pytorch-image-models/timm/optim/lamb.py

""" PyTorch Lamb optimizer w/ behaviour similar to NVIDIA FusedLamb

This optimizer code was adapted from the following (starting with latest)
* https://github.com/HabanaAI/Model-References/blob/2b435114fe8e31f159b1d3063b8280ae37af7423/PyTorch/nlp/bert/pretraining/lamb.py
* https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/Transformer-XL/pytorch/lamb.py
* https://github.com/cybertronai/pytorch-lamb

Use FusedLamb if you can. The reason for including this variant of Lamb is to have a version that is
similar in behaviour to APEX FusedLamb if you aren't using NVIDIA GPUs or cannot install APEX for whatever reason.

Original copyrights for above sources are below.
"""
# Copyright (c) 2021, Habana Labs Ltd.  All rights reserved.

# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
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# Copyright (c) 2019 cybertronai
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import math

import torch
from torch.optim import Optimizer


class Lamb(Optimizer):
    """Implements a pure pytorch variant of FuseLAMB (NvLamb variant) optimizer from apex.optimizers.FusedLAMB
    reference: https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/Transformer-XL/pytorch/lamb.py

    LAMB was proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_.

    Arguments:
        params (iterable): iterable of parameters to optimize or dicts defining
            parameter groups.
        lr (float, optional): learning rate. (default: 1e-3)
        betas (Tuple[float, float], optional): coefficients used for computing
            running averages of gradient and its norm. (default: (0.9, 0.999))
        eps (float, optional): term added to the denominator to improve
            numerical stability. (default: 1e-8)
        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
        grad_averaging (bool, optional): whether apply (1-beta2) to grad when
            calculating running averages of gradient. (default: True)
        set_grad_none (bool, optional): whether set grad to None when zero_grad()
            method is called. (default: True)
        max_grad_norm (float, optional): value used to clip global grad norm
            (default: 1.0)
        use_nvlamb (boolean, optional): Apply adaptive learning rate to 0.0
            weight decay parameter (default: False)

    .. _Large Batch Optimization for Deep Learning - Training BERT in 76 minutes:
        https://arxiv.org/abs/1904.00962
    .. _On the Convergence of Adam and Beyond:
        https://openreview.net/forum?id=ryQu7f-RZ
    """

    def __init__(
            self, params, lr=1e-3, bias_correction=True, betas=(0.9, 0.999), eps=1e-6, weight_decay=0.01,
            grad_averaging=True, max_grad_norm=1.0, decoupled_decay=False, use_nvlamb=False):
        defaults = dict(
            lr=lr, bias_correction=bias_correction,
            betas=betas, eps=eps, weight_decay=weight_decay,
            grad_averaging=grad_averaging, max_grad_norm=max_grad_norm,
            decoupled_decay=decoupled_decay, use_nvlamb=use_nvlamb)
        super().__init__(params, defaults)

    def step(self, closure=None):
        """Performs a single optimization step.
        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        device = self.param_groups[0]["params"][0].device
        one_tensor = torch.tensor(1.0, device=device)

        loss = None
        if closure is not None:
            loss = closure()

        global_grad_norm = torch.zeros(1, device=device)
        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:
                    raise RuntimeError('Lamb does not support sparse gradients, consider SparseAdam instad.')
                global_grad_norm.add_(grad.pow(2).sum())

        global_grad_norm = torch.sqrt(global_grad_norm)
        max_grad_norm = self.defaults['max_grad_norm']
        clip_global_grad_norm = torch.where(
            global_grad_norm > max_grad_norm,
            global_grad_norm / max_grad_norm,
            one_tensor)

        for group in self.param_groups:
            bias_correction = 1 if group['bias_correction'] else 0
            beta1, beta2 = group['betas']
            grad_averaging = 1 if group['grad_averaging'] else 0
            beta3 = 1 - beta1 if grad_averaging else 1.0

            # assume same step across group now to simplify things
            # per parameter step can be easily support by making it tensor, or pass list into kernel
            if 'step' in group:
                group['step'] += 1
            else:
                group['step'] = 1

            step_size = group['lr']

            if bias_correction:
                bias_correction1 = 1 - beta1 ** group['step']
                bias_correction2 = 1 - beta2 ** group['step']
            else:
                bias_correction1, bias_correction2 = 1.0, 1.0

            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data.div_(clip_global_grad_norm)
                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    # Exponential moving average of gradient values
                    state['exp_avg'] = torch.zeros_like(p.data)
                    # Exponential moving average of squared gradient values
                    state['exp_avg_sq'] = torch.zeros_like(p.data)

                decoupled_decay = group['decoupled_decay']
                weight_decay = group['weight_decay']
                if decoupled_decay and weight_decay != 0:
                    p.data.mul_(1. - group['lr'] * weight_decay)

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']

                # Decay the first and second moment running average coefficient
                exp_avg.mul_(beta1).add_(grad, alpha=beta3)  # m_t
                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)  # v_t

                denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
                update = (exp_avg / bias_correction1).div_(denom)

                if not decoupled_decay and weight_decay != 0:
                    update.add_(p.data, alpha=weight_decay)

                trust_ratio = one_tensor
                if weight_decay != 0 or group['use_nvlamb']:
                    # Layer adaptation. By default, skip layer adaptation on parameters that are
                    # excluded from weight norm, unless use_nvlamb == True, then always enabled.
                    w_norm = p.data.norm(2.0)
                    g_norm = update.norm(2.0)
                    trust_ratio = torch.where(
                        w_norm > 0,
                        torch.where(g_norm > 0, w_norm / g_norm, one_tensor),
                        one_tensor,
                    )
                update.mul_(trust_ratio)
                p.data.add_(update, alpha=-step_size)

        return loss