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BatchNorm forward instance/external api/profiler/tests/client example (#511)
* Update to device_batchnorm_forward base class to include all template parameters for problem description * Add batchnorm forward instances and external api * Add batchnorm forward profiler module which uses the external api * Add some comments in batchnorm_forward example to explain the dimensions in lengths[] * Replace the reference_batchnorm_forward_nhwc_c by generic reference_batchnorm_forward * Improvement to the batchnorm infer base API * Add batchnorm forward client example which shows using the batchnorm forward external API * Add test for batchnorm forward * Tuning the batchnorm profiler initialized values and error threshold * Add support for bhalf_t in instances/external api/tests * Add support for int8_t in instances/external api/tests * Add support for double in instances/external api/tests * Let ScaleDataType and BiasDataType be same as XDataType and YDataType when creating instances * Checking before running best instance in batchnorm_fwd_nhwc client example * Add checking for YElementwiseOp in batchnorm_forward external API * Add more types in batchnorm forward profiler * Add more test lengths Co-authored-by: rocking5566 <ChunYu.Lai@amd.com>
This commit is contained in:
Qianfeng
440
profiler/include/profile_batchnorm_forward_impl.hpp
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440
profiler/include/profile_batchnorm_forward_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_forward.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_batchnorm_forward.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_forward_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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bool updateMovingAverage,
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bool saveMeanAndInvVariance,
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double averageFactor,
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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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// 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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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 forward algorithm
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Tensor<XDataType> x(inOutLengths);
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Tensor<ScaleDataType> bnScale(scaleBiasMeanVarLengths);
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Tensor<BiasDataType> bnBias(scaleBiasMeanVarLengths);
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// output data of the batchnorm forward algorithm
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Tensor<YDataType> y_ref(inOutLengths);
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Tensor<YDataType> y(inOutLengths);
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Tensor<MeanVarDataType> resultSaveMean_ref(scaleBiasMeanVarLengths);
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Tensor<MeanVarDataType> resultSaveInvVariance_ref(scaleBiasMeanVarLengths);
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Tensor<MeanVarDataType> resultRunningMean_ref(scaleBiasMeanVarLengths);
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Tensor<MeanVarDataType> resultRunningVariance_ref(scaleBiasMeanVarLengths);
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auto inOutStrides = x.mDesc.GetStrides();
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auto scaleBiasMeanVarStrides = bnScale.mDesc.GetStrides();
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std::size_t num_thread = std::thread::hardware_concurrency();
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if(updateMovingAverage)
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{
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if constexpr(ck::is_same_v<XDataType, int8_t>)
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{
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x.GenerateTensorValue(GeneratorTensor_2<XDataType>{-5, 5}, num_thread);
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const float x_mean = 0.0f;
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const float x_stddev = 2.5f;
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const float noise_stddev = 0.04f;
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resultRunningMean_ref.GenerateTensorValue(
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GeneratorTensor_4<MeanVarDataType>{x_mean, noise_stddev}, num_thread);
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resultRunningVariance_ref.GenerateTensorValue(
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GeneratorTensor_4<MeanVarDataType>{x_stddev * x_stddev, noise_stddev}, num_thread);
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}
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else
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{
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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 runningMean to be values with tiny variation to the mean of the x
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// values
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resultRunningMean_ref.GenerateTensorValue(
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GeneratorTensor_4<MeanVarDataType>{x_mean, noise_stddev}, num_thread);
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// initialize the runningVariance to be values with tiny variation to the variance of
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// the x values
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resultRunningVariance_ref.GenerateTensorValue(
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GeneratorTensor_4<MeanVarDataType>{x_stddev * x_stddev, noise_stddev}, num_thread);
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};
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}
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else
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{
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if constexpr(ck::is_same_v<XDataType, int8_t>)
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x.GenerateTensorValue(GeneratorTensor_2<XDataType>{-5, 5}, num_thread);
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else
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x.GenerateTensorValue(GeneratorTensor_3<XDataType>{-1.0f, 1.0f}, num_thread);
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};
