src/vqvaes/titok/modules/base_model.py

"""This file contains some base class implementation for models.

This file may have been modified by Bytedance Ltd. and/or its affiliates (“Bytedance's Modifications”).
All Bytedance's Modifications are Copyright (year) Bytedance Ltd. and/or its affiliates.

Reference:
    https://github.com/huggingface/open-muse/blob/main/muse/modeling_utils.py
"""

import os
from typing import Union, Callable, Dict, Optional

import torch


class BaseModel(torch.nn.Module):

    def __init__(self):
        super().__init__()

    def save_pretrained_weight(
        self,
        save_directory: Union[str, os.PathLike],
        save_function: Callable = None,
        state_dict: Optional[Dict[str, torch.Tensor]] = None,
    ):
        """Saves a model and its configuration file to a directory.

        Args:
            save_directory: A string or os.PathLike, directory to which to save.
                Will be created if it doesn't exist.
            save_function: A Callable function, the function to use to save the state dictionary.
                Useful on distributed training like TPUs when one need to replace `torch.save` by
                another method. Can be configured with the environment variable `DIFFUSERS_SAVE_MODE`.
            state_dict: A dictionary from str to torch.Tensor, the state dictionary to save.
                If `None`, the model's state dictionary will be saved.
        """
        if os.path.isfile(save_directory):
            print(f"Provided path ({save_directory}) should be a directory, not a file")
            return

        if save_function is None:
            save_function = torch.save

        os.makedirs(save_directory, exist_ok=True)

        model_to_save = self

        if state_dict is None:
            state_dict = model_to_save.state_dict()
        weights_name = "pytorch_model.bin"

        save_function(state_dict, os.path.join(save_directory, weights_name))

        print(f"Model weights saved in {os.path.join(save_directory, weights_name)}")

    def load_pretrained_weight(
        self,
        # pretrained_model_path: Union[str, os.PathLike],
        checkpoint,
        strict_loading: bool = True,
        torch_dtype: Optional[torch.dtype] = None,
    ):
        r"""Instantiates a pretrained pytorch model from a pre-trained model configuration.

        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
        the model, you should first set it back in training mode with `model.train()`.

        Args:
            pretrained_model_path: A string or os.PathLike, a path to a *directory* or *file* containing model weights.

        Raises:
            ValueError: If pretrained_model_path does not exist.
        """
        # If pretrained_model_path is a file, set model_file to this file.
        #         if os.path.isfile(pretrained_model_path):
        #             model_file = pretrained_model_path
        #         # If pretrained_model_path is a directory, set model_file to the path of the
        #         # file "pytorch_model.bin" in this directory.
        #         elif os.path.isdir(pretrained_model_path):
        #             pretrained_model_path = os.path.join(pretrained_model_path, "pytorch_model.bin")
        #             if os.path.isfile(pretrained_model_path):
        #                 model_file = pretrained_model_path
        #             else:
        #                 raise ValueError(f"{pretrained_model_path} does not exist")
        #         else:
        #             raise ValueError(f"{pretrained_model_path} does not exist")
        #
        #         # Load model state from checkpoint.
        #         checkpoint = torch.load(model_file, map_location="cpu")
        # Load state dictionary into self.
        msg = self.load_state_dict(checkpoint, strict=strict_loading)
        # Print information about loading weights.
        print(f"loading weight from {model_file}, msg: {msg}")
        # If torch_dtype is specified and is a valid torch.dtype, convert self to this dtype.
        if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
            raise ValueError(
                f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}."
            )
        elif torch_dtype is not None:
            self.to(torch_dtype)

        # Set model in evaluation mode to deactivate DropOut modules by default.
        self.eval()

    def num_parameters(
        self, only_trainable: bool = False, exclude_embeddings: bool = False
    ) -> int:
        """Gets the number of parameters in the module.

        Args:
            only_trainable: A boolean, whether to only include trainable parameters.
            exclude_embeddings: A boolean, whether to exclude parameters associated with embeddings.

        Returns:
            An integer, the number of parameters.
        """

        if exclude_embeddings:
            embedding_param_names = [
                f"{name}.weight"
                for name, module_type in self.named_modules()
                if isinstance(module_type, torch.nn.Embedding)
            ]
            non_embedding_parameters = [
                parameter
                for name, parameter in self.named_parameters()
                if name not in embedding_param_names
            ]
            return sum(
                p.numel()
                for p in non_embedding_parameters
                if p.requires_grad or not only_trainable
            )
        else:
            return sum(
                p.numel()
                for p in self.parameters()
                if p.requires_grad or not only_trainable
            )