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Unverified Commit 8f5cafaf authored by Qianfeng's avatar Qianfeng Committed by GitHub
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Batchnorm splitk single kernel (#771) 

* Use dim 0 as faster dim for writing mean/var/count workspace in batchnorm multiblock method [performance]

* Add CountDataType as template parameter in blockwise_welford

* Add utility/get_shift.hpp

* Add BatchNorm multiblock single-kernel implementation

* Add smem inline assembly based implementation of gms_init/gms_barrier/gms_reset for gfx90a

* Renaming in device_batchnorm_forward_impl.hpp

* Tiny fix in the batchnorm_fwd profiler

* Revert "Add smem inline assembly based implementation of gms_init/gms_barrier/gms_reset for gfx90a"

This reverts commit d16d00919c43f10759e7b4e4d112125221ed9064.

* Use the old two-kernel batchnorm multiblock method for gfx1030

* Use the old two-kernel batchnorm multiblock method for gfx908

* use the single-kernel batchnorm multiblock method only for gfx90a

* Remove get_wave_id() from utility/get_id.hpp since it is not used

* Set true for testing running mean/variance and saving mean/invvariance in the examples

* Fix to copy-right words

* Remove un-needed including in utility/get_id.hpp

* Add comments to workgroup_synchronization.hpp

* Remove un-used codes in gridwise_multiblock_batchnorm_forward.hpp

* Renaming in the kernels

* Remove un-used kernel file
parent f4dfc060
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Showing with 2334 additions and 123 deletions
example/34_batchnorm/CMakeLists.txt
View file @ 8f5cafaf
add_example_executable(example_batchnorm_forward_training batchnorm_forward_training_nhwc.cpp)
add_example_executable(example_batchnorm_forward_training_obsolete batchnorm_forward_training_nhwc_obsolete.cpp)
add_example_executable(example_batchnorm_forward_inferring batchnorm_forward_inferring_nhwc.cpp)
add_example_executable(example_batchnorm_backward batchnorm_backward_nhwc.cpp)
example/34_batchnorm/batchnorm_forward_training_nhwc.cpp
View file @ 8f5cafaf
......@@ -414,7 +414,7 @@ bool bnorm_fwd_nhwc_test(bool do_verification,
(void)invoker_ptr_ref->Run(argument_ptr_ref.get());
y_dev.FromDevice(y.mData.data());
pass = pass && ck::utils::check_err(y, y_ref);
pass = pass && ck::utils::check_err(y, y_ref, "Incorrect normalized output values");
if(updateMovingAverage)
{
......@@ -424,8 +424,12 @@ bool bnorm_fwd_nhwc_test(bool do_verification,
resultRunningMean_dev.FromDevice(resultRunningMean.mData.data());
resultRunningVariance_dev.FromDevice(resultRunningVariance.mData.data());
pass = pass && ck::utils::check_err(resultRunningMean, resultRunningMean_ref);
pass = pass && ck::utils::check_err(resultRunningVariance, resultRunningVariance_ref);
pass = pass && ck::utils::check_err(resultRunningMean,
resultRunningMean_ref,
"Incorrect running mean values");
pass = pass && ck::utils::check_err(resultRunningVariance,
resultRunningVariance_ref,
"Incorrect running variance values");
};
if(saveMeanAndInvVariance)
......@@ -438,8 +442,11 @@ bool bnorm_fwd_nhwc_test(bool do_verification,
resultSaveMean_dev.FromDevice(resultSaveMean.mData.data());
resultSaveInvVariance_dev.FromDevice(resultSaveInvVariance.mData.data());
pass = pass && ck::utils::check_err(resultSaveMean, resultSaveMean_ref);
pass = pass && ck::utils::check_err(resultSaveInvVariance, resultSaveInvVariance_ref);
pass = pass && ck::utils::check_err(
resultSaveMean, resultSaveMean_ref, "Incorrect saved mean values");
pass = pass && ck::utils::check_err(resultSaveInvVariance,
resultSaveInvVariance_ref,
"Incorrect saved invvariance values");
};
};
......
example/34_batchnorm/batchnorm_forward_training_nhwc_obsolete.cpp 0 → 100644
View file @ 8f5cafaf
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <limits>
#include <iostream>
#include <vector>
#include <array>
#include <algorithm>
#include <getopt.h>
#include "ck/ck.hpp"
#include "ck/library/utility/algorithm.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_forward.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batchnorm_forward_impl_obsolete.hpp"
#include "ck/library/utility/host_common_util.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
static struct option long_options[] = {{"inOutLengths", required_argument, nullptr, 'D'},
{"verify", required_argument, nullptr, 'v'},
{"help", no_argument, nullptr, '?'},
{nullptr, 0, nullptr, 0}};
class BatchNormFwdArg
{
private:
int option_index = 0;
public:
std::vector<size_t> inOutLengths;
bool do_verification = false;
bool updateMovingAverage;
bool saveMeanAndInvVariance;
int data_type = 0;
int init_method = 2;
bool time_kernel = false;
bool use_multiblock_welford = false;
public:
void show_usage(const char* cmd)
{
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 batch-normalization "
"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 update the moving average and variance "
"(0=no, 1=yes)"
<< std::endl;
std::cout << "Arg3: 1/0 to indicate whether to save the calculated mean and invVariance "
"(0=no, 1=yes)"
<< std::endl;
std::cout << "Arg4: init method used for bnScale and bnBias (0=no init, 1=single integer "
"value, 2=scope integer "
"value, 3=decimal value)"
<< std::endl;
std::cout << "Arg5: time kernel (0=no, 1=yes)" << std::endl;
std::cout << "Arg6: use multi-block welford (0=n0, 1=yes)" << std::endl;
};
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 + 6 > argc)
throw std::runtime_error("Invalid cmd-line arguments, more argumetns are needed!");
data_type = std::atoi(argv[optind++]);
updateMovingAverage = std::atoi(argv[optind++]);
saveMeanAndInvVariance = 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]));
if(data_type != 0 && data_type != 1 && data_type != 3 && data_type != 5 && data_type != 6)
return (-1);
return (0);
};
};
using namespace ck;
template <typename InOutDataType, typename AccDataType, bool UseMultiblockInK>
bool bnorm_fwd_nhwc_test(bool do_verification,
int init_method,
bool time_kernel,
const std::vector<size_t> inOutLengths,
bool updateMovingAverage,
bool saveMeanAndInvVariance,
double averageFactor,
double epsilon)
{
// for NHWC BatchNorm calculation of mean and meansquare
constexpr int Rank = 4;
constexpr int NumReduceDim = 3;
// when using lengths[] to create a tensor, lengths[0] is the length of highest dimension
// eg. N of NHWC, so lengths[3] is the dimension C length of NHWC
const std::vector<size_t> scaleBiasMeanVarLengths = {inOutLengths[3]};
// input data of the batchnorm forward algorithm
Tensor<InOutDataType> x(inOutLengths);
Tensor<AccDataType> bnScale(scaleBiasMeanVarLengths);
Tensor<AccDataType> bnBias(scaleBiasMeanVarLengths);
// output data of the batchnorm forward algorithm
Tensor<InOutDataType> y_ref(inOutLengths);
Tensor<InOutDataType> y(inOutLengths);
Tensor<AccDataType> resultSaveMean_ref(scaleBiasMeanVarLengths);
Tensor<AccDataType> resultSaveInvVariance_ref(scaleBiasMeanVarLengths);
Tensor<AccDataType> resultRunningMean_ref(scaleBiasMeanVarLengths);
Tensor<AccDataType> resultRunningVariance_ref(scaleBiasMeanVarLengths);
auto inOutStrides = x.mDesc.GetStrides();
auto scaleBiasMeanVarStrides = bnScale.mDesc.GetStrides();
std::size_t num_thread = std::thread::hardware_concurrency();
if(updateMovingAverage)
{
if constexpr(std::is_same<InOutDataType, int8_t>::value)
{
x.GenerateTensorValue(GeneratorTensor_2<InOutDataType>{-5, 5}, num_thread);
const float x_mean = 0.0f;
const float x_stddev = 2.5f;
const float noise_stddev = 0.04f;
resultRunningMean_ref.GenerateTensorValue(
GeneratorTensor_4<AccDataType>{x_mean, noise_stddev}, num_thread);
resultRunningVariance_ref.GenerateTensorValue(
GeneratorTensor_4<AccDataType>{x_stddev * x_stddev, noise_stddev}, num_thread);
}
else
{
const float x_mean = 0.0f;
const float x_stddev = 1.0f;
const float noise_stddev = 0.04f;
// input data in normal distribution
x.GenerateTensorValue(GeneratorTensor_4<InOutDataType>{x_mean, x_stddev}, num_thread);
// initialize the runningMean to be values with tiny variation to the mean of the x
// values
resultRunningMean_ref.GenerateTensorValue(
GeneratorTensor_4<AccDataType>{x_mean, noise_stddev}, num_thread);
// initialize the runningVariance to be values with tiny variation to the variance of
// the x values
resultRunningVariance_ref.GenerateTensorValue(
GeneratorTensor_4<AccDataType>{x_stddev * x_stddev, noise_stddev}, num_thread);
};
}
else
{
if constexpr(std::is_same<InOutDataType, int8_t>::value)
x.GenerateTensorValue(GeneratorTensor_2<InOutDataType>{-5, 5}, num_thread);
else
x.GenerateTensorValue(GeneratorTensor_3<InOutDataType>{-5.0f, 5.0f}, num_thread);
};
if(do_verification)
{
switch(init_method)
{
case 0:
bnScale.GenerateTensorValue(GeneratorTensor_0<AccDataType>{}, num_thread);
bnBias.GenerateTensorValue(GeneratorTensor_0<AccDataType>{}, num_thread);
break;
case 1:
bnScale.GenerateTensorValue(GeneratorTensor_1<AccDataType>{1}, num_thread);
bnBias.GenerateTensorValue(GeneratorTensor_1<AccDataType>{0}, num_thread);
break;
case 2:
bnScale.GenerateTensorValue(GeneratorTensor_2<AccDataType>{-5, 5}, num_thread);
bnBias.GenerateTensorValue(GeneratorTensor_2<AccDataType>{-5, 5}, num_thread);
break;
default:
bnScale.GenerateTensorValue(GeneratorTensor_3<AccDataType>{-5.0f, 5.0f}, num_thread);
bnBias.GenerateTensorValue(GeneratorTensor_3<AccDataType>{-5.0f, 5.0f}, num_thread);
}
};
// these buffers are usually provided by the user application
DeviceMem x_dev(sizeof(InOutDataType) * x.mDesc.GetElementSpaceSize());
DeviceMem y_dev(sizeof(InOutDataType) * y.mDesc.GetElementSpaceSize());
DeviceMem bnScale_dev(sizeof(AccDataType) * bnScale.mDesc.GetElementSpaceSize());
DeviceMem bnBias_dev(sizeof(AccDataType) * bnBias.mDesc.GetElementSpaceSize());
// mean_dev or resultSaveMean_dev
DeviceMem resultSaveMean_dev(sizeof(AccDataType) *
resultSaveMean_ref.mDesc.GetElementSpaceSize());
// meansquare_dev or resultSaveInvVariance_dev
