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sync_batch_norm-inl.h
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sync_batch_norm-inl.h
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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
/*!
* Copyright (c) 2018 by Contributors
* \file sync_batch_norm-inl.h
* \brief Synchronized BatchNorm modified from BatchNormV1
* \author Hang Zhang
*/
#ifndef MXNET_OPERATOR_CONTRIB_SYNC_BATCH_NORM_INL_H_
#define MXNET_OPERATOR_CONTRIB_SYNC_BATCH_NORM_INL_H_
#include <dmlc/logging.h>
#include <dmlc/parameter.h>
#include <mxnet/operator.h>
#include <condition_variable>
#include <map>
#include <vector>
#include <string>
#include <utility>
#include "../operator_common.h"
#include "../mshadow_op.h"
namespace mxnet {
namespace op {
namespace syncbatchnorm {
enum BatchNormOpInputs {kData, kGamma, kBeta};
enum BatchNormOpOutputs {kOut, kMean, kVar};
enum BatchNormOpAuxiliary {kMovingMean, kMovingVar};
enum BatchNormBackResource {kTempSpace};
} // namespace syncbatchnorm
struct SyncBatchNormParam : public dmlc::Parameter<SyncBatchNormParam> {
float eps;
float momentum;
bool fix_gamma;
bool use_global_stats;
bool output_mean_var;
int ndev;
std::string key;
DMLC_DECLARE_PARAMETER(SyncBatchNormParam) {
DMLC_DECLARE_FIELD(eps).set_default(1e-3f)
.describe("Epsilon to prevent div 0");
DMLC_DECLARE_FIELD(momentum).set_default(0.9f)
.describe("Momentum for moving average");
DMLC_DECLARE_FIELD(fix_gamma).set_default(true)
.describe("Fix gamma while training");
DMLC_DECLARE_FIELD(use_global_stats).set_default(false)
.describe("Whether use global moving statistics instead of local batch-norm. "
"This will force change batch-norm into a scale shift operator.");
DMLC_DECLARE_FIELD(output_mean_var).set_default(false)
.describe("Output All,normal mean and var");
DMLC_DECLARE_FIELD(ndev).set_default(1)
.describe("The count of GPU devices");
DMLC_DECLARE_FIELD(key)
.set_default("")
.describe("Hash key for synchronization, please set the same hash key for same layer, "
"Block.prefix is typically used as in :class:`gluon.nn.contrib.SyncBatchNorm`.");
}
};
// Modified from https://github.com/brucechin/SharedTensor
template<class T>
class SharedND {
private:
int num_devices_;
T mean_;
T *data_;
bool *flag_;
bool mean_ready_ = false;
bool data_inited_ = false;
std::mutex mutex_;
public:
explicit SharedND(int ndev) :num_devices_(ndev) {
flag_ = new bool[ndev];
data_ = new T[ndev];
memset(flag_, false, ndev * sizeof(bool));
}
~SharedND() {
if (data_inited_) mshadow::FreeSpace(&mean_);
delete [] flag_;
delete [] data_;
}
void Init(mshadow::Shape<1> shape) {
std::lock_guard<std::mutex> lock(mutex_);
if (!data_inited_) {
for (int i = 0; i < num_devices_; i++) {
data_[i] = mshadow::NewTensor<cpu, real_t>(shape, 0.0f);
}
mean_ = mshadow::NewTensor<cpu, real_t>(shape, 0.0f);
data_inited_ = true;
}
}
T* Retrieve(mshadow::Shape<1> shape, int index) {
// Retrieve a pointer for copying values
if (!data_inited_) {
Init(shape);
}
if (flag_[index] == false) {
return &data_[index];
} else {
return nullptr;
}
}
bool SetReady(int index) {
// Set data ready after copying
if (flag_[index] == false) {
flag_[index] = true;
