mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-04 05:31:24 +00:00
ad57f6ef0b
* Wrap ck host utitlies in CK namespace. The CK and CK-Tile source code bases are incompatible because CK is not properly using namespaces everywhere. In particular, we need to put hip_check_error in the ck namespace. Move all functions in include/ck_/host_utility that were in global namespace into the ck namespace. There may be additional namespace problems like this, and it's possible we'll have namespace clashes. But it is good design to properly guard our to code bases (CK and CKTile) so that they can both coexist. Moreover, estabilishing this compatiblity is essential if we are going to allow the builder to instantiate kernels from either template library. * Add using declarations to test code. After moving some of the untils into the ck namespace, most examples and a few tests had to be updated to recognize the new namespace declarations. We add using declarations to individual compute units for functions that were previously in the global namespace. * Add using declarations to client examples.
511 lines
22 KiB
C++
511 lines
22 KiB
C++
// SPDX-License-Identifier: MIT
|
|
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
|
|
|
|
#include <limits>
|
|
#include <iostream>
|
|
#include <getopt.h>
|
|
|
|
#include "ck/ck.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/utility/host_common_util.hpp"
|
|
#include "ck/library/reference_tensor_operation/cpu/reference_batchnorm_backward.hpp"
|
|
#include "ck/tensor_operation/gpu/device/impl/device_batchnorm_backward_impl.hpp"
|
|
|
|
using ::ck::DeviceMem;
|
|
using ::ck::HostTensorDescriptor;
|
|
using ::ck::Tensor;
|
|
|
|
static struct option long_options[] = {{"inOutLengths", required_argument, nullptr, 'D'},
|
|
{"verify", required_argument, nullptr, 'v'},
|
|
{"help", no_argument, nullptr, '?'},
|
|
{nullptr, 0, nullptr, 0}};
|
|
|
|
class BatchNormBwdArg
|
|
{
|
|
private:
|
|
int option_index = 0;
|
|
|
|
public:
|
|
std::vector<size_t> inOutLengths;
|
|
|
|
bool do_verification = false;
|
|
|
|
bool haveSavedMeanInvVar;
|
|
|
|
int data_type = 0;
|
|
int init_method = 3;
|
|
bool time_kernel = false;
|
|
bool use_multiblock_welford = false;
|
|
|
|
public:
|
|
void show_usage(const char* cmd)
|
|
{
|
|
// clang-format off
|
|
std::cout << "Usage of " << cmd << std::endl;
|
|
std::cout << "--inOutLengths or -D, comma separated list of input tensor dimension lengths, must have 4 integers for nhwc" << std::endl;
|
|
std::cout << "--verify or -v, 1/0 to indicate whether to verify the result by comparing with the host-based batch-normalization" << std::endl;
|
|
std::cout << "Arg1: data type (0: fp16, 1: fp32, 3: int8, 5: bp16, 6: fp64)" << std::endl;
|
|
std::cout << "Arg2 -- 1/0 to indicate whether to use saved mean and invVariance" << std::endl;
|
|
