tensorplay.nn.modules.activation

Classes

class GELU [source]

GELU(approximate: str = 'none') -> None

Bases: Module

应用高斯误差线性单元 (GELU) 函数。

.. math:: \text{GELU}(x) = x * \Phi(x)

其中 $\mathrm{\Phi }(x)$ 是高斯分布的累积分布函数。

当 approximate 参数为 'tanh' 时，GELU 估计如下：

.. math:: \text{GELU}(x) = 0.5 * x * (1 + \text{Tanh}(\sqrt{2 / \pi} * (x + 0.044715 * x^3)))

Args

• approximate (str, optional): 使用的 GELU 近似算法： 'none' | 'tanh'. 默认值： 'none'

Shape

• 输入： $(\ast )$, 其中 $\ast$ 表示任意维数。
• 输出： $(\ast )$, 与输入形状相同。

Examples

m = nn.GELU()
input = torch.randn(2)
output = m(input)

Methods

__init__(self, approximate: str = 'none') -> None [source]

初始化内部 Module 状态，由 nn.Module 和 ScriptModule 共享。

---

add_module(self, name: str, module: Optional[ForwardRef('Module')]) -> None [source]

将子模块添加到当前模块。

可以使用给定名称作为属性访问该模块。

Args

• name (str): 子模块的名称。可以使用给定名称 从该模块访问子模块
• module (Module): 要添加到模块的子模块。

---

apply(self, fn: Callable[[ForwardRef('Module')], NoneType]) -> Self [source]

递归地将 fn 应用于每个子模块（由 .children() 返回）以及自身。

典型用途包括初始化模型的参数 (see also nn-init-doc).

Args

• fn (``Module -> None): 应用于每个子模块的函数

Returns

• Module: self

Example

@tensorplay.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)
)

---

bfloat16(self) -> Self [source]

将所有浮点参数和缓冲区转换为 bfloat16 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

buffers(self, recurse: bool = True) -> collections.abc.Iterator[tensorplay._C.TensorBase] [source]

返回模块缓冲区的迭代器。

Args

• recurse (bool): 如果为 True，则生成此模块的缓冲区 以及所有子模块的缓冲区。否则，仅生成 此模块的直接成员缓冲区。

Yields

tensorplay.Tensor: 模块缓冲区

Example

# xdoctest: +SKIP("undefined vars")
for buf in model.buffers():
    print(type(buf), buf.size())
<class 'tensorplay.Tensor'> (20L,)
<class 'tensorplay.Tensor'> (20L, 1L, 5L, 5L)

---

children(self) -> collections.abc.Iterator['Module'] [source]

返回直接子模块的迭代器。

Yields

• Module: 一个子模块

---

compile(self, *args, **kwargs) [source]

使用 tensorplay.compile 编译此模块的前向传播。

此模块的 __call__ 方法被编译，所有参数按原样传递 给 tensorplay.compile。

有关此函数的参数详情，请参阅 tensorplay.compile。

---

cpu(self) -> Self [source]

将所有模型参数和缓冲区移动到 CPU。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

cuda(self, device: Union[int, tensorplay.Device, NoneType] = None) -> Self [source]

将所有模型参数和缓冲区移动到 GPU。

这也使得关联的参数和缓冲区成为不同的对象。所以 如果模块将在 GPU 上运行并进行优化， 应在构建优化器之前调用它。

INFO

此方法会就地修改模块。

Args

• device (int, optional): 如果指定，所有参数将被 复制到该设备

Returns

• Module: self

---

double(self) -> Self [source]

将所有浮点参数和缓冲区转换为 double 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

eval(self) -> Self [source]

将模块设置为评估模式。

这仅对某些模块有影响。请参阅特定模块的文档 了解它们在训练/评估模式下的行为细节， 即它们是否受影响，例如 Dropout、BatchNorm 等。

这相当于 self.train(False) <tensorplay.nn.Module.train>。

有关 .eval() 和其他类似机制的比较， .eval() and several similar mechanisms that may be confused with it.

Returns

• Module: self

---

extra_repr(self) -> str [source]

返回模块的额外表示。

---

float(self) -> Self [source]

将所有浮点参数和缓冲区转换为 float 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

forward(self, input: tensorplay._C.TensorBase) -> tensorplay._C.TensorBase [source]

运行前向传播。

---

get_buffer(self, target: str) -> 'Tensor' [source]

如果存在，则返回由 target 指定的缓冲区，否则抛出错误。

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: 缓冲区的完全限定字符串名称 to look for. (See get_submodule for how to specify a fully-qualified string.)

Returns

tensorplay.Tensor: 由 ``target`` 引用的缓冲区

Raises

• AttributeError: If the target string references an invalid path or resolves to something that is not a buffer

---

get_extra_state(self) -> Any [source]

返回要包含在模块 state_dict 中的任何额外状态。

Implement this and a corresponding 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 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

---

get_parameter(self, target: str) -> 'Parameter' [source]

如果存在，则返回由 target 指定的参数，否则抛出错误。

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

tensorplay.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

---

get_submodule(self, target: str) -> 'Module' [source]

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 which 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

tensorplay.nn.Module: The submodule referenced by ``target``

Raises

• AttributeError: If at any point along the path resulting from the target string the (sub)path resolves to a non-existent attribute name or an object that is not an instance of nn.Module.

---

half(self) -> Self [source]

Casts all floating point parameters and buffers to half datatype.

INFO

此方法会就地修改模块。

Returns

• Module: self

---

load_state_dict(self, state_dict: collections.abc.Mapping[str, typing.Any], strict: bool = True, assign: bool = False) [source]

Copy parameters and buffers from state_dict into this module and its descendants.

If strict is True, then the keys of state_dict must exactly match the keys returned by this module's ~tensorplay.nn.Module.state_dict function.

DANGER

If assign is True the optimizer must be created after the call to load_state_dict unless ~tensorplay.__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 state_dict match the keys returned by this module's ~tensorplay.nn.Module.state_dict function. 默认值： True
• assign (bool, optional): When set to False, the properties of the tensors in the current module are preserved whereas setting it to True preserves properties of the Tensors in the state dict. The only exception is the requires_grad field of ~tensorplay.nn.Parameter for which the value from the module is preserved. 默认值： 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 `state_dict`, `load_state_dict` will raise a
``RuntimeError``.

---

modules(self) -> collections.abc.Iterator['Module'] [source]

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)

---

named_buffers(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> collections.abc.Iterator[tuple[str, tensorplay._C.TensorBase]] [source]

Return an iterator over 模块缓冲区s, 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): 如果为 True，则生成此模块的缓冲区 以及所有子模块的缓冲区。否则，仅生成 此模块的直接成员缓冲区。 Defaults to True.
• remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.

Yields

(str, tensorplay.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())

---

named_children(self) -> collections.abc.Iterator[tuple[str, 'Module']] [source]

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)

---

named_modules(self, memo: Optional[set['Module']] = None, prefix: str = '', remove_duplicate: bool = True) [source]

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))

---

named_parameters(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> collections.abc.Iterator[tuple[str, tensorplay.nn.parameter.Parameter]] [source]

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 此模块的直接成员缓冲区。
• 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())

---

parameters(self, recurse: bool = True) -> collections.abc.Iterator[tensorplay.nn.parameter.Parameter] [source]

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 此模块的直接成员缓冲区。

Yields

• Parameter: module parameter

Example

# xdoctest: +SKIP("undefined vars")
for param in model.parameters():
    print(type(param), param.size())
<class 'tensorplay.Tensor'> (20L,)
<class 'tensorplay.Tensor'> (20L, 1L, 5L, 5L)

---

register_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase], Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]]) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a backward hook on the module.

This function is deprecated in favor of ~tensorplay.nn.Module.register_full_backward_hook and the behavior of this function will change in future versions.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_buffer(self, name: str, tensor: Optional[tensorplay._C.TensorBase], persistent: bool = True) -> None [source]

Add a buffer to the module.

This is typically used to register a buffer that should not 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 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 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 cuda, are ignored. If None, the buffer is not included in the module's state_dict.
• persistent (bool): whether the buffer is part of this module's state_dict.

Example

# xdoctest: +SKIP("undefined vars")
self.register_buffer('running_mean', tensorplay.zeros(num_features))

---

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) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a forward hook on the module.

The hook will be called every time after 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 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing forward hooks on this tensorplay.nn.Module. Note that global forward hooks registered with 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

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) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a forward pre-hook on the module.

The hook will be called every time before 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing forward_pre hooks on this tensorplay.nn.Module. Note that global forward_pre hooks registered with 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_full_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase], Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], prepend: bool = False) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a backward hook on the module.

The hook will be called every time the gradients with respect to a module are computed, and its firing rules are as follows:

1. Ordinarily, the hook fires when the gradients are computed with respect to the module inputs.
2. If none of the module inputs require gradients, the hook will fire when the gradients are computed
   with respect to module outputs.
3. If none of the module outputs require gradients, then the hooks will not fire.

The hook should have the following signature

hook(module, grad_input, grad_output) -> tuple(Tensor) or None

The grad_input and 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 grad_input in subsequent computations. grad_input will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in grad_input and 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing backward hooks on this tensorplay.nn.Module. Note that global backward hooks registered with register_module_full_backward_hook will fire before all hooks registered by this method.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_full_backward_pre_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], prepend: bool = False) -> tensorplay.utils.hooks.RemovableHandle [source]

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 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 grad_output in subsequent computations. Entries in 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing backward_pre hooks on this tensorplay.nn.Module. Note that global backward_pre hooks registered with register_module_full_backward_pre_hook will fire before all hooks registered by this method.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_load_state_dict_post_hook(self, hook) [source]

Register a post-hook to be run after module's ~nn.Module.load_state_dict is called.

It should have the following signature

hook(module, incompatible_keys) -> None

The module argument is the current module that this hook is registered on, and the incompatible_keys argument is a NamedTuple consisting of attributes missing_keys and unexpected_keys. missing_keys is a list of str containing the missing keys and unexpected_keys is a list of str containing the unexpected keys.

The given incompatible_keys can be modified inplace if needed.

Note that the checks performed when calling 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_load_state_dict_pre_hook(self, hook) [source]

Register a pre-hook to be run before module's ~nn.Module.load_state_dict is called.

It should have the following signature

hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -> None  # noqa: B950

Arguments

• hook (Callable): Callable hook that will be invoked before loading the state dict.

---

register_module(self, name: str, module: Optional[ForwardRef('Module')]) -> None [source]

Alias for add_module.

---

register_parameter(self, name: str, param: Optional[tensorplay.nn.parameter.Parameter]) -> None [source]

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 cuda, are ignored. If None, the parameter is not included in the module's state_dict.

---

register_state_dict_post_hook(self, hook) [source]

Register a post-hook for the ~tensorplay.nn.Module.state_dict method.

It should have the following signature

hook(module, state_dict, prefix, local_metadata) -> None

The registered hooks can modify the state_dict inplace.

---

register_state_dict_pre_hook(self, hook) [source]

Register a pre-hook for the ~tensorplay.nn.Module.state_dict method.

It should have the following signature

hook(module, prefix, keep_vars) -> None

The registered hooks can be used to perform pre-processing before the state_dict call is made.

---

requires_grad_(self, requires_grad: bool = True) -> Self [source]

Change if autograd should record operations on parameters in this module.

This method sets the parameters' 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).

有关 .eval() 和其他类似机制的比较， .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. 默认值： True.

Returns

• Module: self

---

set_extra_state(self, state: Any) -> None [source]

Set extra state contained in the loaded state_dict.

This function is called from load_state_dict to handle any extra state found within the state_dict. Implement this function and a corresponding 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

---

set_submodule(self, target: str, module: 'Module', strict: bool = False) -> None [source]

Set the submodule given by target if it exists, otherwise throw an error.

