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BatchNorm backward implementation (#461)
* Implemented batchnorm-backward Blockwise and Multiblock kernels
* Add batchnorm-backward device op
* Add batchnorm-backward host-reference op
* Add batchnorm-backward example
* Parameters renaming in batchnorm backward kernels and device op
* Change in the example to loose the threshold for ScaleDiff checking
* Add comments to explain the implementation of batchnorm-backward
* Parameters renaming again in batchnorm backward kernels
* Improve the expression calculation for performance
* Add batchnorm backward to README
* Add comments to explain inv-variance in batchnorm forward and backward
* Renaming the batchnorm forward training and inferring examples
* Add/update the comments for batchnorm-backward kernels
* Renaming again
* Add block_sync_lds between two consecutive blockwise reductions
* Move common expression 1/N out of the static_for loops
* Add dy_elementwise_op
* Renaming in backward example again
* Add checking for reduceDims in reference_batchnorm_backward
* Update to comments and codes format
* Rename in the comments
* Remove common expression out of the loop in reference_batchnorm_backward_nhwc_c
* Add block_sync_lds() between blockwise reduction again
* Fix comments again
* Remove int8 from batchnorm-forward instances since it is not needed for forward training and could fail test
[ROCm/composable_kernel commit: 44789d992a]
This commit is contained in:
Qianfeng
@@ -1,2 +1,3 @@
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add_example_executable(example_batchnorm_forward batchnorm_forward_nhwc.cpp)
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add_example_executable(example_batchnorm_infer batchnorm_infer_nhwc.cpp)
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add_example_executable(example_batchnorm_forward_training batchnorm_forward_training_nhwc.cpp)
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add_example_executable(example_batchnorm_forward_inferring batchnorm_forward_inferring_nhwc.cpp)
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add_example_executable(example_batchnorm_backward batchnorm_backward_nhwc.cpp)
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@@ -53,4 +53,29 @@ Start running 10 times...
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Perf: 1.28235 ms, 523.329 GB/s
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```
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## Run ```batchnorm backward nhwc```
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```bash
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# -D <xxx> : input 4-d tensor lengths
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# -v <x> : verification (0=no, 1=yes)
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Arg1: data type (0: fp16, 1: fp32, 3: int8, 5: bp16, 6: fp64)
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Arg2 -- 1/0 to indicate whether to use saved mean and invVariance
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Arg3 -- init method used for dy and bnScale (0=no init, 1=single integer value, 2=scope integer value, 3=decimal value)
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Arg4 -- time kernel (0=no, 1=yes)
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Arg5: use multi-block welford (0=n0, 1=yes)
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./bin/example_batchnorm_backward -D 128,16,3,1024 -v 1 0 0 3 1 1
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```
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Result
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```
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./bin/example_batchnorm_backward -D 128,16,3,1024 -v 1 0 0 3 1 1
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launch_and_time_kernel: grid_dim {6144, 1, 1}, block_dim {256, 1, 1}
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Warm up 1 time
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Start running 10 times...
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launch_and_time_kernel: grid_dim {6144, 1, 1}, block_dim {256, 1, 1}
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Warm up 1 time
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Start running 10 times...
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launch_and_time_kernel: grid_dim {6144, 1, 1}, block_dim {256, 1, 1}
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Warm up 1 time
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Start running 10 times...
