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022525b003
Custom schedulers are currently initiated by wrapping Pytorch's LambdaLR class and passing a method of the wrapping class to the __init__ function of LambdaLR. This approach is not appropriate for several reasons: 1. one does not need to define a class when it only defines a __init__() method; 2. instantiating the parent class by passing a method of the child class creates a cyclical reference which leads to memory leaks. See issues #1742 and #1134. In this commit we replace the wrapper classes with functions that instantiate `LambdaLR` with a custom learning rate function. We use a closure to specify the parameter of the latter. We also do a bit of renaming within the function to explicit the behaviour and removed docstrings that were subsequently not necessary.
170 lines
7.5 KiB
Python
170 lines
7.5 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch optimization for BERT model."""
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import logging
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import math
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import torch
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import LambdaLR
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logger = logging.getLogger(__name__)
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def get_constant_schedule(optimizer, last_epoch=-1):
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""" Create a schedule with a constant learning rate.
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"""
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return LambdaLR(optimizer, lambda _: 1, last_epoch=last_epoch)
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def get_constant_schedule_with_warmup(optimizer, num_warmup_steps, last_epoch=-1):
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""" Create a schedule with a constant learning rate preceded by a warmup
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period during which the learning rate increases linearly between 0 and 1.
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"""
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def lr_lambda(current_step):
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if current_step < num_warmup_steps:
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return float(current_step) / float(max(1.0, num_warmup_steps))
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return 1.
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return LambdaLR(optimizer, lr_lambda, last_epoch=last_epoch)
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def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1):
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""" Create a schedule with a learning rate that decreases linearly after
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linearly increasing during a warmup period.
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"""
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def lr_lambda(current_step):
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if current_step < num_warmup_steps:
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return float(current_step) / float(max(1, num_warmup_steps))
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return max(0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps)))
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return LambdaLR(optimizer, lr_lambda, last_epoch)
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def get_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_cycles=.5, last_epoch=-1):
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""" Create a schedule with a learning rate that decreases following the
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values of the cosine function between 0 and `pi * cycles` after a warmup
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period during which it increases linearly between 0 and 1.
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"""
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def lr_lambda(current_step):
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if current_step < num_warmup_steps:
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return float(current_step) / float(max(1, num_warmup_steps))
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progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
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return max(0., 0.5 * (1. + math.cos(math.pi * float(num_cycles) * 2. * progress)))
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return LambdaLR(optimizer, lr_lambda, last_epoch)
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def get_cosine_with_hard_restarts_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_cycles=1., last_epoch=-1):
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""" Create a schedule with a learning rate that decreases following the
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values of the cosine function with several hard restarts, after a warmup
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period during which it increases linearly between 0 and 1.
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"""
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def lr_lambda(current_step):
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if current_step < num_warmup_steps:
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return float(current_step) / float(max(1, num_warmup_steps))
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progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
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if progress >= 1.:
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return 0.
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return max(0., 0.5 * (1. + math.cos(math.pi * ((float(num_cycles) * progress) % 1.))))
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return LambdaLR(optimizer, lr_lambda, last_epoch)
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class AdamW(Optimizer):
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""" Implements Adam algorithm with weight decay fix.
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Parameters:
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lr (float): learning rate. Default 1e-3.
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betas (tuple of 2 floats): Adams beta parameters (b1, b2). Default: (0.9, 0.999)
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eps (float): Adams epsilon. Default: 1e-6
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weight_decay (float): Weight decay. Default: 0.0
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correct_bias (bool): can be set to False to avoid correcting bias in Adam (e.g. like in Bert TF repository). Default True.
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"""
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-6, weight_decay=0.0, correct_bias=True):
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if lr < 0.0:
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raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
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if not 0.0 <= betas[0] < 1.0:
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raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[0]))
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[1]))
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if not 0.0 <= eps:
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raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(eps))
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
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correct_bias=correct_bias)
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super(AdamW, self).__init__(params, defaults)
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def step(self, closure=None):
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"""Performs a single optimization step.
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Arguments:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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loss = closure()
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for group in self.param_groups:
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for p in group['params']:
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if p.grad is None:
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continue
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grad = p.grad.data
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if grad.is_sparse:
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raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state['step'] = 0
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# Exponential moving average of gradient values
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state['exp_avg'] = torch.zeros_like(p.data)
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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = torch.zeros_like(p.data)
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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beta1, beta2 = group['betas']
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state['step'] += 1
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# Decay the first and second moment running average coefficient
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# In-place operations to update the averages at the same time
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exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
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exp_avg_sq.mul_(beta2).addcmul_(1.0 - beta2, grad, grad)
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denom = exp_avg_sq.sqrt().add_(group['eps'])
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step_size = group['lr']
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if group['correct_bias']: # No bias correction for Bert
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bias_correction1 = 1.0 - beta1 ** state['step']
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bias_correction2 = 1.0 - beta2 ** state['step']
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step_size = step_size * math.sqrt(bias_correction2) / bias_correction1
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p.data.addcdiv_(-step_size, exp_avg, denom)
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# Just adding the square of the weights to the loss function is *not*
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# the correct way of using L2 regularization/weight decay with Adam,
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# since that will interact with the m and v parameters in strange ways.
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#
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# Instead we want to decay the weights in a manner that doesn't interact
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# with the m/v parameters. This is equivalent to adding the square
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# of the weights to the loss with plain (non-momentum) SGD.
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# Add weight decay at the end (fixed version)
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if group['weight_decay'] > 0.0:
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p.data.add_(-group['lr'] * group['weight_decay'], p.data)
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return loss
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