def from_config(cls, config: Config, tensorizers: Dict[str, Tensorizer]):
label_vocab = tensorizers["tokens"].labels_vocab
vocab = tensorizers["tokens"].vocab
encoder = create_module(
config.encoder,
output_encoded_layers=True,
padding_idx=vocab.get_pad_index(),
vocab_size=vocab.__len__(),
)
decoder = create_module(
config.decoder, in_dim=encoder.representation_dim, out_dim=len(label_vocab)
)
output_layer = create_module(config.output_layer, labels=label_vocab)
return cls(encoder, decoder, output_layer)
def from_config(cls, config: Config, tensorizers: Dict[str, Tensorizer]):
# Although the RNN params are configurable, for DrQA we want to set
# the following parameters for all cases.
config.ques_rnn.dropout = config.dropout
config.doc_rnn.dropout = config.dropout
embedding = cls.create_embedding(config, tensorizers)
ques_aligned_doc_attn = SequenceAlignedAttention(
embedding.embedding_dim)
ques_rnn = create_module(config.ques_rnn,
input_size=embedding.embedding_dim)
doc_rnn = create_module(config.doc_rnn,
input_size=embedding.embedding_dim * 2)
ques_self_attn = DotProductSelfAttention(ques_rnn.representation_dim)
start_attn = MultiplicativeAttention(doc_rnn.representation_dim,
ques_rnn.representation_dim,
normalize=False)
end_attn = MultiplicativeAttention(doc_rnn.representation_dim,
ques_rnn.representation_dim,
normalize=False)
doc_rep_pool = SelfAttention(
SelfAttention.Config(dropout=config.dropout),
n_input=doc_rnn.representation_dim,
)
has_answer_labels = ["False", "True"]
tensorizers["has_answer"].vocab = Vocabulary(has_answer_labels)
has_ans_decoder = MLPDecoder(
config=MLPDecoder.Config(),
in_dim=doc_rnn.representation_dim,
out_dim=len(has_answer_labels),
)
output_layer = create_module(config.output_layer,
labels=has_answer_labels,
is_kd=config.is_kd)
return cls(
dropout=nn.Dropout(config.dropout),
embedding=embedding,
ques_rnn=ques_rnn,
doc_rnn=doc_rnn,
ques_self_attn=ques_self_attn,
ques_aligned_doc_attn=ques_aligned_doc_attn,
start_attn=start_attn,
end_attn=end_attn,
doc_rep_pool=doc_rep_pool,
has_ans_decoder=has_ans_decoder,
output_layer=output_layer,
is_kd=config.is_kd,
)
def from_config(cls, config: Config, tensorizers: Dict[str, Tensorizer]):
vocab = tensorizers["tokens"].vocab
labels = tensorizers["labels"].labels
embedding = WordEmbedding(len(vocab), config.embedding.embed_dim, None,
None, vocab.idx[UNK], [])
representation = create_module(config.representation,
embed_dim=embedding.embedding_dim)
decoder = create_module(
config.decoder,
in_dim=representation.representation_dim,
out_dim=len(labels),
)
output_layer = ClassificationOutputLayer(labels,
CrossEntropyLoss(None))
return cls(embedding, representation, decoder, output_layer)
def from_config(cls, config: Config, tensorizers: Dict[str, Tensorizer]):
labels = tensorizers["labels"].labels
embedding = cls.create_embedding(config, tensorizers)
representation = create_module(
config.representation, embed_dim=embedding.embedding_dim
)
decoder = create_module(
config.decoder,
in_dim=representation.representation_dim,
out_dim=len(labels),
)
# TODO change from_config function of ClassificationOutputLayer after migriting to new design
output_layer = ClassificationOutputLayer(
list(labels), create_loss(config.output_layer.loss)
)
return cls(embedding, representation, decoder, output_layer)
def __init__(self, config: Config, embed_dim: int) -> None:
super().__init__(config)
self.word_rep = create_module(config.word_representation, embed_dim)
self.word_representation_dim = self.word_rep.representation_dim
self.doc_representation_dim = self.word_rep.representation_dim
self.pooling_type = config.pooling_type
