relik/retriever/pytorch_modules/optim.py

import math

import torch
from torch.optim import Optimizer


class RAdamW(Optimizer):
    r"""Implements RAdamW algorithm.

    RAdam from `On the Variance of the Adaptive Learning Rate and Beyond
    <https://arxiv.org/abs/1908.03265v1>`_

    * `Adam: A Method for Stochastic Optimization
      <https://arxiv.org/abs/1412.6980>`_
    * `Decoupled Weight Decay Regularization
      <https://arxiv.org/abs/1711.05101>`_
    * `On the Convergence of Adam and Beyond
      <https://openreview.net/forum?id=ryQu7f-RZ>`_
    * `On the Variance of the Adaptive Learning Rate and Beyond
      <https://arxiv.org/abs/1908.03265v1>`_

    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 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 coefficient (default: 1e-2)
    """

    def __init__(
        self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=1e-2
    ):
        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]))
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
        super(RAdamW, self).__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.
        """
        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

                # Perform optimization step
                grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError(
                        "Adam does not support sparse gradients, please consider SparseAdam instead"
                    )

                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"]
                eps = group["eps"]
                lr = group["lr"]
                if "rho_inf" not in group:
                    group["rho_inf"] = 2 / (1 - beta2) - 1
                rho_inf = group["rho_inf"]

                state["step"] += 1
                t = state["step"]

                # 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)
                rho_t = rho_inf - ((2 * t * (beta2**t)) / (1 - beta2**t))

                # Perform stepweight decay
                p.data.mul_(1 - lr * group["weight_decay"])

                if rho_t >= 5:
                    var = exp_avg_sq.sqrt().add_(eps)
                    r = math.sqrt(
                        (1 - beta2**t)
                        * ((rho_t - 4) * (rho_t - 2) * rho_inf)
                        / ((rho_inf - 4) * (rho_inf - 2) * rho_t)
                    )

                    p.data.addcdiv_(exp_avg, var, value=-lr * r / (1 - beta1**t))
                else:
                    p.data.add_(exp_avg, alpha=-lr / (1 - beta1**t))

        return loss


# from typing import List
# import collections

# import torch
# import transformers
# from classy.optim.factories import Factory
# from transformers import AdamW


# class ElectraOptimizer(Factory):
#     def __init__(
#         self,
#         lr: float,
#         warmup_steps: int,
#         total_steps: int,
#         weight_decay: float,
#         lr_decay: float,
#         no_decay_params: List[str],
#     ):
#         self.lr = lr
#         self.warmup_steps = warmup_steps
#         self.total_steps = total_steps
#         self.weight_decay = weight_decay
#         self.lr_decay = lr_decay
#         self.no_decay_params = no_decay_params

#     def group_layers(self, module) -> dict:
#         grouped_layers = collections.defaultdict(list)
#         module_named_parameters = list(module.named_parameters())
#         for ln, lp in module_named_parameters:
#             if "embeddings" in ln:
#                 grouped_layers["embeddings"].append((ln, lp))
#             elif "encoder.layer" in ln:
#                 layer_num = ln.replace("transformer_model.encoder.layer.", "")
#                 layer_num = layer_num[0 : layer_num.index(".")]
#                 grouped_layers[layer_num].append((ln, lp))
#             else:
#                 grouped_layers["head"].append((ln, lp))

#         depth = len(grouped_layers) - 1
#         final_dict = dict()
#         for key, value in grouped_layers.items():
#             if key == "head":
#                 final_dict[0] = value
#             elif key == "embeddings":
#                 final_dict[depth] = value
#             else:
#                 # -1 because layer number starts from zero
#                 final_dict[depth - int(key) - 1] = value

#         assert len(module_named_parameters) == sum(
#             len(v) for _, v in final_dict.items()
#         )

#         return final_dict

#     def group_params(self, module) -> list:
#         optimizer_grouped_params = []
#         for inverse_depth, layer in self.group_layers(module).items():
#             layer_lr = self.lr * (self.lr_decay**inverse_depth)
#             layer_wd_params = {
#                 "params": [
#                     lp
#                     for ln, lp in layer
#                     if not any(nd in ln for nd in self.no_decay_params)
#                 ],
#                 "weight_decay": self.weight_decay,
#                 "lr": layer_lr,
#             }
#             layer_no_wd_params = {
#                 "params": [
#                     lp
#                     for ln, lp in layer
#                     if any(nd in ln for nd in self.no_decay_params)
#                 ],
#                 "weight_decay": 0,
#                 "lr": layer_lr,
#             }

#             if len(layer_wd_params) != 0:
#                 optimizer_grouped_params.append(layer_wd_params)
#             if len(layer_no_wd_params) != 0:
#                 optimizer_grouped_params.append(layer_no_wd_params)

#         return optimizer_grouped_params

#     def __call__(self, module: torch.nn.Module):
#         optimizer_grouped_parameters = self.group_params(module)
#         optimizer = AdamW(optimizer_grouped_parameters, lr=self.lr)
#         scheduler = transformers.get_linear_schedule_with_warmup(
#             optimizer, self.warmup_steps, self.total_steps
#         )
#         return {
#             "optimizer": optimizer,
#             "lr_scheduler": {
#                 "scheduler": scheduler,
#                 "interval": "step",
#                 "frequency": 1,
#             },
#         }