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Perf: 0.411026 ms, 91.8702 GB/s
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```
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502
example/34_batchnorm/batchnorm_backward_nhwc.cpp
Normal file
502
example/34_batchnorm/batchnorm_backward_nhwc.cpp
Normal file
@@ -0,0 +1,502 @@
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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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#include <limits>
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#include <iostream>
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#include <getopt.h>
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#include "ck/ck.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/reference_tensor_operation/cpu/reference_batchnorm_backward_nhwc_c.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_batchnorm_backward_impl.hpp"
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static struct option long_options[] = {{"inOutLengths", required_argument, nullptr, 'D'},
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{"verify", required_argument, nullptr, 'v'},
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{"help", no_argument, nullptr, '?'},
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{nullptr, 0, nullptr, 0}};
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class BatchNormBwdArg
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{
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private:
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int option_index = 0;
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public:
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std::vector<size_t> inOutLengths;
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bool do_verification = false;
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bool haveSavedMeanInvVar;
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int data_type = 0;
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int init_method = 3;
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bool time_kernel = false;
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bool use_multiblock_welford = false;
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public:
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void show_usage(const char* cmd)
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{
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// clang-format off
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std::cout << "Usage of " << cmd << std::endl;
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std::cout << "--inOutLengths or -D, comma separated list of input tensor dimension lengths, must have 4 integers for nhwc" << std::endl;
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std::cout << "--verify or -v, 1/0 to indicate whether to verify the result by comparing with the host-based batch-normalization" << std::endl;
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std::cout << "Arg1: data type (0: fp16, 1: fp32, 3: int8, 5: bp16, 6: fp64)" << std::endl;
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std::cout << "Arg2 -- 1/0 to indicate whether to use saved mean and invVariance" << std::endl;
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std::cout << "Arg3 -- init method used for dy and bnScale (0=no init, 1=single integer value, 2=scope integer value, 3=decimal value)" << std::endl;
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std::cout << "Arg4 -- time kernel (0=no, 1=yes)" << std::endl;
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std::cout << "Arg5: use multi-block welford (0=n0, 1=yes)" << std::endl;
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// clang-format on
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};
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int processArgs(int argc, char* argv[])
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{
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using ck::host_common::getTypeValuesFromString;
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int ch;
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while(1)
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{
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ch = getopt_long(argc, argv, "D:v:", long_options, &option_index);
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if(ch == -1)
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break;
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switch(ch)
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{
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case 'D':
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if(!optarg)
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throw std::runtime_error("Invalid option format!");
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inOutLengths = getTypeValuesFromString<size_t>(optarg);
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if(inOutLengths.size() != 4)
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throw std::runtime_error(
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"NHWC tensor layout should have 4 length values specified!");
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break;
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case 'v':
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if(!optarg)
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throw std::runtime_error("Invalid option format!");
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do_verification = static_cast<bool>(std::atoi(optarg));
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break;
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case '?':
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if(std::string(long_options[option_index].name) == "help")
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{
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show_usage(argv[0]);
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return (-1);
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};
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break;
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default: show_usage(argv[0]); return (-1);
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};
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};
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if(optind + 5 > argc)
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throw std::runtime_error("Invalid cmd-line arguments, more argumetns are needed!");
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data_type = std::atoi(argv[optind++]);
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haveSavedMeanInvVar = std::atoi(argv[optind++]);
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init_method = std::atoi(argv[optind++]);
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time_kernel = static_cast<bool>(std::atoi(argv[optind++]));
