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- # coding=utf-8
- # Copyright 2018 The Google AI Language 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 BERT 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
- import abc
- import sys
-
- logger = logging.getLogger(__name__)
-
- if sys.version_info >= (3, 4):
- ABC = abc.ABC
- else:
- ABC = abc.ABCMeta('ABC', (), {})
-
-
- class _LRSchedule(ABC):
- """ Parent of all LRSchedules here. """
- warn_t_total = False # is set to True for schedules where progressing beyond t_total steps doesn't make sense
-
- def __init__(self, warmup=0.002, t_total=-1, **kw):
- """
- :param warmup: what fraction of t_total steps will be used for linear warmup
- :param t_total: how many training steps (updates) are planned
- :param kw:
- """
- super(_LRSchedule, self).__init__(**kw)
- if t_total < 0:
- logger.warning("t_total value of {} results in schedule not being applied".format(t_total))
- 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))
- warmup = max(warmup, 0.)
- self.warmup, self.t_total = float(warmup), float(t_total)
- self.warned_for_t_total_at_progress = -1
-
- def get_lr(self, step, nowarn=False):
- """
- :param step: which of t_total steps we're on
- :param nowarn: set to True to suppress warning regarding training beyond specified 't_total' steps
- :return: learning rate multiplier for current update
- """
- if self.t_total < 0:
- return 1.
- progress = float(step) / self.t_total
- ret = self.get_lr_(progress)
- # warning for exceeding t_total (only active with warmup_linear
- if not nowarn and self.warn_t_total and progress > 1. and progress > self.warned_for_t_total_at_progress:
- logger.warning(
- "Training beyond specified 't_total'. Learning rate multiplier set to {}. Please set 't_total' of {} correctly."
- .format(ret, self.__class__.__name__))
- self.warned_for_t_total_at_progress = progress
- # end warning
- return ret
-
- @abc.abstractmethod
- def get_lr_(self, progress):
- """
- :param progress: value between 0 and 1 (unless going beyond t_total steps) specifying training progress
- :return: learning rate multiplier for current update
- """
- return 1.
-
-
- class ConstantLR(_LRSchedule):
- def get_lr_(self, progress):
- return 1.
-
-
- class WarmupCosineSchedule(_LRSchedule):
- """
- Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
- Decreases learning rate from 1. to 0. over remaining `1 - warmup` steps following a cosine curve.
- If `cycles` (default=0.5) is different from default, learning rate follows cosine function after warmup.
- """
- warn_t_total = True
-
- def __init__(self, warmup=0.002, t_total=-1, cycles=.5, **kw):
- """
- :param warmup: see LRSchedule
- :param t_total: see LRSchedule
- :param cycles: number of cycles. Default: 0.5, corresponding to cosine decay from 1. at progress==warmup and 0 at progress==1.
- :param kw:
- """
- super(WarmupCosineSchedule, self).__init__(warmup=warmup, t_total=t_total, **kw)
- self.cycles = cycles
-
- def get_lr_(self, progress):
- if progress < self.warmup:
- return progress / self.warmup
- else:
- progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
- return 0.5 * (1. + math.cos(math.pi * self.cycles * 2 * progress))
-
-
- class WarmupCosineWithHardRestartsSchedule(WarmupCosineSchedule):
- """
- Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
- If `cycles` (default=1.) is different from default, learning rate follows `cycles` times a cosine decaying
- learning rate (with hard restarts).
- """
-
- def __init__(self, warmup=0.002, t_total=-1, cycles=1., **kw):
- super(WarmupCosineWithHardRestartsSchedule, self).__init__(warmup=warmup, t_total=t_total, cycles=cycles, **kw)
- assert (cycles >= 1.)
-
- def get_lr_(self, progress):
- if progress < self.warmup:
- return progress / self.warmup
- else:
- progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
- ret = 0.5 * (1. + math.cos(math.pi * ((self.cycles * progress) % 1)))
- return ret
-
-
- class WarmupCosineWithWarmupRestartsSchedule(WarmupCosineWithHardRestartsSchedule):
- """
- All training progress is divided in `cycles` (default=1.) parts of equal length.
- Every part follows a schedule with the first `warmup` fraction of the training steps linearly increasing from 0. to 1.,
- followed by a learning rate decreasing from 1. to 0. following a cosine curve.
- """
-
- def __init__(self, warmup=0.002, t_total=-1, cycles=1., **kw):
- assert (warmup * cycles < 1.)
- warmup = warmup * cycles if warmup >= 0 else warmup
- super(WarmupCosineWithWarmupRestartsSchedule, self).__init__(warmup=warmup, t_total=t_total, cycles=cycles, **kw)
-
- def get_lr_(self, progress):
- progress = progress * self.cycles % 1.
- if progress < self.warmup:
- return progress / self.warmup
- else:
- progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
- ret = 0.5 * (1. + math.cos(math.pi * progress))
- return ret
-
-
- class WarmupConstantSchedule(_LRSchedule):
- """
- Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
- Keeps learning rate equal to 1. after warmup.
- """
-
- def get_lr_(self, progress):
- if progress < self.warmup:
- return progress / self.warmup
- return 1.
-
-
- class WarmupLinearSchedule(_LRSchedule):
- """
- Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
- Linearly decreases learning rate from 1. to 0. over remaining `1 - warmup` steps.