DeviceMem resultSaveInvVariance_dev(sizeof(AccDataType) *
resultSaveInvVariance_ref.mDesc.GetElementSpaceSize());
// resultRunningMean_dev
DeviceMem resultRunningMean_dev(sizeof(AccDataType) *
resultRunningMean_ref.mDesc.GetElementSpaceSize());
// resultRunningVariance_dev
DeviceMem resultRunningVariance_dev(sizeof(AccDataType) *
resultRunningVariance_ref.mDesc.GetElementSpaceSize());
x_dev.ToDevice(x.mData.data());
bnScale_dev.ToDevice(bnScale.mData.data());
bnBias_dev.ToDevice(bnBias.mData.data());
if(updateMovingAverage)
{
resultRunningMean_dev.ToDevice(resultRunningMean_ref.mData.data());
resultRunningVariance_dev.ToDevice(resultRunningVariance_ref.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;
ck::ranges::copy(inOutLengths, i_inOutLengths.begin());
ck::ranges::copy(inOutStrides, i_inOutStrides.begin());
ck::ranges::copy(scaleBiasMeanVarLengths, i_scaleBiasMeanVarLengths.begin());
ck::ranges::copy(scaleBiasMeanVarStrides, i_scaleBiasMeanVarStrides.begin());
using PassThroughOp = ck::tensor_operation::element_wise::PassThrough;
using DeviceBatchNormFwdInstance =
ck::tensor_operation::device::DeviceBatchNormFwdImpl<InOutDataType,
InOutDataType,
AccDataType,
AccDataType, // ScaleDataType
AccDataType, // BiasDataType
AccDataType, // MeanVarDataType
PassThroughOp, // YElementwiseOp
Rank,
NumReduceDim,
UseMultiblockInK,
256,
16,
16,
1,
2,
0,
1,
1,
1,
1,
1>;
auto batchnorm_fwd = DeviceBatchNormFwdInstance{};
auto argument_ptr = batchnorm_fwd.MakeArgumentPointer(
i_inOutLengths,
i_inOutStrides,
i_inOutStrides,
{0, 1, 2}, // indicates physical indices of reduce dimensions in lengths[] and strides[]
i_scaleBiasMeanVarLengths,
i_scaleBiasMeanVarStrides,
i_scaleBiasMeanVarStrides,
i_scaleBiasMeanVarStrides,
x_dev.GetDeviceBuffer(),
bnScale_dev.GetDeviceBuffer(),
bnBias_dev.GetDeviceBuffer(),
epsilon,
PassThroughOp{},
y_dev.GetDeviceBuffer(),
saveMeanAndInvVariance ? resultSaveMean_dev.GetDeviceBuffer() : nullptr,
saveMeanAndInvVariance ? resultSaveInvVariance_dev.GetDeviceBuffer() : nullptr,
averageFactor,
updateMovingAverage ? resultRunningMean_dev.GetDeviceBuffer() : nullptr,
updateMovingAverage ? resultRunningVariance_dev.GetDeviceBuffer() : nullptr);
if(!batchnorm_fwd.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_fwd.GetWorkSpaceSize(argument_ptr.get());
DeviceMem workspace_dev(workspace_sz);
batchnorm_fwd.SetWorkSpacePointer(argument_ptr.get(), workspace_dev.GetDeviceBuffer());
auto invoker_ptr = batchnorm_fwd.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, scale, bias, outputing of y
num_bytes +=
total_length * sizeof(InOutDataType) * 2 + invariant_length * sizeof(AccDataType) * 2;
// outputing of mean, inv-variance
num_bytes += saveMeanAndInvVariance ? invariant_length * sizeof(AccDataType) * 2 : 0;
// updating of moving mean, variance
num_bytes += updateMovingAverage ? invariant_length * sizeof(AccDataType) * 4 : 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 ReferenceBatchNormFwdInstance =
ck::tensor_operation::host::ReferenceBatchNormFwd<InOutDataType,
InOutDataType,
AccDataType,
AccDataType,
AccDataType,
AccDataType,
PassThroughOp,
Rank,
NumReduceDim>;
auto batchNormFwd_ref = ReferenceBatchNormFwdInstance{};
auto argument_ptr_ref = batchNormFwd_ref.MakeArgumentPointer(
i_inOutLengths,
i_inOutStrides,
i_inOutStrides,
{0, 1, 2}, // indicates physical indices of reduce dimensions in lengths[] and strides[]
i_scaleBiasMeanVarLengths,
i_scaleBiasMeanVarStrides,
i_scaleBiasMeanVarStrides,
i_scaleBiasMeanVarStrides,
x.mData.data(),
bnScale.mData.data(),
bnBias.mData.data(),
epsilon,
PassThroughOp{},
y_ref.mData.data(),
saveMeanAndInvVariance ? resultSaveMean_ref.mData.data() : nullptr,
saveMeanAndInvVariance ? resultSaveInvVariance_ref.mData.data() : nullptr,
averageFactor,
updateMovingAverage ? resultRunningMean_ref.mData.data() : nullptr,
updateMovingAverage ? resultRunningVariance_ref.mData.data() : nullptr);
if(!batchNormFwd_ref.IsSupportedArgument(argument_ptr_ref.get()))
{
std::cout << "The runtime parameters seems not supported by the BatchNorm reference "
"instance, exiting!"
<< std::endl;
return (false);
};
auto invoker_ptr_ref = batchNormFwd_ref.MakeInvokerPointer();
(void)invoker_ptr_ref->Run(argument_ptr_ref.get());
y_dev.FromDevice(y.mData.data());
pass = pass && ck::utils::check_err(y, y_ref, "Incorrect normalized output values");
if(updateMovingAverage)
{
Tensor<AccDataType> resultRunningMean(scaleBiasMeanVarLengths);
Tensor<AccDataType> resultRunningVariance(scaleBiasMeanVarLengths);
resultRunningMean_dev.FromDevice(resultRunningMean.mData.data());
resultRunningVariance_dev.FromDevice(resultRunningVariance.mData.data());
pass = pass && ck::utils::check_err(resultRunningMean,
resultRunningMean_ref,
"Incorrect running mean values");
pass = pass && ck::utils::check_err(resultRunningVariance,
resultRunningVariance_ref,
"Incorrect running variance values");
};
if(saveMeanAndInvVariance)
{
using ck::host_common::dumpBufferToFile;
Tensor<AccDataType> resultSaveMean(scaleBiasMeanVarLengths);
Tensor<AccDataType> resultSaveInvVariance(scaleBiasMeanVarLengths);
resultSaveMean_dev.FromDevice(resultSaveMean.mData.data());
resultSaveInvVariance_dev.FromDevice(resultSaveInvVariance.mData.data());
pass = pass && ck::utils::check_err(
resultSaveMean, resultSaveMean_ref, "Incorrect saved mean values");
pass = pass && ck::utils::check_err(resultSaveInvVariance,
resultSaveInvVariance_ref,
"Incorrect saved invvariance values");
};
};
return (pass);
};
const double epsilon = std::numeric_limits<float>::epsilon();
static const double averageFactor = 0.1;
int main(int argc, char* argv[])
{
bool pass = true;
if(argc > 1)
{
BatchNormFwdArg arg;
if(arg.processArgs(argc, argv) < 0)
return (-1);
if(arg.data_type == 0)
{
if(arg.use_multiblock_welford)
pass = bnorm_fwd_nhwc_test<ck::half_t, float, true>(arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inOutLengths,
arg.updateMovingAverage,
arg.saveMeanAndInvVariance,
averageFactor,
epsilon);
else
pass = bnorm_fwd_nhwc_test<ck::half_t, float, false>(arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inOutLengths,
arg.updateMovingAverage,
arg.saveMeanAndInvVariance,
averageFactor,
epsilon);
}
else if(arg.data_type == 1)
{
if(arg.use_multiblock_welford)
pass = bnorm_fwd_nhwc_test<float, float, true>(arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inOutLengths,
arg.updateMovingAverage,
arg.saveMeanAndInvVariance,
averageFactor,
epsilon);
else
pass = bnorm_fwd_nhwc_test<float, float, false>(arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inOutLengths,
arg.updateMovingAverage,
arg.saveMeanAndInvVariance,
averageFactor,
epsilon);
}
else if(arg.data_type == 3)
{
if(arg.use_multiblock_welford)
pass = bnorm_fwd_nhwc_test<int8_t, float, true>(arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inOutLengths,
arg.updateMovingAverage,
arg.saveMeanAndInvVariance,
averageFactor,
epsilon);
else
pass = bnorm_fwd_nhwc_test<int8_t, float, false>(arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inOutLengths,
arg.updateMovingAverage,
arg.saveMeanAndInvVariance,
averageFactor,
epsilon);
}
else if(arg.data_type == 5)
{
if(arg.use_multiblock_welford)
pass = bnorm_fwd_nhwc_test<ck::bhalf_t, float, true>(arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inOutLengths,
arg.updateMovingAverage,
arg.saveMeanAndInvVariance,
averageFactor,
epsilon);
else
pass = bnorm_fwd_nhwc_test<ck::bhalf_t, float, false>(arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inOutLengths,
arg.updateMovingAverage,
arg.saveMeanAndInvVariance,
averageFactor,
epsilon);
}
else if(arg.data_type == 6)
{
if(arg.use_multiblock_welford)
pass = bnorm_fwd_nhwc_test<double, double, true>(arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inOutLengths,
arg.updateMovingAverage,
arg.saveMeanAndInvVariance,
averageFactor,
epsilon);
else
pass = bnorm_fwd_nhwc_test<double, double, false>(arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inOutLengths,
arg.updateMovingAverage,
arg.saveMeanAndInvVariance,
averageFactor,
epsilon);
}
}
else
{
pass = bnorm_fwd_nhwc_test<ck::half_t, float, true>(true,
2,
false, // don't time kernel
{128, 16, 6, 512},
true,
true,
averageFactor,
epsilon);
pass = pass && bnorm_fwd_nhwc_test<ck::half_t, float, false>(true,
2,
false, // don't time kernel
{128, 16, 3, 1024},
true,
true,
averageFactor,
epsilon);
};
return (pass ? 0 : 1);
}
include/ck/tensor_operation/gpu/block/blockwise_welford.hpp
View file @ 8f5cafaf
......@@ -4,7 +4,7 @@
#pragma once
#include "ck/tensor_description/cluster_descriptor.hpp"
#include "ck/utility/reduction_common.hpp"
#include "ck/utility/get_shift.hpp"
namespace ck {
......@@ -35,10 +35,11 @@ struct BlockwiseWelford
static constexpr auto thread_cluster_desc =
make_cluster_descriptor(ThreadClusterLengths_M_K{}, ThreadClusterArrangeOrder{});
template <typename CountDataType>
__device__ static inline void
Merge(T& mean_a, T& var_a, int& count_a, T mean_b, T var_b, int count_b)
Merge(T& mean_a, T& var_a, CountDataType& count_a, T mean_b, T var_b, CountDataType count_b)
{
int count = count_a + count_b;
CountDataType count = count_a + count_b;
T count_b_over_count = count == 0 ? type_convert<T>(0) : type_convert<T>(count_b) / count;
T delta = mean_b - mean_a;
mean_a += delta * count_b_over_count;
......@@ -46,11 +47,12 @@ struct BlockwiseWelford
count_a = count;
}
__device__ static void Run(T& mean_value, T& var_value, int& count)
template <typename CountDataType>
__device__ static void Run(T& mean_value, T& var_value, CountDataType& count)
{
__shared__ T mean_block_buf[BlockSize];
__shared__ T var_block_buf[BlockSize];
__shared__ int count_block_buf[BlockSize];
__shared__ CountDataType count_block_buf[BlockSize];
constexpr auto cluster_len_shift = get_shift<BufferLength_K>();
......@@ -76,13 +78,13 @@ struct BlockwiseWelford
index_t offset2 = block_buf_desc_m_k.CalculateOffset(thread_cluster_idx +
