schola.ray.utils.WrappedRLLibModel

class schola.ray.utils.WrappedRLLibModel(model)[source]

Bases: TorchModelV2, Module

Methods

__init__(model)	Initialize a TorchModelV2.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module’s forward using torch.compile().
context()	Returns a contextmanager for the current forward pass.
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
custom_loss(policy_loss, loss_inputs)	Override to customize the loss function used to optimize this model.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Set the module in evaluation mode.
extra_repr()	Set the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(*args, **kwargs)	Forward pass through the model.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state()	Return any extra state to include in the module’s state_dict.
get_initial_state()	Get the initial recurrent state values for the model.
get_mask(action_space)
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	Casts all floating point parameters and buffers to half datatype.
import_from_h5(**kwargs)
ipu([device])	Move all model parameters and buffers to the IPU.
is_time_major()	If True, data for calling this ModelV2 must be in time-major format.
last_output()	Returns the last output returned from calling the model.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
metrics()	Override to return custom metrics from your model.
modules()	Return an iterator over all modules in the network.
mtia([device])	Move all model parameters and buffers to the MTIA.
named_buffers([prefix, recurse, …])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, …])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Return an iterator over module parameters.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, …])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, …])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module’s load_state_dict() is called.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module’s load_state_dict() is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_post_hook(hook)	Register a post-hook for the state_dict() method.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_submodule(target, module)	Set the submodule given by target if it exists, otherwise throw an error.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, …])	Return a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
trainable_variables([as_dict])	Returns the list of trainable variables for this model.
type(dst_type)	Casts all parameters and buffers to dst_type.
value_function()	Returns the value function output for the most recent forward pass.
variables([as_dict])	Returns the list (or a dict) of variables for this model.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
call_super_init
dump_patches
training

__init__(model)[source]

Initialize a TorchModelV2.

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

Copied!

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

forward(*args, **kwargs)[source]

Forward pass through the model. Removes variance outputs, to make compatible with Unreal.

get_mask(action_space)[source]

Parameters:

action_space (Space)

Return type:

Tensor

value_function()[source]

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].