torch.nn.modules.module.py
torch.nn.modules.module.py
from collections import OrderedDict, namedtuple import itertools import warnings import functools import torch from ..parameter import Parameter import torch.utils.hooks as hooks from torch import Tensor, device, dtype from typing import Union, Tuple, Any, Callable, Iterator, Set, Optional, overload, TypeVar, Mapping, Dict, List from ...utils.hooks import RemovableHandle _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(_IncompatibleKeys, self).__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 before/after calling forward and backward. This is global state used for debugging/profiling purposes""" _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() def register_module_forward_pre_hook(hook: Callable[..., None]) -> RemovableHandle: r"""Registers 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 = hooks.RemovableHandle(_global_forward_pre_hooks) _global_forward_pre_hooks[handle.id] = hook return handle def register_module_forward_hook(hook: Callable[..., None]) -> RemovableHandle: r"""Registers 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. 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 = hooks.RemovableHandle(_global_forward_hooks) _global_forward_hooks[handle.id] = hook return handle def register_module_backward_hook( hook: Callable[['Module', _grad_t, _grad_t], Union[None, Tensor]] ) -> RemovableHandle: r"""Registers a backward hook common to all the modules. This function is deprecated in favor of :meth:`nn.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 = hooks.RemovableHandle(_global_backward_hooks) _global_backward_hooks[handle.id] = hook return handle def register_module_full_backward_hook( hook: Callable[['Module', _grad_t, _grad_t], Union[None, Tensor]] ) -> RemovableHandle: r"""Registers 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. The current implementation will not have the presented behavior for complex :class:`Module` that perform many operations. In some failure cases, :attr:`grad_input` and :attr:`grad_output` will only contain the gradients for a subset of the inputs and outputs. For such :class:`Module`, you should use :func:`torch.Tensor.register_hook` directly on a specific input or output to get the required gradients. The hook will be called every time the gradients with respect to module inputs are computed. The hook should have the following signature:: hook(module, grad_input, grad_output) -> Tensor or None The :attr:`grad_input` and :attr:`grad_output` are tuples. 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 will not appear in the hook. Entries in :attr:`grad_input` and :attr:`grad_output` will be ``None`` for all non-Tensor arguments. Global hooks are called before hooks registered with `register_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 = hooks.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"""Defines 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 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(Model, self).__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. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool """ dump_patches: bool = False 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.""" _version: int = 1 training: bool _is_full_backward_hook: Optional[bool] def __init__(self): """ Initializes internal Module state, shared by both nn.Module and ScriptModule. """ torch._C._log_api_usage_once("python.nn_module") self.training = True self._parameters = OrderedDict() self._buffers = OrderedDict() self._non_persistent_buffers_set = set() self._backward_hooks = OrderedDict() self._is_full_backward_hook = None self._forward_hooks = OrderedDict() self._forward_pre_hooks = OrderedDict() self._state_dict_hooks = OrderedDict() self._load_state_dict_pre_hooks = OrderedDict() self._modules = OrderedDict() forward: Callable[..., Any] = _forward_unimplemented def register_buffer(self, name: str, tensor: Optional[Tensor], persistent: bool = True) -> None: r"""Adds 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 (string): name of the buffer. The buffer can be accessed from this module using the given name tensor (Tensor): buffer to be registered. persistent (bool): whether the buffer is part of this module's :attr:`state_dict`. Example:: >>> 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, torch._six.string_classes): raise TypeError("buffer name should be a string. " "Got {}".format(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("attribute '{}' already exists".format(name)) elif tensor is not None and not isinstance(tensor, torch.Tensor): raise TypeError("cannot assign '{}' object to buffer '{}' " "(torch Tensor or None required)" .format(torch.typename(tensor), name)) else: 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"""Adds a parameter to the module. The parameter can be accessed as an attribute using given name. Args: name (string): name of the parameter. The parameter can be accessed from this module using the given name param (Parameter): parameter to be added to the module. """ if '_parameters' not in self.__dict__: raise AttributeError( "cannot assign parameter before Module.