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Source code for torch.nn.modules.module

# mypy: allow-untyped-defs

import functools
import inspect
import itertools
import warnings
import weakref
from collections import namedtuple, OrderedDict
from typing import (
    Any,
    Callable,
    Dict,
    Iterator,
    List,
    Mapping,
    Optional,
    overload,
    Set,
    Tuple,
    TypeVar,
    Union,
)
from typing_extensions import Self

import torch
from torch import device, dtype, Tensor
from torch._prims_common import DeviceLikeType
from torch.nn.parameter import Buffer, Parameter
from torch.utils._python_dispatch import is_traceable_wrapper_subclass
from torch.utils.hooks import BackwardHook, RemovableHandle


__all__ = [
    "register_module_forward_pre_hook",
    "register_module_forward_hook",
    "register_module_full_backward_pre_hook",
    "register_module_backward_hook",
    "register_module_full_backward_hook",
    "register_module_buffer_registration_hook",
    "register_module_module_registration_hook",
    "register_module_parameter_registration_hook",
    "Module",
]

_grad_t = Union[Tuple[Tensor, ...], Tensor]
# See https://mypy.readthedocs.io/en/latest/generics.html#generic-methods-and-generic-self for the use
# of `T` to annotate `self`. Many methods of `Module` return `self` and we want those return values to be
# the type of the subclass, not the looser type of `Module`.
T = TypeVar("T", bound="Module")


class _IncompatibleKeys(
    namedtuple("IncompatibleKeys", ["missing_keys", "unexpected_keys"]),
):
    def __repr__(self):
        if not self.missing_keys and not self.unexpected_keys:
            return "<All keys matched successfully>"
        return super().__repr__()

    __str__ = __repr__


def _addindent(s_, numSpaces):
    s = s_.split("\n")
    # don't do anything for single-line stuff
    if len(s) == 1:
        return s_
    first = s.pop(0)
    s = [(numSpaces * " ") + line for line in s]
    s = "\n".join(s)
    s = first + "\n" + s
    return s


r"""This tracks hooks common to all modules that are executed immediately before
.registering the buffer/module/parameter"""
_global_buffer_registration_hooks: Dict[int, Callable] = OrderedDict()
_global_module_registration_hooks: Dict[int, Callable] = OrderedDict()
_global_parameter_registration_hooks: Dict[int, Callable] = OrderedDict()


class _WrappedHook:
    def __init__(self, hook: Callable, module: Optional["Module"] = None):
        self.hook: Callable = hook
        functools.update_wrapper(self, hook)

        self.with_module: bool = False

        if module is not None:
            self.module: weakref.ReferenceType[Module] = weakref.ref(module)
            self.with_module = True

    def __call__(self, *args: Any, **kwargs: Any) -> Any:
        if self.with_module:
            module = self.module()
            if module is None:
                raise RuntimeError("You are trying to call the hook of a dead Module!")
            return self.hook(module, *args, **kwargs)
        return self.hook(*args, **kwargs)

    def __getstate__(self) -> Dict:
        result = {"hook": self.hook, "with_module": self.with_module}
        if self.with_module:
            result["module"] = self.module()

        return result

    def __setstate__(self, state: Dict):
        self.hook = state["hook"]
        self.with_module = state["with_module"]

        if self.with_module:
            if state["module"] is None:
                raise RuntimeError(
                    "You are trying to revive the hook of a dead Module!"
                )
            self.module = weakref.ref(state["module"])


r"""This tracks hooks common to all modules that are executed before/after
calling forward and backward. This is global state used for debugging/profiling
purposes"""
_global_backward_pre_hooks: Dict[int, Callable] = OrderedDict()
_global_backward_hooks: Dict[int, Callable] = OrderedDict()
_global_is_full_backward_hook: Optional[bool] = None
_global_forward_pre_hooks: Dict[int, Callable] = OrderedDict()
_global_forward_hooks: Dict[int, Callable] = OrderedDict()
_global_forward_hooks_always_called: Dict[int, bool] = OrderedDict()

_EXTRA_STATE_KEY_SUFFIX = "_extra_state"


