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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <initializer_list>
#include <cstdlib>
#include <getopt.h>
#include "ck/utility/reduction_enums.hpp"
#include "reduce_blockwise_impl.hpp"
#include "reduce_example_common.hpp"
using namespace ck;
using namespace ck::tensor_operation::device;
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 = {16, 64, 32, 960};
std::vector<int> reduceDims = {0, 1, 2};
std::vector<float> scales = {1.0f, 0.0f};
bool do_verification = true;
int data_type = 1;
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 << "--reduceDims or -R, comma separated list of to-reduce dimensions"
<< 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: data type (0: fp16, 1: fp32, 3: int8, 5: bp16, 6: fp64, 7: int4)"
<< std::endl;
std::cout << "Arg2 -- init method (0=no init, 1=single integer value, 2=scope integer "
"value, 3=decimal value)"
<< std::endl;
std::cout << "Arg3 -- 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:R: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 'R':
if(!optarg)
throw std::runtime_error("Invalid option format!");
reduceDims = getTypeValuesFromString<int>(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 + 3 > argc)
{
throw std::runtime_error("Invalid cmd-line arguments, more argumetns are needed!");
};
data_type = std::atoi(argv[optind++]);
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);
};
};
template <typename InOutDataType,
typename AccDataType,
ReduceTensorOp ReduceOpId,
index_t PropagateNan,
index_t OutputIndex>
bool reduce_blockwise_test(bool do_verification,
int init_method,
bool time_kernel,
const std::vector<size_t>& inLengths,
const std::vector<int>& reduceDims,
float alpha,
float beta)
{
bool matched = false;
int result = 0;
const auto tuple_object = reduce_shape_instances{};
static_for<0, std::tuple_size<reduce_shape_instances>::value, 1>{}([&](auto i) {
if(matched)
return;
using ShapeType = remove_cvref_t<decltype(std::get<i>(tuple_object))>;
if(ShapeType::Rank_ != inLengths.size() || ShapeType::NumReduceDim_ != reduceDims.size())
return;
std::array<int, ShapeType::NumReduceDim_> arrReduceDims;
ck::ranges::copy(reduceDims, arrReduceDims.begin());
result = reduce_blockwise_impl<InOutDataType,
AccDataType,
ReduceOpId,
ShapeType::Rank_,
ShapeType::NumReduceDim_,
PropagateNan,
OutputIndex>(
do_verification, init_method, time_kernel, inLengths, arrReduceDims, alpha, beta);
matched = true;
});
return (result == 0) ? true : false;
};
constexpr ReduceTensorOp ReduceOpId = ReduceTensorOp::AVG;
constexpr bool PropagateNan = true;
constexpr bool OutputIndex = false;
int main(int argc, char* argv[])
{
bool pass = true;
if(argc > 1)
{
SimpleAppArgs arg;
if(arg.processArgs(argc, argv) < 0)
return (-1);
if(arg.data_type == 0)
{
pass = reduce_blockwise_test<ck::half_t, float, ReduceOpId, PropagateNan, OutputIndex>(
arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inLengths,
arg.reduceDims,
arg.scales[0],
arg.scales[1]);
}
else if(arg.data_type == 1)
{
pass = reduce_blockwise_test<float, float, ReduceOpId, PropagateNan, OutputIndex>(
arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inLengths,
arg.reduceDims,
arg.scales[0],
arg.scales[1]);
}
else if(arg.data_type == 3)
{
pass = reduce_blockwise_test<int8_t, float, ReduceOpId, PropagateNan, OutputIndex>(
arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inLengths,
arg.reduceDims,
arg.scales[0],
arg.scales[1]);
}
else if(arg.data_type == 5)
{
pass = reduce_blockwise_test<ck::bhalf_t, float, ReduceOpId, PropagateNan, OutputIndex>(
arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inLengths,
arg.reduceDims,
arg.scales[0],
arg.scales[1]);
}
else if(arg.data_type == 6)
{
pass = reduce_blockwise_test<double, double, ReduceOpId, PropagateNan, OutputIndex>(
arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inLengths,
arg.reduceDims,
arg.scales[0],
arg.scales[1]);
}
#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
else if(arg.data_type == 7)
{
pass = reduce_blockwise_test<int4_t, int32_t, ReduceTensorOp::AVG, false, false>(
arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inLengths,
arg.reduceDims,
arg.scales[0],
arg.scales[1]);
pass = pass && reduce_blockwise_test<int4_t, int8_t, ReduceTensorOp::MAX, false, false>(
arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inLengths,
arg.reduceDims,
arg.scales[0],
arg.scales[1]);
}
#endif
}
else
{
// for testing half_t
pass =
pass && reduce_blockwise_test<ck::half_t, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3}, {0, 1, 2}, 1.0f, 0.0f);
pass =
pass && reduce_blockwise_test<ck::half_t, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {16, 64, 32, 960}, {0, 1, 2}, 1.0f, 0.0f);
// for testing float
pass =
pass && reduce_blockwise_test<float, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3}, {0, 1, 2}, 1.0f, 0.0f);
pass = pass && reduce_blockwise_test<float, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {16, 64, 32, 960}, {0, 1, 2}, 1.0f, 0.0f);
// for testing double
pass =
pass && reduce_blockwise_test<float, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3}, {0, 1, 2}, 1.0f, 0.0f);
pass = pass && reduce_blockwise_test<float, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {16, 64, 32, 960}, {0, 1, 2}, 1.0f, 0.0f);
// for testing bhalf_t
pass = pass &&
reduce_blockwise_test<ck::bhalf_t, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3}, {0, 1, 2}, 1.0f, 0.0f);
pass = pass &&
reduce_blockwise_test<ck::bhalf_t, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {16, 64, 32, 960}, {0, 1, 2}, 1.0f, 0.0f);
// for testing int8_t
pass =
pass && reduce_blockwise_test<int8_t, int32_t, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3}, {0, 1, 2}, 1.0f, 0.0f);
pass =
pass && reduce_blockwise_test<int8_t, int32_t, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {16, 64, 32, 960}, {0, 1, 2}, 1.0f, 0.0f);
#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
// for testing int4_t using AVG operation
pass =
pass && reduce_blockwise_test<int4_t, int32_t, ReduceTensorOp::AVG, false, false>(
true, 2, true, {3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3}, {0, 1, 2}, 1.0f, 0.0f);
pass = pass && reduce_blockwise_test<int4_t, int32_t, ReduceTensorOp::AVG, false, false>(
true, 2, true, {16, 64, 32, 960}, {0, 1, 2}, 1.0f, 0.0f);
// for testing int4_t using MAX operation
pass =
pass && reduce_blockwise_test<int4_t, int8_t, ReduceTensorOp::MAX, false, false>(
true, 2, true, {3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3}, {0, 1, 2}, 1.0f, 0.0f);
pass = pass && reduce_blockwise_test<int4_t, int8_t, ReduceTensorOp::MAX, false, false>(
true, 2, true, {16, 64, 32, 960}, {0, 1, 2}, 1.0f, 0.0f);
#endif
// for testing 3D input
pass = pass && reduce_blockwise_test<float, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {16, 64, 960}, {0, 1}, 1.0f, 0.0f);
// for testing 5D input
pass = pass && reduce_blockwise_test<float, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {16, 64, 32, 2, 960}, {0, 1, 2, 3}, 1.0f, 0.0f);
};
return (pass ? 0 : 1);
};
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