INFO

If strict is set to False (default), the method will replace an existing submodule or create a new submodule if the parent module exists. If strict is set to True, the method will only attempt to replace an existing submodule and throw an error if the submodule does not exist.

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(3, 3, 3) ) (linear): Linear(3, 3) ) )

(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 override the Conv2d with a new submodule Linear, you could call set_submodule("net_b.net_c.conv", nn.Linear(1, 1)) where strict could be True or False

To add a new submodule Conv2d to the existing net_b module, you would call set_submodule("net_b.conv", nn.Conv2d(1, 1, 1)).

In the above if you set strict=True and call set_submodule("net_b.conv", nn.Conv2d(1, 1, 1), strict=True), an AttributeError will be raised because net_b does not have a submodule named conv.

Args

• target: The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
• module: The module to set the submodule to.
• strict: If False, the method will replace an existing submodule or create a new submodule if the parent module exists. If True, the method will only attempt to replace an existing submodule and throw an error if the submodule doesn't already exist.

Raises

• ValueError: If the target string is empty or if module is not an instance of nn.Module.
• AttributeError: If at any point along the path resulting from the target string the (sub)path resolves to a non-existent attribute name or an object that is not an instance of nn.Module.

---

share_memory(self) -> Self [source]

See tensorplay.Tensor.share_memory_.

---

state_dict(self, *args, destination=None, prefix='', keep_vars=False) [source]

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.

INFO

The returned object is a shallow copy. It contains references to the module's parameters and buffers.

DANGER

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.

DANGER

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. 默认值： ''.
• keep_vars (bool, optional): by default the ~tensorplay.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']

---

to(self, *args, **kwargs) [source]

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=tensorplay.channels_last) :noindex:

Its signature is similar to tensorplay.Tensor.to, but only accepts floating point or complex dtype\ s. In addition, this method will only cast the floating point or complex parameters and buffers to dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When 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.

INFO

此方法会就地修改模块。

Args

• device (tensorplay.device): the desired device of the parameters and buffers in this module
• dtype (tensorplay.dtype): the desired floating point or complex dtype of the parameters and buffers in this module
• tensor (tensorplay.Tensor): Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
• memory_format (tensorplay.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(tensorplay.double)
Linear(in_features=2, out_features=2, bias=True)
linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=tensorplay.float64)
# xdoctest: +REQUIRES(env:TENSORPLAY_DOCTEST_CUDA1)
gpu1 = tensorplay.device("cuda:1")
linear.to(gpu1, dtype=tensorplay.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=tensorplay.float16, device='cuda:1')
cpu = tensorplay.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=tensorplay.float16)

linear = nn.Linear(2, 2, bias=None).to(tensorplay.cdouble)
linear.weight
Parameter containing:
tensor([[ 0.3741+0.j,  0.2382+0.j],
        [ 0.5593+0.j, -0.4443+0.j]], dtype=tensorplay.complex128)
linear(tensorplay.ones(3, 2, dtype=tensorplay.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=tensorplay.complex128)

---

to_empty(self, *, device: Union[str, tensorplay.Device, int, NoneType], recurse: bool = True) -> Self [source]

Move the parameters and buffers to the specified device without copying storage.

Args

• device (tensorplay.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

---

train(self, mode: bool = True) -> Self [source]

Set the module in training mode.

这仅对某些模块有影响。请参阅特定模块的文档 了解它们在训练/评估模式下的行为细节， mode, i.e., whether they are affected, e.g. Dropout, BatchNorm, 等。

Args

• mode (bool): whether to set training mode (True) or evaluation mode (False). 默认值： True.

Returns

• Module: self

---

type(self, dst_type: Union[tensorplay.DType, str]) -> Self [source]

Casts all parameters and buffers to dst_type.

INFO

此方法会就地修改模块。

Args

• dst_type (type or string): the desired type

Returns

• Module: self

---

zero_grad(self, set_to_none: bool = True) -> None [source]

Reset gradients of all model parameters.

See similar function under tensorplay.optim.Optimizer for more context.

Args

• set_to_none (bool): instead of setting to zero, set the grads to None. See tensorplay.optim.Optimizer.zero_grad for details.

---

class PReLU [source]

PReLU(num_parameters: int = 1, init: float = 0.25, device=None, dtype=None) -> None

Bases: Module

应用逐元素 PReLU 函数。

.. math

\text{PReLU}(x) = \max(0,x) + a * \min(0,x)

or

.. math

\text{PReLU}(x) =
\begin{cases}
x, & \text{ if } x \ge 0 \\
ax, & \text{ otherwise }
\end{cases}

Here $a$ is a learnable parameter. When called without arguments, nn.PReLU() uses a single parameter $a$ across all input channels. If called with nn.PReLU(nChannels), a separate $a$ is used for each input channel.

INFO

weight decay should not be used when learning $a$ for good performance.

INFO

Channel dim is the 2nd dim of input. When input has dims < 2, then there is no channel dim and the number of channels = 1.

Args

• num_parameters (int): number of $a$ to learn. Although it takes an int as input, there is only two values are legitimate: 1, or the number of channels at input. 默认值： 1
• init (float): the initial value of $a$. 默认值： 0.25

Shape

• 输入： $(\ast )$ where * means, any number of additional dimensions.
• 输出： $(\ast )$, 与输入形状相同。

Attributes

• weight (Tensor): the learnable weights of shape (num_parameters).

Examples

m = nn.PReLU()
input = tensorplay.randn(2)
output = m(input)

Methods

__init__(self, num_parameters: int = 1, init: float = 0.25, device=None, dtype=None) -> None [source]

初始化内部 Module 状态，由 nn.Module 和 ScriptModule 共享。

---

add_module(self, name: str, module: Optional[ForwardRef('Module')]) -> None [source]

将子模块添加到当前模块。

可以使用给定名称作为属性访问该模块。

Args

• name (str): 子模块的名称。可以使用给定名称 从该模块访问子模块
• module (Module): 要添加到模块的子模块。

---

apply(self, fn: Callable[[ForwardRef('Module')], NoneType]) -> Self [source]

递归地将 fn 应用于每个子模块（由 .children() 返回）以及自身。

典型用途包括初始化模型的参数 (see also nn-init-doc).

Args

• fn (``Module -> None): 应用于每个子模块的函数

Returns

• Module: self

Example

@tensorplay.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)
)

---

bfloat16(self) -> Self [source]

将所有浮点参数和缓冲区转换为 bfloat16 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

buffers(self, recurse: bool = True) -> collections.abc.Iterator[tensorplay._C.TensorBase] [source]

返回模块缓冲区的迭代器。

Args

• recurse (bool): 如果为 True，则生成此模块的缓冲区 以及所有子模块的缓冲区。否则，仅生成 此模块的直接成员缓冲区。

Yields

tensorplay.Tensor: 模块缓冲区

Example

# xdoctest: +SKIP("undefined vars")
for buf in model.buffers():
    print(type(buf), buf.size())
<class 'tensorplay.Tensor'> (20L,)
<class 'tensorplay.Tensor'> (20L, 1L, 5L, 5L)

---

children(self) -> collections.abc.Iterator['Module'] [source]

返回直接子模块的迭代器。

Yields

• Module: 一个子模块

---

compile(self, *args, **kwargs) [source]

使用 tensorplay.compile 编译此模块的前向传播。

此模块的 __call__ 方法被编译，所有参数按原样传递 给 tensorplay.compile。

有关此函数的参数详情，请参阅 tensorplay.compile。

---

cpu(self) -> Self [source]

将所有模型参数和缓冲区移动到 CPU。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

cuda(self, device: Union[int, tensorplay.Device, NoneType] = None) -> Self [source]

将所有模型参数和缓冲区移动到 GPU。

这也使得关联的参数和缓冲区成为不同的对象。所以 如果模块将在 GPU 上运行并进行优化， 应在构建优化器之前调用它。

INFO

此方法会就地修改模块。

Args

• device (int, optional): 如果指定，所有参数将被 复制到该设备

Returns

• Module: self

---

double(self) -> Self [source]

将所有浮点参数和缓冲区转换为 double 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

eval(self) -> Self [source]

将模块设置为评估模式。

这仅对某些模块有影响。请参阅特定模块的文档 了解它们在训练/评估模式下的行为细节， 即它们是否受影响，例如 Dropout、BatchNorm 等。

这相当于 self.train(False) <tensorplay.nn.Module.train>。

有关 .eval() 和其他类似机制的比较， .eval() and several similar mechanisms that may be confused with it.

Returns

• Module: self

---

extra_repr(self) -> str [source]

返回模块的额外表示。

---

float(self) -> Self [source]

将所有浮点参数和缓冲区转换为 float 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

forward(self, input: tensorplay._C.TensorBase) -> tensorplay._C.TensorBase [source]

运行前向传播。

---

get_buffer(self, target: str) -> 'Tensor' [source]

如果存在，则返回由 target 指定的缓冲区，否则抛出错误。

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: 缓冲区的完全限定字符串名称 to look for. (See get_submodule for how to specify a fully-qualified string.)

Returns

tensorplay.Tensor: 由 ``target`` 引用的缓冲区

Raises

• AttributeError: If the target string references an invalid path or resolves to something that is not a buffer

---

get_extra_state(self) -> Any [source]

返回要包含在模块 state_dict 中的任何额外状态。

Implement this and a corresponding 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 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

---

get_parameter(self, target: str) -> 'Parameter' [source]

如果存在，则返回由 target 指定的参数，否则抛出错误。

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

tensorplay.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

---

get_submodule(self, target: str) -> 'Module' [source]

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 which 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

tensorplay.nn.Module: The submodule referenced by ``target``

Raises

• AttributeError: If at any point along the path resulting from the target string the (sub)path resolves to a non-existent attribute name or an object that is not an instance of nn.Module.

---

half(self) -> Self [source]

Casts all floating point parameters and buffers to half datatype.

INFO

此方法会就地修改模块。

Returns

• Module: self

---

load_state_dict(self, state_dict: collections.abc.Mapping[str, typing.Any], strict: bool = True, assign: bool = False) [source]

Copy parameters and buffers from state_dict into this module and its descendants.

If strict is True, then the keys of state_dict must exactly match the keys returned by this module's ~tensorplay.nn.Module.state_dict function.

DANGER

If assign is True the optimizer must be created after the call to load_state_dict unless ~tensorplay.__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 state_dict match the keys returned by this module's ~tensorplay.nn.Module.state_dict function. 默认值： True
• assign (bool, optional): When set to False, the properties of the tensors in the current module are preserved whereas setting it to True preserves properties of the Tensors in the state dict. The only exception is the requires_grad field of ~tensorplay.nn.Parameter for which the value from the module is preserved. 默认值： 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 `state_dict`, `load_state_dict` will raise a
``RuntimeError``.

---

modules(self) -> collections.abc.Iterator['Module'] [source]

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)

---

named_buffers(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> collections.abc.Iterator[tuple[str, tensorplay._C.TensorBase]] [source]

Return an iterator over 模块缓冲区s, 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): 如果为 True，则生成此模块的缓冲区 以及所有子模块的缓冲区。否则，仅生成 此模块的直接成员缓冲区。 Defaults to True.
• remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.

Yields

(str, tensorplay.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())

---

named_children(self) -> collections.abc.Iterator[tuple[str, 'Module']] [source]

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)

---

named_modules(self, memo: Optional[set['Module']] = None, prefix: str = '', remove_duplicate: bool = True) [source]

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))

---

named_parameters(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> collections.abc.Iterator[tuple[str, tensorplay.nn.parameter.Parameter]] [source]

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 此模块的直接成员缓冲区。
• 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())

---

parameters(self, recurse: bool = True) -> collections.abc.Iterator[tensorplay.nn.parameter.Parameter] [source]

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 此模块的直接成员缓冲区。

Yields

• Parameter: module parameter

Example

# xdoctest: +SKIP("undefined vars")
for param in model.parameters():
    print(type(param), param.size())
<class 'tensorplay.Tensor'> (20L,)
<class 'tensorplay.Tensor'> (20L, 1L, 5L, 5L)

---

register_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase], Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]]) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a backward hook on the module.

