(YDot, HTilda),
make_embed_transform(make_tuple(YDot, HTilde),
make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
make_embed_transform(make_tuple(XDot, WTilda),
make_embed_transform(make_tuple(XDot, WTilde),
make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
make_pass_through_transform(K)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc =
const auto out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc =
transform_tensor_descriptor(
out_n_ydot_htilda_xdot_wtilda_k_grid_desc,
out_n_ydot_htilde_xdot_wtilde_k_grid_desc,
make_tuple(make_pass_through_transform(N),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
make_unmerge_transform(make_tuple(K0, K1))),
make_tuple(Sequence<0>{},
Sequence<1>{},
......@@ -198,17 +198,17 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
#if 1
const auto out_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
make_pass_through_transform(K1)),
make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
#else
const auto out_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
make_tuple(make_merge_transform(make_tuple(K0, YDotSlice, XDotSlice)),
make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
make_pass_through_transform(K1)),
make_tuple(Sequence<5, 1, 3>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
......@@ -224,24 +224,24 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc = transform_tensor_descriptor(
const auto in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc = transform_tensor_descriptor(
in_n_hip_wip_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(make_tuple(YTilda, HTilda),
make_embed_transform(make_tuple(YTilde, HTilde),
make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(XTilda, WTilda),
make_embed_transform(make_tuple(XTilde, WTilde),
make_tuple(ConvDilationW, ConvStrideW)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto in_n_htildaslice_wtildaslice_c_grid_desc = transform_tensor_descriptor(
in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc,
const auto in_n_htildeslice_wtildeslice_c_grid_desc = transform_tensor_descriptor(
in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_freeze_transform(IYTilda),
make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
make_freeze_transform(IXTilda),
make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
make_freeze_transform(IYTilde),
make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
make_freeze_transform(IXTilde),
make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
......@@ -257,9 +257,9 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
Sequence<3>{}));
const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
in_n_htildaslice_wtildaslice_c_grid_desc,
in_n_htildeslice_wtildeslice_c_grid_desc,
make_tuple(make_pass_through_transform(C),
make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice))),
make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice))),
make_tuple(Sequence<3>{}, Sequence<0, 1, 2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
......
include/ck/problem_transform/transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk.hpp
View file @ cc8df39e
......@@ -10,8 +10,8 @@ namespace ck {
// A: out
// B: wei
// C: in
// Number of GEMMs = YTilda * XTilda
// GemmM = N * HTildaSlice * WTildaSlice
// Number of GEMMs = YTilde * XTilde
// GemmM = N * HTildeSlice * WTildeSlice
// GemmN = C
// GemmK = K * YDotSlice * XDotSlice
template <typename... Wei,
......@@ -21,8 +21,8 @@ template <typename... Wei,
typename ConvDilations,
typename InLeftPads,
typename InRightPads,
typename IYTilda,
typename IXTilda,
typename IYTilde,
typename IXTilde,
index_t GemmK1Value>
__host__ __device__ constexpr auto
transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
......@@ -33,8 +33,8 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
IYTilda i_ytilda,
IXTilda i_xtilda,
IYTilde i_ytilde,
IXTilde i_xtilde,
Number<GemmK1Value>)
{
constexpr auto I0 = Number<0>{};
......@@ -72,32 +72,32 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
const auto YTilda = ConvStrideH / GcdStrideDilationH;
const auto XTilda = ConvStrideW / GcdStrideDilationW;
const auto YTilde = ConvStrideH / GcdStrideDilationH;
const auto XTilde = ConvStrideW / GcdStrideDilationW;
const auto YDot = math::integer_divide_ceil(Y, YTilda);
const auto XDot = math::integer_divide_ceil(X, XTilda);
const auto YDot = math::integer_divide_ceil(Y, YTilde);
const auto XDot = math::integer_divide_ceil(X, XTilde);
const auto HTilda = Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
const auto WTilda = Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
const auto HTilde = Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
const auto WTilde = Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
// only work on HTilda and WTilda that contribute to non-padding area of input tensor
const auto IHTildaSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadH - ConvDilationH * (YTilda - I1)), ConvStrideH);
const auto IWTildaSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadW - ConvDilationW * (XTilda - I1)), ConvStrideW);
// only work on HTilde and WTilde that contribute to non-padding area of input tensor
const auto IHTildeSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadH - ConvDilationH * (YTilde - I1)), ConvStrideH);
const auto IWTildeSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadW - ConvDilationW * (XTilde - I1)), ConvStrideW);
const auto IHTildaSliceEnd =
math::min(HTilda, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
const auto IWTildaSliceEnd =
math::min(WTilda, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
const auto IHTildeSliceEnd =
math::min(HTilde, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
const auto IWTildeSliceEnd =
math::min(WTilde, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
