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9a8ee8a39a0aa6059c55faba05f6abb904fff6dd
composable_kernel/test/reduce/reduce_with_index.cpp
Qianfeng 9a8ee8a39a Reduction for int8 and bfloat16 (#125) 
* Use thread cluster descriptor and explicit M_K 2d descriptor to simply Blockwise Reduction

* Change by replacing ReduceDims by NumReduceDims as Device Reduce interface template parameter

* Rename the folder name for the pool2d and reduce examples

* Update to reduction test scripts

* Add Readme for pool2d_fwd and reduce_blockwise examples

* Add support for int8_t reduction (ADD/AVG, MIN/MAX/AMAX)

* Tiny fix in reduce profiler and tiny update in reduce testing scripts

* Tiny fix in testing script profile_reduce_no_index.sh

* Tiny fix in testing script profile_reduce_no_index.sh

* Add support for bfp16 reduction (using bhalf_t = ushort)

* Tiny fix in amd_buffer_addressing.hpp

* Tiny change in script/profile_reduce_with_index.sh

* Use AccDataType for Beta value and use element_wise::PassThrough

* Use type_convert for type converting in host layer reduction

* Renaming and refining in Reduction profiler/device layer/examples

* Renaming and refining in Reduction profiler/device layer/examples

* Renaming all NumReduceDims to NumReduceDim

* Fix the leaked type_convert in ThreadwiseTensorSliceTransfer_v2

* Update to testing scripts to add bf16 support

* added more static_assert

* Remove buggy tunable configurations defined in device_reduce_instance_xxx.hpp

* Add static_assert to give compile-time warning for incorrect thread slice-size/vector-size configurations

* minor change

* Refine and fix (in GetWorkspaceSizeInBytes of MultiBlockPartialReduce) to make int8 completely pass

* Tiny renaming in gridwise_2d_reduction_multiblock_partial_reduce.hpp

* Tiny fix in script/profile_reduce_no_index.sh

* Refine in DeviceReduce layer with regard to using NumInvariantDim/NumReduceDim or InvariantDims/ReduceDims

* Generic renaming in host reduction and DeviceReduce layer

* Add support for 4-d all dimension reduction in the profiler and add_device_reduce_xxx instances

* Use multi-thread and simplification for host Reduction implementation

* Add ctest for reduction

* Update to clarify the using of data init method in produce_reduce/example_reduce/test_reduce/

* Update to the reduce CTest executables to enable default testing behavior when no command argument

