mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-02 04:31:25 +00:00
c254e5abd2
* start conv2d bwd api * kernel running * add bwd reference * change to no shuffle * fix bwd reference * pass verification * add Filter1x1Stride1Pad0 and start testing * change some tuning parameter * fix test error * add fp16 tuning parameter * add bf16 tuning parameter * add int8 tuning parameters * change fp32 tuning parameter * add bwd to profiler * fix bug for bwd profiler * fix ckProfiler bug * change conv2d_bwd_xdl to fp16 * fix bug in comments * fix precompile id * fix enum conv name * chage _bwd_ to _bwd_data_ * change conv2d_bwd example id * bwd to bwd data * fix prehead * fix MakeDefaultBlock2CTileMap ,import form merge develop * format bwd instance * bwd to bwd data * change name bwd to bwd data * change name bwd to bwd data in example * formate code * change conv2d bwd data id in example * rewrite readme for example * fix CalculateMagicNumbers about div zero * add workaround CK_WORKAROUND_SWDEV_325164 * change test_conf2d_bwd_data show info * format * fix bug for workaround:CK_WORKAROUND_SWDEV_325164 * formate tuning parameters * formate tuning parameters again * formate tuning parameters 3 * formate tuning parameters 4 * remove add function template * format * update comment Co-authored-by: ltqin <letaoqin@amd.com> Co-authored-by: Chao Liu <chao.liu2@amd.com>
279 lines
12 KiB
C++
279 lines
12 KiB
C++
#pragma once
|
|
#include "config.hpp"
|
|
#include "device.hpp"
|
|
#include "host_tensor.hpp"
|
|
#include "host_tensor_generator.hpp"
|
|
#include "tensor_layout.hpp"
|
|
#include "device_tensor.hpp"
|
|
#include "device_conv_bwd_data.hpp"
|
|
#include "element_wise_operation.hpp"
|
|
#include "reference_conv_bwd_data.hpp"
|
|
|
|
using F16 = ck::half_t;
|
|
using F32 = float;
|
|
using BF16 = ushort;
|
|
using INT8 = int8_t;
|
|
namespace ck {
|
|
namespace tensor_operation {
|
|
namespace device {
|
|
namespace device_conv2d_bwd_data_instance {
|
|
|
|
using DeviceConvBwdDataNoOpPtr =
|
|
DeviceConvBwdDataPtr<ck::tensor_operation::element_wise::PassThrough,
|
|
ck::tensor_operation::element_wise::PassThrough,
|
|
ck::tensor_operation::element_wise::PassThrough>;
|
|
void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f32_instances(
|
|
std::vector<DeviceConvBwdDataNoOpPtr>&);
|
|
void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f16_instances(
|
|
std::vector<DeviceConvBwdDataNoOpPtr>&);
|
|
void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_bf16_instances(
|
|
std::vector<DeviceConvBwdDataNoOpPtr>&);
|
|
void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_int8_instances(
|
|
std::vector<DeviceConvBwdDataNoOpPtr>&);
|
|
} // namespace device_conv2d_bwd_data_instance
|
|
} // namespace device
|
|
} // namespace tensor_operation
|
|
} // namespace ck
|
|
|
|
namespace ck {
|
|
namespace profiler {
|
|
|
|
template <int NDimSpatial,
|
|
typename InDataType,
|
|
typename WeiDataType,
|
|
typename OutDataType,
|
|
typename InLayout,
|
|
typename WeiLayout,
|
|
typename OutLayout>
|
|
void profile_conv_bwd_data_impl(int do_verification,
|
|
int init_method,
|
|
bool do_log,
|
|
int nrepeat,
|
|
ck::index_t N,
|
|
ck::index_t K,
|
|
ck::index_t C,
|
|
std::vector<ck::index_t> input_spatial_lengths,
|
|
std::vector<ck::index_t> filter_spatial_lengths,
|
|
std::vector<ck::index_t> output_spatial_lengths,
|
|
std::vector<ck::index_t> conv_filter_strides,
|
|
std::vector<ck::index_t> conv_filter_dilations,