This function is deprecated in favor of ~tensorplay.nn.Module.register_full_backward_hook and the behavior of this function will change in future versions.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_buffer(self, name: str, tensor: Optional[tensorplay._C.TensorBase], persistent: bool = True) -> None [source]

Add a buffer to the module.

This is typically used to register a buffer that should not 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 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 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 cuda, are ignored. If None, the buffer is not included in the module's state_dict.
• persistent (bool): whether the buffer is part of this module's state_dict.

Example

# xdoctest: +SKIP("undefined vars")
self.register_buffer('running_mean', tensorplay.zeros(num_features))

---

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) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a forward hook on the module.

The hook will be called every time after 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 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing forward hooks on this tensorplay.nn.Module. Note that global forward hooks registered with 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

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) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a forward pre-hook on the module.

The hook will be called every time before 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing forward_pre hooks on this tensorplay.nn.Module. Note that global forward_pre hooks registered with 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_full_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase], Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], prepend: bool = False) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a backward hook on the module.

The hook will be called every time the gradients with respect to a module are computed, and its firing rules are as follows:

1. Ordinarily, the hook fires when the gradients are computed with respect to the module inputs.
2. If none of the module inputs require gradients, the hook will fire when the gradients are computed
   with respect to module outputs.
3. If none of the module outputs require gradients, then the hooks will not fire.

The hook should have the following signature

hook(module, grad_input, grad_output) -> tuple(Tensor) or None

The grad_input and 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 grad_input in subsequent computations. grad_input will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in grad_input and 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing backward hooks on this tensorplay.nn.Module. Note that global backward hooks registered with register_module_full_backward_hook will fire before all hooks registered by this method.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_full_backward_pre_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], prepend: bool = False) -> tensorplay.utils.hooks.RemovableHandle [source]

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 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 grad_output in subsequent computations. Entries in 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing backward_pre hooks on this tensorplay.nn.Module. Note that global backward_pre hooks registered with register_module_full_backward_pre_hook will fire before all hooks registered by this method.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_load_state_dict_post_hook(self, hook) [source]

Register a post-hook to be run after module's ~nn.Module.load_state_dict is called.

It should have the following signature

hook(module, incompatible_keys) -> None

The module argument is the current module that this hook is registered on, and the incompatible_keys argument is a NamedTuple consisting of attributes missing_keys and unexpected_keys. missing_keys is a list of str containing the missing keys and unexpected_keys is a list of str containing the unexpected keys.

The given incompatible_keys can be modified inplace if needed.

Note that the checks performed when calling 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_load_state_dict_pre_hook(self, hook) [source]

Register a pre-hook to be run before module's ~nn.Module.load_state_dict is called.

It should have the following signature

hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -> None  # noqa: B950

Arguments

• hook (Callable): Callable hook that will be invoked before loading the state dict.

---

register_module(self, name: str, module: Optional[ForwardRef('Module')]) -> None [source]

Alias for add_module.

---

register_parameter(self, name: str, param: Optional[tensorplay.nn.parameter.Parameter]) -> None [source]

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 cuda, are ignored. If None, the parameter is not included in the module's state_dict.

---

register_state_dict_post_hook(self, hook) [source]

Register a post-hook for the ~tensorplay.nn.Module.state_dict method.

It should have the following signature

hook(module, state_dict, prefix, local_metadata) -> None

The registered hooks can modify the state_dict inplace.

---

register_state_dict_pre_hook(self, hook) [source]

Register a pre-hook for the ~tensorplay.nn.Module.state_dict method.

It should have the following signature

hook(module, prefix, keep_vars) -> None

The registered hooks can be used to perform pre-processing before the state_dict call is made.

---

requires_grad_(self, requires_grad: bool = True) -> Self [source]

Change if autograd should record operations on parameters in this module.

This method sets the parameters' 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).

有关 .eval() 和其他类似机制的比较， .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. 默认值： True.

Returns

• Module: self

---

reset_parameters(self) -> None [source]

Resets parameters based on their initialization used in __init__.

---

set_extra_state(self, state: Any) -> None [source]

Set extra state contained in the loaded state_dict.

This function is called from load_state_dict to handle any extra state found within the state_dict. Implement this function and a corresponding 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

---

set_submodule(self, target: str, module: 'Module', strict: bool = False) -> None [source]

Set the submodule given by target if it exists, otherwise throw an error.

INFO

If strict is set to False (default), the method will replace an existing submodule or create a new submodule if the parent module exists. If strict is set to True, the method will only attempt to replace an existing submodule and throw an error if the submodule does not exist.

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(3, 3, 3) ) (linear): Linear(3, 3) ) )

(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 override the Conv2d with a new submodule Linear, you could call set_submodule("net_b.net_c.conv", nn.Linear(1, 1)) where strict could be True or False

To add a new submodule Conv2d to the existing net_b module, you would call set_submodule("net_b.conv", nn.Conv2d(1, 1, 1)).

In the above if you set strict=True and call set_submodule("net_b.conv", nn.Conv2d(1, 1, 1), strict=True), an AttributeError will be raised because net_b does not have a submodule named conv.

Args

• target: The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
• module: The module to set the submodule to.
• strict: If False, the method will replace an existing submodule or create a new submodule if the parent module exists. If True, the method will only attempt to replace an existing submodule and throw an error if the submodule doesn't already exist.

Raises

• ValueError: If the target string is empty or if module is not an instance of nn.Module.
• AttributeError: If at any point along the path resulting from the target string the (sub)path resolves to a non-existent attribute name or an object that is not an instance of nn.Module.

---

share_memory(self) -> Self [source]

See tensorplay.Tensor.share_memory_.

---

state_dict(self, *args, destination=None, prefix='', keep_vars=False) [source]

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.

INFO

The returned object is a shallow copy. It contains references to the module's parameters and buffers.

DANGER

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.

DANGER

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. 默认值： ''.
• keep_vars (bool, optional): by default the ~tensorplay.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']

---

to(self, *args, **kwargs) [source]

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=tensorplay.channels_last) :noindex:

Its signature is similar to tensorplay.Tensor.to, but only accepts floating point or complex dtype\ s. In addition, this method will only cast the floating point or complex parameters and buffers to dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When 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.

INFO

此方法会就地修改模块。

Args

• device (tensorplay.device): the desired device of the parameters and buffers in this module
• dtype (tensorplay.dtype): the desired floating point or complex dtype of the parameters and buffers in this module
• tensor (tensorplay.Tensor): Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
• memory_format (tensorplay.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(tensorplay.double)
Linear(in_features=2, out_features=2, bias=True)
linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=tensorplay.float64)
# xdoctest: +REQUIRES(env:TENSORPLAY_DOCTEST_CUDA1)
gpu1 = tensorplay.device("cuda:1")
linear.to(gpu1, dtype=tensorplay.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=tensorplay.float16, device='cuda:1')
cpu = tensorplay.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=tensorplay.float16)

linear = nn.Linear(2, 2, bias=None).to(tensorplay.cdouble)
linear.weight
Parameter containing:
tensor([[ 0.3741+0.j,  0.2382+0.j],
        [ 0.5593+0.j, -0.4443+0.j]], dtype=tensorplay.complex128)
linear(tensorplay.ones(3, 2, dtype=tensorplay.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=tensorplay.complex128)

---

to_empty(self, *, device: Union[str, tensorplay.Device, int, NoneType], recurse: bool = True) -> Self [source]

Move the parameters and buffers to the specified device without copying storage.

Args

• device (tensorplay.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

---

train(self, mode: bool = True) -> Self [source]

Set the module in training mode.

这仅对某些模块有影响。请参阅特定模块的文档 了解它们在训练/评估模式下的行为细节， mode, i.e., whether they are affected, e.g. Dropout, BatchNorm, 等。

Args

• mode (bool): whether to set training mode (True) or evaluation mode (False). 默认值： True.

Returns

• Module: self

---

type(self, dst_type: Union[tensorplay.DType, str]) -> Self [source]

Casts all parameters and buffers to dst_type.

INFO

此方法会就地修改模块。

Args

• dst_type (type or string): the desired type

Returns

• Module: self

---

zero_grad(self, set_to_none: bool = True) -> None [source]

Reset gradients of all model parameters.

See similar function under tensorplay.optim.Optimizer for more context.

Args

• set_to_none (bool): instead of setting to zero, set the grads to None. See tensorplay.optim.Optimizer.zero_grad for details.

---

class ReLU [source]

ReLU(inplace: bool = False) -> None

Bases: Module

逐元素应用整流线性单元 (ReLU) 函数。

$\text{ReLU}(x)=(x{)}^{+}=max(0,x)$

Args

• inplace: can optionally do the operation in-place. 默认值： False

Shape

• 输入： $(\ast )$, 其中 $\ast$ 表示任意维数。
• 输出： $(\ast )$, 与输入形状相同。

Examples

m = nn.ReLU()
input = tensorplay.randn(2)
output = m(input)

An implementation of CReLU - https://arxiv.org/abs/1603.05201

m = nn.ReLU()
input = tensorplay.randn(2).unsqueeze(0)
output = tensorplay.cat((m(input), m(-input)))

Methods

__init__(self, inplace: bool = False) -> None [source]

初始化内部 Module 状态，由 nn.Module 和 ScriptModule 共享。

---

add_module(self, name: str, module: Optional[ForwardRef('Module')]) -> None [source]

将子模块添加到当前模块。

可以使用给定名称作为属性访问该模块。

Args

• name (str): 子模块的名称。可以使用给定名称 从该模块访问子模块
• module (Module): 要添加到模块的子模块。

---

apply(self, fn: Callable[[ForwardRef('Module')], NoneType]) -> Self [source]

递归地将 fn 应用于每个子模块（由 .children() 返回）以及自身。

典型用途包括初始化模型的参数 (see also nn-init-doc).

Args

• fn (``Module -> None): 应用于每个子模块的函数

Returns

• Module: self

Example

@tensorplay.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)
)

---

bfloat16(self) -> Self [source]

将所有浮点参数和缓冲区转换为 bfloat16 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

buffers(self, recurse: bool = True) -> collections.abc.Iterator[tensorplay._C.TensorBase] [source]

返回模块缓冲区的迭代器。

Args

• recurse (bool): 如果为 True，则生成此模块的缓冲区 以及所有子模块的缓冲区。否则，仅生成 此模块的直接成员缓冲区。

Yields

tensorplay.Tensor: 模块缓冲区

Example

# xdoctest: +SKIP("undefined vars")
for buf in model.buffers():
    print(type(buf), buf.size())
<class 'tensorplay.Tensor'> (20L,)
<class 'tensorplay.Tensor'> (20L, 1L, 5L, 5L)

---

children(self) -> collections.abc.Iterator['Module'] [source]

返回直接子模块的迭代器。

Yields

• Module: 一个子模块

---

compile(self, *args, **kwargs) [source]

使用 tensorplay.compile 编译此模块的前向传播。

此模块的 __call__ 方法被编译，所有参数按原样传递 给 tensorplay.compile。

有关此函数的参数详情，请参阅 tensorplay.compile。

---

cpu(self) -> Self [source]

将所有模型参数和缓冲区移动到 CPU。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

cuda(self, device: Union[int, tensorplay.Device, NoneType] = None) -> Self [source]

将所有模型参数和缓冲区移动到 GPU。

这也使得关联的参数和缓冲区成为不同的对象。所以 如果模块将在 GPU 上运行并进行优化， 应在构建优化器之前调用它。

INFO

此方法会就地修改模块。

Args

• device (int, optional): 如果指定，所有参数将被 复制到该设备

Returns

• Module: self

---

double(self) -> Self [source]

将所有浮点参数和缓冲区转换为 double 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

eval(self) -> Self [source]

将模块设置为评估模式。

这仅对某些模块有影响。请参阅特定模块的文档 了解它们在训练/评估模式下的行为细节， 即它们是否受影响，例如 Dropout、BatchNorm 等。

这相当于 self.train(False) <tensorplay.nn.Module.train>。

有关 .eval() 和其他类似机制的比较， .eval() and several similar mechanisms that may be confused with it.