* Renaming

Co-authored-by: Jianfeng yan <jfyan008@gmail.com>
2022-03-22 14:35:14 -05:00

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#include "getopt.h"
#include "device_reduce_instance.hpp"
#include "reduction_enums.hpp"
#include "host_tensor.hpp"
#include "host_tensor_generator.hpp"
#include "host_reduction.hpp"
#include "test_util.hpp"
#include "reduce_util.hpp"
using namespace ck;
namespace {
template <index_t Rank, index_t NumReduceDim>
static inline std::vector<int> get_invariant_dims(const std::vector<int>& reduceDims)
{
assert(NumReduceDim == reduceDims.size());
int reduceFlag = 0;
// flag the bits for the reduceDims
for(int i = 0; i < NumReduceDim; i++)
{
reduceFlag |= 1 << reduceDims[i];
};
std::vector<int> invariantDims;
// collect invariant dimensions
for(int i = 0; i < Rank; i++)
if((reduceFlag & (1 << i)) == 0)
{
invariantDims.push_back(i);
};
return invariantDims;
};
// map the data type used by the GPU kernels to the corresponding type used by the host codes
template <typename InType>
struct type_mapping
{
using OutType = InType;
};
template <>
struct type_mapping<ck::half_t>
{
using OutType = half_float::half;
};
constexpr int Rank = 4;
constexpr ReduceTensorOp_t ReduceOpId = ReduceTensorOp_t::AMAX;
constexpr NanPropagation_t NanOpt = NanPropagation_t::PROPAGATE_NAN;
constexpr bool PropagateNan = false;
constexpr ReduceTensorIndices_t IndicesOpt = ReduceTensorIndices_t::FLATTENED_INDICES;
constexpr bool NeedIndices = true;
template <typename InDataType,
typename AccDataType,
typename OutDataType,
int Rank,
int NumReduceDim>
bool test_reduce_with_index_impl(int init_method,
const std::vector<size_t>& inLengths,
const std::vector<int>& reduceDims,
float alpha,
float beta)
{
using namespace ck::tensor_operation::device;
using namespace ck::tensor_operation::device::device_reduce_instance;
using namespace ck::host_reduce;
Tensor<InDataType> in(inLengths);
std::vector<size_t> outLengths;
const auto invariantDims = get_invariant_dims<Rank, NumReduceDim>(reduceDims);
if(reduceDims.size() == Rank)
outLengths.push_back(1);
else
for(auto dim : invariantDims)
outLengths.push_back(inLengths[dim]);
Tensor<OutDataType> out_ref(outLengths);
Tensor<OutDataType> out(outLengths);
Tensor<int32_t> out_indices_ref(outLengths);
Tensor<int32_t> out_indices(outLengths);
// only used when the OutDataType is bhalf_t
Tensor<float> out_ref_fp32(outLengths);
Tensor<float> out_fp32(outLengths);
auto inStrides = in.mDesc.GetStrides();
auto outStrides = out.mDesc.GetStrides();
size_t invariant_total_length = out.mDesc.GetElementSize();
size_t reduce_total_length = in.mDesc.GetElementSize() / invariant_total_length;
std::size_t num_thread = std::thread::hardware_concurrency();
switch(init_method)
{
case 0: break;
case 1:
in.GenerateTensorValue(GeneratorTensor_1<InDataType>{1}, num_thread);
if(beta != 0.0f)
out_ref.GenerateTensorValue(GeneratorTensor_1<InDataType>{1}, num_thread);
break;
case 2:
in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5}, num_thread);
if(beta != 0.0f)
out_ref.GenerateTensorValue(GeneratorTensor_2<InDataType>{-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<InDataType>{-5.0, 5.0}, num_thread);
}
if(beta != 0.0f)
for(size_t i = 0; i < out_ref.mDesc.GetElementSpace(); i++)
out.mData[i] = out_ref.mData[i];
// these buffers are usually provided by the user application
DeviceMem in_dev(sizeof(InDataType) * in.mDesc.GetElementSpace());
DeviceMem out_dev(sizeof(OutDataType) * out.mDesc.GetElementSpace());
in_dev.ToDevice(in.mData.data());
if(beta != 0.0f)
out_dev.ToDevice(out.mData.data());
size_t indicesSizeInBytes = NeedIndices ? out.mDesc.GetElementSize() * sizeof(int) : 0;
DeviceMem out_indices_dev(indicesSizeInBytes);
using InElementwiseOperation_0 =
typename reduce_unary_operator<AccDataType, ReduceOpId, true, true>::InElementwiseOperation;
using AccElementwiseOperation_0 =
typename reduce_unary_operator<AccDataType, ReduceOpId, true, true>::
AccElementwiseOperation;
using InElementwiseOperation_1 =
