def test_get_entity_action_logits(self):
decoder_step = WikiTablesDecoderStep(1, 5, SimilarityFunction.from_params(Params({})), 5, 3)
actions_to_link = [[1, 2], [3, 4, 5], [6]]
# (group_size, num_question_tokens) = (3, 3)
attention_weights = Variable(torch.Tensor([[.2, .8, 0],
[.7, .1, .2],
[.3, .3, .4]]))
action_logits, mask, type_embeddings = decoder_step._get_entity_action_logits(self.state,
actions_to_link,
attention_weights)
assert_almost_equal(mask.data.cpu().numpy(), [[1, 1, 0], [1, 1, 1], [1, 0, 0]])
assert tuple(action_logits.size()) == (3, 3)
assert_almost_equal(action_logits[0, 0].data.cpu().numpy(), .4 * .2 + .5 * .8 + .6 * 0)
assert_almost_equal(action_logits[0, 1].data.cpu().numpy(), .7 * .2 + .8 * .8 + .9 * 0)
assert_almost_equal(action_logits[1, 0].data.cpu().numpy(), -.4 * .7 + -.5 * .1 + -.6 * .2)
assert_almost_equal(action_logits[1, 1].data.cpu().numpy(), -.7 * .7 + -.8 * .1 + -.9 * .2)
assert_almost_equal(action_logits[1, 2].data.cpu().numpy(), -1.0 * .7 + -1.1 * .1 + -1.2 * .2)
assert_almost_equal(action_logits[2, 0].data.cpu().numpy(), 1.0 * .3 + 1.1 * .3 + 1.2 * .4)
embedding_matrix = decoder_step._entity_type_embedding.weight.data.cpu().numpy()
assert_almost_equal(type_embeddings[0, 0].data.cpu().numpy(), embedding_matrix[2])
assert_almost_equal(type_embeddings[0, 1].data.cpu().numpy(), embedding_matrix[1])
assert_almost_equal(type_embeddings[1, 0].data.cpu().numpy(), embedding_matrix[0])
assert_almost_equal(type_embeddings[1, 1].data.cpu().numpy(), embedding_matrix[1])
assert_almost_equal(type_embeddings[1, 2].data.cpu().numpy(), embedding_matrix[2])
assert_almost_equal(type_embeddings[2, 0].data.cpu().numpy(), embedding_matrix[0])
def __init__(self,
vocab,
question_embedder,
action_embedding_dim,
encoder,
entity_encoder,
decoder_beam_search,
max_decoding_steps,
attention,
mixture_feedforward=None,
training_beam_size=None,
use_neighbor_similarity_for_linking=False,
dropout=0.0,
num_linking_features=10,
rule_namespace=u'rule_labels',
tables_directory=u'/wikitables/'):
use_similarity = use_neighbor_similarity_for_linking
super(WikiTablesMmlSemanticParser, self).__init__(
vocab=vocab,
question_embedder=question_embedder,
action_embedding_dim=action_embedding_dim,
encoder=encoder,
entity_encoder=entity_encoder,
max_decoding_steps=max_decoding_steps,
use_neighbor_similarity_for_linking=use_similarity,
dropout=dropout,
num_linking_features=num_linking_features,
rule_namespace=rule_namespace,
tables_directory=tables_directory)
self._beam_search = decoder_beam_search
self._decoder_trainer = MaximumMarginalLikelihood(training_beam_size)
self._decoder_step = WikiTablesDecoderStep(
encoder_output_dim=self._encoder.get_output_dim(),
action_embedding_dim=action_embedding_dim,
input_attention=attention,
num_start_types=self._num_start_types,
num_entity_types=self._num_entity_types,
mixture_feedforward=mixture_feedforward,
dropout=dropout)
def __init__(self,
vocab: Vocabulary,
question_embedder: TextFieldEmbedder,
action_embedding_dim: int,
encoder: Seq2SeqEncoder,
entity_encoder: Seq2VecEncoder,
mixture_feedforward: FeedForward,
decoder_beam_search: BeamSearch,
max_decoding_steps: int,
attention_function: SimilarityFunction,
training_beam_size: int = None,
use_neighbor_similarity_for_linking: bool = False,
dropout: float = 0.0,
num_linking_features: int = 10,
rule_namespace: str = 'rule_labels',
tables_directory: str = '/wikitables/') -> None:
use_similarity = use_neighbor_similarity_for_linking
super().__init__(vocab=vocab,
question_embedder=question_embedder,
action_embedding_dim=action_embedding_dim,
encoder=encoder,
entity_encoder=entity_encoder,
