rbnet.pcfg.AbstractedPCFG

class rbnet.pcfg.AbstractedPCFG(non_terminals, terminals, rules, start, prob_rep=<class 'rbnet.util.LogProb'>, *args, **kwargs)

Bases: PCFG, LightningModule, ConstrainedModuleMixin

An AbstractedPCFG defines an RBN that has only one non-terminal and one terminal variable, both being discrete with a cardinality corresponding to the number of non-terminal and terminal symbols of the PCFG, respectively.

Parameters:

• non_terminals – list or array of non-terminal symbols

• terminals – list or array of terminal symbols

• rules – iterable of rules-weight tuples with rules provided either as strings of the form ("X --> Y Z", w) or ("X --> Y", w) for non-terminal and terminal rules, respectively (symbols have to be strings without whitespace for this), or of the form ((X, (Y, Z)), w) or ((X, (Y,)), w) for arbitrary symbols, where w is the rule weight.

• start – the start symbol

Public Data Attributes:

Inherited from SequentialRBN

root_location	Return the location of the root variables.
inside_chart	Return the chart with inside probabilities for all variables.
terminal_chart	Return the chart with terminal variables.
prior	Return the prior transition (typically an instance of Prior), which has to implement Prior.marginal_likelihood().
dump_patches
training
call_super_init
forward(*input)	Define the computation performed at every call.

Inherited from RBN

root_location	Return the location of the root variables.
inside_chart	Return the chart with inside probabilities for all variables.
terminal_chart	Return the chart with terminal variables.
prior	Return the prior transition (typically an instance of Prior), which has to implement Prior.marginal_likelihood().

Inherited from LightningModule

CHECKPOINT_HYPER_PARAMS_KEY
CHECKPOINT_HYPER_PARAMS_NAME
CHECKPOINT_HYPER_PARAMS_TYPE
trainer
fabric
example_input_array	The example input array is a specification of what the module can consume in the forward() method.
current_epoch	The current epoch in the Trainer, or 0 if not attached.
global_step	Total training batches seen across all epochs.
global_rank	The index of the current process across all nodes and devices.
local_rank	The index of the current process within a single node.
on_gpu	Returns True if this model is currently located on a GPU.
automatic_optimization	If set to False you are responsible for calling .backward(), .step(), .zero_grad().
strict_loading	Determines how Lightning loads this model using .load_state_dict(..., strict=model.strict_loading).
logger	Reference to the logger object in the Trainer.
loggers	Reference to the list of loggers in the Trainer.
device_mesh	Strategies like ModelParallelStrategy will create a device mesh that can be accessed in the configure_model() hook to parallelize the LightningModule.

Inherited from _DeviceDtypeModuleMixin

dtype
device

Inherited from HyperparametersMixin

hparams	The collection of hyperparameters saved with save_hyperparameters().
hparams_initial	The collection of hyperparameters saved with save_hyperparameters().

Inherited from Module

dump_patches
call_super_init
T_destination
training

Public Methods:

__init__(non_terminals, terminals, rules, start)

An AbstractedPCFG defines an RBN that has only one non-terminal and one terminal variable, both being discrete with a cardinality corresponding to the number of non-terminal and terminal symbols of the PCFG, respectively.

Inherited from PCFG

__init__(cells, prior, terminal_indices, ...)
tokenise(sequence)
init_inside(sequence)	Initialise for parsing a new input.
map_inside_chart([precision])

Inherited from SequentialRBN

__init__(cells, prior, *args, **kwargs)
init_inside(sequence)	Initialise for parsing a new input.
inside_schedule(*args, **kwargs)	Iterate through (batches of) non-terminal locations for computing inside probabilities.
cells()	Return iterable over cells (corresponding to the non-terminal variables).
update_inside_chart(var_idx, locations, values)	For the specified variable, update the chart for inside probabilities with given values at given locations.

Inherited from RBN

__init__(*args, **kwargs)
inside_schedule(*args, **kwargs)	Iterate through (batches of) non-terminal locations for computing inside probabilities.
cells()	Return iterable over cells (corresponding to the non-terminal variables).
init_inside(*args, **kwargs)	Initialise for parsing a new input.
update_inside_chart(var_idx, locations, values)	For the specified variable, update the chart for inside probabilities with given values at given locations.
inside(*args, **kwargs)	Compute the inside probabilities and return the marginal data likelihood.

