# hub.solver.ray_rllib.custom_models

Domain specification

Domain

Only show finetuned characteristics Simplify signatures

# TFParametricActionsModel

Parametric action model that handles the dot product and masking and that also learns action embeddings. TensorFlow version.

This assumes the outputs are logits for a single Categorical action dist.

# Constructor TFParametricActionsModel

TFParametricActionsModel(
  obs_space,
  action_space,
  num_outputs,
  model_config,
  name,
  **kw
)

Initializes a TFModelV2 instance.

Here is an example implementation for a subclass MyModelClass(TFModelV2)::

def __init__(self, *args, **kwargs):
    super(MyModelClass, self).__init__(*args, **kwargs)
    input_layer = tf.keras.layers.Input(...)
    hidden_layer = tf.keras.layers.Dense(...)(input_layer)
    output_layer = tf.keras.layers.Dense(...)(hidden_layer)
    value_layer = tf.keras.layers.Dense(...)(hidden_layer)
    self.base_model = tf.keras.Model(
        input_layer, [output_layer, value_layer])

# context ModelV2

context(
  self
) ->

Returns a contextmanager for the current TF graph.

# custom_loss ModelV2

custom_loss(
  self,
  policy_loss: typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')],
  loss_inputs: typing.Dict[str, typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]]
) -> typing.Union[typing.List[typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]], , ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]

Override to customize the loss function used to optimize this model.

This can be used to incorporate self-supervised losses (by defining a loss over existing input and output tensors of this model), and supervised losses (by defining losses over a variable-sharing copy of this model's layers).

You can find an runnable example in examples/custom_loss.py.

Args: policy_loss: List of or single policy loss(es) from the policy. loss_inputs: map of input placeholders for rollout data.

Returns: List of or scalar tensor for the customized loss(es) for this model.

# forward ModelV2

forward(
  self,
  input_dict,
  state,
  seq_lens
)

Call the model with the given input tensors and state.

Any complex observations (dicts, tuples, etc.) will be unpacked by call before being passed to forward(). To access the flattened observation tensor, refer to input_dict["obs_flat"].

This method can be called any number of times. In eager execution, each call to forward() will eagerly evaluate the model. In symbolic execution, each call to forward creates a computation graph that operates over the variables of this model (i.e., shares weights).

Custom models should override this instead of call.

Args: input_dict: dictionary of input tensors, including "obs", "obs_flat", "prev_action", "prev_reward", "is_training", "eps_id", "agent_id", "infos", and "t". state: list of state tensors with sizes matching those returned by get_initial_state + the batch dimension seq_lens: 1d tensor holding input sequence lengths

Returns: A tuple consisting of the model output tensor of size [BATCH, num_outputs] and the list of new RNN state(s) if any.

.. testcode:: :skipif: True

import numpy as np
from ray.rllib.models.modelv2 import ModelV2
class MyModel(ModelV2):
    # ...
    def forward(self, input_dict, state, seq_lens):
        model_out, self._value_out = self.base_model(
            input_dict["obs"])
        return model_out, state

# get_initial_state ModelV2

get_initial_state(
  self
) -> typing.List[typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]]

Get the initial recurrent state values for the model.

Returns: List of np.array (for tf) or Tensor (for torch) objects containing the initial hidden state of an RNN, if applicable.

.. testcode:: :skipif: True

import numpy as np
from ray.rllib.models.modelv2 import ModelV2
class MyModel(ModelV2):
    # ...
    def get_initial_state(self):
        return [
            np.zeros(self.cell_size, np.float32),
            np.zeros(self.cell_size, np.float32),
        ]

# is_time_major ModelV2

is_time_major(
  self
) ->

If True, data for calling this ModelV2 must be in time-major format.

Returns Whether this ModelV2 requires a time-major (TxBx...) data format.

# last_output ModelV2

last_output(
  self
) -> typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]

Returns the last output returned from calling the model.

# metrics ModelV2

metrics(
  self
) -> typing.Dict[str, typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]]

Override to return custom metrics from your model.

The stats will be reported as part of the learner stats, i.e., info.learner.[policy_id, e.g. "default_policy"].model.key1=metric1

Returns: The custom metrics for this model.

# register_variables TFModelV2

register_variables(
  self,
  variables: typing.List[typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]]
)

# trainable_variables ModelV2

trainable_variables(
  self,
  as_dict:  = False
) -> typing.Union[typing.List[typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]], typing.Dict[str, typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]]]

Returns the list of trainable variables for this model.

Args: as_dict: Whether variables should be returned as dict-values (using descriptive keys).