[docs]def register_module_buffer_registration_hook( hook: Callable[..., None], ) -> RemovableHandle: r"""Register a buffer registration hook common to all modules. .. warning :: This adds global state to the `nn.Module` module The hook will be called every time :func:`register_buffer` is invoked. It should have the following signature:: hook(module, name, buffer) -> None or new buffer The hook can modify the input or return a single modified value in the hook. Returns: :class:`torch.utils.hooks.RemovableHandle`: a handle that can be used to remove the added hook by calling ``handle.remove()`` """ handle = RemovableHandle(_global_buffer_registration_hooks) _global_buffer_registration_hooks[handle.id] = hook return handle
[docs]def register_module_module_registration_hook( hook: Callable[..., None], ) -> RemovableHandle: r"""Register a module registration hook common to all modules. .. warning :: This adds global state to the `nn.Module` module The hook will be called every time :func:`register_module` is invoked. It should have the following signature:: hook(module, name, submodule) -> None or new submodule The hook can modify the input or return a single modified value in the hook. Returns: :class:`torch.utils.hooks.RemovableHandle`: a handle that can be used to remove the added hook by calling ``handle.remove()`` """ handle = RemovableHandle(_global_module_registration_hooks) _global_module_registration_hooks[handle.id] = hook return handle
[docs]def register_module_parameter_registration_hook( hook: Callable[..., None], ) -> RemovableHandle: r"""Register a parameter registration hook common to all modules. .. warning :: This adds global state to the `nn.Module` module The hook will be called every time :func:`register_parameter` is invoked. It should have the following signature:: hook(module, name, param) -> None or new parameter The hook can modify the input or return a single modified value in the hook. Returns: :class:`torch.utils.hooks.RemovableHandle`: a handle that can be used to remove the added hook by calling ``handle.remove()`` """ handle = RemovableHandle(_global_parameter_registration_hooks) _global_parameter_registration_hooks[handle.id] = hook return handle
[docs]def register_module_forward_pre_hook(hook: Callable[..., None]) -> RemovableHandle: r"""Register a forward pre-hook common to all modules. .. warning :: This adds global state to the `nn.module` module and it is only intended for debugging/profiling purposes. The hook will be called every time before :func:`forward` is invoked. It should have the following signature:: hook(module, input) -> None or modified input The input contains only the positional arguments given to the module. Keyword arguments won't be passed to the hooks and only to the ``forward``. The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned(unless that value is already a tuple). This hook has precedence over the specific module hooks registered with ``register_forward_pre_hook``. Returns: :class:`torch.utils.hooks.RemovableHandle`: a handle that can be used to remove the added hook by calling ``handle.remove()`` """ handle = RemovableHandle(_global_forward_pre_hooks) _global_forward_pre_hooks[handle.id] = hook return handle
[docs]def register_module_forward_hook( hook: Callable[..., None], *, always_call: bool = False, ) -> RemovableHandle: r"""Register a global forward hook for all the modules. .. warning :: This adds global state to the `nn.module` module and it is only intended for debugging/profiling purposes. The hook will be called every time after :func:`forward` has computed an output. It should have the following signature:: hook(module, input, output) -> None or modified output The input contains only the positional arguments given to the module. Keyword arguments won't be passed to the hooks and only to the ``forward``. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after :func:`forward` is called. Parameters: hook (Callable): The user defined hook to be registered. always_call (bool): If ``True`` the ``hook`` will be run regardless of whether an exception is raised while calling the Module. Default: ``False`` Returns: :class:`torch.utils.hooks.RemovableHandle`: a handle that can be used to remove the added hook by calling ``handle.remove()`` This hook will be executed before specific module hooks registered with ``register_forward_hook``. """ handle = RemovableHandle( _global_forward_hooks, extra_dict=_global_forward_hooks_always_called ) _global_forward_hooks[handle.id] = hook if always_call: _global_forward_hooks_always_called[handle.id] = True return handle
[docs]def register_module_backward_hook( hook: Callable[["Module", _grad_t, _grad_t], Union[None, _grad_t]], ) -> RemovableHandle: r"""Register a backward hook common to all the modules. This function is deprecated in favor of :func:`torch.nn.modules.module.register_module_full_backward_hook` and the behavior of this function will change in future versions. Returns: :class:`torch.utils.hooks.RemovableHandle`: a handle that can be used to remove the added hook by calling ``handle.remove()`` """ global _global_is_full_backward_hook if _global_is_full_backward_hook is True: raise RuntimeError( "Cannot use both regular backward hooks and full backward hooks as a " "global Module hook. Please use only one of them." ) _global_is_full_backward_hook = False handle = RemovableHandle(_global_backward_hooks) _global_backward_hooks[handle.id] = hook return handle
[docs]def register_module_full_backward_pre_hook( hook: Callable[["Module", _grad_t], Union[None, _grad_t]], ) -> RemovableHandle: r"""Register a backward pre-hook common to all the modules. .. warning :: This adds global state to the `nn.module` module and it is only intended for debugging/profiling purposes. Hooks registered using this function behave in the same way as those registered by :meth:`torch.nn.Module.register_full_backward_pre_hook`. Refer to its documentation for more details. Hooks registered using this function will be called before hooks registered using :meth:`torch.nn.Module.register_full_backward_pre_hook`. Returns: :class:`torch.utils.hooks.RemovableHandle`: a handle that can be used to remove the added hook by calling ``handle.remove()`` """ handle = RemovableHandle(_global_backward_pre_hooks) _global_backward_pre_hooks[handle.id] = hook return handle
[docs]def register_module_full_backward_hook( hook: Callable[["Module", _grad_t, _grad_t], Union[None, _grad_t]], ) -> RemovableHandle: r"""Register a backward hook common to all the modules. .. warning :: This adds global state to the `nn.module` module and it is only intended for debugging/profiling purposes. Hooks registered using this function behave in the same way as those registered by :meth:`torch.nn.Module.register_full_backward_hook`. Refer to its documentation for more details. Hooks registered using this function will be called before hooks registered using :meth:`torch.nn.Module.register_full_backward_hook`. Returns: :class:`torch.utils.hooks.RemovableHandle`: a handle that can be used to remove the added hook by calling ``handle.remove()`` """ global _global_is_full_backward_hook if _global_is_full_backward_hook is False: raise RuntimeError( "Cannot use both regular backward hooks and full backward hooks as a " "global Module hook. Please use only one of them." ) _global_is_full_backward_hook = True handle = RemovableHandle(_global_backward_hooks) _global_backward_hooks[handle.id] = hook return handle
# Trick mypy into not applying contravariance rules to inputs by defining # forward as a value, rather than a function. See also # https://github.com/python/mypy/issues/8795 def _forward_unimplemented(self, *input: Any) -> None: r"""Define the computation performed at every call. Should be overridden by all subclasses. .. note:: Although the recipe for forward pass needs to be defined within this function, one should call the :class:`Module` instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them. """ raise NotImplementedError( f'Module [{type(self).__name__}] is missing the required "forward" function' )
[docs]class Module: r"""Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self) -> None: super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool """ dump_patches: bool = False _version: int = 1 r"""This allows better BC support for :meth:`load_state_dict`. In :meth:`state_dict`, the version number will be saved as in the attribute `_metadata` of the returned state dict, and thus pickled. `_metadata` is a dictionary with keys that follow the naming convention of state dict. See ``_load_from_state_dict`` on how to use this information in loading. If new parameters/buffers are added/removed from a module, this number shall be bumped, and the module's `_load_from_state_dict` method can compare the version number and do appropriate changes if the state dict is from before the change.""" training: bool _parameters: Dict[str, Optional[Parameter]] _buffers: Dict[str, Optional[Tensor]] _non_persistent_buffers_set: Set[str] _backward_pre_hooks: Dict[int, Callable] _backward_hooks: Dict[int, Callable] _is_full_backward_hook: Optional[bool] _forward_hooks: Dict[int, Callable] # Marks whether the corresponding _forward_hooks accept kwargs or not. # As JIT does not support Set[int], this dict is used as a set, where all # hooks represented in this dict accept kwargs. _forward_hooks_with_kwargs: Dict[int, bool] # forward hooks that should always be called even if an exception is raised _forward_hooks_always_called: Dict[int, bool] _forward_pre_hooks: Dict[int, Callable] # Marks whether the corresponding _forward_hooks accept kwargs or not. # As JIT does not support Set[int], this dict is used as a set, where all # hooks represented in this dict accept kwargs. _forward_pre_hooks_with_kwargs: Dict[int, bool] _state_dict_hooks: Dict[int, Callable] _load_state_dict_pre_hooks: Dict[int, Callable] _state_dict_pre_hooks: Dict[int, Callable] _load_state_dict_post_hooks: Dict[int, Callable] _modules: Dict[str, Optional["Module"]] call_super_init: bool = False _compiled_call_impl: Optional[Callable] = None def __init__(self, *args, **kwargs) -> None: """Initialize internal Module state, shared by both nn.Module and ScriptModule.""" torch._C._log_api_usage_once("python.nn_module") # Backward compatibility: no args used to be allowed when call_super_init=False if self.call_super_init is False and bool(kwargs): raise TypeError( f"{type(self).__name__}.__init__() got an unexpected keyword argument '{next(iter(kwargs))}'" "" ) if self.call_super_init is False and bool(args): raise TypeError( f"{type(self).__name__}.__init__() takes 1 positional argument but {len(args) + 1} were" " given" ) """ Calls super().__setattr__('a', a) instead of the typical self.a = a to avoid Module.__setattr__ overhead. Module's __setattr__ has special handling for parameters, submodules, and buffers but simply calls into super().__setattr__ for all other attributes. """ super().__setattr__("training", True) super().__setattr__("_parameters", {}) super().__setattr__("_buffers", {}) super().__setattr__("_non_persistent_buffers_set", set()) super().__setattr__("_backward_pre_hooks", OrderedDict()) super().__setattr__("_backward_hooks", OrderedDict()) super().__setattr__("_is_full_backward_hook", None) super().__setattr__("_forward_hooks", OrderedDict()) super().__setattr__("_forward_hooks_with_kwargs", OrderedDict()) super().__setattr__("_forward_hooks_always_called", OrderedDict()) super().__setattr__("_forward_pre_hooks", OrderedDict()) super().__setattr__("_forward_pre_hooks_with_kwargs", OrderedDict()) super().__setattr__("_state_dict_hooks", OrderedDict()) super().__setattr__("_state_dict_pre_hooks", OrderedDict()) super().__setattr__("_load_state_dict_pre_hooks", OrderedDict()) super().__setattr__("_load_state_dict_post_hooks", OrderedDict()) super().__setattr__("_modules", {}) if self.call_super_init: super().__init__(*args, **kwargs) forward: Callable[..., Any] = _forward_unimplemented