make_tuple(0, indOffset));
T mean1 = mean_block_buf[offset1];
T var1 = var_block_buf[offset1];
int count1 = count_block_buf[offset1];
T mean1 = mean_block_buf[offset1];
T var1 = var_block_buf[offset1];
CountDataType count1 = count_block_buf[offset1];
T mean2 = mean_block_buf[offset2];
T var2 = var_block_buf[offset2];
int count2 = count_block_buf[offset2];
T mean2 = mean_block_buf[offset2];
T var2 = var_block_buf[offset2];
CountDataType count2 = count_block_buf[offset2];
Merge(mean1, var1, count1, mean2, var2, count2);
......
include/ck/tensor_operation/gpu/block/reduction_functions_blockwise.hpp
View file @ 8f5cafaf
......@@ -4,7 +4,7 @@
#pragma once
#include "ck/tensor_description/cluster_descriptor.hpp"
#include "ck/utility/reduction_common.hpp"
#include "ck/utility/get_shift.hpp"
#include "ck/utility/reduction_functions_accumulate.hpp"
namespace ck {
......
include/ck/tensor_operation/gpu/device/impl/device_batchnorm_forward_impl.hpp
View file @ 8f5cafaf
......@@ -10,12 +10,14 @@
#include "ck/tensor_operation/gpu/device/device_batchnorm_forward.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_reduce_common.hpp"
#include "ck/tensor_operation/gpu/device/welford_helper.hpp"
#include "ck/tensor_operation/gpu/grid/batchnorm_multiblock/gridwise_multiblock_batchnorm_forward.hpp"
#include "ck/tensor_operation/gpu/grid/batchnorm_multiblock/gridwise_multiblock_welford_first_half.hpp"
#include "ck/tensor_operation/gpu/grid/batchnorm_multiblock/gridwise_multiblock_welford_second_half_batchnorm_forward_final.hpp"
#include "ck/tensor_operation/gpu/grid/batchnorm_multiblock/gridwise_multiblock_welford_second_half_batchnorm_forward_final_obsolete.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_batchnorm_forward_blockwise_welford.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp"
#include "ck/host_utility/hip_check_error.hpp"
namespace ck {
namespace tensor_operation {
......@@ -114,8 +116,8 @@ struct DeviceBatchNormFwdImpl : public DeviceBatchNormFwd<XDataType,
static auto MakeMeanVarCountOutputMG2dDescriptor(int invariantLength, int blkGroupSize)
{
const auto grid_desc_m_g =
make_naive_tensor_descriptor_packed(make_tuple(invariantLength, blkGroupSize));
const auto grid_desc_m_g = make_naive_tensor_descriptor(
make_tuple(invariantLength, blkGroupSize), make_tuple(1, invariantLength));
const auto mPad =
math::integer_least_multiple(invariantLength, M_BlockTileSize) - invariantLength;
......@@ -132,9 +134,9 @@ struct DeviceBatchNormFwdImpl : public DeviceBatchNormFwd<XDataType,
static auto MakeMeanVarCountInputMK2dDescriptor(int invariantLength, int blkGroupSize)
{
const auto reduceLength = blkGroupSize;
const auto grid_desc_m_k =
make_naive_tensor_descriptor_packed(make_tuple(invariantLength, reduceLength));
const auto reduceLength = blkGroupSize;
const auto grid_desc_m_k = make_naive_tensor_descriptor(
make_tuple(invariantLength, reduceLength), make_tuple(1, invariantLength));
const auto mPad =
math::integer_least_multiple(invariantLength, M_BlockTileSize) - invariantLength;
......@@ -244,8 +246,8 @@ struct DeviceBatchNormFwdImpl : public DeviceBatchNormFwd<XDataType,
int testBlkGroupSize = (reduce_length_ + (K_BlockTileSize * iterations) - 1) /
(K_BlockTileSize * iterations);
// we want the blkGroupSize be not more than 128
if(testBlkGroupSize <= 128)
// we want the blkGroupSize be not more than 16
if(testBlkGroupSize <= 16)
break;
iterations++;
......@@ -319,6 +321,8 @@ struct DeviceBatchNormFwdImpl : public DeviceBatchNormFwd<XDataType,
void* workspace_mean_;
void* workspace_variance_;
void* workspace_count_;
void* control_;
};
size_t GetWorkSpaceSize(const BaseArgument* pArg) const override
......@@ -340,6 +344,11 @@ struct DeviceBatchNormFwdImpl : public DeviceBatchNormFwd<XDataType,
// workspace for welford intermediate count
workspace_size +=
pArg_->invariant_length_ * pArg_->blkGroupSize_ * sizeof(int32_t) + 64;
// workspace for barrier objects, each barrier object consists of two integers
// TODO: allocate barrier object memory globally to reuse it by other operators
workspace_size += (pArg_->invariant_length_ + M_BlockTileSize - 1) / M_BlockTileSize *
sizeof(int) * 2;
}
return (workspace_size);
......@@ -353,7 +362,6 @@ struct DeviceBatchNormFwdImpl : public DeviceBatchNormFwd<XDataType,
if(UseMultiblockInK && pArg_->blkGroupSize_ > 1)
{
// setup buffer used for intermediate welford mean
pArg_->workspace_mean_ = static_cast<char*>(pArg_->p_workspace_);
......@@ -374,6 +382,18 @@ struct DeviceBatchNormFwdImpl : public DeviceBatchNormFwd<XDataType,
// setup buffer used for intermediate welfor count
pArg_->workspace_count_ =
reinterpret_cast<char*>(pArg_->workspace_variance_) + variance_space_sz;
index_t count_space_sz =
pArg_->invariant_length_ * pArg_->blkGroupSize_ * sizeof(int32_t);
count_space_sz = math::integer_least_multiple(count_space_sz, 64);
pArg_->control_ = reinterpret_cast<char*>(pArg_->workspace_count_) + count_space_sz;
index_t control_space_sz = (pArg_->invariant_length_ + M_BlockTileSize - 1) /
M_BlockTileSize * sizeof(int) * 2;
hip_check_error(hipMemset(pArg_->control_, 0, control_space_sz));
};
};
......@@ -402,6 +422,32 @@ struct DeviceBatchNormFwdImpl : public DeviceBatchNormFwd<XDataType,
using MeanVarCountGridDesc_M_G = decltype(mean_var_count_grid_desc_m_g);
using MeanVarCountGridDesc_M_K = decltype(mean_var_count_grid_desc_m_k);
using GridwiseMultiblockBatchNormForward_ =
GridwiseMultiblockBatchNormForward<XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
YElementwiseOp,
XYGridDesc_M_K,
MeanVarCountGridDesc_M_G,
MeanVarCountGridDesc_M_K,
ScaleBiasMeanVarGridDesc_M,
ScaleBiasMeanVarGridDesc_M,
GetReduceCountPerThreadFunctor,
BlockSize,
MThreadClusterSize,
KThreadClusterSize,
MThreadSliceSize,
KThreadSliceSize,
XSrcYDstVectorDim,
XSrcVectorSize,
YDstVectorSize,
ScaleSrcVectorSize,
BiasSrcVectorSize,
MeanVarSrcDstVectorSize>;
using GridwiseMultiblockWelfordFirstHalf_ =
GridwiseMultiblockWelfordFirstHalf<XDataType,
AccDataType,
......@@ -441,78 +487,136 @@ struct DeviceBatchNormFwdImpl : public DeviceBatchNormFwd<XDataType,
BiasSrcVectorSize,
MeanVarSrcDstVectorSize>;
index_t numMeanVarCountBlockTileIteration =
(arg.blkGroupSize_ + KThreadClusterSize - 1) / KThreadClusterSize;
const auto kern_multiblock_welford_first_half =
kernel_multiblock_welford_first_half<GridwiseMultiblockWelfordFirstHalf_,
XDataType,
MeanVarDataType,
XYGridDesc_M_K,
MeanVarCountGridDesc_M_G,
GetReduceCountPerThreadFunctor>;
const auto kern_welford_second_half_batchnorm_forward_final =
kernel_welford_second_half_batchnorm_forward_final<
GridwiseWelfordSecondHalfBatchNormForwardFinal_,
XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
YElementwiseOp,
XYGridDesc_M_K,
MeanVarCountGridDesc_M_K,
ScaleBiasMeanVarGridDesc_M,
ScaleBiasMeanVarGridDesc_M>;
avg_time +=
launch_and_time_kernel(stream_config,
kern_multiblock_welford_first_half,
dim3(arg.gridSize_),
dim3(BlockSize),
0,
arg.x_grid_desc_m_k_,
mean_var_count_grid_desc_m_g,
get_reduce_count_per_thread,
arg.numBlockTileIteration_,
arg.p_x_,
static_cast<MeanVarDataType*>(arg.workspace_mean_),
static_cast<MeanVarDataType*>(arg.workspace_variance_),
static_cast<int32_t*>(arg.workspace_count_));
avg_time +=
launch_and_time_kernel(stream_config,
kern_welford_second_half_batchnorm_forward_final,
dim3(arg.gridSize_),
dim3(BlockSize),
0,
arg.x_grid_desc_m_k_,
arg.y_grid_desc_m_k_,
mean_var_count_grid_desc_m_k,
arg.scale_grid_desc_m_,
arg.bias_grid_desc_m_,
arg.mean_var_grid_desc_m_,
arg.blkGroupSize_,
arg.numBlockTileIteration_,
numMeanVarCountBlockTileIteration,
arg.epsilon_,
static_cast<MeanVarDataType*>(arg.workspace_mean_),
static_cast<MeanVarDataType*>(arg.workspace_variance_),
static_cast<int32_t*>(arg.workspace_count_),
arg.p_x_,
arg.p_scale_,
arg.p_bias_,
arg.y_elementwise_op_,
arg.p_y_,
arg.updateMovingAverage_,
arg.averageFactor_,
arg.resultRunningMean_,
arg.resultRunningVariance_,
arg.saveMeanInvVariance_,
arg.resultSaveMean_,
arg.resultSaveInvVariance_);
// It is found that:
// 1) gfx1030 does not support the GLC enabled vector load/store, so using the
// two-kernel method for gfx1030
// 2) Profiler on gfx908 could hang even though it works when running examples
// 3) Single-kernel method works on gfx1100, but the performance it not better
// than two-kernel method (due to more warps participating the barrier)
if(ck::get_device_name() == "gfx90a")
{
const auto kern_multiblock_batchnorm_fwd_ =
kernel_multiblock_batchnorm_forward<GridwiseMultiblockBatchNormForward_,
XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
YElementwiseOp,
XYGridDesc_M_K,
MeanVarCountGridDesc_M_G,
MeanVarCountGridDesc_M_K,
ScaleBiasMeanVarGridDesc_M,
ScaleBiasMeanVarGridDesc_M,
GetReduceCountPerThreadFunctor>;
avg_time += launch_and_time_kernel(
stream_config,
kern_multiblock_batchnorm_fwd_,
dim3(arg.gridSize_),
dim3(BlockSize),
0,
arg.x_grid_desc_m_k_,
arg.y_grid_desc_m_k_,
mean_var_count_grid_desc_m_g, // for writing to mean/variance/count
// workspace by multiple workgroups
mean_var_count_grid_desc_m_k, // for reading from mean/variance/count
// workspace by each workgroup
arg.scale_grid_desc_m_,
arg.bias_grid_desc_m_,
arg.mean_var_grid_desc_m_,
get_reduce_count_per_thread,
arg.numBlockTileIteration_,
arg.epsilon_,
arg.p_x_,
static_cast<MeanVarDataType*>(arg.workspace_mean_),
static_cast<MeanVarDataType*>(arg.workspace_variance_),
static_cast<int32_t*>(arg.workspace_count_),
static_cast<int*>(arg.control_),
arg.p_scale_,
arg.p_bias_,
arg.y_elementwise_op_,
arg.p_y_,
arg.updateMovingAverage_, // true or false
arg.averageFactor_,
arg.resultRunningMean_,
arg.resultRunningVariance_,
arg.saveMeanInvVariance_, // true or false
arg.resultSaveMean_,
arg.resultSaveInvVariance_);
}
else
{
const auto kern_multiblock_welford_first_half =
kernel_multiblock_welford_first_half<GridwiseMultiblockWelfordFirstHalf_,
XDataType,
MeanVarDataType,