Returns: The list (or dict if as_dict is True) of all trainable (tf)/requires_grad (torch) variables of this ModelV2.

# update_ops TFModelV2

update_ops(
  self
) -> typing.List[typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]]

Return the list of update ops for this model.

For example, this should include any BatchNorm update ops.

# value_function ModelV2

value_function(
  self
)

Returns the value function output for the most recent forward pass.

Note that a forward call has to be performed first, before this methods can return anything and thus that calling this method does not cause an extra forward pass through the network.

Returns: Value estimate tensor of shape [BATCH].

# variables ModelV2

variables(
  self,
  as_dict:  = False
) -> typing.Union[typing.List[typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]], typing.Dict[str, typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]]]

Returns the list (or a dict) of variables for this model.

Args: as_dict: Whether variables should be returned as dict-values (using descriptive str keys).

Returns: The list (or dict if as_dict is True) of all variables of this ModelV2.

# _annotated_type ModelV2

# TorchParametricActionsModel

Parametric action model that handles the dot product and masking and that also learns action embeddings. PyTorch version.

This assumes the outputs are logits for a single Categorical action dist.

# Constructor TorchParametricActionsModel

TorchParametricActionsModel(
  obs_space,
  action_space,
  num_outputs,
  model_config,
  name,
  **kw
)

Initialize a TorchModelV2.

Here is an example implementation for a subclass MyModelClass(TorchModelV2, nn.Module)::

def __init__(self, *args, **kwargs):
    TorchModelV2.__init__(self, *args, **kwargs)
    nn.Module.__init__(self)
    self._hidden_layers = nn.Sequential(...)
    self._logits = ...
    self._value_branch = ...

# add_module Module

add_module(
  self,
  name: ,
  module: typing.Optional[ForwardRef('Module')]
)

Add a child module to the current module.

The module can be accessed as an attribute using the given name.

Args: name (str): name of the child module. The child module can be accessed from this module using the given name module (Module): child module to be added to the module.

# apply Module

apply(
  self: ~T,
  fn: typing.Callable[[ForwardRef('Module')], NoneType]
) -> ~T

Apply fn recursively to every submodule (as returned by .children()) as well as self.

Typical use includes initializing the parameters of a model (see also :ref:nn-init-doc).

Args: fn (:class:Module -> None): function to be applied to each submodule

Returns: Module: self

Example::

>>> @torch.no_grad()
>>> def init_weights(m):
>>>     print(m)
>>>     if type(m) == nn.Linear:
>>>         m.weight.fill_(1.0)
>>>         print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[1., 1.],
        [1., 1.]], requires_grad=True)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[1., 1.],
        [1., 1.]], requires_grad=True)
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)

# bfloat16 Module

bfloat16(
  self: ~T
) -> ~T

Casts all floating point parameters and buffers to bfloat16 datatype.

.. note:: This method modifies the module in-place.

Returns: Module: self

# buffers Module

buffers(
  self,
  recurse:  = True
) -> typing.Iterator[torch.Tensor]

Return an iterator over module buffers.

Args: recurse (bool): if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.

Yields: torch.Tensor: module buffer

Example::

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

# children Module

children(
  self
) -> typing.Iterator[ForwardRef('Module')]

Return an iterator over immediate children modules.

Yields: Module: a child module

# compile Module

compile(
  self,
  *args,
  **kwargs
)

Compile this Module's forward using :func:torch.compile.

This Module's __call__ method is compiled and all arguments are passed as-is to :func:torch.compile.

See :func:torch.compile for details on the arguments for this function.

# context ModelV2

context(
  self
) ->

Returns a contextmanager for the current forward pass.

# cpu Module

cpu(
  self: ~T
) -> ~T

Move all model parameters and buffers to the CPU.

.. note:: This method modifies the module in-place.

Returns: Module: self

# cuda Module

cuda(
  self: ~T,
  device: typing.Union[int, torch.device, NoneType] = None
) -> ~T

Move all model parameters and buffers to the GPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.

.. note:: This method modifies the module in-place.

Args: device (int, optional): if specified, all parameters will be copied to that device

Returns: Module: self

# custom_loss ModelV2

custom_loss(
  self,
  policy_loss: typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')],
  loss_inputs: typing.Dict[str, typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]]
) -> typing.Union[typing.List[typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]], , ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]

Override to customize the loss function used to optimize this model.

You can find an runnable example in examples/custom_loss.py.

Args: policy_loss: List of or single policy loss(es) from the policy. loss_inputs: map of input placeholders for rollout data.

Returns: List of or scalar tensor for the customized loss(es) for this model.