Returns

• Module: self

---

extra_repr(self) -> str [source]

返回模块的额外表示。

---

float(self) -> Self [source]

将所有浮点参数和缓冲区转换为 float 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

forward(self, input: tensorplay._C.TensorBase) -> tensorplay._C.TensorBase [source]

运行前向传播。

---

get_buffer(self, target: str) -> 'Tensor' [source]

如果存在，则返回由 target 指定的缓冲区，否则抛出错误。

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: 缓冲区的完全限定字符串名称 to look for. (See get_submodule for how to specify a fully-qualified string.)

Returns

tensorplay.Tensor: 由 ``target`` 引用的缓冲区

Raises

• AttributeError: If the target string references an invalid path or resolves to something that is not a buffer

---

get_extra_state(self) -> Any [source]

返回要包含在模块 state_dict 中的任何额外状态。

Implement this and a corresponding 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 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

---

get_parameter(self, target: str) -> 'Parameter' [source]

如果存在，则返回由 target 指定的参数，否则抛出错误。

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

tensorplay.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

---

get_submodule(self, target: str) -> 'Module' [source]

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 which 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

tensorplay.nn.Module: The submodule referenced by ``target``

Raises

• AttributeError: If at any point along the path resulting from the target string the (sub)path resolves to a non-existent attribute name or an object that is not an instance of nn.Module.

---

half(self) -> Self [source]

Casts all floating point parameters and buffers to half datatype.

INFO

此方法会就地修改模块。

Returns

• Module: self

---

load_state_dict(self, state_dict: collections.abc.Mapping[str, typing.Any], strict: bool = True, assign: bool = False) [source]

Copy parameters and buffers from state_dict into this module and its descendants.

If strict is True, then the keys of state_dict must exactly match the keys returned by this module's ~tensorplay.nn.Module.state_dict function.

DANGER

If assign is True the optimizer must be created after the call to load_state_dict unless ~tensorplay.__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 state_dict match the keys returned by this module's ~tensorplay.nn.Module.state_dict function. 默认值： True
• assign (bool, optional): When set to False, the properties of the tensors in the current module are preserved whereas setting it to True preserves properties of the Tensors in the state dict. The only exception is the requires_grad field of ~tensorplay.nn.Parameter for which the value from the module is preserved. 默认值： 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 `state_dict`, `load_state_dict` will raise a
``RuntimeError``.

---

modules(self) -> collections.abc.Iterator['Module'] [source]

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)

---

named_buffers(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> collections.abc.Iterator[tuple[str, tensorplay._C.TensorBase]] [source]

Return an iterator over 模块缓冲区s, 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): 如果为 True，则生成此模块的缓冲区 以及所有子模块的缓冲区。否则，仅生成 此模块的直接成员缓冲区。 Defaults to True.
• remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.

Yields

(str, tensorplay.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())

---

named_children(self) -> collections.abc.Iterator[tuple[str, 'Module']] [source]

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)

---

named_modules(self, memo: Optional[set['Module']] = None, prefix: str = '', remove_duplicate: bool = True) [source]

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))

---

named_parameters(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> collections.abc.Iterator[tuple[str, tensorplay.nn.parameter.Parameter]] [source]

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 此模块的直接成员缓冲区。
• 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())

---

parameters(self, recurse: bool = True) -> collections.abc.Iterator[tensorplay.nn.parameter.Parameter] [source]

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 此模块的直接成员缓冲区。

Yields

• Parameter: module parameter

Example

# xdoctest: +SKIP("undefined vars")
for param in model.parameters():
    print(type(param), param.size())
<class 'tensorplay.Tensor'> (20L,)
<class 'tensorplay.Tensor'> (20L, 1L, 5L, 5L)

---

register_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase], Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]]) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a backward hook on the module.

This function is deprecated in favor of ~tensorplay.nn.Module.register_full_backward_hook and the behavior of this function will change in future versions.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_buffer(self, name: str, tensor: Optional[tensorplay._C.TensorBase], persistent: bool = True) -> None [source]

Add a buffer to the module.

This is typically used to register a buffer that should not 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 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 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 cuda, are ignored. If None, the buffer is not included in the module's state_dict.
• persistent (bool): whether the buffer is part of this module's state_dict.

Example

# xdoctest: +SKIP("undefined vars")
self.register_buffer('running_mean', tensorplay.zeros(num_features))

---

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) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a forward hook on the module.

The hook will be called every time after 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 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing forward hooks on this tensorplay.nn.Module. Note that global forward hooks registered with 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

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) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a forward pre-hook on the module.

The hook will be called every time before 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing forward_pre hooks on this tensorplay.nn.Module. Note that global forward_pre hooks registered with 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_full_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase], Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], prepend: bool = False) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a backward hook on the module.

The hook will be called every time the gradients with respect to a module are computed, and its firing rules are as follows:

1. Ordinarily, the hook fires when the gradients are computed with respect to the module inputs.
2. If none of the module inputs require gradients, the hook will fire when the gradients are computed
   with respect to module outputs.
3. If none of the module outputs require gradients, then the hooks will not fire.

The hook should have the following signature

hook(module, grad_input, grad_output) -> tuple(Tensor) or None

The grad_input and 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 grad_input in subsequent computations. grad_input will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in grad_input and 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing backward hooks on this tensorplay.nn.Module. Note that global backward hooks registered with register_module_full_backward_hook will fire before all hooks registered by this method.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_full_backward_pre_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], prepend: bool = False) -> tensorplay.utils.hooks.RemovableHandle [source]

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 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 grad_output in subsequent computations. Entries in 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing backward_pre hooks on this tensorplay.nn.Module. Note that global backward_pre hooks registered with register_module_full_backward_pre_hook will fire before all hooks registered by this method.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_load_state_dict_post_hook(self, hook) [source]

Register a post-hook to be run after module's ~nn.Module.load_state_dict is called.

It should have the following signature

hook(module, incompatible_keys) -> None

The module argument is the current module that this hook is registered on, and the incompatible_keys argument is a NamedTuple consisting of attributes missing_keys and unexpected_keys. missing_keys is a list of str containing the missing keys and unexpected_keys is a list of str containing the unexpected keys.

The given incompatible_keys can be modified inplace if needed.

Note that the checks performed when calling 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_load_state_dict_pre_hook(self, hook) [source]

Register a pre-hook to be run before module's ~nn.Module.load_state_dict is called.

It should have the following signature

hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -> None  # noqa: B950

Arguments

• hook (Callable): Callable hook that will be invoked before loading the state dict.

---

register_module(self, name: str, module: Optional[ForwardRef('Module')]) -> None [source]

Alias for add_module.

---

register_parameter(self, name: str, param: Optional[tensorplay.nn.parameter.Parameter]) -> None [source]

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 cuda, are ignored. If None, the parameter is not included in the module's state_dict.

---

register_state_dict_post_hook(self, hook) [source]

Register a post-hook for the ~tensorplay.nn.Module.state_dict method.

It should have the following signature

hook(module, state_dict, prefix, local_metadata) -> None

The registered hooks can modify the state_dict inplace.

---

register_state_dict_pre_hook(self, hook) [source]

Register a pre-hook for the ~tensorplay.nn.Module.state_dict method.

It should have the following signature

hook(module, prefix, keep_vars) -> None

The registered hooks can be used to perform pre-processing before the state_dict call is made.

---

requires_grad_(self, requires_grad: bool = True) -> Self [source]

Change if autograd should record operations on parameters in this module.

This method sets the parameters' 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).

有关 .eval() 和其他类似机制的比较， .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. 默认值： True.

Returns

• Module: self

---

set_extra_state(self, state: Any) -> None [source]

Set extra state contained in the loaded state_dict.

This function is called from load_state_dict to handle any extra state found within the state_dict. Implement this function and a corresponding 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

---

set_submodule(self, target: str, module: 'Module', strict: bool = False) -> None [source]

Set the submodule given by target if it exists, otherwise throw an error.

INFO

If strict is set to False (default), the method will replace an existing submodule or create a new submodule if the parent module exists. If strict is set to True, the method will only attempt to replace an existing submodule and throw an error if the submodule does not exist.

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(3, 3, 3) ) (linear): Linear(3, 3) ) )

(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 override the Conv2d with a new submodule Linear, you could call set_submodule("net_b.net_c.conv", nn.Linear(1, 1)) where strict could be True or False

To add a new submodule Conv2d to the existing net_b module, you would call set_submodule("net_b.conv", nn.Conv2d(1, 1, 1)).

In the above if you set strict=True and call set_submodule("net_b.conv", nn.Conv2d(1, 1, 1), strict=True), an AttributeError will be raised because net_b does not have a submodule named conv.

Args

• target: The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
• module: The module to set the submodule to.
• strict: If False, the method will replace an existing submodule or create a new submodule if the parent module exists. If True, the method will only attempt to replace an existing submodule and throw an error if the submodule doesn't already exist.

Raises

• ValueError: If the target string is empty or if module is not an instance of nn.Module.
• AttributeError: If at any point along the path resulting from the target string the (sub)path resolves to a non-existent attribute name or an object that is not an instance of nn.Module.

---

share_memory(self) -> Self [source]

See tensorplay.Tensor.share_memory_.

---

state_dict(self, *args, destination=None, prefix='', keep_vars=False) [source]

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.

INFO

The returned object is a shallow copy. It contains references to the module's parameters and buffers.

DANGER

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.

DANGER

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. 默认值： ''.
• keep_vars (bool, optional): by default the ~tensorplay.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']

---

to(self, *args, **kwargs) [source]

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=tensorplay.channels_last) :noindex:

Its signature is similar to tensorplay.Tensor.to, but only accepts floating point or complex dtype\ s. In addition, this method will only cast the floating point or complex parameters and buffers to dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When 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.

INFO

此方法会就地修改模块。

Args

• device (tensorplay.device): the desired device of the parameters and buffers in this module
• dtype (tensorplay.dtype): the desired floating point or complex dtype of the parameters and buffers in this module
• tensor (tensorplay.Tensor): Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
• memory_format (tensorplay.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(tensorplay.double)
Linear(in_features=2, out_features=2, bias=True)
linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=tensorplay.float64)
# xdoctest: +REQUIRES(env:TENSORPLAY_DOCTEST_CUDA1)
gpu1 = tensorplay.device("cuda:1")
linear.to(gpu1, dtype=tensorplay.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=tensorplay.float16, device='cuda:1')
cpu = tensorplay.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=tensorplay.float16)

linear = nn.Linear(2, 2, bias=None).to(tensorplay.cdouble)
linear.weight
Parameter containing:
tensor([[ 0.3741+0.j,  0.2382+0.j],
        [ 0.5593+0.j, -0.4443+0.j]], dtype=tensorplay.complex128)
linear(tensorplay.ones(3, 2, dtype=tensorplay.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=tensorplay.complex128)

---

to_empty(self, *, device: Union[str, tensorplay.Device, int, NoneType], recurse: bool = True) -> Self [source]

Move the parameters and buffers to the specified device without copying storage.

Args

• device (tensorplay.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

---

train(self, mode: bool = True) -> Self [source]

Set the module in training mode.

这仅对某些模块有影响。请参阅特定模块的文档 了解它们在训练/评估模式下的行为细节， mode, i.e., whether they are affected, e.g. Dropout, BatchNorm, 等。

Args

• mode (bool): whether to set training mode (True) or evaluation mode (False). 默认值： True.

Returns

• Module: self

---

type(self, dst_type: Union[tensorplay.DType, str]) -> Self [source]

Casts all parameters and buffers to dst_type.