const auto HTildaSlice = IHTildaSliceEnd - IHTildaSliceBegin;
const auto WTildaSlice = IWTildaSliceEnd - IWTildaSliceBegin;
const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
// GemmK is different for each GEMM
const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilda, YTilda);
const auto XDotSlice = math::integer_divide_ceil(X - i_xtilda, XTilda);
const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
const auto K1 = GemmK1;
const auto K0 = K / K1;
......@@ -113,25 +113,25 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto out_n_ydot_htilda_xdot_wtilda_k_grid_desc = transform_tensor_descriptor(
const auto out_n_ydot_htilde_xdot_wtilde_k_grid_desc = transform_tensor_descriptor(
out_n_hop_wop_k_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(make_tuple(YDot, HTilda),
make_embed_transform(make_tuple(YDot, HTilde),
make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
make_embed_transform(make_tuple(XDot, WTilda),
make_embed_transform(make_tuple(XDot, WTilde),
make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
make_pass_through_transform(K)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc =
const auto out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc =
transform_tensor_descriptor(
out_n_ydot_htilda_xdot_wtilda_k_grid_desc,
out_n_ydot_htilde_xdot_wtilde_k_grid_desc,
make_tuple(make_pass_through_transform(N),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
make_unmerge_transform(make_tuple(K0, K1))),
make_tuple(Sequence<0>{},
Sequence<1>{},
......@@ -148,41 +148,41 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
#if 1
const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
make_pass_through_transform(K1)),
make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
#else
const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
make_tuple(make_merge_transform(make_tuple(K0, YDotSlice, XDotSlice)),
make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
make_pass_through_transform(K1)),
make_tuple(Sequence<5, 1, 3>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
#endif
// B: weight tensor
const auto wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc = transform_tensor_descriptor(
const auto wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc = transform_tensor_descriptor(
wei_k_y_x_c_grid_desc,
make_tuple(make_pass_through_transform(K),
make_embed_transform(make_tuple(YDot, YTilda),
make_embed_transform(make_tuple(YDot, YTilde),
make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
make_embed_transform(make_tuple(XDot, XTilda),
make_embed_transform(make_tuple(XDot, XTilde),
make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto wei_k0_k1_ydotslice_xdotslice_c_grid_desc =
transform_tensor_descriptor(wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc,
transform_tensor_descriptor(wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_freeze_transform(i_ytilda),
make_freeze_transform(i_xtilda),
make_freeze_transform(i_ytilde),
make_freeze_transform(i_xtilde),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
......@@ -225,24 +225,24 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc = transform_tensor_descriptor(
const auto in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc = transform_tensor_descriptor(
in_n_hip_wip_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(make_tuple(YTilda, HTilda),
make_embed_transform(make_tuple(YTilde, HTilde),
make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(XTilda, WTilda),
make_embed_transform(make_tuple(XTilde, WTilde),
make_tuple(ConvDilationW, ConvStrideW)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto in_n_htildaslice_wtildaslice_c_grid_desc = transform_tensor_descriptor(
in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc,
const auto in_n_htildeslice_wtildeslice_c_grid_desc = transform_tensor_descriptor(
in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_freeze_transform(i_ytilda),
make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
make_freeze_transform(i_xtilda),
make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
make_freeze_transform(i_ytilde),
make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
make_freeze_transform(i_xtilde),
make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
......@@ -258,8 +258,8 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
Sequence<3>{}));
const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
in_n_htildaslice_wtildaslice_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
in_n_htildeslice_wtildeslice_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
make_pass_through_transform(C)),
make_tuple(Sequence<0, 1, 2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
......
include/ck/tensor_operation/gpu/device/conv_utils.hpp
View file @ cc8df39e
......@@ -108,6 +108,28 @@ struct ConvParams
input_right_pads(2, 1)
{
}
ConvParams(ck::index_t n_dim_spatial,
ck::index_t n,
ck::index_t k,
ck::index_t c,
std::vector<ck::index_t> filter_lengths,
std::vector<ck::index_t> input_lengths,
std::vector<ck::index_t> conv_strides,
std::vector<ck::index_t> conv_dilations,
std::vector<ck::index_t> left_pads,
std::vector<ck::index_t> right_pads)
: num_dim_spatial(n_dim_spatial),
N(n),
K(k),
C(c),
filter_spatial_lengths(filter_lengths),
input_spatial_lengths(input_lengths),
conv_filter_strides(conv_strides),
conv_filter_dilations(conv_dilations),
input_left_pads(left_pads),
input_right_pads(right_pads)
{
}
ck::index_t num_dim_spatial;
ck::index_t N;
......@@ -206,7 +228,7 @@ HostTensorDescriptor GetHostTensorDescriptor(const std::vector<std::size_t>& dim
return HostTensorDescriptor(
dims,
std::vector<std::size_t>{
C * dims[2] * dims[3] * dims[4], 1, C * dims[3] * dims[4], C * dims[4], C});
C * dims[2] * dims[3] * dims[4], 1, dims[3] * dims[4] * C, dims[4] * C, C});
}
std::stringstream err_msg;
......
