Pytorch's Module class
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class Module:
def __init__(self, *args, **kwargs) -> None:
pass
forward: Callable[..., Any] = _forward_unimplemented
def register_buffer(self, name: str, tensor: Optional[Tensor], persistent: bool = True) -> None:
r"""Add a buffer to the module."""
pass
def register_parameter(self, name: str, param: Optional[Parameter]) -> None:
r"""Add a parameter to the module."""
pass
def add_module(self, name: str, module: Optional['Module']) -> None:
r"""Add a child module to the current module."""
pass
def register_module(self, name: str, module: Optional['Module']) -> None:
r"""Alias for :func:`add_module`."""
pass
def get_submodule(self, target: str) -> "Module":
"""Return the submodule given by ``target`` if it exists, otherwise throw an error."""
pass
def get_parameter(self, target: str) -> "Parameter":
"""Return the parameter given by ``target`` if it exists, otherwise throw an error."""
pass
def get_buffer(self, target: str) -> "Tensor":
"""Return the buffer given by ``target`` if it exists, otherwise throw an error."""
pass
def get_extra_state(self) -> Any:
"""Return any extra state to include in the module's state_dict."""
pass
def set_extra_state(self, state: Any) -> None:
"""Set extra state contained in the loaded `state_dict`."""
pass
def _apply(self, fn, recurse=True):
pass
def apply(self: T, fn: Callable[['Module'], None]) -> T:
r"""Apply ``fn`` recursively to every submodule (as returned by ``.children()``) as well as self."""
pass
def cuda(self: T, device: Optional[Union[int, device]] = None) -> T:
r"""Move all model parameters and buffers to the GPU."""
pass
def ipu(self: T, device: Optional[Union[int, device]] = None) -> T:
r"""Move all model parameters and buffers to the IPU."""
pass
def xpu(self: T, device: Optional[Union[int, device]] = None) -> T:
r"""Move all model parameters and buffers to the XPU."""
pass
def cpu(self: T) -> T:
r"""Move all model parameters and buffers to the CPU."""
pass
def type(self: T, dst_type: Union[dtype, str]) -> T:
r"""Casts all parameters and buffers to :attr:`dst_type`."""
pass
def float(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``float`` datatype."""
pass
def double(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``double`` datatype."""
return self._apply(lambda t: t.double() if t.is_floating_point() else t)
def half(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``half`` datatype."""
pass
def bfloat16(self: T) -> T:
r"""Casts all floating point parameters and buffers to ``bfloat16`` datatype."""
pass
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."""
pass
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:
...
def to(self, *args, **kwargs):
pass
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."""
pass
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."""
pass
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."""
pass
def _get_backward_hooks(self):
r"""Return the backward hooks for use in the call function."""
pass
def _get_backward_pre_hooks(self):
pass
def _maybe_warn_non_full_backward_hook(self, inputs, result, grad_fn):
pass
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."""
pass
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."""
pass
def _slow_forward(self, *input, **kwargs):
pass
def _wrapped_call_impl(self, *args, **kwargs):
pass
def _call_impl(self, *args, **kwargs):
pass
__call__ : Callable[..., Any] = _wrapped_call_impl
def __getstate__(self):
pass
def __setstate__(self, state):
pass
def __getattr__(self, name: str) -> Any:
pass
def __setattr__(self, name: str, value: Union[Tensor, 'Module']) -> None:
pass
def __delattr__(self, name):
pass
def _register_state_dict_hook(self, hook):
r"""Register a state-dict hook."""
pass
def register_state_dict_pre_hook(self, hook):
r"""Register a pre-hook for the :meth:`~torch.nn.Module.state_dict` method."""
pass
def _save_to_state_dict(self, destination, prefix, keep_vars):
r"""Save module state to the `destination` dictionary."""
pass
# 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]:
...
def state_dict(self, *args, destination=None, prefix='', keep_vars=False):
r"""Return a dictionary containing references to the whole state of the module."""
pass
def _register_load_state_dict_pre_hook(self, hook, with_module=False):
r"""Register a pre-hook for the :meth:`~torch.nn.Module.load_state_dict` method."""
pass
def register_load_state_dict_post_hook(self, hook):
r"""Register a post hook to be run after module's ``load_state_dict`` is called."""
pass
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."""
pass
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."""
pass
def _named_members(self, get_members_fn, prefix='', recurse=True, remove_duplicate: bool = True):
r"""Help yield various names + members of modules."""
pass
def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
r"""Return an iterator over module parameters."""
pass
def buffers(self, recurse: bool = True) -> Iterator[Tensor]:
r"""Return an iterator over module buffers."""
pass
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."""
