mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-04-19 22:39:03 +00:00
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>
This commit is contained in:
Qianfeng
669
test/reduce/reduce_with_index.cpp
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669
test/reduce/reduce_with_index.cpp
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#include "getopt.h"
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#include "device_reduce_instance.hpp"
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#include "reduction_enums.hpp"
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#include "host_tensor.hpp"
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#include "host_tensor_generator.hpp"
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#include "host_reduction.hpp"
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#include "test_util.hpp"
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#include "reduce_util.hpp"
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using namespace ck;
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namespace {
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template <index_t Rank, index_t NumReduceDim>
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static inline std::vector<int> get_invariant_dims(const std::vector<int>& reduceDims)
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{
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assert(NumReduceDim == reduceDims.size());
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int reduceFlag = 0;
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// flag the bits for the reduceDims
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for(int i = 0; i < NumReduceDim; i++)
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{
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reduceFlag |= 1 << reduceDims[i];
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};
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std::vector<int> invariantDims;
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// collect invariant dimensions
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for(int i = 0; i < Rank; i++)
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if((reduceFlag & (1 << i)) == 0)
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{
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invariantDims.push_back(i);
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};
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return invariantDims;
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};
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// map the data type used by the GPU kernels to the corresponding type used by the host codes
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template <typename InType>
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struct type_mapping
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{
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using OutType = InType;
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};
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template <>
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struct type_mapping<ck::half_t>
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{
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using OutType = half_float::half;
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};
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constexpr int Rank = 4;
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constexpr ReduceTensorOp_t ReduceOpId = ReduceTensorOp_t::AMAX;
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constexpr NanPropagation_t NanOpt = NanPropagation_t::PROPAGATE_NAN;
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constexpr bool PropagateNan = false;
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constexpr ReduceTensorIndices_t IndicesOpt = ReduceTensorIndices_t::FLATTENED_INDICES;
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constexpr bool NeedIndices = true;
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template <typename InDataType,
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typename AccDataType,
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typename OutDataType,
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int Rank,
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int NumReduceDim>
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bool test_reduce_with_index_impl(int init_method,
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const std::vector<size_t>& inLengths,
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const std::vector<int>& reduceDims,
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float alpha,
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float beta)
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{
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using namespace ck::tensor_operation::device;
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using namespace ck::tensor_operation::device::device_reduce_instance;
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using namespace ck::host_reduce;
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Tensor<InDataType> in(inLengths);
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std::vector<size_t> outLengths;
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const auto invariantDims = get_invariant_dims<Rank, NumReduceDim>(reduceDims);
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if(reduceDims.size() == Rank)
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outLengths.push_back(1);
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else
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for(auto dim : invariantDims)
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outLengths.push_back(inLengths[dim]);
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Tensor<OutDataType> out_ref(outLengths);
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Tensor<OutDataType> out(outLengths);
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Tensor<int32_t> out_indices_ref(outLengths);
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Tensor<int32_t> out_indices(outLengths);
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// only used when the OutDataType is bhalf_t
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Tensor<float> out_ref_fp32(outLengths);
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Tensor<float> out_fp32(outLengths);
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auto inStrides = in.mDesc.GetStrides();
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auto outStrides = out.mDesc.GetStrides();
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size_t invariant_total_length = out.mDesc.GetElementSize();
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size_t reduce_total_length = in.mDesc.GetElementSize() / invariant_total_length;
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std::size_t num_thread = std::thread::hardware_concurrency();
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switch(init_method)
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{
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case 0: break;
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case 1:
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in.GenerateTensorValue(GeneratorTensor_1<InDataType>{1}, num_thread);
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if(beta != 0.0f)
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out_ref.GenerateTensorValue(GeneratorTensor_1<InDataType>{1}, num_thread);
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break;
