mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-03 05:01:25 +00:00
f03a1738d9
* Turning compare warnings on * Cleaning part I * Cleaning part II * Explicit static_cast to ck::type_convert * Resolving large tensor size issue. * format * revert change to tensor descriptor; promote lementSpaceSize to 64bit * use integer value for GEMM test * Review remarks * Review remarks + issues with (un)signed arithmetic * Format fix * Format * Clang-format. * fix 2gb limit issue Co-authored-by: Chao Liu <chao.liu2@amd.com> Co-authored-by: Adam Osewski <aosewski@amd.com>
481 lines
18 KiB
C++
481 lines
18 KiB
C++
#pragma once
|
|
#include "config.hpp"
|
|
#include "device.hpp"
|
|
#include "conv_fwd_util.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 = ck::bhalf_t;
|
|
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_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instances(
|
|
std::vector<DeviceConvBwdDataNoOpPtr>&);
|
|
void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f16_instances(
|
|
std::vector<DeviceConvBwdDataNoOpPtr>&);
|
|
void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instances(
|
|
std::vector<DeviceConvBwdDataNoOpPtr>&);
|
|
void add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_int8_instances(
|
|
std::vector<DeviceConvBwdDataNoOpPtr>&);
|
|
|
|
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>&);
|
|
|
|
void add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f32_instances(
|
|
std::vector<DeviceConvBwdDataNoOpPtr>&);
|
|
void add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f16_instances(
|
|
std::vector<DeviceConvBwdDataNoOpPtr>&);
|
|
void add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_bf16_instances(
|
|
std::vector<DeviceConvBwdDataNoOpPtr>&);
|
|
void add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_int8_instances(
|
|
std::vector<DeviceConvBwdDataNoOpPtr>&);
|
|
} // namespace device_conv2d_bwd_data_instance
|
|
} // namespace device
|
|
} // namespace tensor_operation
|
|
} // namespace ck
|
|
|
|
namespace ck {
|
|
namespace profiler {
|
|
using DeviceConvBwdDataNoOpPtr =
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::DeviceConvBwdDataNoOpPtr;
|
|
|
|
template <typename InLayout>
|
|
HostTensorDescriptor get_input_host_tensor_descriptor(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::utils::conv::get_host_tensor_descriptor(dims, InLayout{});
|
|
}
|
|
case 2: {
|
|
return ck::utils::conv::get_host_tensor_descriptor(dims, InLayout{});
|
|
}
|
|
case 1: {
|
|
return ck::utils::conv::get_host_tensor_descriptor(dims, InLayout{});
|
|
}
|
|
default: {
|
|
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
|
|
}
|
|
}
|
|
}
|
|
template <typename WeiLayout>
|
|
HostTensorDescriptor get_filters_host_tensor_descriptor(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::utils::conv::get_host_tensor_descriptor(dims, WeiLayout{});
|
|
}
|
|
case 2: {
|
|
return ck::utils::conv::get_host_tensor_descriptor(dims, WeiLayout{});
|
|
}
|
|
case 1: {
|
|
return ck::utils::conv::get_host_tensor_descriptor(dims, WeiLayout{});
|
|
}
|
|
default: {
|
|
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
|
|
}
|
|
}
|
|
}
|
|
template <typename OutLayout>
|
|
HostTensorDescriptor get_output_host_ensor_descriptor(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::utils::conv::get_host_tensor_descriptor(dims, OutLayout{});
|
|
}
|
|
case 2: {
|
|
return ck::utils::conv::get_host_tensor_descriptor(dims, OutLayout{});
|
|
}
|
|
case 1: {
|
|
return ck::utils::conv::get_host_tensor_descriptor(dims, OutLayout{});
|
|
}
|
|
default: {
|
|
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
|
|
}
|
|
}
|
|
}
|
|
template <typename InDataType, typename WeiDataType, typename OutDataType>
|
|
void get_device_conv_bwd_data_op_ptr(
|
|
InDataType, WeiDataType, OutDataType, std::vector<DeviceConvBwdDataNoOpPtr>&, int)
|
|
{
|
|
std::cout << "can not find device conv bwd data" << std::endl;
|
|
exit(1);
|
|
}
|
|
template <>
|
|
void get_device_conv_bwd_data_op_ptr(
|
|
F32, F32, F32, std::vector<DeviceConvBwdDataNoOpPtr>& conv_ptrs, int num_dim_spatial)
|
|
{
|
|
switch(num_dim_spatial)
|
|
{
|
|
case 1:
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::
|
|
add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f32_instances(conv_ptrs);
|
|
break;
|
|
case 2:
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::
