def __init__(self,
vocab: Vocabulary,
encoder: Seq2SeqEncoder,
entity_encoder: Seq2VecEncoder,
decoder_beam_search: BeamSearch,
question_embedder: TextFieldEmbedder,
input_attention: Attention,
past_attention: Attention,
graph_attention: Attention,
max_decoding_steps: int,
action_embedding_dim: int,
enable_gating: bool = False,
ablation_mode: str = None,
gnn: bool = True,
graph_loss_lambda: float = 0.5,
decoder_use_graph_entities: bool = True,
decoder_self_attend: bool = True,
gnn_timesteps: int = 2,
pruning_gnn_timesteps: int = 2,
parse_sql_on_decoding: bool = True,
add_action_bias: bool = False,
use_neighbor_similarity_for_linking: bool = True,
dataset_path: str = 'dataset',
log_path: str = '',
training_beam_size: int = None,
decoder_num_layers: int = 1,
dropout: float = 0.0,
rule_namespace: str = 'rule_labels') -> None:
super().__init__(vocab, encoder, entity_encoder, question_embedder, gnn_timesteps, dropout, rule_namespace)
self.enable_gating = enable_gating
self.ablation_mode = ablation_mode
self._log_path = log_path
self._max_decoding_steps = max_decoding_steps
self._add_action_bias = add_action_bias
self._parse_sql_on_decoding = parse_sql_on_decoding
self._self_attend = decoder_self_attend
self._decoder_use_graph_entities = decoder_use_graph_entities
self._use_neighbor_similarity_for_linking = use_neighbor_similarity_for_linking
self._action_padding_index = -1 # the padding value used by IndexField
self._exact_match = Average()
self._sql_evaluator_match = Average()
self._action_similarity = Average()
self._beam_hit = Average()
self._action_embedding_dim = action_embedding_dim
self._graph_loss_lambda = graph_loss_lambda
num_actions = vocab.get_vocab_size(self._rule_namespace)
if self._add_action_bias:
input_action_dim = action_embedding_dim + 1
else:
input_action_dim = action_embedding_dim
self._action_embedder = Embedding(num_embeddings=num_actions, embedding_dim=input_action_dim)
self._output_action_embedder = Embedding(num_embeddings=num_actions, embedding_dim=action_embedding_dim)
self._embedding_projector = torch.nn.Linear(question_embedder.get_output_dim(), self._embedding_dim, bias=False)
self._bert_embedding_dim = question_embedder.get_output_dim()
encoder_output_dim = self._encoder.get_output_dim() + self._embedding_dim
self._neighbor_encoder = TimeDistributed(BagOfEmbeddingsEncoder(self._embedding_dim, averaged=True))
self._first_action_embedding = torch.nn.Parameter(torch.FloatTensor(action_embedding_dim))
self._first_attended_utterance = torch.nn.Parameter(torch.FloatTensor(encoder_output_dim))
self._first_attended_output = torch.nn.Parameter(torch.FloatTensor(action_embedding_dim))
torch.nn.init.normal_(self._first_action_embedding)
torch.nn.init.normal_(self._first_attended_utterance)
torch.nn.init.normal_(self._first_attended_output)
self._entity_type_decoder_embedding = Embedding(self._num_entity_types, action_embedding_dim)
self._decoder_num_layers = decoder_num_layers
self._beam_search = decoder_beam_search
self._decoder_trainer = MaximumMarginalLikelihood(training_beam_size)
self._graph_pruning = GraphPruning(3, self._embedding_dim, encoder.get_output_dim(), dropout,
timesteps=pruning_gnn_timesteps)
if decoder_self_attend:
self._transition_function = AttendPastSchemaItemsTransitionFunction(encoder_output_dim=encoder_output_dim,
action_embedding_dim=action_embedding_dim,
input_attention=input_attention,
past_attention=past_attention,
enable_gating=self.enable_gating,
ablation_mode=self.ablation_mode,
predict_start_type_separately=False,
add_action_bias=self._add_action_bias,
dropout=dropout,
num_layers=self._decoder_num_layers)
else:
self._transition_function = LinkingTransitionFunction(encoder_output_dim=encoder_output_dim,
action_embedding_dim=action_embedding_dim,
input_attention=input_attention,
predict_start_type_separately=False,
add_action_bias=self._add_action_bias,
dropout=dropout,
num_layers=self._decoder_num_layers)
if self.enable_gating:
self._graph_attention = graph_attention
else:
self._graph_attention = DotProductAttention()
self._embedding_sim_attn = CosineMatrixAttention()
# TODO: Remove hard-coded dirs
self._evaluate_func = partial(evaluate,