# double Module

double(
  self: ~T
) -> ~T

Casts all floating point parameters and buffers to double datatype.

.. note:: This method modifies the module in-place.

Returns: Module: self

# eval Module

eval(
  self: ~T
) -> ~T

Set the module in evaluation mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. :class:Dropout, :class:BatchNorm, etc.

This is equivalent with :meth:self.train(False) \<torch.nn.Module.train>.

See :ref:locally-disable-grad-doc for a comparison between .eval() and several similar mechanisms that may be confused with it.

Returns: Module: self

# extra_repr Module

extra_repr(
  self
) ->

Set the extra representation of the module.

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

# float Module

float(
  self: ~T
) -> ~T

Casts all floating point parameters and buffers to float datatype.

.. note:: This method modifies the module in-place.

Returns: Module: self

# forward Module

forward(
  self,
  input_dict,
  state,
  seq_lens
)

Call the model with the given input tensors and state.

Any complex observations (dicts, tuples, etc.) will be unpacked by call before being passed to forward(). To access the flattened observation tensor, refer to input_dict["obs_flat"].

Custom models should override this instead of call.

Returns: A tuple consisting of the model output tensor of size [BATCH, num_outputs] and the list of new RNN state(s) if any.

.. testcode:: :skipif: True

import numpy as np
from ray.rllib.models.modelv2 import ModelV2
class MyModel(ModelV2):
    # ...
    def forward(self, input_dict, state, seq_lens):
        model_out, self._value_out = self.base_model(
            input_dict["obs"])
        return model_out, state

# get_buffer Module

get_buffer(
  self,
  target: 
) -> Tensor

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

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

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

Returns: torch.Tensor: The buffer referenced by target

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

# get_extra_state Module

get_extra_state(
  self
) -> typing.Any

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

Implement this and a corresponding :func:set_extra_state for your module if you need to store extra state. This function is called when building the module's state_dict().

Note that extra state should be picklable to ensure working serialization of the state_dict. We only provide provide backwards compatibility guarantees for serializing Tensors; other objects may break backwards compatibility if their serialized pickled form changes.

Returns: object: Any extra state to store in the module's state_dict

# get_initial_state ModelV2

get_initial_state(
  self
) -> typing.List[typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]]

Get the initial recurrent state values for the model.

Returns: List of np.array (for tf) or Tensor (for torch) objects containing the initial hidden state of an RNN, if applicable.

.. testcode:: :skipif: True

import numpy as np
from ray.rllib.models.modelv2 import ModelV2
class MyModel(ModelV2):
    # ...
    def get_initial_state(self):
        return [
            np.zeros(self.cell_size, np.float32),
            np.zeros(self.cell_size, np.float32),
        ]

# get_parameter Module

get_parameter(
  self,
  target: 
) -> Parameter

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

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

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

Returns: torch.nn.Parameter: The Parameter referenced by target

Raises: AttributeError: If the target string references an invalid path or resolves to something that is not an nn.Parameter

# get_submodule Module

get_submodule(
  self,
  target: 
) -> Module

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

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

.. code-block:: text

A(
    (net_b): Module(
        (net_c): Module(
            (conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))
        )
        (linear): Linear(in_features=100, out_features=200, bias=True)
    )
)

(The diagram shows an nn.Module A. A has a nested submodule net_b, which itself has two submodules net_c and linear. net_c then has a submodule conv.)

To check whether or not we have the linear submodule, we would call get_submodule("net_b.linear"). To check whether we have the conv submodule, we would call get_submodule("net_b.net_c.conv").

The runtime of get_submodule is bounded by the degree of module nesting in target. A query against named_modules achieves the same result, but it is O(N) in the number of transitive modules. So, for a simple check to see if some submodule exists, get_submodule should always be used.

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

Returns: torch.nn.Module: The submodule referenced by target

Raises: AttributeError: If the target string references an invalid path or resolves to something that is not an nn.Module

# half Module

half(
  self: ~T
) -> ~T

Casts all floating point parameters and buffers to half datatype.

.. note:: This method modifies the module in-place.

Returns: Module: self

# ipu Module

ipu(
  self: ~T,
  device: typing.Union[int, torch.device, NoneType] = None
) -> ~T

Move all model parameters and buffers to the IPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on IPU while being optimized.

.. note:: This method modifies the module in-place.

Arguments: device (int, optional): if specified, all parameters will be copied to that device

Returns: Module: self

# is_time_major ModelV2

is_time_major(
  self
) ->

If True, data for calling this ModelV2 must be in time-major format.

Returns Whether this ModelV2 requires a time-major (TxBx...) data format.