[docs] def register_buffer( self, name: str, tensor: Optional[Tensor], persistent: bool = True ) -> None: r"""Add a buffer to the module. This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm's ``running_mean`` is not a parameter, but is part of the module's state. Buffers, by default, are persistent and will be saved alongside parameters. This behavior can be changed by setting :attr:`persistent` to ``False``. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module's :attr:`state_dict`. Buffers can be accessed as attributes using given names. Args: name (str): name of the buffer. The buffer can be accessed from this module using the given name tensor (Tensor or None): buffer to be registered. If ``None``, then operations that run on buffers, such as :attr:`cuda`, are ignored. If ``None``, the buffer is **not** included in the module's :attr:`state_dict`. persistent (bool): whether the buffer is part of this module's :attr:`state_dict`. Example:: >>> # xdoctest: +SKIP("undefined vars") >>> self.register_buffer('running_mean', torch.zeros(num_features)) """ if persistent is False and isinstance(self, torch.jit.ScriptModule): raise RuntimeError("ScriptModule does not support non-persistent buffers") if "_buffers" not in self.__dict__: raise AttributeError("cannot assign buffer before Module.__init__() call") elif not isinstance(name, str): raise TypeError( f"buffer name should be a string. Got {torch.typename(name)}" ) elif "." in name: raise KeyError('buffer name can\'t contain "."') elif name == "": raise KeyError('buffer name can\'t be empty string ""') elif hasattr(self, name) and name not in self._buffers: raise KeyError(f"attribute '{name}' already exists") elif tensor is not None and not isinstance(tensor, torch.Tensor): raise TypeError( f"cannot assign '{torch.typename(tensor)}' object to buffer '{name}' " "(torch Tensor or None required)" ) else: for hook in _global_buffer_registration_hooks.values(): output = hook(self, name, tensor) if output is not None: tensor = output self._buffers[name] = tensor if persistent: self._non_persistent_buffers_set.discard(name) else: self._non_persistent_buffers_set.add(name)
[docs] def register_parameter(self, name: str, param: Optional[Parameter]) -> None: r"""Add a parameter to the module. The parameter can be accessed as an attribute using given name. Args: name (str): name of the parameter. The parameter can be accessed from this module using the given name param (Parameter or None): parameter to be added to the module. If ``None``, then operations that run on parameters, such as :attr:`cuda`, are ignored. If ``None``, the parameter is **not** included in the module's :attr:`state_dict`. """ if "_parameters" not in self.__dict__: raise AttributeError( "cannot assign parameter before Module.__init__() call" ) elif not isinstance(name, str): raise TypeError( f"parameter name should be a string. Got {torch.typename(name)}" ) elif "." in name: raise KeyError('parameter name can\'t contain "."') elif name == "": raise KeyError('parameter name can\'t be empty string ""') elif hasattr(self, name) and name not in self._parameters: raise KeyError(f"attribute '{name}' already exists") if param is None: self._parameters[name] = None elif not isinstance(param, Parameter): raise TypeError( f"cannot assign '{torch.typename(param)}' object to parameter '{name}' " "(torch.nn.Parameter or None required)" ) elif param.grad_fn: raise ValueError( f"Cannot assign non-leaf Tensor to parameter '{name}'. Model " f"parameters must be created explicitly. To express '{name}' " "as a function of another Tensor, compute the value in " "the forward() method." ) else: for hook in _global_parameter_registration_hooks.values(): output = hook(self, name, param) if output is not None: param = output self._parameters[name] = param
[docs] def add_module(self, name: str, module: Optional["Module"]) -> None: r"""Add a child module to the current module. The module can be accessed as an attribute using the given name. Args: name (str): name of the child module. The child module can be accessed from this module using the given name module (Module): child module to be added to the module. """ if not isinstance(module, Module) and module is not None: raise TypeError(f"{torch.typename(module)} is not a Module subclass") elif not isinstance(name, str): raise TypeError( f"module name should be a string. Got {torch.typename(name)}" ) elif hasattr(self, name) and name not in self._modules: raise KeyError(f"attribute '{name}' already exists") elif "." in name: raise KeyError(f'module name can\'t contain ".", got: {name}') elif name == "": raise KeyError('module name can\'t be empty string ""') for hook in _global_module_registration_hooks.values(): output = hook(self, name, module) if output is not None: module = output self._modules[name] = module
[docs] def register_module(self, name: str, module: Optional["Module"]) -> None: r"""Alias for :func:`add_module`.""" self.add_module(name, module)
[docs] def get_submodule(self, target: str) -> "Module": """Return the submodule given by ``target`` if it exists, otherwise throw an error. For example, let's say you have an ``nn.Module`` ``A`` that looks like this: .. code-block:: text A( (net_b): Module( (net_c): Module( (conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2)) ) (linear): Linear(in_features=100, out_features=200, bias=True) ) ) (The diagram shows an ``nn.Module`` ``A``. ``A`` has a nested submodule ``net_b``, which itself has two submodules ``net_c`` and ``linear``. ``net_c`` then has a submodule ``conv``.) To check whether or not we have the ``linear`` submodule, we would call ``get_submodule("net_b.linear")``. To check whether we have the ``conv`` submodule, we would call ``get_submodule("net_b.net_c.conv")``. The runtime of ``get_submodule`` is bounded by the degree of module nesting in ``target``. A query against ``named_modules`` achieves the same result, but it is O(N) in the number of transitive modules. So, for a simple check to see if some submodule exists, ``get_submodule`` should always be used. Args: target: The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.) Returns: torch.nn.Module: The submodule referenced by ``target`` Raises: AttributeError: If the target string references an invalid path or resolves to something that is not an ``nn.Module`` """ if target == "": return self atoms: List[str] = target.split(".") mod: torch.nn.Module = self for item in atoms: if not hasattr(mod, item): raise AttributeError( mod._get_name() + " has no " "attribute `" + item + "`" ) mod = getattr(mod, item) if not isinstance(mod, torch.nn.Module): raise AttributeError("`" + item + "` is not " "an nn.Module") return mod
[docs] def set_submodule(self, target: str, module: "Module") -> None: """ Set the submodule given by ``target`` if it exists, otherwise throw an error. For example, let's say you have an ``nn.Module`` ``A`` that looks like this: .. code-block:: text A( (net_b): Module( (net_c): Module( (conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2)) ) (linear): Linear(in_features=100, out_features=200, bias=True) ) ) (The diagram shows an ``nn.Module`` ``A``. ``A`` has a nested submodule ``net_b``, which itself has two submodules ``net_c`` and ``linear``. ``net_c`` then has a submodule ``conv``.) To overide the ``Conv2d`` with a new submodule ``Linear``, you would call ``set_submodule("net_b.net_c.conv", nn.Linear(33, 16))``. Args: target: The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.) module: The module to set the submodule to. Raises: ValueError: If the target string is empty AttributeError: If the target string references an invalid path or resolves to something that is not an ``nn.Module`` """ if target == "": raise ValueError("Cannot set the submodule without a target name!") atoms: List[str] = target.split(".") name = atoms.pop(-1) mod: torch.nn.Module = self for item in atoms: if not hasattr(mod, item): raise AttributeError( mod._get_name() + " has no attribute `" + item + "`" ) mod = getattr(mod, item) # Use isinstance instead of type here to also handle subclass of nn.Module if not isinstance(mod, torch.nn.Module): raise AttributeError("`" + item + "` is not an nn.Module") setattr(mod, name, module)
[docs] def get_parameter(self, target: str) -> "Parameter": """Return the parameter given by ``target`` if it exists, otherwise throw an error. See the docstring for ``get_submodule`` for a more detailed explanation of this method's functionality as well as how to correctly specify ``target``. Args: target: The fully-qualified string name of the Parameter to look for. (See ``get_submodule`` for how to specify a fully-qualified string.) Returns: torch.nn.Parameter: The Parameter referenced by ``target`` Raises: AttributeError: If the target string references an invalid path or resolves to something that is not an ``nn.Parameter`` """ module_path, _, param_name = target.rpartition(".") mod: torch.nn.Module = self.get_submodule(module_path) if not hasattr(mod, param_name): raise AttributeError( mod._get_name() + " has no attribute `" + param_name + "`" ) param: torch.nn.Parameter = getattr(mod, param_name) if not isinstance(param, torch.nn.Parameter): raise AttributeError("`" + param_name + "` is not an " "nn.Parameter") return param
[docs] def get_buffer(self, target: str) -> "Tensor": """Return the buffer given by ``target`` if it exists, otherwise throw an error. See the docstring for ``get_submodule`` for a more detailed explanation of this method's functionality as well as how to correctly specify ``target``. Args: target: The fully-qualified string name of the buffer to look for. (See ``get_submodule`` for how to specify a fully-qualified string.) Returns: torch.Tensor: The buffer referenced by ``target`` Raises: AttributeError: If the target string references an invalid path or resolves to something that is not a buffer """ module_path, _, buffer_name = target.rpartition(".") mod: torch.nn.Module = self.get_submodule(module_path) if not hasattr(mod, buffer_name): raise AttributeError( mod._get_name() + " has no attribute `" + buffer_name + "`" ) buffer: torch.Tensor = getattr(mod, buffer_name) if buffer_name not in mod._buffers: raise AttributeError("`" + buffer_name + "` is not a buffer") return buffer
[docs] def get_extra_state(self) -> Any: """Return any extra state to include in the module's state_dict. Implement this and a corresponding :func:`set_extra_state` for your module if you need to store extra state. This function is called when building the module's `state_dict()`. Note that extra state should be picklable to ensure working serialization of the state_dict. We only provide provide backwards compatibility guarantees for serializing Tensors; other objects may break backwards compatibility if their serialized pickled form changes. Returns: object: Any extra state to store in the module's state_dict """ raise RuntimeError( "Reached a code path in Module.get_extra_state() that should never be called. " "Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml " "to report this bug." )
[docs] def set_extra_state(self, state: Any) -> None: """Set extra state contained in the loaded `state_dict`. This function is called from :func:`load_state_dict` to handle any extra state found within the `state_dict`. Implement this function and a corresponding :func:`get_extra_state` for your module if you need to store extra state within its `state_dict`. Args: state (dict): Extra state from the `state_dict` """ raise RuntimeError( "Reached a code path in Module.set_extra_state() that should never be called. " "Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml " "to report this bug." )