log_class_usage(__class__)
def from_config(cls, config: Config, tensorizers: Dict[str, Tensorizer]):
labels = tensorizers["labels"].vocab
embedding = cls.create_embedding(config, tensorizers)
representation = create_module(
config.representation, embed_dim=embedding.embedding_dim
)
decoder = cls.create_decoder(
config, representation.representation_dim, len(labels)
)
label_weights = (
get_label_weights(labels.idx, config.output_layer.label_weights)
if config.output_layer.label_weights
else None
)
loss = create_loss(config.output_layer.loss, weight=label_weights)
if isinstance(loss, BinaryCrossEntropyLoss):
output_layer_cls = BinaryClassificationOutputLayer
elif isinstance(loss, MultiLabelSoftMarginLoss):
output_layer_cls = MultiLabelOutputLayer
else:
output_layer_cls = MulticlassOutputLayer
output_layer = output_layer_cls(list(labels), loss)
return cls(embedding, representation, decoder, output_layer)
def _create_encoder(cls, config: Config,
tensorizers: Dict[str, Tensorizer]) -> nn.ModuleList:
encoder1 = create_module(
config.encoder,
padding_idx=tensorizers["tokens1"].vocab.get_pad_index(),
vocab_size=len(tensorizers["tokens1"].vocab),
)
if config.shared_encoder:
encoder2 = encoder1
else:
encoder2 = create_module(
config.encoder,
padding_idx=tensorizers["tokens2"].vocab.get_pad_index(),
vocab_size=len(tensorizers["tokens2"].vocab),
)
return encoder1, encoder2
def __init__(self, config: Config, embed_dim: int) -> None:
"""embed_dim is the dimension of embedded_tokens
"""
super().__init__(config)
self.dropout = nn.Dropout(config.dropout)
# Document attention.
self.attention = (
create_module(config.pooling, n_input=embed_dim)
if config.pooling is not None
else None
)
# Non-linear projection over attended representation.
self.dense = None
if (
isinstance(config.pooling, BoundaryPool.Config)
and config.pooling.boundary_type == "firstlast"
):
# the dimension double because of concatenating bos and eos
self.representation_dim = embed_dim * 2
else:
self.representation_dim = embed_dim
if config.mlp_decoder:
self.dense = MLPDecoder(config.mlp_decoder, in_dim=embed_dim)
self.representation_dim = self.dense.out_dim
log_class_usage(__class__)
def from_config(cls, config, tensorizers: Dict[str, Tensorizer]):
model = super().from_config(config, tensorizers)
user_embedding = create_module(
config.user_embedding,
tensorizer=tensorizers["uid"],
init_from_saved_state=config.init_from_saved_state,
)
# Init user embeddings to be a same vector because we assume user features
# are not too different from each other.
emb_shape = user_embedding.word_embedding.weight.data.shape
with torch.no_grad():
user_embedding.word_embedding.weight.copy_(
torch.rand(emb_shape[1]).repeat(emb_shape[0], 1)
)
labels = tensorizers["labels"].vocab
decoder = cls.create_decoder(
config,
model.representation.representation_dim + user_embedding.embedding_dim,
len(labels),
)
return cls(
model.embedding,
model.representation,
decoder,
model.output_layer,
user_embedding,
)
def create_embedding(cls, config: Config, tensorizers: Dict[str,
Tensorizer]):
return create_module(
config.embedding,
tensorizer=tensorizers["tokens"],
init_from_saved_state=config.init_from_saved_state,
)
def __init__(self, config: Config, embed_dim: int) -> None:
super().__init__(config)
self.dropout = nn.Dropout(config.dropout)
# BiLSTM representation.
self.lstm = create_module(config.lstm, embed_dim=embed_dim)
# Slot attention.
self.attention = None
word_representation_dim = self.lstm.representation_dim
if config.slot_attention:
self.attention = SlotAttention(
config.slot_attention, self.lstm.representation_dim, batch_first=True
)
