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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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import math
from collections.abc import Collection

import torch
import torch.distributed as dist
import torch.optim
from common.fairseq.optim.fp16_optimizer import FairseqOptimizer
from common.fairseq.optim.fused_adam import get_fused_adam_class


class FairseqAdam(FairseqOptimizer):
    """Adam optimizer for fairseq.

    Important note: this optimizer corresponds to the "AdamW" variant of
    Adam in its weight decay behavior. As such, it is most closely
    analogous to torch.optim.AdamW from PyTorch.
    """

    def __init__(self, cfg, params):
        super().__init__(cfg)
        fused_adam_cls = get_fused_adam_class()
        use_fused_adam = (
            not getattr(cfg, "use_old_adam", False)
            and fused_adam_cls is not None
            and torch.cuda.is_available()
        )
        if use_fused_adam:
            self._optimizer = fused_adam_cls(params, **self.optimizer_config)
        else:
            self._optimizer = Adam(params, **self.optimizer_config)

    @property
    def optimizer_config(self):
        """
        Return a kwarg dictionary that will be used to override optimizer
        args stored in checkpoints. This allows us to load a checkpoint and
        resume training using a different set of optimizer args, e.g., with a
        different learning rate.
        """
        return {
            "lr": self.cfg.lr[0]
            if isinstance(self.cfg.lr, Collection)
            else self.cfg.lr,
            "betas": eval(self.cfg.adam_betas)
            if isinstance(self.cfg.adam_betas, str)
            else self.cfg.adam_betas,
            "eps": self.cfg.adam_eps,
            "weight_decay": self.cfg.weight_decay,
        }

    def average_params(self):
        """Reduce Params is only used during BMUF distributed training."""
        state_dict = self.optimizer.state_dict()
        total_gpus = float(dist.get_world_size())

        for _, value in state_dict["state"].items():
            value["exp_avg"] /= total_gpus
            value["exp_avg_sq"] /= total_gpus
            dist.all_reduce(value["exp_avg"], op=dist.ReduceOp.SUM)
            dist.all_reduce(value["exp_avg_sq"], op=dist.ReduceOp.SUM)


class Adam(torch.optim.Optimizer):
    r"""Implements Adam algorithm.

    This implementation is modified from torch.optim.Adam based on:
    `Fixed Weight Decay Regularization in Adam`
    (see https://arxiv.org/abs/1711.05101)

    It has been proposed in `Adam: A Method for Stochastic Optimization`_.

    Args:
        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 square (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)
        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
            algorithm from the paper `On the Convergence of Adam and Beyond`_

    .. _Adam\: A Method for Stochastic Optimization:
        https://arxiv.org/abs/1412.6980
    .. _On the Convergence of Adam and Beyond:
        https://openreview.net/forum?id=ryQu7f-RZ
    """

    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        amsgrad=False,
    ):
        defaults = dict(
            lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad
        )
        super(Adam, self).__init__(params, defaults)

    @property
    def supports_memory_efficient_fp16(self):
        return True

    @property
    def supports_flat_params(self):
        return True

    def step(self, closure=None):
        """Performs a single optimization step.

        Args:
            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.dtype in {torch.float16, torch.bfloat16}:
                    grad = grad.float()
                if grad.is_sparse:
                    raise RuntimeError(
                        "Adam does not support sparse gradients, please consider SparseAdam instead"
                    )
                amsgrad = group.get("amsgrad", False)

                p_data_fp32 = p.data
                if p.data.dtype in {torch.float16, torch.bfloat16}:
                    p_data_fp32 = p_data_fp32.float()

                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_fp32)
                    # Exponential moving average of squared gradient values
                    state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
                    if amsgrad:
                        # Maintains max of all exp. moving avg. of sq. grad. values
                        state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32)
                else:
                    state["exp_avg"] = state["exp_avg"].to(p_data_fp32)
                    state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data_fp32)
                    if amsgrad:
                        state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to(
                            p_data_fp32
                        )

                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
                if amsgrad:
                    max_exp_avg_sq = state["max_exp_avg_sq"]
                beta1, beta2 = group["betas"]

                state["step"] += 1

                # Decay the first and second moment running average coefficient
                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
                if amsgrad:
                    # Maintains the maximum of all 2nd moment running avg. till now
                    torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
                    # Use the max. for normalizing running avg. of gradient
                    denom = max_exp_avg_sq.sqrt().add_(group["eps"])
                else:
                    denom = exp_avg_sq.sqrt().add_(group["eps"])

                bias_correction1 = 1 - beta1 ** state["step"]
                bias_correction2 = 1 - beta2 ** state["step"]
                step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1

                if group["weight_decay"] != 0:
                    p_data_fp32.add_(
                        p_data_fp32, alpha=-group["weight_decay"] * group["lr"]
                    )

                p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size)

                if p.data.dtype in {torch.float16, torch.bfloat16}:
                    p.data.copy_(p_data_fp32)

        return loss