|
|
add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f32_instances(conv_ptrs);
|
|
break;
|
|
case 3:
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::
|
|
add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f32_instances(conv_ptrs);
|
|
break;
|
|
default: break;
|
|
}
|
|
}
|
|
template <>
|
|
void get_device_conv_bwd_data_op_ptr(
|
|
F16, F16, F16, std::vector<DeviceConvBwdDataNoOpPtr>& conv_ptrs, int num_dim_spatial)
|
|
{
|
|
switch(num_dim_spatial)
|
|
{
|
|
case 1:
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::
|
|
add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_f16_instances(conv_ptrs);
|
|
break;
|
|
case 2:
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::
|
|
add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f16_instances(conv_ptrs);
|
|
break;
|
|
case 3:
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::
|
|
add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_f16_instances(conv_ptrs);
|
|
break;
|
|
default: break;
|
|
}
|
|
}
|
|
template <>
|
|
void get_device_conv_bwd_data_op_ptr(
|
|
BF16, BF16, BF16, std::vector<DeviceConvBwdDataNoOpPtr>& conv_ptrs, int num_dim_spatial)
|
|
{
|
|
switch(num_dim_spatial)
|
|
{
|
|
case 1:
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::
|
|
add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_bf16_instances(conv_ptrs);
|
|
break;
|
|
case 2:
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::
|
|
add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_bf16_instances(conv_ptrs);
|
|
break;
|
|
case 3:
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::
|
|
add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_bf16_instances(conv_ptrs);
|
|
break;
|
|
default: break;
|
|
}
|
|
}
|
|
template <>
|
|
void get_device_conv_bwd_data_op_ptr(
|
|
INT8, INT8, INT8, std::vector<DeviceConvBwdDataNoOpPtr>& conv_ptrs, int num_dim_spatial)
|
|
{
|
|
switch(num_dim_spatial)
|
|
{
|
|
case 1:
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::
|
|
add_device_conv1d_bwd_data_xdl_nwc_kxc_nwk_int8_instances(conv_ptrs);
|
|
break;
|
|
case 2:
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::
|
|
add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_int8_instances(conv_ptrs);
|
|
break;
|
|
case 3:
|
|
ck::tensor_operation::device::device_conv2d_bwd_data_instance::
|
|
add_device_conv3d_bwd_data_xdl_ndhwc_kzyxc_ndhwk_int8_instances(conv_ptrs);
|
|
break;
|
|
default: break;
|
|
}
|
|
}
|
|
|
|
template <typename T>
|
|
static bool check_out(const Tensor<T>& ref, const Tensor<T>& result)
|
|
{
|
|
float max_diff = 1e-6;
|
|
|
|
for(std::size_t i = 0; i < ref.mData.size(); ++i)
|
|
{
|
|
float diff = std::abs(double(ref.mData[i]) - double(result.mData[i]));
|
|
if(max_diff < diff)
|
|
{
|
|
return false;
|
|
}
|
|
}
|
|
return true;
|
|
}
|
|
template <typename DataType>
|
|
void show_data_nhwc_layout(Tensor<DataType>& nhwc)
|
|
{
|
|
std::cout << "[";
|
|
for(int n = 0; n < ck::type_convert<int>(nhwc.mDesc.GetLengths()[0]); n++)
|
|
{
|
|
std::cout << "[";
|
|
for(int hi = 0; hi < ck::type_convert<int>(nhwc.mDesc.GetLengths()[2]); hi++)
|
|
{
|
|
std::cout << "[";
|
|
for(int wi = 0; wi < ck::type_convert<int>(nhwc.mDesc.GetLengths()[3]); wi++)
|
|
{
|
|
std::cout << "[";
|
|
for(int c = 0; c < ck::type_convert<int>(nhwc.mDesc.GetLengths()[1]); c++)
|
|
{
|
|
std::cout << static_cast<float>(nhwc(n, c, hi, wi)) << " ";
|
|
}
|
|
std::cout << "]";
|
|
}
|
|
std::cout << "]";
|
|
}
|
|
std::cout << "]";
|
|
}
|
|
std::cout << "]";
|
|
}
|
|
|
|
template <int NDimSpatial,
|
|
typename InDataType,
|
|
typename WeiDataType,
|
|
typename OutDataType,
|
|
typename AccDataType,
|
|
typename InLayout,
|
|
typename WeiLayout,
|
|
typename OutLayout>
|
|
bool profile_convnd_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,
|
|
const std::vector<ck::index_t>& input_spatial_lengths,
|
|
const std::vector<ck::index_t>& filter_spatial_lengths,
|
|
const std::vector<ck::index_t>& output_spatial_lengths,
|
|
const std::vector<ck::index_t>& conv_filter_strides,
|
|
const std::vector<ck::index_t>& conv_filter_dilations,
|
|
const std::vector<ck::index_t>& input_left_pads,
|
|
const std::vector<ck::index_t>& input_right_pads)
|
|
{
|
|
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{};
|
|
|
|
std::vector<std::size_t> input_dims{static_cast<std::size_t>(N), static_cast<std::size_t>(C)};