return true;
} else {
return false;
}
}
T Pop(int index) {
// Pop the mean value after suming up
std::lock_guard<std::mutex> lock(mutex_);
while (!MeanReady()) {}
flag_[index] = false;
T tmp = mean_;
ResetMean();
return tmp;
}
bool MeanReady() {
if (mean_ready_) {
return true;
}
for (int i = 0; i < num_devices_; i++) {
if (!flag_[i]) {
return false;
}
}
for (int i = 1; i < num_devices_; i++) {
data_[0] += data_[i];
}
mean_ = data_[0] * 1.0f / num_devices_;
mean_ready_ = true;
return true;
}
void ResetMean() {
for (int i = 0; i < num_devices_; i++) {
if (flag_[i]) return;
}
mean_ready_ = false;
}
};
template<class T>
class GlobalShared {
public:
T* Register(const std::string &key, int ndev) {
std::lock_guard<std::mutex> lock(mutex_);
auto it = registry_.find(key);
if (it != registry_.end()) return it->second;
T *newT = new T(ndev);
registry_[key] = newT;
return newT;
}
~GlobalShared() {
for (auto it = registry_.begin(); it != registry_.end(); it++) {
T *ptr = it->second;
delete ptr;
}
}
private:
std::mutex mutex_;
std::map<std::string, T*> registry_;
};
template<class T>
class GlobalSharedRank {
public:
T Register(const std::string &key, int ndev) {
std::lock_guard<std::mutex> lock(mutex_);
auto it = registry_.find(key);
if (it != registry_.end()) {
T* tmpT = it->second;
*tmpT = (*tmpT == ndev - 1) ? 0 : *tmpT + 1;
return *tmpT;
}
T *newT = new T(0);
registry_[key] = newT;
return *newT;
}
~GlobalSharedRank() {
for (auto it = registry_.begin(); it != registry_.end(); it++) {
T *ptr = it->second;
delete ptr;
}
}
private:
std::mutex mutex_;
std::map<std::string, T*> registry_;
};
class Barrier {
private:
std::mutex mutex_;
std::condition_variable cv_;
std::size_t count_;
std::size_t total_count_;
public:
explicit Barrier(std::size_t count) : count_{count}, total_count_{count} { }
void Wait() {
std::unique_lock<std::mutex> lock{mutex_};
if (--count_ == 0) {
count_ = total_count_;
cv_.notify_all();
} else {
cv_.wait(lock, [this] { return count_ == total_count_; });
}
}
};
// Global variables for Synchronizations
static GlobalSharedRank<int> global_shared_rank_forward;
static GlobalSharedRank<int> global_shared_rank_backward;
static GlobalShared<Barrier> global_shared_barrier_forward;
static GlobalShared<Barrier> global_shared_barrier_backward;
static GlobalShared<SharedND<mshadow::Tensor<cpu, 1, real_t>>> global_shared_mean;
static GlobalShared<SharedND<mshadow::Tensor<cpu, 1, real_t>>> global_shared_var;
static GlobalShared<SharedND<mshadow::Tensor<cpu, 1, real_t>>> global_shared_grad;
static GlobalShared<SharedND<mshadow::Tensor<cpu, 1, real_t>>> global_shared_prod;
template<typename xpu>
class SyncBatchNorm : public Operator {
public:
explicit SyncBatchNorm(SyncBatchNormParam param) {
this->param_ = param;
}
virtual void Forward(const OpContext &ctx,
const std::vector<TBlob> &in_data,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &out_data,
const std::vector<TBlob> &aux_states) {
using namespace mshadow;
using namespace mshadow::expr;
CHECK_EQ(in_data.size(), 3U);
CHECK_EQ(aux_states.size(), 2U);
if (ctx.is_train) {
CHECK_EQ(out_data.size(), 3U);
CHECK_EQ(req.size(), 3U);
} else {
CHECK_GE(out_data.size(), 1U);