std::cout << "Arg3 -- init method used for dy and bnScale (0=no init, 1=single integer value, 2=scope integer value, 3=decimal value)" << std::endl;
|
|
std::cout << "Arg4 -- time kernel (0=no, 1=yes)" << std::endl;
|
|
std::cout << "Arg5: use multi-block welford (0=n0, 1=yes)" << std::endl;
|
|
// clang-format on
|
|
};
|
|
|
|
int processArgs(int argc, char* argv[])
|
|
{
|
|
using ck::host_common::getTypeValuesFromString;
|
|
|
|
int ch;
|
|
|
|
while(1)
|
|
{
|
|
ch = getopt_long(argc, argv, "D:v:", long_options, &option_index);
|
|
if(ch == -1)
|
|
break;
|
|
switch(ch)
|
|
{
|
|
case 'D':
|
|
if(!optarg)
|
|
throw std::runtime_error("Invalid option format!");
|
|
|
|
inOutLengths = getTypeValuesFromString<size_t>(optarg);
|
|
|
|
if(inOutLengths.size() != 4)
|
|
throw std::runtime_error(
|
|
"NHWC tensor layout should have 4 length values specified!");
|
|
break;
|
|
case 'v':
|
|
if(!optarg)
|
|
throw std::runtime_error("Invalid option format!");
|
|
|
|
do_verification = static_cast<bool>(std::atoi(optarg));
|
|
break;
|
|
case '?':
|
|
if(std::string(long_options[option_index].name) == "help")
|
|
{
|
|
show_usage(argv[0]);
|
|
return (-1);
|
|
};
|
|
break;
|
|
default: show_usage(argv[0]); return (-1);
|
|
};
|
|
};
|
|
|
|
if(optind + 5 > argc)
|
|
throw std::runtime_error("Invalid cmd-line arguments, more argumetns are needed!");
|
|
|
|
data_type = std::atoi(argv[optind++]);
|
|
haveSavedMeanInvVar = std::atoi(argv[optind++]);
|
|
init_method = std::atoi(argv[optind++]);
|
|
time_kernel = static_cast<bool>(std::atoi(argv[optind++]));
|
|
use_multiblock_welford = static_cast<bool>(std::atoi(argv[optind]));
|
|
|
|
return (0);
|
|
};
|
|
};
|
|
|
|
using namespace ck;
|
|
|
|
template <typename XDataType, typename AccDataType, bool UseMultiblockInK>
|
|
bool bnorm_bwd_nhwc_test(bool do_verification,
|
|
int init_method,
|
|
bool time_kernel,
|
|
const std::vector<size_t> inOutLengths,
|
|
bool haveSavedMeanInvVar,
|
|
double epsilon)
|
|
{
|
|
// for NHWC BatchNorm calculation of mean and meansquare
|
|
constexpr index_t Rank = 4;
|
|
constexpr index_t NumReduceDim = 3;
|
|
|
|
using ScaleDataType = XDataType;
|
|
|
|
const std::vector<size_t> scaleBiasMeanVarLengths = {inOutLengths[3]};
|
|
|
|
// input data of the batchnorm backward algorithm
|
|
Tensor<XDataType> x(inOutLengths);
|
|
Tensor<AccDataType> dy(inOutLengths);
|
|
|
|
Tensor<ScaleDataType> bnScale(scaleBiasMeanVarLengths);
|
|
|
|
Tensor<AccDataType> savedMean(scaleBiasMeanVarLengths);
|
|
Tensor<AccDataType> savedInvVar(scaleBiasMeanVarLengths);
|
|
// savedVariance is only used for initializing savedInvVar
|
|
Tensor<AccDataType> savedVariance(scaleBiasMeanVarLengths);
|
|
|
|
// output data of the batchnorm backward algorithm
|
|
Tensor<AccDataType> dx_ref(inOutLengths);
|
|
Tensor<AccDataType> dx(inOutLengths);
|
|
|
|
Tensor<AccDataType> dscale(scaleBiasMeanVarLengths);