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if(do_verification)
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{
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if constexpr(ck::is_same_v<ScaleDataType, int8_t> && ck::is_same_v<BiasDataType, int8_t>)
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{
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bnScale.GenerateTensorValue(GeneratorTensor_2<ScaleDataType>{-5, 5}, num_thread);
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bnBias.GenerateTensorValue(GeneratorTensor_2<BiasDataType>{-5, 5}, num_thread);
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}
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else
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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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bnScale.GenerateTensorValue(GeneratorTensor_0<ScaleDataType>{}, num_thread);
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bnBias.GenerateTensorValue(GeneratorTensor_0<BiasDataType>{}, num_thread);
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break;
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case 1:
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bnScale.GenerateTensorValue(GeneratorTensor_1<ScaleDataType>{1}, num_thread);
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bnBias.GenerateTensorValue(GeneratorTensor_1<BiasDataType>{0}, num_thread);
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break;
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case 2:
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bnScale.GenerateTensorValue(GeneratorTensor_2<ScaleDataType>{-5, 5}, num_thread);
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bnBias.GenerateTensorValue(GeneratorTensor_2<BiasDataType>{-5, 5}, num_thread);
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break;
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default:
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bnScale.GenerateTensorValue(GeneratorTensor_3<ScaleDataType>{-1.0f, 1.0f},
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num_thread);
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bnBias.GenerateTensorValue(GeneratorTensor_3<BiasDataType>{-1.0f, 1.0f},
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num_thread);
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}
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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 bnScale_dev(sizeof(ScaleDataType) * bnScale.mDesc.GetElementSpaceSize());
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DeviceMem bnBias_dev(sizeof(BiasDataType) * bnBias.mDesc.GetElementSpaceSize());
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// mean_dev or resultSaveMean_dev
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DeviceMem resultSaveMean_dev(sizeof(MeanVarDataType) *
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resultSaveMean_ref.mDesc.GetElementSpaceSize());
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// meansquare_dev or resultSaveInvVariance_dev
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DeviceMem resultSaveInvVariance_dev(sizeof(MeanVarDataType) *
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resultSaveInvVariance_ref.mDesc.GetElementSpaceSize());
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// resultRunningMean_dev
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DeviceMem resultRunningMean_dev(sizeof(MeanVarDataType) *
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resultRunningMean_ref.mDesc.GetElementSpaceSize());
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// resultRunningVariance_dev
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DeviceMem resultRunningVariance_dev(sizeof(MeanVarDataType) *
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resultRunningVariance_ref.mDesc.GetElementSpaceSize());
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x_dev.ToDevice(x.mData.data());
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bnScale_dev.ToDevice(bnScale.mData.data());
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bnBias_dev.ToDevice(bnBias.mData.data());
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if(updateMovingAverage)
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{
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resultRunningMean_dev.ToDevice(resultRunningMean_ref.mData.data());
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resultRunningVariance_dev.ToDevice(resultRunningVariance_ref.mData.data());
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};
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// used for storing the device result for verification when updateMovingAverage is enabled
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Tensor<MeanVarDataType> resultRunningMean(scaleBiasMeanVarLengths);
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Tensor<MeanVarDataType> resultRunningVariance(scaleBiasMeanVarLengths);
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// used for storing the device result for verification when saveMeanAndInvVariance is enabled
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Tensor<MeanVarDataType> resultSaveMean(scaleBiasMeanVarLengths);
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Tensor<MeanVarDataType> resultSaveInvVariance(scaleBiasMeanVarLengths);
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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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using PassThroughOp = ck::tensor_operation::element_wise::PassThrough;
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// add device batchnorm-forward instances
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using DeviceOp = ck::tensor_operation::device::DeviceBatchNormFwd<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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// 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 ReferenceBatchNormFwdInstance =
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ck::tensor_operation::host::ReferenceBatchNormFwd<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 batchNormFwd_ref = ReferenceBatchNormFwdInstance{};
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auto argument_ptr_ref = batchNormFwd_ref.MakeArgumentPointer(
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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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bnScale.mData.data(),