XYGridDesc_M_K,
MeanVarCountGridDesc_M_G,
GetReduceCountPerThreadFunctor>;
const auto kern_welford_second_half_batchnorm_forward_final =
kernel_welford_second_half_batchnorm_forward_final<
GridwiseWelfordSecondHalfBatchNormForwardFinal_,
XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
YElementwiseOp,
XYGridDesc_M_K,
MeanVarCountGridDesc_M_K,
ScaleBiasMeanVarGridDesc_M,
ScaleBiasMeanVarGridDesc_M>;
avg_time += launch_and_time_kernel(
stream_config,
kern_multiblock_welford_first_half,
dim3(arg.gridSize_),
dim3(BlockSize),
0,
arg.x_grid_desc_m_k_,
mean_var_count_grid_desc_m_g,
get_reduce_count_per_thread,
arg.numBlockTileIteration_,
arg.p_x_,
static_cast<MeanVarDataType*>(arg.workspace_mean_),
static_cast<MeanVarDataType*>(arg.workspace_variance_),
static_cast<int32_t*>(arg.workspace_count_));
avg_time += launch_and_time_kernel(
stream_config,
kern_welford_second_half_batchnorm_forward_final,
dim3(arg.gridSize_),
dim3(BlockSize),
0,
arg.x_grid_desc_m_k_,
arg.y_grid_desc_m_k_,
mean_var_count_grid_desc_m_k,
arg.scale_grid_desc_m_,
arg.bias_grid_desc_m_,
arg.mean_var_grid_desc_m_,
arg.blkGroupSize_,
arg.numBlockTileIteration_,
arg.epsilon_,
static_cast<MeanVarDataType*>(arg.workspace_mean_),
static_cast<MeanVarDataType*>(arg.workspace_variance_),
static_cast<int32_t*>(arg.workspace_count_),
arg.p_x_,
arg.p_scale_,
arg.p_bias_,
arg.y_elementwise_op_,
arg.p_y_,
arg.updateMovingAverage_,
arg.averageFactor_,
arg.resultRunningMean_,
arg.resultRunningVariance_,
arg.saveMeanInvVariance_,
arg.resultSaveMean_,
arg.resultSaveInvVariance_);
};
}
else
{
......
include/ck/tensor_operation/gpu/device/impl/device_batchnorm_forward_impl_obsolete.hpp 0 → 100644
View file @ 8f5cafaf
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <sstream>
#include "ck/utility/reduction_operator.hpp"
#include "ck/tensor_operation/gpu/device/device_batchnorm_forward.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_reduce_common.hpp"
#include "ck/tensor_operation/gpu/device/welford_helper.hpp"
#include "ck/tensor_operation/gpu/grid/batchnorm_multiblock/gridwise_multiblock_welford_first_half.hpp"
#include "ck/tensor_operation/gpu/grid/batchnorm_multiblock/gridwise_multiblock_welford_second_half_batchnorm_forward_final_obsolete.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_batchnorm_forward_blockwise_welford.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
template <typename XDataType,
typename YDataType,
typename AccDataType,
typename ScaleDataType,
typename BiasDataType,
typename MeanVarDataType,
typename YElementwiseOp,
index_t Rank,
index_t NumBatchNormReduceDim,
bool UseMultiblockInK,
index_t BlockSize,
index_t MThreadClusterSize,
index_t KThreadClusterSize,
index_t MThreadSliceSize,
index_t KThreadSliceSize,
index_t XSrcYDstVectorDim,
index_t XSrcVectorSize,
index_t YDstVectorSize,
index_t ScaleSrcVectorSize,
index_t BiasSrcVectorSize,
index_t MeanVarSrcDstVectorSize>
struct DeviceBatchNormFwdImpl : public DeviceBatchNormFwd<XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
YElementwiseOp,
Rank,
NumBatchNormReduceDim>
{
static_assert(Rank <= 6, "Bigger Rank size is not supported!");
static_assert(BlockSize == MThreadClusterSize * KThreadClusterSize,
"Invalid thread cluster size assignments!");
static_assert((XSrcYDstVectorDim == 0 && MThreadSliceSize % XSrcVectorSize == 0) ||
(XSrcYDstVectorDim == 1 && KThreadSliceSize % XSrcVectorSize == 0),
"Invalid thread slice sizes and/or vector sizes configuration, please check!");
static constexpr index_t NumInvariantDim = Rank - NumBatchNormReduceDim;
static constexpr index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
static constexpr index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
static auto MakeXY2dDescriptor(const std::array<index_t, Rank>& xyLengths,
const std::array<index_t, Rank>& xyStrides,
int blkGroupSize,
int numBlockTileIteration)
{
const auto tupleXYLengths =
generate_tuple([&](auto I) { return xyLengths[I]; }, Number<Rank>{});
const auto tupleXYStrides =
generate_tuple([&](auto I) { return xyStrides[I]; }, Number<Rank>{});
const auto raw_grid_desc = make_naive_tensor_descriptor(tupleXYLengths, tupleXYStrides);
const auto grid_desc_m_k = [&]() {
using InvariantDims = typename arithmetic_sequence_gen<0, NumInvariantDim, 1>::type;
using ReduceDims = typename arithmetic_sequence_gen<NumInvariantDim, Rank, 1>::type;
const auto reduceDimLengths =
generate_tuple([&](auto I) { return xyLengths[NumInvariantDim + I]; },
Number<NumBatchNormReduceDim>{});
const auto invariantDimLengths =
generate_tuple([&](auto I) { return xyLengths[I]; }, Number<NumInvariantDim>{});
return transform_tensor_descriptor(raw_grid_desc,
make_tuple(make_merge_transform(invariantDimLengths),
make_merge_transform(reduceDimLengths)),
make_tuple(InvariantDims{}, ReduceDims{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
}();
const auto invariantLength = grid_desc_m_k.GetLength(Number<0>{});
const auto reduceLength = grid_desc_m_k.GetLength(Number<1>{});
const int workSizePerBlock = K_BlockTileSize * numBlockTileIteration;
const auto mPad =
math::integer_least_multiple(invariantLength, M_BlockTileSize) - invariantLength;
const auto kPad = workSizePerBlock * blkGroupSize - reduceLength;
auto grid_desc_m_k_padded =
transform_tensor_descriptor(grid_desc_m_k,
make_tuple(make_right_pad_transform(invariantLength, mPad),
make_right_pad_transform(reduceLength, kPad)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return (grid_desc_m_k_padded);
};
static auto MakeMeanVarCountOutputMG2dDescriptor(int invariantLength, int blkGroupSize)
{
const auto grid_desc_m_g = make_naive_tensor_descriptor(
make_tuple(invariantLength, blkGroupSize), make_tuple(1, invariantLength));
const auto mPad =
math::integer_least_multiple(invariantLength, M_BlockTileSize) - invariantLength;
auto grid_desc_m_g_padded =
transform_tensor_descriptor(grid_desc_m_g,
make_tuple(make_right_pad_transform(invariantLength, mPad),
make_pass_through_transform(blkGroupSize)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return (grid_desc_m_g_padded);
};
static auto MakeMeanVarCountInputMK2dDescriptor(int invariantLength, int blkGroupSize)
{
const auto reduceLength = blkGroupSize;
const auto grid_desc_m_k = make_naive_tensor_descriptor(
make_tuple(invariantLength, reduceLength), make_tuple(1, invariantLength));
const auto mPad =
math::integer_least_multiple(invariantLength, M_BlockTileSize) - invariantLength;
const auto kPad =
math::integer_least_multiple(reduceLength, KThreadClusterSize) - reduceLength;
auto grid_desc_m_k_padded =
transform_tensor_descriptor(grid_desc_m_k,
make_tuple(make_right_pad_transform(invariantLength, mPad),
make_right_pad_transform(reduceLength, kPad)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return (grid_desc_m_k_padded);
};
static auto
MakeScaleBiasMeanVar1dDescriptor(const std::array<index_t, NumInvariantDim>& lengths,
const std::array<index_t, NumInvariantDim>& strides)
{
const auto tupleLengths =
generate_tuple([&](auto I) { return lengths[I]; }, Number<NumInvariantDim>{});
const auto tupleStrides =
generate_tuple([&](auto I) { return strides[I]; }, Number<NumInvariantDim>{});
auto raw_grid_desc = make_naive_tensor_descriptor(tupleLengths, tupleStrides);
auto grid_desc_m = transform_tensor_descriptor(
raw_grid_desc,
make_tuple(make_merge_transform(tupleLengths)),
make_tuple(typename arithmetic_sequence_gen<0, NumInvariantDim, 1>::type{}),
make_tuple(Sequence<0>{}));
const auto invariantLength = grid_desc_m.GetLength(Number<0>{});
const auto mPad =
math::integer_least_multiple(invariantLength, M_BlockTileSize) - invariantLength;
auto grid_desc_m_padded =
transform_tensor_descriptor(grid_desc_m,
make_tuple(make_right_pad_transform(invariantLength, mPad)),
make_tuple(Sequence<0>{}),
make_tuple(Sequence<0>{}));
return (grid_desc_m_padded);
};
using XYGridDesc_M_K = decltype(MakeXY2dDescriptor({1}, {1}, 1, 1));
using ScaleBiasMeanVarGridDesc_M = decltype(MakeScaleBiasMeanVar1dDescriptor({1}, {1}));
struct Argument : public BaseArgument
{
Argument(const std::array<index_t, Rank> xyLengths,
const std::array<index_t, Rank> xStrides,
const std::array<index_t, Rank> yStrides,
const std::array<int, NumBatchNormReduceDim> reduceDims,
const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleBiasMeanVarLengths,
const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleStrides,
const std::array<index_t, Rank - NumBatchNormReduceDim> bnBiasStrides,
const std::array<index_t, Rank - NumBatchNormReduceDim> bnMeanVarStrides,
const XDataType* p_x,
const ScaleDataType* p_scale,
const BiasDataType* p_bias,
const YElementwiseOp y_elementwise_op,
double epsilon,
YDataType* p_y,
MeanVarDataType* resultSaveMean,
MeanVarDataType* resultSaveInvVariance,
double averageFactor,
MeanVarDataType* resultRunningMean,
MeanVarDataType* resultRunningVariance)
: bnScaleBiasMeanVarLengths_(bnScaleBiasMeanVarLengths),
bnScaleStrides_(bnScaleStrides),
bnBiasStrides_(bnBiasStrides),
bnMeanVarStrides_(bnMeanVarStrides),
p_x_(p_x),
p_scale_(p_scale),
p_bias_(p_bias),
y_elementwise_op_(y_elementwise_op),
p_y_(p_y),
resultSaveMean_(resultSaveMean),
resultSaveInvVariance_(resultSaveInvVariance),
resultRunningMean_(resultRunningMean),
resultRunningVariance_(resultRunningVariance)
{
xyLengths_ =
shuffle_tensor_dimensions<Rank, NumBatchNormReduceDim>(xyLengths, reduceDims);
xStrides_ =
shuffle_tensor_dimensions<Rank, NumBatchNormReduceDim>(xStrides, reduceDims);
yStrides_ =
shuffle_tensor_dimensions<Rank, NumBatchNormReduceDim>(yStrides, reduceDims);
std::tie(invariant_length_, reduce_length_) =
get_2d_lengths<Rank, NumBatchNormReduceDim>(xyLengths_);
epsilon_ = type_convert<AccDataType>(epsilon);
averageFactor_ = type_convert<AccDataType>(averageFactor);
updateMovingAverage_ =
(resultRunningMean != nullptr && resultRunningVariance != nullptr);
saveMeanInvVariance_ = (resultSaveMean != nullptr && resultSaveInvVariance_ != nullptr);
