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Batchnorm inference instances, external API, client examples and gtests (#531)
* File renaming and class renaming for device element-wise operation
* Add batchnorm-infer instances, external API and client example
* Add batchnorm-infer profiler module and gtests
* Remove file device_elementwise_extension.hpp and move NormalizeInInfer operation to element_wise_operation.hpp
* Remove the using of class aliasing for DeviceElementwiseForBatchNormInfer
* Rename class and file due to conflict from device_elementwise_2d.hpp
* Fix namespace in batcnnorm_infer_nhwc client example
[ROCm/composable_kernel commit: a1b2441f8d]
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Qianfeng
335
profiler/include/profiler/profile_batchnorm_infer_impl.hpp
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335
profiler/include/profiler/profile_batchnorm_infer_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 <iomanip>
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#include <stdexcept>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.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/tensor_operation_instance/gpu/batchnorm_infer.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_batchnorm_infer.hpp"
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namespace ck {
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namespace profiler {
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template <typename XDataType,
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typename YDataType,
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typename AccDataType,
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typename ScaleDataType,
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typename BiasDataType,
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typename MeanVarDataType,
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index_t Rank,
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index_t NumBatchNormReduceDim>
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bool profile_batchnorm_infer_impl(int do_verification,
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int init_method,
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bool do_dumpout,
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bool time_kernel,
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const std::vector<size_t> inOutLengths,
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const std::vector<int> reduceDims,
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double epsilon)
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{
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if(inOutLengths.size() != Rank || reduceDims.size() != NumBatchNormReduceDim)
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{
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throw std::runtime_error("Invalid tensor lengths or number of reduce dimensions!");
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};
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std::vector<size_t> scaleBiasMeanVarLengths;
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std::vector<int> invariantDims;
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// used for calculating the effective transferred bytes by each operation
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size_t total_length;
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size_t invariant_length = 1;
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total_length =
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std::accumulate(inOutLengths.begin(), inOutLengths.end(), 1, std::multiplies<size_t>{});
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if(std::any_of(reduceDims.begin(), reduceDims.end(), [](int d) { return d < 0 || d >= Rank; }))
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throw std::runtime_error("Invalid reduce dimensions!");
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for(int dim = 0; dim < Rank; dim++)
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{
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if(std::none_of(reduceDims.begin(), reduceDims.end(), [&](int d) { return dim == d; }))
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{
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invariantDims.push_back(dim);
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scaleBiasMeanVarLengths.push_back(inOutLengths[dim]);
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invariant_length *= inOutLengths[dim];
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};
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}
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// input data of the batchnorm infer algorithm
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Tensor<XDataType> x(inOutLengths);
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Tensor<ScaleDataType> scale(scaleBiasMeanVarLengths);
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Tensor<BiasDataType> bias(scaleBiasMeanVarLengths);
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Tensor<MeanVarDataType> estimatedMean(scaleBiasMeanVarLengths);
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Tensor<MeanVarDataType> estimatedVariance(scaleBiasMeanVarLengths);
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// output data of the batchnorm infer algorithm
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Tensor<YDataType> y_ref(inOutLengths);
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Tensor<YDataType> y(inOutLengths);
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auto inOutStrides = x.mDesc.GetStrides();
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auto scaleBiasMeanVarStrides = scale.mDesc.GetStrides();
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std::size_t num_thread = std::thread::hardware_concurrency();
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const float x_mean = 0.0f;
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const float x_stddev = 1.0f;
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const float noise_stddev = 0.04f;
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// input data in normal distribution
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x.GenerateTensorValue(GeneratorTensor_4<XDataType>{x_mean, x_stddev}, num_thread);
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// initialize the estimatedMean to be values with tiny variation to the mean of the x
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// values
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estimatedMean.GenerateTensorValue(GeneratorTensor_4<MeanVarDataType>{x_mean, noise_stddev},
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num_thread);
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// initialize the estimatedVariance to be values with tiny variation to the variance of
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// the x values
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estimatedVariance.GenerateTensorValue(
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GeneratorTensor_4<MeanVarDataType>{x_stddev * x_stddev, noise_stddev}, num_thread);
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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:
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scale.GenerateTensorValue(GeneratorTensor_0<ScaleDataType>{}, num_thread);
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bias.GenerateTensorValue(GeneratorTensor_0<BiasDataType>{}, num_thread);
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break;
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case 1:
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scale.GenerateTensorValue(GeneratorTensor_1<ScaleDataType>{1}, num_thread);