word_representation_dim += self.lstm.representation_dim
# Projection over attended representation.
self.dense = None
self.representation_dim: int = self.lstm.representation_dim
if config.mlp_decoder:
self.dense = MLPDecoder(
config.mlp_decoder, in_dim=self.lstm.representation_dim
)
self.representation_dim = self.dense.out_dim
def __init__(self, config: Config, embed_dim: Tuple[int, ...]) -> None:
super().__init__(config)
assert len(embed_dim) == 2
self.sen_rep = create_module(config.sen_representation,
embed_dim=embed_dim[1])
self.sen_representation_dim = self.sen_rep.representation_dim
self.seq_rep = create_module(config.seq_representation,
embed_dim=self.sen_representation_dim)
self.seq_representation_dim = self.seq_rep.representation_dim
self.joint_rep = create_module(
config.joint_representation,
embed_dim=embed_dim[0] + self.seq_representation_dim,
)
self.doc_representation_dim = self.joint_rep.doc_representation_dim
self.word_representation_dim = self.joint_rep.word_representation_dim
def from_config(cls, config, kernel_size):
conv = create_module(
config.encoder_conv_type,
input_size=config.encoder_conv_dim,
kernel_size=kernel_size,
convolution_type="non-causal",
)
if config.self_attention_type is not None:
self_attention = create_module(
config.self_attention_type,
config.encoder_embed_dim,
config.self_attention_heads,
)
else:
self_attention = None
return cls(**config._asdict(), conv=conv, self_attention=self_attention)
def from_config(cls, config: Config, tensorizers: Dict[str, Tensorizer]):
encoder1, encoder2 = cls._create_encoder(config, tensorizers)
decoder = cls._create_decoder(config, [encoder1, encoder2], tensorizers)
output_layer = create_module(
config.output_layer, labels=tensorizers["labels"].vocab
)
return cls(encoder1, encoder2, decoder, output_layer, config.encode_relations)
def __init__(self, config: Config, output_encoded_layers: bool,
**kwarg) -> None:
super().__init__(config, output_encoded_layers=output_encoded_layers)
assert config.pretrained_encoder.load_path, "Load path cannot be empty."
self.encoder = create_module(config.pretrained_encoder)
self.representation_dim = self.encoder.encoder.token_embedding.weight.size(
-1)
def __init__(
self,
model: CNNModel,
length_prediction_model: MaskedLengthPredictionModule,
output_layer: NARSeq2SeqOutputLayer,
src_vocab: Vocabulary,
trg_vocab: Vocabulary,
dictfeat_vocab: Vocabulary,
tensorizer=None,
generator_config=None,
config: Config = None,
):
super().__init__(
model,
output_layer,
src_vocab,
trg_vocab,
dictfeat_vocab,
generator_config=None,
)
self.quantize = generator_config.quantize
self.length_prediction_model = length_prediction_model
self.sequence_generator_builder = (
lambda model, length_prediction_model, quantize: create_module(
generator_config, model, length_prediction_model, trg_vocab, quantize
)
)
self.force_eval_predictions = generator_config.force_eval_predictions
self.generate_predictions_every = generator_config.generate_predictions_every
self.tensorizer = tensorizer
def construct_length_prediction_module(
cls,
config: Config,
):
return create_module(
config.length_prediction_model,
config.encoder_decoder.encoder.encoder_config.encoder_embed_dim,
)
def from_config(cls, config: Config, tensorizers: Dict[str, Tensorizer]):
embeddings = cls._create_embeddings(config, tensorizers)
representations = cls._create_representations(config, embeddings)
decoder = cls._create_decoder(config, representations, tensorizers)
output_layer = create_module(config.output_layer,
labels=tensorizers["labels"].vocab)
return cls(embeddings, representations, decoder, output_layer,
config.encode_relations)
def from_config(cls, config: Config, tensorizers: Dict[str, Tensorizer]):
if config.use_shared_embedding:
token_embedding = torch.nn.Embedding(
config.vocab_size,
config.hidden_dim,
padding_idx=config.padding_idx)
else:
token_embedding = None
right_vocab = tensorizers["right_tokens"].vocab
right_encoder = create_module(
config.right_encoder,
token_embedding=token_embedding,
padding_idx=right_vocab.get_pad_index(),