include/ck/tensor_operation/gpu/device/device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk.hpp
View file @ cc8df39e
......@@ -95,8 +95,8 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads,
index_t i_ytilda,
index_t i_xtilda)
index_t i_ytilde,
index_t i_xtilde)
{
using namespace ck;
......@@ -177,34 +177,34 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
const auto YTilda = ConvStrideH / GcdStrideDilationH;
const auto XTilda = ConvStrideW / GcdStrideDilationW;
const auto YTilde = ConvStrideH / GcdStrideDilationH;
const auto XTilde = ConvStrideW / GcdStrideDilationW;
const auto YDot = math::integer_divide_ceil(Y, YTilda);
const auto XDot = math::integer_divide_ceil(X, XTilda);
const auto YDot = math::integer_divide_ceil(Y, YTilde);
const auto XDot = math::integer_divide_ceil(X, XTilde);
const auto HTilda =
const auto HTilde =
Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
const auto WTilda =
const auto WTilde =
Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
// only work on HTilda and WTilda that contribute to non-padding area of input tensor
const auto IHTildaSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadH - ConvDilationH * (YTilda - I1)), ConvStrideH);
const auto IWTildaSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadW - ConvDilationW * (XTilda - I1)), ConvStrideW);
// only work on HTilde and WTilde that contribute to non-padding area of input tensor
const auto IHTildeSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadH - ConvDilationH * (YTilde - I1)), ConvStrideH);
const auto IWTildeSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadW - ConvDilationW * (XTilde - I1)), ConvStrideW);
const auto IHTildaSliceEnd = math::min(
HTilda, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
const auto IWTildaSliceEnd = math::min(
WTilda, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
const auto IHTildeSliceEnd = math::min(
HTilde, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
const auto IWTildeSliceEnd = math::min(
WTilde, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
const auto HTildaSlice = IHTildaSliceEnd - IHTildaSliceBegin;
const auto WTildaSlice = IWTildaSliceEnd - IWTildaSliceBegin;
const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
// GemmK is different for each GEMM
const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilda, YTilda);
const auto XDotSlice = math::integer_divide_ceil(X - i_xtilda, XTilda);
const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
// A: output tensor
const auto out_n_hop_wop_k_grid_desc = transform_tensor_descriptor(
......@@ -216,26 +216,26 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto out_n_ydot_htilda_xdot_wtilda_k_grid_desc = transform_tensor_descriptor(
const auto out_n_ydot_htilde_xdot_wtilde_k_grid_desc = transform_tensor_descriptor(
out_n_hop_wop_k_grid_desc,
make_tuple(
make_pass_through_transform(N),
make_embed_transform(make_tuple(YDot, HTilda),
make_embed_transform(make_tuple(YDot, HTilde),
make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
make_embed_transform(make_tuple(XDot, WTilda),
make_embed_transform(make_tuple(XDot, WTilde),
make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
make_pass_through_transform(K)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc =
const auto out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc =
transform_tensor_descriptor(
out_n_ydot_htilda_xdot_wtilda_k_grid_desc,
out_n_ydot_htilde_xdot_wtilde_k_grid_desc,
make_tuple(make_pass_through_transform(N),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
make_unmerge_transform(make_tuple(K0, K1))),
make_tuple(Sequence<0>{},
Sequence<1>{},
......@@ -251,32 +251,32 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
Sequence<5, 6>{}));
const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
make_pass_through_transform(K1)),
make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
// B weight tensor
const auto wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc = transform_tensor_descriptor(
const auto wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc = transform_tensor_descriptor(
wei_k_y_x_c_grid_desc,
make_tuple(make_pass_through_transform(K),
make_embed_transform(make_tuple(YDot, YTilda),
make_embed_transform(make_tuple(YDot, YTilde),
make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
make_embed_transform(make_tuple(XDot, XTilda),
make_embed_transform(make_tuple(XDot, XTilde),
make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto wei_k0_k1_ydotslice_xdotslice_c_grid_desc =
transform_tensor_descriptor(wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc,
transform_tensor_descriptor(wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_freeze_transform(i_ytilda),