typename reduce_unary_operator<AccDataType, ReduceOpId, true, false>::
InElementwiseOperation;
using AccElementwiseOperation_1 =
typename reduce_unary_operator<AccDataType, ReduceOpId, true, false>::
AccElementwiseOperation;
using InElementwiseOperation_2 =
typename reduce_unary_operator<AccDataType, ReduceOpId, false, true>::
InElementwiseOperation;
using AccElementwiseOperation_2 =
typename reduce_unary_operator<AccDataType, ReduceOpId, false, true>::
AccElementwiseOperation;
using DeviceReduceInstPtr0 =
DeviceReducePtr<InElementwiseOperation_0, AccElementwiseOperation_0>;
using DeviceReduceInstPtr1 =
DeviceReducePtr<InElementwiseOperation_1, AccElementwiseOperation_1>;
using DeviceReduceInstPtr2 =
DeviceReducePtr<InElementwiseOperation_2, AccElementwiseOperation_2>;
std::vector<DeviceReduceInstPtr0> reduce0_ptrs;
std::vector<DeviceReduceInstPtr1> reduce1_ptrs;
std::vector<DeviceReduceInstPtr2> reduce2_ptrs;
add_device_reduce_instance_threadwise<InDataType,
AccDataType,
OutDataType,
Rank,
NumReduceDim,
ReduceOpId,
NanOpt,
IndicesOpt>(reduce0_ptrs);
add_device_reduce_instance_blockwise<InDataType,
AccDataType,
OutDataType,
Rank,
NumReduceDim,
ReduceOpId,
NanOpt,
IndicesOpt>(reduce0_ptrs);
add_device_reduce_instance_multiblock_partial_reduce<InDataType,
AccDataType,
OutDataType,
Rank,
NumReduceDim,
ReduceOpId,
NanOpt,
IndicesOpt>(reduce1_ptrs);
add_device_reduce_instance_blockwise_second_call<AccDataType,
AccDataType,
OutDataType,
Rank,
NumReduceDim,
ReduceOpId,
NanOpt,
IndicesOpt>(reduce2_ptrs);
if(reduce0_ptrs.empty() && reduce1_ptrs.empty())
{
throw std::runtime_error("Wrong! No device REDUCE instance found");
};
bool result = true;
using HostInDataType = typename type_mapping<InDataType>::OutType;
using HostOutDataType = typename type_mapping<OutDataType>::OutType;
using HostAccDataType = typename type_mapping<AccDataType>::OutType;
ReductionHost<HostInDataType,
HostAccDataType,
HostOutDataType,
ReduceOpId,
Rank,
NumReduceDim,
PropagateNan,
NeedIndices>
hostReduce(in.mDesc, out_ref.mDesc, invariantDims, reduceDims);
hostReduce.Run(alpha,
reinterpret_cast<const HostInDataType*>(in.mData.data()),
beta,
reinterpret_cast<HostOutDataType*>(out_ref.mData.data()),
out_indices_ref.mData.data());
const auto i_inLengths = to_int_vector(inLengths);
const auto i_inStrides = to_int_vector(inStrides);
const auto i_outLengths = to_int_vector(outLengths);
const auto i_outStrides = to_int_vector(outStrides);
for(auto& reduce_ptr : reduce0_ptrs)
{
auto wsSizeInBytes = reduce_ptr->GetWorkspaceSizeInBytes(i_inLengths, reduceDims);
DeviceMem ws_dev(wsSizeInBytes);
InElementwiseOperation_0 in_elementwise_op_0(static_cast<int32_t>(reduce_total_length));
AccElementwiseOperation_0 acc_elementwise_op_0(static_cast<int32_t>(reduce_total_length));
auto argument_ptr = reduce_ptr->MakeArgumentPointer(i_inLengths,
i_inStrides,
i_outLengths,
i_outStrides,
reduceDims,
alpha,
beta,
in_dev.GetDeviceBuffer(),
out_dev.GetDeviceBuffer(),
out_indices_dev.GetDeviceBuffer(),
ws_dev.GetDeviceBuffer(),
in_elementwise_op_0,
acc_elementwise_op_0);
if(!reduce_ptr->IsSupportedArgument(argument_ptr.get()))
continue;
auto invoker_ptr = reduce_ptr->MakeInvokerPointer();
(void)invoker_ptr->Run(argument_ptr.get());
out_dev.FromDevice(out.mData.data());
bool single_result = true;
if constexpr(std::is_same<OutDataType, ck::half_t>::value ||
std::is_same<OutDataType, ck::bhalf_t>::value)
{
reduce_util::to_f32_vector(out, out_fp32);
reduce_util::to_f32_vector(out_ref, out_ref_fp32);
single_result = test_util::check_err(
out_fp32.mData, out_ref_fp32.mData, "Error: incorrect data result!");
}
else
{
single_result =
test_util::check_err(out.mData, out_ref.mData, "Error: incorrect data result!");
};
if(NeedIndices)
{
out_indices_dev.FromDevice(out_indices.mData.data());
single_result = single_result && test_util::check_err(out_indices_ref.mData,
out_indices.mData,
"Error: incorrect index result!");
};
if(!single_result)
{
std::cout << "Fail Info: " << reduce_ptr->GetTypeString() << std::endl;
result = false;
}
};