# last_output ModelV2

last_output(
  self
) -> typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]

Returns the last output returned from calling the model.

# load_state_dict Module

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

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

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

.. warning:: If :attr:assign is True the optimizer must be created after the call to :attr:load_state_dict unless :func:~torch.__future__.get_swap_module_params_on_conversion is True.

Args: state_dict (dict): a dict containing parameters and persistent buffers. strict (bool, optional): whether to strictly enforce that the keys in :attr:state_dict match the keys returned by this module's :meth:~torch.nn.Module.state_dict function. Default: True assign (bool, optional): When False, the properties of the tensors in the current module are preserved while when True, the properties of the Tensors in the state dict are preserved. The only exception is the requires_grad field of :class:~torch.nn.Parameters for which the value from the module is preserved. Default: False

Returns: NamedTuple with missing_keys and unexpected_keys fields: * missing_keys is a list of str containing any keys that are expected by this module but missing from the provided state_dict. * unexpected_keys is a list of str containing the keys that are not expected by this module but present in the provided state_dict.

Note: If a parameter or buffer is registered as None and its corresponding key exists in :attr:state_dict, :meth:load_state_dict will raise a RuntimeError.

# metrics ModelV2

metrics(
  self
) -> typing.Dict[str, typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]]

Override to return custom metrics from your model.

The stats will be reported as part of the learner stats, i.e., info.learner.[policy_id, e.g. "default_policy"].model.key1=metric1

Returns: The custom metrics for this model.

# modules Module

modules(
  self
) -> typing.Iterator[ForwardRef('Module')]

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)

# mtia Module

mtia(
  self: ~T,
  device: typing.Union[int, torch.device, NoneType] = None
) -> ~T

Move all model parameters and buffers to the MTIA.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on MTIA while being optimized.

.. note:: This method modifies the module in-place.

Arguments: device (int, optional): if specified, all parameters will be copied to that device

Returns: Module: self

# named_buffers Module

named_buffers(
  self,
  prefix: ,
  recurse:  = True,
  remove_duplicate:  = True
) -> typing.Iterator[typing.Tuple[str, torch.Tensor]]

Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Args: prefix (str): prefix to prepend to all buffer names. recurse (bool, optional): if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True. remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.

Yields: (str, torch.Tensor): Tuple containing the name and buffer

Example::

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, buf in self.named_buffers():
>>>     if name in ['running_var']:
>>>         print(buf.size())

# named_children Module

named_children(
  self
) -> typing.Iterator[typing.Tuple[str, ForwardRef('Module')]]

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 Module

named_modules(
  self,
  memo: typing.Optional[typing.Set[ForwardRef('Module')]] = None,
  prefix: ,
  remove_duplicate:  = True
)

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 Module

named_parameters(
  self,
  prefix: ,
  recurse:  = True,
  remove_duplicate:  = True
) -> typing.Iterator[typing.Tuple[str, torch.nn.parameter.Parameter]]

Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Args: prefix (str): prefix to prepend to all parameter names. recurse (bool): if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. remove_duplicate (bool, optional): whether to remove the duplicated parameters in the result. Defaults to True.

Yields: (str, Parameter): Tuple containing the name and parameter

Example::

>>> # xdoctest: +SKIP("undefined vars")
>>> for name, param in self.named_parameters():
>>>     if name in ['bias']:
>>>         print(param.size())

# parameters Module

parameters(
  self,
  recurse:  = True
) -> typing.Iterator[torch.nn.parameter.Parameter]

Return an iterator over module parameters.

This is typically passed to an optimizer.

Args: recurse (bool): if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.

Yields: Parameter: module parameter

Example::

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

# register_backward_hook Module

register_backward_hook(
  self,
  hook: typing.Callable[[ForwardRef('Module'), typing.Union[typing.Tuple[torch.Tensor, ...], torch.Tensor], typing.Union[typing.Tuple[torch.Tensor, ...], torch.Tensor]], typing.Union[NoneType, typing.Tuple[torch.Tensor, ...], torch.Tensor]]
) ->

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

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

# register_buffer Module

register_buffer(
  self,
  name: ,
  tensor: typing.Optional[torch.Tensor],
  persistent:  = True
)

Add a buffer to the module.

This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm's running_mean is not a parameter, but is part of the module's state. Buffers, by default, are persistent and will be saved alongside parameters. This behavior can be changed by setting :attr:persistent to False. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module's :attr:state_dict.

Buffers can be accessed as attributes using given names.