__init__() call") elif not isinstance(name, torch._six.string_classes): raise TypeError("parameter name should be a string. " "Got {}".format(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("attribute '{}' already exists".format(name)) if param is None: self._parameters[name] = None elif not isinstance(param, Parameter): raise TypeError("cannot assign '{}' object to parameter '{}' " "(torch.nn.Parameter or None required)" .format(torch.typename(param), name)) elif param.grad_fn: raise ValueError( "Cannot assign non-leaf Tensor to parameter '{0}'. Model " "parameters must be created explicitly. To express '{0}' " "as a function of another Tensor, compute the value in " "the forward() method.".format(name)) else: self._parameters[name] = param def add_module(self, name: str, module: Optional['Module']) -> None: r"""Adds a child module to the current module. The module can be accessed as an attribute using the given name. Args: name (string): 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("{} is not a Module subclass".format( torch.typename(module))) elif not isinstance(name, torch._six.string_classes): raise TypeError("module name should be a string. Got {}".format( torch.typename(name))) elif hasattr(self, name) and name not in self._modules: raise KeyError("attribute '{}' already exists".format(name)) elif '.' in name: raise KeyError("module name can't contain \".\", got: {}".format(name)) elif name == '': raise KeyError("module name can't be empty string \"\"") self._modules[name] = module def _apply(self, fn): 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 for key, param in self._parameters.items(): if param is not None: # 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) should_use_set_data = compute_should_use_set_data(param, param_applied) if should_use_set_data: param.data = param_applied else: assert isinstance(param, Parameter) assert param.is_leaf self._parameters[key] = Parameter(param_applied, param.requires_grad) if param.grad is not None: with torch.no_grad(): grad_applied = fn(param.grad) should_use_set_data = compute_should_use_set_data(param.grad, grad_applied) if should_use_set_data: param.grad.data = grad_applied else: assert param.grad.is_leaf self._parameters[key].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"""Applies ``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.]]) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[ 1., 1.], [ 1., 1.]]) Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=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"""Moves 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. 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 xpu(self: T, device: Optional[Union[int, device]] = None) -> T: r"""Moves 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. 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"""Moves all model parameters and buffers to the CPU. 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`. 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. 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. 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. 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. Returns: Module: self """ return self._apply(lambda t: t.bfloat16() if t.is_floating_point() else t) @overload def to(self: T, device: Optional[Union[int, device]] = ..., dtype: Optional[Union[dtype, str]] = ..., non_blocking: bool = ...) -> T: ... @overload def to(self: T, dtype: Union[dtype, str], non_blocking: bool = ...) -> T: ... @overload def to(self: T, tensor: Tensor, non_blocking: bool = ...) -> T: ... def to(self, *args, **kwargs): r"""Moves and/or casts the parameters and buffers. This can be called as .. function:: to(device=None, dtype=None, non_blocking=False) .. function:: to(dtype, non_blocking=False) .. function:: to(tensor, non_blocking=False) .. function:: to(memory_format=torch.channels_last) 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:: >>> 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) >>> 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 ' 'dtypes, but got desired dtype={}'.format(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.md " "if a complex module does not work as expected.") def convert(t): if convert_to_format is not None and t.dim() == 4: 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) return self._apply(convert) def register_backward_hook( self, hook: Callable[['Module', _grad_t, _grad_t], Union[None, Tensor]] ) -> RemovableHandle: r"""Registers a backward hook on the module. This function is deprecated in favor of :meth:`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, Tensor]] ) -> RemovableHandle: r"""Registers a backward hook on the module. The hook will be called every time the gradients with respect to module inputs 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. .. warning :: Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error. 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 return handle def _get_backward_hooks(self): r"""Returns 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 _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.") 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.") return else: inputs = (inputs,) # At this point we are sure that inputs and result are tuple of Tensors out_grad_fn = set([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.") 