meta_init_context.py

from contextlib import contextmanager
from typing import Any, Callable, Optional
import torch
import torch.nn as nn

@contextmanager
def init_empty_weights(include_buffers: bool=False):
    """Meta initialization context manager.

    A context manager under which models are initialized with all parameters
    on the meta device, therefore creating an empty model. Useful when just
    initializing the model would blow the available RAM.

    Args:
        include_buffers (`bool`, *optional*, defaults to `False`): Whether or
            not to also put all buffers on the meta device while initializing.

    Example:
    ```python
    import torch.nn as nn

    # Initialize a model with 100 billions parameters in no time and without using any RAM.
    with init_empty_weights():
        tst = nn.Sequential(*[nn.Linear(10000, 10000) for _ in range(1000)])
    ```

    <Tip warning={true}>

    Any model created under this context manager has no weights. As such you can't do something like
    `model.to(some_device)` with it. To load weights inside your empty model, see [`load_checkpoint_and_dispatch`].

    </Tip>
    """
    with init_on_device(torch.device('meta'), include_buffers=include_buffers) as f:
        yield f

@contextmanager
def init_on_device(device: torch.device, include_buffers: bool=False):
    """Device initialization context manager.

    A context manager under which models are initialized with all parameters
    on the specified device.

    Args:
        device (`torch.device`): Device to initialize all parameters on.
        include_buffers (`bool`, *optional*, defaults to `False`): Whether or
            not to also put all buffers on the meta device while initializing.

    Example:
    ```python
    import torch.nn as nn

    with init_on_device(device=torch.device("cuda")):
        tst = nn.Liner(100, 100)  # on `cuda` device
    ```
    """
    old_register_parameter = nn.Module.register_parameter
    if include_buffers:
        old_register_buffer = nn.Module.register_buffer

    def register_empty_parameter(self: torch.nn.Module, name: str, param: Optional[torch.nn.Parameter]):
        old_register_parameter(self, name, param)
        if param is not None:
            parameter = self._parameters[name]
            assert parameter is not None
            param_cls = type(parameter)
            kwargs = parameter.__dict__
            self._parameters[name] = param_cls(parameter.to(device), **kwargs)

    def register_empty_buffer(self: torch.nn.Module, name: str, tensor: Optional[torch.Tensor], persistent: bool=True):
        old_register_buffer(self, name, tensor, persistent=persistent)
        if tensor is not None:
            named_buffer = self._buffers[name]
            assert named_buffer is not None
            self._buffers[name] = named_buffer.to(device)
    if include_buffers:
        tensor_constructors_to_patch = {torch_function_name: getattr(torch, torch_function_name) for torch_function_name in ['empty', 'zeros', 'ones', 'full']}
    else:
        tensor_constructors_to_patch = {}

    def patch_tensor_constructor(fn: Callable):

        def wrapper(*args: Any, **kwargs: Any):
            kwargs['device'] = device
            return fn(*args, **kwargs)
        return wrapper
    try:
        nn.Module.register_parameter = register_empty_parameter
        if include_buffers:
            nn.Module.register_buffer = register_empty_buffer
        for torch_function_name in tensor_constructors_to_patch.keys():
            setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name)))
        yield
    finally:
        nn.Module.register_parameter = old_register_parameter
        if include_buffers:
            nn.Module.register_buffer = old_register_buffer
        for (torch_function_name, old_torch_function) in tensor_constructors_to_patch.items():
            setattr(torch, torch_function_name, old_torch_function)