INFO

此方法会就地修改模块。

Args

• dst_type (type or string): the desired type

Returns

• Module: self

---

zero_grad(self, set_to_none: bool = True) -> None [source]

Reset gradients of all model parameters.

See similar function under tensorplay.optim.Optimizer for more context.

Args

• set_to_none (bool): instead of setting to zero, set the grads to None. See tensorplay.optim.Optimizer.zero_grad for details.

---

class Sigmoid [source]

Sigmoid(*args, **kwargs) -> None

Bases: Module

Applies the Sigmoid function element-wise.

.. math

\text{Sigmoid}(x) = \sigma(x) = \frac{1}{1 + \exp(-x)}

Shape

• 输入： $(\ast )$, 其中 $\ast$ 表示任意维数。
• 输出： $(\ast )$, 与输入形状相同。

Examples

m = nn.Sigmoid()
input = tensorplay.randn(2)
output = m(input)

Methods

__init__(self, *args, **kwargs) -> None [source]

初始化内部 Module 状态，由 nn.Module 和 ScriptModule 共享。

---

add_module(self, name: str, module: Optional[ForwardRef('Module')]) -> None [source]

将子模块添加到当前模块。

可以使用给定名称作为属性访问该模块。

Args

• name (str): 子模块的名称。可以使用给定名称 从该模块访问子模块
• module (Module): 要添加到模块的子模块。

---

apply(self, fn: Callable[[ForwardRef('Module')], NoneType]) -> Self [source]

递归地将 fn 应用于每个子模块（由 .children() 返回）以及自身。

典型用途包括初始化模型的参数 (see also nn-init-doc).

Args

• fn (``Module -> None): 应用于每个子模块的函数

Returns

• Module: self

Example

@tensorplay.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)
)

---

bfloat16(self) -> Self [source]

将所有浮点参数和缓冲区转换为 bfloat16 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

buffers(self, recurse: bool = True) -> collections.abc.Iterator[tensorplay._C.TensorBase] [source]

返回模块缓冲区的迭代器。

Args

• recurse (bool): 如果为 True，则生成此模块的缓冲区 以及所有子模块的缓冲区。否则，仅生成 此模块的直接成员缓冲区。

Yields

tensorplay.Tensor: 模块缓冲区

Example

# xdoctest: +SKIP("undefined vars")
for buf in model.buffers():
    print(type(buf), buf.size())
<class 'tensorplay.Tensor'> (20L,)
<class 'tensorplay.Tensor'> (20L, 1L, 5L, 5L)

---

children(self) -> collections.abc.Iterator['Module'] [source]

返回直接子模块的迭代器。

Yields

• Module: 一个子模块

---

compile(self, *args, **kwargs) [source]

使用 tensorplay.compile 编译此模块的前向传播。

此模块的 __call__ 方法被编译，所有参数按原样传递 给 tensorplay.compile。

有关此函数的参数详情，请参阅 tensorplay.compile。

---

cpu(self) -> Self [source]

将所有模型参数和缓冲区移动到 CPU。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

cuda(self, device: Union[int, tensorplay.Device, NoneType] = None) -> Self [source]

将所有模型参数和缓冲区移动到 GPU。

这也使得关联的参数和缓冲区成为不同的对象。所以 如果模块将在 GPU 上运行并进行优化， 应在构建优化器之前调用它。

INFO

此方法会就地修改模块。

Args

• device (int, optional): 如果指定，所有参数将被 复制到该设备

Returns

• Module: self

---

double(self) -> Self [source]

将所有浮点参数和缓冲区转换为 double 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

eval(self) -> Self [source]

将模块设置为评估模式。

这仅对某些模块有影响。请参阅特定模块的文档 了解它们在训练/评估模式下的行为细节， 即它们是否受影响，例如 Dropout、BatchNorm 等。

这相当于 self.train(False) <tensorplay.nn.Module.train>。

有关 .eval() 和其他类似机制的比较， .eval() and several similar mechanisms that may be confused with it.

Returns

• Module: self

---

extra_repr(self) -> str [source]

返回模块的额外表示。

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

---

float(self) -> Self [source]

将所有浮点参数和缓冲区转换为 float 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

forward(self, input: tensorplay._C.TensorBase) -> tensorplay._C.TensorBase [source]

运行前向传播。

---

get_buffer(self, target: str) -> 'Tensor' [source]

如果存在，则返回由 target 指定的缓冲区，否则抛出错误。

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: 缓冲区的完全限定字符串名称 to look for. (See get_submodule for how to specify a fully-qualified string.)

Returns

tensorplay.Tensor: 由 ``target`` 引用的缓冲区

Raises

• AttributeError: If the target string references an invalid path or resolves to something that is not a buffer

---

get_extra_state(self) -> Any [source]

返回要包含在模块 state_dict 中的任何额外状态。

Implement this and a corresponding 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 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

---

get_parameter(self, target: str) -> 'Parameter' [source]

如果存在，则返回由 target 指定的参数，否则抛出错误。

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

tensorplay.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

---

get_submodule(self, target: str) -> 'Module' [source]

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 which 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

tensorplay.nn.Module: The submodule referenced by ``target``

Raises

• AttributeError: If at any point along the path resulting from the target string the (sub)path resolves to a non-existent attribute name or an object that is not an instance of nn.Module.

---

half(self) -> Self [source]

Casts all floating point parameters and buffers to half datatype.

INFO

此方法会就地修改模块。

Returns

• Module: self

---

load_state_dict(self, state_dict: collections.abc.Mapping[str, typing.Any], strict: bool = True, assign: bool = False) [source]

Copy parameters and buffers from state_dict into this module and its descendants.

If strict is True, then the keys of state_dict must exactly match the keys returned by this module's ~tensorplay.nn.Module.state_dict function.

DANGER

If assign is True the optimizer must be created after the call to load_state_dict unless ~tensorplay.__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 state_dict match the keys returned by this module's ~tensorplay.nn.Module.state_dict function. 默认值： True
• assign (bool, optional): When set to False, the properties of the tensors in the current module are preserved whereas setting it to True preserves properties of the Tensors in the state dict. The only exception is the requires_grad field of ~tensorplay.nn.Parameter for which the value from the module is preserved. 默认值： 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 `state_dict`, `load_state_dict` will raise a
``RuntimeError``.

---

modules(self) -> collections.abc.Iterator['Module'] [source]

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)

---

named_buffers(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> collections.abc.Iterator[tuple[str, tensorplay._C.TensorBase]] [source]

Return an iterator over 模块缓冲区s, 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): 如果为 True，则生成此模块的缓冲区 以及所有子模块的缓冲区。否则，仅生成 此模块的直接成员缓冲区。 Defaults to True.
• remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.

Yields

(str, tensorplay.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())

---

named_children(self) -> collections.abc.Iterator[tuple[str, 'Module']] [source]

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)

---

named_modules(self, memo: Optional[set['Module']] = None, prefix: str = '', remove_duplicate: bool = True) [source]

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))

---

named_parameters(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> collections.abc.Iterator[tuple[str, tensorplay.nn.parameter.Parameter]] [source]

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 此模块的直接成员缓冲区。
• 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())

---

parameters(self, recurse: bool = True) -> collections.abc.Iterator[tensorplay.nn.parameter.Parameter] [source]

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 此模块的直接成员缓冲区。

Yields

• Parameter: module parameter

Example

# xdoctest: +SKIP("undefined vars")
for param in model.parameters():
    print(type(param), param.size())
<class 'tensorplay.Tensor'> (20L,)
<class 'tensorplay.Tensor'> (20L, 1L, 5L, 5L)

---

register_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase], Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]]) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a backward hook on the module.

This function is deprecated in favor of ~tensorplay.nn.Module.register_full_backward_hook and the behavior of this function will change in future versions.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_buffer(self, name: str, tensor: Optional[tensorplay._C.TensorBase], persistent: bool = True) -> None [source]

Add a buffer to the module.

This is typically used to register a buffer that should not 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 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 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 cuda, are ignored. If None, the buffer is not included in the module's state_dict.
• persistent (bool): whether the buffer is part of this module's state_dict.

Example

# xdoctest: +SKIP("undefined vars")
self.register_buffer('running_mean', tensorplay.zeros(num_features))

---

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) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a forward hook on the module.

The hook will be called every time after 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 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing forward hooks on this tensorplay.nn.Module. Note that global forward hooks registered with 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

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) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a forward pre-hook on the module.

The hook will be called every time before 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing forward_pre hooks on this tensorplay.nn.Module. Note that global forward_pre hooks registered with 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_full_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase], Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], prepend: bool = False) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a backward hook on the module.

The hook will be called every time the gradients with respect to a module are computed, and its firing rules are as follows:

1. Ordinarily, the hook fires when the gradients are computed with respect to the module inputs.
2. If none of the module inputs require gradients, the hook will fire when the gradients are computed
   with respect to module outputs.
3. If none of the module outputs require gradients, then the hooks will not fire.

The hook should have the following signature

hook(module, grad_input, grad_output) -> tuple(Tensor) or None

The grad_input and 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 grad_input in subsequent computations. grad_input will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in grad_input and 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing backward hooks on this tensorplay.nn.Module. Note that global backward hooks registered with register_module_full_backward_hook will fire before all hooks registered by this method.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_full_backward_pre_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], prepend: bool = False) -> tensorplay.utils.hooks.RemovableHandle [source]

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 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 grad_output in subsequent computations. Entries in 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing backward_pre hooks on this tensorplay.nn.Module. Note that global backward_pre hooks registered with register_module_full_backward_pre_hook will fire before all hooks registered by this method.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_load_state_dict_post_hook(self, hook) [source]

Register a post-hook to be run after module's ~nn.Module.load_state_dict is called.

It should have the following signature

hook(module, incompatible_keys) -> None

The module argument is the current module that this hook is registered on, and the incompatible_keys argument is a NamedTuple consisting of attributes missing_keys and unexpected_keys. missing_keys is a list of str containing the missing keys and unexpected_keys is a list of str containing the unexpected keys.

The given incompatible_keys can be modified inplace if needed.

Note that the checks performed when calling 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_load_state_dict_pre_hook(self, hook) [source]

Register a pre-hook to be run before module's ~nn.Module.load_state_dict is called.

It should have the following signature

hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -> None  # noqa: B950

Arguments

• hook (Callable): Callable hook that will be invoked before loading the state dict.

---

register_module(self, name: str, module: Optional[ForwardRef('Module')]) -> None [source]

Alias for add_module.

---

register_parameter(self, name: str, param: Optional[tensorplay.nn.parameter.Parameter]) -> None [source]

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 cuda, are ignored. If None, the parameter is not included in the module's state_dict.

---

register_state_dict_post_hook(self, hook) [source]

Register a post-hook for the ~tensorplay.nn.Module.state_dict method.

It should have the following signature

hook(module, state_dict, prefix, local_metadata) -> None

The registered hooks can modify the state_dict inplace.

---

register_state_dict_pre_hook(self, hook) [source]

Register a pre-hook for the ~tensorplay.nn.Module.state_dict method.

It should have the following signature

hook(module, prefix, keep_vars) -> None

The registered hooks can be used to perform pre-processing before the state_dict call is made.

---

requires_grad_(self, requires_grad: bool = True) -> Self [source]

Change if autograd should record operations on parameters in this module.

This method sets the parameters' 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).

有关 .eval() 和其他类似机制的比较， .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. 默认值： True.

Returns

• Module: self

---

set_extra_state(self, state: Any) -> None [source]

Set extra state contained in the loaded state_dict.

This function is called from load_state_dict to handle any extra state found within the state_dict. Implement this function and a corresponding 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

---

set_submodule(self, target: str, module: 'Module', strict: bool = False) -> None [source]

Set the submodule given by target if it exists, otherwise throw an error.

INFO

If strict is set to False (default), the method will replace an existing submodule or create a new submodule if the parent module exists. If strict is set to True, the method will only attempt to replace an existing submodule and throw an error if the submodule does not exist.