|
|
Tensor<AccDataType> dbias(scaleBiasMeanVarLengths);
|
|
|
|
Tensor<AccDataType> dscale_ref(scaleBiasMeanVarLengths);
|
|
Tensor<AccDataType> dbias_ref(scaleBiasMeanVarLengths);
|
|
|
|
auto inOutStrides = dy.mDesc.GetStrides();
|
|
auto scaleBiasMeanVarStrides = dscale.mDesc.GetStrides();
|
|
|
|
std::size_t num_thread = std::thread::hardware_concurrency();
|
|
|
|
if(haveSavedMeanInvVar)
|
|
{
|
|
const float x_mean = 0.0f;
|
|
const float x_stddev = 1.0f;
|
|
const float noise_stddev = 0.0001f;
|
|
|
|
// input data in normal distribution
|
|
x.GenerateTensorValue(GeneratorTensor_4<XDataType>{x_mean, x_stddev}, num_thread);
|
|
|
|
// initialize the savedMean to be values with tiny variation to the mean of the x values
|
|
savedMean.GenerateTensorValue(GeneratorTensor_4<AccDataType>{x_mean, noise_stddev},
|
|
num_thread);
|
|
|
|
// initialize the variance to be values with tiny variation to the variance of the x values
|
|
savedVariance.GenerateTensorValue(
|
|
GeneratorTensor_4<AccDataType>{x_stddev * x_stddev, noise_stddev}, num_thread);
|
|
|
|
auto it_src = savedVariance.mData.begin();
|
|
auto it_dst = savedInvVar.mData.begin();
|
|
float tmp_epsilon = std::numeric_limits<float>::epsilon();
|
|
|
|
while(it_src != savedVariance.mData.end())
|
|
{
|
|
*it_dst = type_convert<AccDataType>(
|
|
1.0f / std::sqrtf(type_convert<float>(*it_src) + tmp_epsilon));
|
|
|
|
it_src++;
|
|
it_dst++;
|
|
};
|
|
}
|
|
else
|
|
{
|
|
const float x_mean = 0.0f;
|
|
const float x_stddev = 1.0f;
|
|
|
|
// input data in normal distribution
|
|
x.GenerateTensorValue(GeneratorTensor_4<XDataType>{x_mean, x_stddev}, num_thread);
|
|
};
|
|
|
|
if(do_verification)
|
|
{
|
|
switch(init_method)
|
|
{
|
|
case 0:
|
|
dy.GenerateTensorValue(GeneratorTensor_0<AccDataType>{}, num_thread);
|
|
bnScale.GenerateTensorValue(GeneratorTensor_0<ScaleDataType>{}, num_thread);
|
|
break;
|
|
case 1:
|
|
dy.GenerateTensorValue(GeneratorTensor_1<AccDataType>{1}, num_thread);
|
|
bnScale.GenerateTensorValue(GeneratorTensor_1<ScaleDataType>{1}, num_thread);
|
|
break;
|
|
case 2:
|
|
dy.GenerateTensorValue(GeneratorTensor_2<AccDataType>{-2, 2}, num_thread);
|
|
bnScale.GenerateTensorValue(GeneratorTensor_2<ScaleDataType>{-5, 5}, num_thread);
|
|
break;
|
|
default:
|
|
dy.GenerateTensorValue(GeneratorTensor_3<AccDataType>{-0.2f, 0.2f}, num_thread);
|
|
bnScale.GenerateTensorValue(GeneratorTensor_3<ScaleDataType>{-0.5f, 0.5f}, num_thread);
|
|
}
|
|
};
|
|
|
|
// input data of the batchnorm backward algorithm
|
|
DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpaceSize());
|
|
DeviceMem dy_dev(sizeof(AccDataType) * dy.mDesc.GetElementSpaceSize());
|
|
|
|
DeviceMem bnScale_dev(sizeof(ScaleDataType) * bnScale.mDesc.GetElementSpaceSize());
|
|
|
|
DeviceMem savedMean_dev(sizeof(AccDataType) * savedMean.mDesc.GetElementSpaceSize());