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.") else: # At this point the grad_ouput part of the hook will most likely be correct inputs_grad_fn = set([i.grad_fn for i in inputs if i.grad_fn is not None]) next_functions = set([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.") def register_forward_pre_hook(self, hook: Callable[..., None]) -> RemovableHandle: r"""Registers a forward pre-hook on the module. 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). 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) self._forward_pre_hooks[handle.id] = hook return handle def register_forward_hook(self, hook: Callable[..., None]) -> RemovableHandle: r"""Registers a forward hook on the module. 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. 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) self._forward_hooks[handle.id] = hook 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 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 _call_impl(self, *input, **kwargs): # Do not call functions when jit is used full_backward_hooks, non_full_backward_hooks = [], [] if len(self._backward_hooks) > 0 or len(_global_backward_hooks) > 0: full_backward_hooks, non_full_backward_hooks = self._get_backward_hooks() for hook in itertools.chain( _global_forward_pre_hooks.values(), self._forward_pre_hooks.values()): result = hook(self, input) if result is not None: if not isinstance(result, tuple): result = (result,) input = result bw_hook = None if len(full_backward_hooks) > 0: bw_hook = hooks.BackwardHook(self, full_backward_hooks) input = bw_hook.setup_input_hook(input) if torch._C._get_tracing_state(): result = self._slow_forward(*input, **kwargs) else: result = self.forward(*input, **kwargs) for hook in itertools.chain( _global_forward_hooks.values(), self._forward_hooks.values()): hook_result = hook(self, input, result) if hook_result is not None: result = hook_result if bw_hook: result = bw_hook.setup_output_hook(result) # Handle the non-full backward hooks if len(non_full_backward_hooks) > 0: 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: wrapper = functools.partial(hook, self) functools.update_wrapper(wrapper, hook) grad_fn.register_hook(wrapper) self._maybe_warn_non_full_backward_hook(input, result, grad_fn) return result __call__ : Callable[..., Any] = _call_impl 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 '_state_dict_hooks' not in self.__dict__: self._state_dict_hooks = OrderedDict() if '_load_state_dict_pre_hooks' not in self.__dict__: self._load_state_dict_pre_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 def __getattr__(self, name: str) -> Union[Tensor, 'Module']: 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("'{}' object has no attribute '{}'".format( type(self).__name__, 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("cannot assign '{}' as parameter '{}' " "(torch.nn.Parameter or None expected)" .format(torch.typename(value), name)) 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) modules[name] = value elif modules is not None and name in modules: if value is not None: raise TypeError("cannot assign '{}' as child module '{}' " "(torch.nn.Module or None expected)" .format(torch.typename(value), name)) 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("cannot assign '{}' as buffer '{}' " "(torch.Tensor or None expected)" .format(torch.typename(value), name)) buffers[name] = value else: object.__setattr__(self, 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: object.__delattr__(self, name) def _register_state_dict_hook(self, hook): r"""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 _save_to_state_dict(self, destination, prefix, keep_vars): r"""Saves module state to `destination` dictionary, containing a 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() # 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 `OrederedDict` is created and returned. T_destination = TypeVar('T_destination', bound=Mapping[str, Tensor]) @overload def state_dict(self, destination: T_destination, prefix: str = ..., keep_vars: bool = ...) -> T_destination: ... # TODO: annotate with OrderedDict not Dict, but there is a problem: # https://docs.python.org/3/library/typing.html#typing.OrderedDict @overload def state_dict(self, prefix: str = ..., keep_vars: bool = ...) -> Dict[str, Tensor]: ... def state_dict(self, destination=None, prefix='', keep_vars=False): r"""Returns a dictionary containing a whole state of the module. Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Returns: dict: a dictionary containing a whole state of the module Example:: >>> module.state_dict().keys() ['bias', 'weight'] """ if destination is None: destination = OrderedDict() destination._metadata = OrderedDict() destination._metadata[prefix[:-1]] = local_metadata = dict(version=self._version) 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, 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): r"""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`. """ handle = hooks.RemovableHandle(self._load_state_dict_pre_hooks) self._load_state_dict_pre_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"""Copies 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)`. .. 