def _apply(self, fn, recurse=True): if recurse: for module in self.children(): module._apply(fn) def compute_should_use_set_data(tensor, tensor_applied): if torch._has_compatible_shallow_copy_type(tensor, tensor_applied): # If the new tensor has compatible tensor type as the existing tensor, # the current behavior is to change the tensor in-place using `.data =`, # and the future behavior is to overwrite the existing tensor. However, # changing the current behavior is a BC-breaking change, and we want it # to happen in future releases. So for now we introduce the # `torch.__future__.get_overwrite_module_params_on_conversion()` # global flag to let the user control whether they want the future # behavior of overwriting the existing tensor or not. return not torch.__future__.get_overwrite_module_params_on_conversion() else: return False should_use_swap_tensors = ( torch.__future__.get_swap_module_params_on_conversion() ) for key, param in self._parameters.items(): if param is None: continue # Tensors stored in modules are graph leaves, and we don't want to # track autograd history of `param_applied`, so we have to use # `with torch.no_grad():` with torch.no_grad(): param_applied = fn(param) p_should_use_set_data = compute_should_use_set_data(param, param_applied) # subclasses may have multiple child tensors so we need to use swap_tensors p_should_use_swap_tensors = ( should_use_swap_tensors or is_traceable_wrapper_subclass(param_applied) ) param_grad = param.grad if p_should_use_swap_tensors: try: if param_grad is not None: # Accessing param.grad makes its at::Tensor's use_count 2, which will prevent swapping. # Decrement use count of the gradient by setting to None param.grad = None param_applied = torch.nn.Parameter( param_applied, requires_grad=param.requires_grad ) torch.utils.swap_tensors(param, param_applied) except Exception as e: if param_grad is not None: param.grad = param_grad raise RuntimeError( f"_apply(): Couldn't swap {self._get_name()}.{key}" ) from e out_param = param elif p_should_use_set_data: param.data = param_applied out_param = param else: assert isinstance(param, Parameter) assert param.is_leaf out_param = Parameter(param_applied, param.requires_grad) self._parameters[key] = out_param if param_grad is not None: with torch.no_grad(): grad_applied = fn(param_grad) g_should_use_set_data = compute_should_use_set_data( param_grad, grad_applied ) if p_should_use_swap_tensors: grad_applied.requires_grad_(param_grad.requires_grad) try: torch.utils.swap_tensors(param_grad, grad_applied) except Exception as e: raise RuntimeError( f"_apply(): Couldn't swap {self._get_name()}.{key}.grad" ) from e out_param.grad = param_grad elif g_should_use_set_data: assert out_param.grad is not None out_param.grad.data = grad_applied else: assert param_grad.is_leaf out_param.grad = grad_applied.requires_grad_( param_grad.requires_grad ) for key, buf in self._buffers.items(): if buf is not None: self._buffers[key] = fn(buf) return self
[docs] def apply(self: T, fn: Callable[["Module"], None]) -> T: r"""Apply ``fn`` recursively to every submodule (as returned by ``.children()``) as well as self. Typical use includes initializing the parameters of a model (see also :ref:`nn-init-doc`). Args: fn (:class:`Module` -> None): function to be applied to each submodule Returns: Module: self Example:: >>> @torch.no_grad() >>> def init_weights(m): >>> print(m) >>> if type(m) == nn.Linear: >>> m.weight.fill_(1.0) >>> print(m.weight) >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2)) >>> net.apply(init_weights) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[1., 1.], [1., 1.]], requires_grad=True) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[1., 1.], [1., 1.]], requires_grad=True) Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) ) """ for module in self.children(): module.apply(fn) fn(self) return self
[docs] def cuda(self: T, device: Optional[Union[int, device]] = None) -> T: r"""Move all model parameters and buffers to the GPU. This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized. .. note:: This method modifies the module in-place. Args: device (int, optional): if specified, all parameters will be copied to that device Returns: Module: self """ return self._apply(lambda t: t.cuda(device))
[docs] def ipu(self: T, device: Optional[Union[int, device]] = None) -> T: r"""Move all model parameters and buffers to the IPU. This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on IPU while being optimized. .. note:: This method modifies the module in-place. Arguments: device (int, optional): if specified, all parameters will be copied to that device Returns: Module: self """ return self._apply(lambda t: t.ipu(device))
[docs] def xpu(self: T, device: Optional[Union[int, device]] = None) -> T: r"""Move all model parameters and buffers to the XPU. This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on XPU while being optimized. .. note:: This method modifies the module in-place. Arguments: device (int, optional): if specified, all parameters will be copied to that device Returns: Module: self """ return self._apply(lambda t: t.xpu(device))
[docs] def mtia(self: T, device: Optional[Union[int, device]] = None) -> T: r"""Move all model parameters and buffers to the MTIA. This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on MTIA while being optimized. .. note:: This method modifies the module in-place. Arguments: device (int, optional): if specified, all parameters will be copied to that device Returns: Module: self """ return self._apply(lambda t: t.mtia(device))
[docs] def cpu(self: T) -> T: r"""Move all model parameters and buffers to the CPU. .. note:: This method modifies the module in-place. Returns: Module: self """ return self._apply(lambda t: t.cpu())
[docs] def type(self: T, dst_type: Union[dtype, str]) -> T: r"""Casts all parameters and buffers to :attr:`dst_type`. .. note:: This method modifies the module in-place. Args: dst_type (type or string): the desired type Returns: Module: self """ return self._apply(lambda t: t.type(dst_type))
[docs] def float(self: T) -> T: r"""Casts all floating point parameters and buffers to ``float`` datatype. .. note:: This method modifies the module in-place. Returns: Module: self """ return self._apply(lambda t: t.float() if t.is_floating_point() else t)
[docs] def double(self: T) -> T: r"""Casts all floating point parameters and buffers to ``double`` datatype. .. note:: This method modifies the module in-place. Returns: Module: self """ return self._apply(lambda t: t.double() if t.is_floating_point() else t)
[docs] def half(self: T) -> T: r"""Casts all floating point parameters and buffers to ``half`` datatype. .. note:: This method modifies the module in-place. Returns: Module: self """ return self._apply(lambda t: t.half() if t.is_floating_point() else t)
[docs] def bfloat16(self: T) -> T: r"""Casts all floating point parameters and buffers to ``bfloat16`` datatype. .. note:: This method modifies the module in-place. Returns: Module: self """ return self._apply(lambda t: t.bfloat16() if t.is_floating_point() else t)
[docs] def to_empty( self: T, *, device: Optional[DeviceLikeType], recurse: bool = True ) -> T: r"""Move the parameters and buffers to the specified device without copying storage. Args: device (:class:`torch.device`): The desired device of the parameters and buffers in this module. recurse (bool): Whether parameters and buffers of submodules should be recursively moved to the specified device. Returns: Module: self """ return self._apply( lambda t: torch.empty_like(t, device=device), recurse=recurse )
@overload def to( self, device: Optional[DeviceLikeType] = ..., dtype: Optional[dtype] = ..., non_blocking: bool = ..., ) -> Self: ... @overload def to(self, dtype: dtype, non_blocking: bool = ...) -> Self: ... @overload def to(self, tensor: Tensor, non_blocking: bool = ...) -> Self: ...
[docs] def to(self, *args, **kwargs): r"""Move and/or cast the parameters and buffers. This can be called as .. function:: to(device=None, dtype=None, non_blocking=False) :noindex: .. function:: to(dtype, non_blocking=False) :noindex: .. function:: to(tensor, non_blocking=False) :noindex: .. function:: to(memory_format=torch.channels_last) :noindex: Its signature is similar to :meth:`torch.Tensor.to`, but only accepts floating point or complex :attr:`dtype`\ s. In addition, this method will only cast the floating point or complex parameters and buffers to :attr:`dtype` (if given). The integral parameters and buffers will be moved :attr:`device`, if that is given, but with dtypes unchanged. When :attr:`non_blocking` is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices. See below for examples. .. note:: This method modifies the module in-place. Args: device (:class:`torch.device`): the desired device of the parameters and buffers in this module dtype (:class:`torch.dtype`): the desired floating point or complex dtype of the parameters and buffers in this module tensor (torch.Tensor): Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module memory_format (:class:`torch.memory_format`): the desired memory format for 4D parameters and buffers in this module (keyword only argument) Returns: Module: self Examples:: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> linear = nn.Linear(2, 2) >>> linear.weight Parameter containing: tensor([[ 0.1913, -0.3420], [-0.5113, -0.2325]]) >>> linear.to(torch.double) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1913, -0.3420], [-0.5113, -0.2325]], dtype=torch.float64) >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1) >>> gpu1 = torch.device("cuda:1") >>> linear.to(gpu1, dtype=torch.half, non_blocking=True) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1914, -0.3420], [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1') >>> cpu = torch.device("cpu") >>> linear.to(cpu) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1914, -0.3420], [-0.5112, -0.2324]], dtype=torch.float16) >>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble) >>> linear.weight Parameter containing: tensor([[ 0.3741+0.j, 0.2382+0.j], [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128) >>> linear(torch.ones(3, 2, dtype=torch.cdouble)) tensor([[0.6122+0.j, 0.1150+0.j], [0.6122+0.j, 0.1150+0.j], [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128) """ device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to( *args, **kwargs ) if dtype is not None: if not (dtype.is_floating_point or dtype.is_complex): raise TypeError( "nn.Module.to only accepts floating point or complex " f"dtypes, but got desired dtype={dtype}" ) if dtype.is_complex: warnings.warn( "Complex modules are a new feature under active development whose design may change, " "and some modules might not work as expected when using complex tensors as parameters or buffers. " "Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml " "if a complex module does not work as expected." ) def convert(t): try: if convert_to_format is not None and t.dim() in (4, 5): return t.to( device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking, memory_format=convert_to_format, ) return t.to( device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking, ) except NotImplementedError as e: if str(e) == "Cannot copy out of meta tensor; no data!": raise NotImplementedError( f"{e} Please use torch.nn.Module.to_empty() instead of torch.nn.Module.to() " f"when moving module from meta to a different device." ) from None else: raise return self._apply(convert)
[docs] def register_full_backward_pre_hook( self, hook: Callable[["Module", _grad_t], Union[None, _grad_t]], prepend: bool = False, ) -> RemovableHandle: r"""Register a backward pre-hook on the module. The hook will be called every time the gradients for the module are computed. The hook should have the following signature:: hook(module, grad_output) -> tuple[Tensor] or None The :attr:`grad_output` is a tuple. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the output that will be used in place of :attr:`grad_output` in subsequent computations. Entries in :attr:`grad_output` will be ``None`` for all non-Tensor arguments. For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function. .. warning :: Modifying inputs inplace is not allowed when using backward hooks and will raise an error. Args: hook (Callable): The user-defined hook to be registered. prepend (bool): If true, the provided ``hook`` will be fired before all existing ``backward_pre`` hooks on this :class:`torch.nn.modules.Module`. Otherwise, the provided ``hook`` will be fired after all existing ``backward_pre`` hooks on this :class:`torch.nn.modules.Module`. Note that global ``backward_pre`` hooks registered with :func:`register_module_full_backward_pre_hook` will fire before all hooks registered by this method. Returns: :class:`torch.utils.hooks.RemovableHandle`: a handle that can be used to remove the added hook by calling ``handle.remove()`` """ handle = RemovableHandle(self._backward_pre_hooks) self._backward_pre_hooks[handle.id] = hook if prepend: self._backward_pre_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined] return handle
[docs] def register_backward_hook( self, hook: Callable[["Module", _grad_t, _grad_t], Union[None, _grad_t]] ) -> RemovableHandle: r"""Register a backward hook on the module. This function is deprecated in favor of :meth:`~torch.nn.Module.register_full_backward_hook` and the behavior of this function will change in future versions. Returns: :class:`torch.utils.hooks.RemovableHandle`: a handle that can be used to remove the added hook by calling ``handle.remove()`` """ if self._is_full_backward_hook is True: raise RuntimeError( "Cannot use both regular backward hooks and full backward hooks on a " "single Module. Please use only one of them." ) self._is_full_backward_hook = False handle = RemovableHandle(self._backward_hooks) self._backward_hooks[handle.id] = hook return handle