for(auto& reduce_ptr : reduce1_ptrs)
{
auto wsSizeInBytes = reduce_ptr->GetWorkspaceSizeInBytes(i_inLengths, reduceDims);
DeviceMem ws_dev(wsSizeInBytes);
InElementwiseOperation_1 in_elementwise_op_1(static_cast<int32_t>(reduce_total_length));
AccElementwiseOperation_1 acc_elementwise_op_1(static_cast<int32_t>(reduce_total_length));
auto argument_ptr = reduce_ptr->MakeArgumentPointer(i_inLengths,
i_inStrides,
i_outLengths,
i_outStrides,
reduceDims,
alpha,
beta,
in_dev.GetDeviceBuffer(),
out_dev.GetDeviceBuffer(),
out_indices_dev.GetDeviceBuffer(),
ws_dev.GetDeviceBuffer(),
in_elementwise_op_1,
acc_elementwise_op_1);
if(!reduce_ptr->IsSupportedArgument(argument_ptr.get()))
continue;
std::string reduce_name = reduce_ptr->GetTypeString();
auto invoker_ptr = reduce_ptr->MakeInvokerPointer();
(void)invoker_ptr->Run(argument_ptr.get());
std::vector<int> inLengths2 = reduce_ptr->GetWorkspace2dLengths(argument_ptr.get());
std::vector<int> inStrides2{inLengths2[1], 1};
for(auto& reduce2_ptr : reduce2_ptrs)
{
InElementwiseOperation_2 in_elementwise_op_2(static_cast<int32_t>(reduce_total_length));
AccElementwiseOperation_2 acc_elementwise_op_2(
static_cast<int32_t>(reduce_total_length));
auto argument2_ptr = reduce2_ptr->MakeArgumentPointer(inLengths2,
inStrides2,
i_outLengths,
i_outStrides,
reduceDims,
alpha,
beta,
ws_dev.GetDeviceBuffer(),
out_dev.GetDeviceBuffer(),
out_indices_dev.GetDeviceBuffer(),
ws_dev.GetDeviceBuffer(),
in_elementwise_op_2,
acc_elementwise_op_2);
if(!reduce2_ptr->IsSupportedArgument(argument2_ptr.get()))
continue;
std::string reduce2_name = reduce2_ptr->GetTypeString();
auto invoker2_ptr = reduce2_ptr->MakeInvokerPointer();
(void)invoker2_ptr->Run(argument2_ptr.get());
out_dev.FromDevice(out.mData.data());
bool single_result = true;
if constexpr(std::is_same<OutDataType, ck::half_t>::value ||
std::is_same<OutDataType, ck::bhalf_t>::value)
{
reduce_util::to_f32_vector(out, out_fp32);
reduce_util::to_f32_vector(out_ref, out_ref_fp32);
single_result = test_util::check_err(
out_fp32.mData, out_ref_fp32.mData, "Error: incorrect data result!");
}
else
{
single_result =
test_util::check_err(out.mData, out_ref.mData, "Error: incorrect data result!");
};
if(NeedIndices)
{
out_indices_dev.FromDevice(out_indices.mData.data());
single_result =
single_result && test_util::check_err(out_indices_ref.mData,
out_indices.mData,
"Error: incorrect index result!");
};
if(!single_result)
{
std::cout << "Fail Info: " << reduce_ptr->GetTypeString() << " => "
<< reduce2_ptr->GetTypeString() << std::endl;
result = false;
}
};
};
return (result);
};
} // anonymous namespace
static struct option long_options[] = {{"inLengths", required_argument, nullptr, 'D'},
{"reduceDimensions", required_argument, nullptr, 'R'},
{"scales", required_argument, nullptr, 'S'},
{"help", no_argument, nullptr, '?'},
{nullptr, 0, nullptr, 0}};
class SimpleAppArgs
{
template <typename T>
static T getSingleValueFromString(const std::string& valueStr)
{
std::istringstream iss(valueStr);
T ret;
iss >> ret;
return (ret);
};
template <typename T>
static std::vector<T> getTypeValuesFromString(const char* cstr_values)
{
std::string valuesStr(cstr_values);
std::vector<T> values;
std::size_t pos = 0;
std::size_t new_pos;
new_pos = valuesStr.find(',', pos);
while(new_pos != std::string::npos)
{
const std::string sliceStr = valuesStr.substr(pos, new_pos - pos);
T val = getSingleValueFromString<T>(sliceStr);
values.push_back(val);
pos = new_pos + 1;
new_pos = valuesStr.find(',', pos);
};
std::string sliceStr = valuesStr.substr(pos);
T val = getSingleValueFromString<T>(sliceStr);
values.push_back(val);
return (values);
};
private:
int option_index = 0;
public:
std::vector<size_t> inLengths;
std::vector<int> reduceDims;
std::vector<float> scales;
int data_type;
int init_method = 1;
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 "
"(only 4-d tensor supported)"
<< std::endl;
std::cout << "--reduceDimensions or -R comma seperated list of dimension indexes to reduce "
"(only 1 or 3 or 4 dimensions supported)"
<< std::endl;
