#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved

from typing import Optional

import torch
from pytext.optimizer.optimizers import Optimizer
from torch.optim import Optimizer as PT_Optimizer


class Lamb(Optimizer, PT_Optimizer):
    r"""Implements Lamb algorithm.
    THIS WAS DIRECTLY COPIED OVER FROM pytorch/contrib:
    https://github.com/cybertronai/pytorch-lamb
    It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`.
    https://arxiv.org/abs/1904.00962

    Has the option for minimum trust LAMB as described in "Single Headed
    Attention RNN: Stop Thinking With Your Head" section 6.3
    https://arxiv.org/abs/1911.11423
    """

    class Config(Optimizer.Config):
        lr: float = 0.001
        weight_decay: float = 0.00001
        eps: float = 1e-8
        min_trust: Optional[float] = None

    @classmethod
    def from_config(cls, config: Config, model: torch.nn.Module):
        return cls(
            model.parameters(),
            lr=config.lr,
            weight_decay=config.weight_decay,
            eps=config.eps,
            min_trust=config.min_trust,
        )

    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-6,
        weight_decay=0,
        min_trust=None,
    ):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= eps:
            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]))
        PT_Optimizer.__init__(
            self,
            params,
            {"lr": lr, "betas": betas, "eps": eps, "weight_decay": weight_decay},
        )

        self.min_trust = min_trust

    def step(self, closure=None, **kwargs):
        """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:
            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:
                    raise RuntimeError(
                        "Lamb does not support sparse gradients, consider SparseAdam instad."
                    )

                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.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"]
                beta1, beta2 = group["betas"]

                state["step"] += 1

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

                # Paper v3 does not use debiasing.
                # bias_correction1 = 1 - beta1 ** state['step']
                # bias_correction2 = 1 - beta2 ** state['step']
                # Apply bias to lr to avoid broadcast.
                step_size = group["lr"]
                # * math.sqrt(bias_correction2) / bias_correction1

                weight_norm = p.data.pow(2).sum().sqrt().clamp(0, 10)

                adam_step = exp_avg / exp_avg_sq.sqrt().add(group["eps"])
                if group["weight_decay"] != 0:
                    adam_step.add_(group["weight_decay"], p.data)

                adam_norm = adam_step.pow(2).sum().sqrt()
                if weight_norm == 0 or adam_norm == 0:
                    trust_ratio = 1
                else:
                    trust_ratio = weight_norm / adam_norm
                if self.min_trust:
                    trust_ratio = max(self.min_trust, trust_ratio)
                state["weight_norm"] = weight_norm
                state["adam_norm"] = adam_norm
                state["trust_ratio"] = trust_ratio
                p.data.add_(-step_size * trust_ratio, adam_step)

        return loss