CHECK_GE(req.size(), 1U);
CHECK_EQ(req[syncbatchnorm::kOut], kWriteTo);
}
Stream<xpu> *s = ctx.get_stream<xpu>();
const real_t scale = static_cast<real_t>(in_data[syncbatchnorm::kData].shape_[1]) /
static_cast<real_t>(in_data[syncbatchnorm::kData].shape_.Size());
Tensor<xpu, 4> data;
Tensor<xpu, 4> out;
if (in_data[syncbatchnorm::kData].ndim() == 2) {
Shape<4> dshape = Shape4(in_data[syncbatchnorm::kData].shape_[0],
in_data[syncbatchnorm::kData].shape_[1], 1, 1);
data = in_data[syncbatchnorm::kData].get_with_shape<xpu, 4, real_t>(dshape, s);
out = out_data[syncbatchnorm::kOut].get_with_shape<xpu, 4, real_t>(dshape, s);
} else {
data = in_data[syncbatchnorm::kData].get<xpu, 4, real_t>(s);
out = out_data[syncbatchnorm::kOut].get<xpu, 4, real_t>(s);
}
Tensor<xpu, 1> slope = in_data[syncbatchnorm::kGamma].get<xpu, 1, real_t>(s);
Tensor<xpu, 1> bias = in_data[syncbatchnorm::kBeta].get<xpu, 1, real_t>(s);
Tensor<xpu, 1> moving_mean = aux_states[syncbatchnorm::kMovingMean].get<xpu, 1, real_t>(s);
Tensor<xpu, 1> moving_var = aux_states[syncbatchnorm::kMovingVar].get<xpu, 1, real_t>(s);
if (param_.fix_gamma) slope = 1.f;
// whether use global statistics
if (ctx.is_train && !param_.use_global_stats) {
// get my rank
Barrier *global_barrier = global_shared_barrier_forward.Register(param_.key, param_.ndev);
int myRank = global_shared_rank_forward.Register(param_.key, param_.ndev);
// get the mean and var
Tensor<xpu, 1> mean = out_data[syncbatchnorm::kMean].get<xpu, 1, real_t>(s);
Tensor<xpu, 1> var = out_data[syncbatchnorm::kVar].get<xpu, 1, real_t>(s);
CHECK(req[syncbatchnorm::kMean] == kNullOp || req[syncbatchnorm::kMean] == kWriteTo);
CHECK(req[syncbatchnorm::kVar] == kNullOp || req[syncbatchnorm::kVar] == kWriteTo);
// E(x) and E(x^2)
mean = scale * sumall_except_dim<1>(data);
var = scale * sumall_except_dim<1>(F<mshadow_op::square>(data));
SharedND<mshadow::Tensor<cpu, 1, real_t>> *sharedMean =
global_shared_mean.Register(param_.key, param_.ndev);
SharedND<mshadow::Tensor<cpu, 1, real_t>> *sharedVar =
global_shared_var.Register(param_.key, param_.ndev);
// copy to cpu, push and pull
Tensor<cpu, 1, real_t>* mean_cpu_ptr = sharedMean->Retrieve(mean.shape_, myRank);
Tensor<cpu, 1, real_t>* var_cpu_ptr = sharedVar->Retrieve(mean.shape_, myRank);
mshadow::Copy(*mean_cpu_ptr, mean, s);
mshadow::Copy(*var_cpu_ptr, var, s);
sharedMean->SetReady(myRank);
sharedVar->SetReady(myRank);
global_barrier->Wait();
Tensor<cpu, 1, real_t> mean_cpu = sharedMean->Pop(myRank);
Tensor<cpu, 1, real_t> var_cpu = sharedVar->Pop(myRank);
// copy back to gpu
mshadow::Copy(mean, mean_cpu, s);
mshadow::Copy(var, var_cpu, s);
var = var-F<mshadow_op::square>(mean);
Assign(out, req[syncbatchnorm::kOut], broadcast<1>(slope, out.shape_) *
(data - broadcast<1>(mean, data.shape_)) /
F<mshadow_op::square_root>(broadcast<1>(var + param_.eps, data.shape_)) +
broadcast<1>(bias, out.shape_));
} else {
Assign(out, req[syncbatchnorm::kOut], broadcast<1>(slope /
F<mshadow_op::square_root>(moving_var + param_.eps),
data.shape_) * data +
broadcast<1>(bias - (slope * moving_mean) /
F<mshadow_op::square_root>(moving_var + param_.eps), data.shape_));
}
}
virtual void Backward(const OpContext &ctx,
