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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# MIT License
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# Copyright (c) 2019 cybertronai
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import torch
from torch.optim import Optimizer


class NvLamb(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, set_grad_none=True,
                 max_grad_norm=1.0, 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)
        super().__init__(params, defaults)
        self.set_grad_none = set_grad_none
        self.use_nvlamb = use_nvlamb

    def zero_grad(self):
        if self.set_grad_none:
            for group in self.param_groups:
                for p in group['params']:
                    p.grad = None
        else:
            super(NvLamb, self).zero_grad()

    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

        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']

        if global_grad_norm_ > max_grad_norm:
            clip_global_grad_norm = global_grad_norm_ / max_grad_norm
        else:
            clip_global_grad_norm = 1.0

        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
            if grad_averaging:
                beta3 = 1 - beta1
            else:
                beta3 = 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)

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

                # Decay the first and second moment running average coefficient
                # m_t
                exp_avg_.mul_(beta1).add_(grad, alpha=beta3)
                # v_t
                exp_avg_sq_.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
                # create clones to avoid modifying runner stats
                exp_avg = exp_avg_.div(bias_correction1)
                exp_avg_sq = exp_avg_sq_.div(bias_correction2)

                # || w_t ||
                weight_norm = p.data.norm(2.0)
                # u_t
                exp_avg_sq_sqrt = torch.sqrt(exp_avg_sq)
                adam_step = exp_avg.div_(exp_avg_sq_sqrt.add_(group['eps']))
                if group['weight_decay'] != 0:
                    adam_step.add_(p.data, alpha=group['weight_decay'])
                # || u_t ||
                adam_norm = adam_step.norm(2.0)
                if (group['weight_decay'] != 0 or self.use_nvlamb) and adam_norm > 0 and weight_norm > 0:
                    trust_ratio = weight_norm / adam_norm
                    trust_ratio = trust_ratio.item()
                else:
                    trust_ratio = 1

                state['weight_norm'] = weight_norm
                state['adam_norm'] = adam_norm
                state['trust_ratio'] = trust_ratio

                p.data.add_(adam_step, alpha=-step_size * trust_ratio)

        return loss