mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-04-19 22:39:03 +00:00
1f543bfa79
* Remove template from Reducton operation classes and add template to their operator() and GetIdentityValue() interfaces * Change to unary elementwise operators and the reduce_unary_operator (class for mapping) and dependent variations in all host layers * Remove the data type template parameter from reduce_binary_operator (class for mapping) and dependent variations in host layers * Add InMemoryDataOperatonSupportedOnDataType to check the matching between data type and InMemoryDataOperation * Use struct-scope operator template instantiation for binary and unary element-wise operations * Change a few more elementwise operations to use template for operator() * Tiny correction in Normalize operator * Add static_assert to check the data type appliability for some reduction accumulator and element-wise operatons * Correction in some examples with regard to using ReduceAccDataType * Use static_assert for UnaryDivide * Update to merged codes to use Element-wise operations and Reduction Accumulator operations correctly * Tiny fix with regard to SetWorkSpacePointer()
302 lines
12 KiB
C++
302 lines
12 KiB
C++
#include <iostream>
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#include <numeric>
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#include <sstream>
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#include <initializer_list>
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#include <cstdlib>
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#include <getopt.h>
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#include "check_err.hpp"
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#include "config.hpp"
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#include "print.hpp"
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#include "device.hpp"
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#include "host_tensor.hpp"
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#include "host_tensor_generator.hpp"
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#include "device_tensor.hpp"
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#include "device_base.hpp"
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#include "device_reduce_multiblock.hpp"
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#include "host_common_util.hpp"
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#include "host_reduction.hpp"
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#include "reduction_enums.hpp"
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#include "reduction_operator_mapping.hpp"
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using namespace ck;
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using namespace ck::tensor_operation::device;
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using InOutDataType = ck::half_t;
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using InOutDataType = ck::half_t;
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using AccDataType = float;
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constexpr ReduceTensorOp ReduceOpId = ReduceTensorOp::NORM2;
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constexpr bool PropagateNan = true;
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constexpr bool OutputIndex = false;
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using ReduceOperation = typename reduce_binary_operator<ReduceOpId>::opType;
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using InElementwiseOperation =
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typename reduce_unary_operator<ReduceOpId, true, true>::InElementwiseOperation;
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using AccElementwiseOperation =
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typename reduce_unary_operator<ReduceOpId, true, true>::AccElementwiseOperation;
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using PassThroughOp = tensor_operation::element_wise::PassThrough;
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using DeviceReduceInstance_1 = DeviceReduceMultiBlock<InOutDataType,
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AccDataType,
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InOutDataType,
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5, // Rank
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1, // NumReduceDim
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ReduceOperation,
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InElementwiseOperation,
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PassThroughOp,
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InMemoryDataOperationEnum::Set,
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PropagateNan,
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OutputIndex,
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false, // HaveIndexInputIfOutputIndex
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256,
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32,
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8,
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1,
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1,
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1, // vector dim
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1,
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1>;
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using DeviceReduceInstance_2 = DeviceReduceMultiBlock<InOutDataType,
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AccDataType,
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InOutDataType,
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4, // Rank
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1, // NumReduceDim
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ReduceOperation,
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PassThroughOp,
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AccElementwiseOperation,
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InMemoryDataOperationEnum::Set,
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PropagateNan,
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OutputIndex,
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false, // HaveIndexInputIfOutputIndex
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256,
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128,
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2,
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1,
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1,
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1, // vector dim
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1,
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1>;
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static bool do_verify;
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static int init_method;
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static float alpha;
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static float beta;
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static bool time_kernel;
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int main(int argc, char* argv[])
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{
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// used by the device reduction
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const std::vector<int> reduceDims_1 = {4};
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const std::vector<int> invariantDims_1 = {0, 1, 2, 3};
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const std::vector<int> reduceDims_2 = {3};
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const std::vector<int> invariantDims_2 = {0, 1, 2};
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// used by the host reduction
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const std::vector<int> reduceDims = {3, 4};
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const std::vector<int> invariantDims = {0, 1, 2};
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const std::vector<size_t> inLengths_1 = {64, 320, 80, 4, 128};
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// input lengths of the second reduction, which is also the output lengths of the first
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// reduction
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const std::vector<size_t> inLengths_2 = {64, 320, 80, 4};
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const std::vector<size_t> outLengths = {64, 320, 80};
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if(argc == 1)
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{
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do_verify = true;
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init_method = 2;
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time_kernel = true;
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}
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else if(argc == 4)
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{
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do_verify = static_cast<bool>(argv[1]);
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init_method = atoi(argv[2]);
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time_kernel = static_cast<bool>(atoi(argv[3]));
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}
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else
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{
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std::ostringstream ostr;
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ostr << "Wrong parameter! " << std::endl
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<< "Usage: " << argv[0] << "[verify 0/1] init_method time_kernel" << std::endl;
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throw std::runtime_error(ostr.str());
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};
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alpha = 1.0f;
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beta = 0.0f;
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Tensor<InOutDataType> in_1(inLengths_1);
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Tensor<InOutDataType> out_ref(outLengths);
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Tensor<InOutDataType> in_2(inLengths_2); // also the output tensor of the first reduction
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Tensor<InOutDataType> out(outLengths);