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bias.GenerateTensorValue(GeneratorTensor_1<BiasDataType>{0}, num_thread);
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break;
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case 2:
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scale.GenerateTensorValue(GeneratorTensor_2<ScaleDataType>{-5, 5}, num_thread);
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bias.GenerateTensorValue(GeneratorTensor_2<BiasDataType>{-5, 5}, num_thread);
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break;
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default:
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scale.GenerateTensorValue(GeneratorTensor_3<ScaleDataType>{-1.0f, 1.0f}, num_thread);
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bias.GenerateTensorValue(GeneratorTensor_3<BiasDataType>{-1.0f, 1.0f}, num_thread);
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}
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};
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// these buffers are usually provided by the user application
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DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpaceSize());
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DeviceMem y_dev(sizeof(XDataType) * y.mDesc.GetElementSpaceSize());
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DeviceMem scale_dev(sizeof(ScaleDataType) * scale.mDesc.GetElementSpaceSize());
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DeviceMem bias_dev(sizeof(BiasDataType) * bias.mDesc.GetElementSpaceSize());
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// estimatedMean_dev
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DeviceMem estimatedMean_dev(sizeof(MeanVarDataType) *
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estimatedMean.mDesc.GetElementSpaceSize());
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// estimatedVariance_dev
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DeviceMem estimatedVariance_dev(sizeof(MeanVarDataType) *
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estimatedVariance.mDesc.GetElementSpaceSize());
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x_dev.ToDevice(x.mData.data());
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scale_dev.ToDevice(scale.mData.data());
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bias_dev.ToDevice(bias.mData.data());
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estimatedMean_dev.ToDevice(estimatedMean.mData.data());
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estimatedVariance_dev.ToDevice(estimatedVariance.mData.data());
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std::array<index_t, Rank> arrInOutLengths;
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std::array<index_t, Rank> arrInOutStrides;
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std::array<index_t, Rank - NumBatchNormReduceDim> arrScaleBiasMeanVarLengths;
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std::array<index_t, Rank - NumBatchNormReduceDim> arrScaleBiasMeanVarStrides;
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std::array<int, NumBatchNormReduceDim> arrReduceDims;
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std::copy(inOutLengths.begin(), inOutLengths.end(), arrInOutLengths.begin());
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std::copy(inOutStrides.begin(), inOutStrides.end(), arrInOutStrides.begin());
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std::copy(scaleBiasMeanVarLengths.begin(),
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scaleBiasMeanVarLengths.end(),
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arrScaleBiasMeanVarLengths.begin());
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std::copy(scaleBiasMeanVarStrides.begin(),
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scaleBiasMeanVarStrides.end(),
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arrScaleBiasMeanVarStrides.begin());
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std::copy(reduceDims.begin(), reduceDims.end(), arrReduceDims.begin());
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std::array<index_t, Rank> aligned_scaleBiasMeanVarStrides{0};
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int i = 0;
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for(auto dim : invariantDims)
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{
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assert(inOutLengths[dim] == scaleBiasMeanVarLengths[i]);
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aligned_scaleBiasMeanVarStrides[dim] = scaleBiasMeanVarStrides[i];
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i++;
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};
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using Normalize = ck::tensor_operation::element_wise::NormalizeInInfer;
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// add device batchnorm-infer instances
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using DeviceOp = ck::tensor_operation::device::DeviceElementwise<
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ck::Tuple<XDataType, MeanVarDataType, MeanVarDataType, ScaleDataType, BiasDataType>,
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ck::Tuple<YDataType>,
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Normalize,
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Rank>;
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// get device op instances
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const auto instance_ptrs =
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ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << instance_ptrs.size() << " instances" << std::endl;
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std::string best_instance_name;
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float best_avg_time = std::numeric_limits<float>::max();
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float best_gb_per_sec = 0;
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if(do_verification)
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{
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using PassThroughOp = ck::tensor_operation::element_wise::PassThrough;
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using ReferenceBatchNormInferInstance =
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ck::tensor_operation::host::ReferenceBatchNormInfer<XDataType,
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YDataType,
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AccDataType,
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ScaleDataType,
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BiasDataType,
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MeanVarDataType,
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PassThroughOp,
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Rank,
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NumBatchNormReduceDim>;
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auto batchNormInfer_ref = ReferenceBatchNormInferInstance{};
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auto argument_ptr_ref =
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batchNormInfer_ref.MakeArgumentPointer(arrInOutLengths,
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arrInOutStrides,
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arrInOutStrides,
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arrReduceDims,
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arrScaleBiasMeanVarLengths,
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arrScaleBiasMeanVarStrides,
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arrScaleBiasMeanVarStrides,
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arrScaleBiasMeanVarStrides,