|
|
std::vector<ck::index_t> input_left_pads,
|
|
std::vector<ck::index_t> input_right_pads)
|
|
{
|
|
const ck::index_t Y = filter_spatial_lengths[0];
|
|
const ck::index_t X = filter_spatial_lengths[1];
|
|
|
|
const ck::index_t Hi = input_spatial_lengths[0];
|
|
const ck::index_t Wi = input_spatial_lengths[1];
|
|
|
|
const ck::index_t Ho = output_spatial_lengths[0];
|
|
const ck::index_t Wo = output_spatial_lengths[1];
|
|
|
|
auto f_host_tensor_descriptor =
|
|
[](std::size_t N_, std::size_t C_, std::size_t H, std::size_t W, auto layout) {
|
|
if constexpr(is_same<decltype(layout), ck::tensor_layout::convolution::NCHW>::value ||
|
|
is_same<decltype(layout), ck::tensor_layout::convolution::KCYX>::value ||
|
|
is_same<decltype(layout), ck::tensor_layout::convolution::NKHW>::value)
|
|
{
|
|
return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
|
|
std::vector<std::size_t>({C_ * H * W, H * W, W, 1}));
|
|
}
|
|
else if constexpr(is_same<decltype(layout), tensor_layout::convolution::NHWC>::value ||
|
|
is_same<decltype(layout), tensor_layout::convolution::KYXC>::value ||
|
|
is_same<decltype(layout), tensor_layout::convolution::NHWK>::value)
|
|
{
|
|
return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
|
|
std::vector<std::size_t>({C_ * H * W, 1, W * C_, C_}));
|
|
}
|
|
};
|
|
|
|
Tensor<InDataType> in_n_c_hi_wi_host_result(f_host_tensor_descriptor(N, C, Hi, Wi, InLayout{}));
|
|
Tensor<InDataType> in_n_c_hi_wi_device_result(
|
|
f_host_tensor_descriptor(N, C, Hi, Wi, InLayout{}));
|
|
Tensor<WeiDataType> wei_k_c_y_x(f_host_tensor_descriptor(K, C, Y, X, WeiLayout{}));
|
|
Tensor<OutDataType> out_n_k_ho_wo(f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));
|
|
|
|
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_2<InDataType>{-5, 5});
|
|
wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
|
|
break;
|
|
default:
|
|
out_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
|
|
wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
|
|
}
|
|
|
|
using InElementOp = ck::tensor_operation::element_wise::PassThrough;
|
|
using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
|
|
using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
|
|
|
|
const auto in_element_op = InElementOp{};
|
|
const auto wei_element_op = WeiElementOp{};
|
|
const auto out_element_op = OutElementOp{};
|
|
|
|
if(do_verification)
|
|
{
|
|
using ReferenceConvBwdDataInstance =
|
|
ck::tensor_operation::host::ReferenceConvBwdData<InDataType,
|
|
WeiDataType,
|
|
OutDataType,
|
|
InElementOp,
|
|
WeiElementOp,
|
|
OutElementOp>;
|
|
|
|
auto ref_conv = ReferenceConvBwdDataInstance{};
|
|
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,
|
|
conv_filter_strides,
|
|
conv_filter_dilations,
|
|
input_left_pads,
|
|
input_right_pads,
|
|
in_element_op,
|
|
wei_element_op,
|
|
out_element_op);
|
|
|
|
ref_invoker.Run(ref_argument);
|
|
}
|
|
|
|
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());
|
|
|
|
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
|
|
|
using DeviceConvBwdDataNoOpPtr =
|
|
ck::tensor_operation::device::DeviceConvBwdDataPtr<PassThrough, PassThrough, PassThrough>;
|
|
|
|
// add device Conv instances
|
|
std::vector<DeviceConvBwdDataNoOpPtr> conv_ptrs;
|
|
if constexpr(ck::is_same_v<ck::remove_cv_t<InDataType>, float> &&
|
|
ck::is_same_v<ck::remove_cv_t<WeiDataType>, float> &&
|
|
ck::is_same_v<ck::remove_cv_t<OutDataType>, float>)
|
|
{