make_freeze_transform(i_xtilda),
make_freeze_transform(i_ytilde),
make_freeze_transform(i_xtilde),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
......@@ -309,24 +309,24 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc = transform_tensor_descriptor(
const auto in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc = transform_tensor_descriptor(
in_n_hip_wip_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(make_tuple(YTilda, HTilda),
make_embed_transform(make_tuple(YTilde, HTilde),
make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(XTilda, WTilda),
make_embed_transform(make_tuple(XTilde, WTilde),
make_tuple(ConvDilationW, ConvStrideW)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto in_n_htildaslice_wtildaslice_c_grid_desc = transform_tensor_descriptor(
in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc,
const auto in_n_htildeslice_wtildeslice_c_grid_desc = transform_tensor_descriptor(
in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_freeze_transform(i_ytilda),
make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
make_freeze_transform(i_xtilda),
make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
make_freeze_transform(i_ytilde),
make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
make_freeze_transform(i_xtilde),
make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
......@@ -342,8 +342,8 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
Sequence<3>{}));
const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
in_n_htildaslice_wtildaslice_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
in_n_htildeslice_wtildeslice_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
make_pass_through_transform(C)),
make_tuple(Sequence<0, 1, 2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
......@@ -452,18 +452,18 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
const auto YTilda = ConvStrideH / GcdStrideDilationH;
const auto XTilda = ConvStrideW / GcdStrideDilationW;
const auto YTilde = ConvStrideH / GcdStrideDilationH;
const auto XTilde = ConvStrideW / GcdStrideDilationW;
for(index_t i_ytilda = 0; i_ytilda < YTilda; ++i_ytilda)
for(index_t i_ytilde = 0; i_ytilde < YTilde; ++i_ytilde)
{
for(index_t i_xtilda = 0; i_xtilda < XTilda; ++i_xtilda)
for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
{
// check slice is valid
const index_t Y = filter_spatial_lengths_[0];
const index_t X = filter_spatial_lengths_[1];
const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilda, YTilda);
const auto XDotSlice = math::integer_divide_ceil(X - i_xtilda, XTilda);
const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
if(YDotSlice * XDotSlice <= 0)
{
continue;
......@@ -480,8 +480,8 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
conv_filter_dilations,
input_left_pads,
input_right_pads,
i_ytilda,
i_xtilda);
i_ytilde,
i_xtilde);
a_grid_desc_k0_m_k1_container_.push_back(descs[I0]);
b_grid_desc_k0_n_k1_container_.push_back(descs[I1]);
c_grid_desc_m_n_container_.push_back(descs[I2]);
......@@ -533,7 +533,6 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
float Run(const Argument& arg, int nrepeat = 1)
{
nrepeat = 1;
float ave_time = 0;
for(size_t i = 0; i < arg.a_grid_desc_k0_m_k1_container_.size(); i++)
{
......
include/ck/tensor_operation/gpu/device/tensor_layout.hpp
View file @ cc8df39e
......@@ -100,7 +100,6 @@ struct NDHWK : public BaseTensorLayout
{
static constexpr const char* name = "NDHWK";
};
struct NCDHW : public BaseTensorLayout
{
static constexpr const char* name = "NCDHW";
......
library/include/ck/library/obselete_driver_offline/device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk.hpp
View file @ cc8df39e
......@@ -303,14 +303,14 @@ void device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk
const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
const auto YTilda = ConvStrideH / GcdStrideDilationH;
const auto XTilda = ConvStrideW / GcdStrideDilationW;
const auto YTilde = ConvStrideH / GcdStrideDilationH;
const auto XTilde = ConvStrideW / GcdStrideDilationW;
float ave_time = 0;
for(index_t i_ytilda = 0; i_ytilda < YTilda; ++i_ytilda)
for(index_t i_ytilde = 0; i_ytilde < YTilde; ++i_ytilde)
{
for(index_t i_xtilda = 0; i_xtilda < XTilda; ++i_xtilda)
for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
{
const auto descs =
transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
......@@ -321,8 +321,8 @@ void device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk
conv_dilations,
in_left_pads,
in_right_pads,
i_ytilda,
i_xtilda,
i_ytilde,
i_xtilde,
Number<GemmK1>{});
const auto out_gemmk0_gemmm_gemmk1_grid_desc = descs[I0];
......