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x.mData.data(),
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scale.mData.data(),
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bias.mData.data(),
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epsilon,
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PassThroughOp{},
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estimatedMean.mData.data(),
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estimatedVariance.mData.data(),
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y_ref.mData.data());
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if(!batchNormInfer_ref.IsSupportedArgument(argument_ptr_ref.get()))
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{
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std::cout << "The runtime parameters not supported by the reference instance, 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 = batchNormInfer_ref.MakeInvokerPointer();
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(void)invoker_ptr_ref->Run(argument_ptr_ref.get());
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}
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int num_kernel = 0;
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bool pass = true;
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for(auto& inst_ptr : instance_ptrs)
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{
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auto argument_ptr = inst_ptr->MakeArgumentPointer(arrInOutLengths,
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{arrInOutStrides,
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aligned_scaleBiasMeanVarStrides,
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aligned_scaleBiasMeanVarStrides,
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aligned_scaleBiasMeanVarStrides,
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aligned_scaleBiasMeanVarStrides},
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{arrInOutStrides},
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{x_dev.GetDeviceBuffer(),
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estimatedMean_dev.GetDeviceBuffer(),
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estimatedVariance_dev.GetDeviceBuffer(),
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scale_dev.GetDeviceBuffer(),
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bias_dev.GetDeviceBuffer()},
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{y_dev.GetDeviceBuffer()},
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Normalize{epsilon});
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if(inst_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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num_kernel++;
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}
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else
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{
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if(time_kernel)
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{
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std::cout << inst_ptr->GetTypeString()
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<< " skipped due to unsupported argument: " << std::endl;
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}
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continue;
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};
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auto invoker_ptr = inst_ptr->MakeInvokerPointer();
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float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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size_t num_bytes = 0;
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// inputing of x, scale, bias, outputing of y
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num_bytes += total_length * (sizeof(XDataType) + sizeof(YDataType)) +
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invariant_length *
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(sizeof(ScaleDataType) + sizeof(BiasDataType) + sizeof(MeanVarDataType));
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float gb_per_sec = num_bytes / 1.E6 / avg_time;
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if(time_kernel)
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std::cout << "Perf: " << avg_time << " ms, " << gb_per_sec << " GB/s, "
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<< inst_ptr->GetTypeString() << std::endl;
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if(avg_time < best_avg_time)
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{
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best_instance_name = inst_ptr->GetTypeString();
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best_avg_time = avg_time;
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best_gb_per_sec = gb_per_sec;
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}
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if(do_verification)
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{
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using ck::utils::check_err;
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bool single_pass;
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y_dev.FromDevice(y.mData.data());
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if constexpr(ck::is_same_v<YDataType, ck::bhalf_t>)
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single_pass = check_err(y.mData, y_ref.mData, "y results", 1e-2, 1e-2);
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else
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single_pass = check_err(y.mData, y_ref.mData, "y results", 4e-3, 4e-3);
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pass = pass && single_pass;
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};
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if(do_dumpout)
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{
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using ck::host_common::dumpBufferToFile;
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// clang-format off
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dumpBufferToFile("dump_x.bin", x.mData.data(), x.mDesc.GetElementSize());
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dumpBufferToFile("dump_y.bin", y.mData.data(), y.mDesc.GetElementSize());
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dumpBufferToFile("dump_y_ref.bin", y_ref.mData.data(), y_ref.mDesc.GetElementSize());
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// clang-format off
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};
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}
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if(time_kernel)
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{
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std::cout << "best perf = " << best_avg_time << " ms, " << best_gb_per_sec << " GB/s, "
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<< best_instance_name << std::endl;
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}
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if(num_kernel == 0)
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{
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std::cout << "Error: No kernel is applicable" << std::endl;
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return false;
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}
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return pass;
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}
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} // namespace profiler
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} // namespace ck
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