# coding=utf-8
# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch optimization for OpenAI GPT model."""
import math
import torch
from torch.optim import Optimizer
from torch.optim.optimizer import required
from torch.nn.utils import clip_grad_norm_
import logging
logger = logging.getLogger(__name__)
def warmup_cosine(x, warmup=0.002):
if x < warmup:
return x/warmup
x_ = (x - warmup) / (1 - warmup) # progress after warmup
return 0.5 * (1. + math.cos(math.pi * x_))
def warmup_constant(x, warmup=0.002):
""" Linearly increases learning rate over `warmup`*`t_total` (as provided to OpenAIAdam) training steps.
Learning rate is 1. afterwards. """
if x < warmup:
return x/warmup
return 1.0
def warmup_linear(x, warmup=0.002):
""" Specifies a triangular learning rate schedule where peak is reached at `warmup`*`t_total`-th (as provided to OpenAIAdam) training step.
After `t_total`-th training step, learning rate is zero. """
if x < warmup:
return x/warmup
return max((x-1.)/(warmup-1.), 0)
SCHEDULES = {
'warmup_cosine':warmup_cosine,
'warmup_constant':warmup_constant,
'warmup_linear':warmup_linear,
}
class OpenAIAdam(Optimizer):
"""Implements Open AI version of Adam algorithm with weight decay fix.
"""
def __init__(self, params, lr=required, schedule='warmup_linear', warmup=-1, t_total=-1,
b1=0.9, b2=0.999, e=1e-8, weight_decay=0,
vector_l2=False, max_grad_norm=-1, **kwargs):
if lr is not required and lr < 0.0:
raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
if schedule not in SCHEDULES:
raise ValueError("Invalid schedule parameter: {}".format(schedule))
if not 0.0 <= warmup < 1.0 and not warmup == -1:
raise ValueError("Invalid warmup: {} - should be in [0.0, 1.0[ or -1".format(warmup))
if not 0.0 <= b1 < 1.0:
raise ValueError("Invalid b1 parameter: {}".format(b1))
if not 0.0 <= b2 < 1.0:
raise ValueError("Invalid b2 parameter: {}".format(b2))
if not e >= 0.0:
raise ValueError("Invalid epsilon value: {}".format(e))
defaults = dict(lr=lr, schedule=schedule, warmup=warmup, t_total=t_total,
b1=b1, b2=b2, e=e, weight_decay=weight_decay, vector_l2=vector_l2,
max_grad_norm=max_grad_norm)
super(OpenAIAdam, self).__init__(params, defaults)
def get_lr(self):
lr = []
for group in self.param_groups:
for p in group['params']:
state = self.state[p]
if len(state) == 0:
return [0]
if group['t_total'] != -1:
schedule_fct = SCHEDULES[group['schedule']]
lr_scheduled = group['lr'] * schedule_fct(state['step']/group['t_total'], group['warmup'])
else:
lr_scheduled = group['lr']
lr.append(lr_scheduled)
return lr
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()
warned_for_t_total = False
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('Adam does not support sparse gradients, please consider SparseAdam 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)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['b1'], group['b2']
state['step'] += 1
# Add grad clipping
if group['max_grad_norm'] > 0:
clip_grad_norm_(p, group['max_grad_norm'])
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
denom = exp_avg_sq.sqrt().add_(group['e'])
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
if group['t_total'] != -1:
schedule_fct = SCHEDULES[group['schedule']]
progress = state['step']/group['t_total']
lr_scheduled = group['lr'] * schedule_fct(progress, group['warmup'])
# warning for exceeding t_total (only active with warmup_linear
if group['schedule'] == "warmup_linear" and progress > 1. and not warned_for_t_total:
logger.warning(
"Training beyond specified 't_total' steps with schedule '{}'. Learning rate set to {}. "
"Please set 't_total' of {} correctly.".format(group['schedule'], lr_scheduled, self.__class__.__name__))
warned_for_t_total = True
# end warning
else:
lr_scheduled = group['lr']
step_size = lr_scheduled * math.sqrt(bias_correction2) / bias_correction1
p.data.addcdiv_(-step_size, exp_avg, denom)
# Add weight decay at the end (fixed version)
if (len(p.size()) > 1 or group['vector_l2']) and group['weight_decay'] > 0:
p.data.add_(-lr_scheduled * group['weight_decay'], p.data)
return loss