const std::vector<TBlob> &out_grad,
const std::vector<TBlob> &in_data,
const std::vector<TBlob> &out_data,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &in_grad,
const std::vector<TBlob> &aux_states) {
using namespace mshadow;
using namespace mshadow::expr;
CHECK_EQ(out_grad.size(), param_.output_mean_var ? 3U : 1U);
CHECK_EQ(in_data.size(), 3U);
CHECK_EQ(out_data.size(), 3U);
CHECK_EQ(in_grad.size(), 3U);
Stream<xpu> *s = ctx.get_stream<xpu>();
Tensor<xpu, 4> data, grad, grad_in;
const real_t scale = static_cast<real_t>(out_grad[syncbatchnorm::kOut].shape_[1]) /
static_cast<real_t>(out_grad[syncbatchnorm::kOut].shape_.Size());
if (in_data[syncbatchnorm::kData].ndim() == 2) {
Shape<4> dshape = Shape4(out_grad[syncbatchnorm::kOut].shape_[0],
out_grad[syncbatchnorm::kOut].shape_[1], 1, 1);
data = in_data[syncbatchnorm::kData].get_with_shape<xpu, 4, real_t>(dshape, s);
grad = out_grad[syncbatchnorm::kOut].get_with_shape<xpu, 4, real_t>(dshape, s);
grad_in = in_grad[syncbatchnorm::kData].get_with_shape<xpu, 4, real_t>(dshape, s);
} else {
data = in_data[syncbatchnorm::kData].get<xpu, 4, real_t>(s);
grad = out_grad[syncbatchnorm::kOut].get<xpu, 4, real_t>(s);
grad_in = in_grad[syncbatchnorm::kData].get<xpu, 4, real_t>(s);
}
Tensor<xpu, 1> mean = out_data[syncbatchnorm::kMean].get<xpu, 1, real_t>(s);
Tensor<xpu, 1> var = out_data[syncbatchnorm::kVar].get<xpu, 1, real_t>(s);
Tensor<xpu, 1> slope = in_data[syncbatchnorm::kGamma].get<xpu, 1, real_t>(s);
// Tensor<xpu, 1> bias = in_data[kBeta].get<xpu, 1, real_t>(s);
Tensor<xpu, 1> gslope = in_grad[syncbatchnorm::kGamma].get<xpu, 1, real_t>(s);
Tensor<xpu, 1> gbias = in_grad[syncbatchnorm::kBeta].get<xpu, 1, real_t>(s);
// update moving avg
Tensor<xpu, 1> moving_mean = aux_states[syncbatchnorm::kMovingMean].get<xpu, 1, real_t>(s);
Tensor<xpu, 1> moving_var = aux_states[syncbatchnorm::kMovingVar].get<xpu, 1, real_t>(s);
if (param_.fix_gamma) slope = 1.f;
if (ctx.is_train && !param_.use_global_stats) {
// get my rank
Barrier *global_barrier = global_shared_barrier_backward.Register(param_.key, param_.ndev);
int myRank = global_shared_rank_backward.Register(param_.key, param_.ndev);
// get requested temp space
Tensor<xpu, 2> workspace = ctx.requested[syncbatchnorm::kTempSpace].get_space<xpu>(
mshadow::Shape2(5, mean.shape_[0]), s);
Tensor<xpu, 1> gmean = workspace[0];
Tensor<xpu, 1> gvar = workspace[1];
moving_mean = moving_mean * param_.momentum + mean * (1 - param_.momentum);
moving_var = moving_var * param_.momentum + var * (1 - param_.momentum);
// cal
Tensor<xpu, 1> sumGrad = workspace[3];
Tensor<xpu, 1> sumProd = workspace[4];
sumGrad = sumall_except_dim<1>(grad);
sumProd = sumall_except_dim<1>(grad * (data - broadcast<1>(mean, data.shape_)));
SharedND<mshadow::Tensor<cpu, 1, real_t>> *sharedGrad =
global_shared_grad.Register(param_.key, param_.ndev);
SharedND<mshadow::Tensor<cpu, 1, real_t>> *sharedProd =
global_shared_prod.Register(param_.key, param_.ndev);
// copy to cpu, push and pull
Tensor<cpu, 1, real_t>* grad_cpu_ptr = sharedGrad->Retrieve(sumGrad.shape_, myRank);
Tensor<cpu, 1, real_t>* prod_cpu_ptr = sharedProd->Retrieve(sumGrad.shape_, myRank);