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_bwd_data.hpp
View file @ cc8df39e
......@@ -14,17 +14,20 @@ namespace host {
template <typename InDataType,
typename WeiDataType,
typename OutDataType,
typename AccDataType,
typename InElementwiseOperation,
typename WeiElementwiseOperation,
typename OutElementwiseOperation>
typename OutElementwiseOperation,
ck::index_t NumDimSpatial = 2,
typename std::enable_if<NumDimSpatial >= 1 && NumDimSpatial <= 3, bool>::type = false>
struct ReferenceConvBwdData : public device::BaseOperator
{
// Argument
struct Argument : public device::BaseArgument
{
Argument(Tensor<InDataType>& in_n_c_hi_wi,
const Tensor<WeiDataType>& wei_k_c_y_x,
const Tensor<OutDataType>& out_n_k_ho_wo,
Argument(Tensor<InDataType>& input,
const Tensor<WeiDataType>& weight,
const Tensor<OutDataType>& output,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
......@@ -32,9 +35,9 @@ struct ReferenceConvBwdData : public device::BaseOperator
InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op)
: in_n_c_hi_wi_{in_n_c_hi_wi},
wei_k_c_y_x_{wei_k_c_y_x},
out_n_k_ho_wo_{out_n_k_ho_wo},
: input_{input},
weight_{weight},
output_{output},
conv_strides_{conv_filter_strides},
conv_dilations_{conv_filter_dilations},
in_left_pads_{input_left_pads},
......@@ -45,9 +48,9 @@ struct ReferenceConvBwdData : public device::BaseOperator
{
}
Tensor<InDataType>& in_n_c_hi_wi_;
const Tensor<WeiDataType>& wei_k_c_y_x_;
const Tensor<OutDataType>& out_n_k_ho_wo_;
Tensor<InDataType>& input_;
const Tensor<WeiDataType>& weight_;
const Tensor<OutDataType>& output_;
std::vector<index_t> conv_strides_;
std::vector<index_t> conv_dilations_;
......@@ -66,67 +69,199 @@ struct ReferenceConvBwdData : public device::BaseOperator
float Run(const Argument& arg)
{
auto f_nchw = [&](auto n, auto c, auto hi, auto wi) {
std::size_t K = arg.wei_k_c_y_x_.mDesc.GetLengths()[0];
std::size_t Y = arg.wei_k_c_y_x_.mDesc.GetLengths()[2];
std::size_t X = arg.wei_k_c_y_x_.mDesc.GetLengths()[3];
if constexpr(NumDimSpatial == 1)
{
auto f_nchw = [&](auto n, auto c, auto wi) {
std::size_t K = arg.weight_.mDesc.GetLengths()[0];
std::size_t X = arg.weight_.mDesc.GetLengths()[2];
std::size_t Wo = arg.output_.mDesc.GetLengths()[2];
std::size_t Ho = arg.out_n_k_ho_wo_.mDesc.GetLengths()[2];
std::size_t Wo = arg.out_n_k_ho_wo_.mDesc.GetLengths()[3];
AccDataType v_acc = 0;
float v_acc = 0;
for(int x = 0; x < X; ++x)
{
int w_tmp = wi + arg.in_left_pads_[0] - x * arg.conv_dilations_[0];
if(w_tmp % arg.conv_strides_[0] == 0)
{
int wo = w_tmp / arg.conv_strides_[0];
if(wo >= 0 && wo < Wo)
{
for(int k = 0; k < K; ++k)
{
AccDataType v_out = 0;
AccDataType v_wei = 0;
arg.out_element_op_(
v_out,
ck::type_convert<AccDataType>(arg.output_(n, k, wo)));
arg.wei_element_op_(
v_wei, ck::type_convert<AccDataType>(arg.weight_(k, c, x)));
v_acc += v_out * v_wei;
}
}
}
}
for(int y = 0; y < Y; ++y)
{
int h_tmp = hi + arg.in_left_pads_[0] - y * arg.conv_dilations_[0];
if(h_tmp % arg.conv_strides_[0] == 0)
float v_in;
arg.in_element_op_(v_in, v_acc);
arg.input_(n, c, wi) = ck::type_convert<InDataType>(v_in);
};
make_ParallelTensorFunctor(f_nchw,
arg.input_.mDesc.GetLengths()[0],
arg.input_.mDesc.GetLengths()[1],
arg.input_.mDesc.GetLengths()[2])(
std::thread::hardware_concurrency());
return 0;
}
else if constexpr(NumDimSpatial == 2)
{
auto f_nchw = [&](auto n, auto c, auto hi, auto wi) {
std::size_t K = arg.weight_.mDesc.GetLengths()[0];
std::size_t Y = arg.weight_.mDesc.GetLengths()[2];
std::size_t X = arg.weight_.mDesc.GetLengths()[3];
std::size_t Ho = arg.output_.mDesc.GetLengths()[2];
std::size_t Wo = arg.output_.mDesc.GetLengths()[3];
AccDataType v_acc = 0;
for(int y = 0; y < Y; ++y)
{
int ho = h_tmp / arg.conv_strides_[0];
if(ho >= 0 && ho < Ho)
int h_tmp = hi + arg.in_left_pads_[0] - y * arg.conv_dilations_[0];
if(h_tmp % arg.conv_strides_[0] == 0)
{
for(int x = 0; x < X; ++x)
int ho = h_tmp / arg.conv_strides_[0];
if(ho >= 0 && ho < Ho)
{
int w_tmp = wi + arg.in_left_pads_[1] - x * arg.conv_dilations_[1];
if(w_tmp % arg.conv_strides_[1] == 0)
for(int x = 0; x < X; ++x)
{
int wo = w_tmp / arg.conv_strides_[1];
if(wo >= 0 && wo < Wo)
int w_tmp =
wi + arg.in_left_pads_[1] - x * arg.conv_dilations_[1];
if(w_tmp % arg.conv_strides_[1] == 0)
{
for(int k = 0; k < K; ++k)
int wo = w_tmp / arg.conv_strides_[1];
if(wo >= 0 && wo < Wo)
{
float v_out = 0;
float v_wei = 0;
arg.out_element_op_(
v_out,
ck::type_convert<float>(
arg.out_n_k_ho_wo_(n, k, ho, wo)));
arg.wei_element_op_(v_wei,
ck::type_convert<float>(
arg.wei_k_c_y_x_(k, c, y, x)));
v_acc += v_out * v_wei;
for(int k = 0; k < K; ++k)
{
AccDataType v_out = 0;
AccDataType v_wei = 0;
arg.out_element_op_(v_out,
ck::type_convert<AccDataType>(
arg.output_(n, k, ho, wo)));
arg.wei_element_op_(v_wei,
ck::type_convert<AccDataType>(
arg.weight_(k, c, y, x)));
v_acc += v_out * v_wei;
}
}
}
}
}
}
}
AccDataType v_in;
arg.in_element_op_(v_in, v_acc);
arg.input_(n, c, hi, wi) = ck::type_convert<InDataType>(v_in);
};
make_ParallelTensorFunctor(f_nchw,
arg.input_.mDesc.GetLengths()[0],
arg.input_.mDesc.GetLengths()[1],
arg.input_.mDesc.GetLengths()[2],