max_decoding_steps=max_decoding_steps,
use_neighbor_similarity_for_linking=use_similarity,
dropout=dropout,
num_linking_features=num_linking_features,
rule_namespace=rule_namespace,
tables_directory=tables_directory)
self._beam_search = decoder_beam_search
self._decoder_trainer = MaximumMarginalLikelihood(training_beam_size)
self._decoder_step = WikiTablesDecoderStep(
encoder_output_dim=self._encoder.get_output_dim(),
action_embedding_dim=action_embedding_dim,
attention_function=attention_function,
num_start_types=self._num_start_types,
num_entity_types=self._num_entity_types,
mixture_feedforward=mixture_feedforward,
dropout=dropout)
def __init__(self,
vocab: Vocabulary,
question_embedder: TextFieldEmbedder,
action_embedding_dim: int,
encoder: Seq2SeqEncoder,
entity_encoder: Seq2VecEncoder,
mixture_feedforward: FeedForward,
input_attention: Attention,
decoder_beam_size: int,
decoder_num_finished_states: int,
max_decoding_steps: int,
normalize_beam_score_by_length: bool = False,
checklist_cost_weight: float = 0.6,
use_neighbor_similarity_for_linking: bool = False,
dropout: float = 0.0,
num_linking_features: int = 10,
rule_namespace: str = 'rule_labels',
tables_directory: str = '/wikitables/',
initial_mml_model_file: str = None) -> None:
use_similarity = use_neighbor_similarity_for_linking
super().__init__(vocab=vocab,
question_embedder=question_embedder,
action_embedding_dim=action_embedding_dim,
encoder=encoder,
entity_encoder=entity_encoder,
max_decoding_steps=max_decoding_steps,
use_neighbor_similarity_for_linking=use_similarity,
dropout=dropout,
num_linking_features=num_linking_features,
rule_namespace=rule_namespace,
tables_directory=tables_directory)
# Not sure why mypy needs a type annotation for this!
self._decoder_trainer: ExpectedRiskMinimization = \
ExpectedRiskMinimization(beam_size=decoder_beam_size,
normalize_by_length=normalize_beam_score_by_length,
max_decoding_steps=self._max_decoding_steps,
max_num_finished_states=decoder_num_finished_states)
unlinked_terminals_global_indices = []
global_vocab = self.vocab.get_token_to_index_vocabulary(rule_namespace)
for production, index in global_vocab.items():
right_side = production.split(" -> ")[1]
if right_side in types.COMMON_NAME_MAPPING:
# This is a terminal production.
unlinked_terminals_global_indices.append(index)
self._num_unlinked_terminals = len(unlinked_terminals_global_indices)
self._decoder_step = WikiTablesDecoderStep(
encoder_output_dim=self._encoder.get_output_dim(),
action_embedding_dim=action_embedding_dim,
input_attention=input_attention,
num_start_types=self._num_start_types,
num_entity_types=self._num_entity_types,
mixture_feedforward=mixture_feedforward,
dropout=dropout,
unlinked_terminal_indices=unlinked_terminals_global_indices)
self._checklist_cost_weight = checklist_cost_weight
self._agenda_coverage = Average()
# TODO (pradeep): Checking whether file exists here to avoid raising an error when we've
# copied a trained ERM model from a different machine and the original MML model that was
# used to initialize it does not exist on the current machine. This may not be the best
# solution for the problem.
if initial_mml_model_file is not None:
if os.path.isfile(initial_mml_model_file):
archive = load_archive(initial_mml_model_file)
self._initialize_weights_from_archive(archive)
else:
# A model file is passed, but it does not exist. This is expected to happen when
# you're using a trained ERM model to decode. But it may also happen if the path to
# the file is really just incorrect. So throwing a warning.
logger.warning(
"MML model file for initializing weights is passed, but does not exist."