Inherited from LightningModule

__init__(*args, **kwargs)
optimizers([use_pl_optimizer])	Returns the optimizer(s) that are being used during training.
lr_schedulers()	Returns the learning rate scheduler(s) that are being used during training.
print(*args, **kwargs)	Prints only from process 0.
log(name, value[, prog_bar, logger, ...])	Log a key, value pair.
log_dict(dictionary[, prog_bar, logger, ...])	Log a dictionary of values at once.
all_gather(data[, group, sync_grads])	Gather tensors or collections of tensors from multiple processes.
forward(*args, **kwargs)	Same as torch.nn.Module.forward().
training_step(*args, **kwargs)	Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger.
validation_step(*args, **kwargs)	Operates on a single batch of data from the validation set.
test_step(*args, **kwargs)	Operates on a single batch of data from the test set.
predict_step(*args, **kwargs)	Step function called during predict().
configure_callbacks()	Configure model-specific callbacks.
configure_optimizers()	Choose what optimizers and learning-rate schedulers to use in your optimization.
manual_backward(loss, *args, **kwargs)	Call this directly from your training_step() when doing optimizations manually.
backward(loss, *args, **kwargs)	Called to perform backward on the loss returned in training_step().
toggle_optimizer(optimizer)	Makes sure only the gradients of the current optimizer's parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup.
untoggle_optimizer(optimizer)	Resets the state of required gradients that were toggled with toggle_optimizer().
clip_gradients(optimizer[, ...])	Handles gradient clipping internally.
configure_gradient_clipping(optimizer[, ...])	Perform gradient clipping for the optimizer parameters.
lr_scheduler_step(scheduler, metric)	Override this method to adjust the default way the Trainer calls each scheduler.
optimizer_step(epoch, batch_idx, optimizer)	Override this method to adjust the default way the Trainer calls the optimizer.
optimizer_zero_grad(epoch, batch_idx, optimizer)	Override this method to change the default behaviour of optimizer.zero_grad().
freeze()	Freeze all params for inference.
unfreeze()	Unfreeze all parameters for training.
to_onnx(file_path[, input_sample])	Saves the model in ONNX format.
to_torchscript([file_path, method, ...])	By default compiles the whole model to a ScriptModule.
load_from_checkpoint(checkpoint_path[, ...])	Primary way of loading a model from a checkpoint.
__getstate__()

Inherited from _DeviceDtypeModuleMixin

__init__()	Initialize internal Module state, shared by both nn.Module and ScriptModule.
to(*args, **kwargs)	See torch.nn.Module.to().
cuda([device])	Moves all model parameters and buffers to the GPU.
cpu()	See torch.nn.Module.cpu().
type(dst_type)	See torch.nn.Module.type().
float()	See torch.nn.Module.float().
double()	See torch.nn.Module.double().
half()	See torch.nn.Module.half().