pass
def children(self) -> Iterator['Module']:
r"""Return an iterator over immediate children modules."""
pass
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."""
pass
def modules(self) -> Iterator['Module']:
r"""Return an iterator over all modules in the network."""
pass
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."""
pass
def train(self: T, mode: bool = True) -> T:
r"""Set the module in training mode."""
pass
def eval(self: T) -> T:
r"""Set the module in evaluation mode."""
pass
def requires_grad_(self: T, requires_grad: bool = True) -> T:
r"""Change if autograd should record operations on parameters in this module."""
pass
def zero_grad(self, set_to_none: bool = True) -> None:
r"""Reset gradients of all model parameters."""
pass
def share_memory(self: T) -> T:
r"""See :meth:`torch.Tensor.share_memory_`."""
pass
def _get_name(self):
return self.__class__.__name__
def extra_repr(self) -> str:
r"""Set the extra representation of the module."""
return ''
def __repr__(self):
pass
def __dir__(self):
pass
def _replicate_for_data_parallel(self):
pass
def compile(self, *args, **kwargs):
pass
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):
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', OrderedDict())
super().__setattr__('_buffers', OrderedDict())
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', OrderedDict())
if self.call_super_init:
super().__init__(*args, **kwargs)
forward: Callable[..., Any] = _forward_unimplemented
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)
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
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
def register_module(self, name: str, module: Optional['Module']) -> None:
r"""Alias for :func:`add_module`."""
self.add_module(name, module)
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
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
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
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.")
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()
def compute_should_use_swap_tensors(tensor, tensor_applied):
return (should_use_swap_tensors
# subclasses may have multiple child tensors so we need to use swap_tensors
or is_traceable_wrapper_subclass(tensor_applied)
or tensor.device.type == 'xla'
or tensor_applied.device.type == 'xla')
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)
p_should_use_swap_tensors = compute_should_use_swap_tensors(param, 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
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
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))
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))
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))
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())
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))
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)
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)
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)
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)
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:
...
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)
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 = hooks.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
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 = hooks.RemovableHandle(self._backward_hooks)
self._backward_hooks[handle.id] = hook
return handle
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 = hooks.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,
)
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 = hooks.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
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 = hooks.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)
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)
try:
result = None
called_always_called_hooks = set()
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 = hooks.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
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
__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 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.Tensor or None expected)"
)
for hook in _global_buffer_registration_hooks.values():
output = hook(self, name, value)
if output is not None:
value = output
buffers[name] = value
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 state-dict hook.
These hooks will be called with arguments: `self`, `state_dict`,
`prefix`, `local_metadata`, after the `state_dict` of `self` is set.
Note that only parameters and buffers of `self` or its children are
guaranteed to exist in `state_dict`. The hooks may modify `state_dict`
inplace or return a new one.
"""
handle = hooks.RemovableHandle(self._state_dict_hooks)
self._state_dict_hooks[handle.id] = hook
return handle
def register_state_dict_pre_hook(self, hook):
r"""Register a pre-hook for the :meth:`~torch.nn.Module.state_dict` method.
These hooks will be called with arguments: ``self``, ``prefix``,
and ``keep_vars`` before calling ``state_dict`` on ``self``. The registered
hooks can be used to perform pre-processing before the ``state_dict``
call is made.
"""
handle = hooks.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.
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 hook_result is not None:
destination = hook_result
return destination
def _register_load_state_dict_pre_hook(self, hook, with_module=False):
r"""Register a pre-hook for the :meth:`~torch.nn.Module.load_state_dict` method.
These hooks will be called with arguments: `state_dict`, `prefix`,
`local_metadata`, `strict`, `missing_keys`, `unexpected_keys`,
`error_msgs`, before loading `state_dict` into `self`. These arguments
are exactly the same as those of `_load_from_state_dict`.
If ``with_module`` is ``True``, then the first argument to the hook is
an instance of the module.
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 = hooks.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
def register_load_state_dict_post_hook(self, hook):
r"""Register a post hook to be run after module's ``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 = hooks.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)
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
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())
lt;class 'torch.tensor'=""> (20L,)
lt;class 'torch.tensor'=""> (20L, 1L, 5L, 5L)
"""
for name, param in self.named_parameters(recurse=recurse):
yield param
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
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())
lt;class 'torch.tensor'=""> (20L,)
lt;class 'torch.tensor'=""> (20L, 1L, 5L, 5L)
"""
for _, buf in self.named_buffers(recurse=recurse):
yield buf
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
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
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
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
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)
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
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)
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
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_()
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__
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 = OrderedDict()
replica._buffers = replica._buffers.copy()
replica._modules = replica._modules.copy()
replica._is_replica = True # type: ignore[assignment]
return replica
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)