Source code for lmflow.optim.lamb

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import math
import torch
from torch.optim.optimizer import Optimizer

[docs] class Lamb(Optimizer): r"""Implements Lamb algorithm. It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes` https://arxiv.org/abs/1904.00962 Note: Reference code: https://github.com/cybertronai/pytorch-lamb """ def __init__( self, params, lr: float = 1e-3, betas = (0.9, 0.999), eps: float = 1e-6, weight_decay: float = 0, clamp_value: float = 10, adam: bool = False, debias: bool = False, ) -> None: if lr <= 0.0: raise ValueError("Invalid learning rate: {}".format(lr)) if eps < 0.0: raise ValueError("Invalid epsilon value: {}".format(eps)) if not 0.0 <= betas[0] < 1.0: raise ValueError( "Invalid beta parameter at index 0: {}".format(betas[0]) ) if not 0.0 <= betas[1] < 1.0: raise ValueError( "Invalid beta parameter at index 1: {}".format(betas[1]) ) if weight_decay < 0: raise ValueError( "Invalid weight_decay value: {}".format(weight_decay) ) if clamp_value < 0.0: raise ValueError("Invalid clamp value: {}".format(clamp_value)) defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
[docs] self.clamp_value = clamp_value
[docs] self.adam = adam
[docs] self.debias = debias
super(Lamb, self).__init__(params, defaults)
[docs] def step(self, closure = None): r"""Performs a single optimization step. Arguments: closure: 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: msg = ( "Lamb does not support sparse gradients, " "please consider SparseAdam instead" ) raise RuntimeError(msg) state = self.state[p] # State initialization if len(state) == 0: state["step"] = 0 # Exponential moving average of gradient values state["exp_avg"] = torch.zeros_like( p, memory_format=torch.preserve_format ) # Exponential moving average of squared gradient values state["exp_avg_sq"] = torch.zeros_like( p, memory_format=torch.preserve_format ) exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] beta1, beta2 = group["betas"] state["step"] += 1 # Decay the first and second moment running average coefficient # m_t exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) # v_t exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) # Paper v3 does not use debiasing. if self.debias: bias_correction = math.sqrt(1 - beta2 ** state["step"]) bias_correction /= 1 - beta1 ** state["step"] else: bias_correction = 1 # Apply bias to lr to avoid broadcast. step_size = group["lr"] * bias_correction weight_norm = torch.norm(p.data).clamp(0, self.clamp_value) adam_step = exp_avg / exp_avg_sq.sqrt().add(group["eps"]) if group["weight_decay"] != 0: adam_step.add_(p.data, alpha=group["weight_decay"]) adam_norm = torch.norm(adam_step) if weight_norm == 0 or adam_norm == 0: trust_ratio = 1 else: trust_ratio = weight_norm / adam_norm state["weight_norm"] = weight_norm state["adam_norm"] = adam_norm state["trust_ratio"] = trust_ratio if self.adam: trust_ratio = 1 p.data.add_(adam_step, alpha=-step_size * trust_ratio) return loss