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(3, 3, 3) ) (linear): Linear(3, 3) ) )

(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 override the Conv2d with a new submodule Linear, you could call set_submodule("net_b.net_c.conv", nn.Linear(1, 1)) where strict could be True or False

To add a new submodule Conv2d to the existing net_b module, you would call set_submodule("net_b.conv", nn.Conv2d(1, 1, 1)).

In the above if you set strict=True and call set_submodule("net_b.conv", nn.Conv2d(1, 1, 1), strict=True), an AttributeError will be raised because net_b does not have a submodule named conv.

Args

• target: The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
• module: The module to set the submodule to.
• strict: If False, the method will replace an existing submodule or create a new submodule if the parent module exists. If True, the method will only attempt to replace an existing submodule and throw an error if the submodule doesn't already exist.

Raises

• ValueError: If the target string is empty or if module is not an instance of nn.Module.
• AttributeError: If at any point along the path resulting from the target string the (sub)path resolves to a non-existent attribute name or an object that is not an instance of nn.Module.

---

share_memory(self) -> Self [source]

See tensorplay.Tensor.share_memory_.

---

state_dict(self, *args, destination=None, prefix='', keep_vars=False) [source]

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.

INFO

The returned object is a shallow copy. It contains references to the module's parameters and buffers.

DANGER

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.

DANGER

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. 默认值： ''.
• keep_vars (bool, optional): by default the ~tensorplay.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']

---

to(self, *args, **kwargs) [source]

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=tensorplay.channels_last) :noindex:

Its signature is similar to tensorplay.Tensor.to, but only accepts floating point or complex dtype\ s. In addition, this method will only cast the floating point or complex parameters and buffers to dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When 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.

INFO

此方法会就地修改模块。

Args

• device (tensorplay.device): the desired device of the parameters and buffers in this module
• dtype (tensorplay.dtype): the desired floating point or complex dtype of the parameters and buffers in this module
• tensor (tensorplay.Tensor): Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
• memory_format (tensorplay.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(tensorplay.double)
Linear(in_features=2, out_features=2, bias=True)
linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=tensorplay.float64)
# xdoctest: +REQUIRES(env:TENSORPLAY_DOCTEST_CUDA1)
gpu1 = tensorplay.device("cuda:1")
linear.to(gpu1, dtype=tensorplay.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=tensorplay.float16, device='cuda:1')
cpu = tensorplay.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=tensorplay.float16)

linear = nn.Linear(2, 2, bias=None).to(tensorplay.cdouble)
linear.weight
Parameter containing:
tensor([[ 0.3741+0.j,  0.2382+0.j],
        [ 0.5593+0.j, -0.4443+0.j]], dtype=tensorplay.complex128)
linear(tensorplay.ones(3, 2, dtype=tensorplay.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=tensorplay.complex128)

---

to_empty(self, *, device: Union[str, tensorplay.Device, int, NoneType], recurse: bool = True) -> Self [source]

Move the parameters and buffers to the specified device without copying storage.

Args

• device (tensorplay.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

---

train(self, mode: bool = True) -> Self [source]

Set the module in training mode.

这仅对某些模块有影响。请参阅特定模块的文档 了解它们在训练/评估模式下的行为细节， mode, i.e., whether they are affected, e.g. Dropout, BatchNorm, 等。

Args

• mode (bool): whether to set training mode (True) or evaluation mode (False). 默认值： True.

Returns

• Module: self

---

type(self, dst_type: Union[tensorplay.DType, str]) -> Self [source]

Casts all parameters and buffers to dst_type.

INFO

此方法会就地修改模块。

Args

• dst_type (type or string): the desired type

Returns

• Module: self

---

zero_grad(self, set_to_none: bool = True) -> None [source]

Reset gradients of all model parameters.

See similar function under tensorplay.optim.Optimizer for more context.

Args

• set_to_none (bool): instead of setting to zero, set the grads to None. See tensorplay.optim.Optimizer.zero_grad for details.

---

class Tanh [source]

Tanh(*args, **kwargs) -> None

Bases: Module

逐元素应用双曲正切 (Tanh) 函数。

Tanh is defined as:

.. math

\text{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)} {\exp(x) + \exp(-x)}

Shape

• 输入： $(\ast )$, 其中 $\ast$ 表示任意维数。
• 输出： $(\ast )$, 与输入形状相同。

Examples

m = nn.Tanh()
input = tensorplay.randn(2)
output = m(input)

Methods

__init__(self, *args, **kwargs) -> None [source]

初始化内部 Module 状态，由 nn.Module 和 ScriptModule 共享。

---

add_module(self, name: str, module: Optional[ForwardRef('Module')]) -> None [source]

将子模块添加到当前模块。

可以使用给定名称作为属性访问该模块。

Args

• name (str): 子模块的名称。可以使用给定名称 从该模块访问子模块
• module (Module): 要添加到模块的子模块。

---

apply(self, fn: Callable[[ForwardRef('Module')], NoneType]) -> Self [source]

递归地将 fn 应用于每个子模块（由 .children() 返回）以及自身。

典型用途包括初始化模型的参数 (see also nn-init-doc).

Args

• fn (``Module -> None): 应用于每个子模块的函数

Returns

• Module: self

Example

@tensorplay.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)
)

---

bfloat16(self) -> Self [source]

将所有浮点参数和缓冲区转换为 bfloat16 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

buffers(self, recurse: bool = True) -> collections.abc.Iterator[tensorplay._C.TensorBase] [source]

返回模块缓冲区的迭代器。

Args

• recurse (bool): 如果为 True，则生成此模块的缓冲区 以及所有子模块的缓冲区。否则，仅生成 此模块的直接成员缓冲区。

Yields

tensorplay.Tensor: 模块缓冲区

Example

# xdoctest: +SKIP("undefined vars")
for buf in model.buffers():
    print(type(buf), buf.size())
<class 'tensorplay.Tensor'> (20L,)
<class 'tensorplay.Tensor'> (20L, 1L, 5L, 5L)

---

children(self) -> collections.abc.Iterator['Module'] [source]

返回直接子模块的迭代器。

Yields

• Module: 一个子模块

---

compile(self, *args, **kwargs) [source]

使用 tensorplay.compile 编译此模块的前向传播。

此模块的 __call__ 方法被编译，所有参数按原样传递 给 tensorplay.compile。

有关此函数的参数详情，请参阅 tensorplay.compile。

---

cpu(self) -> Self [source]

将所有模型参数和缓冲区移动到 CPU。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

cuda(self, device: Union[int, tensorplay.Device, NoneType] = None) -> Self [source]

将所有模型参数和缓冲区移动到 GPU。

这也使得关联的参数和缓冲区成为不同的对象。所以 如果模块将在 GPU 上运行并进行优化， 应在构建优化器之前调用它。

INFO

此方法会就地修改模块。

Args

• device (int, optional): 如果指定，所有参数将被 复制到该设备

Returns

• Module: self

---

double(self) -> Self [source]

将所有浮点参数和缓冲区转换为 double 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

eval(self) -> Self [source]

将模块设置为评估模式。

这仅对某些模块有影响。请参阅特定模块的文档 了解它们在训练/评估模式下的行为细节， 即它们是否受影响，例如 Dropout、BatchNorm 等。

这相当于 self.train(False) <tensorplay.nn.Module.train>。

有关 .eval() 和其他类似机制的比较， .eval() and several similar mechanisms that may be confused with it.

Returns

• Module: self

---

extra_repr(self) -> str [source]

返回模块的额外表示。

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

---

float(self) -> Self [source]

将所有浮点参数和缓冲区转换为 float 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

forward(self, input: tensorplay._C.TensorBase) -> tensorplay._C.TensorBase [source]

运行前向传播。

---

get_buffer(self, target: str) -> 'Tensor' [source]

如果存在，则返回由 target 指定的缓冲区，否则抛出错误。

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: 缓冲区的完全限定字符串名称 to look for. (See get_submodule for how to specify a fully-qualified string.)

Returns

tensorplay.Tensor: 由 ``target`` 引用的缓冲区

Raises

• AttributeError: If the target string references an invalid path or resolves to something that is not a buffer

---

get_extra_state(self) -> Any [source]

返回要包含在模块 state_dict 中的任何额外状态。

Implement this and a corresponding 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 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

---

get_parameter(self, target: str) -> 'Parameter' [source]

如果存在，则返回由 target 指定的参数，否则抛出错误。

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

tensorplay.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

---

get_submodule(self, target: str) -> 'Module' [source]

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 which 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

tensorplay.nn.Module: The submodule referenced by ``target``

Raises

• AttributeError: If at any point along the path resulting from the target string the (sub)path resolves to a non-existent attribute name or an object that is not an instance of nn.Module.

---

half(self) -> Self [source]

Casts all floating point parameters and buffers to half datatype.

INFO

此方法会就地修改模块。

Returns

• Module: self

---

load_state_dict(self, state_dict: collections.abc.Mapping[str, typing.Any], strict: bool = True, assign: bool = False) [source]

Copy parameters and buffers from state_dict into this module and its descendants.

If strict is True, then the keys of state_dict must exactly match the keys returned by this module's ~tensorplay.nn.Module.state_dict function.

DANGER

If assign is True the optimizer must be created after the call to load_state_dict unless ~tensorplay.__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 state_dict match the keys returned by this module's ~tensorplay.nn.Module.state_dict function. 默认值： True
• assign (bool, optional): When set to False, the properties of the tensors in the current module are preserved whereas setting it to True preserves properties of the Tensors in the state dict. The only exception is the requires_grad field of ~tensorplay.nn.Parameter for which the value from the module is preserved. 默认值： 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 `state_dict`, `load_state_dict` will raise a
``RuntimeError``.

---

modules(self) -> collections.abc.Iterator['Module'] [source]

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)

---

named_buffers(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> collections.abc.Iterator[tuple[str, tensorplay._C.TensorBase]] [source]

Return an iterator over 模块缓冲区s, 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): 如果为 True，则生成此模块的缓冲区 以及所有子模块的缓冲区。否则，仅生成 此模块的直接成员缓冲区。 Defaults to True.
• remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.

Yields

(str, tensorplay.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())

---

named_children(self) -> collections.abc.Iterator[tuple[str, 'Module']] [source]

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)

---

named_modules(self, memo: Optional[set['Module']] = None, prefix: str = '', remove_duplicate: bool = True) [source]

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))

---

named_parameters(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> collections.abc.Iterator[tuple[str, tensorplay.nn.parameter.Parameter]] [source]

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 此模块的直接成员缓冲区。
• 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())

---

parameters(self, recurse: bool = True) -> collections.abc.Iterator[tensorplay.nn.parameter.Parameter] [source]

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 此模块的直接成员缓冲区。

Yields

• Parameter: module parameter

Example

# xdoctest: +SKIP("undefined vars")
for param in model.parameters():
    print(type(param), param.size())
<class 'tensorplay.Tensor'> (20L,)
<class 'tensorplay.Tensor'> (20L, 1L, 5L, 5L)

---

register_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase], Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]]) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a backward hook on the module.

This function is deprecated in favor of ~tensorplay.nn.Module.register_full_backward_hook and the behavior of this function will change in future versions.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_buffer(self, name: str, tensor: Optional[tensorplay._C.TensorBase], persistent: bool = True) -> None [source]

Add a buffer to the module.

This is typically used to register a buffer that should not 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 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 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 cuda, are ignored. If None, the buffer is not included in the module's state_dict.
• persistent (bool): whether the buffer is part of this module's state_dict.

Example

# xdoctest: +SKIP("undefined vars")
self.register_buffer('running_mean', tensorplay.zeros(num_features))

---

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) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a forward hook on the module.

The hook will be called every time after 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 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing forward hooks on this tensorplay.nn.Module. Note that global forward hooks registered with 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

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) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a forward pre-hook on the module.