[docs] def register_full_backward_hook( self, hook: Callable[["Module", _grad_t, _grad_t], Union[None, _grad_t]], prepend: bool = False, ) -> RemovableHandle: r"""Register a backward hook on the module. The hook will be called every time the gradients with respect to a module are computed, i.e. the hook will execute if and only if the gradients with respect to module outputs are computed. The hook should have the following signature:: hook(module, grad_input, grad_output) -> tuple(Tensor) or None The :attr:`grad_input` and :attr:`grad_output` are tuples that contain the gradients with respect to the inputs and outputs respectively. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the input that will be used in place of :attr:`grad_input` in subsequent computations. :attr:`grad_input` will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in :attr:`grad_input` and :attr:`grad_output` will be ``None`` for all non-Tensor arguments. For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function. .. warning :: Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error. Args: hook (Callable): The user-defined hook to be registered. prepend (bool): If true, the provided ``hook`` will be fired before all existing ``backward`` hooks on this :class:`torch.nn.modules.Module`. Otherwise, the provided ``hook`` will be fired after all existing ``backward`` hooks on this :class:`torch.nn.modules.Module`. Note that global ``backward`` hooks registered with :func:`register_module_full_backward_hook` will fire before all hooks registered by this method. Returns: :class:`torch.utils.hooks.RemovableHandle`: a handle that can be used to remove the added hook by calling ``handle.remove()`` """ if self._is_full_backward_hook is False: raise RuntimeError( "Cannot use both regular backward hooks and full backward hooks on a " "single Module. Please use only one of them." ) self._is_full_backward_hook = True handle = RemovableHandle(self._backward_hooks) self._backward_hooks[handle.id] = hook if prepend: self._backward_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined] return handle
def _get_backward_hooks(self): r"""Return the backward hooks for use in the call function. It returns two lists, one with the full backward hooks and one with the non-full backward hooks. """ full_backward_hooks: List[Callable] = [] if _global_is_full_backward_hook is True: full_backward_hooks += _global_backward_hooks.values() if self._is_full_backward_hook is True: full_backward_hooks += self._backward_hooks.values() non_full_backward_hooks: List[Callable] = [] if _global_is_full_backward_hook is False: non_full_backward_hooks += _global_backward_hooks.values() if self._is_full_backward_hook is False: non_full_backward_hooks += self._backward_hooks.values() return full_backward_hooks, non_full_backward_hooks def _get_backward_pre_hooks(self): backward_pre_hooks: List[Callable] = [] backward_pre_hooks += _global_backward_pre_hooks.values() backward_pre_hooks += self._backward_pre_hooks.values() return backward_pre_hooks def _maybe_warn_non_full_backward_hook(self, inputs, result, grad_fn): if not isinstance(result, torch.Tensor): if not ( isinstance(result, tuple) and all(isinstance(r, torch.Tensor) for r in result) ): warnings.warn( "Using non-full backward hooks on a Module that does not return a " "single Tensor or a tuple of Tensors is deprecated and will be removed " "in future versions. This hook will be missing some of the grad_output. " "Please use register_full_backward_hook to get the documented behavior.", FutureWarning, stacklevel=2, ) return else: result = (result,) if not isinstance(inputs, torch.Tensor): if not ( isinstance(inputs, tuple) and all(isinstance(i, torch.Tensor) for i in inputs) ): warnings.warn( "Using non-full backward hooks on a Module that does not take as input a " "single Tensor or a tuple of Tensors is deprecated and will be removed " "in future versions. This hook will be missing some of the grad_input. " "Please use register_full_backward_hook to get the documented behavior.", FutureWarning, stacklevel=2, ) return else: inputs = (inputs,) # At this point we are sure that inputs and result are tuple of Tensors out_grad_fn = {r.grad_fn for r in result if r.grad_fn is not None} if len(out_grad_fn) == 0 or ( len(out_grad_fn) == 1 and grad_fn not in out_grad_fn ): warnings.warn( "Using a non-full backward hook when outputs are nested in python data structure " "is deprecated and will be removed in future versions. This hook will be missing " "some grad_output.", FutureWarning, stacklevel=2, ) elif len(out_grad_fn) > 1: warnings.warn( "Using a non-full backward hook when outputs are generated by different autograd Nodes " "is deprecated and will be removed in future versions. This hook will be missing " "some grad_output. Please use register_full_backward_hook to get the documented behavior.", FutureWarning, stacklevel=2, ) else: # At this point the grad_output part of the hook will most likely be correct inputs_grad_fn = {i.grad_fn for i in inputs if i.grad_fn is not None} next_functions = {n[0] for n in grad_fn.next_functions} if inputs_grad_fn != next_functions: warnings.warn( "Using a non-full backward hook when the forward contains multiple autograd Nodes " "is deprecated and will be removed in future versions. This hook will be missing " "some grad_input. Please use register_full_backward_hook to get the documented " "behavior.", FutureWarning, stacklevel=2, )
[docs] def register_forward_pre_hook( self, hook: Union[ Callable[[T, Tuple[Any, ...]], Optional[Any]], Callable[ [T, Tuple[Any, ...], Dict[str, Any]], Optional[Tuple[Any, Dict[str, Any]]], ], ], *, prepend: bool = False, with_kwargs: bool = False, ) -> RemovableHandle: r"""Register a forward pre-hook on the module. The hook will be called every time before :func:`forward` is invoked. If ``with_kwargs`` is false or not specified, the input contains only the positional arguments given to the module. Keyword arguments won't be passed to the hooks and only to the ``forward``. The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned (unless that value is already a tuple). The hook should have the following signature:: hook(module, args) -> None or modified input If ``with_kwargs`` is true, the forward pre-hook will be passed the kwargs given to the forward function. And if the hook modifies the input, both the args and kwargs should be returned. The hook should have the following signature:: hook(module, args, kwargs) -> None or a tuple of modified input and kwargs Args: hook (Callable): The user defined hook to be registered. prepend (bool): If true, the provided ``hook`` will be fired before all existing ``forward_pre`` hooks on this :class:`torch.nn.modules.Module`. Otherwise, the provided ``hook`` will be fired after all existing ``forward_pre`` hooks on this :class:`torch.nn.modules.Module`. Note that global ``forward_pre`` hooks registered with :func:`register_module_forward_pre_hook` will fire before all hooks registered by this method. Default: ``False`` with_kwargs (bool): If true, the ``hook`` will be passed the kwargs given to the forward function. Default: ``False`` Returns: :class:`torch.utils.hooks.RemovableHandle`: a handle that can be used to remove the added hook by calling ``handle.remove()`` """ handle = RemovableHandle( self._forward_pre_hooks, extra_dict=self._forward_pre_hooks_with_kwargs ) self._forward_pre_hooks[handle.id] = hook if with_kwargs: self._forward_pre_hooks_with_kwargs[handle.id] = True if prepend: self._forward_pre_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined] return handle
[docs] def register_forward_hook( self, hook: Union[ Callable[[T, Tuple[Any, ...], Any], Optional[Any]], Callable[[T, Tuple[Any, ...], Dict[str, Any], Any], Optional[Any]], ], *, prepend: bool = False, with_kwargs: bool = False, always_call: bool = False, ) -> RemovableHandle: r"""Register a forward hook on the module. The hook will be called every time after :func:`forward` has computed an output. If ``with_kwargs`` is ``False`` or not specified, the input contains only the positional arguments given to the module. Keyword arguments won't be passed to the hooks and only to the ``forward``. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after :func:`forward` is called. The hook should have the following signature:: hook(module, args, output) -> None or modified output If ``with_kwargs`` is ``True``, the forward hook will be passed the ``kwargs`` given to the forward function and be expected to return the output possibly modified. The hook should have the following signature:: hook(module, args, kwargs, output) -> None or modified output Args: hook (Callable): The user defined hook to be registered. prepend (bool): If ``True``, the provided ``hook`` will be fired before all existing ``forward`` hooks on this :class:`torch.nn.modules.Module`. Otherwise, the provided ``hook`` will be fired after all existing ``forward`` hooks on this :class:`torch.nn.modules.Module`. Note that global ``forward`` hooks registered with :func:`register_module_forward_hook` will fire before all hooks registered by this method. Default: ``False`` with_kwargs (bool): If ``True``, the ``hook`` will be passed the kwargs given to the forward function. Default: ``False`` always_call (bool): If ``True`` the ``hook`` will be run regardless of whether an exception is raised while calling the Module. Default: ``False`` Returns: :class:`torch.utils.hooks.RemovableHandle`: a handle that can be used to remove the added hook by calling ``handle.remove()`` """ handle = RemovableHandle( self._forward_hooks, extra_dict=[ self._forward_hooks_with_kwargs, self._forward_hooks_always_called, ], ) self._forward_hooks[handle.id] = hook if with_kwargs: self._forward_hooks_with_kwargs[handle.id] = True if always_call: self._forward_hooks_always_called[handle.id] = True if prepend: self._forward_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined] return handle
def _slow_forward(self, *input, **kwargs): tracing_state = torch._C._get_tracing_state() if not tracing_state or isinstance(self.forward, torch._C.ScriptMethod): return self.forward(*input, **kwargs) recording_scopes = torch.jit._trace._trace_module_map is not None if recording_scopes: # type ignore was added because at this point one knows that # torch.jit._trace._trace_module_map is not Optional and has type Dict[Any, Any] name = torch.jit._trace._trace_module_map[self] if self in torch.jit._trace._trace_module_map else None # type: ignore[index, operator] # noqa: B950 if name: tracing_state.push_scope(name) else: recording_scopes = False try: result = self.forward(*input, **kwargs) finally: if recording_scopes: tracing_state.pop_scope() return result def _wrapped_call_impl(self, *args, **kwargs): if self._compiled_call_impl is not None: return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc] else: return self._call_impl(*args, **kwargs) # torchrec tests the code consistency with the following code # fmt: off def _call_impl(self, *args, **kwargs): forward_call = (self._slow_forward if torch._C._get_tracing_state() else self.forward) # If we don't have any hooks, we want to skip the rest of the logic in # this function, and just call forward. if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_pre_hooks or _global_backward_hooks or _global_forward_hooks or _global_forward_pre_hooks): return forward_call(*args, **kwargs) result = None called_always_called_hooks = set() def inner(): nonlocal result, args, kwargs full_backward_hooks, non_full_backward_hooks = [], [] backward_pre_hooks = [] if self._backward_pre_hooks or _global_backward_pre_hooks: backward_pre_hooks = self._get_backward_pre_hooks() if self._backward_hooks or _global_backward_hooks: full_backward_hooks, non_full_backward_hooks = self._get_backward_hooks() if _global_forward_pre_hooks or self._forward_pre_hooks: for hook_id, hook in ( *_global_forward_pre_hooks.items(), *self._forward_pre_hooks.items(), ): if hook_id in self._forward_pre_hooks_with_kwargs: args_kwargs_result = hook(self, args, kwargs) # type: ignore[misc] if args_kwargs_result is not None: if isinstance(args_kwargs_result, tuple) and len(args_kwargs_result) == 2: args, kwargs = args_kwargs_result else: raise RuntimeError( "forward pre-hook must return None or a tuple " f"of (new_args, new_kwargs), but got {args_kwargs_result}." ) else: args_result = hook(self, args) if args_result is not None: if not