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d85f065b15
* chore(copyright): update copyright header for codegen directory * chore(copyright): update copyright header for example directory
308 lines
13 KiB
C++
308 lines
13 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#pragma once
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#include <iostream>
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#include "ck/ck.hpp"
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#include "ck/utility/reduction_enums.hpp"
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#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_reduce_threadwise_multi_d.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_reduce.hpp"
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#include "ck/library/utility/algorithm.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/library/utility/host_common_util.hpp"
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#include "reduce_example_common.hpp"
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template <typename InOutDataType,
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typename AccDataType,
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ck::ReduceTensorOp ReduceOpId,
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ck::index_t Rank,
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ck::index_t NumReduceDim,
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bool PropagateNan,
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bool OutputIndex>
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int reduce_threadwise_multi_d_impl(bool do_verification,
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int init_method,
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bool time_kernel,
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const std::vector<size_t>& inLengths,
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const std::array<int, NumReduceDim>& reduceDims,
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float alpha,
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float beta)
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{
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using namespace ck;
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using namespace ck::tensor_operation::device;
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constexpr index_t NumOutDim = (Rank - NumReduceDim == 0) ? 1 : Rank - NumReduceDim;
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constexpr bool op_support_indices =
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(ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX ||
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ReduceOpId == ReduceTensorOp::AMAX);
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constexpr bool invalid_reduce_1 = OutputIndex && !op_support_indices;
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// 1) If InOutDataType is half_t, must use half_t as AccDataType for indexable reduction
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// operations 2) If InOutDataType is half_t, must use float as AccDataType for non-indexable
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// reduction operations
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constexpr bool invalid_reduce_2 =
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std::is_same<InOutDataType, half_t>::value &&
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((!op_support_indices && !std::is_same<AccDataType, float>::value) ||
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(op_support_indices && !std::is_same<AccDataType, half_t>::value));
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// 1) If InOutDataType is float, must use float as AccDataType for indexable reduction
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// operations
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constexpr bool invalid_reduce_3 =
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std::is_same<InOutDataType, float>::value &&
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(op_support_indices && !std::is_same<AccDataType, float>::value);
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// 1) If InOutDataType is int8_t or int4_t, must use int8_t as AccDataType for indexable
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// reduction operations 2) If InOutDataType is int8_t or int4_t, must use int32_t as AccDataType
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// for non-indexable reduction operations
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constexpr bool invalid_reduce_4 =
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std::is_same<InOutDataType, int8_t>::value &&
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((!op_support_indices && !std::is_same<AccDataType, int32_t>::value) ||
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(op_support_indices && !std::is_same<AccDataType, int8_t>::value));
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// 1) If InOutDataType is int8_t or int4_t, the supported operation must be either indexable
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// operations or ADD/AVG
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constexpr bool invalid_reduce_5 = std::is_same<InOutDataType, int8_t>::value &&
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(!op_support_indices && ReduceOpId != ReduceTensorOp::ADD &&
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ReduceOpId != ReduceTensorOp::AVG);
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// 1) If InOutDataType is bhalf_t, must use float as AccDataType for all reduction operations
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constexpr bool invalid_reduce_6 =
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std::is_same<InOutDataType, bhalf_t>::value && !std::is_same<AccDataType, float>::value;
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constexpr bool invalid_reduce = (invalid_reduce_1 || invalid_reduce_2 || invalid_reduce_3 ||
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invalid_reduce_4 || invalid_reduce_5 || invalid_reduce_6);
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if constexpr(invalid_reduce)
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{
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std::cerr << "The reduction setting is invalid, exiting!" << std::endl;
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return (-1);
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};
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using PassThrough = tensor_operation::element_wise::PassThrough;
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using Add = tensor_operation::element_wise::Add;
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using ReduceOperation = typename reduce_binary_operator<ReduceOpId>::opType;
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using InElementwiseOperation = PassThrough;
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using OutElementwiseOperation = Add;
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using InOutDataTypeInDevice = InOutDataType;
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using DeviceReduceInstance =
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ck::tensor_operation::device::DeviceReduceThreadWiseMultiD<InOutDataTypeInDevice,
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ck::Tuple<InOutDataTypeInDevice>,
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AccDataType,
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InOutDataTypeInDevice,
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Rank,
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NumReduceDim,
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ReduceOperation,
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InElementwiseOperation,
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OutElementwiseOperation,
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256, // BlockSize
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4, // MThreadSliceSize
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1, // KThreadSliceSize
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0, // InSrcVectorDim
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1, // InSrceVectorSize
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1,
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Sequence<1>>; // OutDstVectorSize
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Tensor<InOutDataType> in(inLengths);
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std::vector<size_t> outLengths;
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auto invariantDims = get_invariant_dims<Rank, NumReduceDim>(reduceDims);
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if(invariantDims.empty())
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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<InOutDataType> out_ref(outLengths);
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Tensor<InOutDataType> out(outLengths);
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Tensor<InOutDataType> d0(outLengths);
