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import torch
from . import _functional as F
from .optimizer import Optimizer
__all__ = ['SparseAdam']
class SparseAdam(Optimizer):
r"""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.
Args:
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)
maximize (bool, optional): maximize the params based on the objective, instead of
minimizing (default: False)
.. _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, maximize: bool = False):
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]))
params = list(params)
sparse_params = []
for index, param in enumerate(params):
if isinstance(param, dict):
for d_index, d_param in enumerate(param.get("params", [])):
if d_param.is_sparse:
sparse_params.append([index, d_index])
elif param.is_sparse:
sparse_params.append(index)
if sparse_params:
raise ValueError(
f"Sparse params at indices {sparse_params}: SparseAdam requires dense parameter tensors"
)
defaults = dict(lr=lr, betas=betas, eps=eps, maximize=maximize)
super(SparseAdam, 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()
for group in self.param_groups:
params_with_grad = []
grads = []
exp_avgs = []
exp_avg_sqs = []
state_steps = []
eps = group['eps']
lr = group['lr']
beta1, beta2 = group['betas']
maximize = group.get('maximize', False)
for p in group['params']:
if p.grad is not None:
params_with_grad.append(p)
if not p.grad.is_sparse:
raise RuntimeError('SparseAdam does not support dense gradients, please consider Adam instead')
grads.append(p.grad)
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, memory_format=torch.preserve_format)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
exp_avgs.append(state['exp_avg'])
exp_avg_sqs.append(state['exp_avg_sq'])
# update the steps for each param group update
state['step'] += 1
# record the step after step update
state_steps.append(state['step'])
F.sparse_adam(params_with_grad,
grads,
exp_avgs,
exp_avg_sqs,
state_steps,
beta1=beta1,
beta2=beta2,
lr=group['lr'],
eps=group['eps'],
maximize=maximize)
return loss
|