mshadow::Copy(*grad_cpu_ptr, sumGrad, s);
mshadow::Copy(*prod_cpu_ptr, sumProd, s);
sharedGrad->SetReady(myRank);
sharedProd->SetReady(myRank);
global_barrier->Wait();
Tensor<cpu, 1, real_t> grad_cpu = sharedGrad->Pop(myRank);
Tensor<cpu, 1, real_t> prod_cpu = sharedProd->Pop(myRank);
// copy back to gpu
mshadow::Copy(sumGrad, grad_cpu, s);
mshadow::Copy(sumProd, prod_cpu, s);
gvar = -1.0f * sumProd * slope *
F<mshadow_op::power>(var + param_.eps, -1.5f);
gmean = sumGrad * slope;
gmean *= -1.0f / F<mshadow_op::square_root>(var + param_.eps);
// assign
if (!param_.fix_gamma) {
Assign(gslope, req[syncbatchnorm::kGamma],
sumall_except_dim<1>(
grad * (data - broadcast<1>(mean, data.shape_)) /
F<mshadow_op::square_root>(broadcast<1>(var + param_.eps, data.shape_))));
} else {
Assign(gslope, req[syncbatchnorm::kGamma], 0.0f);
}
Assign(grad_in, req[syncbatchnorm::kData],
(grad * broadcast<1>(slope, data.shape_)) *
broadcast<1>(1.0f / F<mshadow_op::square_root>(var + param_.eps), data.shape_) +
broadcast<1>(gvar, data.shape_) *
scale * (data - broadcast<1>(mean, data.shape_)) +
broadcast<1>(gmean, data.shape_) * scale);
Assign(gbias, req[syncbatchnorm::kBeta], sumall_except_dim<1>(grad));
} else {
// use global statistics with freeze moving mean and var.
if (!param_.fix_gamma) {
Assign(gslope, req[syncbatchnorm::kGamma],
sumall_except_dim<1>(
grad * (data - broadcast<1>(moving_mean, data.shape_)) /
F<mshadow_op::square_root>(broadcast<1>(moving_var + param_.eps, data.shape_))));
} else {
Assign(gslope, req[syncbatchnorm::kGamma], 0.0f);
}
Assign(gbias, req[syncbatchnorm::kBeta], sumall_except_dim<1>(grad));
Assign(grad_in, req[syncbatchnorm::kData], (grad * broadcast<1>(slope, data.shape_)) *
broadcast<1>(
1.0f / F<mshadow_op::square_root>(moving_var + param_.eps), data.shape_));
}
}
private:
SyncBatchNormParam param_;
}; // class SyncBatchNorm
template<typename xpu>
Operator *CreateOp(SyncBatchNormParam param, int dtype);
#if DMLC_USE_CXX11
class SyncBatchNormProp : public OperatorProperty {
public:
void Init(const std::vector<std::pair<std::string, std::string> >& kwargs) override {
param_.Init(kwargs);
}
std::map<std::string, std::string> GetParams() const override {
return param_.__DICT__();
}
bool InferShape(std::vector<TShape> *in_shape,
std::vector<TShape> *out_shape,
std::vector<TShape> *aux_shape) const override {
using namespace mshadow;
CHECK_EQ(in_shape->size(), 3U) << "Input:[data, gamma, beta]";
const TShape &dshape = in_shape->at(0);
if (dshape.ndim() == 0) return false;
in_shape->at(1) = TShape(Shape1(dshape[1]));
in_shape->at(2) = TShape(Shape1(dshape[1]));
out_shape->clear();
out_shape->push_back(dshape);
out_shape->push_back(Shape1(dshape[1]));
out_shape->push_back(Shape1(dshape[1]));
aux_shape->clear();
aux_shape->push_back(Shape1(dshape[1]));
aux_shape->push_back(Shape1(dshape[1]));
return true;
}
bool InferType(std::vector<int> *in_type,
std::vector<int> *out_type,
std::vector<int> *aux_type) const override {
using namespace mshadow;
CHECK_GE(in_type->size(), 1U);
int dtype = (*in_type)[0];
CHECK_NE(dtype, -1) << "First input must have specified type";
// For float16 input type beta, gamma, mean, and average are stored in float32.