Args: name (str): name of the buffer. The buffer can be accessed from this module using the given name tensor (Tensor or None): buffer to be registered. If None, then operations that run on buffers, such as :attr:cuda, are ignored. If None, the buffer is not included in the module's :attr:state_dict. persistent (bool): whether the buffer is part of this module's :attr:state_dict.

Example::

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

# register_forward_hook Module

register_forward_hook(
  self,
  hook: typing.Union[typing.Callable[[~T, typing.Tuple[typing.Any, ...], typing.Any], typing.Optional[typing.Any]], typing.Callable[[~T, typing.Tuple[typing.Any, ...], typing.Dict[str, typing.Any], typing.Any], typing.Optional[typing.Any]]],
  prepend:  = False,
  with_kwargs:  = False,
  always_call:  = False
) ->

The hook will be called every time after :func:forward has computed an output.

If with_kwargs is False or not specified, the input contains only the positional arguments given to the module. Keyword arguments won't be passed to the hooks and only to the forward. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after :func:forward is called. The hook should have the following signature::

hook(module, args, output) -> None or modified output

If with_kwargs is True, the forward hook will be passed the kwargs given to the forward function and be expected to return the output possibly modified. The hook should have the following signature::

hook(module, args, kwargs, output) -> None or modified output

Args: hook (Callable): The user defined hook to be registered. prepend (bool): If True, the provided hook will be fired before all existing forward hooks on this :class:torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing forward hooks on this :class:torch.nn.modules.Module. Note that global forward hooks registered with :func:register_module_forward_hook will fire before all hooks registered by this method. Default: False with_kwargs (bool): If True, the hook will be passed the kwargs given to the forward function. Default: False always_call (bool): If True the hook will be run regardless of whether an exception is raised while calling the Module. Default: False

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

# register_forward_pre_hook Module

register_forward_pre_hook(
  self,
  hook: typing.Union[typing.Callable[[~T, typing.Tuple[typing.Any, ...]], typing.Optional[typing.Any]], typing.Callable[[~T, typing.Tuple[typing.Any, ...], typing.Dict[str, typing.Any]], typing.Optional[typing.Tuple[typing.Any, typing.Dict[str, typing.Any]]]]],
  prepend:  = False,
  with_kwargs:  = False
) ->

The hook will be called every time before :func:forward is invoked.

If with_kwargs is false or not specified, the input contains only the positional arguments given to the module. Keyword arguments won't be passed to the hooks and only to the forward. The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned (unless that value is already a tuple). The hook should have the following signature::

hook(module, args) -> None or modified input

If with_kwargs is true, the forward pre-hook will be passed the kwargs given to the forward function. And if the hook modifies the input, both the args and kwargs should be returned. The hook should have the following signature::

hook(module, args, kwargs) -> None or a tuple of modified input and kwargs

Args: hook (Callable): The user defined hook to be registered. prepend (bool): If true, the provided hook will be fired before all existing forward_pre hooks on this :class:torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing forward_pre hooks on this :class:torch.nn.modules.Module. Note that global forward_pre hooks registered with :func:register_module_forward_pre_hook will fire before all hooks registered by this method. Default: False with_kwargs (bool): If true, the hook will be passed the kwargs given to the forward function. Default: False

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

# register_full_backward_hook Module

register_full_backward_hook(
  self,
  hook: typing.Callable[[ForwardRef('Module'), typing.Union[typing.Tuple[torch.Tensor, ...], torch.Tensor], typing.Union[typing.Tuple[torch.Tensor, ...], torch.Tensor]], typing.Union[NoneType, typing.Tuple[torch.Tensor, ...], torch.Tensor]],
  prepend:  = False
) ->

The hook will be called every time the gradients with respect to a module are computed, i.e. the hook will execute if and only if the gradients with respect to module outputs are computed. The hook should have the following signature::

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

The :attr:grad_input and :attr:grad_output are tuples that contain the gradients with respect to the inputs and outputs respectively. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the input that will be used in place of :attr:grad_input in subsequent computations. :attr:grad_input will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in :attr:grad_input and :attr:grad_output will be None for all non-Tensor arguments.

For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function.

.. warning :: Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error.

Args: hook (Callable): The user-defined hook to be registered. prepend (bool): If true, the provided hook will be fired before all existing backward hooks on this :class:torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing backward hooks on this :class:torch.nn.modules.Module. Note that global backward hooks registered with :func:register_module_full_backward_hook will fire before all hooks registered by this method.

