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use pytorch lightning remove potential leakage, misc
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wassname
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@@ -1,304 +1,304 @@
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# 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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# # 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 math
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import torch
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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.optimizer import required
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from torch.nn.utils import clip_grad_norm_
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import logging
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import abc
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import sys
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# from torch.optim import Optimizer
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# from torch.optim.optimizer import required
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# from torch.nn.utils import clip_grad_norm_
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# import logging
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# import abc
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# import sys
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logger = logging.getLogger(__name__)
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# logger = logging.getLogger(__name__)
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if sys.version_info >= (3, 4):
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ABC = abc.ABC
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else:
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ABC = abc.ABCMeta('ABC', (), {})
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# if sys.version_info >= (3, 4):
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# ABC = abc.ABC
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# else:
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# ABC = abc.ABCMeta('ABC', (), {})
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class _LRSchedule(ABC):
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""" Parent of all LRSchedules here. """
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warn_t_total = False # is set to True for schedules where progressing beyond t_total steps doesn't make sense
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def __init__(self, warmup=0.002, t_total=-1, **kw):
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"""
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:param warmup: what fraction of t_total steps will be used for linear warmup
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:param t_total: how many training steps (updates) are planned
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:param kw:
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"""
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super(_LRSchedule, self).__init__(**kw)
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if t_total < 0:
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logger.warning("t_total value of {} results in schedule not being applied".format(t_total))
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if not 0.0 <= warmup < 1.0 and not warmup == -1:
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raise ValueError("Invalid warmup: {} - should be in [0.0, 1.0[ or -1".format(warmup))
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warmup = max(warmup, 0.)
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self.warmup, self.t_total = float(warmup), float(t_total)
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self.warned_for_t_total_at_progress = -1
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# class _LRSchedule(ABC):
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# """ Parent of all LRSchedules here. """
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# warn_t_total = False # is set to True for schedules where progressing beyond t_total steps doesn't make sense
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# def __init__(self, warmup=0.002, t_total=-1, **kw):
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# """
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# :param warmup: what fraction of t_total steps will be used for linear warmup
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# :param t_total: how many training steps (updates) are planned
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# :param kw:
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# """
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# super(_LRSchedule, self).__init__(**kw)
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# if t_total < 0:
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# logger.warning("t_total value of {} results in schedule not being applied".format(t_total))
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# if not 0.0 <= warmup < 1.0 and not warmup == -1:
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# raise ValueError("Invalid warmup: {} - should be in [0.0, 1.0[ or -1".format(warmup))
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# warmup = max(warmup, 0.)
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# self.warmup, self.t_total = float(warmup), float(t_total)
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# self.warned_for_t_total_at_progress = -1
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def get_lr(self, step, nowarn=False):
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"""
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:param step: which of t_total steps we're on
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:param nowarn: set to True to suppress warning regarding training beyond specified 't_total' steps
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:return: learning rate multiplier for current update
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"""
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if self.t_total < 0:
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return 1.
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progress = float(step) / self.t_total
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ret = self.get_lr_(progress)
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# warning for exceeding t_total (only active with warmup_linear
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if not nowarn and self.warn_t_total and progress > 1. and progress > self.warned_for_t_total_at_progress:
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logger.warning(
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"Training beyond specified 't_total'. Learning rate multiplier set to {}. Please set 't_total' of {} correctly."
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.format(ret, self.__class__.__name__))
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self.warned_for_t_total_at_progress = progress
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# end warning
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return ret
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# def get_lr(self, step, nowarn=False):
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# """
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# :param step: which of t_total steps we're on
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# :param nowarn: set to True to suppress warning regarding training beyond specified 't_total' steps
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# :return: learning rate multiplier for current update
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# """
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# if self.t_total < 0:
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# return 1.
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# progress = float(step) / self.t_total
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# ret = self.get_lr_(progress)
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# # warning for exceeding t_total (only active with warmup_linear
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# if not nowarn and self.warn_t_total and progress > 1. and progress > self.warned_for_t_total_at_progress:
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# logger.warning(
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# "Training beyond specified 't_total'. Learning rate multiplier set to {}. Please set 't_total' of {} correctly."
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# .format(ret, self.__class__.__name__))
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# self.warned_for_t_total_at_progress = progress
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# # end warning
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# return ret
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@abc.abstractmethod
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def get_lr_(self, progress):
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"""
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:param progress: value between 0 and 1 (unless going beyond t_total steps) specifying training progress
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:return: learning rate multiplier for current update
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"""
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return 1.
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# @abc.abstractmethod
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# def get_lr_(self, progress):
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# """
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# :param progress: value between 0 and 1 (unless going beyond t_total steps) specifying training progress
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# :return: learning rate multiplier for current update
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# """
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# return 1.
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class ConstantLR(_LRSchedule):
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def get_lr_(self, progress):
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return 1.
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# class ConstantLR(_LRSchedule):
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# def get_lr_(self, progress):
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# return 1.
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class WarmupCosineSchedule(_LRSchedule):
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"""
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Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
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Decreases learning rate from 1. to 0. over remaining `1 - warmup` steps following a cosine curve.