Inherited from HyperparametersMixin

__init__()
save_hyperparameters(*args[, ignore, frame, ...])	Save arguments to hparams attribute.

Inherited from ModelHooks

on_fit_start()	Called at the very beginning of fit.
on_fit_end()	Called at the very end of fit.
on_train_start()	Called at the beginning of training after sanity check.
on_train_end()	Called at the end of training before logger experiment is closed.
on_validation_start()	Called at the beginning of validation.
on_validation_end()	Called at the end of validation.
on_test_start()	Called at the beginning of testing.
on_test_end()	Called at the end of testing.
on_predict_start()	Called at the beginning of predicting.
on_predict_end()	Called at the end of predicting.
on_train_batch_start(batch, batch_idx)	Called in the training loop before anything happens for that batch.
on_train_batch_end(outputs, batch, batch_idx)	Called in the training loop after the batch.
on_validation_batch_start(batch, batch_idx)	Called in the validation loop before anything happens for that batch.
on_validation_batch_end(outputs, batch, ...)	Called in the validation loop after the batch.
on_test_batch_start(batch, batch_idx[, ...])	Called in the test loop before anything happens for that batch.
on_test_batch_end(outputs, batch, batch_idx)	Called in the test loop after the batch.
on_predict_batch_start(batch, batch_idx[, ...])	Called in the predict loop before anything happens for that batch.
on_predict_batch_end(outputs, batch, batch_idx)	Called in the predict loop after the batch.
on_validation_model_zero_grad()	Called by the training loop to release gradients before entering the validation loop.
on_validation_model_eval()	Called when the validation loop starts.
on_validation_model_train()	Called when the validation loop ends.
on_test_model_eval()	Called when the test loop starts.
on_test_model_train()	Called when the test loop ends.
on_predict_model_eval()	Called when the predict loop starts.
on_train_epoch_start()	Called in the training loop at the very beginning of the epoch.
on_train_epoch_end()	Called in the training loop at the very end of the epoch.
on_validation_epoch_start()	Called in the validation loop at the very beginning of the epoch.
on_validation_epoch_end()	Called in the validation loop at the very end of the epoch.
on_test_epoch_start()	Called in the test loop at the very beginning of the epoch.
on_test_epoch_end()	Called in the test loop at the very end of the epoch.
on_predict_epoch_start()	Called at the beginning of predicting.
on_predict_epoch_end()	Called at the end of predicting.
on_before_zero_grad(optimizer)	Called after training_step() and before optimizer.zero_grad().
on_before_backward(loss)	Called before loss.backward().
on_after_backward()	Called after loss.backward() and before optimizers are stepped.
on_before_optimizer_step(optimizer)	Called before optimizer.step().
configure_sharded_model()	Deprecated.
configure_model()	Hook to create modules in a strategy and precision aware context.

Inherited from DataHooks

__init__()	Attributes:
prepare_data()	Use this to download and prepare data.
setup(stage)	Called at the beginning of fit (train + validate), validate, test, or predict.
teardown(stage)	Called at the end of fit (train + validate), validate, test, or predict.
train_dataloader()	An iterable or collection of iterables specifying training samples.
test_dataloader()	An iterable or collection of iterables specifying test samples.
val_dataloader()	An iterable or collection of iterables specifying validation samples.
predict_dataloader()	An iterable or collection of iterables specifying prediction samples.
transfer_batch_to_device(batch, device, ...)	Override this hook if your DataLoader returns tensors wrapped in a custom data structure.
on_before_batch_transfer(batch, dataloader_idx)	Override to alter or apply batch augmentations to your batch before it is transferred to the device.
on_after_batch_transfer(batch, dataloader_idx)	Override to alter or apply batch augmentations to your batch after it is transferred to the device.

Inherited from CheckpointHooks

on_load_checkpoint(checkpoint)	Called by Lightning to restore your model.
on_save_checkpoint(checkpoint)	Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save.

Inherited from Module

__init__(*args, **kwargs)	Initialize internal Module state, shared by both nn.Module and ScriptModule.
forward(*input)	Define the computation performed at every call.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_parameter(name, param)	Add a parameter to the module.
add_module(name, module)	Add a child module to the current module.
register_module(name, module)	Alias for add_module().
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
set_submodule(target, module)	Set the submodule given by target if it exists, otherwise throw an error.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
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.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
cuda([device])	Move all model parameters and buffers to the GPU.
ipu([device])	Move all model parameters and buffers to the IPU.
xpu([device])	Move all model parameters and buffers to the XPU.
mtia([device])	Move all model parameters and buffers to the MTIA.
cpu()	Move all model parameters and buffers to the CPU.
type(dst_type)	Casts all parameters and buffers to dst_type.
float()	Casts all floating point parameters and buffers to float datatype.
double()	Casts all floating point parameters and buffers to double datatype.
half()	Casts all floating point parameters and buffers to half datatype.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_backward_hook(hook)	Register a backward hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
__call__(*args, **kwargs)	Call self as a function.
__getstate__()
__setstate__(state)
__getattr__(name)
__setattr__(name, value)	Implement setattr(self, name, value).
__delattr__(name)	Implement delattr(self, name).
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.
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module's load_state_dict() is called.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module's load_state_dict() is called.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
parameters([recurse])	Return an iterator over module parameters.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
buffers([recurse])	Return an iterator over module buffers.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
children()	Return an iterator over immediate children modules.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
modules()	Return an iterator over all modules in the network.
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.
train([mode])	Set the module in training mode.
eval()	Set the module in evaluation mode.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
zero_grad([set_to_none])	Reset gradients of all model parameters.
share_memory()	See torch.Tensor.share_memory_().
extra_repr()	Set the extra representation of the module.
__repr__()	Return repr(self).
__dir__()	Default dir() implementation.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().