isinstance(args_result, tuple): args_result = (args_result,) args = args_result bw_hook = None if full_backward_hooks or backward_pre_hooks: bw_hook = BackwardHook(self, full_backward_hooks, backward_pre_hooks) args = bw_hook.setup_input_hook(args) result = forward_call(*args, **kwargs) if _global_forward_hooks or self._forward_hooks: for hook_id, hook in ( *_global_forward_hooks.items(), *self._forward_hooks.items(), ): # mark that always called hook is run if hook_id in self._forward_hooks_always_called or hook_id in _global_forward_hooks_always_called: called_always_called_hooks.add(hook_id) if hook_id in self._forward_hooks_with_kwargs: hook_result = hook(self, args, kwargs, result) else: hook_result = hook(self, args, result) if hook_result is not None: result = hook_result if bw_hook: if not isinstance(result, (torch.Tensor, tuple)): warnings.warn("For backward hooks to be called," " module output should be a Tensor or a tuple of Tensors" f" but received {type(result)}") result = bw_hook.setup_output_hook(result) # Handle the non-full backward hooks if non_full_backward_hooks: var = result while not isinstance(var, torch.Tensor): if isinstance(var, dict): var = next(v for v in var.values() if isinstance(v, torch.Tensor)) else: var = var[0] grad_fn = var.grad_fn if grad_fn is not None: for hook in non_full_backward_hooks: grad_fn.register_hook(_WrappedHook(hook, self)) self._maybe_warn_non_full_backward_hook(args, result, grad_fn) return result from torch.compiler import is_compiling # This is technically not behavior equivalent when compiling, but it's # incredibly unlikely we will ever support throwing an exception in NN # module, and then catching it here, and then reraising it, and then # catching it again, and expecting the resulting frame to be compiled. # The reraise here just gunks up our exception handling for no good # reason. Don't try to run the always called hooks in event of # exception. if is_compiling(): return inner() try: return inner() except Exception: # run always called hooks if they have not already been run # For now only forward hooks have the always_call option but perhaps # this functionality should be added to full backward hooks as well. for hook_id, hook in _global_forward_hooks.items(): if hook_id in _global_forward_hooks_always_called and hook_id not in called_always_called_hooks: # type: ignore[possibly-undefined] try: hook_result = hook(self, args, result) # type: ignore[possibly-undefined] if hook_result is not None: result = hook_result except Exception as e: warnings.warn("global module forward hook with ``always_call=True`` raised an exception " f"that was silenced as another error was raised in forward: {str(e)}") continue for hook_id, hook in self._forward_hooks.items(): if hook_id in self._forward_hooks_always_called and hook_id not in called_always_called_hooks: # type: ignore[possibly-undefined] try: if hook_id in self._forward_hooks_with_kwargs: hook_result = hook(self, args, kwargs, result) # type: ignore[possibly-undefined] else: hook_result = hook(self, args, result) # type: ignore[possibly-undefined] if hook_result is not None: result = hook_result except Exception as e: warnings.warn("module forward hook with ``always_call=True`` raised an exception " f"that was silenced as another error was raised in forward: {str(e)}") continue # raise exception raised in try block raise # fmt: on __call__: Callable[..., Any] = _wrapped_call_impl def __getstate__(self): state = self.__dict__.copy() state.pop("_compiled_call_impl", None) return state def __setstate__(self, state): self.__dict__.update(state) # Support loading old checkpoints that don't have the following attrs: if "_forward_pre_hooks" not in self.__dict__: self._forward_pre_hooks = OrderedDict() if "_forward_pre_hooks_with_kwargs" not in self.__dict__: self._forward_pre_hooks_with_kwargs = OrderedDict() if "_forward_hooks_with_kwargs" not in self.__dict__: self._forward_hooks_with_kwargs = OrderedDict() if "_forward_hooks_always_called" not in self.__dict__: self._forward_hooks_always_called = OrderedDict() if "_state_dict_hooks" not in self.__dict__: self._state_dict_hooks = OrderedDict() if "_state_dict_pre_hooks" not in self.__dict__: self._state_dict_pre_hooks = OrderedDict() if "_load_state_dict_pre_hooks" not in self.__dict__: self._load_state_dict_pre_hooks = OrderedDict() if "_load_state_dict_post_hooks" not in self.__dict__: self._load_state_dict_post_hooks = OrderedDict() if "_non_persistent_buffers_set" not in self.__dict__: self._non_persistent_buffers_set = set() if "_is_full_backward_hook" not in self.__dict__: self._is_full_backward_hook = None if "_backward_pre_hooks" not in self.__dict__: self._backward_pre_hooks = OrderedDict() # On the return type: # We choose to return `Any` in the `__getattr__` type signature instead of a more strict `Union[Tensor, Module]`. # This is done for better interop with various type checkers for the end users. # Having a stricter return type doesn't play nicely with `register_buffer()` and forces # people to excessively use type-ignores, asserts, casts, etc. # See full discussion on the problems with returning `Union` here # https://github.com/microsoft/pyright/issues/4213 def __getattr__(self, name: str) -> Any: if "_parameters" in self.__dict__: _parameters = self.__dict__["_parameters"] if name in _parameters: return _parameters[name] if "_buffers" in self.__dict__: _buffers = self.__dict__["_buffers"] if name in _buffers: return _buffers[name] if "_modules" in self.__dict__: modules = self.__dict__["_modules"] if name in modules: return modules[name] raise AttributeError( f"'{type(self).__name__}' object has no attribute '{name}'" ) def __setattr__(self, name: str, value: Union[Tensor, "Module"]) -> None: def remove_from(*dicts_or_sets): for d in dicts_or_sets: if name in d: if isinstance(d, dict): del d[name] else: d.discard(name) params = self.__dict__.get("_parameters") if isinstance(value, Parameter): if params is None: raise AttributeError( "cannot assign parameters before Module.__init__() call" ) remove_from( self.__dict__, self._buffers, self._modules, self._non_persistent_buffers_set, ) self.register_parameter(name, value) elif params is not None and name in params: if value is not None: raise TypeError( f"cannot assign '{torch.typename(value)}' as parameter '{name}' " "(torch.nn.Parameter or None expected)" ) self.register_parameter(name, value) else: modules = self.__dict__.get("_modules") if isinstance(value, Module): if modules is None: raise AttributeError( "cannot assign module before Module.__init__() call" ) remove_from( self.__dict__, self._parameters, self._buffers, self._non_persistent_buffers_set, ) for hook in _global_module_registration_hooks.values(): output = hook(self, name, value) if output is not None: value = output modules[name] = value elif modules is not None and name in modules: if value is not None: raise TypeError( f"cannot assign '{torch.typename(value)}' as child module '{name}' " "(torch.nn.Module or None expected)" ) for hook in _global_module_registration_hooks.values(): output = hook(self, name, value) if output is not None: value = output modules[name] = value else: buffers = self.__dict__.get("_buffers") if isinstance(value, Buffer) or buffers is not None and name in buffers: if value is not None and not isinstance(value, torch.Tensor): raise TypeError( f"cannot assign '{torch.typename(value)}' as buffer '{name}' " "(torch.nn.Buffer, torch.Tensor or None expected)" ) if isinstance(value, Buffer): persistent = value.persistent else: persistent = name not in self._non_persistent_buffers_set # === HACK === # This whole block below should just be: # self.register_buffer(name, value, persistent) # But to support subclasses of nn.Module that (wrongfully) implement a # register_buffer() method that doesn't have the "persistent" # argument. Only pass it in if it is accepted otherwise assume # it is always true if self.register_buffer is torch.nn.Module.register_buffer: self.register_buffer(name, value, persistent) else: sign = inspect.signature(self.register_buffer) if "persistent" in sign.parameters: self.register_buffer(name, value, persistent) else: if not persistent: raise RuntimeError( "Registering a non-persistent buffer " "on a Module subclass that implements " "register_buffer() without the persistent " "argument is not allowed." ) # Assume that the implementation without the argument has the # behavior from before the argument was added: persistent=True self.register_buffer(name, value) # === HACK END === else: super().__setattr__(name, value) def __delattr__(self, name): if name in self._parameters: del self._parameters[name] elif name in self._buffers: del self._buffers[name] self._non_persistent_buffers_set.discard(name) elif name in self._modules: del self._modules[name] else: super().__delattr__(name) def _register_state_dict_hook(self, hook): r"""Register a post-hook for the :meth:`~torch.nn.Module.state_dict` method. It should have the following signature:: hook(module, state_dict, prefix, local_metadata) -> None or state_dict The registered hooks can modify the ``state_dict`` inplace or return a new one. If a new ``state_dict`` is returned, it will only be respected if it is the root module that :meth:`~nn.Module.state_dict` is called from. """ if getattr(hook, "_from_public_api", False): raise RuntimeError( "Cannot register the same function as the state dict post hook that was " "previously registered via register_state_dict_post_hook" ) handle = RemovableHandle(self._state_dict_hooks) self._state_dict_hooks[handle.id] = hook return handle
[docs] def register_state_dict_post_hook(self, hook): r"""Register a post-hook for the :meth:`~torch.nn.Module.state_dict` method. It should have the following signature:: hook(module, state_dict, prefix, local_metadata) -> None The registered hooks can modify the ``state_dict`` inplace. """ # In _register_state_dict_hook there was a bug described in # https://github.com/pytorch/pytorch/issues/117437 where the return value # was only respected for the root module but not child submodules. # We fix this in this public version by only allowing inplace modifications on # the state_dict by the hook. However, since hooks registered via both these # APIs will be added to `_state_dict_hooks` and the type of `_state_dict_hooks` # cannot be changed due to many dependencies on it, we mark a hook # as being registered via the public API by setting `_from_public_api` on it. # In the implementation of `state_dict`, if the callable does not have this # flag, the old behavior of respecting the return value will be preserved # for the root module, otherwise, we ensure that the hook returns None. hook._from_public_api = True handle = RemovableHandle(self._state_dict_hooks) self._state_dict_hooks[handle.id] = hook return handle
[docs] def register_state_dict_pre_hook(self, hook): r"""Register a pre-hook for the :meth:`~torch.nn.Module.state_dict` method. It should have the following signature:: hook(module, prefix, keep_vars) -> None The registered hooks can be used to perform pre-processing before the ``state_dict`` call is made. """ handle = RemovableHandle(self._state_dict_pre_hooks) self._state_dict_pre_hooks[handle.id] = hook return handle
def _save_to_state_dict(self, destination, prefix, keep_vars): r"""Save module state to the `destination` dictionary. The `destination` dictionary will contain the state of the module, but not its descendants. This is called on every submodule in :meth:`~torch.nn.Module.state_dict`. In rare cases, subclasses can achieve class-specific behavior by overriding this method with custom logic. Args: destination (dict): a dict where state will be stored prefix (str): the prefix for parameters and buffers used in this module """ for name, param in self._parameters.items(): if param is not None: destination[prefix + name] = param if keep_vars else param.detach() for name, buf in self._buffers.items(): if buf is not None and name not in self._non_persistent_buffers_set: destination[prefix + name] = buf if keep_vars else buf.detach() extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX if ( getattr(self.__class__, "get_extra_state", Module.get_extra_state) is not Module.get_extra_state ): destination[extra_state_key] = self.get_extra_state() # The user can pass an optional arbitrary mappable object to `state_dict`, in which case `state_dict` returns # back that same object. But if they pass nothing, an `OrderedDict` is created and returned. T_destination = TypeVar("T_destination", bound=Dict[str, Any]) @overload def state_dict( self, *, destination: T_destination, prefix: str = ..., keep_vars: bool = ... ) -> T_destination: ... @overload def state_dict(self, *, prefix: str = ..., keep_vars: bool = ...) -> Dict[str, Any]: ... # TODO: Change `*args` to `*` and remove the corresponding warning in docs when BC allows. # Also remove the logic for arg parsing together.