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use_multiblock_welford = static_cast<bool>(std::atoi(argv[optind]));
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return (0);
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};
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};
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using namespace ck;
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template <typename InOutDataType, typename AccDataType, bool UseMultiblockInK>
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bool bnorm_bwd_nhwc_test(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> inOutLengths,
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bool haveSavedMeanInvVar,
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double epsilon)
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{
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// for NHWC BatchNorm calculation of mean and meansquare
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constexpr index_t Rank = 4;
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constexpr index_t NumReduceDim = 3;
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const std::vector<size_t> scaleBiasMeanVarLengths = {inOutLengths[3]};
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// input data of the batchnorm backward algorithm
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Tensor<InOutDataType> x(inOutLengths);
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Tensor<InOutDataType> dy(inOutLengths);
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Tensor<AccDataType> bnScale(scaleBiasMeanVarLengths);
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Tensor<AccDataType> savedMean(scaleBiasMeanVarLengths);
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Tensor<AccDataType> savedInvVar(scaleBiasMeanVarLengths);
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// savedVariance is only used for initializing savedInvVar
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Tensor<AccDataType> savedVariance(scaleBiasMeanVarLengths);
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// output data of the batchnorm backward algorithm
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Tensor<InOutDataType> dx_ref(inOutLengths);
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Tensor<InOutDataType> dx(inOutLengths);
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Tensor<AccDataType> dscale(scaleBiasMeanVarLengths);
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Tensor<AccDataType> dbias(scaleBiasMeanVarLengths);
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Tensor<AccDataType> dscale_ref(scaleBiasMeanVarLengths);
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Tensor<AccDataType> dbias_ref(scaleBiasMeanVarLengths);
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auto inOutStrides = dy.mDesc.GetStrides();
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auto scaleBiasMeanVarStrides = dscale.mDesc.GetStrides();
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std::size_t num_thread = std::thread::hardware_concurrency();
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if(haveSavedMeanInvVar)
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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 = 0.0001f;
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// input data in normal distribution
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x.GenerateTensorValue(GeneratorTensor_4<InOutDataType>{x_mean, x_stddev}, num_thread);
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// initialize the savedMean to be values with tiny variation to the mean of the x values
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savedMean.GenerateTensorValue(GeneratorTensor_4<AccDataType>{x_mean, noise_stddev},
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num_thread);
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// initialize the variance to be values with tiny variation to the variance of the x values
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savedVariance.GenerateTensorValue(
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GeneratorTensor_4<AccDataType>{x_stddev * x_stddev, noise_stddev}, num_thread);
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auto it_src = savedVariance.mData.begin();
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auto it_dst = savedInvVar.mData.begin();
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float tmp_epsilon = std::numeric_limits<float>::epsilon();
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while(it_src != savedVariance.mData.end())
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{
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*it_dst = type_convert<AccDataType>(
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1.0f / std::sqrtf(type_convert<float>(*it_src) + tmp_epsilon));
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it_src++;
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it_dst++;
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};
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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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// input data in normal distribution
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x.GenerateTensorValue(GeneratorTensor_4<InOutDataType>{x_mean, x_stddev}, num_thread);
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};
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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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dy.GenerateTensorValue(GeneratorTensor_0<InOutDataType>{}, num_thread);
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bnScale.GenerateTensorValue(GeneratorTensor_0<InOutDataType>{}, num_thread);
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break;
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case 1:
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dy.GenerateTensorValue(GeneratorTensor_1<InOutDataType>{1}, num_thread);
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bnScale.GenerateTensorValue(GeneratorTensor_1<InOutDataType>{1}, num_thread);
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break;
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case 2:
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dy.GenerateTensorValue(GeneratorTensor_2<InOutDataType>{-5, 5}, num_thread);
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bnScale.GenerateTensorValue(GeneratorTensor_2<InOutDataType>{-5, 5}, num_thread);
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break;
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default:
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dy.GenerateTensorValue(GeneratorTensor_3<InOutDataType>{-0.2f, 0.2f}, num_thread);
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bnScale.GenerateTensorValue(GeneratorTensor_3<InOutDataType>{-0.5f, 0.5f}, num_thread);
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}
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};