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auto inStrides_1 = in_1.mDesc.GetStrides();
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auto inStrides_2 = in_2.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_1.mDesc.GetElementSize() / invariant_total_length;
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std::size_t num_thread = 1;
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if(do_verify)
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{
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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_1.GenerateTensorValue(GeneratorTensor_1<InOutDataType>{1}, num_thread);
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if(beta != 0.0f)
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out_ref.GenerateTensorValue(GeneratorTensor_1<InOutDataType>{1}, num_thread);
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break;
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case 2:
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in_1.GenerateTensorValue(GeneratorTensor_2<InOutDataType>{-5, 5}, num_thread);
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if(beta != 0.0f)
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out_ref.GenerateTensorValue(GeneratorTensor_2<InOutDataType>{-5, 5}, num_thread);
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break;
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default:
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in_1.GenerateTensorValue(GeneratorTensor_3<InOutDataType>{-5.0, 5.0}, num_thread);
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if(beta != 0.0f)
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out_ref.GenerateTensorValue(GeneratorTensor_3<InOutDataType>{-5.0, 5.0},
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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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};
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DeviceMem in_1_dev(sizeof(InOutDataType) * in_1.mDesc.GetElementSpace());
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DeviceMem in_2_dev(sizeof(InOutDataType) * in_2.mDesc.GetElementSpace());
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DeviceMem out_dev(sizeof(InOutDataType) * out.mDesc.GetElementSpace());
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in_1_dev.ToDevice(in_1.mData.data());
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if(beta != 0.0f)
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out_dev.ToDevice(out.mData.data());
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InElementwiseOperation in_elementwise_op;
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AccElementwiseOperation acc_elementwise_op;
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std::tie(in_elementwise_op, acc_elementwise_op) =
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reduce_unary_operator<ReduceOpId, true, true>::GetElementwiseOperator(
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static_cast<int32_t>(reduce_total_length));
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if(do_verify)
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{
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ReductionHost<InOutDataType,
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AccDataType,
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InOutDataType,
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ReduceOperation,
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InElementwiseOperation,
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AccElementwiseOperation,
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5, // Rank
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2, // NumReduceDim
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PropagateNan,
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OutputIndex>
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hostReduce(in_1.mDesc, out_ref.mDesc, invariantDims, reduceDims);
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hostReduce.Run(alpha,
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in_1.mData.data(),
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beta,
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out_ref.mData.data(),
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nullptr,
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in_elementwise_op,
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acc_elementwise_op);
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};
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std::vector<ck::index_t> i_inLengths_1;
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std::vector<ck::index_t> i_inStrides_1;
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std::vector<ck::index_t> i_inLengths_2;
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std::vector<ck::index_t> i_inStrides_2;
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std::vector<ck::index_t> i_outLengths;
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std::vector<ck::index_t> i_outStrides;
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i_inLengths_1.assign(inLengths_1.begin(), inLengths_1.end());
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i_inStrides_1.assign(inStrides_1.begin(), inStrides_1.end());
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i_inLengths_2.assign(inLengths_2.begin(), inLengths_2.end());
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i_inStrides_2.assign(inStrides_2.begin(), inStrides_2.end());
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i_outLengths.assign(outLengths.begin(), outLengths.end());
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i_outStrides.assign(outStrides.begin(), outStrides.end());
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auto reduce_1 = DeviceReduceInstance_1{};
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auto argument_ptr_1 = reduce_1.MakeArgumentPointer(i_inLengths_1,
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i_inStrides_1,
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i_inLengths_2,
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i_inStrides_2,
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reduceDims_1,
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1.0f,
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0.0f,
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in_1_dev.GetDeviceBuffer(),
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nullptr,
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in_2_dev.GetDeviceBuffer(),
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nullptr,
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in_elementwise_op,
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PassThroughOp{});
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if(!reduce_1.IsSupportedArgument(argument_ptr_1.get()))
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{
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std::cout
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<< "The runtime parameters seems not supported by the DeviceReduce instance, exiting!"
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<< std::endl;
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};
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auto invoker_ptr_1 = reduce_1.MakeInvokerPointer();
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auto reduce_2 = DeviceReduceInstance_2{};
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auto argument_ptr_2 = reduce_2.MakeArgumentPointer(i_inLengths_2,
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i_inStrides_2,
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i_outLengths,
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i_outStrides,
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reduceDims_2,
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alpha,
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beta,
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in_2_dev.GetDeviceBuffer(),
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nullptr,
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out_dev.GetDeviceBuffer(),
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nullptr,
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PassThroughOp{},
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acc_elementwise_op);
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if(!reduce_2.IsSupportedArgument(argument_ptr_2.get()))
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{
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std::cout
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<< "The runtime parameters seems not supported by the DeviceReduce instance, exiting!"
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<< std::endl;
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};
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auto invoker_ptr_2 = reduce_2.MakeInvokerPointer();
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float avg_time_1 = invoker_ptr_1->Run(argument_ptr_1.get(), StreamConfig{nullptr, time_kernel});
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float avg_time_2 = invoker_ptr_2->Run(argument_ptr_2.get(), StreamConfig{nullptr, time_kernel});
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std::size_t num_bytes = invariant_total_length * reduce_total_length * sizeof(InOutDataType) +
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invariant_total_length * sizeof(InOutDataType);
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float gb_per_sec = num_bytes / 1.E6 / (avg_time_1 + avg_time_2);
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std::cout << "Perf: " << avg_time_1 + avg_time_2 << " ms, " << gb_per_sec << " GB/s, "
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<< reduce_1.GetTypeString() << " => " << reduce_2.GetTypeString() << std::endl;
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bool pass = true;
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if(do_verify)
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{
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out_dev.FromDevice(out.mData.data());
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pass = pass && ck::utils::check_err(out.mData, out_ref.mData);
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};
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return (pass ? 0 : 1);
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}
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