/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/sgd
Edit: /usr/local/lib64/python3.6/site-packages/torch/include/caffe2/sgd/learning_rate_op.h (12696B)
#ifndef CAFFE2_SGD_LEARNING_RATE_OP_H_
#define CAFFE2_SGD_LEARNING_RATE_OP_H_
#include
#include
#include "caffe2/core/context.h"
#include "caffe2/core/export_caffe2_op_to_c10.h"
#include "caffe2/core/operator.h"
#include "caffe2/sgd/learning_rate_functors.h"
C10_DECLARE_EXPORT_CAFFE2_OP_TO_C10(LearningRate);
namespace caffe2 {
template
class LearningRateOp final : public Operator {
public:
template
LearningRateOp(Args&&... args)
: Operator(std::forward(args)...),
functor_(nullptr),
base_lr_(this->template GetSingleArgument("base_lr", FLT_MAX)) {
CAFFE_ENFORCE_NE(base_lr_, FLT_MAX, "Base learning rate must be set.");
const string policy =
this->template GetSingleArgument("policy", "");
CAFFE_ENFORCE(policy.size(), "Must specify a learning rate policy.");
functor_.reset(createLearningRateFunctor(policy));
}
USE_OPERATOR_CONTEXT_FUNCTIONS;
bool RunOnDevice() override {
int64_t iter =
OperatorBase::Input(0, CPU).template data()[0];
T learning_rate = base_lr_ * (*functor_)(iter);
// Write to output.
auto* output = Output(0);
output->Resize(vector());
context_.template CopyFromCPU(
1, &learning_rate, Output(0)->template mutable_data());
return true;
}
private:
unique_ptr> functor_;
T base_lr_;
LearningRateFunctor* createLearningRateFunctor(
const string& policy,
const string& arg_prefix = "") {
if (policy == "fixed") {
return new FixedLearningRate();
} else if (policy == "alter") {
bool active_first = this->template GetSingleArgument(
arg_prefix + "active_first", true);
int64_t active_period = this->template GetSingleArgument(
arg_prefix + "active_period", -1);
int64_t inactive_period = this->template GetSingleArgument(
arg_prefix + "inactive_period", -1);
DCHECK_GE(active_period, 0);
DCHECK_GE(inactive_period, 0);
return new AlternateLearningRate(
active_period, inactive_period, active_first);
} else if (policy == "hill") {
int64_t num_iter =
this->template GetSingleArgument(arg_prefix + "num_iter", 0);
DCHECK_GT(num_iter, 0);
T start_multiplier = this->template GetSingleArgument(
arg_prefix + "start_multiplier", 0.);
DCHECK_GE(start_multiplier, 0); // start_multiplier in range [0, 1]
DCHECK_LE(start_multiplier, 1);
T gamma =
this->template GetSingleArgument(arg_prefix + "gamma", 0);
DCHECK_GT(gamma, 0);
T power =
this->template GetSingleArgument(arg_prefix + "power", 0);
DCHECK_GT(power, 0);
T end_multiplier = this->template GetSingleArgument(
arg_prefix + "end_multiplier", 0);
DCHECK_GE(end_multiplier, 0); // end_multiplier in range [0, 1]
DCHECK_LE(end_multiplier, 1);
return new HillLearningRate(
num_iter, start_multiplier, gamma, power, end_multiplier);
} else if (policy == "slope") {
int64_t num_iter_1 = this->template GetSingleArgument(
arg_prefix + "num_iter_1", 0);
DCHECK_GT(num_iter_1, 0);
T multiplier_1 = this->template GetSingleArgument(
arg_prefix + "multiplier_1", 0.);
int64_t num_iter_2 = this->template GetSingleArgument(
arg_prefix + "num_iter_2", 0);
DCHECK_GT(num_iter_1, 0);
T multiplier_2 = this->template GetSingleArgument(
arg_prefix + "multiplier_2", 0.);
DCHECK_GT(num_iter_2, num_iter_1);
return new SlopeLearningRate(
num_iter_1, multiplier_1, num_iter_2, multiplier_2);
} else if (policy == "step") {
int stepsize =
this->template GetSingleArgument(arg_prefix + "stepsize", 0);
T gamma =