|
|
DeviceMem savedInvVar_dev(sizeof(AccDataType) * savedInvVar.mDesc.GetElementSpaceSize());
|
|
|
|
// output data of the batchnorm backward algorithm
|
|
DeviceMem dx_dev(sizeof(AccDataType) * dx.mDesc.GetElementSpaceSize());
|
|
|
|
DeviceMem dscale_dev(sizeof(AccDataType) * dscale.mDesc.GetElementSpaceSize());
|
|
DeviceMem dbias_dev(sizeof(AccDataType) * dbias.mDesc.GetElementSpaceSize());
|
|
|
|
x_dev.ToDevice(x.mData.data());
|
|
dy_dev.ToDevice(dy.mData.data());
|
|
bnScale_dev.ToDevice(bnScale.mData.data());
|
|
|
|
if(haveSavedMeanInvVar)
|
|
{
|
|
savedMean_dev.ToDevice(savedMean.mData.data());
|
|
savedInvVar_dev.ToDevice(savedInvVar.mData.data());
|
|
};
|
|
|
|
std::array<index_t, Rank> i_inOutLengths;
|
|
std::array<index_t, Rank> i_inOutStrides;
|
|
std::array<index_t, Rank - NumReduceDim> i_scaleBiasMeanVarLengths;
|
|
std::array<index_t, Rank - NumReduceDim> i_scaleBiasMeanVarStrides;
|
|
|
|
std::copy(inOutLengths.begin(), inOutLengths.end(), i_inOutLengths.begin());
|
|
std::copy(inOutStrides.begin(), inOutStrides.end(), i_inOutStrides.begin());
|
|
std::copy(scaleBiasMeanVarLengths.begin(),
|
|
scaleBiasMeanVarLengths.end(),
|
|
i_scaleBiasMeanVarLengths.begin());
|
|
std::copy(scaleBiasMeanVarStrides.begin(),
|
|
scaleBiasMeanVarStrides.end(),
|
|
i_scaleBiasMeanVarStrides.begin());
|
|
|
|
using PassThroughOp = ck::tensor_operation::element_wise::PassThrough;
|
|
|
|
using DeviceBatchNormBwdInstance =
|
|
ck::tensor_operation::device::DeviceBatchNormBwdImpl<XDataType,
|
|
AccDataType,
|
|
AccDataType,
|
|
AccDataType,
|
|
ScaleDataType, // ScaleDataType
|
|
AccDataType, // DscaleDbiasDataType
|
|
AccDataType, // MeanVarDataType
|
|
PassThroughOp,
|
|
Rank,
|
|
NumReduceDim,
|
|
UseMultiblockInK,
|
|
256,
|
|
16,
|
|
16,
|
|
1,
|
|
2,
|
|
0,
|
|
1, // XSrcVectorSize
|
|
1, // DySrcVectorSize
|
|
1, // DxDstVectorSize
|
|
1, // ScaleSrcVectorSize
|
|
1, // DscaleDbiasDstVectorSize
|
|
1>; // MeanVarSrcVectorSize
|
|
|
|
auto batchnorm_bwd = DeviceBatchNormBwdInstance{};
|
|
|
|
auto argument_ptr = batchnorm_bwd.MakeArgumentPointer(
|
|
i_inOutLengths,
|
|
i_inOutStrides,
|
|
i_inOutStrides,
|
|
i_inOutStrides,
|
|
{0, 1, 2},
|
|
i_scaleBiasMeanVarLengths,
|
|
i_scaleBiasMeanVarStrides,
|
|
i_scaleBiasMeanVarStrides,
|
|
i_scaleBiasMeanVarStrides,
|
|
x_dev.GetDeviceBuffer(),
|
|
dy_dev.GetDeviceBuffer(),
|
|
bnScale_dev.GetDeviceBuffer(),
|
|
haveSavedMeanInvVar ? savedMean_dev.GetDeviceBuffer() : nullptr,
|
|
haveSavedMeanInvVar ? savedInvVar_dev.GetDeviceBuffer() : nullptr,
|
|
epsilon,
|
|
PassThroughOp{},
|
|
dx_dev.GetDeviceBuffer(),
|
|
dscale_dev.GetDeviceBuffer(),
|
|
dbias_dev.GetDeviceBuffer());
|
|
|
|
if(!batchnorm_bwd.IsSupportedArgument(argument_ptr.get()))
|
|
{
|
|
std::cout << "The runtime parameters seems not supported by the BatchNorm device instance, "
|
|
"exiting!"
