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BatchNorm forward instance/external api/profiler/tests/client example (#511)

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* Update to device_batchnorm_forward base class to include all template parameters for problem description

* Add batchnorm forward instances and external api

* Add batchnorm forward profiler module which uses the external api

* Add some comments in batchnorm_forward example to explain the dimensions in lengths[]

* Replace the reference_batchnorm_forward_nhwc_c by generic reference_batchnorm_forward

* Improvement to the batchnorm infer base API

* Add batchnorm forward client example which shows using the batchnorm forward external API

* Add test for batchnorm forward

* Tuning the batchnorm profiler initialized values and error threshold

* Add support for bhalf_t in instances/external api/tests

* Add support for int8_t in instances/external api/tests

* Add support for double in instances/external api/tests

* Let ScaleDataType and BiasDataType be same as XDataType and YDataType when creating instances

* Checking before running best instance in batchnorm_fwd_nhwc client example

* Add checking for YElementwiseOp in batchnorm_forward external API

* Add more types in batchnorm forward profiler

* Add more test lengths

Co-authored-by: rocking5566 <ChunYu.Lai@amd.com>
This commit is contained in:
Qianfeng
2022-11-25 08:02:27 +08:00
committed by GitHub
parent 43a889b72e
commit 4e6a5575be
26 changed files with 2685 additions and 522 deletions
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client_example/13_batchnorm/CMakeLists.txt Normal file
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add_executable(client_batchnorm_fwd_nhwc batchnorm_fwd_nhwc.cpp)
target_link_libraries(client_batchnorm_fwd_nhwc PRIVATE composable_kernel::device_operations)

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client_example/13_batchnorm/batchnorm_fwd_nhwc.cpp Normal file
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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <functional>
#include <numeric>
#include <iomanip>
#include <iostream>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/device_reduce.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/batchnorm_forward.hpp"
using XDataType = float;
using YDataType = float;
using AccDataType = float;
using ScaleDataType = AccDataType;
using BiasDataType = AccDataType;
using MeanVarDataType = AccDataType;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
constexpr int Rank = 4;
constexpr int NumBatchNormReduceDim = 3;
const double epsilon = std::numeric_limits<float>::epsilon();
const double averageFactor = 0.1;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
int main(int argc, char* argv[])
{
std::array<ck::index_t, Rank> xyLengths{16, 8, 128, 256};
std::array<ck::index_t, Rank> xyStrides{8 * 128 * 256, 128 * 256, 256, 1};
std::array<ck::index_t, Rank - NumBatchNormReduceDim> scaleBiasMeanVarLengths{256};
std::array<ck::index_t, Rank - NumBatchNormReduceDim> scaleBiasMeanVarStrides{1};
std::array<int, NumBatchNormReduceDim> reduceDims{0, 1, 2};
ck::index_t numXYElement =
std::accumulate(xyLengths.begin(), xyLengths.end(), 1, std::multiplies<ck::index_t>());
ck::index_t numScaleBiasMeanVarElement = std::accumulate(scaleBiasMeanVarLengths.begin(),
scaleBiasMeanVarLengths.end(),
1,
std::multiplies<ck::index_t>());
SimpleDeviceMem x(sizeof(XDataType) * numXYElement);
SimpleDeviceMem y(sizeof(YDataType) * numXYElement);
SimpleDeviceMem scale(sizeof(ScaleDataType) * numScaleBiasMeanVarElement);
SimpleDeviceMem bias(sizeof(BiasDataType) * numScaleBiasMeanVarElement);
SimpleDeviceMem mean(sizeof(MeanVarDataType) * numScaleBiasMeanVarElement);
SimpleDeviceMem invVariance(sizeof(MeanVarDataType) * numScaleBiasMeanVarElement);
using DeviceOp = ck::tensor_operation::device::DeviceBatchNormFwd<XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
PassThrough,
Rank,
NumBatchNormReduceDim>;
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
std::string best_op_name;
bool found = false;
int best_op_id = -1;
float best_ave_time = std::numeric_limits<float>::max();
float best_gb_per_sec = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr = op_ptr->MakeArgumentPointer(xyLengths,
xyStrides,
xyStrides,
reduceDims,
scaleBiasMeanVarLengths,
scaleBiasMeanVarStrides,
scaleBiasMeanVarStrides,
scaleBiasMeanVarStrides,
x.GetDeviceBuffer(),
scale.GetDeviceBuffer(),
bias.GetDeviceBuffer(),
epsilon,
PassThrough{},
y.GetDeviceBuffer(),
mean.GetDeviceBuffer(),
invVariance.GetDeviceBuffer(),
averageFactor,
nullptr,
nullptr);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
size_t workspace_sz = op_ptr->GetWorkSpaceSize(argument_ptr.get());
SimpleDeviceMem workspace(workspace_sz);
op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace.GetDeviceBuffer());
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
std::size_t num_bytes =
numXYElement * (sizeof(XDataType) + sizeof(YDataType)) +
numScaleBiasMeanVarElement * (sizeof(ScaleDataType) + sizeof(BiasDataType) +
sizeof(MeanVarDataType) + sizeof(MeanVarDataType));
float gb_per_sec = num_bytes / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << gb_per_sec << " GB/s, "
<< op_name << std::endl;
if(ave_time < best_ave_time)
{
found = true;
best_op_id = i;
best_op_name = op_name;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
if(found)
{
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
<< best_op_name << std::endl;
// run the best intance
auto& op_ptr = op_ptrs[best_op_id];
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
<< std::endl;
auto argument_ptr = op_ptr->MakeArgumentPointer(xyLengths,
xyStrides,
xyStrides,
reduceDims,
scaleBiasMeanVarLengths,
scaleBiasMeanVarStrides,
scaleBiasMeanVarStrides,
scaleBiasMeanVarStrides,
x.GetDeviceBuffer(),
scale.GetDeviceBuffer(),
bias.GetDeviceBuffer(),
epsilon,
PassThrough{},
y.GetDeviceBuffer(),
mean.GetDeviceBuffer(),
invVariance.GetDeviceBuffer(),
averageFactor,
nullptr,
nullptr);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
}
std::cout << "Done" << std::endl;
}
return 0;
}