" This is fine if you're just decoding.")
def __init__(self,
vocab: Vocabulary,
question_embedder: TextFieldEmbedder,
action_embedding_dim: int,
encoder: Seq2SeqEncoder,
entity_encoder: Seq2VecEncoder,
mixture_feedforward: FeedForward,
max_decoding_steps: int,
attention_function: SimilarityFunction,
use_neighbor_similarity_for_linking: bool = False,
dropout: float = 0.0,
num_linking_features: int = 10,
rule_namespace: str = 'rule_labels',
tables_directory: str = '/wikitables/') -> None:
super(WikiTablesSemanticParser, self).__init__(vocab)
self._question_embedder = question_embedder
self._encoder = encoder
self._entity_encoder = TimeDistributed(entity_encoder)
self._max_decoding_steps = max_decoding_steps
self._use_neighbor_similarity_for_linking = use_neighbor_similarity_for_linking
if dropout > 0:
self._dropout = torch.nn.Dropout(p=dropout)
else:
self._dropout = lambda x: x
self._rule_namespace = rule_namespace
self._denotation_accuracy = WikiTablesAccuracy(tables_directory)
self._action_sequence_accuracy = Average()
self._has_logical_form = Average()
self._action_padding_index = -1 # the padding value used by IndexField
num_actions = vocab.get_vocab_size(self._rule_namespace)
self._action_embedder = Embedding(num_embeddings=num_actions, embedding_dim=action_embedding_dim)
self._output_action_embedder = Embedding(num_embeddings=num_actions, embedding_dim=action_embedding_dim)
self._action_biases = Embedding(num_embeddings=num_actions, embedding_dim=1)
# This is what we pass as input in the first step of decoding, when we don't have a
# previous action, or a previous question attention.
self._first_action_embedding = torch.nn.Parameter(torch.FloatTensor(action_embedding_dim))
self._first_attended_question = torch.nn.Parameter(torch.FloatTensor(encoder.get_output_dim()))
torch.nn.init.normal(self._first_action_embedding)
torch.nn.init.normal(self._first_attended_question)
check_dimensions_match(entity_encoder.get_output_dim(), question_embedder.get_output_dim(),
"entity word average embedding dim", "question embedding dim")
self._num_entity_types = 4 # TODO(mattg): get this in a more principled way somehow?
self._num_start_types = 5 # TODO(mattg): get this in a more principled way somehow?
self._embedding_dim = question_embedder.get_output_dim()
self._type_params = torch.nn.Linear(self._num_entity_types, self._embedding_dim)
self._neighbor_params = torch.nn.Linear(self._embedding_dim, self._embedding_dim)
if num_linking_features > 0:
self._linking_params = torch.nn.Linear(num_linking_features, 1)
else:
self._linking_params = None
if self._use_neighbor_similarity_for_linking:
self._question_entity_params = torch.nn.Linear(1, 1)
self._question_neighbor_params = torch.nn.Linear(1, 1)
else:
self._question_entity_params = None
self._question_neighbor_params = None
self._decoder_step = WikiTablesDecoderStep(encoder_output_dim=self._encoder.get_output_dim(),
action_embedding_dim=action_embedding_dim,
attention_function=attention_function,
num_start_types=self._num_start_types,
num_entity_types=self._num_entity_types,
mixture_feedforward=mixture_feedforward,
dropout=dropout)