- """
- warn_t_total = True
-
- def get_lr_(self, progress):
- if progress < self.warmup:
- return progress / self.warmup
- return max((progress - 1.) / (self.warmup - 1.), 0.)
-
-
- SCHEDULES = {
- None: ConstantLR,
- "none": ConstantLR,
- "warmup_cosine": WarmupCosineSchedule,
- "warmup_constant": WarmupConstantSchedule,
- "warmup_linear": WarmupLinearSchedule
- }
-
-
- class BertAdam(Optimizer):
- """Implements BERT version of Adam algorithm with weight decay fix.
- Params:
- lr: learning rate
- warmup: portion of t_total for the warmup, -1 means no warmup. Default: -1
- t_total: total number of training steps for the learning
- rate schedule, -1 means constant learning rate of 1. (no warmup regardless of warmup setting). Default: -1
- schedule: schedule to use for the warmup (see above).
- Can be `'warmup_linear'`, `'warmup_constant'`, `'warmup_cosine'`, `'none'`, `None` or a `_LRSchedule` object (see below).
- If `None` or `'none'`, learning rate is always kept constant.
- Default : `'warmup_linear'`
- b1: Adams b1. Default: 0.9
- b2: Adams b2. Default: 0.999
- e: Adams epsilon. Default: 1e-6
- weight_decay: Weight decay. Default: 0.01
- max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0
- """
-
- def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear',
- b1=0.9, b2=0.999, e=1e-6, weight_decay=0.01, max_grad_norm=1.0, **kwargs):
- if lr is not required and lr < 0.0:
- raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
- if not isinstance(schedule, _LRSchedule) and schedule not in SCHEDULES:
- raise ValueError("Invalid schedule parameter: {}".format(schedule))
- if not 0.0 <= b1 < 1.0:
- raise ValueError("Invalid b1 parameter: {} - should be in [0.0, 1.0[".format(b1))
- if not 0.0 <= b2 < 1.0:
- raise ValueError("Invalid b2 parameter: {} - should be in [0.0, 1.0[".format(b2))
- if not e >= 0.0:
- raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(e))
- # initialize schedule object
- if not isinstance(schedule, _LRSchedule):
- schedule_type = SCHEDULES[schedule]
- schedule = schedule_type(warmup=warmup, t_total=t_total)
- else:
- if warmup != -1 or t_total != -1:
- logger.warning("warmup and t_total on the optimizer are ineffective when _LRSchedule object is provided as schedule. "
- "Please specify custom warmup and t_total in _LRSchedule object.")
- defaults = dict(lr=lr, schedule=schedule,
- b1=b1, b2=b2, e=e, weight_decay=weight_decay,
- max_grad_norm=max_grad_norm)
- super(BertAdam, 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]
- lr_scheduled = group['lr']
- lr_scheduled *= group['schedule'].get_lr(state['step'])
- 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()
-
- 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['next_m'] = torch.zeros_like(p.data)
- # Exponential moving average of squared gradient values
- state['next_v'] = torch.zeros_like(p.data)
-
- next_m, next_v = state['next_m'], state['next_v']
- beta1, beta2 = group['b1'], group['b2']
-
- # 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
- # In-place operations to update the averages at the same time
- next_m.mul_(beta1).add_(1 - beta1, grad)
- next_v.mul_(beta2).addcmul_(1 - beta2, grad, grad)
- update = next_m / (next_v.sqrt() + group['e'])
-
- # Just adding the square of the weights to the loss function is *not*
- # the correct way of using L2 regularization/weight decay with Adam,
- # since that will interact with the m and v parameters in strange ways.
- #
- # Instead we want to decay the weights in a manner that doesn't interact
- # with the m/v parameters. This is equivalent to adding the square
- # of the weights to the loss with plain (non-momentum) SGD.
- if group['weight_decay'] > 0.0:
- update += group['weight_decay'] * p.data
-
- lr_scheduled = group['lr']
- lr_scheduled *= group['schedule'].get_lr(state['step'])
-
- update_with_lr = lr_scheduled * update
- p.data.add_(-update_with_lr)
-
- state['step'] += 1
-
- # step_size = lr_scheduled * math.sqrt(bias_correction2) / bias_correction1
- # No bias correction
- # bias_correction1 = 1 - beta1 ** state['step']
- # bias_correction2 = 1 - beta2 ** state['step']
-
- return loss
-
-
- def optim4GPU(cfg, model, discriminator=True):
- """ optimizer for GPU training """
- param_optimizer = list(model.named_parameters())
- no_decay = ['bias', 'gamma', 'beta']
- if discriminator:
- optimizer_grouped_parameters = [
- {'params': [p for n, p in param_optimizer if n not in no_decay and "OOD_classifier" in n], 'weight_decay_rate': 0.01},
- {'params': [p for n, p in param_optimizer if n in no_decay and "OOD_classifier" in n], 'weight_decay_rate': 0.0}]
- else:
- optimizer_grouped_parameters = [
- {'params': [p for n, p in param_optimizer if n not in no_decay and "OOD_classifier" not in n], 'weight_decay_rate': 0.01},
- {'params': [p for n, p in param_optimizer if n in no_decay and "OOD_classifier" not in n], 'weight_decay_rate': 0.0}]
- return BertAdam(optimizer_grouped_parameters, lr=cfg.lr, warmup=cfg.warmup, t_total=cfg.total_steps)
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