db_dir=os.path.join(dataset_path, 'database'),
table=os.path.join(dataset_path, 'tables.json'),
check_valid=False)
def __init__(self,
vocab: Vocabulary,
encoder: Seq2SeqEncoder,
entity_encoder: Seq2VecEncoder,
decoder_beam_search: BeamSearch,
question_embedder: TextFieldEmbedder,
input_attention: Attention,
past_attention: Attention,
max_decoding_steps: int,
action_embedding_dim: int,
gnn: bool = True,
decoder_use_graph_entities: bool = True,
decoder_self_attend: bool = True,
gnn_timesteps: int = 2,
parse_sql_on_decoding: bool = True,
add_action_bias: bool = True,
use_neighbor_similarity_for_linking: bool = True,
dataset_path: str = 'dataset',
training_beam_size: int = None,
decoder_num_layers: int = 1,
dropout: float = 0.0,
rule_namespace: str = 'rule_labels',
scoring_dev_params: dict = None,
debug_parsing: bool = False) -> None:
super().__init__(vocab)
self.vocab = vocab
self._encoder = encoder
self._max_decoding_steps = max_decoding_steps
if dropout > 0:
self._dropout = torch.nn.Dropout(p=dropout)
else:
self._dropout = lambda x: x
self._rule_namespace = rule_namespace
self._question_embedder = question_embedder
self._add_action_bias = add_action_bias
self._scoring_dev_params = scoring_dev_params or {}
self.parse_sql_on_decoding = parse_sql_on_decoding
self._entity_encoder = TimeDistributed(entity_encoder)
self._use_neighbor_similarity_for_linking = use_neighbor_similarity_for_linking
self._self_attend = decoder_self_attend
self._decoder_use_graph_entities = decoder_use_graph_entities
self._action_padding_index = -1 # the padding value used by IndexField
self._exact_match = Average()
self._sql_evaluator_match = Average()
self._action_similarity = Average()
self._acc_single = Average()
self._acc_multi = Average()
self._beam_hit = Average()
self._action_embedding_dim = action_embedding_dim
num_actions = vocab.get_vocab_size(self._rule_namespace)
if self._add_action_bias:
input_action_dim = action_embedding_dim + 1
else:
input_action_dim = action_embedding_dim
self._action_embedder = Embedding(num_embeddings=num_actions,
embedding_dim=input_action_dim)
self._output_action_embedder = Embedding(
num_embeddings=num_actions, embedding_dim=action_embedding_dim)
encoder_output_dim = encoder.get_output_dim()
if gnn:
encoder_output_dim += action_embedding_dim
self._first_action_embedding = torch.nn.Parameter(
torch.FloatTensor(action_embedding_dim))
self._first_attended_utterance = torch.nn.Parameter(
torch.FloatTensor(encoder_output_dim))
self._first_attended_output = torch.nn.Parameter(
torch.FloatTensor(action_embedding_dim))
torch.nn.init.normal_(self._first_action_embedding)
torch.nn.init.normal_(self._first_attended_utterance)
torch.nn.init.normal_(self._first_attended_output)
self._num_entity_types = 9
self._embedding_dim = question_embedder.get_output_dim()
self._entity_type_encoder_embedding = Embedding(
self._num_entity_types, self._embedding_dim)
self._entity_type_decoder_embedding = Embedding(
self._num_entity_types, action_embedding_dim)
self._linking_params = torch.nn.Linear(16, 1)
torch.nn.init.uniform_(self._linking_params.weight, 0, 1)
num_edge_types = 3
self._gnn = GatedGraphConv(self._embedding_dim,
gnn_timesteps,
num_edge_types=num_edge_types,
dropout=dropout)
self._decoder_num_layers = decoder_num_layers
self._beam_search = decoder_beam_search
self._decoder_trainer = MaximumMarginalLikelihood(training_beam_size)
if decoder_self_attend:
self._transition_function = AttendPastSchemaItemsTransitionFunction(
encoder_output_dim=encoder_output_dim,
action_embedding_dim=action_embedding_dim,
input_attention=input_attention,
past_attention=past_attention,
predict_start_type_separately=False,
add_action_bias=self._add_action_bias,
dropout=dropout,
num_layers=self._decoder_num_layers)
else:
self._transition_function = LinkingTransitionFunction(
encoder_output_dim=encoder_output_dim,
action_embedding_dim=action_embedding_dim,
input_attention=input_attention,
predict_start_type_separately=False,
add_action_bias=self._add_action_bias,
dropout=dropout,
num_layers=self._decoder_num_layers)
self._ent2ent_ff = FeedForward(action_embedding_dim, 1,
action_embedding_dim,
Activation.by_name('relu')())