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} for name, param in local_state.items(): key = prefix + name if key in state_dict: input_param = state_dict[key] # 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 = isinstance(param, torch.nn.parameter.UninitializedParameter) # 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('size mismatch for {}: copying a param with shape {} from checkpoint, ' 'the shape in current model is {}.' .format(key, input_param.shape, param.shape)) continue try: with torch.no_grad(): param.copy_(input_param) except Exception as ex: error_msgs.append('While copying the parameter named "{}", ' 'whose dimensions in the model are {} and ' 'whose dimensions in the checkpoint are {}, ' 'an exception occurred : {}.' .format(key, param.size(), input_param.size(), ex.args)) elif strict: missing_keys.append(key) if strict: for key in state_dict.keys(): if key.startswith(prefix): input_name = key[len(prefix):] input_name = input_name.split('.', 1)[0] # get the name of param/buffer/child if input_name not in self._modules and input_name not in local_state: unexpected_keys.append(key) def load_state_dict(self, state_dict: 'OrderedDict[str, Tensor]', strict: bool = True): r"""Copies 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. 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`` Returns: ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields: * **missing_keys** is a list of str containing the missing keys * **unexpected_keys** is a list of str containing the unexpected keys """ 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 = state_dict.copy() 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, prefix=''): local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {}) module._load_from_state_dict( state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs) for name, child in module._modules.items(): if child is not None: load(child, prefix + name + '.') load(self) del load if strict: if len(unexpected_keys) > 0: error_msgs.insert( 0, 'Unexpected key(s) in state_dict: {}. '.format( ', '.join('"{}"'.format(k) for k in unexpected_keys))) if len(missing_keys) > 0: error_msgs.insert( 0, 'Missing key(s) in state_dict: {}. '.format( ', '.join('"{}"'.format(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): r"""Helper method for yielding various names + members of modules.""" memo = set() modules = self.named_modules(prefix=prefix) 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 memo.add(v) name = module_prefix + ('.' if module_prefix else '') + k yield name, v def parameters(self, recurse: bool = True) -> Iterator[Parameter]: r"""Returns 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:: >>> 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 def named_parameters(self, prefix: str = '', recurse: bool = True) -> Iterator[Tuple[str, Parameter]]: r"""Returns 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. Yields: (string, Parameter): Tuple containing the name and parameter Example:: >>> 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) for elem in gen: yield elem def buffers(self, recurse: bool = True) -> Iterator[Tensor]: r"""Returns 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:: >>> for buf in model.buffers(): >>> print(type(buf), buf.size()) <class 'torch.Tensor'> (20L,) <class 'torch.Tensor'> (20L, 1L, 5L, 5L) """ for name, buf in self.named_buffers(recurse=recurse): yield buf def named_buffers(self, prefix: str = '', recurse: bool = True) -> Iterator[Tuple[str, Tensor]]: r"""Returns 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): if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Yields: (string, torch.Tensor): Tuple containing the name and buffer Example:: >>> 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) for elem in gen: yield elem def children(self) -> Iterator['Module']: r"""Returns 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"""Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself. Yields: (string, Module): Tuple containing a name and child module Example:: >>> 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"""Returns 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 name, module in self.named_modules(): yield module def named_modules(self, memo: Optional[Set['Module']] = None, prefix: str = ''): r"""Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself. Yields: (string, 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: 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 for m in module.named_modules(memo, submodule_prefix): yield m def train(self: T, mode: bool = True) -> T: r"""Sets 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 """ self.training = mode for module in self.children(): module.train(mode) return self def eval(self: T) -> T: r"""Sets 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>`. 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). 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 = False) -> None: r"""Sets gradients of all model parameters to zero. 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: 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 return replica
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