if(UseMultiblockInK)
{
int iterations = 1;
while(true)
{
int testBlkGroupSize = (reduce_length_ + (K_BlockTileSize * iterations) - 1) /
(K_BlockTileSize * iterations);
// we want the blkGroupSize be not more than 16
if(testBlkGroupSize <= 16)
break;
iterations++;
};
blkGroupSize_ = (reduce_length_ + (K_BlockTileSize * iterations) - 1) /
(K_BlockTileSize * iterations);
numBlockTileIteration_ = iterations;
}
else
{
blkGroupSize_ = 1;
numBlockTileIteration_ = (reduce_length_ + K_BlockTileSize - 1) / K_BlockTileSize;
};
gridSize_ = (invariant_length_ + M_BlockTileSize - 1) / M_BlockTileSize * blkGroupSize_;
x_grid_desc_m_k_ =
MakeXY2dDescriptor(xyLengths_, xStrides_, blkGroupSize_, numBlockTileIteration_);
y_grid_desc_m_k_ =
MakeXY2dDescriptor(xyLengths_, yStrides_, blkGroupSize_, numBlockTileIteration_);
scale_grid_desc_m_ =
MakeScaleBiasMeanVar1dDescriptor(bnScaleBiasMeanVarLengths, bnScaleStrides_);
bias_grid_desc_m_ =
MakeScaleBiasMeanVar1dDescriptor(bnScaleBiasMeanVarLengths, bnBiasStrides_);
mean_var_grid_desc_m_ =
MakeScaleBiasMeanVar1dDescriptor(bnScaleBiasMeanVarLengths, bnMeanVarStrides_);
}
AccDataType epsilon_;
AccDataType averageFactor_;
bool updateMovingAverage_;
bool saveMeanInvVariance_;
std::array<index_t, Rank> xyLengths_;
std::array<index_t, Rank> xStrides_;
std::array<index_t, Rank> yStrides_;
std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleBiasMeanVarLengths_;
std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleStrides_;
std::array<index_t, Rank - NumBatchNormReduceDim> bnBiasStrides_;
std::array<index_t, Rank - NumBatchNormReduceDim> bnMeanVarStrides_;
const XDataType* p_x_;
const ScaleDataType* p_scale_;
const BiasDataType* p_bias_;
const YElementwiseOp y_elementwise_op_;
YDataType* p_y_;
MeanVarDataType* resultSaveMean_;
MeanVarDataType* resultSaveInvVariance_;
MeanVarDataType* resultRunningMean_;
MeanVarDataType* resultRunningVariance_;
long_index_t invariant_length_;
long_index_t reduce_length_;
int blkGroupSize_;
int numBlockTileIteration_;
size_t gridSize_;
XYGridDesc_M_K x_grid_desc_m_k_;
XYGridDesc_M_K y_grid_desc_m_k_;
ScaleBiasMeanVarGridDesc_M scale_grid_desc_m_;
ScaleBiasMeanVarGridDesc_M bias_grid_desc_m_;
ScaleBiasMeanVarGridDesc_M mean_var_grid_desc_m_;
void* workspace_mean_;
void* workspace_variance_;
void* workspace_count_;
};
size_t GetWorkSpaceSize(const BaseArgument* pArg) const override
{
const Argument* pArg_ = dynamic_cast<const Argument*>(pArg);
size_t workspace_size = 0;
if(UseMultiblockInK && pArg_->blkGroupSize_ > 1)
{
// workspace for welford intermediate mean
workspace_size +=
pArg_->invariant_length_ * pArg_->blkGroupSize_ * sizeof(MeanVarDataType) + 64;
// workspace for welford intermediate variance
workspace_size +=
pArg_->invariant_length_ * pArg_->blkGroupSize_ * sizeof(MeanVarDataType) + 64;
// workspace for welford intermediate count
workspace_size +=
pArg_->invariant_length_ * pArg_->blkGroupSize_ * sizeof(int32_t) + 64;
}
return (workspace_size);
};
void SetWorkSpacePointer(BaseArgument* pArg, void* p_workspace) const override
{
Argument* pArg_ = dynamic_cast<Argument*>(pArg);
pArg_->p_workspace_ = p_workspace;
if(UseMultiblockInK && pArg_->blkGroupSize_ > 1)
{
// setup buffer used for intermediate welford mean
pArg_->workspace_mean_ = static_cast<char*>(pArg_->p_workspace_);
index_t mean_space_sz =
pArg_->invariant_length_ * pArg_->blkGroupSize_ * sizeof(MeanVarDataType);
mean_space_sz = math::integer_least_multiple(mean_space_sz, 64);
// setup buffer used for intermediate welford varirance
pArg_->workspace_variance_ =
reinterpret_cast<char*>(pArg_->workspace_mean_) + mean_space_sz;
index_t variance_space_sz =
pArg_->invariant_length_ * pArg_->blkGroupSize_ * sizeof(MeanVarDataType);
variance_space_sz = math::integer_least_multiple(variance_space_sz, 64);
// setup buffer used for intermediate welfor count
pArg_->workspace_count_ =
reinterpret_cast<char*>(pArg_->workspace_variance_) + variance_space_sz;
};
};
struct Invoker : public BaseInvoker
{
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
float avg_time = 0;
if(UseMultiblockInK && arg.blkGroupSize_ > 1)
{
using GetReduceCountPerThreadFunctor =
GetReduceCountPerThreadForMultiblockWelford<K_BlockTileSize, KThreadSliceSize>;
GetReduceCountPerThreadFunctor get_reduce_count_per_thread(
arg.blkGroupSize_, arg.numBlockTileIteration_, arg.reduce_length_);
const auto mean_var_count_grid_desc_m_g =
DeviceBatchNormFwdImpl::MakeMeanVarCountOutputMG2dDescriptor(
arg.invariant_length_, arg.blkGroupSize_);
const auto mean_var_count_grid_desc_m_k =
DeviceBatchNormFwdImpl::MakeMeanVarCountInputMK2dDescriptor(
arg.invariant_length_, arg.blkGroupSize_);
using MeanVarCountGridDesc_M_G = decltype(mean_var_count_grid_desc_m_g);
using MeanVarCountGridDesc_M_K = decltype(mean_var_count_grid_desc_m_k);
using GridwiseMultiblockWelfordFirstHalf_ =
GridwiseMultiblockWelfordFirstHalf<XDataType,
AccDataType,
MeanVarDataType,
XYGridDesc_M_K,
MeanVarCountGridDesc_M_G,
GetReduceCountPerThreadFunctor,
BlockSize,
MThreadClusterSize,
KThreadClusterSize,
MThreadSliceSize,
KThreadSliceSize,
XSrcYDstVectorDim,
XSrcVectorSize>;
using GridwiseWelfordSecondHalfBatchNormForwardFinal_ =
GridwiseWelfordSecondHalfBatchNormForwardFinal<XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
YElementwiseOp,
XYGridDesc_M_K,
MeanVarCountGridDesc_M_K,
ScaleBiasMeanVarGridDesc_M,
ScaleBiasMeanVarGridDesc_M,
BlockSize,
MThreadClusterSize,
KThreadClusterSize,
MThreadSliceSize,
KThreadSliceSize,
XSrcYDstVectorDim,
XSrcVectorSize,
YDstVectorSize,
ScaleSrcVectorSize,
BiasSrcVectorSize,
MeanVarSrcDstVectorSize>;
const auto kern_multiblock_welford_first_half =
kernel_multiblock_welford_first_half<GridwiseMultiblockWelfordFirstHalf_,
XDataType,
MeanVarDataType,
XYGridDesc_M_K,
MeanVarCountGridDesc_M_G,
GetReduceCountPerThreadFunctor>;
const auto kern_welford_second_half_batchnorm_forward_final =
kernel_welford_second_half_batchnorm_forward_final<
GridwiseWelfordSecondHalfBatchNormForwardFinal_,
XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
YElementwiseOp,
XYGridDesc_M_K,
MeanVarCountGridDesc_M_K,
ScaleBiasMeanVarGridDesc_M,
ScaleBiasMeanVarGridDesc_M>;
avg_time +=
launch_and_time_kernel(stream_config,
kern_multiblock_welford_first_half,
dim3(arg.gridSize_),
dim3(BlockSize),
0,
arg.x_grid_desc_m_k_,
mean_var_count_grid_desc_m_g,
get_reduce_count_per_thread,
arg.numBlockTileIteration_,
arg.p_x_,
static_cast<MeanVarDataType*>(arg.workspace_mean_),
static_cast<MeanVarDataType*>(arg.workspace_variance_),
static_cast<int32_t*>(arg.workspace_count_));
avg_time +=
launch_and_time_kernel(stream_config,
kern_welford_second_half_batchnorm_forward_final,
dim3(arg.gridSize_),
dim3(BlockSize),
0,
arg.x_grid_desc_m_k_,
arg.y_grid_desc_m_k_,
mean_var_count_grid_desc_m_k,
arg.scale_grid_desc_m_,
arg.bias_grid_desc_m_,
arg.mean_var_grid_desc_m_,
arg.blkGroupSize_,
arg.numBlockTileIteration_,
arg.epsilon_,
static_cast<MeanVarDataType*>(arg.workspace_mean_),
static_cast<MeanVarDataType*>(arg.workspace_variance_),
static_cast<int32_t*>(arg.workspace_count_),
arg.p_x_,
arg.p_scale_,
arg.p_bias_,
arg.y_elementwise_op_,
arg.p_y_,
arg.updateMovingAverage_,
arg.averageFactor_,
arg.resultRunningMean_,
arg.resultRunningVariance_,
arg.saveMeanInvVariance_,
arg.resultSaveMean_,
arg.resultSaveInvVariance_);
}
else
{
using GetReduceCountPerThreadFunctor =
GetReduceCountPerThreadForBlockwiseWelford<K_BlockTileSize, KThreadSliceSize>;
GetReduceCountPerThreadFunctor get_reduce_count_per_thread(
arg.numBlockTileIteration_, arg.reduce_length_);
using GridwiseBatchNormForwardWithBlockwiseWelford_ =
GridwiseBatchNormForwardWithBlockwiseWelford<XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
YElementwiseOp,
XYGridDesc_M_K,
ScaleBiasMeanVarGridDesc_M,
ScaleBiasMeanVarGridDesc_M,
GetReduceCountPerThreadFunctor,
BlockSize,
MThreadClusterSize,
KThreadClusterSize,
MThreadSliceSize,
KThreadSliceSize,
XSrcYDstVectorDim,
XSrcVectorSize,
YDstVectorSize,
ScaleSrcVectorSize,
BiasSrcVectorSize,
MeanVarSrcDstVectorSize>;
const auto kern_batchnorm_fwd = kernel_batchnorm_forward_with_blockwise_welford<
GridwiseBatchNormForwardWithBlockwiseWelford_,
XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
YElementwiseOp,
XYGridDesc_M_K,
ScaleBiasMeanVarGridDesc_M,
ScaleBiasMeanVarGridDesc_M,
GetReduceCountPerThreadFunctor>;
avg_time += launch_and_time_kernel(stream_config,
kern_batchnorm_fwd,
dim3(arg.gridSize_),
dim3(BlockSize),
0,
arg.x_grid_desc_m_k_,
arg.y_grid_desc_m_k_,
arg.scale_grid_desc_m_,
arg.bias_grid_desc_m_,
arg.mean_var_grid_desc_m_,
get_reduce_count_per_thread,
arg.numBlockTileIteration_,
arg.epsilon_,
arg.p_x_,
arg.p_scale_,
arg.p_bias_,
arg.y_elementwise_op_,
arg.p_y_,
arg.updateMovingAverage_, // true or false
arg.averageFactor_,
arg.resultRunningMean_,
arg.resultRunningVariance_,
arg.saveMeanInvVariance_, // true or false
arg.resultSaveMean_,
arg.resultSaveInvVariance_);
};
return (avg_time);
};
float Run(const BaseArgument* pArg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(pArg), stream_config);
};
};
bool IsSupportedArgument(const BaseArgument* pArg) override
{
const Argument* pArg_ = dynamic_cast<const Argument*>(pArg);
if constexpr(XSrcYDstVectorDim == 0)
{
if(pArg_->xStrides_[NumInvariantDim - 1] != 1 ||
pArg_->yStrides_[NumInvariantDim - 1] != 1)
return false;
if(pArg_->xyLengths_[NumInvariantDim - 1] % XSrcVectorSize != 0 ||
pArg_->xyLengths_[NumInvariantDim - 1] % YDstVectorSize != 0)
return false;
}
else
{
if(pArg_->xStrides_[Rank - 1] != 1 || pArg_->yStrides_[Rank - 1] != 1)
return false;
if(pArg_->xyLengths_[Rank - 1] % XSrcVectorSize != 0 ||
pArg_->xyLengths_[Rank - 1] % YDstVectorSize != 0)
return false;
};
if(pArg_->bnScaleStrides_[NumInvariantDim - 1] != 1 && ScaleSrcVectorSize != 1)
return false;
if(pArg_->bnBiasStrides_[NumInvariantDim - 1] != 1 && BiasSrcVectorSize != 1)
return false;