[docs] def state_dict(self, *args, destination=None, prefix="", keep_vars=False): r"""Return a dictionary containing references to the whole state of the module. Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to ``None`` are not included. .. note:: The returned object is a shallow copy. It contains references to the module's parameters and buffers. .. warning:: Currently ``state_dict()`` also accepts positional arguments for ``destination``, ``prefix`` and ``keep_vars`` in order. However, this is being deprecated and keyword arguments will be enforced in future releases. .. warning:: Please avoid the use of argument ``destination`` as it is not designed for end-users. Args: destination (dict, optional): If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an ``OrderedDict`` will be created and returned. Default: ``None``. prefix (str, optional): a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ``''``. keep_vars (bool, optional): by default the :class:`~torch.Tensor` s returned in the state dict are detached from autograd. If it's set to ``True``, detaching will not be performed. Default: ``False``. Returns: dict: a dictionary containing a whole state of the module Example:: >>> # xdoctest: +SKIP("undefined vars") >>> module.state_dict().keys() ['bias', 'weight'] """ # TODO: Remove `args` and the parsing logic when BC allows. if len(args) > 0: # DeprecationWarning is ignored by default warnings.warn( "Positional args are being deprecated, use kwargs instead. Refer to " "https://pytorch.org/docs/main/generated/torch.nn.Module.html#torch.nn.Module.state_dict" " for details.", FutureWarning, stacklevel=2, ) if destination is None: destination = args[0] if len(args) > 1 and prefix == "": prefix = args[1] if len(args) > 2 and keep_vars is False: keep_vars = args[2] if destination is None: destination = OrderedDict() destination._metadata = OrderedDict() local_metadata = dict(version=self._version) if hasattr(destination, "_metadata"): destination._metadata[prefix[:-1]] = local_metadata for hook in self._state_dict_pre_hooks.values(): hook(self, prefix, keep_vars) self._save_to_state_dict(destination, prefix, keep_vars) for name, module in self._modules.items(): if module is not None: module.state_dict( destination=destination, prefix=prefix + name + ".", keep_vars=keep_vars, ) for hook in self._state_dict_hooks.values(): hook_result = hook(self, destination, prefix, local_metadata) if not getattr(hook, "_from_public_api", False): if hook_result is not None: destination = hook_result else: if hook_result is not None: raise RuntimeError("state_dict post-hook must return None") return destination
def _register_load_state_dict_pre_hook(self, hook, with_module=False): r"""See :meth:`~torch.nn.Module.register_load_state_dict_pre_hook` for details. A subtle difference is that if ``with_module`` is set to ``False``, then the hook will not take the ``module`` as the first argument whereas :meth:`~torch.nn.Module.register_load_state_dict_pre_hook` always takes the ``module`` as the first argument. Arguments: hook (Callable): Callable hook that will be invoked before loading the state dict. with_module (bool, optional): Whether or not to pass the module instance to the hook as the first parameter. """ handle = RemovableHandle(self._load_state_dict_pre_hooks) self._load_state_dict_pre_hooks[handle.id] = _WrappedHook( hook, self if with_module else None ) return handle
[docs] def register_load_state_dict_pre_hook(self, hook): r"""Register a pre-hook to be run before module's :meth:`~nn.Module.load_state_dict` is called. It should have the following signature:: hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -> None # noqa: B950 Arguments: hook (Callable): Callable hook that will be invoked before loading the state dict. """ return self._register_load_state_dict_pre_hook(hook, with_module=True)
[docs] def register_load_state_dict_post_hook(self, hook): r"""Register a post-hook to be run after module's :meth:`~nn.Module.load_state_dict` is called. It should have the following signature:: hook(module, incompatible_keys) -> None The ``module`` argument is the current module that this hook is registered on, and the ``incompatible_keys`` argument is a ``NamedTuple`` consisting of attributes ``missing_keys`` and ``unexpected_keys``. ``missing_keys`` is a ``list`` of ``str`` containing the missing keys and ``unexpected_keys`` is a ``list`` of ``str`` containing the unexpected keys. The given incompatible_keys can be modified inplace if needed. Note that the checks performed when calling :func:`load_state_dict` with ``strict=True`` are affected by modifications the hook makes to ``missing_keys`` or ``unexpected_keys``, as expected. Additions to either set of keys will result in an error being thrown when ``strict=True``, and clearing out both missing and unexpected keys will avoid an error. Returns: :class:`torch.utils.hooks.RemovableHandle`: a handle that can be used to remove the added hook by calling ``handle.remove()`` """ handle = RemovableHandle(self._load_state_dict_post_hooks) self._load_state_dict_post_hooks[handle.id] = hook return handle
def _load_from_state_dict( self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, ): r"""Copy parameters and buffers from :attr:`state_dict` into only this module, but not its descendants. This is called on every submodule in :meth:`~torch.nn.Module.load_state_dict`. Metadata saved for this module in input :attr:`state_dict` is provided as :attr:`local_metadata`. For state dicts without metadata, :attr:`local_metadata` is empty. Subclasses can achieve class-specific backward compatible loading using the version number at `local_metadata.get("version", None)`. Additionally, :attr:`local_metadata` can also contain the key `assign_to_params_buffers` that indicates whether keys should be assigned their corresponding tensor in the state_dict. .. note:: :attr:`state_dict` is not the same object as the input :attr:`state_dict` to :meth:`~torch.nn.Module.load_state_dict`. So it can be modified. Args: state_dict (dict): a dict containing parameters and persistent buffers. prefix (str): the prefix for parameters and buffers used in this module local_metadata (dict): a dict containing the metadata for this module. See strict (bool): whether to strictly enforce that the keys in :attr:`state_dict` with :attr:`prefix` match the names of parameters and buffers in this module missing_keys (list of str): if ``strict=True``, add missing keys to this list unexpected_keys (list of str): if ``strict=True``, add unexpected keys to this list error_msgs (list of str): error messages should be added to this list, and will be reported together in :meth:`~torch.nn.Module.load_state_dict` """ for hook in self._load_state_dict_pre_hooks.values(): hook( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, ) persistent_buffers = { k: v for k, v in self._buffers.items() if k not in self._non_persistent_buffers_set } local_name_params = itertools.chain( self._parameters.items(), persistent_buffers.items() ) local_state = {k: v for k, v in local_name_params if v is not None} assign_to_params_buffers = local_metadata.get("assign_to_params_buffers", False) use_swap_tensors = torch.__future__.get_swap_module_params_on_conversion() for name, param in local_state.items(): key = prefix + name if key in state_dict: input_param = state_dict[key] if not torch.overrides.is_tensor_like(input_param): error_msgs.append( f'While copying the parameter named "{key}", ' "expected torch.Tensor or Tensor-like object from checkpoint but " f"received {type(input_param)}" ) continue # This is used to avoid copying uninitialized parameters into # non-lazy modules, since they dont have the hook to do the checks # in such case, it will error when accessing the .shape attribute. is_param_lazy = torch.nn.parameter.is_lazy(param) # Backward compatibility: loading 1-dim tensor from 0.3.* to version 0.4+ if ( not is_param_lazy and len(param.shape) == 0 and len(input_param.shape) == 1 ): input_param = input_param[0] if not is_param_lazy and input_param.shape != param.shape: # local shape should match the one in checkpoint error_msgs.append( f"size mismatch for {key}: copying a param with shape {input_param.shape} from checkpoint, " f"the shape in current model is {param.shape}." ) continue if ( param.is_meta and not input_param.is_meta and not assign_to_params_buffers ): warnings.warn( f"for {key}: copying from a non-meta parameter in the checkpoint to a meta " "parameter in the current model, which is a no-op. (Did you mean to " "pass `assign=True` to assign items in the state dictionary to their " "corresponding key in the module instead of copying them in place?)" ) try: with torch.no_grad(): if use_swap_tensors: new_input_param = param.module_load( input_param, assign=assign_to_params_buffers ) if id(new_input_param) == id(input_param) or id( new_input_param ) == id(param): raise RuntimeError( "module_load returned one of self or other, please .detach() " "the result if returning one of the inputs in module_load" ) if isinstance(param, torch.nn.Parameter): if not isinstance(new_input_param, torch.nn.Parameter): new_input_param = torch.nn.Parameter( new_input_param, requires_grad=param.requires_grad, ) else: new_input_param.requires_grad_(param.requires_grad) torch.utils.swap_tensors(param, new_input_param) del new_input_param elif assign_to_params_buffers: # Shape checks are already done above if isinstance(param, torch.nn.Parameter): if not isinstance(input_param, torch.nn.Parameter): input_param = torch.nn.Parameter( input_param, requires_grad=param.requires_grad ) else: input_param.requires_grad_(param.requires_grad) setattr(self, name, input_param) else: param.copy_(input_param) except Exception as ex: action = "swapping" if use_swap_tensors else "copying" error_msgs.append( f'While {action} the parameter named "{key}", ' f"whose dimensions in the model are {param.size()} and " f"whose dimensions in the checkpoint are {input_param.size()}, " f"an exception occurred : {ex.args}." ) elif strict: missing_keys.append(key) extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX if ( getattr(self.__class__, "set_extra_state", Module.set_extra_state) is not Module.set_extra_state ): if extra_state_key in state_dict: self.set_extra_state(state_dict[extra_state_key]) elif strict: missing_keys.append(extra_state_key) elif strict and (extra_state_key in state_dict): unexpected_keys.append(extra_state_key) if strict: for key in state_dict.keys(): if key.startswith(prefix) and key != extra_state_key: input_name = key[len(prefix) :].split(".", 1) # Must be Module if it have attributes if len(input_name) > 1: if input_name[0] not in self._modules: unexpected_keys.append(key) elif input_name[0] not in local_state: unexpected_keys.append(key)
