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shared_optim.py
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import os
os.environ["OMP_NUM_THREADS"] = "1"
import torch
from torch.optim import Optimizer
from collections import defaultdict
from math import sqrt
import time
import torch.nn.functional as F
CONST_1 = torch.ones(()).float()
CONST_2 = torch.ones(()).float() * 2.0
class SharedRMSprop(Optimizer):
"""Implements RMSprop algorithm with shared states."""
def __init__(self, params, lr=7e-4, alpha=0.99, eps=0.1, weight_decay=0, momentum=0, centered=False):
defaults = defaultdict(
lr=lr, alpha=alpha, eps=eps, weight_decay=weight_decay, momentum=momentum, centered=centered
)
super(SharedRMSprop, self).__init__(params, defaults)
self.ONE = torch.ones(())
for group in self.param_groups:
for p in group["params"]:
state = self.state[p]
state["step"] = torch.zeros(())
state["grad_avg"] = p.data.new().resize_as_(p.data).zero_()
state["square_avg"] = p.data.new().resize_as_(p.data).zero_()
state["momentum_buffer"] = p.data.new().resize_as_(p.data).zero_()
def share_memory(self):
for group in self.param_groups:
for p in group["params"]:
state = self.state[p]
state["square_avg"].share_memory_()
state["step"].share_memory_()
state["grad_avg"].share_memory_()
state["momentum_buffer"].share_memory_()
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 grad.is_sparse:
raise RuntimeError("RMSprop does not support sparse gradients")
state = self.state[p]
square_avg = state["square_avg"]
alpha = group["alpha"]
state["step"].add_(self.ONE)
if group["weight_decay"] != 0:
grad = grad.add(p, alpha=group["weight_decay"])
square_avg.mul_(alpha).addcmul_(grad, grad, value=1 - alpha)
if group["centered"]:
grad_avg = state["grad_avg"]
grad_avg.mul_(alpha).add_(grad, alpha=1 - alpha)
avg = square_avg.addcmul(grad_avg, grad_avg, value=-1).sqrt_().add_(group["eps"])
else:
avg = square_avg.sqrt().add_(group["eps"])
if group["momentum"] > 0:
buf = state["momentum_buffer"]
buf.mul_(group["momentum"]).addcdiv_(grad, avg)
# Need to avoid version tracking for parameter.
p.data.add_(buf, alpha=-group["lr"])
else:
# Need to avoid version tracking for parameter.
p.data.addcdiv_(grad, avg, value=-group["lr"])
return loss
class SharedAdam(Optimizer):
"""Implements Adam algorithm with shared states."""
def __init__(self, params, lr=1e-4, betas=(0.9, 0.999), eps=1e-3, weight_decay=0, amsgrad=True):
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad)
super(SharedAdam, self).__init__(params, defaults)
defaults["torch_eps"] = torch.tensor(eps).float()
defaults["beta1"], defaults["beta2"] = betas
defaults["beta2T"] = torch.tensor(defaults["beta2"]).float()
defaults["stepNum"] = torch.zeros(()).float()
defaults["OneMinusBeta1"] = CONST_1.sub(defaults["beta1"]).float()
defaults["OneMinusBeta2"] = CONST_1.sub(defaults["beta2T"]).float()
defaults["NEG_LR"] = -lr
for group in self.param_groups:
for p in group["params"]:
state = self.state[p]
state["exp_avg"] = torch.zeros_like(p)
state["exp_avg_sq"] = torch.zeros_like(p)
state["max_exp_avg_sq"] = torch.zeros_like(p) + defaults["torch_eps"].square()
def share_memory(self):
self.defaults["stepNum"].share_memory_()
for group in self.param_groups:
for p in group["params"]:
state = self.state[p]
state["exp_avg"].share_memory_()
state["exp_avg_sq"].share_memory_()
state["max_exp_avg_sq"].share_memory_()
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:
with torch.enable_grad():
loss = closure()
stepFlag = 1
for group in self.param_groups:
for p in group["params"]:
if p.grad is None:
continue
if stepFlag:
defaults = self.defaults
amsgrad = defaults["amsgrad"]
OneMinusBeta1 = defaults["OneMinusBeta1"]
OneMinusBeta2 = defaults["OneMinusBeta2"]
beta2T = defaults["beta2T"]
defaults["stepNum"].add_(CONST_1)
step_t = defaults["stepNum"].item()
bias_correction1 = 1 - defaults["beta1"] ** step_t
bias_correction2 = 1 - defaults["beta2"] ** step_t
bias_correction2_sqrt = sqrt(bias_correction2)
step_size_neg = defaults["NEG_LR"] * bias_correction2_sqrt / bias_correction1
stepFlag = 0
grad = p.grad
state = self.state[p]
exp_avg = state["exp_avg"]
exp_avg.lerp_(grad, OneMinusBeta1)
exp_avg_sq = state["exp_avg_sq"]
exp_avg_sq.mul_(beta2T).addcmul_(grad, grad, value=OneMinusBeta2)
if amsgrad:
max_exp_avg_sq = state["max_exp_avg_sq"]
# Maintains the maximum of all 2nd moment running avg till now
torch.maximum(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the mean of 2nd moment running avg and maximum of all 2nd moment running avg till now for normalizing running avg of gradient
denom = exp_avg_sq.add(max_exp_avg_sq).div(CONST_2).sqrt()
else:
denom = exp_avg_sq.sqrt().add(defaults["torch_eps"])
p.data.addcdiv_(exp_avg, denom, value=step_size_neg)
return loss