SignIDD_CodeFiles/builders.py

# coding: utf-8
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

import torch.nn as nn

from helpers import ConfigurationError
from typing import Optional, Callable, Generator
from torch.optim import Optimizer
from torch.optim.lr_scheduler import _LRScheduler, ReduceLROnPlateau, StepLR, ExponentialLR

def build_gradient_clipper(config: dict) -> Optional[Callable]:
    """
    Define the function for gradient clipping as specified in configuration.
    If not specified, returns None.

    Current options:
        - "clip_grad_val": clip the gradients if they exceed this value,
            see `torch.nn.utils.clip_grad_value_`
        - "clip_grad_norm": clip the gradients if their norm exceeds this value,
            see `torch.nn.utils.clip_grad_norm_`

    :param config: dictionary with training configurations
    :return: clipping function (in-place) or None if no gradient clipping
    """
    clip_grad_fun = None
    if "clip_grad_val" in config.keys():
        clip_value = config["clip_grad_val"]
        clip_grad_fun = lambda params: \
            nn.utils.clip_grad_value_(parameters=params,
                                      clip_value=clip_value)
    elif "clip_grad_norm" in config.keys():
        max_norm = config["clip_grad_norm"]
        clip_grad_fun = lambda params: \
            nn.utils.clip_grad_norm_(parameters=params, max_norm=max_norm)

    if "clip_grad_val" in config.keys() and "clip_grad_norm" in config.keys():
        raise ConfigurationError(
            "You can only specify either clip_grad_val or clip_grad_norm.")

    return clip_grad_fun

def build_optimizer(config: dict, parameters: Generator) -> Optimizer:
    """
    Create an optimizer for the given parameters as specified in config.

    Except for the weight decay and initial learning rate,
    default optimizer settings are used.

    Currently supported configuration settings for "optimizer":
        - "sgd" (default): see `torch.optim.SGD`
        - "adam": see `torch.optim.adam`
        - "adagrad": see `torch.optim.adagrad`
        - "adadelta": see `torch.optim.adadelta`
        - "rmsprop": see `torch.optim.RMSprop`

    The initial learning rate is set according to "learning_rate" in the config.
    The weight decay is set according to "weight_decay" in the config.
    If they are not specified, the initial learning rate is set to 3.0e-4, the
    weight decay to 0.

    Note that the scheduler state is saved in the checkpoint, so if you load
    a model for further training you have to use the same type of scheduler.

    :param config: configuration dictionary
    :param parameters:
    :return: optimizer
    """
    optimizer_name = config.get("optimizer", "sgd").lower()
    learning_rate = config.get("learning_rate", 3.0e-4)
    weight_decay = config.get("weight_decay", 0)

    if optimizer_name == "adam":
        adam_betas = config.get("adam_betas", (0.9, 0.999))
        optimizer = torch.optim.Adam(parameters, weight_decay=weight_decay,
                                     lr=learning_rate, betas=adam_betas)
    elif optimizer_name == "adagrad":
        optimizer = torch.optim.Adagrad(parameters, weight_decay=weight_decay,
                                        lr=learning_rate)
    elif optimizer_name == "adadelta":
        optimizer = torch.optim.Adadelta(parameters, weight_decay=weight_decay,
                                         lr=learning_rate)
    elif optimizer_name == "rmsprop":
        optimizer = torch.optim.RMSprop(parameters, weight_decay=weight_decay,
                                        lr=learning_rate)
    elif optimizer_name == "sgd":
        # default
        optimizer = torch.optim.SGD(parameters, weight_decay=weight_decay,
                                    lr=learning_rate)
    else:
        raise ConfigurationError("Invalid optimizer. Valid options: 'adam', "
                                 "'adagrad', 'adadelta', 'rmsprop', 'sgd'.")
    return optimizer


def build_scheduler(config: dict, optimizer: Optimizer, scheduler_mode: str,
                    hidden_size: int = 0) \
        -> (Optional[_LRScheduler], Optional[str]):
    """
    Create a learning rate scheduler if specified in config and
    determine when a scheduler step should be executed.

    Current options:
        - "plateau": see `torch.optim.lr_scheduler.ReduceLROnPlateau`
        - "decaying": see `torch.optim.lr_scheduler.StepLR`
        - "exponential": see `torch.optim.lr_scheduler.ExponentialLR`
        - "noam": see `SignProdJoey.transformer.NoamScheduler`

    If no scheduler is specified, returns (None, None) which will result in
    a constant learning rate.

    :param config: training configuration
    :param optimizer: optimizer for the scheduler, determines the set of
        parameters which the scheduler sets the learning rate for
    :param scheduler_mode: "min" or "max", depending on whether the validation
        score should be minimized or maximized.
        Only relevant for "plateau".
    :param hidden_size: encoder hidden size (required for NoamScheduler)
    :return:
        - scheduler: scheduler object,
        - scheduler_step_at: either "validation" or "epoch"
    """
    scheduler, scheduler_step_at = None, None
    if "scheduling" in config.keys() and \
            config["scheduling"]:
        if config["scheduling"].lower() == "plateau":
            # learning rate scheduler
            scheduler = ReduceLROnPlateau(
                optimizer=optimizer,
                mode=scheduler_mode,
                # verbose=False,
                threshold_mode='abs',
                threshold=1e-8,
                factor=config.get("decrease_factor", 0.1),
                patience=config.get("patience", 10))
            # scheduler step is executed after every validation
            scheduler_step_at = "validation"
        elif config["scheduling"].lower() == "decaying":
            scheduler = StepLR(
                optimizer=optimizer,
                step_size=config.get("decaying_step_size", 1))
            # scheduler step is executed after every epoch
            scheduler_step_at = "epoch"
        elif config["scheduling"].lower() == "exponential":
            scheduler = ExponentialLR(
                optimizer=optimizer,
                gamma=config.get("decrease_factor", 0.99))
            # scheduler step is executed after every epoch
            scheduler_step_at = "epoch"
        elif config["scheduling"].lower() == "noam":
            factor = config.get("learning_rate_factor", 1)
            warmup = config.get("learning_rate_warmup", 4000)
            scheduler = NoamScheduler(hidden_size=hidden_size, factor=factor,
                                      warmup=warmup, optimizer=optimizer)

            scheduler_step_at = "step"
    return scheduler, scheduler_step_at

class NoamScheduler:
    """
    The Noam learning rate scheduler used in "Attention is all you need"
    See Eq. 3 in https://arxiv.org/pdf/1706.03762.pdf
    """

    def __init__(self, hidden_size: int, optimizer: torch.optim.Optimizer,
                 factor: float = 1, warmup: int = 4000):
        """
        Warm-up, followed by learning rate decay.

        :param hidden_size:
        :param optimizer:
        :param factor: decay factor
        :param warmup: number of warmup steps
        """
        self.optimizer = optimizer
        self._step = 0
        self.warmup = warmup
        self.factor = factor
        self.hidden_size = hidden_size
        self._rate = 0

    def step(self):
        """Update parameters and rate"""
        self._step += 1
        rate = self._compute_rate()
        for p in self.optimizer.param_groups:
            p['lr'] = rate
        self._rate = rate

    def _compute_rate(self):
        """Implement `lrate` above"""
        step = self._step
        return self.factor * \
            (self.hidden_size ** (-0.5) *
                min(step ** (-0.5), step * self.warmup ** (-1.5)))

    #pylint: disable=no-self-use
    def state_dict(self):
        return None