532 lines
22 KiB
Python
532 lines
22 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 math
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from typing import Callable, Iterable, Tuple
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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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from .utils import logging
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logger = logging.get_logger(__name__)
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def get_constant_schedule(optimizer: Optimizer, last_epoch: int = -1):
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"""
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Create a schedule with a constant learning rate, using the learning rate set in optimizer.
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Args:
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optimizer (:class:`~torch.optim.Optimizer`):
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The optimizer for which to schedule the learning rate.
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last_epoch (:obj:`int`, `optional`, defaults to -1):
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The index of the last epoch when resuming training.
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Return:
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:obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
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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: Optimizer, num_warmup_steps: int, last_epoch: int = -1):
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"""
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Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate
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increases linearly between 0 and the initial lr set in the optimizer.
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Args:
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optimizer (:class:`~torch.optim.Optimizer`):
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The optimizer for which to schedule the learning rate.
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num_warmup_steps (:obj:`int`):
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The number of steps for the warmup phase.
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last_epoch (:obj:`int`, `optional`, defaults to -1):
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The index of the last epoch when resuming training.
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Return:
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:obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
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"""
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def lr_lambda(current_step: int):
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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 1.0
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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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"""
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Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0,
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after a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.
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Args:
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optimizer (:class:`~torch.optim.Optimizer`):
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The optimizer for which to schedule the learning rate.
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num_warmup_steps (:obj:`int`):
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The number of steps for the warmup phase.
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num_training_steps (:obj:`int`):
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The total number of training steps.
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last_epoch (:obj:`int`, `optional`, defaults to -1):
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The index of the last epoch when resuming training.
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Return:
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:obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
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"""
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def lr_lambda(current_step: int):
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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(
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0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps))
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)
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return LambdaLR(optimizer, lr_lambda, last_epoch)
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def get_cosine_schedule_with_warmup(
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optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: float = 0.5, last_epoch: int = -1
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):
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"""
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Create a schedule with a learning rate that decreases following the values of the cosine function between the
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initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
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initial lr set in the optimizer.
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Args:
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optimizer (:class:`~torch.optim.Optimizer`):
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The optimizer for which to schedule the learning rate.
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num_warmup_steps (:obj:`int`):
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The number of steps for the warmup phase.
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num_training_steps (:obj:`int`):
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The total number of training steps.
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num_cycles (:obj:`float`, `optional`, defaults to 0.5):
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The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
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following a half-cosine).
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last_epoch (:obj:`int`, `optional`, defaults to -1):
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The index of the last epoch when resuming training.
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Return:
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:obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
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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, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * 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(
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optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: int = 1, last_epoch: int = -1
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):
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"""
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Create a schedule with a learning rate that decreases following the values of the cosine function between the
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initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases
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linearly between 0 and the initial lr set in the optimizer.
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Args:
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optimizer (:class:`~torch.optim.Optimizer`):
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The optimizer for which to schedule the learning rate.
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num_warmup_steps (:obj:`int`):
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The number of steps for the warmup phase.
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num_training_steps (:obj:`int`):
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The total number of training steps.
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num_cycles (:obj:`int`, `optional`, defaults to 1):
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The number of hard restarts to use.
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last_epoch (:obj:`int`, `optional`, defaults to -1):
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The index of the last epoch when resuming training.
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Return:
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:obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
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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 >= 1.0:
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return 0.0
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return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(num_cycles) * progress) % 1.0))))
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return LambdaLR(optimizer, lr_lambda, last_epoch)
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def get_polynomial_decay_schedule_with_warmup(
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optimizer, num_warmup_steps, num_training_steps, lr_end=1e-7, power=1.0, last_epoch=-1
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):
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"""
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Create a schedule with a learning rate that decreases as a polynomial decay
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from the initial lr set in the optimizer to end lr defined by `lr_end`,
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after a warmup period during which it increases linearly from 0 to the
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initial lr set in the optimizer.
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Args:
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optimizer (:class:`~torch.optim.Optimizer`):
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The optimizer for which to schedule the learning rate.
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num_warmup_steps (:obj:`int`):
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The number of steps for the warmup phase.
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num_training_steps (:obj:`int`):
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The total number of training steps.
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lr_end (:obj:`float`, `optional`, defaults to 1e-7):
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The end LR.
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power (:obj:`float`, `optional`, defaults to 1.0):
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Power factor.
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last_epoch (:obj:`int`, `optional`, defaults to -1):
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The index of the last epoch when resuming training.