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If `cycles` (default=0.5) is different from default, learning rate follows cosine function after warmup.
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"""
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warn_t_total = True
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def __init__(self, warmup=0.002, t_total=-1, cycles=.5, **kw):
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"""
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:param warmup: see LRSchedule
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:param t_total: see LRSchedule
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:param cycles: number of cycles. Default: 0.5, corresponding to cosine decay from 1. at progress==warmup and 0 at progress==1.
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:param kw:
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"""
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super(WarmupCosineSchedule, self).__init__(warmup=warmup, t_total=t_total, **kw)
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self.cycles = cycles
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# class WarmupCosineSchedule(_LRSchedule):
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# """
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# Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
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# Decreases learning rate from 1. to 0. over remaining `1 - warmup` steps following a cosine curve.
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# If `cycles` (default=0.5) is different from default, learning rate follows cosine function after warmup.
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# """
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# warn_t_total = True
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# def __init__(self, warmup=0.002, t_total=-1, cycles=.5, **kw):
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# """
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# :param warmup: see LRSchedule
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# :param t_total: see LRSchedule
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# :param cycles: number of cycles. Default: 0.5, corresponding to cosine decay from 1. at progress==warmup and 0 at progress==1.
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# :param kw:
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# """
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# super(WarmupCosineSchedule, self).__init__(warmup=warmup, t_total=t_total, **kw)
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# self.cycles = cycles
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def get_lr_(self, progress):
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if progress < self.warmup:
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return progress / self.warmup
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else:
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progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
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return 0.5 * (1. + math.cos(math.pi * self.cycles * 2 * progress))
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# def get_lr_(self, progress):
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# if progress < self.warmup:
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# return progress / self.warmup
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# else:
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# progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
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# return 0.5 * (1. + math.cos(math.pi * self.cycles * 2 * progress))
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class WarmupCosineWithHardRestartsSchedule(WarmupCosineSchedule):
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"""
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Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
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If `cycles` (default=1.) is different from default, learning rate follows `cycles` times a cosine decaying
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learning rate (with hard restarts).
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"""
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def __init__(self, warmup=0.002, t_total=-1, cycles=1., **kw):
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super(WarmupCosineWithHardRestartsSchedule, self).__init__(warmup=warmup, t_total=t_total, cycles=cycles, **kw)
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assert(cycles >= 1.)
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# class WarmupCosineWithHardRestartsSchedule(WarmupCosineSchedule):
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# """
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# Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
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# If `cycles` (default=1.) is different from default, learning rate follows `cycles` times a cosine decaying
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# learning rate (with hard restarts).
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# """
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# def __init__(self, warmup=0.002, t_total=-1, cycles=1., **kw):
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# super(WarmupCosineWithHardRestartsSchedule, self).__init__(warmup=warmup, t_total=t_total, cycles=cycles, **kw)
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# assert(cycles >= 1.)
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def get_lr_(self, progress):
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if progress < self.warmup:
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return progress / self.warmup
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else:
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progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
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ret = 0.5 * (1. + math.cos(math.pi * ((self.cycles * progress) % 1)))
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return ret
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# def get_lr_(self, progress):
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# if progress < self.warmup:
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# return progress / self.warmup
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# else:
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# progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
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# ret = 0.5 * (1. + math.cos(math.pi * ((self.cycles * progress) % 1)))
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# return ret
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class WarmupCosineWithWarmupRestartsSchedule(WarmupCosineWithHardRestartsSchedule):
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"""
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All training progress is divided in `cycles` (default=1.) parts of equal length.
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Every part follows a schedule with the first `warmup` fraction of the training steps linearly increasing from 0. to 1.,
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followed by a learning rate decreasing from 1. to 0. following a cosine curve.
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"""
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def __init__(self, warmup=0.002, t_total=-1, cycles=1., **kw):
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assert(warmup * cycles < 1.)
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warmup = warmup * cycles if warmup >= 0 else warmup
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super(WarmupCosineWithWarmupRestartsSchedule, self).__init__(warmup=warmup, t_total=t_total, cycles=cycles, **kw)
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# class WarmupCosineWithWarmupRestartsSchedule(WarmupCosineWithHardRestartsSchedule):
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# """
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# All training progress is divided in `cycles` (default=1.) parts of equal length.
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# Every part follows a schedule with the first `warmup` fraction of the training steps linearly increasing from 0. to 1.,
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# followed by a learning rate decreasing from 1. to 0. following a cosine curve.
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# """
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# def __init__(self, warmup=0.002, t_total=-1, cycles=1., **kw):
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# assert(warmup * cycles < 1.)
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# warmup = warmup * cycles if warmup >= 0 else warmup
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# super(WarmupCosineWithWarmupRestartsSchedule, self).__init__(warmup=warmup, t_total=t_total, cycles=cycles, **kw)
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def get_lr_(self, progress):
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progress = progress * self.cycles % 1.