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case 2:
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in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5}, num_thread);
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if(beta != 0.0f)
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out_ref.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5}, num_thread);
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break;
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default:
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in.GenerateTensorValue(GeneratorTensor_3<InDataType>{-5.0, 5.0}, num_thread);
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if(beta != 0.0f)
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out_ref.GenerateTensorValue(GeneratorTensor_3<InDataType>{-5.0, 5.0}, num_thread);
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}
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if(beta != 0.0f)
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for(size_t i = 0; i < out_ref.mDesc.GetElementSpace(); i++)
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out.mData[i] = out_ref.mData[i];
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// these buffers are usually provided by the user application
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DeviceMem in_dev(sizeof(InDataType) * in.mDesc.GetElementSpace());
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DeviceMem out_dev(sizeof(OutDataType) * out.mDesc.GetElementSpace());
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in_dev.ToDevice(in.mData.data());
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if(beta != 0.0f)
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out_dev.ToDevice(out.mData.data());
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size_t indicesSizeInBytes = NeedIndices ? out.mDesc.GetElementSize() * sizeof(int) : 0;
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DeviceMem out_indices_dev(indicesSizeInBytes);
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using InElementwiseOperation_0 =
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typename reduce_unary_operator<AccDataType, ReduceOpId, true, true>::InElementwiseOperation;
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using AccElementwiseOperation_0 =
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typename reduce_unary_operator<AccDataType, ReduceOpId, true, true>::
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AccElementwiseOperation;
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using InElementwiseOperation_1 =
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typename reduce_unary_operator<AccDataType, ReduceOpId, true, false>::
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InElementwiseOperation;
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using AccElementwiseOperation_1 =
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typename reduce_unary_operator<AccDataType, ReduceOpId, true, false>::
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AccElementwiseOperation;
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using InElementwiseOperation_2 =
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typename reduce_unary_operator<AccDataType, ReduceOpId, false, true>::
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InElementwiseOperation;
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using AccElementwiseOperation_2 =
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typename reduce_unary_operator<AccDataType, ReduceOpId, false, true>::
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AccElementwiseOperation;
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using DeviceReduceInstPtr0 =
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DeviceReducePtr<InElementwiseOperation_0, AccElementwiseOperation_0>;
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using DeviceReduceInstPtr1 =
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DeviceReducePtr<InElementwiseOperation_1, AccElementwiseOperation_1>;
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using DeviceReduceInstPtr2 =
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DeviceReducePtr<InElementwiseOperation_2, AccElementwiseOperation_2>;
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std::vector<DeviceReduceInstPtr0> reduce0_ptrs;
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std::vector<DeviceReduceInstPtr1> reduce1_ptrs;
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std::vector<DeviceReduceInstPtr2> reduce2_ptrs;
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add_device_reduce_instance_threadwise<InDataType,
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AccDataType,
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OutDataType,
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Rank,
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NumReduceDim,
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ReduceOpId,
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NanOpt,
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IndicesOpt>(reduce0_ptrs);
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add_device_reduce_instance_blockwise<InDataType,
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AccDataType,
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OutDataType,
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Rank,
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NumReduceDim,
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ReduceOpId,
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NanOpt,
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IndicesOpt>(reduce0_ptrs);
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add_device_reduce_instance_multiblock_partial_reduce<InDataType,
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AccDataType,
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OutDataType,
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Rank,
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NumReduceDim,
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ReduceOpId,
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NanOpt,
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IndicesOpt>(reduce1_ptrs);
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add_device_reduce_instance_blockwise_second_call<AccDataType,
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AccDataType,
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OutDataType,
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Rank,
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NumReduceDim,
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ReduceOpId,
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NanOpt,
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IndicesOpt>(reduce2_ptrs);
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if(reduce0_ptrs.empty() && reduce1_ptrs.empty())
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{
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throw std::runtime_error("Wrong! No device REDUCE instance found");
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};
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bool result = true;
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using HostInDataType = typename type_mapping<InDataType>::OutType;
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using HostOutDataType = typename type_mapping<OutDataType>::OutType;
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using HostAccDataType = typename type_mapping<AccDataType>::OutType;
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ReductionHost<HostInDataType,