vocab_size=len(right_vocab),
)
left_vocab = tensorizers["left_tokens"].vocab
left_encoder = create_module(
config.left_encoder,
token_embedding=token_embedding,
padding_idx=left_vocab.get_pad_index(),
vocab_size=len(left_vocab),
)
right_dense_dim = tensorizers["right_dense"].dim
left_dense_dim = tensorizers["left_dense"].dim
decoder = create_module(
config.decoder,
right_dim=right_encoder.representation_dim + right_dense_dim,
left_dim=left_encoder.representation_dim + left_dense_dim,
to_dim=1,
)
output_layer = RegressionOutputLayer.from_config(config.output_layer)
return cls(
right_encoder,
left_encoder,
decoder,
output_layer,
config.use_shared_encoder,
config.use_shared_embedding,
config.vocab_size,
config.hidden_dim,
config.padding_idx,
)
def from_config(cls, config, tgt_dict, tgt_embedding):
kernel_size_list = config.decoder_kernel_size_list
layers = []
for size in kernel_size_list:
assert (config.decoder_config.decoder_embed_dim ==
config.layer_config.decoder_embed_dim)
layers.append(create_module(config.layer_config, kernel_size=size))
return cls(tgt_dict, tgt_embedding, layers, config.decoder_config)
def from_config(
cls,
config: Config,
doc_meta: Optional[FieldMeta] = None,
word_meta: Optional[FieldMeta] = None,
doc_labels: Optional[Vocabulary] = None,
word_labels: Optional[Vocabulary] = None,
):
if word_labels and doc_labels:
return cls(
create_module(config.doc_output, labels=doc_labels),
create_module(config.word_output, labels=word_labels),
)
else:
return cls(
create_module(config.doc_output, metadata=doc_meta),
create_module(config.word_output, metadata=word_meta),
)
def _create_decoder(
cls,
config: Config,
representations: nn.ModuleList,
tensorizers: Dict[str, Tensorizer],
):
labels = tensorizers["labels"].vocab
num_reps = len(representations)
rep_dim = representations[0].representation_dim
decoder_in_dim = num_reps * rep_dim
if config.encode_relations:
decoder_in_dim += 2 * comb(num_reps, 2, exact=True) * rep_dim
decoder = create_module(
config.decoder, in_dim=decoder_in_dim, out_dim=len(labels)
)
output_layer = create_module(config.output_layer, labels=labels)
return decoder, output_layer
def _create_representations(cls, config: Config,
embeddings: nn.ModuleList):
if config.shared_representations:
# create representation once and used for all embeddings
embedding_dim = embeddings[0].embedding_dim
representations = nn.ModuleList(
itertools.repeat(
create_module(config.representation,
embed_dim=embedding_dim),
len(embeddings),
))
else:
representations = nn.ModuleList([
create_module(config.representation,
embed_dim=embedding.embedding_dim)
for embedding in embeddings
])
return representations
def create_decoder(cls, config: Config, representation_dim: int, num_labels: int):
num_decoder_modules = 0
in_dim = representation_dim
if hasattr(config.inputs, "dense") and config.inputs.dense:
num_decoder_modules += 1
in_dim += config.inputs.dense.dim
decoder = create_module(config.decoder, in_dim=in_dim, out_dim=num_labels)
decoder.num_decoder_modules = num_decoder_modules
return decoder
def from_config(cls, config: Config, tensorizers: Dict[str, Tensorizer]):
# merge tensorizer vocab
vocab_builder = VocabBuilder()
for tensorizer in tensorizers.values():
vocab_builder.add_all(tensorizer.vocab.counts)
merged_vocab = vocab_builder.make_vocab()
for tensorizer in tensorizers.values():
tensorizer.vocab = merged_vocab
# create embeddings
positive_emb = create_module(
config.embedding, None, tensorizers["pos_response"]
)
negative_emb = positive_emb
query_emb = positive_emb
embeddings = nn.ModuleList([positive_emb, negative_emb, query_emb])
embedding_dim = embeddings[0].embedding_dim
# create representations
positive_repr = create_module(config.representation, embed_dim=embedding_dim)
negative_repr = positive_repr
query_repr = (
positive_repr
if config.shared_representations
else create_module(config.representation, embed_dim=embedding_dim)
)
representations = nn.ModuleList([positive_repr, negative_repr, query_repr])