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Tensor<int> out_indices_ref(outLengths);
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Tensor<int> out_indices(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 = 1;
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if(do_verification)
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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.GenerateTensorValue(GeneratorTensor_1<InOutDataType>{1}, num_thread);
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d0.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.GenerateTensorValue(GeneratorTensor_2<InOutDataType>{-5, 5}, num_thread);
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d0.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.GenerateTensorValue(GeneratorTensor_3<InOutDataType>{-5.0, 5.0}, num_thread);
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d0.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.GetElementSpaceSize(); i++)
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out.mData[i] = out_ref.mData[i];
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};
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// these buffers are usually provided by the user application
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DeviceMem in_dev(sizeof(InOutDataTypeInDevice) * in.mDesc.GetElementSpaceSize());
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DeviceMem d0_dev(sizeof(InOutDataTypeInDevice) * d0.mDesc.GetElementSpaceSize());
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DeviceMem out_dev(sizeof(InOutDataTypeInDevice) * out.mDesc.GetElementSpaceSize());
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in_dev.ToDevice(in.mData.data());
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d0_dev.ToDevice(d0.mData.data());
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if(beta != 0.0f)
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{
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out_dev.ToDevice(out.mData.data());
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};
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size_t indicesSizeInBytes = OutputIndex ? out.mDesc.GetElementSize() * sizeof(int32_t) : 0;
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DeviceMem out_index_dev(indicesSizeInBytes);
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InElementwiseOperation in_elementwise_op;
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OutElementwiseOperation out_elementwise_op;
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std::array<index_t, Rank> arrInLengths;
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std::array<index_t, Rank> arrInStrides;
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std::array<index_t, NumOutDim> arrOutLengths;
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std::array<index_t, NumOutDim> arrOutStrides;
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ck::ranges::copy(inLengths, arrInLengths.begin());
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ck::ranges::copy(inStrides, arrInStrides.begin());
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ck::ranges::copy(outLengths, arrOutLengths.begin());
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ck::ranges::copy(outStrides, arrOutStrides.begin());
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if(do_verification)
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{
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using ReferenceReduceInstance =
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ck::tensor_operation::host::ReferenceReduce<InOutDataType,
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AccDataType,
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InOutDataType,
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Rank,
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NumReduceDim,
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ReduceOperation,
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InElementwiseOperation,
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PassThrough,
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PropagateNan,
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OutputIndex>;
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auto reduce_ref = ReferenceReduceInstance{};
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auto argument_ptr_ref = reduce_ref.MakeArgumentPointer(arrInLengths,
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arrInStrides,
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arrOutLengths,
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arrOutStrides,
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reduceDims,
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static_cast<double>(alpha),
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static_cast<double>(beta),
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in.mData.data(),
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nullptr,
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out_ref.mData.data(),
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out_indices_ref.mData.data(),
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in_elementwise_op,
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PassThrough{});
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if(!reduce_ref.IsSupportedArgument(argument_ptr_ref.get()))
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{
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std::cout << "The runtime parameters not supported by the reduce reference, exiting!"
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<< std::endl;
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return (false);
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};
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auto invoker_ptr_ref = reduce_ref.MakeInvokerPointer();
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invoker_ptr_ref->Run(argument_ptr_ref.get());
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for(std::size_t i = 0; i < out_ref.GetElementSize(); i++)
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out_elementwise_op(out_ref.mData[i], out_ref.mData[i], d0.mData[i]);
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};
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auto reduce = DeviceReduceInstance{};
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auto argument_ptr = reduce.MakeArgumentPointer(arrInLengths,
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arrInStrides,
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{arrOutLengths},
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{arrOutStrides},
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arrOutLengths,
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arrOutStrides,
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reduceDims,
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in_dev.GetDeviceBuffer(),
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{d0_dev.GetDeviceBuffer()},
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out_dev.GetDeviceBuffer(),
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in_elementwise_op,
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out_elementwise_op);
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if(!reduce.IsSupportedArgument(argument_ptr.get()))
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{
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std::cerr << "The runtime parameters not supported by the DeviceReduce instance, exiting!"
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<< std::endl;
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return (-2);
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};
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std::string reduce_name = reduce.GetTypeString();
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auto invoker_ptr = reduce.MakeInvokerPointer();
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float avg_time = invoker_ptr->Run(argument_ptr.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;
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std::cout << "Perf: " << avg_time << " ms, " << gb_per_sec << " GB/s, " << reduce_name
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<< std::endl;
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bool pass = true;
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if(do_verification)
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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, out_ref);
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if(OutputIndex)
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{
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out_index_dev.FromDevice(out_indices.mData.data());
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pass = pass && ck::utils::check_err(out_indices, out_indices_ref);
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};
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};
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return (pass ? 0 : 1);
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}
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