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// input data of the batchnorm backward algorithm
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DeviceMem x_dev(sizeof(InOutDataType) * x.mDesc.GetElementSpaceSize());
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DeviceMem dy_dev(sizeof(InOutDataType) * dy.mDesc.GetElementSpaceSize());
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DeviceMem bnScale_dev(sizeof(AccDataType) * bnScale.mDesc.GetElementSpaceSize());
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DeviceMem savedMean_dev(sizeof(AccDataType) * savedMean.mDesc.GetElementSpaceSize());
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DeviceMem savedInvVar_dev(sizeof(AccDataType) * savedInvVar.mDesc.GetElementSpaceSize());
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// output data of the batchnorm backward algorithm
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DeviceMem dx_dev(sizeof(InOutDataType) * dx.mDesc.GetElementSpaceSize());
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DeviceMem dscale_dev(sizeof(AccDataType) * dscale.mDesc.GetElementSpaceSize());
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DeviceMem dbias_dev(sizeof(AccDataType) * dbias.mDesc.GetElementSpaceSize());
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x_dev.ToDevice(x.mData.data());
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dy_dev.ToDevice(dy.mData.data());
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bnScale_dev.ToDevice(bnScale.mData.data());
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if(haveSavedMeanInvVar)
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{
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savedMean_dev.ToDevice(savedMean.mData.data());
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savedInvVar_dev.ToDevice(savedInvVar.mData.data());
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};
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std::array<index_t, Rank> i_inOutLengths;
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std::array<index_t, Rank> i_inOutStrides;
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std::array<index_t, Rank - NumReduceDim> i_scaleBiasMeanVarLengths;
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std::array<index_t, Rank - NumReduceDim> i_scaleBiasMeanVarStrides;
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std::copy(inOutLengths.begin(), inOutLengths.end(), i_inOutLengths.begin());
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std::copy(inOutStrides.begin(), inOutStrides.end(), i_inOutStrides.begin());
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std::copy(scaleBiasMeanVarLengths.begin(),
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scaleBiasMeanVarLengths.end(),
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i_scaleBiasMeanVarLengths.begin());
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std::copy(scaleBiasMeanVarStrides.begin(),
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scaleBiasMeanVarStrides.end(),
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i_scaleBiasMeanVarStrides.begin());
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using PassThroughOp = ck::tensor_operation::element_wise::PassThrough;
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using DeviceBatchNormBwdInstance =
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ck::tensor_operation::device::DeviceBatchNormBwdImpl<InOutDataType,
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InOutDataType,
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InOutDataType,
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AccDataType,
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AccDataType, // ScaleDataType
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AccDataType, // BiasDataType
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AccDataType, // MeanVarDataType
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PassThroughOp,
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Rank,
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NumReduceDim,
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UseMultiblockInK,
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256,
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16,
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16,
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1,
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2,
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0,
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1, // XSrcVectorSize
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1, // DySrcVectorSize
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1, // DxDstVectorSize
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1, // ScaleSrcDstVectorSize
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1, // BiasDstVectorSize
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1>; // MeanVarSrcVectorSize
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auto batchnorm_bwd = DeviceBatchNormBwdInstance{};
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auto argument_ptr = batchnorm_bwd.MakeArgumentPointer(
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i_inOutLengths,
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i_inOutStrides,
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i_inOutStrides,
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i_inOutStrides,
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{0, 1, 2},
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i_scaleBiasMeanVarLengths,
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i_scaleBiasMeanVarStrides,
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i_scaleBiasMeanVarStrides,
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i_scaleBiasMeanVarStrides,
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x_dev.GetDeviceBuffer(),
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dy_dev.GetDeviceBuffer(),
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bnScale_dev.GetDeviceBuffer(),
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haveSavedMeanInvVar ? savedMean_dev.GetDeviceBuffer() : nullptr,
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haveSavedMeanInvVar ? savedInvVar_dev.GetDeviceBuffer() : nullptr,
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epsilon,
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PassThroughOp{},
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dx_dev.GetDeviceBuffer(),
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dscale_dev.GetDeviceBuffer(),
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dbias_dev.GetDeviceBuffer());
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if(!batchnorm_bwd.IsSupportedArgument(argument_ptr.get()))
|
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{
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std::cout << "The runtime parameters seems not supported by the BatchNorm device instance, "
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"exiting!"