# representation.representation_dim: tuple(2, actual repr dim)
decoder = create_module(
config.decoder,
from_dim=representations[0].representation_dim,
to_dim=config.decoder_output_dim,
)
output_layer = create_module(config.output_layer)
return cls(
embeddings,
representations,
decoder,
output_layer,
encode_relations=False,
shared_representations=config.shared_representations,
)
def from_config(cls, config: Config, tensorizers: Dict[str, Tensorizer]):
labels = tensorizers["labels"].vocab
if not labels:
raise ValueError("Labels were not created, see preceding errors")
right_vocab = tensorizers["right_tokens"].vocab
right_encoder = create_module(
config.right_encoder,
padding_idx=right_vocab.get_pad_index(),
vocab_size=len(right_vocab),
)
left_vocab = tensorizers["left_tokens"].vocab
left_encoder = create_module(
config.left_encoder,
padding_idx=left_vocab.get_pad_index(),
vocab_size=len(left_vocab),
)
right_dense_dim = tensorizers["right_dense"].dim
left_dense_dim = tensorizers["left_dense"].dim
decoder = create_module(
config.decoder,
right_dim=right_encoder.representation_dim + right_dense_dim,
left_dim=left_encoder.representation_dim + left_dense_dim,
to_dim=len(labels),
)
label_weights = (get_label_weights(labels.idx,
config.output_layer.label_weights)
if config.output_layer.label_weights else None)
loss = create_loss(config.output_layer.loss, weight=label_weights)
if isinstance(loss, BinaryCrossEntropyLoss):
output_layer_cls = BinaryClassificationOutputLayer
elif isinstance(loss, MultiLabelSoftMarginLoss):
output_layer_cls = MultiLabelOutputLayer
else:
output_layer_cls = MulticlassOutputLayer
output_layer = output_layer_cls(list(labels), loss)
return cls(right_encoder, left_encoder, decoder, output_layer)
def from_config(cls, config, src_dict, src_embedding):
kernel_size_list = config.encoder_kernel_size_list
layers = []
# Overwrite the config.layer_config.encoder_embed_dim so that it will always match with config.encoder_config.encoder_embed_dim
config.layer_config.encoder_embed_dim = config.encoder_config.encoder_embed_dim
for size in kernel_size_list:
assert (config.encoder_config.encoder_embed_dim ==
config.layer_config.encoder_embed_dim)
layers.append(create_module(config.layer_config, kernel_size=size))
return cls(src_dict, src_embedding, layers, config.encoder_config)
def from_config(cls, config: Config, tensorizers):
has_answer_labels = ["False", "True"]
tensorizers["has_answer"].vocab = Vocabulary(has_answer_labels)
vocab = tensorizers["squad_input"].vocab
encoder = create_module(
config.encoder,
output_encoded_layers=True,
padding_idx=vocab.get_pad_index(),
vocab_size=vocab.__len__(),
)
decoder = create_module(
config.decoder, in_dim=encoder.representation_dim, out_dim=2
)
has_ans_decoder = create_module(
config.decoder,
in_dim=encoder.representation_dim,
out_dim=len(has_answer_labels),
)
output_layer = create_module(config.output_layer, labels=has_answer_labels)
return cls(encoder, decoder, has_ans_decoder, output_layer)
def __init__(self, config: Config, output_encoded_layers: bool, **kwarg) -> None:
config.pretrained_encoder.load_path = (
resources.roberta.RESOURCE_MAP[config.pretrained_encoder.load_path]
if config.pretrained_encoder.load_path in resources.roberta.RESOURCE_MAP
else config.pretrained_encoder.load_path
)
super().__init__(config, output_encoded_layers=output_encoded_layers)
assert config.pretrained_encoder.load_path, "Load path cannot be empty."
self.encoder = create_module(config.pretrained_encoder)
self.representation_dim = self.encoder.encoder.token_embedding.weight.size(-1)
log_class_usage(__class__)
def create_embedding(cls, model_config: Config, tensorizers: Dict[str, Tensorizer]):
squad_tensorizer = tensorizers["squad_input"]
# Initialize the embedding module.
embedding_module = create_module(model_config.embedding, None, squad_tensorizer)
# Set ques and doc tensorizer vocab to squad_tensorizer.vocab.
squad_tensorizer.ques_tensorizer.vocab = squad_tensorizer.vocab
squad_tensorizer.doc_tensorizer.vocab = squad_tensorizer.vocab
return embedding_module