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

# register_full_backward_pre_hook Module

register_full_backward_pre_hook(
  self,
  hook: typing.Callable[[ForwardRef('Module'), typing.Union[typing.Tuple[torch.Tensor, ...], torch.Tensor]], typing.Union[NoneType, typing.Tuple[torch.Tensor, ...], torch.Tensor]],
  prepend:  = False
) ->

The hook will be called every time the gradients for the module are computed. The hook should have the following signature::

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

The :attr:grad_output is a tuple. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the output that will be used in place of :attr:grad_output in subsequent computations. Entries in :attr:grad_output will be None for all non-Tensor arguments.

.. warning :: Modifying inputs inplace is not allowed when using backward hooks and will raise an error.

Args: hook (Callable): The user-defined hook to be registered. prepend (bool): If true, the provided hook will be fired before all existing backward_pre hooks on this :class:torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing backward_pre hooks on this :class:torch.nn.modules.Module. Note that global backward_pre hooks registered with :func:register_module_full_backward_pre_hook will fire before all hooks registered by this method.

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

# register_load_state_dict_post_hook Module

register_load_state_dict_post_hook(
  self,
  hook
)

It should have the following signature:: hook(module, incompatible_keys) -> None

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

The given incompatible_keys can be modified inplace if needed.

Note that the checks performed when calling :func:load_state_dict with strict=True are affected by modifications the hook makes to missing_keys or unexpected_keys, as expected. Additions to either set of keys will result in an error being thrown when strict=True, and clearing out both missing and unexpected keys will avoid an error.

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

# register_load_state_dict_pre_hook Module

register_load_state_dict_pre_hook(
  self,
  hook
)

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 Module

register_module(
  self,
  name: ,
  module: typing.Optional[ForwardRef('Module')]
)

Alias for :func:add_module.

# register_parameter Module

register_parameter(
  self,
  name: ,
  param: typing.Optional[torch.nn.parameter.Parameter]
)

Add a parameter to the module.

The parameter can be accessed as an attribute using given name.

Args: name (str): name of the parameter. The parameter can be accessed from this module using the given name param (Parameter or None): parameter to be added to the module. If None, then operations that run on parameters, such as :attr:cuda, are ignored. If None, the parameter is not included in the module's :attr:state_dict.

# register_state_dict_post_hook Module

register_state_dict_post_hook(
  self,
  hook
)

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 Module

register_state_dict_pre_hook(
  self,
  hook
)

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_ Module

requires_grad_(
  self: ~T,
  requires_grad:  = True
) -> ~T

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

This method sets the parameters' :attr:requires_grad attributes in-place.

This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training).

See :ref:locally-disable-grad-doc for a comparison between .requires_grad_() and several similar mechanisms that may be confused with it.

Args: requires_grad (bool): whether autograd should record operations on parameters in this module. Default: True.

Returns: Module: self

# set_extra_state Module

set_extra_state(
  self,
  state: typing.Any
)

Set extra state contained in the loaded state_dict.

This function is called from :func:load_state_dict to handle any extra state found within the state_dict. Implement this function and a corresponding :func:get_extra_state for your module if you need to store extra state within its state_dict.

Args: state (dict): Extra state from the state_dict

# set_submodule Module

set_submodule(
  self,
  target: ,
  module: Module
)

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

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

.. code-block:: text

A(
    (net_b): Module(
        (net_c): Module(
            (conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))
        )
        (linear): Linear(in_features=100, out_features=200, bias=True)
    )
)

(The diagram shows an nn.Module A. A has a nested submodule net_b, which itself has two submodules net_c and linear. net_c then has a submodule conv.)

To overide the Conv2d with a new submodule Linear, you would call set_submodule("net_b.net_c.conv", nn.Linear(33, 16)).

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

Raises: ValueError: If the target string is empty AttributeError: If the target string references an invalid path or resolves to something that is not an nn.Module

share_memory(
  self: ~T
) -> ~T

See :meth:torch.Tensor.share_memory_.

# state_dict Module

state_dict(
  self,
  *args,
  destination = None,
  prefix,
  keep_vars = False
)

Return a dictionary containing references to the whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to None are not included.

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

.. warning:: Currently state_dict() also accepts positional arguments for destination, prefix and keep_vars in order. However, this is being deprecated and keyword arguments will be enforced in future releases.

.. warning:: Please avoid the use of argument destination as it is not designed for end-users.

Args: destination (dict, optional): If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned. Default: None. prefix (str, optional): a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''. keep_vars (bool, optional): by default the :class:~torch.Tensor s returned in the state dict are detached from autograd. If it's set to True, detaching will not be performed. Default: False.

Returns: dict: a dictionary containing a whole state of the module

Example::

>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']

# to Module

to(
  self,
  *args,
  **kwargs
)

Move and/or cast the parameters and buffers.