|
|
<< std::endl;
|
|
return (false);
|
|
};
|
|
|
|
size_t workspace_sz = batchnorm_bwd.GetWorkSpaceSize(argument_ptr.get());
|
|
|
|
DeviceMem workspace_dev(workspace_sz);
|
|
|
|
batchnorm_bwd.SetWorkSpacePointer(argument_ptr.get(), workspace_dev.GetDeviceBuffer());
|
|
|
|
auto invoker_ptr = batchnorm_bwd.MakeInvokerPointer();
|
|
|
|
if(time_kernel)
|
|
{
|
|
float avg_time = 0.0f;
|
|
size_t num_bytes = 0;
|
|
|
|
size_t total_length = inOutLengths[0] * inOutLengths[1] * inOutLengths[2] * inOutLengths[3];
|
|
size_t invariant_length = inOutLengths[3];
|
|
|
|
avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
|
|
|
|
// inputing of x, dy, scale, outputing of dx, dscale, dbias
|
|
num_bytes +=
|
|
total_length * sizeof(XDataType) * 3 + invariant_length * sizeof(AccDataType) * 3;
|
|
|
|
// outputing of mean, inv-variance
|
|
num_bytes += haveSavedMeanInvVar ? invariant_length * sizeof(AccDataType) * 2 : 0;
|
|
|
|
float gb_per_sec = num_bytes / 1.E6 / avg_time;
|
|
|
|
std::cout << "Perf: " << avg_time << " ms, " << gb_per_sec << " GB/s" << std::endl;
|
|
}
|
|
else
|
|
(void)invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
|
|
|
|
bool pass = true;
|
|
|
|
if(do_verification)
|
|
{
|
|
using ReferenceBatchNormBwdInstance =
|
|
ck::tensor_operation::host::ReferenceBatchNormBwd<XDataType,
|
|
AccDataType,
|
|
AccDataType,
|
|
AccDataType,
|
|
ScaleDataType, // ScaleDataType
|
|
AccDataType,
|
|
AccDataType,
|
|
PassThroughOp,
|
|
Rank,
|
|
NumReduceDim>;
|
|
|
|
auto batchNormBwd_ref = ReferenceBatchNormBwdInstance{};
|
|
|
|
auto argument_ptr_ref = batchNormBwd_ref.MakeArgumentPointer(
|
|
i_inOutLengths,
|
|
i_inOutStrides,
|
|
i_inOutStrides,
|
|
i_inOutStrides,
|
|
{0, 1, 2},
|
|
i_scaleBiasMeanVarLengths,
|
|
i_scaleBiasMeanVarStrides,
|
|
i_scaleBiasMeanVarStrides,
|
|
i_scaleBiasMeanVarStrides,
|
|
x.mData.data(),
|
|
dy.mData.data(),
|
|
bnScale.mData.data(),
|
|
haveSavedMeanInvVar ? savedMean.mData.data() : nullptr,
|
|
haveSavedMeanInvVar ? savedInvVar.mData.data() : nullptr,
|
|
epsilon,
|
|
PassThroughOp{},
|
|
dx_ref.mData.data(),
|
|
dscale_ref.mData.data(),
|
|
dbias_ref.mData.data());
|
|
|
|
if(!batchNormBwd_ref.IsSupportedArgument(argument_ptr_ref.get()))
|
|
{
|
|
std::cout
|
|
<< "The runtime parameters seems not supported by the device instance, exiting!"
