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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <getopt.h>
#include "ck/ck.hpp"
#include "ck/utility/reduction_enums.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_softmax_impl.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_common_util.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
using namespace ck::tensor_operation::device;
using InDataType = ck::half_t;
using OutDataType = ck::half_t;
using AccDataType = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
constexpr int Rank = 3;
constexpr int NumReduceDim = 1;
using DeviceInstance = DeviceSoftmaxImpl<InDataType,
AccDataType,
OutDataType,
PassThrough, // InElementwiseOperation
PassThrough, // AccElementwiseOperation
Rank,
NumReduceDim,
256, // BlockSize
8, // ClusterM
32, // ClusterK
1, // SliceM
8, // SliceK
1, // SrcVecDim (0=M, 1=K)
8, // SrcScalarPerVector
8>; // OutScalarPerVector
static struct option long_options[] = {{"inLengths", required_argument, nullptr, 'D'},
{"verify", required_argument, nullptr, 'v'},
{"help", no_argument, nullptr, '?'},
{nullptr, 0, nullptr, 0}};
class SimpleAppArgs
{
private:
int option_index = 0;
public:
std::vector<size_t> inLengths = {8, 128, 2048};
std::vector<double> scales = {2.0, 2.0};
bool do_verification = true;
int init_method = 2;
bool time_kernel = true;
public:
void show_usage(const char* cmd)
{
std::cout << "Usage of " << cmd << std::endl;
std::cout << "--inLengths or -D, comma separated list of input tensor dimension lengths"
<< std::endl;
std::cout << "--verify or -v, 1/0 to indicate whether to verify the reduction result by "
"comparing with the host-based reduction"
<< std::endl;
std::cout << "Arg1 -- init method (0=no init, 1=single integer value, 2=scope integer "
"value, 3=decimal value)"
<< std::endl;
std::cout << "Arg2 -- time kernel (0=no, 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:l:", long_options, &option_index);
if(ch == -1)
break;
switch(ch)
{
case 'D':
if(!optarg)
throw std::runtime_error("Invalid option format!");
inLengths = getTypeValuesFromString<size_t>(optarg);
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 + 2 > argc)
throw std::runtime_error("Invalid cmd-line arguments, more argumetns are needed!");
init_method = std::atoi(argv[optind++]);
time_kernel = static_cast<bool>(std::atoi(argv[optind]));
if(scales.empty())
{
scales.push_back(1.0f);
scales.push_back(0.0f);
};
return (0);
};
};
int main(int argc, char* argv[])
{
// Example: batched gemm C[G, M, N] applies max/sum reduction along N internally
const std::vector<int> invariantDims{0, 1};
const std::vector<int> reduceDims{2};
SimpleAppArgs args;
if(argc > 1)
{
if(args.processArgs(argc, argv) < 0)
return (-1);
};
Tensor<InDataType> in(args.inLengths);
Tensor<OutDataType> out_ref(args.inLengths);
Tensor<OutDataType> out(args.inLengths);
auto inStrides = in.mDesc.GetStrides();
auto outStrides = out.mDesc.GetStrides();
double alpha = args.scales[0];
double beta = args.scales[1];
std::cout << "in: " << in.mDesc << std::endl;
std::cout << "out: " << out.mDesc << std::endl;
std::size_t num_thread = 1;
if(args.do_verification)
{
switch(args.init_method)
{
case 0: break;
case 1:
in.GenerateTensorValue(GeneratorTensor_1<InDataType>{1}, num_thread);
if(beta != 0.0f)
out_ref.GenerateTensorValue(GeneratorTensor_1<OutDataType>{1}, num_thread);
break;
case 2:
in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5}, num_thread);
if(beta != 0.0f)
out_ref.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5}, num_thread);
break;
default:
in.GenerateTensorValue(GeneratorTensor_3<InDataType>{-5.0, 5.0}, num_thread);
if(beta != 0.0f)
out_ref.GenerateTensorValue(GeneratorTensor_3<OutDataType>{-5.0, 5.0}, num_thread);
}
if(beta != 0.0f)
for(size_t i = 0; i < out_ref.mDesc.GetElementSpaceSize(); i++)
out.mData[i] = out_ref.mData[i];
};
// std::cout << "beta = " << beta << std::endl;
// LogRangeAsType<float>(std::cout << "tensor in: " , in.mData, ",") << std::endl;
// LogRangeAsType<float>(std::cout << "tensor prior out: " , out.mData, ",") << std::endl;
// these buffers are usually provided by the user application
DeviceMem in_dev(sizeof(InDataType) * in.mDesc.GetElementSpaceSize());
DeviceMem out_dev(sizeof(OutDataType) * out.mDesc.GetElementSpaceSize());
in_dev.ToDevice(in.mData.data());
if(beta != 0.0f)
out_dev.ToDevice(out.mData.data());
if(args.do_verification)
{
using ReferenceInstance =
ck::tensor_operation::host::ReferenceSoftmax<InDataType, OutDataType, AccDataType>;
ReferenceInstance ref;
auto ref_arg = ref.MakeArgument(in, out_ref, alpha, beta, reduceDims);
auto invoker = ref.MakeInvoker();
invoker.Run(ref_arg);
// LogRangeAsType<float>(std::cout << "tensor out_ref: ", out_ref.mData, ",") << std::endl;
};
std::vector<ck::index_t> i_inLengths;
std::vector<ck::index_t> i_inStrides;
i_inLengths.assign(args.inLengths.begin(), args.inLengths.end());
i_inStrides.assign(inStrides.begin(), inStrides.end());
auto device_instance = DeviceInstance{};
std::cout << i_inLengths.size() << ", " << i_inStrides.size() << std::endl;
auto argument_ptr = device_instance.MakeArgumentPointer(i_inLengths,
i_inStrides,
reduceDims,
alpha,
beta,
in_dev.GetDeviceBuffer(),
out_dev.GetDeviceBuffer(),
PassThrough{},
PassThrough{});
if(!device_instance.IsSupportedArgument(argument_ptr.get()))
{
std::cout
<< "The runtime parameters seems not supported by the DeviceReduce instance, exiting!"
<< std::endl;
return 1;
};
std::string instance_name = device_instance.GetTypeString();
auto invoker_ptr = device_instance.MakeInvokerPointer();
bool pass = true;
if(args.do_verification)
{
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
out_dev.FromDevice(out.mData.data());
// LogRangeAsType<float>(std::cout << "tensor out: " , out.mData, ",") << std::endl;
pass = pass && ck::utils::check_err(out, out_ref);
};
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, args.time_kernel});
std::size_t num_bytes =
in.mDesc.GetElementSize() * sizeof(InDataType) +
(beta == 0.0f ? 1 : 2) * out.mDesc.GetElementSize() * sizeof(OutDataType);
float gb_per_sec = num_bytes / 1.E6 / avg_time;
std::cout << "Perf: " << avg_time << " ms, " << gb_per_sec << " GB/s, " << instance_name
<< std::endl;
return (pass ? 0 : 1);
}
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