arg.input_.mDesc.GetLengths()[3])(
std::thread::hardware_concurrency());
return 0;
}
else if constexpr(NumDimSpatial == 3)
{
auto f_nchw = [&](auto n, auto c, auto di, auto hi, auto wi) {
std::size_t K = arg.weight_.mDesc.GetLengths()[0];
std::size_t Z = arg.weight_.mDesc.GetLengths()[2];
std::size_t Y = arg.weight_.mDesc.GetLengths()[3];
std::size_t X = arg.weight_.mDesc.GetLengths()[4];
std::size_t Do = arg.output_.mDesc.GetLengths()[2];
std::size_t Ho = arg.output_.mDesc.GetLengths()[3];
std::size_t Wo = arg.output_.mDesc.GetLengths()[4];
AccDataType v_acc = 0;
for(int z = 0; z < Z; ++z)
{
int d_tmp = di + arg.in_left_pads_[0] - z * arg.conv_dilations_[0];
if(d_tmp % arg.conv_strides_[0] == 0)
{
int do_ = d_tmp / arg.conv_strides_[0];
if(do_ >= 0 && do_ < Do)
{
for(int y = 0; y < Y; ++y)
{
int h_tmp =
hi + arg.in_left_pads_[1] - y * arg.conv_dilations_[1];
if(h_tmp % arg.conv_strides_[1] == 0)
{
int ho = h_tmp / arg.conv_strides_[1];
if(ho >= 0 && ho < Ho)
{
for(int x = 0; x < X; ++x)
{
int w_tmp = wi + arg.in_left_pads_[2] -
x * arg.conv_dilations_[2];
if(w_tmp % arg.conv_strides_[2] == 0)
{
int wo = w_tmp / arg.conv_strides_[2];
if(wo >= 0 && wo < Wo)
{
for(int k = 0; k < K; ++k)
{
AccDataType v_out = 0;
AccDataType v_wei = 0;
arg.out_element_op_(
v_out,
ck::type_convert<AccDataType>(
arg.output_(
n, k, do_, ho, wo)));
arg.wei_element_op_(
v_wei,
ck::type_convert<AccDataType>(
arg.weight_(k, c, z, y, x)));
v_acc += v_out * v_wei;
}
}
}
}
}
}
}
}
}
}
}
float v_in;
arg.in_element_op_(v_in, v_acc);
arg.in_n_c_hi_wi_(n, c, hi, wi) = ck::type_convert<InDataType>(v_in);
};
AccDataType v_in;
arg.in_element_op_(v_in, v_acc);
arg.input_(n, c, di, hi, wi) = ck::type_convert<InDataType>(v_in);
};
make_ParallelTensorFunctor(f_nchw,
arg.in_n_c_hi_wi_.mDesc.GetLengths()[0],
arg.in_n_c_hi_wi_.mDesc.GetLengths()[1],
arg.in_n_c_hi_wi_.mDesc.GetLengths()[2],
arg.in_n_c_hi_wi_.mDesc.GetLengths()[3])(
std::thread::hardware_concurrency());
make_ParallelTensorFunctor(f_nchw,
arg.input_.mDesc.GetLengths()[0],
arg.input_.mDesc.GetLengths()[1],
arg.input_.mDesc.GetLengths()[2],
arg.input_.mDesc.GetLengths()[3],
arg.input_.mDesc.GetLengths()[4])(
std::thread::hardware_concurrency());
return 0;
return 0;
}
}
float Run(const device::BaseArgument* p_arg, int) override
......@@ -143,9 +278,9 @@ struct ReferenceConvBwdData : public device::BaseOperator
bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
static auto MakeArgument(Tensor<InDataType>& in_n_c_hi_wi,
const Tensor<WeiDataType>& wei_k_c_y_x,
const Tensor<OutDataType>& out_n_k_ho_wo,
static auto MakeArgument(Tensor<InDataType>& input,
const Tensor<WeiDataType>& weight,
const Tensor<OutDataType>& output,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
......@@ -154,9 +289,9 @@ struct ReferenceConvBwdData : public device::BaseOperator
WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op)
{
return Argument{in_n_c_hi_wi,
wei_k_c_y_x,
out_n_k_ho_wo,
return Argument{input,
weight,
output,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
......
library/src/tensor_operation_instance/gpu/CMakeLists.txt
View file @ cc8df39e
......@@ -37,4 +37,5 @@ add_subdirectory(conv2d_fwd_bias_relu_add)
add_subdirectory(conv2d_fwd_bias_relu_atomic_add)
add_subdirectory(conv2d_bwd_data)
add_subdirectory(reduce)
add_subdirectory(convnd_bwd_data)
add_subdirectory(grouped_gemm)
library/src/tensor_operation_instance/gpu/convnd_bwd_data/CMakeLists.txt 0 → 100644
View file @ cc8df39e
# device_convnd_bwd_data_instance
set(DEVICE_CONVND_BWD_DATA_INSTANCE_SOURCE
device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f16_instance.cpp;
device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instance.cpp;
device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instance.cpp;
device_conv1d_bwd_data_xdl_nwc_kxc_nwk_int8_instance.cpp;
device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f16_instance.cpp;
device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f32_instance.cpp;
device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_bf16_instance.cpp;
device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_int8_instance.cpp;
device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f16_instance.cpp;
device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f32_instance.cpp;
device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_bf16_instance.cpp;
device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_int8_instance.cpp;
)
add_library(device_convnd_bwd_data_instance SHARED ${DEVICE_CONVND_BWD_DATA_INSTANCE_SOURCE})
target_compile_features(device_convnd_bwd_data_instance PUBLIC)
set_target_properties(device_convnd_bwd_data_instance PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS device_convnd_bwd_data_instance LIBRARY DESTINATION lib)
clang_tidy_check(device_convnd_bwd_data_instance)
library/src/tensor_operation_instance/gpu/convnd_bwd_data/device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instance.cpp 0 → 100644
View file @ cc8df39e
#include <stdlib.h>
#include "config.hpp"
#include "device_convnd_bwd_data_xdl_ndhwc_kzyxc_ndhwk.hpp"