The hook will be called every time before 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing forward_pre hooks on this tensorplay.nn.Module. Note that global forward_pre hooks registered with 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_full_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase], Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], prepend: bool = False) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a backward hook on the module.

The hook will be called every time the gradients with respect to a module are computed, and its firing rules are as follows:

1. Ordinarily, the hook fires when the gradients are computed with respect to the module inputs.
2. If none of the module inputs require gradients, the hook will fire when the gradients are computed
   with respect to module outputs.
3. If none of the module outputs require gradients, then the hooks will not fire.

The hook should have the following signature

hook(module, grad_input, grad_output) -> tuple(Tensor) or None

The grad_input and 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 grad_input in subsequent computations. grad_input will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in grad_input and 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing backward hooks on this tensorplay.nn.Module. Note that global backward hooks registered with register_module_full_backward_hook will fire before all hooks registered by this method.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_full_backward_pre_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], prepend: bool = False) -> tensorplay.utils.hooks.RemovableHandle [source]

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 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 grad_output in subsequent computations. Entries in 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing backward_pre hooks on this tensorplay.nn.Module. Note that global backward_pre hooks registered with register_module_full_backward_pre_hook will fire before all hooks registered by this method.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_load_state_dict_post_hook(self, hook) [source]

Register a post-hook to be run after module's ~nn.Module.load_state_dict is called.

It should have the following signature

hook(module, incompatible_keys) -> None

The module argument is the current module that this hook is registered on, and the incompatible_keys argument is a NamedTuple consisting of attributes missing_keys and unexpected_keys. missing_keys is a list of str containing the missing keys and unexpected_keys is a list of str containing the unexpected keys.

The given incompatible_keys can be modified inplace if needed.

Note that the checks performed when calling 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_load_state_dict_pre_hook(self, hook) [source]

Register a pre-hook to be run before module's ~nn.Module.load_state_dict is called.

It should have the following signature

hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -> None  # noqa: B950

Arguments

• hook (Callable): Callable hook that will be invoked before loading the state dict.

---

register_module(self, name: str, module: Optional[ForwardRef('Module')]) -> None [source]

Alias for add_module.

---

register_parameter(self, name: str, param: Optional[tensorplay.nn.parameter.Parameter]) -> None [source]

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 cuda, are ignored. If None, the parameter is not included in the module's state_dict.

---

register_state_dict_post_hook(self, hook) [source]

Register a post-hook for the ~tensorplay.nn.Module.state_dict method.

It should have the following signature

hook(module, state_dict, prefix, local_metadata) -> None

The registered hooks can modify the state_dict inplace.

---

register_state_dict_pre_hook(self, hook) [source]

Register a pre-hook for the ~tensorplay.nn.Module.state_dict method.

It should have the following signature

hook(module, prefix, keep_vars) -> None

The registered hooks can be used to perform pre-processing before the state_dict call is made.

---

requires_grad_(self, requires_grad: bool = True) -> Self [source]

Change if autograd should record operations on parameters in this module.

This method sets the parameters' 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).

有关 .eval() 和其他类似机制的比较， .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. 默认值： True.

Returns

• Module: self

---

set_extra_state(self, state: Any) -> None [source]

Set extra state contained in the loaded state_dict.

This function is called from load_state_dict to handle any extra state found within the state_dict. Implement this function and a corresponding 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

---

set_submodule(self, target: str, module: 'Module', strict: bool = False) -> None [source]

Set the submodule given by target if it exists, otherwise throw an error.

INFO

If strict is set to False (default), the method will replace an existing submodule or create a new submodule if the parent module exists. If strict is set to True, the method will only attempt to replace an existing submodule and throw an error if the submodule does not exist.

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(3, 3, 3) ) (linear): Linear(3, 3) ) )

(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 override the Conv2d with a new submodule Linear, you could call set_submodule("net_b.net_c.conv", nn.Linear(1, 1)) where strict could be True or False

To add a new submodule Conv2d to the existing net_b module, you would call set_submodule("net_b.conv", nn.Conv2d(1, 1, 1)).

In the above if you set strict=True and call set_submodule("net_b.conv", nn.Conv2d(1, 1, 1), strict=True), an AttributeError will be raised because net_b does not have a submodule named conv.

Args

• target: The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
• module: The module to set the submodule to.
• strict: If False, the method will replace an existing submodule or create a new submodule if the parent module exists. If True, the method will only attempt to replace an existing submodule and throw an error if the submodule doesn't already exist.

Raises

• ValueError: If the target string is empty or if module is not an instance of nn.Module.
• AttributeError: If at any point along the path resulting from the target string the (sub)path resolves to a non-existent attribute name or an object that is not an instance of nn.Module.

---

share_memory(self) -> Self [source]

See tensorplay.Tensor.share_memory_.

---

state_dict(self, *args, destination=None, prefix='', keep_vars=False) [source]

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.

INFO

The returned object is a shallow copy. It contains references to the module's parameters and buffers.

DANGER

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.

DANGER

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. 默认值： ''.
• keep_vars (bool, optional): by default the ~tensorplay.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']

---

to(self, *args, **kwargs) [source]

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=tensorplay.channels_last) :noindex:

Its signature is similar to tensorplay.Tensor.to, but only accepts floating point or complex dtype\ s. In addition, this method will only cast the floating point or complex parameters and buffers to dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When 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.

INFO

此方法会就地修改模块。

Args

• device (tensorplay.device): the desired device of the parameters and buffers in this module
• dtype (tensorplay.dtype): the desired floating point or complex dtype of the parameters and buffers in this module
• tensor (tensorplay.Tensor): Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
• memory_format (tensorplay.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(tensorplay.double)
Linear(in_features=2, out_features=2, bias=True)
linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=tensorplay.float64)
# xdoctest: +REQUIRES(env:TENSORPLAY_DOCTEST_CUDA1)
gpu1 = tensorplay.device("cuda:1")
linear.to(gpu1, dtype=tensorplay.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=tensorplay.float16, device='cuda:1')
cpu = tensorplay.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=tensorplay.float16)

linear = nn.Linear(2, 2, bias=None).to(tensorplay.cdouble)
linear.weight
Parameter containing:
tensor([[ 0.3741+0.j,  0.2382+0.j],
        [ 0.5593+0.j, -0.4443+0.j]], dtype=tensorplay.complex128)
linear(tensorplay.ones(3, 2, dtype=tensorplay.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=tensorplay.complex128)

---

to_empty(self, *, device: Union[str, tensorplay.Device, int, NoneType], recurse: bool = True) -> Self [source]

Move the parameters and buffers to the specified device without copying storage.

Args

• device (tensorplay.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

---

train(self, mode: bool = True) -> Self [source]

Set the module in training mode.

这仅对某些模块有影响。请参阅特定模块的文档 了解它们在训练/评估模式下的行为细节， mode, i.e., whether they are affected, e.g. Dropout, BatchNorm, 等。

Args

• mode (bool): whether to set training mode (True) or evaluation mode (False). 默认值： True.

Returns

• Module: self

---

type(self, dst_type: Union[tensorplay.DType, str]) -> Self [source]

Casts all parameters and buffers to dst_type.

INFO

此方法会就地修改模块。

Args

• dst_type (type or string): the desired type

Returns

• Module: self

---

zero_grad(self, set_to_none: bool = True) -> None [source]

Reset gradients of all model parameters.

See similar function under tensorplay.optim.Optimizer for more context.

Args

• set_to_none (bool): instead of setting to zero, set the grads to None. See tensorplay.optim.Optimizer.zero_grad for details.

---

class Threshold [source]

Threshold(threshold: float, value: float, inplace: bool = False) -> None

Bases: Module

对输入张量的每个元素进行阈值处理。

Threshold is defined as:

.. math

y =
\begin{cases}
x, &\text{ if } x > \text{threshold} \\
\text{value}, &\text{ otherwise }
\end{cases}

Args

• threshold: The value to threshold at
• value: The value to replace with
• inplace: can optionally do the operation in-place. 默认值： False

Shape

• 输入： $(\ast )$, 其中 $\ast$ 表示任意维数。
• 输出： $(\ast )$, 与输入形状相同。

Examples

m = tensorplay.nn.Threshold(0, 0.5)
input = tensorplay.arange(-3, 3)
output = m(input)

Methods

__init__(self, threshold: float, value: float, inplace: bool = False) -> None [source]

初始化内部 Module 状态，由 nn.Module 和 ScriptModule 共享。

---

add_module(self, name: str, module: Optional[ForwardRef('Module')]) -> None [source]

将子模块添加到当前模块。

可以使用给定名称作为属性访问该模块。

Args

• name (str): 子模块的名称。可以使用给定名称 从该模块访问子模块
• module (Module): 要添加到模块的子模块。

---

apply(self, fn: Callable[[ForwardRef('Module')], NoneType]) -> Self [source]

递归地将 fn 应用于每个子模块（由 .children() 返回）以及自身。

典型用途包括初始化模型的参数 (see also nn-init-doc).

Args

• fn (``Module -> None): 应用于每个子模块的函数

Returns

• Module: self

Example

@tensorplay.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)
)

---

bfloat16(self) -> Self [source]

将所有浮点参数和缓冲区转换为 bfloat16 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

buffers(self, recurse: bool = True) -> collections.abc.Iterator[tensorplay._C.TensorBase] [source]

返回模块缓冲区的迭代器。

Args

• recurse (bool): 如果为 True，则生成此模块的缓冲区 以及所有子模块的缓冲区。否则，仅生成 此模块的直接成员缓冲区。

Yields

tensorplay.Tensor: 模块缓冲区

Example

# xdoctest: +SKIP("undefined vars")
for buf in model.buffers():
    print(type(buf), buf.size())
<class 'tensorplay.Tensor'> (20L,)
<class 'tensorplay.Tensor'> (20L, 1L, 5L, 5L)

---

children(self) -> collections.abc.Iterator['Module'] [source]

返回直接子模块的迭代器。

Yields

• Module: 一个子模块

---

compile(self, *args, **kwargs) [source]

使用 tensorplay.compile 编译此模块的前向传播。

此模块的 __call__ 方法被编译，所有参数按原样传递 给 tensorplay.compile。

有关此函数的参数详情，请参阅 tensorplay.compile。

---

cpu(self) -> Self [source]

将所有模型参数和缓冲区移动到 CPU。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

cuda(self, device: Union[int, tensorplay.Device, NoneType] = None) -> Self [source]

将所有模型参数和缓冲区移动到 GPU。

这也使得关联的参数和缓冲区成为不同的对象。所以 如果模块将在 GPU 上运行并进行优化， 应在构建优化器之前调用它。

INFO

此方法会就地修改模块。

Args

• device (int, optional): 如果指定，所有参数将被 复制到该设备

Returns

• Module: self

---

double(self) -> Self [source]

将所有浮点参数和缓冲区转换为 double 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

eval(self) -> Self [source]

将模块设置为评估模式。

这仅对某些模块有影响。请参阅特定模块的文档 了解它们在训练/评估模式下的行为细节， 即它们是否受影响，例如 Dropout、BatchNorm 等。

这相当于 self.train(False) <tensorplay.nn.Module.train>。

有关 .eval() 和其他类似机制的比较， .eval() and several similar mechanisms that may be confused with it.

Returns

• Module: self

---

extra_repr(self) -> str [source]

返回模块的额外表示。

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

---

float(self) -> Self [source]

将所有浮点参数和缓冲区转换为 float 数据类型。

INFO

此方法会就地修改模块。

Returns

• Module: self

---

forward(self, input: tensorplay._C.TensorBase) -> tensorplay._C.TensorBase [source]

运行前向传播。

---

get_buffer(self, target: str) -> 'Tensor' [source]

如果存在，则返回由 target 指定的缓冲区，否则抛出错误。

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: 缓冲区的完全限定字符串名称 to look for. (See get_submodule for how to specify a fully-qualified string.)

