mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-21 13:29:20 +00:00
b64f30e733
* Rename folder
* Add layernorm 4d fwd example
* Rename original layernorm example
* Add layernorm 4d f16 test
* Add layernorm4d_fwd client example
* Support layernorm4D in ckProfiler
* Rename groupnorm to groupnorm fwd in example
* Rename layernorm and group fwd in test
* Rename normalization to normalization_fwd (instances)
* Add fwd to DeviceNormalization
* Rename external api header
* Rename folder, because we can also add bwd in this folder
* Add fwd in layernorm and groupnorm (profiler
* Fix compile error
---------
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>
[ROCm/composable_kernel commit: a3d9a2cd42]
275 lines
11 KiB
C++
275 lines
11 KiB
C++
// SPDX-License-Identifier: MIT
|
|
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
|
|
|
|
#pragma once
|
|
|
|
#include <iomanip>
|
|
|
|
#include "ck/ck.hpp"
|
|
|
|
#include "ck/library/tensor_operation_instance/gpu/normalization_fwd.hpp"
|
|
|
|
#include "ck/library/utility/check_err.hpp"
|
|
#include "ck/library/utility/device_memory.hpp"
|
|
#include "ck/library/utility/host_tensor.hpp"
|
|
#include "ck/library/utility/host_tensor_generator.hpp"
|
|
#include "ck/library/reference_tensor_operation/cpu/reference_groupnorm.hpp"
|
|
|
|
namespace ck {
|
|
namespace profiler {
|
|
|
|
template <typename XDataType,
|
|
typename GammaDataType,
|
|
typename BetaDataType,
|
|
typename ComputeDataType,
|
|
typename YDataType,
|
|
typename SaveMeanInvStdDataType,
|
|
bool SaveMeanInvStd>
|
|
bool profile_groupnorm_impl(int do_verification,
|
|
int init_method,
|
|
bool do_log,
|
|
bool time_kernel,
|
|
std::vector<index_t> length)
|
|
{
|
|
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
|
|
|
if(length.size() != 5)
|
|
return false;
|
|
|
|
index_t N = length[0];
|
|
index_t G = length[3];
|
|
index_t C = length[4];
|
|
|
|
std::vector<index_t> reduce_dim = {1, 2, 4};
|
|
std::vector<index_t> gammaBetaLength = {G, C};
|
|
std::vector<index_t> gammaBetaStride = {0, 0, 0, C, 1};
|
|
|
|
Tensor<XDataType> x(length);
|
|
Tensor<GammaDataType> gamma(gammaBetaLength);
|
|
Tensor<BetaDataType> beta(gammaBetaLength);
|
|
Tensor<YDataType> y(length);
|
|
Tensor<SaveMeanInvStdDataType> save_mean({N, G});
|
|
Tensor<SaveMeanInvStdDataType> save_inv_std({N, G});
|
|
|
|
Tensor<YDataType> host_y(length);
|
|
Tensor<SaveMeanInvStdDataType> host_save_mean({N, G});
|
|
Tensor<SaveMeanInvStdDataType> host_save_inv_std({N, G});
|
|
|
|
std::vector<index_t> strideSaveMeanInvStd = {1};
|
|
|
|
switch(init_method)
|
|
{
|
|
case 0:
|
|
x.GenerateTensorValue(GeneratorTensor_1<XDataType>{});
|
|
gamma.GenerateTensorValue(GeneratorTensor_1<GammaDataType>{});
|
|
beta.GenerateTensorValue(GeneratorTensor_1<BetaDataType>{});
|
|
break;
|
|
case 1:
|
|
x.GenerateTensorValue(GeneratorTensor_2<XDataType>{-5, 5});
|
|
gamma.GenerateTensorValue(GeneratorTensor_2<GammaDataType>{-5, 5});
|
|
beta.GenerateTensorValue(GeneratorTensor_2<BetaDataType>{-5, 5});
|
|
break;
|
|
default:
|
|
x.GenerateTensorValue(GeneratorTensor_3<XDataType>{0, 1});
|
|
gamma.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{-0.5, 0.5});
