def from_config(cls, model_config, feature_config, metadata: CommonMetadata):
if model_config.compositional_type == RNNGParser.Config.CompositionalType.SUM:
p_compositional = CompositionalSummationNN(
lstm_dim=model_config.lstm.lstm_dim
)
elif (
model_config.compositional_type == RNNGParser.Config.CompositionalType.BLSTM
):
p_compositional = CompositionalNN(lstm_dim=model_config.lstm.lstm_dim)
else:
raise ValueError(
"Cannot understand compositional flag {}".format(
model_config.compositional_type
)
)
return cls(
ablation=model_config.ablation,
constraints=model_config.constraints,
lstm_num_layers=model_config.lstm.num_layers,
lstm_dim=model_config.lstm.lstm_dim,
max_open_NT=model_config.max_open_NT,
dropout=model_config.dropout,
actions_vocab=metadata.actions_vocab,
shift_idx=metadata.shift_idx,
reduce_idx=metadata.reduce_idx,
ignore_subNTs_roots=metadata.ignore_subNTs_roots,
valid_NT_idxs=metadata.valid_NT_idxs,
valid_IN_idxs=metadata.valid_IN_idxs,
valid_SL_idxs=metadata.valid_SL_idxs,
embedding=Model.create_embedding(feature_config, metadata=metadata),
p_compositional=p_compositional,
)
def from_config(
cls,
model_config,
feature_config=None,
metadata: CommonMetadata = None,
tensorizers: Dict[str, Tensorizer] = None,
):
if model_config.compositional_type == RNNGParser.Config.CompositionalType.SUM:
p_compositional = CompositionalSummationNN(
lstm_dim=model_config.lstm.lstm_dim)
elif (model_config.compositional_type ==
RNNGParser.Config.CompositionalType.BLSTM):
p_compositional = CompositionalNN(
lstm_dim=model_config.lstm.lstm_dim)
else:
raise ValueError("Cannot understand compositional flag {}".format(
model_config.compositional_type))
if tensorizers is not None:
embedding = EmbeddingList(
[
create_module(model_config.embedding,
tensorizer=tensorizers["tokens"])
],
concat=True,
)
actions_params = tensorizers["actions"]
actions_vocab = actions_params.vocab
else:
embedding = Model.create_embedding(feature_config,
metadata=metadata)
actions_params = metadata
actions_vocab = metadata.actions_vocab
return cls(
ablation=model_config.ablation,
constraints=model_config.constraints,
lstm_num_layers=model_config.lstm.num_layers,
lstm_dim=model_config.lstm.lstm_dim,
max_open_NT=model_config.max_open_NT,
dropout=model_config.dropout,
actions_vocab=actions_vocab,
shift_idx=actions_params.shift_idx,
reduce_idx=actions_params.reduce_idx,
ignore_subNTs_roots=actions_params.ignore_subNTs_roots,
valid_NT_idxs=actions_params.valid_NT_idxs,
valid_IN_idxs=actions_params.valid_IN_idxs,
valid_SL_idxs=actions_params.valid_SL_idxs,
embedding=embedding,
p_compositional=p_compositional,
)
def from_config(cls, model_config, feature_config,
metadata: CommonMetadata):
device = ("cuda:{}".format(torch.cuda.current_device())
if cuda.CUDA_ENABLED else "cpu")
if model_config.compositional_type == RNNGParser.Config.CompositionalType.SUM:
p_compositional = CompositionalSummationNN(
lstm_dim=model_config.lstm.lstm_dim)
elif (model_config.compositional_type ==
RNNGParser.Config.CompositionalType.BLSTM):
p_compositional = CompositionalNN(
lstm_dim=model_config.lstm.lstm_dim, device=device)
else:
raise ValueError("Cannot understand compositional flag {}".format(
model_config.compositional_type))
emb_module = Model.create_embedding(feature_config, metadata=metadata)
contextual_emb_dim = feature_config.contextual_token_embedding.embed_dim
return cls(
cls.get_input_for_trace(contextual_emb_dim),
embedding=emb_module,
ablation=model_config.ablation,
constraints=model_config.constraints,
lstm_num_layers=model_config.lstm.num_layers,
lstm_dim=model_config.lstm.lstm_dim,
max_open_NT=model_config.max_open_NT,
dropout=model_config.dropout,
num_actions=len(metadata.actions_vocab),
shift_idx=metadata.shift_idx,
reduce_idx=metadata.reduce_idx,
ignore_subNTs_roots=metadata.ignore_subNTs_roots,
valid_NT_idxs=metadata.valid_NT_idxs,
valid_IN_idxs=metadata.valid_IN_idxs,
valid_SL_idxs=metadata.valid_SL_idxs,
embedding_dim=emb_module.embedding_dim,
p_compositional=p_compositional,
device=device,
)