|
|
<< std::endl;
|
|
return (false);
|
|
};
|
|
|
|
auto invoker_ptr_ref = batchNormBwd_ref.MakeInvokerPointer();
|
|
|
|
(void)invoker_ptr_ref->Run(argument_ptr_ref.get());
|
|
|
|
dx_dev.FromDevice(dx.mData.data());
|
|
dscale_dev.FromDevice(dscale.data());
|
|
dbias_dev.FromDevice(dbias.data());
|
|
|
|
// clang-format off
|
|
pass = pass && ck::utils::check_err(dbias.mData, dbias_ref.mData, "dBias result:", 2e-4, 2e-4);
|
|
pass = pass && ck::utils::check_err(dscale.mData, dscale_ref.mData, "dScale result:", 2e-4, 2e-4);
|
|
pass = pass && ck::utils::check_err(dx.mData, dx_ref.mData, "dx result:");
|
|
// clang-format on
|
|
};
|
|
|
|
return (pass);
|
|
};
|
|
|
|
int main(int argc, char* argv[])
|
|
{
|
|
static const double epsilon = std::numeric_limits<float>::epsilon();
|
|
|
|
bool pass = true;
|
|
|
|
if(argc > 1)
|
|
{
|
|
BatchNormBwdArg arg;
|
|
|
|
if(arg.processArgs(argc, argv) < 0)
|
|
return (-1);
|
|
|
|
if(arg.data_type == 0)
|
|
{
|
|
if(arg.use_multiblock_welford)
|
|
pass = bnorm_bwd_nhwc_test<ck::half_t, float, true>(arg.do_verification,
|
|
arg.init_method,
|
|
arg.time_kernel,
|
|
arg.inOutLengths,
|
|
arg.haveSavedMeanInvVar,
|
|
epsilon);
|
|
else
|
|
pass = bnorm_bwd_nhwc_test<ck::half_t, float, false>(arg.do_verification,
|
|
arg.init_method,
|
|
arg.time_kernel,
|
|
arg.inOutLengths,
|
|
arg.haveSavedMeanInvVar,
|
|
epsilon);
|
|
}
|
|
else if(arg.data_type == 1)
|
|
{
|
|
if(arg.use_multiblock_welford)
|
|
pass = bnorm_bwd_nhwc_test<float, float, true>(arg.do_verification,
|
|
arg.init_method,
|
|
arg.time_kernel,
|
|
arg.inOutLengths,
|
|
arg.haveSavedMeanInvVar,
|
|
epsilon);
|
|
else
|
|
pass = bnorm_bwd_nhwc_test<float, float, false>(arg.do_verification,
|
|
arg.init_method,
|
|
arg.time_kernel,
|
|
arg.inOutLengths,
|
|
arg.haveSavedMeanInvVar,
|
|
epsilon);
|
|
}
|
|
else if(arg.data_type == 5)
|
|
{
|
|
if(arg.use_multiblock_welford)
|
|
pass = bnorm_bwd_nhwc_test<ck::bhalf_t, float, true>(arg.do_verification,
|
|
arg.init_method,
|
|
arg.time_kernel,
|
|
arg.inOutLengths,
|
|
arg.haveSavedMeanInvVar,
|
|
epsilon);
|
|
else
|
|
pass = bnorm_bwd_nhwc_test<ck::bhalf_t, float, false>(arg.do_verification,
|
|
arg.init_method,
|
|
arg.time_kernel,
|
|
arg.inOutLengths,
|
|
arg.haveSavedMeanInvVar,
|
|
epsilon);
|
|
}
|
|
else if(arg.data_type == 6)
|
|
{
|
|
if(arg.use_multiblock_welford)
|
|
pass = bnorm_bwd_nhwc_test<double, double, true>(arg.do_verification,
|
|
arg.init_method,
|
|
arg.time_kernel,
|
|
arg.inOutLengths,
|
|
arg.haveSavedMeanInvVar,
|
|
epsilon);
|
|
else
|
|
pass = bnorm_bwd_nhwc_test<double, double, false>(arg.do_verification,
|
|
arg.init_method,
|
|
arg.time_kernel,
|
|
arg.inOutLengths,
|
|
arg.haveSavedMeanInvVar,
|
|
epsilon);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
pass = bnorm_bwd_nhwc_test<ck::half_t, float, true>(true,
|
|
3,
|
|
false, // don't time kernel
|
|
{128, 16, 6, 512},
|
|
false,
|
|
epsilon);
|
|
|
|
pass = pass && bnorm_bwd_nhwc_test<ck::half_t, float, false>(true,
|
|
3,
|
|
false, // don't time kernel
|
|
{128, 16, 3, 1024},
|
|
false,
|
|
epsilon);
|
|
};
|
|
|
|
return (pass ? 0 : 1);
|
|
}
|