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63239ac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | import math
import torch
from torch.optim.optimizer import Optimizer
class DenseSparseAdam(Optimizer):
"""
"""
def __init__(self, params, lr=3e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0.0):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {}".format(eps))
if not 0.0 <= betas[0] < 1.0:
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
super(DenseSparseAdam, self).__init__(params, defaults)
def step(self, closure=None):
"""
Performs a single optimization step.
Parameters
----------
closure : ``callable``, optional.
A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
state = self.state[p]
# State initialization
if 'step' not in state:
state['step'] = 0
if 'exp_avg' not in state:
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
if 'exp_avg_sq' not in state:
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
state['step'] += 1
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['betas']
weight_decay = group['weight_decay']
if grad.is_sparse:
grad = grad.coalesce() # the update is non-linear so indices must be unique
grad_indices = grad._indices()
grad_values = grad._values()
size = grad.size()
def make_sparse(values):
constructor = grad.new
if grad_indices.dim() == 0 or values.dim() == 0:
return constructor().resize_as_(grad)
return constructor(grad_indices, values, size)
# Decay the first and second moment running average coefficient
# old <- b * old + (1 - b) * new
# <==> old += (1 - b) * (new - old)
old_exp_avg_values = exp_avg.sparse_mask(grad)._values()
exp_avg_update_values = grad_values.sub(old_exp_avg_values).mul_(1 - beta1)
exp_avg.add_(make_sparse(exp_avg_update_values))
old_exp_avg_sq_values = exp_avg_sq.sparse_mask(grad)._values()
exp_avg_sq_update_values = grad_values.pow(2).sub_(old_exp_avg_sq_values).mul_(1 - beta2)
exp_avg_sq.add_(make_sparse(exp_avg_sq_update_values))
# Dense addition again is intended, avoiding another sparse_mask
numer = exp_avg_update_values.add_(old_exp_avg_values)
exp_avg_sq_update_values.add_(old_exp_avg_sq_values)
denom = exp_avg_sq_update_values.sqrt_().add_(group['eps'])
del exp_avg_update_values, exp_avg_sq_update_values
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
p.data.add_(make_sparse(-step_size * numer.div_(denom)))
if weight_decay > 0.0:
p.data.add_(-group['lr'] * weight_decay, p.data.sparse_mask(grad))
else:
# Decay the first and second moment running average coefficient
# exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad,value=1 - beta2)
denom = exp_avg_sq.sqrt().add_(group['eps'])
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
p.data.addcdiv_(exp_avg, denom, value=-step_size)
if weight_decay > 0.0:
p.data.add_(-group['lr'] * weight_decay, p.data)
return loss
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