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<< std::endl;
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return (false);
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};
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size_t workspace_sz = batchnorm_bwd.GetWorkSpaceSize(argument_ptr.get());
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DeviceMem workspace_dev(workspace_sz);
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batchnorm_bwd.SetWorkSpacePointer(argument_ptr.get(), workspace_dev.GetDeviceBuffer());
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auto invoker_ptr = batchnorm_bwd.MakeInvokerPointer();
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if(time_kernel)
|
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{
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float avg_time = 0.0f;
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size_t num_bytes = 0;
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size_t total_length = inOutLengths[0] * inOutLengths[1] * inOutLengths[2] * inOutLengths[3];
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size_t invariant_length = inOutLengths[3];
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avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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// inputing of x, dy, scale, outputing of dx, dscale, dbias
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num_bytes +=
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total_length * sizeof(InOutDataType) * 3 + invariant_length * sizeof(AccDataType) * 3;
|
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// outputing of mean, inv-variance
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num_bytes += haveSavedMeanInvVar ? invariant_length * sizeof(AccDataType) * 2 : 0;
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|
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float gb_per_sec = num_bytes / 1.E6 / avg_time;
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|
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std::cout << "Perf: " << avg_time << " ms, " << gb_per_sec << " GB/s" << std::endl;
|
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}
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else
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(void)invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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|
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bool pass = true;
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||||
|
||||
if(do_verification)
|
||||
{
|
||||
using ReferenceBatchNormBwdInstance =
|
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ck::tensor_operation::host::ReferenceBatchNormBwd_Input_N_H_W_C_Output_C<InOutDataType,
|
||||
InOutDataType,
|
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InOutDataType,
|
||||
AccDataType,
|
||||
AccDataType,
|
||||
AccDataType,
|
||||
AccDataType,
|
||||
PassThroughOp>;
|
||||
|
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auto batchNormBwd_ref = ReferenceBatchNormBwdInstance{};
|
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|
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auto argument_ptr_ref = batchNormBwd_ref.MakeArgumentPointer(
|
||||
i_inOutLengths,
|
||||
i_inOutStrides,
|
||||
i_inOutStrides,
|
||||
i_inOutStrides,
|
||||
{0, 1, 2},
|
||||
i_scaleBiasMeanVarLengths,
|
||||
i_scaleBiasMeanVarStrides,
|
||||
i_scaleBiasMeanVarStrides,
|
||||
i_scaleBiasMeanVarStrides,
|
||||
x.mData.data(),
|
||||
dy.mData.data(),
|
||||
bnScale.mData.data(),
|
||||
haveSavedMeanInvVar ? savedMean.mData.data() : nullptr,
|
||||
haveSavedMeanInvVar ? savedInvVar.mData.data() : nullptr,
|
||||
epsilon,
|
||||
PassThroughOp{},
|
||||
dx_ref.mData.data(),
|
||||
dscale_ref.mData.data(),
|
||||
dbias_ref.mData.data());
|
||||
|
||||
if(!batchNormBwd_ref.IsSupportedArgument(argument_ptr_ref.get()))
|
||||
{
|
||||
std::cout
|
||||
<< "The runtime parameters seems not supported by the device instance, exiting!"
