import math
import torch
from .optimizer import Optimizer
[docs]class SparseAdam(Optimizer):
"""Implements lazy version of Adam algorithm suitable for sparse tensors.
In this variant, only moments that show up in the gradient get updated, and
only those portions of the gradient get applied to the parameters.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
.. _Adam\: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
"""
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8):
defaults = dict(lr=lr, betas=betas, eps=eps)
super(SparseAdam, self).__init__(params, defaults)
[docs] def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
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
if not grad.is_sparse:
raise RuntimeError('SparseAdam does not support dense gradients, please consider Adam instead')
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
state['step'] += 1
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)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['betas']
# 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)
denom = exp_avg_sq_update_values.add_(old_exp_avg_sq_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)))
return loss