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103 lines
4.3 KiB
103 lines
4.3 KiB
"""
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AdamW optimizer (weight decay fix)
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copied from hugginface
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"""
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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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class AdamW(Optimizer):
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""" Implements Adam algorithm with weight decay fix.
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Parameters:
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lr (float): learning rate. Default 1e-3.
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betas (tuple of 2 floats): Adams beta parameters (b1, b2).
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Default: (0.9, 0.999)
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eps (float): Adams epsilon. Default: 1e-6
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weight_decay (float): Weight decay. Default: 0.0
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correct_bias (bool): can be set to False to avoid correcting bias
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in Adam (e.g. like in Bert TF repository). Default True.
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"""
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-6,
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weight_decay=0.0, correct_bias=True):
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if lr < 0.0:
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raise ValueError(
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"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: {} - "
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"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: {} - "
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"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: {} - "
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"should be >= 0.0".format(eps))
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
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correct_bias=correct_bias)
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super(AdamW, self).__init__(params, defaults)
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def step(self, closure=None):
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"""Performs a single optimization step.
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Arguments:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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loss = closure()
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for group in self.param_groups:
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for p in group['params']:
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if p.grad is None:
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continue
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grad = p.grad.data
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if grad.is_sparse:
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raise RuntimeError(
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'Adam does not support sparse '
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'gradients, please consider SparseAdam instead')
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state['step'] = 0
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# Exponential moving average of gradient values
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state['exp_avg'] = torch.zeros_like(p.data)
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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = torch.zeros_like(p.data)
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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beta1, beta2 = group['betas']
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state['step'] += 1
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# Decay the first and second moment running average coefficient
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# In-place operations to update the averages at the same time
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exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
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exp_avg_sq.mul_(beta2).addcmul_(1.0 - beta2, grad, grad)
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denom = exp_avg_sq.sqrt().add_(group['eps'])
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step_size = group['lr']
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if group['correct_bias']: # No bias correction for Bert
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bias_correction1 = 1.0 - beta1 ** state['step']
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bias_correction2 = 1.0 - beta2 ** state['step']
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step_size = (step_size * math.sqrt(bias_correction2)
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/ bias_correction1)
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p.data.addcdiv_(-step_size, exp_avg, denom)
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# Just adding the square of the weights to the loss function is
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# *not* the correct way of using L2 regularization/weight decay
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# with Adam, since that will interact with the m and v
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# 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
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# interact with the m/v parameters. This is equivalent to
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# adding the square of the weights to the loss with plain
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# (non-momentum) SGD.
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# Add weight decay at the end (fixed version)
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if group['weight_decay'] > 0.0:
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p.data.add_(-group['lr'] * group['weight_decay'], p.data)
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return loss
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