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Note: `power` defaults to 1.0 as in the fairseq implementation, which in turn is
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based on the original BERT implementation at
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https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37
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Return:
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:obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
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"""
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lr_init = optimizer.defaults["lr"]
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assert lr_init > lr_end, f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"
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def lr_lambda(current_step: int):
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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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elif current_step > num_training_steps:
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return lr_end / lr_init # as LambdaLR multiplies by lr_init
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else:
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lr_range = lr_init - lr_end
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decay_steps = num_training_steps - num_warmup_steps
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pct_remaining = 1 - (current_step - num_warmup_steps) / decay_steps
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decay = lr_range * pct_remaining ** power + lr_end
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return decay / lr_init # as LambdaLR multiplies by lr_init
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return LambdaLR(optimizer, lr_lambda, last_epoch)
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class AdamW(Optimizer):
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"""
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Implements Adam algorithm with weight decay fix as introduced in
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`Decoupled Weight Decay Regularization <https://arxiv.org/abs/1711.05101>`__.
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Parameters:
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params (:obj:`Iterable[torch.nn.parameter.Parameter]`):
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Iterable of parameters to optimize or dictionaries defining parameter groups.
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lr (:obj:`float`, `optional`, defaults to 1e-3):
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The learning rate to use.
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betas (:obj:`Tuple[float,float]`, `optional`, defaults to (0.9, 0.999)):
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Adam's betas parameters (b1, b2).
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eps (:obj:`float`, `optional`, defaults to 1e-6):
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Adam's epsilon for numerical stability.
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weight_decay (:obj:`float`, `optional`, defaults to 0):
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Decoupled weight decay to apply.
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correct_bias (:obj:`bool`, `optional`, defaults to `True`):
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Whether ot not to correct bias in Adam (for instance, in Bert TF repository they use :obj:`False`).
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"""
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def __init__(
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self,
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params: Iterable[torch.nn.parameter.Parameter],
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lr: float = 1e-3,
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betas: Tuple[float, float] = (0.9, 0.999),
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eps: float = 1e-6,
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weight_decay: float = 0.0,
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correct_bias: bool = True,
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):
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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, correct_bias=correct_bias)
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super().__init__(params, defaults)
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def step(self, closure: Callable = None):
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"""
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Performs a single optimization step.
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Arguments:
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closure (:obj:`Callable`, `optional`): A closure that reevaluates the model 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_(grad, alpha=1.0 - beta1)
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exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2)
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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_(exp_avg, denom, value=-step_size)
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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_(p.data, alpha=-group["lr"] * group["weight_decay"])
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return loss
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class Adafactor(Optimizer):
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"""
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AdaFactor pytorch implementation can be used as a drop in replacement for Adam
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original fairseq code: https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py
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Paper: `Adafactor: Adaptive Learning Rates with Sublinear Memory Cost` https://arxiv.org/abs/1804.04235
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Note that this optimizer internally adjusts the learning rate depending on the *scale_parameter*, *relative_step* and
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*warmup_init* options. To use a manual (external) learning rate schedule you should set `scale_parameter=False` and `relative_step=False`.
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Arguments:
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params (:obj:`Iterable[torch.nn.parameter.Parameter]`):
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Iterable of parameters to optimize or dictionaries defining parameter groups.
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lr (:obj:`float`, `optional`):
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The external learning rate.
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eps (:obj:`Tuple[float, float]`, `optional`, defaults to (1e-30, 1e-3)):
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Regularization constants for square gradient and parameter scale respectively
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clip_threshold (:obj:`float`, `optional`, defaults 1.0):
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Threshold of root mean square of final gradient update
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decay_rate (:obj:`float`, `optional`, defaults to -0.8):
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Coefficient used to compute running averages of square
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beta1 (:obj:`float`, `optional`):
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Coefficient used for computing running averages of gradient
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weight_decay (:obj:`float`, `optional`, defaults to 0):
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Weight decay (L2 penalty)
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scale_parameter (:obj:`bool`, `optional`, defaults to :obj:`True`):
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If True, learning rate is scaled by root mean square
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relative_step (:obj:`bool`, `optional`, defaults to :obj:`True`):
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If True, time-dependent learning rate is computed instead of external learning rate
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warmup_init (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Time-dependent learning rate computation depends on whether warm-up initialization is being used
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This implementation handles low-precision (FP16, bfloat) values, but we have not thoroughly tested.
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Recommended T5 finetuning settings:
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- Scheduled LR warm-up to fixed LR
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- disable relative updates
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- use clip threshold: https://arxiv.org/abs/2004.14546
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Example::
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Adafactor(model.parameters(), lr=1e-3, relative_step=False, warmup_init=True)
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- Alternatively, relative_step with warmup_init can be used.
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- Training without LR warmup or clip threshold is not recommended. Additional optimizer operations like
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gradient clipping should not be used alongside Adafactor.