#include "element_wise_operation.hpp"
#include "device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_conv2d_bwd_data_instance {
using BF16 = ushort;
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdDataDefault =
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization_t::Default;
static constexpr auto ConvBwdDataFilter1x1Stride1Pad0 =
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization_t::Filter1x1Stride1Pad0;
// Compilation parameters for in[n, hi, wi, c] * wei[k, y, x, c] = out[n, ho, wo, k]
using device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instances =
std::tuple<
// clang-format off
//#############################################################################| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Num| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
//#############################################################################| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Data| Dim| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
//#############################################################################| | | | | Operation| Operation| Operation| Specialization|Spatial| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
//#############################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>
// clang-format on
>;
using device_conv1d_bwd_data_xdl_nwc_kxc_nwk_1x1_s1_p0_bf16_instances =
std::tuple<
// clang-format off
//#############################################################################| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Num| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
//#############################################################################| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Data| Dim| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
//#############################################################################| | | | | Operation| Operation| Operation| Specialization|Spatial| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
//#############################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>
// clang-format on
>;
void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instances(
std::vector<DeviceConvBwdDataPtr<PassThrough, PassThrough, PassThrough>>& instances)
{
add_device_operation_instances(instances,
device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instances{});
add_device_operation_instances(
instances, device_conv1d_bwd_data_xdl_nwc_kxc_nwk_1x1_s1_p0_bf16_instances{});
}
} // namespace device_conv2d_bwd_data_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/convnd_bwd_data/device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f16_instance.cpp 0 → 100644
View file @ cc8df39e
#include <stdlib.h>
#include "config.hpp"
#include "device_convnd_bwd_data_xdl_ndhwc_kzyxc_ndhwk.hpp"
#include "element_wise_operation.hpp"
#include "device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_conv2d_bwd_data_instance {
using F16 = ck::half_t;
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdDataDefault =
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization_t::Default;
static constexpr auto ConvBwdDataFilter1x1Stride1Pad0 =
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization_t::Filter1x1Stride1Pad0;
// Compilation parameters for in[n, hi, wi, c] * wei[k, y, x, c] = out[n, ho, wo, k]
using device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f16_instances =
std::tuple<
// clang-format off
//#############################################################################| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Num| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
//#############################################################################| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Data| Dim| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
//#############################################################################| | | | | Operation| Operation| Operation| Specialization|Spatial| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
//#############################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
#if 1
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
#endif
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>
// clang-format on
>;
using device_conv1d_bwd_data_xdl_nwc_kxc_nwk_1x1_s1_p0_f16_instances =
std::tuple<
// clang-format off
//#############################################################################| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Num| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
//#############################################################################| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Data| Dim| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
//#############################################################################| | | | | Operation| Operation| Operation| Specialization|Spatial| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
//#############################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>
// clang-format on
>;
void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f16_instances(