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if progress < self.warmup:
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return progress / self.warmup
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else:
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progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
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ret = 0.5 * (1. + math.cos(math.pi * progress))
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return ret
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# def get_lr_(self, progress):
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# progress = progress * self.cycles % 1.
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# if progress < self.warmup:
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# return progress / self.warmup
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# else:
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# progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
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# ret = 0.5 * (1. + math.cos(math.pi * progress))
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# return ret
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class WarmupConstantSchedule(_LRSchedule):
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"""
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Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
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Keeps learning rate equal to 1. after warmup.
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"""
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def get_lr_(self, progress):
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if progress < self.warmup:
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return progress / self.warmup
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return 1.
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# class WarmupConstantSchedule(_LRSchedule):
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# """
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# Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
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# Keeps learning rate equal to 1. after warmup.
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# """
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# def get_lr_(self, progress):
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# if progress < self.warmup:
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# return progress / self.warmup
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# return 1.
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class WarmupLinearSchedule(_LRSchedule):
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"""
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Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
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Linearly decreases learning rate from 1. to 0. over remaining `1 - warmup` steps.
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"""
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warn_t_total = True
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def get_lr_(self, progress):
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if progress < self.warmup:
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return progress / self.warmup
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return max((progress - 1.) / (self.warmup - 1.), 0.)
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# class WarmupLinearSchedule(_LRSchedule):
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# """
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# Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
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# Linearly decreases learning rate from 1. to 0. over remaining `1 - warmup` steps.
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# """
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# warn_t_total = True
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# def get_lr_(self, progress):
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# if progress < self.warmup:
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# return progress / self.warmup
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# return max((progress - 1.) / (self.warmup - 1.), 0.)
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SCHEDULES = {
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None: ConstantLR,
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"none": ConstantLR,
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"warmup_cosine": WarmupCosineSchedule,
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"warmup_constant": WarmupConstantSchedule,
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"warmup_linear": WarmupLinearSchedule
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}
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# SCHEDULES = {
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# None: ConstantLR,
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# "none": ConstantLR,
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# "warmup_cosine": WarmupCosineSchedule,
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# "warmup_constant": WarmupConstantSchedule,
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# "warmup_linear": WarmupLinearSchedule
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# }
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class BertAdam(Optimizer):
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"""Implements BERT version of Adam algorithm with weight decay fix.
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Params:
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lr: learning rate
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warmup: portion of t_total for the warmup, -1 means no warmup. Default: -1
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t_total: total number of training steps for the learning
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rate schedule, -1 means constant learning rate of 1. (no warmup regardless of warmup setting). Default: -1
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schedule: schedule to use for the warmup (see above).
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Can be `'warmup_linear'`, `'warmup_constant'`, `'warmup_cosine'`, `'none'`, `None` or a `_LRSchedule` object (see below).
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If `None` or `'none'`, learning rate is always kept constant.
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Default : `'warmup_linear'`
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b1: Adams b1. Default: 0.9
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b2: Adams b2. Default: 0.999
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e: Adams epsilon. Default: 1e-6
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weight_decay: Weight decay. Default: 0.01
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max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0
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"""
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def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear',
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b1=0.9, b2=0.999, e=1e-6, weight_decay=0.01, max_grad_norm=1.0, **kwargs):
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if lr is not required and lr < 0.0:
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raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
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if not isinstance(schedule, _LRSchedule) and schedule not in SCHEDULES:
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raise ValueError("Invalid schedule parameter: {}".format(schedule))
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if not 0.0 <= b1 < 1.0:
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raise ValueError("Invalid b1 parameter: {} - should be in [0.0, 1.0[".format(b1))
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if not 0.0 <= b2 < 1.0:
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raise ValueError("Invalid b2 parameter: {} - should be in [0.0, 1.0[".format(b2))
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if not e >= 0.0:
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raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(e))
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# initialize schedule object
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||||
if not isinstance(schedule, _LRSchedule):
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schedule_type = SCHEDULES[schedule]
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schedule = schedule_type(warmup=warmup, t_total=t_total)
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else:
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||||
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.")
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defaults = dict(lr=lr, schedule=schedule,
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||||
b1=b1, b2=b2, e=e, weight_decay=weight_decay,
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||||
max_grad_norm=max_grad_norm)
|
||||
super(BertAdam, self).__init__(params, defaults)
|
||||
# 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 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.
|
||||
# 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()
|
||||
# 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')
|
||||
# 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 = 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)
|
||||
# # 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']
|
||||
# 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'])
|
||||
# # 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'])
|
||||
# # 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
|
||||
# # 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'])
|
||||
# lr_scheduled = group['lr']
|
||||
# lr_scheduled *= group['schedule'].get_lr(state['step'])
|
||||
|
||||
update_with_lr = lr_scheduled * update
|
||||
p.data.add_(-update_with_lr)
|
||||
# update_with_lr = lr_scheduled * update
|
||||
# p.data.add_(-update_with_lr)
|
||||
|
||||
state['step'] += 1
|
||||
# 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']
|
||||
# # 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
|
||||
# return loss
|
||||
|
||||
Reference in New Issue
Block a user