Source code for lmflow.optim.adamp

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

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


[docs] class AdamP(Optimizer): r"""Implements AdamP algorithm. It has been proposed in `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` https://arxiv.org/abs/2006.08217 Note: Reference code: https://github.com/clovaai/AdamP """ def __init__( self, params, lr: float = 1e-3, betas = (0.9, 0.999), eps: float = 1e-8, weight_decay: float = 0, delta: float = 0.1, wd_ratio: float = 0.1, nesterov: 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 delta < 0: raise ValueError("Invalid delta value: {}".format(delta)) if wd_ratio < 0: raise ValueError("Invalid wd_ratio value: {}".format(wd_ratio)) defaults = dict( lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, delta=delta, wd_ratio=wd_ratio, nesterov=nesterov, ) super(AdamP, self).__init__(params, defaults) @staticmethod
[docs] def _channel_view(x): return x.view(x.size(0), -1)
@staticmethod
[docs] def _layer_view(x): return x.view(1, -1)
@staticmethod
[docs] def _cosine_similarity(x, y, eps, view_func): x = view_func(x) y = view_func(y) x_norm = x.norm(dim=1).add_(eps) y_norm = y.norm(dim=1).add_(eps) dot = (x * y).sum(dim=1) return dot.abs() / x_norm / y_norm
[docs] def _projection(self, p, grad, perturb, delta, wd_ratio, eps): wd = 1 expand_size = [-1] + [1] * (len(p.shape) - 1) for view_func in [self._channel_view, self._layer_view]: cosine_sim = self._cosine_similarity(grad, p.data, eps, view_func) if cosine_sim.max() < delta / math.sqrt(view_func(p.data).size(1)): p_n = p.data / view_func(p.data).norm(dim=1).view( expand_size ).add_(eps) perturb -= p_n * view_func(p_n * perturb).sum(dim=1).view( expand_size ) wd = wd_ratio return perturb, wd return perturb, wd
[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 beta1, beta2 = group["betas"] nesterov = group["nesterov"] state = self.state[p] # State initialization if len(state) == 0: state["step"] = 0 state["exp_avg"] = torch.zeros_like( p.data, memory_format=torch.preserve_format ) state["exp_avg_sq"] = torch.zeros_like( p.data, memory_format=torch.preserve_format ) # Adam exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] state["step"] += 1 bias_correction1 = 1 - beta1 ** state["step"] bias_correction2 = 1 - beta2 ** state["step"] exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_( group["eps"] ) step_size = group["lr"] / bias_correction1 if nesterov: perturb = (beta1 * exp_avg + (1 - beta1) * grad) / denom else: perturb = exp_avg / denom # Projection wd_ratio = 1 if len(p.shape) > 1: perturb, wd_ratio = self._projection( p, grad, perturb, group["delta"], group["wd_ratio"], group["eps"], ) # Weight decay if group["weight_decay"] > 0: p.data.mul_( 1 - group["lr"] * group["weight_decay"] * wd_ratio ) # Step p.data.add_(perturb, alpha=-step_size) return loss