if(pArg_->bnScaleBiasMeanVarLengths_[NumInvariantDim - 1] % ScaleSrcVectorSize != 0)
return false;
if(pArg_->bnScaleBiasMeanVarLengths_[NumInvariantDim - 1] % BiasSrcVectorSize != 0)
return false;
if(pArg_->bnMeanVarStrides_[NumInvariantDim - 1] != 1 && MeanVarSrcDstVectorSize != 1)
return false;
if(pArg_->bnScaleBiasMeanVarLengths_[NumInvariantDim - 1] % MeanVarSrcDstVectorSize != 0)
return false;
bool is_valid = true;
static_for<0, NumInvariantDim, 1>{}([&](auto I) {
if(pArg_->xyLengths_[I] != pArg_->bnScaleBiasMeanVarLengths_[I])
is_valid = false;
});
if(!is_valid)
return false;
return true;
};
std::unique_ptr<BaseArgument> MakeArgumentPointer(
const std::array<index_t, Rank> xyLengths,
const std::array<index_t, Rank> xStrides,
const std::array<index_t, Rank> yStrides,
const std::array<int, NumBatchNormReduceDim> reduceDims,
const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleBiasMeanVarLengths,
const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleStrides,
const std::array<index_t, Rank - NumBatchNormReduceDim> bnBiasStrides,
const std::array<index_t, Rank - NumBatchNormReduceDim> bnMeanVarStrides,
const void* p_x,
const void* p_scale,
const void* p_bias,
double epsilon,
const YElementwiseOp y_elementwise_op,
void* p_y,
void* resultSaveMean,
void* resultSaveInvVariance,
double averageFactor,
void* resultRunningMean,
void* resultRunningVariance) override
{
return std::make_unique<Argument>(xyLengths,
xStrides,
yStrides,
reduceDims,
bnScaleBiasMeanVarLengths,
bnScaleStrides,
bnBiasStrides,
bnMeanVarStrides,
static_cast<const XDataType*>(p_x),
static_cast<const ScaleDataType*>(p_scale),
static_cast<const BiasDataType*>(p_bias),
y_elementwise_op,
epsilon,
static_cast<YDataType*>(p_y),
static_cast<MeanVarDataType*>(resultSaveMean),
static_cast<MeanVarDataType*>(resultSaveInvVariance),
averageFactor,
static_cast<MeanVarDataType*>(resultRunningMean),
static_cast<MeanVarDataType*>(resultRunningVariance));
};
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
{
return std::make_unique<Invoker>();
};
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "DeviceBatchNormFwdImpl<" << BlockSize << ",";
str << "M_C" << MThreadClusterSize << "_S" << MThreadSliceSize << ",";
str << "K_C" << KThreadClusterSize << "_S" << KThreadSliceSize << ",";
str << "XSrcYDstVectorDim_" << XSrcYDstVectorDim << ",";
str << "VectorSize_X" << XSrcVectorSize << "_scale_" << ScaleSrcVectorSize << "_bias_" << BiasSrcVectorSize << "_mean_var_" << MeanVarSrcDstVectorSize << "_Y" << YDstVectorSize << ">";
// clang-format on
return str.str();
}
};
} // namespace device
} // namespace tensor_operation
} // namespace ck
include/ck/tensor_operation/gpu/grid/batchnorm_multiblock/gridwise_multiblock_batchnorm_forward.hpp 0 → 100644
View file @ 8f5cafaf
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/utility/data_type.hpp"
#include "ck/utility/math_v2.hpp"
#include "ck/tensor_operation/gpu/block/blockwise_welford.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_welford.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/utility/workgroup_synchronization.hpp"
namespace ck {
template <typename GridwiseMultiblockBatchNormForward_,
typename XDataType,
typename YDataType,
typename AccDataType,
typename ScaleDataType,
typename BiasDataType,
typename MeanVarDataType,
typename YElementwiseOp,
typename XYGridDesc_M_K,
typename MeanVarCountGridDesc_M_G,
typename MeanVarCountGridDesc_M_K,
typename ScaleBiasGridDesc_M,
typename MeanVarGridDesc_M,
typename GetReduceCountPerThreadFunctor>
__global__ void kernel_multiblock_batchnorm_forward(
const XYGridDesc_M_K x_grid_desc_m_k,
const XYGridDesc_M_K y_grid_desc_m_k,
const MeanVarCountGridDesc_M_G mean_var_count_grid_desc_m_g,
const MeanVarCountGridDesc_M_K mean_var_count_grid_desc_m_k,
const ScaleBiasGridDesc_M scale_grid_desc_m,
const ScaleBiasGridDesc_M bias_grid_desc_m,
const MeanVarGridDesc_M mean_var_grid_desc_m,
const GetReduceCountPerThreadFunctor get_reduce_count_per_thread,
index_t num_k_block_tile_iteration,
AccDataType epsilon,
const XDataType* const __restrict__ p_x,
MeanVarDataType* const __restrict__ p_welford_mean,
MeanVarDataType* const __restrict__ p_welford_variance,
int32_t* const __restrict__ p_welford_count,
int32_t* const __restrict__ p_control,
const ScaleDataType* const __restrict__ p_scale,
const BiasDataType* const __restrict__ p_bias,
const YElementwiseOp y_elementwise_op,
YDataType* const __restrict__ p_y,
bool updateMovingAverage,
AccDataType averageFactor,
MeanVarDataType* const __restrict__ resultRunningMean,
MeanVarDataType* const __restrict__ resultRunningVariance,
bool saveMeanInvVariance,
MeanVarDataType* const __restrict__ resultSaveMean,
MeanVarDataType* const __restrict__ resultSaveInvVariance)
{
GridwiseMultiblockBatchNormForward_::Run(x_grid_desc_m_k,
y_grid_desc_m_k,
mean_var_count_grid_desc_m_g,
mean_var_count_grid_desc_m_k,
scale_grid_desc_m,
bias_grid_desc_m,
mean_var_grid_desc_m,
get_reduce_count_per_thread,
num_k_block_tile_iteration,
epsilon,
p_x,
p_welford_mean,
p_welford_variance,
p_welford_count,
p_control,
p_scale,
p_bias,
y_elementwise_op,
p_y,
updateMovingAverage,
averageFactor,
resultRunningMean,
resultRunningVariance,
saveMeanInvVariance,
resultSaveMean,
resultSaveInvVariance);
};
template <typename XDataType,
typename YDataType,
typename AccDataType,
typename ScaleDataType,
typename BiasDataType,
typename MeanVarDataType,
typename YElementwiseOp,
typename XYGridDesc_M_K,
typename MeanVarCountGridDesc_M_G,
typename MeanVarCountGridDesc_M_K,
typename ScaleBiasGridDesc_M,
typename MeanVarGridDesc_M,
typename GetReduceCountPerThreadFunctor,
index_t BlockSize,
index_t MThreadClusterSize,
index_t KThreadClusterSize,
index_t MThreadSliceSize,
index_t KThreadSliceSize,
index_t XSrcYDstVectorDim,
index_t XSrcVectorSize,
index_t YDstVectorSize,
index_t ScaleSrcVectorSize,
index_t BiasSrcVectorSize,
index_t MeanVarSrcDstVectorSize>
struct GridwiseMultiblockBatchNormForward
{
static_assert((XSrcYDstVectorDim == 0 && MThreadSliceSize % XSrcVectorSize == 0) ||
(XSrcYDstVectorDim == 1 && KThreadSliceSize % XSrcVectorSize == 0),
"Invalid thread slice sizes and/or vector sizes configuration, please check!");
static_assert((XSrcYDstVectorDim == 0 && MThreadSliceSize % YDstVectorSize == 0) ||
(XSrcYDstVectorDim == 1 && KThreadSliceSize % YDstVectorSize == 0),
"Invalid thread slice sizes and/or vector sizes configuration, please check!");
static constexpr bool reorder_thread_cluster = (XSrcYDstVectorDim == 0);
using ThreadClusterLengths_M_K = Sequence<MThreadClusterSize, KThreadClusterSize>;
using ThreadBufferDimAccessOrder =
typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
using ThreadClusterArrangeOrder =
typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
static constexpr auto thread_cluster_desc =
make_cluster_descriptor(ThreadClusterLengths_M_K{}, ThreadClusterArrangeOrder{});
using ThreadReduceSrcDesc_M_K = decltype(make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{})));
using ThreadReduceDstDesc_M =
decltype(make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{})));
using ThreadReduceSrcDesc_M_1 = decltype(
make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}, Number<1>{})));
using ThreadwiseWelford1 =
ThreadwiseWelford<AccDataType, ThreadReduceSrcDesc_M_K, ThreadReduceDstDesc_M>;
using ThreadwiseWelford2 =
ThreadwiseWelfordMerge<AccDataType, ThreadReduceSrcDesc_M_1, ThreadReduceDstDesc_M>;
using BlockwiseWelford1 = BlockwiseWelford<AccDataType,
BlockSize,
ThreadClusterLengths_M_K,
ThreadClusterArrangeOrder,
false>;
using BlockwiseWelford2 = BlockwiseWelford<AccDataType,
BlockSize,
ThreadClusterLengths_M_K,
ThreadClusterArrangeOrder,
true>;
using PassThroughOp = tensor_operation::element_wise::PassThrough;
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
static constexpr index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
__device__ static void Run(const XYGridDesc_M_K& x_grid_desc_m_k,
const XYGridDesc_M_K& y_grid_desc_m_k,
const MeanVarCountGridDesc_M_G& mean_var_count_grid_desc_m_g,
const MeanVarCountGridDesc_M_K& mean_var_count_grid_desc_m_k,
const ScaleBiasGridDesc_M& scale_grid_desc_m,
const ScaleBiasGridDesc_M& bias_grid_desc_m,
const MeanVarGridDesc_M& mean_var_grid_desc_m,
const GetReduceCountPerThreadFunctor& get_reduce_count_per_thread,
index_t num_k_block_tile_iteration,
AccDataType epsilon,
const XDataType* const __restrict__ p_x,
MeanVarDataType* const __restrict__ p_welford_mean,
MeanVarDataType* const __restrict__ p_welford_variance,
int32_t* const __restrict__ p_welford_count,
int32_t* const __restrict__ p_control,
const ScaleDataType* const __restrict__ p_scale,
const BiasDataType* const __restrict__ p_bias,
const YElementwiseOp y_elementwise_op,
YDataType* const __restrict__ p_y,
bool updateMovingAverage,
AccDataType averageFactor,
MeanVarDataType* const __restrict__ resultRunningMean,
MeanVarDataType* const __restrict__ resultRunningVariance,
bool saveMeanInvVariance,
MeanVarDataType* const __restrict__ resultSaveMean,
MeanVarDataType* const __restrict__ resultSaveInvVariance)
{
using ck::math::sqrt;
const index_t blkgroup_size = mean_var_count_grid_desc_m_g.GetLength(I1);
const index_t thread_local_id = get_thread_local_1d_id();
const index_t block_global_id = get_block_1d_id();
const index_t blkgroup_id = block_global_id / blkgroup_size;
const index_t block_local_id = block_global_id % blkgroup_size;
if(block_local_id == 0)
gms_init(BlockSize / warpSize * blkgroup_size, &p_control[blkgroup_id * 2]);
const auto thread_cluster_idx =
thread_cluster_desc.CalculateBottomIndex(make_multi_index(thread_local_id));
const auto thread_m_cluster_id = thread_cluster_idx[I0];
const auto thread_k_cluster_id = thread_cluster_idx[I1];
using ThreadBufferLengths_M_K = Sequence<MThreadSliceSize, KThreadSliceSize>;
using ThreadBufferLengths_M = Sequence<MThreadSliceSize>;