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HostAccDataType,
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HostOutDataType,
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ReduceOpId,
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Rank,
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NumReduceDim,
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PropagateNan,
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NeedIndices>
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hostReduce(in.mDesc, out_ref.mDesc, invariantDims, reduceDims);
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hostReduce.Run(alpha,
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reinterpret_cast<const HostInDataType*>(in.mData.data()),
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beta,
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reinterpret_cast<HostOutDataType*>(out_ref.mData.data()),
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out_indices_ref.mData.data());
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const auto i_inLengths = to_int_vector(inLengths);
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const auto i_inStrides = to_int_vector(inStrides);
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const auto i_outLengths = to_int_vector(outLengths);
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const auto i_outStrides = to_int_vector(outStrides);
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for(auto& reduce_ptr : reduce0_ptrs)
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{
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auto wsSizeInBytes = reduce_ptr->GetWorkspaceSizeInBytes(i_inLengths, reduceDims);
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DeviceMem ws_dev(wsSizeInBytes);
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InElementwiseOperation_0 in_elementwise_op_0(static_cast<int32_t>(reduce_total_length));
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AccElementwiseOperation_0 acc_elementwise_op_0(static_cast<int32_t>(reduce_total_length));
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auto argument_ptr = reduce_ptr->MakeArgumentPointer(i_inLengths,
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i_inStrides,
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i_outLengths,
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i_outStrides,
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reduceDims,
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alpha,
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beta,
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in_dev.GetDeviceBuffer(),
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out_dev.GetDeviceBuffer(),
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out_indices_dev.GetDeviceBuffer(),
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ws_dev.GetDeviceBuffer(),
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in_elementwise_op_0,
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acc_elementwise_op_0);
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if(!reduce_ptr->IsSupportedArgument(argument_ptr.get()))
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continue;
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auto invoker_ptr = reduce_ptr->MakeInvokerPointer();
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(void)invoker_ptr->Run(argument_ptr.get());
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out_dev.FromDevice(out.mData.data());
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bool single_result = true;
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if constexpr(std::is_same<OutDataType, ck::half_t>::value ||
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std::is_same<OutDataType, ck::bhalf_t>::value)
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{
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reduce_util::to_f32_vector(out, out_fp32);
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reduce_util::to_f32_vector(out_ref, out_ref_fp32);
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single_result = test_util::check_err(
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out_fp32.mData, out_ref_fp32.mData, "Error: incorrect data result!");
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}
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else
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{
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single_result =
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test_util::check_err(out.mData, out_ref.mData, "Error: incorrect data result!");
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};
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if(NeedIndices)
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{
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out_indices_dev.FromDevice(out_indices.mData.data());
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single_result = single_result && test_util::check_err(out_indices_ref.mData,
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out_indices.mData,
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"Error: incorrect index result!");
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};
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if(!single_result)
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{
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std::cout << "Fail Info: " << reduce_ptr->GetTypeString() << std::endl;
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result = false;
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}
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};
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for(auto& reduce_ptr : reduce1_ptrs)
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{
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auto wsSizeInBytes = reduce_ptr->GetWorkspaceSizeInBytes(i_inLengths, reduceDims);
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DeviceMem ws_dev(wsSizeInBytes);
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InElementwiseOperation_1 in_elementwise_op_1(static_cast<int32_t>(reduce_total_length));
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AccElementwiseOperation_1 acc_elementwise_op_1(static_cast<int32_t>(reduce_total_length));
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auto argument_ptr = reduce_ptr->MakeArgumentPointer(i_inLengths,
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i_inStrides,
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i_outLengths,
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i_outStrides,
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reduceDims,
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alpha,
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beta,
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in_dev.GetDeviceBuffer(),
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out_dev.GetDeviceBuffer(),
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out_indices_dev.GetDeviceBuffer(),
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ws_dev.GetDeviceBuffer(),
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in_elementwise_op_1,
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acc_elementwise_op_1);
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if(!reduce_ptr->IsSupportedArgument(argument_ptr.get()))
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continue;
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std::string reduce_name = reduce_ptr->GetTypeString();