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::
|
|
add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f32_instances(conv_ptrs);
|
|
}
|
|
else if constexpr(ck::is_same_v<ck::remove_cv_t<InDataType>, ck::half_t> &&
|
|
ck::is_same_v<ck::remove_cv_t<WeiDataType>, ck::half_t> &&
|
|
ck::is_same_v<ck::remove_cv_t<OutDataType>, ck::half_t>)
|
|
{
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::
|
|
add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f16_instances(conv_ptrs);
|
|
}
|
|
else if constexpr(ck::is_same_v<ck::remove_cv_t<InDataType>, ushort> &&
|
|
ck::is_same_v<ck::remove_cv_t<WeiDataType>, ushort> &&
|
|
ck::is_same_v<ck::remove_cv_t<OutDataType>, ushort>)
|
|
{
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::
|
|
add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_bf16_instances(conv_ptrs);
|
|
}
|
|
else if constexpr(ck::is_same_v<ck::remove_cv_t<InDataType>, int8_t> &&
|
|
ck::is_same_v<ck::remove_cv_t<WeiDataType>, int8_t> &&
|
|
ck::is_same_v<ck::remove_cv_t<OutDataType>, int8_t>)
|
|
{
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::
|
|
add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_int8_instances(conv_ptrs);
|
|
}
|
|
|
|
if(conv_ptrs.size() <= 0)
|
|
{
|
|
throw std::runtime_error("wrong! no device Conv instance found");
|
|
}
|
|
|
|
std::string best_conv_name;
|
|
float best_ave_time = 0;
|
|
float best_tflops = 0;
|
|
float best_gb_per_sec = 0;
|
|
|
|
// profile device Conv instances
|
|
for(auto& conv_ptr : conv_ptrs)
|
|
{
|
|
auto argument_ptr = conv_ptr->MakeArgumentPointer(
|
|
static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
|
|
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
|
|
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
|
|
N,
|
|
K,
|
|
C,
|
|
input_spatial_lengths,
|
|
filter_spatial_lengths,
|
|
output_spatial_lengths,
|
|
conv_filter_strides,
|
|
conv_filter_dilations,
|
|
input_left_pads,
|
|
input_right_pads,
|
|
in_element_op,
|
|
wei_element_op,
|
|
out_element_op);
|
|
|
|
auto invoker_ptr = conv_ptr->MakeInvokerPointer();
|
|
|
|
if(conv_ptr->IsSupportedArgument(argument_ptr.get()))
|
|
{
|
|
std::string conv_name = conv_ptr->GetTypeString();
|
|
|
|
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);
|
|
|
|
std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;
|
|
|
|
std::size_t num_btype = sizeof(InDataType) * (N * C * Hi * Wi) +
|
|
sizeof(WeiDataType) * (K * C * Y * X) +
|
|
sizeof(OutDataType) * (N * K * Ho * Wo);
|
|
|
|
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, " << conv_name << std::endl;
|
|
|
|
if(tflops > best_tflops)
|
|
{
|
|
best_conv_name = conv_name;
|
|
best_tflops = tflops;
|
|
best_ave_time = ave_time;
|
|
best_gb_per_sec = gb_per_sec;
|
|
}
|
|
|
|
if(do_verification)
|
|
{
|
|
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);
|
|
|
|
if(do_log)
|
|
{
|
|
LogRangeAsType<float>(std::cout << "in : ", out_n_k_ho_wo.mData, ",")
|
|
<< std::endl;
|
|
LogRangeAsType<float>(std::cout << "wei: ", wei_k_c_y_x.mData, ",")
|
|
<< std::endl;
|
|
LogRangeAsType<float>(
|
|
std::cout << "out_host : ", in_n_c_hi_wi_host_result.mData, ",")
|
|
<< std::endl;
|
|
LogRangeAsType<float>(
|
|
std::cout << "out_device: ", in_n_c_hi_wi_device_result.mData, ",")
|
|
<< std::endl;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
|
|
<< best_gb_per_sec << " GB/s, " << best_conv_name << std::endl;
|
|
}
|
|
|
|
} // namespace profiler
|
|
} // namespace ck
|