self._neighbor_params = torch.nn.Linear(self._embedding_dim,
self._embedding_dim)
# TODO: Remove hard-coded dirs
self._evaluate_func = partial(
evaluate,
db_dir=os.path.join(dataset_path, 'database'),
table=os.path.join(dataset_path, 'tables.json'),
check_valid=False)
self.debug_parsing = debug_parsing
def __init__(self,
question_embedder: TextFieldEmbedder,
input_memory_embedder: TextFieldEmbedder,
output_memory_embedder: TextFieldEmbedder,
question_encoder: Seq2SeqEncoder,
input_memory_encoder: Seq2VecEncoder,
output_memory_encoder: Seq2VecEncoder,
decoder_beam_search: BeamSearch,
input_attention: Attention,
past_attention: Attention,
action_embedding_dim: int,
max_decoding_steps: int,
nhop: int,
decoding_nhop: int,
vocab: Vocabulary,
dataset_path: str = 'dataset',
parse_sql_on_decoding: bool = True,
training_beam_size: int = None,
add_action_bias: bool = True,
decoder_self_attend: bool = True,
decoder_num_layers: int = 1,
dropout: float = 0.0,
rule_namespace: str = 'rule_labels') -> None:
super().__init__(vocab)
self.question_embedder = question_embedder
self._input_mm_embedder = input_memory_embedder
self._output_mm_embedder = output_memory_embedder
self._question_encoder = question_encoder
self._input_mm_encoder = TimeDistributed(input_memory_encoder)
self._output_mm_encoder = TimeDistributed(output_memory_encoder)
self.parse_sql_on_decoding = parse_sql_on_decoding
self._self_attend = decoder_self_attend
self._max_decoding_steps = max_decoding_steps
self._add_action_bias = add_action_bias
self._rule_namespace = rule_namespace
num_actions = vocab.get_vocab_size(self._rule_namespace)
if self._add_action_bias:
input_action_dim = action_embedding_dim + 1
else:
input_action_dim = action_embedding_dim
self._action_embedder = Embedding(num_embeddings=num_actions,
embedding_dim=input_action_dim)
self._input_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._num_entity_types = 9
self._entity_type_decoder_input_embedding = Embedding(
self._num_entity_types, action_embedding_dim)
self._entity_type_decoder_output_embedding = Embedding(
self._num_entity_types, action_embedding_dim)
self._entity_type_encoder_embedding = Embedding(
self._num_entity_types,
(int)(question_encoder.get_output_dim() / 2))
self._decoder_num_layers = decoder_num_layers
self._action_embedding_dim = action_embedding_dim
self._ent2ent_ff = FeedForward(action_embedding_dim, 1,
action_embedding_dim,
Activation.by_name('relu')())
if dropout > 0:
self._dropout = torch.nn.Dropout(p=dropout)
else:
self._dropout = lambda x: x
self._first_action_embedding = torch.nn.Parameter(
torch.FloatTensor(action_embedding_dim))
self._first_attended_utterance = torch.nn.Parameter(
torch.FloatTensor(question_encoder.get_output_dim()))
torch.nn.init.normal_(self._first_action_embedding)
torch.nn.init.normal_(self._first_attended_utterance)
if self._self_attend:
self._transition_function = AttendPastSchemaItemsTransitionFunction(
encoder_output_dim=question_encoder.get_output_dim(),
action_embedding_dim=action_embedding_dim,
input_attention=input_attention,
past_attention=past_attention,
decoding_nhop=decoding_nhop,
predict_start_type_separately=False,
add_action_bias=self._add_action_bias,
dropout=dropout,
num_layers=self._decoder_num_layers)
else:
self._transition_function = LinkingTransitionFunction(
encoder_output_dim=question_encoder.get_output_dim(),
action_embedding_dim=action_embedding_dim,
input_attention=input_attention,
predict_start_type_separately=False,
add_action_bias=self._add_action_bias,
dropout=dropout,
num_layers=self._decoder_num_layers)
self._mm_attn = MemAttn(question_encoder.get_output_dim(), nhop)
self._beam_search = decoder_beam_search
self._decoder_trainer = MaximumMarginalLikelihood(training_beam_size)
self._action_padding_index = -1 # the padding value used by IndexField
self._exact_match = Average()
self._sql_evaluator_match = Average()
self._action_similarity = Average()
self._acc_single = Average()
self._acc_multi = Average()
self._beam_hit = Average()
# TODO: Remove hard-coded dirs
self._evaluate_func = partial(
evaluate,
db_dir=os.path.join(dataset_path, 'database'),
table=os.path.join(dataset_path, 'tables.json'),
check_valid=False)