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auto invoker_ptr = reduce_ptr->MakeInvokerPointer();
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(void)invoker_ptr->Run(argument_ptr.get());
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std::vector<int> inLengths2 = reduce_ptr->GetWorkspace2dLengths(argument_ptr.get());
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std::vector<int> inStrides2{inLengths2[1], 1};
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for(auto& reduce2_ptr : reduce2_ptrs)
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{
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InElementwiseOperation_2 in_elementwise_op_2(static_cast<int32_t>(reduce_total_length));
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AccElementwiseOperation_2 acc_elementwise_op_2(
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static_cast<int32_t>(reduce_total_length));
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auto argument2_ptr = reduce2_ptr->MakeArgumentPointer(inLengths2,
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inStrides2,
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i_outLengths,
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i_outStrides,
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reduceDims,
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alpha,
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beta,
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ws_dev.GetDeviceBuffer(),
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out_dev.GetDeviceBuffer(),
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out_indices_dev.GetDeviceBuffer(),
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ws_dev.GetDeviceBuffer(),
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in_elementwise_op_2,
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acc_elementwise_op_2);
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if(!reduce2_ptr->IsSupportedArgument(argument2_ptr.get()))
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continue;
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std::string reduce2_name = reduce2_ptr->GetTypeString();
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auto invoker2_ptr = reduce2_ptr->MakeInvokerPointer();
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(void)invoker2_ptr->Run(argument2_ptr.get());
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out_dev.FromDevice(out.mData.data());
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bool single_result = true;
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if constexpr(std::is_same<OutDataType, ck::half_t>::value ||
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std::is_same<OutDataType, ck::bhalf_t>::value)
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{
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reduce_util::to_f32_vector(out, out_fp32);
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reduce_util::to_f32_vector(out_ref, out_ref_fp32);
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single_result = test_util::check_err(
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out_fp32.mData, out_ref_fp32.mData, "Error: incorrect data result!");
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}
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else
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{
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single_result =
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test_util::check_err(out.mData, out_ref.mData, "Error: incorrect data result!");
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};
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if(NeedIndices)
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{
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out_indices_dev.FromDevice(out_indices.mData.data());
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single_result =
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single_result && test_util::check_err(out_indices_ref.mData,
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out_indices.mData,
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"Error: incorrect index result!");
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};
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if(!single_result)
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{
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std::cout << "Fail Info: " << reduce_ptr->GetTypeString() << " => "
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<< reduce2_ptr->GetTypeString() << std::endl;
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result = false;
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}
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};
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};
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return (result);
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};
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} // anonymous namespace
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static struct option long_options[] = {{"inLengths", required_argument, nullptr, 'D'},
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{"reduceDimensions", required_argument, nullptr, 'R'},
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{"scales", required_argument, nullptr, 'S'},
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{"help", no_argument, nullptr, '?'},
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{nullptr, 0, nullptr, 0}};
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|
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class SimpleAppArgs
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{
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template <typename T>
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static T getSingleValueFromString(const std::string& valueStr)
|
||||
{
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std::istringstream iss(valueStr);
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|
||||
T ret;
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iss >> ret;
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return (ret);
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};
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||||
|
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template <typename T>
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static std::vector<T> getTypeValuesFromString(const char* cstr_values)
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||||
{
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||||
std::string valuesStr(cstr_values);
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||||
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std::vector<T> values;
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std::size_t pos = 0;
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std::size_t new_pos;
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new_pos = valuesStr.find(',', pos);
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while(new_pos != std::string::npos)
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{
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const std::string sliceStr = valuesStr.substr(pos, new_pos - pos);
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||||
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T val = getSingleValueFromString<T>(sliceStr);
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values.push_back(val);
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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);
|
||||
}
|
||||
Reference in New Issue
Block a user