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bnBias.mData.data(),
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epsilon,
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PassThroughOp{},
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y_ref.mData.data(),
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saveMeanAndInvVariance ? resultSaveMean_ref.mData.data() : nullptr,
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saveMeanAndInvVariance ? resultSaveInvVariance_ref.mData.data() : nullptr,
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averageFactor,
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updateMovingAverage ? resultRunningMean_ref.mData.data() : nullptr,
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updateMovingAverage ? resultRunningVariance_ref.mData.data() : nullptr);
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if(!batchNormFwd_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 = batchNormFwd_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(
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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_dev.GetDeviceBuffer(),
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bnScale_dev.GetDeviceBuffer(),
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bnBias_dev.GetDeviceBuffer(),
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epsilon,
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PassThroughOp{},
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y_dev.GetDeviceBuffer(),
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saveMeanAndInvVariance ? resultSaveMean_dev.GetDeviceBuffer() : nullptr,
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saveMeanAndInvVariance ? resultSaveInvVariance_dev.GetDeviceBuffer() : nullptr,
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averageFactor,
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updateMovingAverage ? resultRunningMean_dev.GetDeviceBuffer() : nullptr,
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updateMovingAverage ? resultRunningVariance_dev.GetDeviceBuffer() : nullptr);
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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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size_t workspace_sz = inst_ptr->GetWorkSpaceSize(argument_ptr.get());
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DeviceMem workspace_dev(workspace_sz);
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inst_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace_dev.GetDeviceBuffer());
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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 * (sizeof(ScaleDataType) + sizeof(BiasDataType));
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// outputing of mean, inv-variance
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num_bytes += saveMeanAndInvVariance ? invariant_length * sizeof(MeanVarDataType) * 2 : 0;
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// updating of moving mean, variance
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num_bytes += updateMovingAverage ? invariant_length * sizeof(MeanVarDataType) * 4 : 0;
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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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if(updateMovingAverage)
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{
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resultRunningMean_dev.FromDevice(resultRunningMean.mData.data());
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resultRunningVariance_dev.FromDevice(resultRunningVariance.mData.data());
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// clang-format off
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single_pass = single_pass && check_err(resultRunningMean.mData, resultRunningMean_ref.mData, "average mean results", 1.5e-5, 1.5e-5);
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single_pass = single_pass && check_err(resultRunningVariance.mData, resultRunningVariance_ref.mData, "average variance results", 1e-5, 1e-5);
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// clang-format on
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};
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if(saveMeanAndInvVariance)
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{
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resultSaveMean_dev.FromDevice(resultSaveMean.mData.data());
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resultSaveInvVariance_dev.FromDevice(resultSaveInvVariance.mData.data());
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// clang-format off
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single_pass = single_pass && check_err(resultSaveMean.mData, resultSaveMean_ref.mData, "mean results", 3e-5, 3e-5);
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single_pass = single_pass && check_err(resultSaveInvVariance.mData, resultSaveInvVariance_ref.mData, "inv-variance results", 7e-5, 7e-5);
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// clang-format on
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};
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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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if(saveMeanAndInvVariance)
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{
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// clang-format off
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dumpBufferToFile("dump_mean.bin", resultSaveMean.mData.data(), resultSaveMean.mDesc.GetElementSize());
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dumpBufferToFile("dump_mean_ref.bin", resultSaveMean_ref.mData.data(), resultSaveMean_ref.mDesc.GetElementSize());
|
||||
dumpBufferToFile("dump_invvar.bin", resultSaveInvVariance.mData.data(), resultSaveInvVariance.mDesc.GetElementSize());
|
||||
dumpBufferToFile("dump_invvar_ref.bin", resultSaveInvVariance_ref.mData.data(), resultSaveInvVariance_ref.mDesc.GetElementSize());
|
||||
// clang-format on
|
||||
};
|
||||
};
|
||||
}
|
||||
|
||||
if(time_kernel)
|
||||
{
|
||||
std::cout << "best perf = " << best_avg_time << " ms, " << best_gb_per_sec << " GB/s, "
|
||||
<< best_instance_name << std::endl;
|
||||
}
|
||||
|
||||
if(num_kernel == 0)
|
||||
{
|
||||
std::cout << "Error: No kernel is applicable" << std::endl;
|
||||
return false;
|
||||
}
|
||||
|
||||
return pass;
|
||||
}
|
||||
|
||||
} // namespace profiler
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user