Source code for fairseq.optim.adafactor

# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import math

import torch
import torch.optim

from . import LegacyFairseqOptimizer, register_optimizer

[docs]@register_optimizer("adafactor") class FairseqAdafactor(LegacyFairseqOptimizer): def __init__(self, args, params): super().__init__(args) 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('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', help='weight decay') parser.add_argument('--scale-parameter', action='store_true', help='scale learning rate by root mean square of parameter') parser.add_argument('--relative-step', action='store_true', help='set learning rate to inverse square root of timestep,' 'otherwise use external learning rate') parser.add_argument('--warmup-init', action='store_true', help='use relative step for warm-up learning rate schedule')
# 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":[0], "eps": eval(self.args.adafactor_eps), "clip_threshold": self.args.clip_threshold, "decay_rate": self.args.decay_rate, "beta1": self.args.beta1, "weight_decay": self.args.weight_decay, "scale_parameter": self.args.scale_parameter, # defaults to False "relative_step": self.args.relative_step, # defaults to False "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 Note that this optimizer internally adjusts the learning rate depending on the *scale_parameter*, *relative_step* and *warmup_init* options. To use a manual (external) learning rate schedule you should set `scale_parameter=False` and `relative_step=False`. Args: 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, ): if lr is not None and relative_step: raise ValueError("Cannot combine manual lr and relative_step options") if warmup_init and not relative_step: raise ValueError("warmup_init requires relative_step=True") 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 @property def supports_flat_params(self): return False 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): r_factor = ( (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)) .rsqrt_() .unsqueeze(-1) ) c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt() return torch.mul(r_factor, c_factor) def step(self, closure=None): """Performs a single optimization step. Args: 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 = 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 = if 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_( update.mean(dim=-1), alpha=1.0 - beta2t ) exp_avg_sq_col.mul_(beta2t).add_( update.mean(dim=-2), alpha=1.0 - beta2t ) # 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_(update, alpha=1.0 - beta2t) 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_(update, alpha=1 - group["beta1"]) update = exp_avg if group["weight_decay"] != 0: p_data_fp32.add_( p_data_fp32, alpha=-group["weight_decay"] * group["lr"] ) p_data_fp32.add_(-update) if in {torch.float16, torch.bfloat16}: return loss