this->template GetSingleArgument(arg_prefix + "gamma", 0);
DCHECK_GT(stepsize, 0);
DCHECK_GT(gamma, 0);
return new StepLearningRate(stepsize, gamma);
} else if (policy == "exp") {
T gamma =
this->template GetSingleArgument(arg_prefix + "gamma", 0);
DCHECK_GT(gamma, 0);
return new ExpLearningRate(gamma);
} else if (policy == "gate") {
T multiplier_1 = this->template GetSingleArgument(
arg_prefix + "multiplier_1", 1);
T multiplier_2 = this->template GetSingleArgument(
arg_prefix + "multiplier_2", 1);
int num_iter =
this->template GetSingleArgument(arg_prefix + "num_iter", 0);
// no constraint on the range of multiplier_1 and multiplier_2
return new GateLearningRate(multiplier_1, multiplier_2, num_iter);
} else if (policy == "inv") {
T gamma =
this->template GetSingleArgument(arg_prefix + "gamma", 0);
T power =
this->template GetSingleArgument(arg_prefix + "power", 0);
DCHECK_GT(gamma, 0);
DCHECK_GT(power, 0);
return new InvLearningRate(gamma, power);
} else if (policy == "poly") {
int max_iter =
this->template GetSingleArgument(arg_prefix + "max_iter", -1);
T power =
this->template GetSingleArgument(arg_prefix + "power", 0);
DCHECK_GT(power, 0);
return new PolyLearningRate(power, max_iter);
} else if (policy == "linearWarmup") {
T start_multiplier = this->template GetSingleArgument(
arg_prefix + "start_multiplier", 0.);
int num_iter =
this->template GetSingleArgument(arg_prefix + "num_iter", 0);
DCHECK_GE(start_multiplier, 0);
return new LinearWarmupLearningRate(start_multiplier, num_iter);
} else if (policy == "constantWarmup") {
T multiplier = this->template GetSingleArgument(
arg_prefix + "multiplier", 0.5);
int num_iter =
this->template GetSingleArgument(arg_prefix + "num_iter", 0);
DCHECK_GT(multiplier, 0);
return new ConstantWarmupLearningRate(multiplier, num_iter);
} else if (policy == "pieceWarmup") {
T m1 = this->template GetSingleArgument(arg_prefix + "m1", 0.5);
int64_t n1 =
this->template GetSingleArgument(arg_prefix + "n1", 0);
T m2 = this->template GetSingleArgument(arg_prefix + "m2", 0.5);
int64_t n2 =
this->template GetSingleArgument(arg_prefix + "n2", 0);
T m3 = this->template GetSingleArgument(arg_prefix + "m3", 0.5);
return new PieceWarmupLearningRate(m1, n1, m2, n2, m3);
} else if (policy == "composite") {
std::vector sub_policy_num_iters =
this->template GetRepeatedArgument("sub_policy_num_iters");
std::list> sub_policies;
CAFFE_ENFORCE_GT(
sub_policy_num_iters.size(),
0,
"Must specify at least one sub learning rate policy.");
for (size_t i = 0; i < sub_policy_num_iters.size(); ++i) {
CAFFE_ENFORCE_GT(
sub_policy_num_iters[i],
0,
"The number of iterations for sub learning rate policy should be positive.");
std::stringstream sub_policy_arg_prefix;
sub_policy_arg_prefix << "sub_policy_" << i << "_";
const string sub_policy_arg_prefix_str = sub_policy_arg_prefix.str();
const string sub_policy = this->template GetSingleArgument(
sub_policy_arg_prefix_str + "policy", "");
if (sub_policy == "composite") {
CAFFE_THROW(
"Defining composite LR policy as a subpolicy of composite LR "
"policy is not allowed.");
}
const float scale_lr = this->template GetSingleArgument(
sub_policy_arg_prefix_str + "lr_scale", 1.0);
sub_policies.push_back(CompositeLearningRateItem(
sub_policy_num_iters[i],
scale_lr,
createLearningRateFunctor(sub_policy, sub_policy_arg_prefix_str)));
}
return new CompositeLearningRate(sub_policies);
} else if (policy == "cyclical") {
T max_lr =
this->template GetSingleArgument(arg_prefix + "max_lr", 0.005);