std::vector<DeviceConvBwdDataPtr<PassThrough, PassThrough, PassThrough>>& instances)
{
add_device_operation_instances(instances,
device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f16_instances{});
add_device_operation_instances(
instances, device_conv1d_bwd_data_xdl_nwc_kxc_nwk_1x1_s1_p0_f16_instances{});
}
} // namespace device_conv2d_bwd_data_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/convnd_bwd_data/device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instance.cpp 0 → 100644
View file @ cc8df39e
#include <stdlib.h>
#include "config.hpp"
#include "device_convnd_bwd_data_xdl_ndhwc_kzyxc_ndhwk.hpp"
#include "element_wise_operation.hpp"
#include "device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_conv2d_bwd_data_instance {
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdDataDefault =
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization_t::Default;
static constexpr auto ConvBwdDataFilter1x1Stride1Pad0 =
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization_t::Filter1x1Stride1Pad0;
// Compilation parameters for in[n, hi, wi, c] * wei[k, y, x, c] = out[n, ho, wo, k]
using device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instances =
std::tuple<
// clang-format off
//#############################################################################| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Num| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
//#############################################################################| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Data| Dim| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
//#############################################################################| | | | | Operation| Operation| Operation| Specialization|Spatial| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
//#############################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 256, 256, 128, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 128, 128, 128, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 256, 128, 128, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 128, 64, 128, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 64, 64, 64, 4, 4, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 256, 64, 128, 4, 4, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 128, 128, 32, 4, 4, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 128, 32, 128, 4, 4, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 64, 64, 32, 4, 4, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataDefault, 1, 64, 32, 64, 4, 4, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>
// clang-format on
>;
using device_conv1d_bwd_data_xdl_nwc_kxc_nwk_1x1_s1_p0_f32_instances =
std::tuple<
// clang-format off
//#############################################################################| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Num| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
//#############################################################################| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Data| Dim| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
//#############################################################################| | | | | Operation| Operation| Operation| Specialization|Spatial| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
//#############################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 256, 256, 128, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 128, 128, 128, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 256, 128, 128, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 128, 64, 128, 4, 4, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 64, 64, 64, 4, 4, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 256, 64, 128, 4, 4, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 64, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 128, 128, 32, 4, 4, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 1, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 128, 32, 128, 4, 4, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 32, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 64, 64, 32, 4, 4, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 2, 4, true, 7, 1>,
DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K< F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdDataFilter1x1Stride1Pad0, 1, 64, 32, 64, 4, 4, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, true, S<4, 16, 1>, S<2, 0, 1>, S<0, 2, 1>, 1, 4, 4, true, 7, 1>
// clang-format on
>;
void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instances(
std::vector<DeviceConvBwdDataPtr<PassThrough, PassThrough, PassThrough>>& instances)
{
add_device_operation_instances(instances,
device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instances{});
add_device_operation_instances(
instances, device_conv1d_bwd_data_xdl_nwc_kxc_nwk_1x1_s1_p0_f32_instances{});
}
} // namespace device_conv2d_bwd_data_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
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