This can be called as

.. function:: to(device=None, dtype=None, non_blocking=False) :noindex:

.. function:: to(dtype, non_blocking=False) :noindex:

.. function:: to(tensor, non_blocking=False) :noindex:

.. function:: to(memory_format=torch.channels_last) :noindex:

Its signature is similar to :meth:torch.Tensor.to, but only accepts floating point or complex :attr:dtype\ s. In addition, this method will only cast the floating point or complex parameters and buffers to :attr:dtype (if given). The integral parameters and buffers will be moved :attr:device, if that is given, but with dtypes unchanged. When :attr:non_blocking is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.

See below for examples.

.. note:: This method modifies the module in-place.

Args: device (:class:torch.device): the desired device of the parameters and buffers in this module dtype (:class:torch.dtype): the desired floating point or complex dtype of the parameters and buffers in this module tensor (torch.Tensor): Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module memory_format (:class:torch.memory_format): the desired memory format for 4D parameters and buffers in this module (keyword only argument)

Returns: Module: self

Examples::

>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=torch.float64)
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16)

>>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble)
>>> linear.weight
Parameter containing:
tensor([[ 0.3741+0.j,  0.2382+0.j],
        [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)
>>> linear(torch.ones(3, 2, dtype=torch.cdouble))
tensor([[0.6122+0.j, 0.1150+0.j],
        [0.6122+0.j, 0.1150+0.j],
        [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)

# to_empty Module

to_empty(
  self: ~T,
  device: typing.Union[int, str, torch.device, NoneType],
  recurse:  = True
) -> ~T

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

Args: device (:class:torch.device): The desired device of the parameters and buffers in this module. recurse (bool): Whether parameters and buffers of submodules should be recursively moved to the specified device.

Returns: Module: self

# train Module

train(
  self: ~T,
  mode:  = True
) -> ~T

Set the module in training mode.

Args: mode (bool): whether to set training mode (True) or evaluation mode (False). Default: True.

Returns: Module: self

# trainable_variables ModelV2

trainable_variables(
  self,
  as_dict:  = False
) -> typing.Union[typing.List[typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]], typing.Dict[str, typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]]]

Returns the list of trainable variables for this model.

Args: as_dict: Whether variables should be returned as dict-values (using descriptive keys).

Returns: The list (or dict if as_dict is True) of all trainable (tf)/requires_grad (torch) variables of this ModelV2.

# type Module

type(
  self: ~T,
  dst_type: typing.Union[torch.dtype, str]
) -> ~T

Casts all parameters and buffers to :attr:dst_type.

.. note:: This method modifies the module in-place.

Args: dst_type (type or string): the desired type

Returns: Module: self

# value_function ModelV2

value_function(
  self
)

Returns the value function output for the most recent forward pass.

Note that a forward call has to be performed first, before this methods can return anything and thus that calling this method does not cause an extra forward pass through the network.

Returns: Value estimate tensor of shape [BATCH].

# variables ModelV2

variables(
  self,
  as_dict:  = False
) -> typing.Union[typing.List[typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]], typing.Dict[str, typing.Union[, ForwardRef('jnp.ndarray'), ForwardRef('tf.Tensor'), ForwardRef('torch.Tensor')]]]

Returns the list (or a dict) of variables for this model.

Args: as_dict: Whether variables should be returned as dict-values (using descriptive str keys).

Returns: The list (or dict if as_dict is True) of all variables of this ModelV2.

# xpu Module

xpu(
  self: ~T,
  device: typing.Union[int, torch.device, NoneType] = None
) -> ~T

Move all model parameters and buffers to the XPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on XPU while being optimized.

.. note:: This method modifies the module in-place.

Arguments: device (int, optional): if specified, all parameters will be copied to that device

Returns: Module: self

# zero_grad Module

zero_grad(
  self,
  set_to_none:  = True
)

Reset gradients of all model parameters.

See similar function under :class:torch.optim.Optimizer for more context.

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

# _annotated_type ModelV2

# _get_backward_hooks Module

_get_backward_hooks(
  self
)

Return the backward hooks for use in the call function.

It returns two lists, one with the full backward hooks and one with the non-full backward hooks.

# _load_from_state_dict Module

_load_from_state_dict(
  self,
  state_dict,
  prefix,
  local_metadata,
  strict,
  missing_keys,
  unexpected_keys,
  error_msgs
)

Copy parameters and buffers from :attr:state_dict into only this module, but not its descendants.