int stepsize =
this->template GetSingleArgument(arg_prefix + "stepsize", 0);
T decay =
this->template GetSingleArgument(arg_prefix + "decay", 1.0);
DCHECK_GT(stepsize, 0);
DCHECK_GE(max_lr, base_lr_);
return new CyclicalLearningRate(base_lr_, max_lr, stepsize, decay);
} else if (policy == "constantThenLinearWarmup") {
T start_warmup_multiplier = this->template GetSingleArgument(
arg_prefix + "start_warmup_multiplier", 0.1);
int64_t constant_warmup_num_iter = this->template GetSingleArgument(
arg_prefix + "constant_warmup_num_iter", 10000000);
int64_t linear_warmup_num_iter = this->template GetSingleArgument(
arg_prefix + "linear_warmup_num_iter", 10000000);
return new ConstantThenLinearWarmupLearningRate(
start_warmup_multiplier,
constant_warmup_num_iter,
linear_warmup_num_iter);
} else if (policy == "compositeCyclical") {
T start_warmup_multiplier = this->template GetSingleArgument(
arg_prefix + "start_warmup_multiplier", 0.1);
int64_t constant_warmup_num_iter = this->template GetSingleArgument(
arg_prefix + "constant_warmup_num_iter", 10000000);
int64_t linear_warmup_num_iter = this->template GetSingleArgument(
arg_prefix + "linear_warmup_num_iter", 10000000);
T cyclical_max_lr = this->template GetSingleArgument(
arg_prefix + "cyclical_max_lr", 0.05);
int cyclical_step_size = this->template GetSingleArgument(
arg_prefix + "cyclical_step_size", 1000000);
T cyclical_decay = this->template GetSingleArgument(
arg_prefix + "cyclical_decay", 1.0);
DCHECK_GE(cyclical_max_lr, base_lr_);
return new CompositeCyclicalLearningRate(
base_lr_,
start_warmup_multiplier,
constant_warmup_num_iter,
linear_warmup_num_iter,
cyclical_max_lr,
cyclical_step_size,
cyclical_decay);
} else if (policy == "cosine") {
T max_lr =
this->template GetSingleArgument(arg_prefix + "max_lr", 0.5);
T min_lr =
this->template GetSingleArgument(arg_prefix + "min_lr", 0.1);
int64_t period =
this->template GetSingleArgument(arg_prefix + "period", 50);
T t_mult =
this->template GetSingleArgument(arg_prefix + "t_mult", 1.0);
T lr_shrink = this->template GetSingleArgument(
arg_prefix + "lr_shrink", 0.99);
DCHECK_GE(max_lr, min_lr);
return new CosineLearningRate(
min_lr, max_lr, period, t_mult, lr_shrink);
} else if (policy == "compositeCosine") {
T start_warmup_multiplier = this->template GetSingleArgument(
arg_prefix + "start_warmup_multiplier", 0.1);
int64_t constant_warmup_num_iter = this->template GetSingleArgument(
arg_prefix + "constant_warmup_num_iter", 10000000);
int64_t linear_warmup_num_iter = this->template GetSingleArgument(
arg_prefix + "linear_warmup_num_iter", 10000000);
T cosine_max_lr = this->template GetSingleArgument(
arg_prefix + "cosine_max_lr", 0.5);
T cosine_min_lr = this->template GetSingleArgument(
arg_prefix + "cosine_min_lr", 0.1);
int64_t cosine_period = this->template GetSingleArgument(
arg_prefix + "cosine_period", 50);
T cosine_t_mult = this->template GetSingleArgument(
arg_prefix + "cosine_t_mult", 1.0);
T cosine_lr_shrink = this->template GetSingleArgument(
arg_prefix + "cosine_lr_shrink", 0.99);
DCHECK_GE(cosine_max_lr, cosine_min_lr);
return new CompositeCosineLearningRate(
start_warmup_multiplier,
constant_warmup_num_iter,
linear_warmup_num_iter,
cosine_min_lr,
cosine_max_lr,
cosine_period,
cosine_t_mult,
cosine_lr_shrink);
} else {
CAFFE_THROW("Unknown learning rate policy: ", policy);
return NULL;
}
}
};
} // namespace caffe2
#endif // CAFFE2_SGD_LEARNING_RATE_OP_H_