Inherited from ConstrainedModuleMixin

enforce_constraints([recurse])	Enforce constraints for module parameters and child modules.
remap(param[, _top_level, prefix])

Private Data Attributes:

_abc_impl

Inherited from PCFG

_abc_impl

Inherited from SequentialRBN

_abc_impl
_version	This allows better BC support for load_state_dict().
_parameters
_buffers
_non_persistent_buffers_set
_backward_pre_hooks
_backward_hooks
_is_full_backward_hook
_forward_hooks
_forward_hooks_with_kwargs
_forward_hooks_always_called
_forward_pre_hooks
_forward_pre_hooks_with_kwargs
_state_dict_hooks
_load_state_dict_pre_hooks
_state_dict_pre_hooks
_load_state_dict_post_hooks
_modules
_compiled_call_impl

Inherited from RBN

_abc_impl

Inherited from ABC

_abc_impl

Inherited from LightningModule

_jit_is_scripting
_trainer
_example_input_array
_automatic_optimization
_strict_loading
_current_fx_name
_param_requires_grad_state
_metric_attributes
_compiler_ctx
_fabric
_fabric_optimizers
_device_mesh
_dtype
_version	This allows better BC support for load_state_dict().
_parameters
_buffers
_non_persistent_buffers_set
_backward_pre_hooks
_backward_hooks
_is_full_backward_hook
_forward_hooks
_forward_hooks_with_kwargs
_forward_hooks_always_called
_forward_pre_hooks
_forward_pre_hooks_with_kwargs
_state_dict_hooks
_load_state_dict_pre_hooks
_state_dict_pre_hooks
_load_state_dict_post_hooks
_modules
_compiled_call_impl

Inherited from Module

_version	This allows better BC support for load_state_dict().
_compiled_call_impl
_parameters
_buffers
_non_persistent_buffers_set
_backward_pre_hooks
_backward_hooks
_is_full_backward_hook
_forward_hooks
_forward_hooks_with_kwargs
_forward_hooks_always_called
_forward_pre_hooks
_forward_pre_hooks_with_kwargs
_state_dict_hooks
_load_state_dict_pre_hooks
_state_dict_pre_hooks
_load_state_dict_post_hooks
_modules

Private Methods:

Inherited from LightningModule

_call_batch_hook(hook_name, *args)
_on_before_batch_transfer(batch[, ...])
_apply_batch_transfer_handler(batch[, ...])
_log_dict_through_fabric(dictionary[, logger])
_LightningModule__check_not_nested(value, name)
_LightningModule__check_allowed(v, name, value)
_LightningModule__to_tensor(value, name)
_verify_is_manual_optimization(fn_name)

Inherited from HyperparametersMixin

_set_hparams(hp)
_to_hparams_dict(hp)

Inherited from Module

_apply(fn[, recurse])
_get_backward_hooks()	Return the backward hooks for use in the call function.
_get_backward_pre_hooks()
_maybe_warn_non_full_backward_hook(inputs, ...)
_slow_forward(*input, **kwargs)
_wrapped_call_impl(*args, **kwargs)
_call_impl(*args, **kwargs)
_register_state_dict_hook(hook)	Register a post-hook for the state_dict() method.
_save_to_state_dict(destination, prefix, ...)	Save module state to the destination dictionary.
_register_load_state_dict_pre_hook(hook[, ...])	See register_load_state_dict_pre_hook() for details.
_load_from_state_dict(state_dict, prefix, ...)	Copy parameters and buffers from state_dict into only this module, but not its descendants.
_named_members(get_members_fn[, prefix, ...])	Help yield various names + members of modules.
_get_name()
_replicate_for_data_parallel()

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