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https://github.com/ROCm/composable_kernel.git
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Add examples for reduction fp16/fp32/bp16/int8/fp64 for 3d/4d/5d (#342)
* Update the reduce_blockwise example to support user specified data type and input+reducing dimensions
* Add examples for using reduce_multiblock_atomic_add
* Add more running examples to the default command-line
* Remove un-necessary header including
* Update to the example README.md
[ROCm/composable_kernel commit: 14932e8de3]
This commit is contained in:
Qianfeng
275
example/12_reduce/reduce_blockwise_impl.hpp
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275
example/12_reduce/reduce_blockwise_impl.hpp
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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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/device_reduce_multiblock.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 "ck/library/utility/host_reduction.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_blockwise_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::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;
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using namespace ck::tensor_operation::device;
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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, must use int8_t as AccDataType for indexable reduction
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// operations 2) If InOutDataType is int8_t, must use int32_t as AccDataType for non-indexable
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// 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, the supported operation must be either indexable operations or
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// 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(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 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 DeviceReduceInstance =
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ck::tensor_operation::device::DeviceReduceMultiBlock<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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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, // BlockSize
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4, // MThreadClusterSize
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64, // KThreadClusterSize
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1, // MThreadSliceSize
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1, // KThreadSliceSize
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0, // InSrcVectorDim
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1, // InSrceVectorSize
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1>; // OutDstVectorSize
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Tensor<InOutDataType> in(inLengths);
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std::vector<size_t> outLengths;
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std::vector<int> 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<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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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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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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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(InOutDataType) * in.mDesc.GetElementSpaceSize());
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DeviceMem out_dev(sizeof(InOutDataType) * out.mDesc.GetElementSpaceSize());
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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 = 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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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_verification)
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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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Rank,
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NumReduceDim,
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PropagateNan,
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OutputIndex>
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hostReduce(in.mDesc, out_ref.mDesc, invariantDims, reduceDims);
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hostReduce.Run(alpha,
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in.mData.data(),
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beta,
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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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acc_elementwise_op);
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};
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std::vector<ck::index_t> i_inLengths;
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std::vector<ck::index_t> i_inStrides;
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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.assign(inLengths.begin(), inLengths.end());
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i_inStrides.assign(inStrides.begin(), inStrides.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 = DeviceReduceInstance{};
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auto argument_ptr = reduce.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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nullptr,
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out_dev.GetDeviceBuffer(),
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out_index_dev.GetDeviceBuffer(),
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in_elementwise_op,
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acc_elementwise_op);
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if(!reduce.IsSupportedArgument(argument_ptr.get()))
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{
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std::cerr
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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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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.mData, out_ref.mData);
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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.mData, out_indices_ref.mData);
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};
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};
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return (pass ? 0 : 1);
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}
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