This is called on every submodule in :meth:~torch.nn.Module.load_state_dict. Metadata saved for this module in input :attr:state_dict is provided as :attr:local_metadata. For state dicts without metadata, :attr:local_metadata is empty. Subclasses can achieve class-specific backward compatible loading using the version number at local_metadata.get("version", None). Additionally, :attr:local_metadata can also contain the key assign_to_params_buffers that indicates whether keys should be assigned their corresponding tensor in the state_dict.

.. note:: :attr:state_dict is not the same object as the input :attr:state_dict to :meth:~torch.nn.Module.load_state_dict. So it can be modified.

Args: state_dict (dict): a dict containing parameters and persistent buffers. prefix (str): the prefix for parameters and buffers used in this module local_metadata (dict): a dict containing the metadata for this module. See strict (bool): whether to strictly enforce that the keys in :attr:state_dict with :attr:prefix match the names of parameters and buffers in this module missing_keys (list of str): if strict=True, add missing keys to this list unexpected_keys (list of str): if strict=True, add unexpected keys to this list error_msgs (list of str): error messages should be added to this list, and will be reported together in :meth:~torch.nn.Module.load_state_dict

# _named_members Module

_named_members(
  self,
  get_members_fn,
  prefix,
  recurse = True,
  remove_duplicate:  = True
)

Help yield various names + members of modules.

# _register_load_state_dict_pre_hook Module

_register_load_state_dict_pre_hook(
  self,
  hook,
  with_module = False
)

See :meth:~torch.nn.Module.register_load_state_dict_pre_hook for details.

A subtle difference is that if with_module is set to False, then the hook will not take the module as the first argument whereas :meth:~torch.nn.Module.register_load_state_dict_pre_hook always takes the module as the first argument.

Arguments: hook (Callable): Callable hook that will be invoked before loading the state dict. with_module (bool, optional): Whether or not to pass the module instance to the hook as the first parameter.

# _register_state_dict_hook Module

_register_state_dict_hook(
  self,
  hook
)

It should have the following signature:: hook(module, state_dict, prefix, local_metadata) -> None or state_dict

The registered hooks can modify the state_dict inplace or return a new one. If a new state_dict is returned, it will only be respected if it is the root module that :meth:~nn.Module.state_dict is called from.

# _save_to_state_dict Module

_save_to_state_dict(
  self,
  destination,
  prefix,
  keep_vars
)

Save module state to the destination dictionary.

The destination dictionary will contain the state of the module, but not its descendants. This is called on every submodule in :meth:~torch.nn.Module.state_dict.

In rare cases, subclasses can achieve class-specific behavior by overriding this method with custom logic.

Args: destination (dict): a dict where state will be stored prefix (str): the prefix for parameters and buffers used in this module

# hub.solver.ray_rllib.custom_models

# TFParametricActionsModel

# Constructor TFParametricActionsModel

# context ModelV2

# custom_loss ModelV2

# forward ModelV2

# get_initial_state ModelV2

# is_time_major ModelV2

# last_output ModelV2

# metrics ModelV2

# register_variables TFModelV2

# trainable_variables ModelV2

# update_ops TFModelV2

# value_function ModelV2

# variables ModelV2

# _annotated_type ModelV2

# TorchParametricActionsModel

# Constructor TorchParametricActionsModel

# add_module Module

# apply Module

# bfloat16 Module

# buffers Module

# children Module

# compile Module

# context ModelV2

# cpu Module

# cuda Module

# custom_loss ModelV2

# double Module

# eval Module

# extra_repr Module

# float Module

# forward Module

# get_buffer Module

# get_extra_state Module

# get_initial_state ModelV2

# get_parameter Module

# get_submodule Module

# half Module

# ipu Module

# is_time_major ModelV2

# last_output ModelV2

# load_state_dict Module

# metrics ModelV2

# modules Module

# mtia Module

# named_buffers Module

# named_children Module

# named_modules Module

# named_parameters Module

# parameters Module

# register_backward_hook Module

# register_buffer Module

# register_forward_hook Module

# register_forward_pre_hook Module

# register_full_backward_hook Module

# register_full_backward_pre_hook Module

# register_load_state_dict_post_hook Module

# register_load_state_dict_pre_hook Module

# register_module Module

# register_parameter Module

# register_state_dict_post_hook Module

# register_state_dict_pre_hook Module

# requires_grad_ Module

# set_extra_state Module

# set_submodule Module

# share_memory Module

# state_dict Module

# to Module

# to_empty Module

# train Module

# trainable_variables ModelV2

# type Module

# value_function ModelV2

# variables ModelV2

# xpu Module

# zero_grad Module

# _annotated_type ModelV2

# _get_backward_hooks Module

# _load_from_state_dict Module