using ThreadBufferLengths_M_1 = Sequence<MThreadSliceSize, 1>;
constexpr auto thread_buffer_desc_m_k = make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{}));
constexpr auto thread_buffer_desc_m =
make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{}));
constexpr auto thread_buffer_desc_m_1 = make_naive_tensor_descriptor_packed(
make_tuple(Number<MThreadSliceSize>{}, Number<1>{}));
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
x_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> mean_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> var_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, int32_t, MThreadSliceSize, true> count_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
tmp_mean_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
tmp_var_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, int32_t, MThreadSliceSize, true> tmp_count_thread_buf;
const index_t reduceSizePerBlock = K_BlockTileSize * num_k_block_tile_iteration;
auto threadwise_x_load = ThreadwiseTensorSliceTransfer_v2<XDataType,
AccDataType,
XYGridDesc_M_K,
decltype(thread_buffer_desc_m_k),
ThreadBufferLengths_M_K,
ThreadBufferDimAccessOrder,
XSrcYDstVectorDim,
XSrcVectorSize,
1,
true>(
x_grid_desc_m_k,
make_multi_index(blkgroup_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
block_local_id * reduceSizePerBlock +
thread_k_cluster_id * KThreadSliceSize));
constexpr auto xy_copy_fwd_step_m_k = make_multi_index(0, K_BlockTileSize);
const auto x_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_x, x_grid_desc_m_k.GetElementSpaceSize());
// Step 1: each workgroup does local welford reduction
auto threadwise_welford_1 = ThreadwiseWelford1();
threadwise_welford_1.max_count_ =
get_reduce_count_per_thread(block_local_id, thread_k_cluster_id);
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
mean_thread_buf(I) = type_convert<AccDataType>(0.0f);
var_thread_buf(I) = type_convert<AccDataType>(0.0f);
});
for(index_t reducedTiles = 0; reducedTiles < num_k_block_tile_iteration; ++reducedTiles)
{
threadwise_x_load.Run(x_grid_desc_m_k,
x_global_val_buf,
thread_buffer_desc_m_k,
make_tuple(I0, I0),
x_thread_buf);
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, xy_copy_fwd_step_m_k);
threadwise_welford_1.Run(x_thread_buf, mean_thread_buf, var_thread_buf);
}
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
if constexpr(I > 0)
block_sync_lds();
count_thread_buf(I) = threadwise_welford_1.cur_count_;
BlockwiseWelford1::Run(mean_thread_buf(I), var_thread_buf(I), count_thread_buf(I));
});
// Step 2: each workgroup writes its local welford result to workspace memory
auto mean_global_val_buf =
make_dynamic_buffer<AddressSpaceEnum::Global, AmdBufferCoherenceEnum::GLC>(
p_welford_mean, mean_var_count_grid_desc_m_g.GetElementSpaceSize());
auto var_global_val_buf =
make_dynamic_buffer<AddressSpaceEnum::Global, AmdBufferCoherenceEnum::GLC>(
p_welford_variance, mean_var_count_grid_desc_m_g.GetElementSpaceSize());
auto count_global_val_buf =
make_dynamic_buffer<AddressSpaceEnum::Global, AmdBufferCoherenceEnum::GLC>(
p_welford_count, mean_var_count_grid_desc_m_g.GetElementSpaceSize());
auto threadwise_mean_var_store_m_g =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
MeanVarDataType,
decltype(thread_buffer_desc_m_1),
MeanVarCountGridDesc_M_G,
PassThroughOp,
ThreadBufferLengths_M_1,
Sequence<0, 1>,
0,
1,
InMemoryDataOperationEnum::Set,
1,
true>(
mean_var_count_grid_desc_m_g,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
block_local_id),
PassThroughOp{});
auto threadwise_count_store_m_g =
ThreadwiseTensorSliceTransfer_v1r3<int32_t,
int32_t,
decltype(thread_buffer_desc_m_1),
MeanVarCountGridDesc_M_G,
PassThroughOp,
ThreadBufferLengths_M_1,
Sequence<0, 1>,
0,
1,
InMemoryDataOperationEnum::Set,
1,
true>(
mean_var_count_grid_desc_m_g,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
block_local_id),
PassThroughOp{});
if(thread_k_cluster_id == 0)
{
threadwise_mean_var_store_m_g.Run(thread_buffer_desc_m_1,
make_tuple(I0, I0),
mean_thread_buf,
mean_var_count_grid_desc_m_g,
mean_global_val_buf);
threadwise_mean_var_store_m_g.Run(thread_buffer_desc_m_1,
make_tuple(I0, I0),
var_thread_buf,
mean_var_count_grid_desc_m_g,
var_global_val_buf);
threadwise_count_store_m_g.Run(thread_buffer_desc_m_1,
make_tuple(I0, I0),
count_thread_buf,
mean_var_count_grid_desc_m_g,
count_global_val_buf);
};
gms_barrier(&p_control[blkgroup_id * 2]);
if(block_local_id == 0)
gms_reset(&p_control[blkgroup_id * 2]);
// Step 3: each workgroup reads welford results from workspace memory and does final welford
// reduction
auto threadwise_mean_var_load_m_k =
ThreadwiseTensorSliceTransfer_v2<MeanVarDataType,
AccDataType,
MeanVarCountGridDesc_M_K,
decltype(thread_buffer_desc_m_1),
ThreadBufferLengths_M_1,
Sequence<0, 1>,
0,
1,
1,
true>(
mean_var_count_grid_desc_m_k,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
thread_k_cluster_id * 1));
auto threadwise_count_load_m_k =
ThreadwiseTensorSliceTransfer_v2<int32_t,
int32_t,
MeanVarCountGridDesc_M_K,
decltype(thread_buffer_desc_m_1),
ThreadBufferLengths_M_1,
Sequence<0, 1>,
0,
1,
1,
true>(
mean_var_count_grid_desc_m_k,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
thread_k_cluster_id * 1));
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
mean_thread_buf(I) = type_convert<AccDataType>(0.0f);
var_thread_buf(I) = type_convert<AccDataType>(0.0f);
count_thread_buf(I) = 0;
});
constexpr auto mean_var_count_read_fwd_step_m_k = make_multi_index(0, KThreadClusterSize);
int32_t reducedSize = 0;
while(reducedSize < blkgroup_size)
{
threadwise_mean_var_load_m_k.Run(mean_var_count_grid_desc_m_k,
mean_global_val_buf,
thread_buffer_desc_m_1,
make_tuple(I0, I0),
tmp_mean_thread_buf);
threadwise_mean_var_load_m_k.Run(mean_var_count_grid_desc_m_k,
var_global_val_buf,
thread_buffer_desc_m_1,
make_tuple(I0, I0),
tmp_var_thread_buf);
threadwise_count_load_m_k.Run(mean_var_count_grid_desc_m_k,
count_global_val_buf,
thread_buffer_desc_m_1,
make_tuple(I0, I0),
tmp_count_thread_buf);
ThreadwiseWelford2::Run(tmp_mean_thread_buf,
tmp_var_thread_buf,
tmp_count_thread_buf,
mean_thread_buf,
var_thread_buf,
count_thread_buf);
reducedSize += KThreadClusterSize;
threadwise_mean_var_load_m_k.MoveSrcSliceWindow(mean_var_count_grid_desc_m_k,
mean_var_count_read_fwd_step_m_k);
threadwise_count_load_m_k.MoveSrcSliceWindow(mean_var_count_grid_desc_m_k,
mean_var_count_read_fwd_step_m_k);
};
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
if constexpr(I > 0)
block_sync_lds();
BlockwiseWelford2::Run(mean_thread_buf(I), var_thread_buf(I), count_thread_buf(I));
});
// Step 4: do normalization using the mean/variance
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> scale_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> bias_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
y_thread_buf;
auto threadwise_y_store =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
YDataType,
decltype(thread_buffer_desc_m_k),
XYGridDesc_M_K,
YElementwiseOp,
ThreadBufferLengths_M_K,
ThreadBufferDimAccessOrder,
XSrcYDstVectorDim,
YDstVectorSize,
InMemoryDataOperationEnum::Set,
1,
true>(
y_grid_desc_m_k,
make_multi_index(
blkgroup_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
block_local_id * reduceSizePerBlock + thread_k_cluster_id * KThreadSliceSize),
y_elementwise_op);
auto threadwise_scale_load =
ThreadwiseTensorSliceTransfer_v2<ScaleDataType,
AccDataType,
ScaleBiasGridDesc_M,
decltype(thread_buffer_desc_m),
ThreadBufferLengths_M,
Sequence<0>,
0,
ScaleSrcVectorSize,
1,
true>(
scale_grid_desc_m,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize));
auto threadwise_bias_load = ThreadwiseTensorSliceTransfer_v2<BiasDataType,
AccDataType,
ScaleBiasGridDesc_M,
decltype(thread_buffer_desc_m),
ThreadBufferLengths_M,
Sequence<0>,
0,
BiasSrcVectorSize,
1,
true>(
bias_grid_desc_m,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize));
const auto scale_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_scale, scale_grid_desc_m.GetElementSpaceSize());
const auto bias_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_bias, bias_grid_desc_m.GetElementSpaceSize());
auto y_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_y, y_grid_desc_m_k.GetElementSpaceSize());
threadwise_scale_load.Run(scale_grid_desc_m,
scale_global_val_buf,
thread_buffer_desc_m,
make_tuple(I0),
scale_thread_buf);
threadwise_bias_load.Run(bias_grid_desc_m,
bias_global_val_buf,
thread_buffer_desc_m,
make_tuple(I0),
bias_thread_buf);
threadwise_x_load.SetSrcSliceOrigin(
x_grid_desc_m_k,
make_multi_index(blkgroup_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
block_local_id * reduceSizePerBlock +
thread_k_cluster_id * KThreadSliceSize));
for(index_t reducedTiles = 0; reducedTiles < num_k_block_tile_iteration; ++reducedTiles)
{
threadwise_x_load.Run(x_grid_desc_m_k,
x_global_val_buf,
thread_buffer_desc_m_k,
make_tuple(I0, I0),
x_thread_buf);
static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
AccDataType multiplier =
scale_thread_buf[Number<iM>{}] / sqrt(var_thread_buf[iM] + epsilon);
AccDataType fused_mean_bias =
bias_thread_buf[Number<iM>{}] - mean_thread_buf[iM] * multiplier;
static_for<0, KThreadSliceSize, 1>{}([&](auto iK) {
constexpr auto offset =
thread_buffer_desc_m_k.CalculateOffset(make_tuple(iM, iK));
// normalize
y_thread_buf(Number<offset>{}) =
x_thread_buf[Number<offset>{}] * multiplier + fused_mean_bias;
});
});
threadwise_y_store.Run(thread_buffer_desc_m_k,
make_tuple(I0, I0),
y_thread_buf,
y_grid_desc_m_k,
y_global_val_buf);
threadwise_x_load.MoveSrcSliceWindow(x_grid_desc_m_k, xy_copy_fwd_step_m_k);
threadwise_y_store.MoveDstSliceWindow(y_grid_desc_m_k, xy_copy_fwd_step_m_k);
}
// Step 5: update the moving average of mean and variance (optional)
if(updateMovingAverage && block_local_id == 0 && thread_k_cluster_id == 0)
{
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