|
|
input_dims.insert(
|
|
std::end(input_dims), std::begin(input_spatial_lengths), std::end(input_spatial_lengths));
|
|
|
|
std::vector<std::size_t> filter_dims{static_cast<std::size_t>(K), static_cast<std::size_t>(C)};
|
|
filter_dims.insert(std::end(filter_dims),
|
|
std::begin(filter_spatial_lengths),
|
|
std::end(filter_spatial_lengths));
|
|
|
|
std::vector<std::size_t> output_dims{static_cast<std::size_t>(N), static_cast<std::size_t>(K)};
|
|
output_dims.insert(std::end(output_dims),
|
|
std::begin(output_spatial_lengths),
|
|
std::end(output_spatial_lengths));
|
|
|
|
Tensor<InDataType> input_host_result(
|
|
get_input_host_tensor_descriptor<InLayout>(input_dims, NDimSpatial));
|
|
Tensor<InDataType> input_device_result(
|
|
get_input_host_tensor_descriptor<InLayout>(input_dims, NDimSpatial));
|
|
Tensor<WeiDataType> weights(
|
|
get_filters_host_tensor_descriptor<WeiLayout>(filter_dims, NDimSpatial));
|
|
Tensor<OutDataType> output(
|
|
get_output_host_ensor_descriptor<OutLayout>(output_dims, NDimSpatial));
|
|
|
|
std::cout << "input: " << input_host_result.mDesc << std::endl;
|
|
std::cout << "weights: " << weights.mDesc << std::endl;
|
|
std::cout << "output: " << output.mDesc << std::endl;
|
|
|
|
switch(init_method)
|
|
{
|
|
case 0: break;
|
|
case 1:
|
|
output.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
|
|
weights.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
|
|
break;
|
|
default:
|
|
output.GenerateTensorValue(GeneratorTensor_1<OutDataType>{1});
|
|
weights.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{1});
|
|
}
|
|
|
|
DeviceMem in_device_buf(sizeof(InDataType) * input_device_result.mDesc.GetElementSpace());
|
|
DeviceMem wei_device_buf(sizeof(WeiDataType) * weights.mDesc.GetElementSpace());
|
|
DeviceMem out_device_buf(sizeof(OutDataType) * output.mDesc.GetElementSpace());
|
|
|
|
out_device_buf.ToDevice(output.mData.data());
|
|
wei_device_buf.ToDevice(weights.mData.data());
|
|
|
|
// reset input to zero
|
|
in_device_buf.SetZero();
|
|
|
|
if(do_verification)
|
|
{
|
|
auto RunReference = [&](auto& ref_conv) {
|
|
auto ref_invoker = ref_conv.MakeInvoker();
|
|
|
|
auto ref_argument = ref_conv.MakeArgument(input_host_result,
|
|
weights,
|
|
output,
|
|
conv_filter_strides,
|
|
conv_filter_dilations,
|
|
input_left_pads,
|
|
input_right_pads,
|
|
InElementOp{},
|
|
WeiElementOp{},
|
|
OutElementOp{});
|
|
ref_invoker.Run(ref_argument);
|
|
};
|
|
|
|
auto ref_conv = ck::tensor_operation::host::ReferenceConvBwdData<InDataType,
|
|
WeiDataType,
|
|
OutDataType,
|
|
AccDataType,
|
|
InElementOp,
|
|
WeiElementOp,
|
|
OutElementOp,
|
|
NDimSpatial>();
|
|
RunReference(ref_conv);
|
|
}
|
|
|
|
// add device Conv instances
|
|
std::vector<DeviceConvBwdDataNoOpPtr> conv_ptrs;
|
|
get_device_conv_bwd_data_op_ptr(
|
|
InDataType{}, WeiDataType{}, OutDataType{}, conv_ptrs, NDimSpatial);
|
|
|
|
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
|
|
bool success = true;
|
|
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 =
|
|
ck::utils::conv::get_flops(N, C, K, filter_spatial_lengths, output_spatial_lengths);
|
|
std::size_t num_btype =
|
|
ck::utils::conv::get_btype<InDataType, WeiDataType, OutDataType>(
|
|
N, C, K, input_spatial_lengths, 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(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(input_device_result.mData.data());
|
|
|
|
if(!check_out(input_host_result, input_device_result))
|
|
{
|
|
std::cout << "Fail Info: " << conv_ptr->GetTypeString() << std::endl;
|
|
|
|
success = false;
|
|
}
|
|
else
|
|
{
|
|
std::cout << "Pass Info: " << conv_ptr->GetTypeString() << std::endl;
|
|
}
|
|
|
|
check_error(input_host_result, input_device_result);
|
|
|
|
if(do_log)
|
|
{
|
|
std::cout << "in : ";
|
|
show_data_nhwc_layout(output);
|
|
std::cout << std::endl;
|
|
|
|
std::cout << "wei: ";
|
|
show_data_nhwc_layout(weights);
|
|
std::cout << std::endl;
|
|
|
|
std::cout << "out_host : ";
|
|
show_data_nhwc_layout(input_host_result);
|
|
std::cout << std::endl;
|
|
|
|
std::cout << "out_device: ";
|
|
show_data_nhwc_layout(input_device_result);
|
|
std::cout << std::endl;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
|
|
<< best_gb_per_sec << " GB/s, " << best_conv_name << std::endl;
|
|
return success;
|
|
}
|
|
|
|
} // namespace profiler
|
|
} // namespace ck
|