// For other input types, these parameters have the same type as input
// NOTE: This requirement is from cuDNN (v. 4 and 5)
int dtype_param = (dtype == kFloat16) ? kFloat32 : dtype;
for (size_t i = 1; i < in_type->size(); ++i) {
if ((*in_type)[i] == -1) {
(*in_type)[i] = dtype_param;
} else {
UNIFORM_TYPE_CHECK((*in_type)[i], dtype_param, ListArguments()[i]);
}
}
for (size_t i = 0; i < aux_type->size(); ++i) {
if ((*aux_type)[i] != -1) {
UNIFORM_TYPE_CHECK((*aux_type)[i], dtype_param, ListArguments()[i]);
}
}
int n_aux = this->ListAuxiliaryStates().size();
aux_type->clear();
for (int i = 0; i < n_aux; ++i ) aux_type->push_back(dtype_param);
int n_out = this->ListOutputs().size();
out_type->clear();
out_type->push_back(dtype);
for (int i = 1; i < n_out; ++i ) out_type->push_back(dtype_param);
return true;
}
OperatorProperty* Copy() const override {
auto ptr = new SyncBatchNormProp();
ptr->param_ = param_;
return ptr;
}
std::string TypeString() const override {
return "_contrib_SyncBatchNorm";
}
std::vector<int> DeclareBackwardDependency(
const std::vector<int> &out_grad,
const std::vector<int> &in_data,
const std::vector<int> &out_data) const override {
return {out_grad[syncbatchnorm::kOut],
out_data[syncbatchnorm::kMean],
out_data[syncbatchnorm::kVar],
in_data[syncbatchnorm::kData],
in_data[syncbatchnorm::kGamma]
};
}
std::vector<ResourceRequest> BackwardResource(
const std::vector<TShape> &in_shape) const override {
return {ResourceRequest::kTempSpace};
}
int NumVisibleOutputs() const override {
if (param_.output_mean_var) {
return 3;
}
return 1;
}
int NumOutputs() const override {
return 3;
}
std::vector<std::string> ListArguments() const override {
return {"data", "gamma", "beta"};
}
std::vector<std::string> ListOutputs() const override {
return {"output", "mean", "var"};
}
std::vector<std::string> ListAuxiliaryStates() const override {
return {"moving_mean", "moving_var"};
}
Operator* CreateOperator(Context ctx) const override {
LOG(FATAL) << "Not Implemented.";
return NULL;
}
Operator* CreateOperatorEx(Context ctx, std::vector<TShape> *in_shape,
std::vector<int> *in_type) const override;
inline const SyncBatchNormParam& getParam() const {
return param_;
}
private:
SyncBatchNormParam param_;
}; // class SyncBatchNormProp
#endif // DMLC_USE_CXX11
} // namespace op
} // namespace mxnet
#endif // MXNET_OPERATOR_CONTRIB_SYNC_BATCH_NORM_INL_H_