Source code for fairseq.optim.adafactor

# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.

import math
import torch
import torch.optim

from . import FairseqOptimizer, register_optimizer


[docs]@register_optimizer('adafactor') class FairseqAdafactor(FairseqOptimizer): def __init__(self, args, params): super().__init__(args, params) self._optimizer = Adafactor(params, **self.optimizer_config)
[docs] @staticmethod def add_args(parser): """Add optimizer-specific arguments to the parser.""" # fmt: off parser.add_argument('--adafactor-eps', default='(1e-30, 1e-3)', metavar="E", help='epsilons for Adafactor optimizer') parser.add_argument('--clip-threshold', type=float, default=1.0, metavar="C", help='threshold for clipping update root mean square') parser.add_argument('--decay-rate', type=float, default=-0.8, metavar="D", help='decay rate of the second moment estimator') parser.add_argument('--beta1', type=float, default=None, metavar="B", help='beta for first moment estimator. Optional') parser.add_argument('--scale-parameter', action='store_true', help='scale learning rate by root mean square of parameter.') parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', help='weight decay') parser.add_argument('--warmup-init', action='store_true', help='use relative step for warm-up learning rate schedule') parser.add_argument('--relative-step', action='store_true', help='set learning rate to inverse square root of timestep.' 'If false, external learning rate applied')
# fmt: on @property def optimizer_config(self): """ Return a kwarg dictionary that will be used to override optimizer args stored in checkpoints. This allows us to load a checkpoint and resume training using a different set of optimizer args, e.g., with a different learning rate. Note : Convergence issues empirically observed with fp16 on. Might require search for appropriate configuration. """ return { 'lr': self.args.lr[0], 'eps': eval(self.args.adafactor_eps), 'clip_threshold': self.args.clip_threshold, 'beta1': self.args.beta1, 'decay_rate': self.args.decay_rate, 'scale_parameter': self.args.scale_parameter, 'weight_decay': self.args.weight_decay, 'relative_step': self.args.relative_step, 'warmup_init': self.args.warmup_init, }
class Adafactor(torch.optim.Optimizer): """Implements Adafactor algorithm. This implementation is based on: `Adafactor: Adaptive Learning Rates with Sublinear Memory Cost` (see https://arxiv.org/abs/1804.04235) Arguments: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): external learning rate (default: None) eps (tuple[float, float]): regularization constans for square gradient and parameter scale respectively (default: (1e-30, 1e-3)) clip_threshold (float): threshold of root mean square of final gradient update (default: 1.0) decay_rate (float): coefficient used to compute running averages of square gradient (default: -0.8) beta1 (float): coefficient used for computing running averages of gradient (default: None) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) scale_parameter (bool): if true, learning rate is scaled by root mean square of parameter (default: True) relative_step (bool): if true, time-dependent learning rate is computed instead of external learning rate (default: True) warmup_init (bool): time-dependent learning rate computation depends on whether warm-up initialization is being used (default: False) """ def __init__(self, params, lr=None, eps=(1e-30, 1e-3), clip_threshold=1.0, decay_rate=-0.8, beta1=None, weight_decay=0.0, scale_parameter=True, relative_step=True, warmup_init=False): defaults = dict(lr=lr, eps=eps, clip_threshold=clip_threshold, decay_rate=decay_rate, beta1=beta1, weight_decay=weight_decay, scale_parameter=scale_parameter, relative_step=relative_step, warmup_init=warmup_init) super(Adafactor, self).__init__(params, defaults) @property def supports_memory_efficient_fp16(self): return True def _get_lr(self, param_group, param_state): rel_step_sz = param_group['lr'] if param_group['relative_step']: min_step = 1e-6 * param_state['step'] if param_group['warmup_init'] else 1e-2 rel_step_sz = min(min_step, 1.0/math.sqrt(param_state['step'])) param_scale = 1.0 if param_group['scale_parameter']: param_scale = max(param_group['eps'][1], param_state['RMS']) return param_scale * rel_step_sz def _get_options(self, param_group, param_shape): factored = len(param_shape) >= 2 use_first_moment = param_group['beta1'] is not None return factored, use_first_moment def _rms(self, tensor): return tensor.norm(2) / (tensor.numel() ** 0.5) def _approx_sq_grad(self, exp_avg_sq_row, exp_avg_sq_col, output): r_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1)).rsqrt_().unsqueeze(-1) c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt() torch.mul(r_factor, c_factor, out=output) 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.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]).type_as(grad) state['exp_avg_sq_col'] = torch.zeros(grad_shape[:-2] + grad_shape[-1:]).type_as(grad) else: state['exp_avg_sq'] = torch.zeros_like(grad) state['RMS'] = 0 else: if use_first_moment: state['exp_avg'] = state['exp_avg'].type_as(grad) if factored: state['exp_avg_sq_row'] = state['exp_avg_sq_row'].type_as(grad) state['exp_avg_sq_col'] = state['exp_avg_sq_col'].type_as(grad) else: state['exp_avg_sq'] = state['exp_avg_sq'].type_as(grad) p_data_fp32 = p.data.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 self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col, update) update.mul_(grad) else: exp_avg_sq = state['exp_avg_sq'] exp_avg_sq.mul_(beta2t).add_(1.0 - beta2t, update) torch.rsqrt(exp_avg_sq, out=update).mul_(grad) update.div_(max(1.0, self._rms(update) / group['clip_threshold'])) 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) p.data.copy_(p_data_fp32) return loss