# Source code for pyro.optim.clipped_adam

# Copyright (c) 2017-2019 Uber Technologies, Inc.

import math
from typing import Any, Callable, Optional, Tuple

import torch
from torch.optim.optimizer import Optimizer

"""
:param params: iterable of parameters to optimize or dicts defining parameter groups
:param lr: learning rate (default: 1e-3)
:param Tuple betas: coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
:param eps: term added to the denominator to improve
numerical stability (default: 1e-8)
:param weight_decay: weight decay (L2 penalty) (default: 0)
:param clip_norm: magnitude of norm to which gradients are clipped (default: 10.0)
:param lrd: rate at which learning rate decays (default: 1.0)

Small modification to the Adam algorithm implemented in torch.optim.Adam
to include gradient clipping and learning rate decay.

Reference

A Method for Stochastic Optimization, Diederik P. Kingma, Jimmy Ba
https://arxiv.org/abs/1412.6980
"""

def __init__(
self,
params,
lr: float = 1e-3,
betas: Tuple = (0.9, 0.999),
eps: float = 1e-8,
weight_decay=0,
clip_norm: float = 10.0,
lrd: float = 1.0,
):
defaults = dict(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
clip_norm=clip_norm,
lrd=lrd,
)
super().__init__(params, defaults)

[docs]    def step(self, closure: Optional[Callable] = None) -> Optional[Any]:
"""
:param closure: An optional closure that reevaluates the model and returns the loss.

Performs a single optimization step.
"""
loss = None
if closure is not None:
loss = closure()

for group in self.param_groups:
group["lr"] *= group["lrd"]

for p in group["params"]:
if p.grad is None:
continue
state = self.state[p]

# State initialization
if len(state) == 0:
state["step"] = 0
# Exponential moving average of gradient values
# Exponential moving average of squared gradient values

exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
beta1, beta2 = group["betas"]

state["step"] += 1

if group["weight_decay"] != 0:

# Decay the first and second moment running average coefficient