|
|
beta.GenerateTensorValue(GeneratorTensor_3<BetaDataType>{-0.5, 0.5});
|
|
}
|
|
|
|
DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpaceSize());
|
|
DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize());
|
|
DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpaceSize());
|
|
DeviceMem y_dev(sizeof(YDataType) * y.mDesc.GetElementSpaceSize());
|
|
DeviceMem save_mean_dev(sizeof(SaveMeanInvStdDataType) * save_mean.mDesc.GetElementSpaceSize());
|
|
DeviceMem save_inv_std_dev(sizeof(SaveMeanInvStdDataType) *
|
|
save_inv_std.mDesc.GetElementSpaceSize());
|
|
|
|
x_dev.ToDevice(x.mData.data());
|
|
gamma_dev.ToDevice(gamma.mData.data());
|
|
beta_dev.ToDevice(beta.mData.data());
|
|
|
|
// add device normalization instances
|
|
using DeviceOp = ck::tensor_operation::device::DeviceNormalizationFwd<XDataType,
|
|
GammaDataType,
|
|
BetaDataType,
|
|
YDataType,
|
|
SaveMeanInvStdDataType,
|
|
PassThrough,
|
|
5,
|
|
3>;
|
|
|
|
// get device op instances
|
|
const auto instance_ptrs =
|
|
ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
|
|
DeviceOp>::GetInstances();
|
|
|
|
std::cout << "found " << instance_ptrs.size() << " instances" << std::endl;
|
|
|
|
std::string best_instance_name;
|
|
float best_avg_time = std::numeric_limits<float>::max();
|
|
float best_gb_per_sec = 0;
|
|
|
|
if(do_verification)
|
|
{
|
|
using ReferenceInstance =
|
|
ck::tensor_operation::host::ReferenceGroupnorm<XDataType,
|
|
GammaDataType,
|
|
BetaDataType,
|
|
YDataType,
|
|
SaveMeanInvStdDataType,
|
|
ComputeDataType,
|
|
PassThrough>;
|
|
|
|
ReferenceInstance ref;
|
|
auto ref_argument = ref.MakeArgument(
|
|
x, gamma, beta, host_y, host_save_mean, host_save_inv_std, PassThrough{}, length, 1e-6);
|
|
auto ref_invoker = ref.MakeInvoker();
|
|
ref_invoker.Run(ref_argument);
|
|
}
|
|
|
|
int num_kernel = 0;
|
|
|
|
auto f_get_argument = [&](auto& inst_ptr) {
|
|
if constexpr(SaveMeanInvStd)
|
|
return inst_ptr->MakeArgumentPointer(
|
|
length,
|
|
std::vector<ck::index_t>{x.mDesc.GetStrides().begin(), x.mDesc.GetStrides().end()},
|
|
gammaBetaStride,
|
|
gammaBetaStride,
|
|
std::vector<ck::index_t>{y.mDesc.GetStrides().begin(), y.mDesc.GetStrides().end()},
|
|
std::vector<ck::index_t>{save_mean.mDesc.GetStrides().begin(),
|
|
save_mean.mDesc.GetStrides().end()},
|
|
std::vector<ck::index_t>{save_inv_std.mDesc.GetStrides().begin(),
|
|
save_inv_std.mDesc.GetStrides().end()},
|
|
reduce_dim,
|
|
1e-6,
|
|
x_dev.GetDeviceBuffer(),
|
|
gamma_dev.GetDeviceBuffer(),
|
|
beta_dev.GetDeviceBuffer(),
|
|
y_dev.GetDeviceBuffer(),
|
|
save_mean_dev.GetDeviceBuffer(),
|
|
save_inv_std_dev.GetDeviceBuffer(),
|
|
PassThrough{});
|
|
else
|
|
return inst_ptr->MakeArgumentPointer(
|
|
length,
|
|
std::vector<ck::index_t>{x.mDesc.GetStrides().begin(), x.mDesc.GetStrides().end()},
|
|
gammaBetaStride,
|
|
gammaBetaStride,
|
|
std::vector<ck::index_t>{y.mDesc.GetStrides().begin(), y.mDesc.GetStrides().end()},
|
|
std::vector<ck::index_t>{save_mean.mDesc.GetStrides().begin(),
|
|
save_mean.mDesc.GetStrides().end()},
|
|
std::vector<ck::index_t>{save_inv_std.mDesc.GetStrides().begin(),
|
|
save_inv_std.mDesc.GetStrides().end()},
