""" 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 (GPU). 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/use APEX.

In addition to some cleanup, this Lamb impl has been modified to support PyTorch XLA and has been tested on TPU.

Original copyrights for above sources are below.

Modifications Copyright 2021 Ross Wightman

"""

# Copyright (c) 2021, Habana Labs Ltd.  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)

        max_grad_norm (float, optional): value used to clip global grad norm (default: 1.0)

        trust_clip (bool): enable LAMBC trust ratio clipping (default: False)

        always_adapt (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, trust_clip=False, always_adapt=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,

            trust_clip=trust_clip, always_adapt=always_adapt)

        super().__init__(params, defaults)



    @torch.no_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.

        """

        loss = None

        if closure is not None:

            with torch.enable_grad():

                loss = closure()



        device = self.param_groups[0]['params'][0].device

        if not hasattr(self, 'one_tensor'):

            self.one_tensor = torch.tensor(1.0, device=device)  # because torch.where doesn't handle scalars correctly

        one_tensor = self.one_tensor

        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

                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)

        # FIXME it'd be nice to remove explicit tensor conversion of scalars when torch.where promotes

        # scalar types properly https://github.com/pytorch/pytorch/issues/9190

        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



            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.div_(clip_global_grad_norm)

                state = self.state[p]



                # State initialization

                if len(state) == 0:

                    # Exponential moving average of gradient valuesa

                    state['exp_avg'] = torch.zeros_like(p)

                    # Exponential moving average of squared gradient values

                    state['exp_avg_sq'] = torch.zeros_like(p)



                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)



                weight_decay = group['weight_decay']

                if weight_decay != 0:

                    update.add_(p, alpha=weight_decay)



                if weight_decay != 0 or group['always_adapt']:

                    # Layer-wise LR adaptation. By default, skip adaptation on parameters that are

                    # excluded from weight decay, unless always_adapt == True, then always enabled.

                    w_norm = p.norm(2.0)

                    g_norm = update.norm(2.0)

                    # FIXME nested where required since logical and/or not working in PT XLA

                    trust_ratio = torch.where(

                        w_norm > 0,

                        torch.where(g_norm > 0, w_norm / g_norm, one_tensor),

                        one_tensor,

                    )

                    if group['trust_clip']:

                        # LAMBC trust clipping, upper bound fixed at one

                        trust_ratio = torch.minimum(trust_ratio, one_tensor)

                    update.mul_(trust_ratio)



                p.add_(update, alpha=-group['lr'])



        return loss