[docs] def load_state_dict( self, state_dict: Mapping[str, Any], strict: bool = True, assign: bool = False ): r"""Copy parameters and buffers from :attr:`state_dict` into this module and its descendants. If :attr:`strict` is ``True``, then the keys of :attr:`state_dict` must exactly match the keys returned by this module's :meth:`~torch.nn.Module.state_dict` function. .. warning:: If :attr:`assign` is ``True`` the optimizer must be created after the call to :attr:`load_state_dict` unless :func:`~torch.__future__.get_swap_module_params_on_conversion` is ``True``. Args: state_dict (dict): a dict containing parameters and persistent buffers. strict (bool, optional): whether to strictly enforce that the keys in :attr:`state_dict` match the keys returned by this module's :meth:`~torch.nn.Module.state_dict` function. Default: ``True`` assign (bool, optional): When ``False``, the properties of the tensors in the current module are preserved while when ``True``, the properties of the Tensors in the state dict are preserved. The only exception is the ``requires_grad`` field of :class:`~torch.nn.Parameter`s for which the value from the module is preserved. Default: ``False`` Returns: ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields: * **missing_keys** is a list of str containing any keys that are expected by this module but missing from the provided ``state_dict``. * **unexpected_keys** is a list of str containing the keys that are not expected by this module but present in the provided ``state_dict``. Note: If a parameter or buffer is registered as ``None`` and its corresponding key exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a ``RuntimeError``. """ if not isinstance(state_dict, Mapping): raise TypeError( f"Expected state_dict to be dict-like, got {type(state_dict)}." ) missing_keys: List[str] = [] unexpected_keys: List[str] = [] error_msgs: List[str] = [] # copy state_dict so _load_from_state_dict can modify it metadata = getattr(state_dict, "_metadata", None) state_dict = OrderedDict(state_dict) if metadata is not None: # mypy isn't aware that "_metadata" exists in state_dict state_dict._metadata = metadata # type: ignore[attr-defined] def load(module, local_state_dict, prefix=""): local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {}) if assign: local_metadata["assign_to_params_buffers"] = assign module._load_from_state_dict( local_state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs, ) for name, child in module._modules.items(): if child is not None: child_prefix = prefix + name + "." child_state_dict = { k: v for k, v in local_state_dict.items() if k.startswith(child_prefix) } load(child, child_state_dict, child_prefix) # noqa: F821 # Note that the hook can modify missing_keys and unexpected_keys. incompatible_keys = _IncompatibleKeys(missing_keys, unexpected_keys) for hook in module._load_state_dict_post_hooks.values(): out = hook(module, incompatible_keys) assert out is None, ( "Hooks registered with ``register_load_state_dict_post_hook`` are not" "expected to return new values, if incompatible_keys need to be modified," "it should be done inplace." ) load(self, state_dict) del load if strict: if len(unexpected_keys) > 0: error_msgs.insert( 0, "Unexpected key(s) in state_dict: {}. ".format( ", ".join(f'"{k}"' for k in unexpected_keys) ), ) if len(missing_keys) > 0: error_msgs.insert( 0, "Missing key(s) in state_dict: {}. ".format( ", ".join(f'"{k}"' for k in missing_keys) ), ) if len(error_msgs) > 0: raise RuntimeError( "Error(s) in loading state_dict for {}:\n\t{}".format( self.__class__.__name__, "\n\t".join(error_msgs) ) ) return _IncompatibleKeys(missing_keys, unexpected_keys)
def _named_members( self, get_members_fn, prefix="", recurse=True, remove_duplicate: bool = True ): r"""Help yield various names + members of modules.""" memo = set() modules = ( self.named_modules(prefix=prefix, remove_duplicate=remove_duplicate) if recurse else [(prefix, self)] ) for module_prefix, module in modules: members = get_members_fn(module) for k, v in members: if v is None or v in memo: continue if remove_duplicate: memo.add(v) name = module_prefix + ("." if module_prefix else "") + k yield name, v
[docs] def parameters(self, recurse: bool = True) -> Iterator[Parameter]: r"""Return an iterator over module parameters. This is typically passed to an optimizer. Args: recurse (bool): if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. Yields: Parameter: module parameter Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for param in model.parameters(): >>> print(type(param), param.size()) <class 'torch.Tensor'> (20L,) <class 'torch.Tensor'> (20L, 1L, 5L, 5L) """ for name, param in self.named_parameters(recurse=recurse): yield param
[docs] def named_parameters( self, prefix: str = "", recurse: bool = True, remove_duplicate: bool = True ) -> Iterator[Tuple[str, Parameter]]: r"""Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself. Args: prefix (str): prefix to prepend to all parameter names. recurse (bool): if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. remove_duplicate (bool, optional): whether to remove the duplicated parameters in the result. Defaults to True. Yields: (str, Parameter): Tuple containing the name and parameter Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for name, param in self.named_parameters(): >>> if name in ['bias']: >>> print(param.size()) """ gen = self._named_members( lambda module: module._parameters.items(), prefix=prefix, recurse=recurse, remove_duplicate=remove_duplicate, ) yield from gen
[docs] def buffers(self, recurse: bool = True) -> Iterator[Tensor]: r"""Return an iterator over module buffers. Args: recurse (bool): if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Yields: torch.Tensor: module buffer Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for buf in model.buffers(): >>> print(type(buf), buf.size()) <class 'torch.Tensor'> (20L,) <class 'torch.Tensor'> (20L, 1L, 5L, 5L) """ for _, buf in self.named_buffers(recurse=recurse): yield buf
[docs] def named_buffers( self, prefix: str = "", recurse: bool = True, remove_duplicate: bool = True ) -> Iterator[Tuple[str, Tensor]]: r"""Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself. Args: prefix (str): prefix to prepend to all buffer names. recurse (bool, optional): if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True. remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True. Yields: (str, torch.Tensor): Tuple containing the name and buffer Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for name, buf in self.named_buffers(): >>> if name in ['running_var']: >>> print(buf.size()) """ gen = self._named_members( lambda module: module._buffers.items(), prefix=prefix, recurse=recurse, remove_duplicate=remove_duplicate, ) yield from gen
[docs] def children(self) -> Iterator["Module"]: r"""Return an iterator over immediate children modules. Yields: Module: a child module """ for name, module in self.named_children(): yield module
[docs] def named_children(self) -> Iterator[Tuple[str, "Module"]]: r"""Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself. Yields: (str, Module): Tuple containing a name and child module Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for name, module in model.named_children(): >>> if name in ['conv4', 'conv5']: >>> print(module) """ memo = set() for name, module in self._modules.items(): if module is not None and module not in memo: memo.add(module) yield name, module
[docs] def modules(self) -> Iterator["Module"]: r"""Return an iterator over all modules in the network. Yields: Module: a module in the network Note: Duplicate modules are returned only once. In the following example, ``l`` will be returned only once. Example:: >>> l = nn.Linear(2, 2) >>> net = nn.Sequential(l, l) >>> for idx, m in enumerate(net.modules()): ... print(idx, '->', m) 0 -> Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) ) 1 -> Linear(in_features=2, out_features=2, bias=True) """ for _, module in self.named_modules(): yield module
[docs] def named_modules( self, memo: Optional[Set["Module"]] = None, prefix: str = "", remove_duplicate: bool = True, ): r"""Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself. Args: memo: a memo to store the set of modules already added to the result prefix: a prefix that will be added to the name of the module remove_duplicate: whether to remove the duplicated module instances in the result or not Yields: (str, Module): Tuple of name and module Note: Duplicate modules are returned only once. In the following example, ``l`` will be returned only once. Example:: >>> l = nn.Linear(2, 2) >>> net = nn.Sequential(l, l) >>> for idx, m in enumerate(net.named_modules()): ... print(idx, '->', m) 0 -> ('', Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) )) 1 -> ('0', Linear(in_features=2, out_features=2, bias=True)) """ if memo is None: memo = set() if self not in memo: if remove_duplicate: memo.add(self) yield prefix, self for name, module in self._modules.items(): if module is None: continue submodule_prefix = prefix + ("." if prefix else "") + name yield from module.named_modules( memo, submodule_prefix, remove_duplicate )
[docs] def train(self: T, mode: bool = True) -> T: r"""Set the module in training mode. This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`, etc. Args: mode (bool): whether to set training mode (``True``) or evaluation mode (``False``). Default: ``True``. Returns: Module: self """ if not isinstance(mode, bool): raise ValueError("training mode is expected to be boolean") self.training = mode for module in self.children(): module.train(mode) return self
[docs] def eval(self: T) -> T: r"""Set the module in evaluation mode. This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`, etc. This is equivalent with :meth:`self.train(False) <torch.nn.Module.train>`. See :ref:`locally-disable-grad-doc` for a comparison between `.eval()` and several similar mechanisms that may be confused with it. Returns: Module: self """ return self.train(False)
[docs] def requires_grad_(self: T, requires_grad: bool = True) -> T: r"""Change if autograd should record operations on parameters in this module. This method sets the parameters' :attr:`requires_grad` attributes in-place. This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training). See :ref:`locally-disable-grad-doc` for a comparison between `.requires_grad_()` and several similar mechanisms that may be confused with it. Args: requires_grad (bool): whether autograd should record operations on parameters in this module. Default: ``True``. Returns: Module: self """ for p in self.parameters(): p.requires_grad_(requires_grad) return self
[docs] def zero_grad(self, set_to_none: bool = True) -> None: r"""Reset gradients of all model parameters. See similar function under :class:`torch.optim.Optimizer` for more context. Args: set_to_none (bool): instead of setting to zero, set the grads to None. See :meth:`torch.optim.Optimizer.zero_grad` for details. """ if getattr(self, "_is_replica", False): warnings.warn( "Calling .zero_grad() from a module created with nn.DataParallel() has no effect. " "The parameters are copied (in a differentiable manner) from the original module. " "This means they are not leaf nodes in autograd and so don't accumulate gradients. " "If you need gradients in your forward method, consider using autograd.grad instead." ) for p in self.parameters(): if p.grad is not None: if set_to_none: p.grad = None else: if p.grad.grad_fn is not None: p.grad.detach_() else: p.grad.requires_grad_(False) p.grad.zero_()
[docs] def share_memory(self: T) -> T: r"""See :meth:`torch.Tensor.share_memory_`.""" return self._apply(lambda t: t.share_memory_())
def _get_name(self): return self.__class__.__name__
[docs] def extra_repr(self) -> str: r"""Set the extra representation of the module. To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable. """ return ""
def __repr__(self): # We treat the extra repr like the sub-module, one item per line extra_lines = [] extra_repr = self.extra_repr() # empty string will be split into list [''] if extra_repr: extra_lines = extra_repr.split("\n") child_lines = [] for key, module in self._modules.items(): mod_str = repr(module) mod_str = _addindent(mod_str, 2) child_lines.append("(" + key + "): " + mod_str) lines = extra_lines + child_lines main_str = self._get_name() + "(" if lines: # simple one-liner info, which most builtin Modules will use if len(extra_lines) == 1 and not child_lines: main_str += extra_lines[0] else: main_str += "\n " + "\n ".join(lines) + "\n" main_str += ")" return main_str def __dir__(self): module_attrs = dir(self.__class__) attrs = list(self.__dict__.keys()) parameters = list(self._parameters.keys()) modules = list(self._modules.keys()) buffers = list(self._buffers.keys()) keys = module_attrs + attrs + parameters + modules + buffers # Eliminate attrs that are not legal Python variable names keys = [key for key in keys if not key[0].isdigit()] return sorted(keys) def _replicate_for_data_parallel(self): replica = self.__new__(type(self)) replica.__dict__ = self.__dict__.copy() # replicas do not have parameters themselves, the replicas reference the original # module. replica._parameters = {} replica._buffers = replica._buffers.copy() replica._modules = replica._modules.copy() replica._is_replica = True # type: ignore[assignment] return replica
[docs] def compile(self, *args, **kwargs): """ Compile this Module's forward using :func:`torch.compile`. This Module's `__call__` method is compiled and all arguments are passed as-is to :func:`torch.compile`. See :func:`torch.compile` for details on the arguments for this function. """ self._compiled_call_impl = torch.compile(self._call_impl, *args, **kwargs)

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