|
||||
<< std::endl;
|
||||
return (false);
|
||||
};
|
||||
|
||||
auto invoker_ptr_ref = batchNormBwd_ref.MakeInvokerPointer();
|
||||
|
||||
(void)invoker_ptr_ref->Run(argument_ptr_ref.get());
|
||||
|
||||
dx_dev.FromDevice(dx.mData.data());
|
||||
dscale_dev.FromDevice(dscale.data());
|
||||
dbias_dev.FromDevice(dbias.data());
|
||||
|
||||
// clang-format off
|
||||
pass = pass && ck::utils::check_err(dbias.mData, dbias_ref.mData, "dBias result:", 1e-5, 1e-5);
|
||||
pass = pass && ck::utils::check_err(dscale.mData, dscale_ref.mData, "dScale result:", 1e-5, 2e-4);
|
||||
pass = pass && ck::utils::check_err(dx.mData, dx_ref.mData, "dx result:");
|
||||
// clang-format on
|
||||
};
|
||||
|
||||
return (pass);
|
||||
};
|
||||
|
||||
static const double epsilon = std::numeric_limits<float>::epsilon();
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
bool pass = true;
|
||||
|
||||
if(argc > 1)
|
||||
{
|
||||
BatchNormBwdArg arg;
|
||||
|
||||
if(arg.processArgs(argc, argv) < 0)
|
||||
return (-1);
|
||||
|
||||
if(arg.data_type == 0)
|
||||
{
|
||||
if(arg.use_multiblock_welford)
|
||||
pass = bnorm_bwd_nhwc_test<ck::half_t, float, true>(arg.do_verification,
|
||||
arg.init_method,
|
||||
arg.time_kernel,
|
||||
arg.inOutLengths,
|
||||
arg.haveSavedMeanInvVar,
|
||||
epsilon);
|
||||
else
|
||||
pass = bnorm_bwd_nhwc_test<ck::half_t, float, false>(arg.do_verification,
|
||||
arg.init_method,
|
||||
arg.time_kernel,
|
||||
arg.inOutLengths,
|
||||
arg.haveSavedMeanInvVar,
|
||||
epsilon);
|
||||
}
|
||||
else if(arg.data_type == 1)
|
||||
{
|
||||
if(arg.use_multiblock_welford)
|
||||
pass = bnorm_bwd_nhwc_test<float, float, true>(arg.do_verification,
|
||||
arg.init_method,
|
||||
arg.time_kernel,
|
||||
arg.inOutLengths,
|
||||
arg.haveSavedMeanInvVar,
|
||||
epsilon);
|
||||
else
|
||||
pass = bnorm_bwd_nhwc_test<float, float, false>(arg.do_verification,
|
||||
arg.init_method,
|
||||
arg.time_kernel,
|
||||
arg.inOutLengths,
|
||||
arg.haveSavedMeanInvVar,
|
||||
epsilon);
|
||||
}
|
||||
else if(arg.data_type == 5)
|
||||
{
|
||||
if(arg.use_multiblock_welford)
|
||||
pass = bnorm_bwd_nhwc_test<ck::bhalf_t, float, true>(arg.do_verification,
|
||||
arg.init_method,
|
||||
arg.time_kernel,
|
||||
arg.inOutLengths,
|
||||
arg.haveSavedMeanInvVar,
|
||||
epsilon);
|
||||
else
|
||||
pass = bnorm_bwd_nhwc_test<ck::bhalf_t, float, false>(arg.do_verification,
|
||||
arg.init_method,
|
||||
arg.time_kernel,
|
||||
arg.inOutLengths,
|
||||
arg.haveSavedMeanInvVar,
|
||||
epsilon);
|
||||
}
|
||||
else if(arg.data_type == 6)
|
||||
{
|
||||
if(arg.use_multiblock_welford)
|
||||
pass = bnorm_bwd_nhwc_test<double, double, true>(arg.do_verification,
|
||||
arg.init_method,
|
||||
arg.time_kernel,
|
||||
arg.inOutLengths,
|
||||
arg.haveSavedMeanInvVar,
|
||||
epsilon);
|
||||
else
|
||||
pass = bnorm_bwd_nhwc_test<double, double, false>(arg.do_verification,
|
||||
arg.init_method,
|
||||
arg.time_kernel,
|
||||
arg.inOutLengths,
|
||||
arg.haveSavedMeanInvVar,
|
||||
epsilon);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
pass = bnorm_bwd_nhwc_test<ck::half_t, float, true>(true,
|
||||
3,
|
||||
false, // don't time kernel
|
||||
{128, 16, 6, 512},
|
||||
false,
|
||||
epsilon);
|
||||
|
||||
pass = pass && bnorm_bwd_nhwc_test<ck::half_t, float, false>(true,
|
||||
3,
|
||||
false, // don't time kernel
|
||||
{128, 16, 3, 1024},
|
||||
false,
|
||||
epsilon);
|
||||
};
|
||||
|
||||
return (pass ? 0 : 1);
|
||||
}
|
||||
Reference in New Issue
Block a user