concepts.language.neural_ccg.tensorized_syntax.TensorizedSyntaxOnlyCCG#

class TensorizedSyntaxOnlyCCG[source]#

Bases: Module

Data structures and parsing implementation for a syntax-only CCG grammar. It uses a tensor-based representation for the chart, therefore is highly parallelizable.

Methods

`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()`.
`cpu`()	Move all model parameters and buffers to the CPU.
`cuda`([device])	Move all model parameters and buffers to the GPU.
`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`(*input)	Define the computation performed at every call.
`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_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.
`ipu`([device])	Move all model parameters and buffers to the IPU.
`load_state_dict`(state_dict[, strict, assign])	Copy parameters and buffers from `state_dict` into this module and its descendants.
`merge`(A, B, transition_matrix)	The underlying merge function for chart parsing.
`merge_beamsearch`(a_list, b_list, ...)	The underlying merge function for chart parsing with beam search.
`modules`()	Return an iterator over all modules in the network.
`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.
`parse`(words, words_length, ...)	Parse a batch of sentences.
`parse_beamsearch`(words, words_length, ...[, ...])	Parse a batch of sentences using beam search.
`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_module`(name, module)	Alias for `add_module()`.
`register_parameter`(name, param)	Add a parameter to the module.
`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.
`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.
`type`(dst_type)	Casts all parameters and buffers to `dst_type`.
`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`
`syntax_name2index`
`training`
`candidate_syntax_types`	A list of candidate syntax types.
`composition_system`	The composition system.

__init__(candidate_syntax_types, composition_system=None)[source]#

Initialize the CCG grammar.

Parameters:

candidate_syntax_types (Iterable[CCGSyntaxType]) – the candidate syntax types.
composition_system (CCGCompositionSystem | None) – the composition system. If None, the default composition system will be used.

merge(A, B, transition_matrix)[source]#

The underlying merge function for chart parsing.

Parameters:

A (Tensor) – the distribution over syntax types for the left child, should have shape [batch_size, nr_types].
B (Tensor) – the distribution over syntax types for the right child, should have shape [batch_size, nr_types].
transition_matrix (Tensor) – the transition matrix, should have shape [A: nr_types, B: nr_types, C: nr_types]. Basically, each entry [A, B, C] means the probability of composing a node of type A and a node of type B to a node of type C.

Returns:

The distribution over syntax types for the merged node, should have shape [batch_size, nr_types].

merge_beamsearch(a_list, b_list, transition_matrix)[source]#

The underlying merge function for chart parsing with beam search.

Parameters:

a_list (List[CCGNode]) – the list of nodes for the left child.
b_list (List[CCGNode]) – the list of nodes for the right child.
transition_matrix (Tensor) – the transition matrix, should have shape [A: nr_types, B: nr_types, C: nr_types]. Basically, each entry [A, B, C] means the probability of composing a node of type A and a node of type B to a node of type C.

Returns:

The list of merged nodes.

Return type:

List[CCGNode]

parse(words, words_length, distribution_over_syntax_types, transition_matrix, allowed_root_type_indices)[source]#

Parse a batch of sentences.

Parameters:

words (Tensor) – the word indices, should have shape [batch_size, max_seqlen].
words_length (Tensor) – the length of each sentence, should have shape [batch_size].
distribution_over_syntax_types (Tensor) – the distribution over syntax types for each word, should have shape [batch_size, max_seqlen, nr_types].
transition_matrix (Tensor) – the transition matrix, should have shape [A: nr_types, B: nr_types, C: nr_types]. Basically, each entry [A, B, C] means the probability of composing a node of type A and a node of type B to a node of type C.
allowed_root_type_indices (int | List[int]) – the indices of the syntax types that are allowed to be the root of the tree. It can be either a single integer or a list of integers.

Returns:

The root nodes of the trees, with shape [batch_size] (when allowed_root_type_indices is a single integer) or [batch_size, nr_allowed_root_types] (when allowed_root_type_indices is a list of integers). Each entry i can be interpreted as the log-probability of the root node being syntax type allowed_root_type_indices[i].

Return type:

Tensor

parse_beamsearch(words, words_length, words_tokenized, distribution_over_syntax_types, transition_matrix, allowed_root_types, beam_size=3)[source]#

Parse a batch of sentences using beam search. This function will return candidate parsing trees (instead of just the probability distribution of the root note). Therefore, this function is significantly slower than parse() and should not be used for training.

Parameters:

words (Tensor) – the word indices, should have shape [batch_size, max_seqlen].
words_length (Tensor) – the length of each sentence, should have shape [batch_size].
words_tokenized (List[List[str]]) – the tokenized words.
distribution_over_syntax_types (Tensor) – the distribution over syntax types for each word, should have shape [batch_size, max_seqlen, nr_types].
transition_matrix (Tensor) – the transition matrix, should have shape [A: nr_types, B: nr_types, C: nr_types]. Basically, each entry [A, B, C] means the probability of composing a node of type A and a node of type B to a node of type C.
allowed_root_types (List[str]) – the syntax types that are allowed to be the root of the tree.
beam_size (int | None) – the beam size.

Returns:

A list of candidate parsing trees for each sentence. Each entry is a list of trees, where each tree is a CCGNode object.

Return type:

List[List[CCGNode]]

candidate_syntax_types: Tuple[CCGSyntaxType, ...]#: A list of candidate syntax types.

composition_system: CCGCompositionSystem#: The composition system.

property syntax_name2index#

training: bool#