running_mean_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true>
running_var_thread_buf;
auto running_mean_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
resultRunningMean, mean_var_grid_desc_m.GetElementSpaceSize());
auto running_var_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
resultRunningVariance, mean_var_grid_desc_m.GetElementSpaceSize());
auto threadwise_mean_var_load =
ThreadwiseTensorSliceTransfer_v2<MeanVarDataType,
AccDataType,
MeanVarGridDesc_M,
decltype(thread_buffer_desc_m),
ThreadBufferLengths_M,
Sequence<0>,
0,
MeanVarSrcDstVectorSize,
1,
true>(
mean_var_grid_desc_m,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize));
threadwise_mean_var_load.Run(mean_var_grid_desc_m,
running_mean_global_buf,
thread_buffer_desc_m,
make_tuple(I0),
running_mean_thread_buf);
threadwise_mean_var_load.Run(mean_var_grid_desc_m,
running_var_global_buf,
thread_buffer_desc_m,
make_tuple(I0),
running_var_thread_buf);
AccDataType oneMinusAverageFactor = type_convert<AccDataType>(1.0) - averageFactor;
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
running_mean_thread_buf(I) = running_mean_thread_buf[I] * oneMinusAverageFactor +
mean_thread_buf[I] * averageFactor;
running_var_thread_buf(I) = running_var_thread_buf[I] * oneMinusAverageFactor +
var_thread_buf[I] * averageFactor;
});
auto threadwise_mean_var_store =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
MeanVarDataType,
decltype(thread_buffer_desc_m),
MeanVarGridDesc_M,
PassThroughOp,
ThreadBufferLengths_M,
Sequence<0>,
0,
MeanVarSrcDstVectorSize,
InMemoryDataOperationEnum::Set,
1,
true>(
mean_var_grid_desc_m,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize),
PassThroughOp{});
threadwise_mean_var_store.Run(thread_buffer_desc_m,
make_tuple(I0),
running_mean_thread_buf,
mean_var_grid_desc_m,
running_mean_global_buf);
threadwise_mean_var_store.Run(thread_buffer_desc_m,
make_tuple(I0),
running_var_thread_buf,
mean_var_grid_desc_m,
running_var_global_buf);
};
// Step 6: save mean and inv-variance (optional)
if(saveMeanInvVariance && block_local_id == 0 && thread_k_cluster_id == 0)
{
auto result_mean_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
resultSaveMean, mean_var_grid_desc_m.GetElementSpaceSize());
auto result_inv_var_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
resultSaveInvVariance, mean_var_grid_desc_m.GetElementSpaceSize());
// calculate inv-variance as 1/sqrt(epsilon+variance), stored in place of variance
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
var_thread_buf(I) =
type_convert<AccDataType>(1.0f) / sqrt(epsilon + var_thread_buf[I]);
});
auto threadwise_mean_inv_var_store =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
MeanVarDataType,
decltype(thread_buffer_desc_m),
MeanVarGridDesc_M,
PassThroughOp,
ThreadBufferLengths_M,
Sequence<0>,
0,
MeanVarSrcDstVectorSize,
InMemoryDataOperationEnum::Set,
1,
true>(
mean_var_grid_desc_m,
make_multi_index(blkgroup_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize),
PassThroughOp{});
threadwise_mean_inv_var_store.Run(thread_buffer_desc_m,
make_tuple(I0),
mean_thread_buf,
mean_var_grid_desc_m,
result_mean_global_buf);
threadwise_mean_inv_var_store.Run(thread_buffer_desc_m,
make_tuple(I0),
var_thread_buf,
mean_var_grid_desc_m,
result_inv_var_global_buf);
};
}
}; // namespace ck
} // namespace ck
include/ck/tensor_operation/gpu/grid/batchnorm_multiblock/gridwise_multiblock_welford_first_half.hpp
View file @ 8f5cafaf
......@@ -161,7 +161,7 @@ struct GridwiseMultiblockWelfordFirstHalf
PassThroughOp,
ThreadBufferLengths_M_1,
Sequence<0, 1>,
1,
0,
1,
InMemoryDataOperationEnum::Set,
1,
......@@ -180,7 +180,7 @@ struct GridwiseMultiblockWelfordFirstHalf
PassThroughOp,
ThreadBufferLengths_M_1,
Sequence<0, 1>,
1,
0,
1,
InMemoryDataOperationEnum::Set,
1,
......
include/ck/tensor_operation/gpu/grid/batchnorm_multiblock/gridwise_multiblock_welford_second_half_batchnorm_forward_final.hpp → include/ck/tensor_operation/gpu/grid/batchnorm_multiblock/gridwise_multiblock_welford_second_half_batchnorm_forward_final_obsolete.hpp
View file @ 8f5cafaf
......@@ -33,7 +33,6 @@ __global__ void kernel_welford_second_half_batchnorm_forward_final(
const MeanVarGridDesc_M mean_var_grid_desc_m,
index_t blkgroup_size,
index_t num_xy_k_block_tile_iteration,
index_t num_mean_var_count_k_block_tile_iteration,
AccDataType epsilon,
const MeanVarDataType* const __restrict__ p_in_welford_mean,
const MeanVarDataType* const __restrict__ p_in_welford_variance,
......@@ -59,7 +58,6 @@ __global__ void kernel_welford_second_half_batchnorm_forward_final(
mean_var_grid_desc_m,
blkgroup_size,
num_xy_k_block_tile_iteration,
num_mean_var_count_k_block_tile_iteration,
epsilon,
p_in_welford_mean,
p_in_welford_variance,
......@@ -152,7 +150,6 @@ struct GridwiseWelfordSecondHalfBatchNormForwardFinal
const MeanVarGridDesc_M& mean_var_grid_desc_m,
index_t blkgroup_size,
index_t num_xy_k_block_tile_iteration,
index_t num_mean_var_count_k_block_tile_iteration,
AccDataType epsilon,
const MeanVarDataType* const __restrict__ p_in_welford_mean,
const MeanVarDataType* const __restrict__ p_in_welford_variance,
......@@ -223,7 +220,7 @@ struct GridwiseWelfordSecondHalfBatchNormForwardFinal
decltype(thread_buffer_desc_m_1),
ThreadBufferLengths_M_1,
Sequence<0, 1>,
1,
0,
1,
1,
true>(
......@@ -239,7 +236,7 @@ struct GridwiseWelfordSecondHalfBatchNormForwardFinal
decltype(thread_buffer_desc_m_1),
ThreadBufferLengths_M_1,
Sequence<0, 1>,
1,
0,
1,
1,
true>(
......@@ -257,9 +254,6 @@ struct GridwiseWelfordSecondHalfBatchNormForwardFinal
const auto welford_count_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_in_welford_count, mean_var_count_grid_desc_m_k.GetElementSpaceSize());
constexpr auto mean_var_count_thread_copy_step_m_k =
make_multi_index(0, KThreadClusterSize * 1);
// Step 1: do final welford reduction to get mean and variance
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
......@@ -268,8 +262,11 @@ struct GridwiseWelfordSecondHalfBatchNormForwardFinal
welford_count_thread_buf(I) = 0;
});
for(index_t reducedTiles = 0; reducedTiles < num_mean_var_count_k_block_tile_iteration;
++reducedTiles)
constexpr auto mean_var_count_thread_copy_step_m_k =
make_multi_index(0, KThreadClusterSize);
int32_t reducedSize = 0;
while(reducedSize < blkgroup_size)
{
threadwise_mean_var_load_m_k.Run(mean_var_count_grid_desc_m_k,
welford_mean_global_val_buf,
......@@ -296,6 +293,8 @@ struct GridwiseWelfordSecondHalfBatchNormForwardFinal
welford_var_thread_buf,
welford_count_thread_buf);
reducedSize += KThreadClusterSize;
threadwise_mean_var_load_m_k.MoveSrcSliceWindow(mean_var_count_grid_desc_m_k,
mean_var_count_thread_copy_step_m_k);
threadwise_count_load_m_k.MoveSrcSliceWindow(mean_var_count_grid_desc_m_k,
......
include/ck/utility/get_shift.hpp 0 → 100644
View file @ 8f5cafaf
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
namespace ck {
template <index_t N>
static constexpr __device__ index_t get_shift()
{
return (get_shift<N / 2>() + 1);
};
template <>
constexpr __device__ index_t get_shift<1>()
{
return (0);
}
} // namespace ck
include/ck/utility/reduction_common.hpp
View file @ 8f5cafaf
......@@ -25,16 +25,4 @@ struct float_equal_zero
};
};
template <index_t N>
static constexpr __device__ index_t get_shift()
{
return (get_shift<N / 2>() + 1);
};
template <>
constexpr __device__ index_t get_shift<1>()
{
return (0);
}
} // namespace ck
include/ck/utility/workgroup_synchronization.hpp 0 → 100644
View file @ 8f5cafaf
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/host_utility/hip_check_error.hpp"
namespace ck {
// Initialization flag of Barrier object, can be any value except for zero
static constexpr int BarrierInitFlag = 0x7856;
// 1) only the first thread-block in the synchronizaton group is supposed to call this function. It
// is the responsibility of the user to ensure the two integer values in p_control_bits are zeros
// before calling gms_init().
// 2) Aftercalling gms_reset(), the two integer values in p_control_bits will be zeros, so no
// repetitious initialization of p_control_bits buffer is required
static __device__ void gms_init(int NumWarps, int* p_control_bits)
{
union
{
int two32[2];
unsigned long one64;
} regs;
regs.two32[0] = BarrierInitFlag;
regs.two32[1] = NumWarps;
if(threadIdx.x == 0)
atomicCAS(reinterpret_cast<unsigned long*>(p_control_bits), 0, regs.one64);
};
// all the workgroups in the synchronization group is supposed to call this function
static __device__ void gms_barrier(int* p_control_bits)
{
constexpr int mask = warpSize - 1;
if((threadIdx.x & mask) == 0)
{
// ensure the barrier object is initialized
do
{
const int r0 = __atomic_load_n(&p_control_bits[0], __ATOMIC_RELAXED);
if(r0 == BarrierInitFlag)
break;
} while(true);
// go ahead toward the barrier line
atomicSub(&p_control_bits[1], 1);
// wait until all warps have arrived
do
{
const int r1 = __atomic_load_n(&p_control_bits[1], __ATOMIC_RELAXED);
if(r1 == 0)
break;
} while(true);
};
};
// 1) Only the first thread-block in the synchronizaton group is supposed to call this function.
// 2) Aftercalling gms_reset(), the two integer values in p_control_bits will be zeros, so no
// repetitious initialization of p_control_bits buffer is required
static __device__ void gms_reset(int* p_control_bits)
{
// reset the barrier object
if(threadIdx.x == 0)
(void)atomicCAS(&p_control_bits[0], BarrierInitFlag, 0);
};
} // namespace ck
profiler/src/profile_batchnorm_fwd.cpp
View file @ 8f5cafaf
......@@ -148,7 +148,7 @@ int profile_batchnorm_forward(int argc, char* argv[])
{
if(arg_parser.inLengths.size() == 4 && arg_parser.reduceDims.size() == 3)
{
profile_batchnorm_forward_impl<F16, F16, F32, F16, F16, F16, 4, 3>(
profile_batchnorm_forward_impl<F16, F16, F32, F16, F16, F32, 4, 3>(
arg_parser.do_verification,
arg_parser.init_method,
arg_parser.do_dumpout,
......
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