std::cout << "--scales or -S, comma separated two float values for alpha and beta"
<< std::endl;
std::cout << "Arg1 -- data type (1: fp32, 3: int8, 5: bp16, 6: fp64)" << std::endl;
std::cout << "Arg2 -- init method(0=no init, 1=single integer value, 2=scope integer "
"value, 3=decimal value)"
<< std::endl;
};
int processArgs(int argc, char* argv[])
{
unsigned int ch;
while(1)
{
ch = getopt_long(argc, argv, "D:R:S:", 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 'S':
if(!optarg)
throw std::runtime_error("Invalid option format!");
scales = getTypeValuesFromString<float>(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!");
data_type = std::atoi(argv[optind++]);
init_method = std::atoi(argv[optind]);
if(scales.empty())
{
scales.push_back(1.0f);
scales.push_back(0.0f);
};
if(inLengths.size() != 4 ||
(reduceDims.size() != 1 && reduceDims.size() != 3 && reduceDims.size() != 4))
return (-1);
if(data_type != 0 && data_type != 1 && data_type != 3 && data_type != 5)
return (-1);
return (0);
};
};
bool test_reduce_with_index(int data_type,
int init_method,
std::vector<int> reduceDims,
std::vector<size_t> inLengths,
float alpha,
float beta)
{
bool result = true;
if(data_type == 0)
{
switch(reduceDims.size())
{
case 1:
result = test_reduce_with_index_impl<float, float, float, Rank, 1>(
init_method, inLengths, reduceDims, alpha, beta);
break;
case 3:
result = test_reduce_with_index_impl<float, float, float, Rank, 3>(
init_method, inLengths, reduceDims, alpha, beta);
break;
case 4:
result = test_reduce_with_index_impl<float, float, float, Rank, 4>(
init_method, inLengths, reduceDims, alpha, beta);
break;
};
}
else if(data_type == 1)
{
switch(reduceDims.size())
{
case 1:
result = test_reduce_with_index_impl<ck::half_t, ck::half_t, ck::half_t, Rank, 1>(
init_method, inLengths, reduceDims, alpha, beta);
break;
case 3:
result = test_reduce_with_index_impl<ck::half_t, ck::half_t, ck::half_t, Rank, 3>(
init_method, inLengths, reduceDims, alpha, beta);
break;
case 4:
result = test_reduce_with_index_impl<ck::half_t, ck::half_t, ck::half_t, Rank, 4>(
init_method, inLengths, reduceDims, alpha, beta);
break;
};
}
else if(data_type == 3)
{
switch(reduceDims.size())
{
case 1:
result = test_reduce_with_index_impl<int8_t, int8_t, int8_t, Rank, 1>(
init_method, inLengths, reduceDims, alpha, beta);
break;
case 3:
result = test_reduce_with_index_impl<int8_t, int8_t, int8_t, Rank, 3>(
init_method, inLengths, reduceDims, alpha, beta);
break;
case 4:
result = test_reduce_with_index_impl<int8_t, int8_t, int8_t, Rank, 4>(
init_method, inLengths, reduceDims, alpha, beta);
break;
};
}
else if(data_type == 5)
{
switch(reduceDims.size())
{
case 1:
result = test_reduce_with_index_impl<ck::bhalf_t, float, ck::bhalf_t, Rank, 1>(
init_method, inLengths, reduceDims, alpha, beta);
break;
case 3:
result = test_reduce_with_index_impl<ck::bhalf_t, float, ck::bhalf_t, Rank, 3>(
init_method, inLengths, reduceDims, alpha, beta);
break;
case 4:
result = test_reduce_with_index_impl<ck::bhalf_t, float, ck::bhalf_t, Rank, 4>(
init_method, inLengths, reduceDims, alpha, beta);
break;
};
}
return (result);
};
int main(int argc, char* argv[])
{
SimpleAppArgs args;
bool result = true;
if(argc == 1)
{
int data_type = 1;
int init_method = 2;
std::vector<size_t> inLengths{64, 4, 280, 80};
std::vector<std::vector<int>> v_reduceDims{
{0, 1, 2, 3}, {0, 1, 2}, {1, 2, 3}, {0, 1, 3}, {0, 2, 3}, {0}, {1}, {2}, {3}};
for(auto& reduceDims : v_reduceDims)
result = result && test_reduce_with_index(
data_type, init_method, reduceDims, inLengths, 1.0f, 0.0f);
}
else
{
if(args.processArgs(argc, argv) < 0)
{
throw std::runtime_error(
"Invalid input arguments, test_reduce_with_index could not be executed!");
};
result = test_reduce_with_index(args.data_type,
args.init_method,
args.reduceDims,
args.inLengths,
args.scales[0],
args.scales[1]);
}
std::cout << "test_reduce_with_index ..... " << (result ? "SUCCESS" : "FAILURE") << std::endl;
return (result ? 0 : -1);
}
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