Returns

tensorplay.Tensor: 由 ``target`` 引用的缓冲区

Raises

• AttributeError: If the target string references an invalid path or resolves to something that is not a buffer

---

get_extra_state(self) -> Any [source]

返回要包含在模块 state_dict 中的任何额外状态。

Implement this and a corresponding 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 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

---

get_parameter(self, target: str) -> 'Parameter' [source]

如果存在，则返回由 target 指定的参数，否则抛出错误。

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

tensorplay.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

---

get_submodule(self, target: str) -> 'Module' [source]

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 which 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

tensorplay.nn.Module: The submodule referenced by ``target``

Raises

• AttributeError: If at any point along the path resulting from the target string the (sub)path resolves to a non-existent attribute name or an object that is not an instance of nn.Module.

---

half(self) -> Self [source]

Casts all floating point parameters and buffers to half datatype.

INFO

此方法会就地修改模块。

Returns

• Module: self

---

load_state_dict(self, state_dict: collections.abc.Mapping[str, typing.Any], strict: bool = True, assign: bool = False) [source]

Copy parameters and buffers from state_dict into this module and its descendants.

If strict is True, then the keys of state_dict must exactly match the keys returned by this module's ~tensorplay.nn.Module.state_dict function.

DANGER

If assign is True the optimizer must be created after the call to load_state_dict unless ~tensorplay.__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 state_dict match the keys returned by this module's ~tensorplay.nn.Module.state_dict function. 默认值： True
• assign (bool, optional): When set to False, the properties of the tensors in the current module are preserved whereas setting it to True preserves properties of the Tensors in the state dict. The only exception is the requires_grad field of ~tensorplay.nn.Parameter for which the value from the module is preserved. 默认值： 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 `state_dict`, `load_state_dict` will raise a
``RuntimeError``.

---

modules(self) -> collections.abc.Iterator['Module'] [source]

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)

---

named_buffers(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> collections.abc.Iterator[tuple[str, tensorplay._C.TensorBase]] [source]

Return an iterator over 模块缓冲区s, 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): 如果为 True，则生成此模块的缓冲区 以及所有子模块的缓冲区。否则，仅生成 此模块的直接成员缓冲区。 Defaults to True.
• remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.

Yields

(str, tensorplay.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())

---

named_children(self) -> collections.abc.Iterator[tuple[str, 'Module']] [source]

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)

---

named_modules(self, memo: Optional[set['Module']] = None, prefix: str = '', remove_duplicate: bool = True) [source]

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))

---

named_parameters(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> collections.abc.Iterator[tuple[str, tensorplay.nn.parameter.Parameter]] [source]

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 此模块的直接成员缓冲区。
• 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())

---

parameters(self, recurse: bool = True) -> collections.abc.Iterator[tensorplay.nn.parameter.Parameter] [source]

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 此模块的直接成员缓冲区。

Yields

• Parameter: module parameter

Example

# xdoctest: +SKIP("undefined vars")
for param in model.parameters():
    print(type(param), param.size())
<class 'tensorplay.Tensor'> (20L,)
<class 'tensorplay.Tensor'> (20L, 1L, 5L, 5L)

---

register_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase], Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]]) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a backward hook on the module.

This function is deprecated in favor of ~tensorplay.nn.Module.register_full_backward_hook and the behavior of this function will change in future versions.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_buffer(self, name: str, tensor: Optional[tensorplay._C.TensorBase], persistent: bool = True) -> None [source]

Add a buffer to the module.

This is typically used to register a buffer that should not 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 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 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 cuda, are ignored. If None, the buffer is not included in the module's state_dict.
• persistent (bool): whether the buffer is part of this module's state_dict.

Example

# xdoctest: +SKIP("undefined vars")
self.register_buffer('running_mean', tensorplay.zeros(num_features))

---

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) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a forward hook on the module.

The hook will be called every time after 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 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing forward hooks on this tensorplay.nn.Module. Note that global forward hooks registered with 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

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) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a forward pre-hook on the module.

The hook will be called every time before 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing forward_pre hooks on this tensorplay.nn.Module. Note that global forward_pre hooks registered with 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_full_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase], Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], prepend: bool = False) -> tensorplay.utils.hooks.RemovableHandle [source]

Register a backward hook on the module.

The hook will be called every time the gradients with respect to a module are computed, and its firing rules are as follows:

1. Ordinarily, the hook fires when the gradients are computed with respect to the module inputs.
2. If none of the module inputs require gradients, the hook will fire when the gradients are computed
   with respect to module outputs.
3. If none of the module outputs require gradients, then the hooks will not fire.

The hook should have the following signature

hook(module, grad_input, grad_output) -> tuple(Tensor) or None

The grad_input and 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 grad_input in subsequent computations. grad_input will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in grad_input and 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing backward hooks on this tensorplay.nn.Module. Note that global backward hooks registered with register_module_full_backward_hook will fire before all hooks registered by this method.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_full_backward_pre_hook(self, hook: Callable[[ForwardRef('Module'), Union[tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], Union[NoneType, tuple[tensorplay._C.TensorBase, ...], tensorplay._C.TensorBase]], prepend: bool = False) -> tensorplay.utils.hooks.RemovableHandle [source]

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 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 grad_output in subsequent computations. Entries in 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 tensorplay.nn.Module. Otherwise, the provided hook will be fired after all existing backward_pre hooks on this tensorplay.nn.Module. Note that global backward_pre hooks registered with register_module_full_backward_pre_hook will fire before all hooks registered by this method.

Returns

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_load_state_dict_post_hook(self, hook) [source]

Register a post-hook to be run after module's ~nn.Module.load_state_dict is called.

It should have the following signature

hook(module, incompatible_keys) -> None

The module argument is the current module that this hook is registered on, and the incompatible_keys argument is a NamedTuple consisting of attributes missing_keys and unexpected_keys. missing_keys is a list of str containing the missing keys and unexpected_keys is a list of str containing the unexpected keys.

The given incompatible_keys can be modified inplace if needed.

Note that the checks performed when calling 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

`tensorplay.utils.hooks.RemovableHandle`:
    a handle that can be used to remove the added hook by calling
    ``handle.remove()``

---

register_load_state_dict_pre_hook(self, hook) [source]

Register a pre-hook to be run before module's ~nn.Module.load_state_dict is called.

It should have the following signature

hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -> None  # noqa: B950

Arguments

• hook (Callable): Callable hook that will be invoked before loading the state dict.

---

register_module(self, name: str, module: Optional[ForwardRef('Module')]) -> None [source]

Alias for add_module.

---

register_parameter(self, name: str, param: Optional[tensorplay.nn.parameter.Parameter]) -> None [source]

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 cuda, are ignored. If None, the parameter is not included in the module's state_dict.

---

register_state_dict_post_hook(self, hook) [source]

Register a post-hook for the ~tensorplay.nn.Module.state_dict method.

It should have the following signature

hook(module, state_dict, prefix, local_metadata) -> None

The registered hooks can modify the state_dict inplace.

---

register_state_dict_pre_hook(self, hook) [source]

Register a pre-hook for the ~tensorplay.nn.Module.state_dict method.

It should have the following signature

hook(module, prefix, keep_vars) -> None

The registered hooks can be used to perform pre-processing before the state_dict call is made.

---

requires_grad_(self, requires_grad: bool = True) -> Self [source]

Change if autograd should record operations on parameters in this module.

This method sets the parameters' 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).

有关 .eval() 和其他类似机制的比较， .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. 默认值： True.

Returns

• Module: self

---

set_extra_state(self, state: Any) -> None [source]

Set extra state contained in the loaded state_dict.

This function is called from load_state_dict to handle any extra state found within the state_dict. Implement this function and a corresponding 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

---

set_submodule(self, target: str, module: 'Module', strict: bool = False) -> None [source]

Set the submodule given by target if it exists, otherwise throw an error.

INFO

If strict is set to False (default), the method will replace an existing submodule or create a new submodule if the parent module exists. If strict is set to True, the method will only attempt to replace an existing submodule and throw an error if the submodule does not exist.

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(3, 3, 3) ) (linear): Linear(3, 3) ) )

(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 override the Conv2d with a new submodule Linear, you could call set_submodule("net_b.net_c.conv", nn.Linear(1, 1)) where strict could be True or False

To add a new submodule Conv2d to the existing net_b module, you would call set_submodule("net_b.conv", nn.Conv2d(1, 1, 1)).

In the above if you set strict=True and call set_submodule("net_b.conv", nn.Conv2d(1, 1, 1), strict=True), an AttributeError will be raised because net_b does not have a submodule named conv.

Args

• target: The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.)
• module: The module to set the submodule to.
• strict: If False, the method will replace an existing submodule or create a new submodule if the parent module exists. If True, the method will only attempt to replace an existing submodule and throw an error if the submodule doesn't already exist.

Raises

• ValueError: If the target string is empty or if module is not an instance of nn.Module.
• AttributeError: If at any point along the path resulting from the target string the (sub)path resolves to a non-existent attribute name or an object that is not an instance of nn.Module.

---

share_memory(self) -> Self [source]

See tensorplay.Tensor.share_memory_.

---

state_dict(self, *args, destination=None, prefix='', keep_vars=False) [source]

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.

INFO

The returned object is a shallow copy. It contains references to the module's parameters and buffers.

DANGER

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.

DANGER

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. 默认值： ''.
• keep_vars (bool, optional): by default the ~tensorplay.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']

---

to(self, *args, **kwargs) [source]

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=tensorplay.channels_last) :noindex:

Its signature is similar to tensorplay.Tensor.to, but only accepts floating point or complex dtype\ s. In addition, this method will only cast the floating point or complex parameters and buffers to dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When 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.

INFO

此方法会就地修改模块。

Args

• device (tensorplay.device): the desired device of the parameters and buffers in this module
• dtype (tensorplay.dtype): the desired floating point or complex dtype of the parameters and buffers in this module
• tensor (tensorplay.Tensor): Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
• memory_format (tensorplay.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(tensorplay.double)
Linear(in_features=2, out_features=2, bias=True)
linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=tensorplay.float64)
# xdoctest: +REQUIRES(env:TENSORPLAY_DOCTEST_CUDA1)
gpu1 = tensorplay.device("cuda:1")
linear.to(gpu1, dtype=tensorplay.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=tensorplay.float16, device='cuda:1')
cpu = tensorplay.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=tensorplay.float16)

linear = nn.Linear(2, 2, bias=None).to(tensorplay.cdouble)
linear.weight
Parameter containing:
tensor([[ 0.3741+0.j,  0.2382+0.j],
        [ 0.5593+0.j, -0.4443+0.j]], dtype=tensorplay.complex128)
linear(tensorplay.ones(3, 2, dtype=tensorplay.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=tensorplay.complex128)

---

to_empty(self, *, device: Union[str, tensorplay.Device, int, NoneType], recurse: bool = True) -> Self [source]

Move the parameters and buffers to the specified device without copying storage.

Args

• device (tensorplay.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

---

train(self, mode: bool = True) -> Self [source]

Set the module in training mode.

这仅对某些模块有影响。请参阅特定模块的文档 了解它们在训练/评估模式下的行为细节， mode, i.e., whether they are affected, e.g. Dropout, BatchNorm, 等。

Args

• mode (bool): whether to set training mode (True) or evaluation mode (False). 默认值： True.

Returns

• Module: self

---

type(self, dst_type: Union[tensorplay.DType, str]) -> Self [source]

Casts all parameters and buffers to dst_type.

INFO

此方法会就地修改模块。

Args

• dst_type (type or string): the desired type

Returns

• Module: self

---

zero_grad(self, set_to_none: bool = True) -> None [source]

Reset gradients of all model parameters.

See similar function under tensorplay.optim.Optimizer for more context.

Args

• set_to_none (bool): instead of setting to zero, set the grads to None. See tensorplay.optim.Optimizer.zero_grad for details.

---