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Usage::
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# replace AdamW with Adafactor
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optimizer = Adafactor(
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model.parameters(),
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lr=1e-3,
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eps=(1e-30, 1e-3),
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clip_threshold=1.0,
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decay_rate=-0.8,
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beta1=None,
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weight_decay=0.0,
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relative_step=False,
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scale_parameter=False,
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warmup_init=False
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)
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"""
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def __init__(
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self,
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params,
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lr=None,
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eps=(1e-30, 1e-3),
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clip_threshold=1.0,
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decay_rate=-0.8,
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beta1=None,
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weight_decay=0.0,
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scale_parameter=True,
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relative_step=True,
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warmup_init=False,
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):
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if lr is not None and relative_step:
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raise ValueError("Cannot combine manual lr and relative_step options")
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if warmup_init and not relative_step:
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raise ValueError("warmup_init requires relative_step=True")
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defaults = dict(
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lr=lr,
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eps=eps,
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clip_threshold=clip_threshold,
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decay_rate=decay_rate,
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beta1=beta1,
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weight_decay=weight_decay,
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scale_parameter=scale_parameter,
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relative_step=relative_step,
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warmup_init=warmup_init,
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)
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super().__init__(params, defaults)
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@staticmethod
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def _get_lr(param_group, param_state):
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rel_step_sz = param_group["lr"]
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if param_group["relative_step"]:
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min_step = 1e-6 * param_state["step"] if param_group["warmup_init"] else 1e-2
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rel_step_sz = min(min_step, 1.0 / math.sqrt(param_state["step"]))
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param_scale = 1.0
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if param_group["scale_parameter"]:
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param_scale = max(param_group["eps"][1], param_state["RMS"])
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return param_scale * rel_step_sz
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@staticmethod
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def _get_options(param_group, param_shape):
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factored = len(param_shape) >= 2
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use_first_moment = param_group["beta1"] is not None
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return factored, use_first_moment
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@staticmethod
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def _rms(tensor):
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return tensor.norm(2) / (tensor.numel() ** 0.5)
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@staticmethod
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def _approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col):
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r_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_()
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c_factor = exp_avg_sq_col.rsqrt()
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return torch.mm(r_factor.unsqueeze(-1), c_factor.unsqueeze(0))
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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.dtype in {torch.float16, torch.bfloat16}:
|
|
grad = grad.float()
|
|
if grad.is_sparse:
|
|
raise RuntimeError("Adafactor does not support sparse gradients.")
|
|
|
|
state = self.state[p]
|
|
grad_shape = grad.shape
|
|
|
|
factored, use_first_moment = self._get_options(group, grad_shape)
|
|
# State Initialization
|
|
if len(state) == 0:
|
|
state["step"] = 0
|
|
|
|
if use_first_moment:
|
|
# Exponential moving average of gradient values
|
|
state["exp_avg"] = torch.zeros_like(grad)
|
|
if factored:
|
|
state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad)
|
|
state["exp_avg_sq_col"] = torch.zeros(grad_shape[:-2] + grad_shape[-1:]).to(grad)
|
|
else:
|
|
state["exp_avg_sq"] = torch.zeros_like(grad)
|
|
|
|
state["RMS"] = 0
|
|
else:
|
|
if use_first_moment:
|
|
state["exp_avg"] = state["exp_avg"].to(grad)
|
|
if factored:
|
|
state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad)
|
|
state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad)
|
|
else:
|
|
state["exp_avg_sq"] = state["exp_avg_sq"].to(grad)
|
|
|
|
p_data_fp32 = p.data
|
|
if p.data.dtype in {torch.float16, torch.bfloat16}:
|
|
p_data_fp32 = p_data_fp32.float()
|
|
|
|
state["step"] += 1
|
|
state["RMS"] = self._rms(p_data_fp32)
|
|
group["lr"] = self._get_lr(group, state)
|
|
|
|
beta2t = 1.0 - math.pow(state["step"], group["decay_rate"])
|
|
update = (grad ** 2) + group["eps"][0]
|
|
if factored:
|
|
exp_avg_sq_row = state["exp_avg_sq_row"]
|
|
exp_avg_sq_col = state["exp_avg_sq_col"]
|
|
|
|
exp_avg_sq_row.mul_(beta2t).add_(1.0 - beta2t, update.mean(dim=-1))
|
|
exp_avg_sq_col.mul_(beta2t).add_(1.0 - beta2t, update.mean(dim=-2))
|
|
|
|
# Approximation of exponential moving average of square of gradient
|
|
update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col)
|
|
update.mul_(grad)
|
|
else:
|
|
exp_avg_sq = state["exp_avg_sq"]
|
|
|
|
exp_avg_sq.mul_(beta2t).add_(1.0 - beta2t, update)
|
|
update = exp_avg_sq.rsqrt().mul_(grad)
|
|
|
|
update.div_((self._rms(update) / group["clip_threshold"]).clamp_(min=1.0))
|
|
update.mul_(group["lr"])
|
|
|
|
if use_first_moment:
|
|
exp_avg = state["exp_avg"]
|
|
exp_avg.mul_(group["beta1"]).add_(1 - group["beta1"], update)
|
|
update = exp_avg
|
|
|
|
if group["weight_decay"] != 0:
|
|
p_data_fp32.add_(-group["weight_decay"] * group["lr"], p_data_fp32)
|
|
|
|
p_data_fp32.add_(-update)
|
|
|
|
if p.data.dtype in {torch.float16, torch.bfloat16}:
|
|
p.data.copy_(p_data_fp32)
|
|
|
|
return loss
|