# RemoteModule

Source: https://www.tensorplay.cn/docs/generated/tensorplay.distributed.nn.RemoteModule.html

```python
class tensorplay.distributed.nn.RemoteModule(remote_device: str, module_cls: type[Module], args: tuple[Any, ...] | None = None, kwargs: dict[str, Any] | None = None)
```

```python
compile(*args, **kwargs)
```

Compile this Module’s forward using tensorplay.compile().

This Module’s __call__ method is compiled and all arguments are passed as-is
to tensorplay.compile().

See tensorplay.compile() for details on the arguments for this function.

```python
forward(*input: Any) → None
```

Define the computation performed at every call.

Should be overridden by all subclasses.

> **Note**
>
> Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

```python
get_buffer(target: str) → Tensor
```

Return the buffer given by target if it exists, otherwise throw an error.

See the docstring for get_submodule for a more detailed
explanation of this method’s functionality as well as how to
correctly specify target.

Parameters:

target – The fully-qualified string name of the buffer
to look for. (See get_submodule for how to specify a
fully-qualified string.)

Returns:

The buffer referenced by target

Return type:

[tensorplay.Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)

Raises:

[AttributeError](https://docs.python.org/3/builtins/exceptions.html#AttributeError) – If the target string references an invalid
    path or resolves to something that is not a
    buffer

```python
get_extra_state() → Any
```

Return any extra state to include in the module’s state_dict.

Implement this and a corresponding [set_extra_state()](#tensorplay.distributed.nn.RemoteModule.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:

Any extra state to store in the module’s state_dict

Return type:

[object](https://docs.python.org/3/builtins/functions.html#object)

```python
get_parameter(target: str) → Parameter
```

Return the parameter given by target if it exists, otherwise throw an error.

See the docstring for get_submodule for a more detailed
explanation of this method’s functionality as well as how to
correctly specify target.

Parameters:

target – The fully-qualified string name of the Parameter
to look for. (See get_submodule for how to specify a
fully-qualified string.)

Returns:

The Parameter referenced by target

Return type:

tensorplay.nn.Parameter

Raises:

[AttributeError](https://docs.python.org/3/builtins/exceptions.html#AttributeError) – If the target string references an invalid
    path or resolves to something that is not an
    nn.Parameter

```python
get_submodule(target: str) → Module
```

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

For example, let’s say you have an nn.Module A that
looks like this:

```
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.

Parameters:

target – The fully-qualified string name of the submodule
to look for. (See above example for how to specify a
fully-qualified string.)

Returns:

The submodule referenced by target

Return type:

tensorplay.nn.Module

Raises:

[AttributeError](https://docs.python.org/3/builtins/exceptions.html#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.

```python
register_full_backward_hook(hook: Callable[[Module, tuple[Tensor, ...] | Tensor, tuple[Tensor, ...] | Tensor], None | tuple[Tensor, ...] | Tensor], prepend: bool = False) → RemovableHandle
```

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:

- Ordinarily, the hook fires when the gradients are computed with respect to the module inputs.

- If none of the module inputs require gradients, the hook will fire when the gradients are computed with respect to module outputs.

- 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.

Parameters:

- hook (Callable) – The user-defined hook to be registered.

- prepend ([bool](https://docs.python.org/3/builtins/functions.html#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:

a handle that can be used to remove the added hook by calling
handle.remove()

Return type:

tensorplay.utils.hooks.RemovableHandle

```python
register_full_backward_pre_hook(hook: Callable[[Module, tuple[Tensor, ...] | Tensor], None | tuple[Tensor, ...] | Tensor], prepend: bool = False) → RemovableHandle
```

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.

Parameters:

- hook (Callable) – The user-defined hook to be registered.

- prepend ([bool](https://docs.python.org/3/builtins/functions.html#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:

a handle that can be used to remove the added hook by calling
handle.remove()

Return type:

tensorplay.utils.hooks.RemovableHandle

```python
register_load_state_dict_post_hook(hook)
```

Register a post-hook to be run after module’s load_state_dict() is called.

It should have the following signature::

hook(module, incompatible_keys) -> None

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

The given incompatible_keys can be modified inplace if needed.

Note that the checks performed when calling 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:

a handle that can be used to remove the added hook by calling
handle.remove()

Return type:

tensorplay.utils.hooks.RemovableHandle

```python
register_load_state_dict_pre_hook(hook)
```

Register a pre-hook to be run before module’s 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

Parameters:

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

```python
register_module(name: str, module: Module | None) → None
```

Alias for add_module().

```python
register_state_dict_post_hook(hook)
```

Register a post-hook for the 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.

```python
register_state_dict_pre_hook(hook)
```

Register a pre-hook for the 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.

```python
set_extra_state(state: Any) → None
```

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()](#tensorplay.distributed.nn.RemoteModule.get_extra_state) for your module if you need to store extra state within its
state_dict.

Parameters:

state ([dict](https://docs.python.org/3/builtins/stdtypes.html#dict)) – Extra state from the state_dict

```python
set_submodule(target: str, module: Module, strict: bool = False) → None
```

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

> **Note**
>
> 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:

```
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.

Parameters:

- 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](https://docs.python.org/3/builtins/exceptions.html#ValueError) – If the target string is empty or if module is not an instance of nn.Module.

- [AttributeError](https://docs.python.org/3/builtins/exceptions.html#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.

```python
to_empty(*, device: str | device | int | None, recurse: bool = True) → Self
```

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

Parameters:

- device ([tensorplay.device](/docs/generated/tensorplay.Device.html#tensorplay.Device)) – The desired device of the parameters and buffers in this module.

- recurse ([bool](https://docs.python.org/3/builtins/functions.html#bool)) – Whether parameters and buffers of submodules should be recursively moved to the specified device.

Returns:

self

Return type:

Module