/usr/local/lib64/python3.6/site-packages/torch/optim/_multi_tensor
Edit: /usr/local/lib64/python3.6/site-packages/torch/optim/_multi_tensor/rprop.py (4537B)
import torch
from ..optimizer import Optimizer
from collections import defaultdict
class Rprop(Optimizer):
"""Implements the resilient backpropagation algorithm.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-2)
etas (Tuple[float, float], optional): pair of (etaminus, etaplis), that
are multiplicative increase and decrease factors
(default: (0.5, 1.2))
step_sizes (Tuple[float, float], optional): a pair of minimal and
maximal allowed step sizes (default: (1e-6, 50))
"""
def __init__(self, params, lr=1e-2, etas=(0.5, 1.2), step_sizes=(1e-6, 50)):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 < etas[0] < 1.0 < etas[1]:
raise ValueError("Invalid eta values: {}, {}".format(etas[0], etas[1]))
defaults = dict(lr=lr, etas=etas, step_sizes=step_sizes)
super(Rprop, self).__init__(params, defaults)
@torch.no_grad()
def step(self, closure=None):
"""Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
grads = []
states = []
params_with_grad = []
step_sizes = []
for group in self.param_groups:
for p in group['params']:
etaminus, etaplus = group['etas']
step_size_min, step_size_max = group['step_sizes']
if p.grad is not None:
if p.grad.is_sparse:
raise RuntimeError('RMSprop does not support sparse gradients')
grads.append(p.grad)
params_with_grad.append(p)
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
state['prev'] = torch.zeros_like(p, memory_format=torch.preserve_format)
state['step_size'] = p.grad.new().resize_as_(p.grad).fill_(group['lr'])
state['step'] += 1
states.append(state)
step_sizes.append(state['step_size'])
signs = torch._foreach_mul(grads, [s['prev'] for s in states])
signs = [s.sign() for s in signs]
for sign in signs:
sign[sign.gt(0)] = etaplus
sign[sign.lt(0)] = etaminus
sign[sign.eq(0)] = 1
# update stepsizes with step size updates
torch._foreach_mul_(step_sizes, signs)
for step_size in step_sizes:
step_size.clamp_(step_size_min, step_size_max)
# for dir<0, dfdx=0
# for dir>=0 dfdx=dfdx
for i in range(len(grads)):
grads[i] = grads[i].clone(memory_format=torch.preserve_format)
grads[i][signs[i].eq(etaminus)] = 0
# update parameters
grad_signs = [grad.sign() for grad in grads]
torch._foreach_addcmul_(params_with_grad, grad_signs, step_sizes, value=-1)
for i in range(len(states)):
states[i]['prev'].copy_(grads[i])
return loss
# TODO: refactor to a base class once foreach ops are in a good shape.
def zero_grad(self, set_to_none: bool = False):
per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list))
for group in self.param_groups:
for p in group['params']:
if p.grad is not None:
if set_to_none:
p.grad = None
else:
if p.grad.grad_fn is not None:
p.grad.detach_()
else:
p.grad.requires_grad_(False)
if p.grad.is_sparse:
p.grad.zero_()
else:
per_device_and_dtype_grads[p.grad.device][p.grad.dtype].append(p.grad)
for _, per_dtype_grads in per_device_and_dtype_grads.items():
for grads in per_dtype_grads.values():
torch._foreach_zero_(grads)