|
|
reduce_dim,
|
|
1e-6,
|
|
x_dev.GetDeviceBuffer(),
|
|
gamma_dev.GetDeviceBuffer(),
|
|
beta_dev.GetDeviceBuffer(),
|
|
y_dev.GetDeviceBuffer(),
|
|
nullptr,
|
|
nullptr,
|
|
PassThrough{});
|
|
};
|
|
|
|
for(auto& inst_ptr : instance_ptrs)
|
|
{
|
|
auto argument_ptr = f_get_argument(inst_ptr);
|
|
|
|
if(inst_ptr->IsSupportedArgument(argument_ptr.get()))
|
|
{
|
|
++num_kernel;
|
|
}
|
|
else
|
|
{
|
|
continue;
|
|
}
|
|
|
|
size_t workspace_sz = inst_ptr->GetWorkSpaceSize(argument_ptr.get());
|
|
DeviceMem workspace_dev(workspace_sz);
|
|
inst_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace_dev.GetDeviceBuffer());
|
|
|
|
auto invoker_ptr = inst_ptr->MakeInvokerPointer();
|
|
|
|
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
|
|
|
|
std::size_t num_bytes = x.mDesc.GetElementSize() * sizeof(XDataType) +
|
|
gamma.mDesc.GetElementSize() * sizeof(GammaDataType) +
|
|
beta.mDesc.GetElementSize() * sizeof(BetaDataType) +
|
|
y.mDesc.GetElementSize() * sizeof(YDataType);
|
|
|
|
if constexpr(SaveMeanInvStd)
|
|
num_bytes += save_mean.mDesc.GetElementSpaceSize() * sizeof(SaveMeanInvStdDataType) +
|
|
save_inv_std.mDesc.GetElementSpaceSize() * sizeof(SaveMeanInvStdDataType);
|
|
|
|
float gb_per_sec = num_bytes / 1.E6 / avg_time;
|
|
|
|
if(time_kernel)
|
|
std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << gb_per_sec << " GB/s, "
|
|
<< inst_ptr->GetTypeString() << std::endl;
|
|
|
|
if(avg_time < best_avg_time)
|
|
{
|
|
best_instance_name = inst_ptr->GetTypeString();
|
|
best_avg_time = avg_time;
|
|
best_gb_per_sec = gb_per_sec;
|
|
}
|
|
|
|
if(do_verification)
|
|
{
|
|
y_dev.FromDevice(y.mData.data());
|
|
bool pass = ck::utils::check_err(y, host_y, "Error: Incorrect results", 1e-3, 1e-3);
|
|
|
|
if constexpr(SaveMeanInvStd)
|
|
{
|
|
save_mean_dev.FromDevice(save_mean.mData.data());
|
|
pass &= ck::utils::check_err(
|
|
save_mean.mData, host_save_mean.mData, "Error: Incorrect results", 1e-3, 1e-3);
|
|
|
|
save_inv_std_dev.FromDevice(save_inv_std.mData.data());
|
|
pass &= ck::utils::check_err(save_inv_std.mData,
|
|
host_save_inv_std.mData,
|
|
"Error: Incorrect results",
|
|
1e-3,
|
|
1e-3);
|
|
}
|
|
|
|
if(do_log)
|
|
{
|
|
LogRangeAsType<float>(std::cout << "x : ", x.mData, ",") << std::endl;
|
|
LogRangeAsType<float>(std::cout << "host_y : ", host_y.mData, ",") << std::endl;
|
|
LogRangeAsType<float>(std::cout << "y : ", y.mData, ",") << std::endl;
|
|
}
|
|
|
|
if(!pass)
|
|
{
|
|
std::cout << inst_ptr->GetTypeString() << " failed verification: ";
|
|
LogRange(std::cout << "lengths = [", length, ", ") << "]." << std::endl;
|
|
return false;
|
|
}
|
|
else
|
|
{
|
|
if(time_kernel)
|
|
std::cout << "pass" << std::endl;
|
|
}
|
|
}
|
|
}
|
|
|
|
if(time_kernel)
|
|
{
|
|
LogRange(std::cout << "length = ", length, ",") << std::endl;
|
|
std::cout << "best perf = " << best_avg_time << " ms, " << best_gb_per_sec << " GB/s, "
|
|
<< best_instance_name << std::endl;
|
|
}
|
|
|
|
if(num_kernel == 0)
|
|
{
|
|
std::cout << "Error: No kernel is applicable" << std::endl;
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
} // namespace profiler
|
|
} // namespace ck
|