def build_pair_sentence_module(task, d_inp, model, params):
""" Build a pair classifier, shared if necessary """
def build_pair_attn(d_in, d_hid_attn):
""" Build the pair model """
d_inp_model = 2 * d_in
modeling_layer = s2s_e.by_name("lstm").from_params(
Params(
{
"input_size": d_inp_model,
"hidden_size": d_hid_attn,
"num_layers": 1,
"bidirectional": True,
}
)
)
pair_attn = AttnPairEncoder(model.vocab, modeling_layer, dropout=params["dropout"])
return pair_attn
# Build the "pooler", which does pools a variable length sequence
# possibly with a projection layer beforehand
if params["attn"] and not model.use_bert:
pooler = Pooler(project=False, d_inp=params["d_hid_attn"], d_proj=params["d_hid_attn"])
d_out = params["d_hid_attn"] * 2
else:
pooler = Pooler(
project=not model.use_bert,
d_inp=d_inp,
d_proj=params["d_proj"],
pool_type=params["pool_type"],
)
d_out = d_inp if model.use_bert else params["d_proj"]
# Build an attention module if necessary
if params["shared_pair_attn"] and params["attn"] and not model.use_bert: # shared attn
if not hasattr(model, "pair_attn"):
pair_attn = build_pair_attn(d_inp, params["d_hid_attn"])
model.pair_attn = pair_attn
else:
pair_attn = model.pair_attn
elif params["attn"] and not model.use_bert: # non-shared attn
pair_attn = build_pair_attn(d_inp, params["d_hid_attn"])
else: # no attn
pair_attn = None
# Build the classifier
n_classes = task.n_classes if hasattr(task, "n_classes") else 1
if model.use_bert:
# BERT handles pair tasks by concatenating the inputs and classifying the joined
# sequence, so we use a single sentence classifier
if isinstance(task, WiCTask):
d_out *= 3 # also pass the two contextual word representations
classifier = Classifier.from_params(d_out, n_classes, params)
module = SingleClassifier(pooler, classifier)
else:
d_out = d_out + d_inp if isinstance(task, WiCTask) else d_out
classifier = Classifier.from_params(4 * d_out, n_classes, params)
module = PairClassifier(pooler, classifier, pair_attn)
return module
def build_multiple_choice_module(task, d_sent, use_bert, params):
""" Basic parts for MC task: reduce a vector representation for each model into a scalar. """
pooler = Pooler(
project=not use_bert, d_inp=d_sent, d_proj=params["d_proj"], pool_type=params["pool_type"]
)
d_out = d_sent if use_bert else params["d_proj"]
choice2scalar = Classifier(d_out, n_classes=1, cls_type=params["cls_type"])
return SingleClassifier(pooler, choice2scalar)
def build_qa_module(task, d_inp, use_bert, params):
""" Build a simple QA module that
1) pools representations (either of the joint (context, question, answer) or individually
2) projects down to two logits
3) classifier
This module models each question-answer pair _individually_ """
pooler = Pooler(
project=not use_bert, d_inp=d_inp, d_proj=params["d_proj"], pool_type=params["pool_type"]
)
d_out = d_inp if use_bert else params["d_proj"]
classifier = Classifier.from_params(d_out, 2, params)
return SingleClassifier(pooler, classifier)
def build_single_sentence_module(task, d_inp: int, use_bert: bool, params: Params):
""" Build a single sentence classifier
args:
- task (Task): task object, used to get the number of output classes
- d_inp (int): input dimension to the module, needed for optional linear projection
- use_bert (bool): if using BERT, skip projection before pooling.
- params (Params): Params object with task-specific parameters
returns:
- SingleClassifier (nn.Module): single-sentence classifier consisting of
(optional) a linear projection, pooling, and an MLP classifier
"""
pooler = Pooler(
project=not use_bert, d_inp=d_inp, d_proj=params["d_proj"], pool_type=params["pool_type"]
)
d_out = d_inp if use_bert else params["d_proj"]
classifier = Classifier.from_params(d_out, task.n_classes, params)
module = SingleClassifier(pooler, classifier)
return module
def __init__(
self,
vocab: Vocabulary,
input_dim: int,
decoder_hidden_size: int,
max_decoding_steps: int,
output_proj_input_dim: int,
target_namespace: str = "targets",
target_embedding_dim: int = None,
attention: str = "none",
dropout: float = 0.0,
scheduled_sampling_ratio: float = 0.0,
) -> None:
super(Seq2SeqDecoder, self).__init__(vocab)
self._max_decoding_steps = max_decoding_steps
self._target_namespace = target_namespace
# We need the start symbol to provide as the input at the first timestep of decoding, and
# end symbol as a way to indicate the end of the decoded sequence.
self._start_index = self.vocab.get_token_index(START_SYMBOL,
self._target_namespace)
self._end_index = self.vocab.get_token_index(END_SYMBOL,
self._target_namespace)
self._unk_index = self.vocab.get_token_index("@@UNKNOWN@@",
self._target_namespace)
num_classes = self.vocab.get_vocab_size(self._target_namespace)
# Decoder output dim needs to be the same as the encoder output dim since we initialize the
# hidden state of the decoder with that of the final hidden states of the encoder. Also, if
# we're using attention with ``DotProductSimilarity``, this is needed.
self._encoder_output_dim = input_dim
self._decoder_hidden_dim = decoder_hidden_size
if self._encoder_output_dim != self._decoder_hidden_dim:
self._projection_encoder_out = Linear(self._encoder_output_dim,
self._decoder_hidden_dim)
else:
self._projection_encoder_out = lambda x: x
self._decoder_output_dim = self._decoder_hidden_dim
self._output_proj_input_dim = output_proj_input_dim
self._target_embedding_dim = target_embedding_dim
self._target_embedder = Embedding(num_classes,
self._target_embedding_dim)
# Used to get an initial hidden state from the encoder states
self._sent_pooler = Pooler(project=True,
d_inp=input_dim,
d_proj=decoder_hidden_size)
if attention == "Bahdanau":
self._decoder_attention = BahdanauAttention(
decoder_hidden_size + target_embedding_dim, input_dim)
# The output of attention, a weighted average over encoder outputs, will be
# concatenated to the input vector of the decoder at each time
# step.
self._decoder_input_dim = input_dim + target_embedding_dim
elif attention == "bilinear":
self._decoder_attention = BilinearAttention(
decoder_hidden_size + target_embedding_dim, input_dim)
# The output of attention, a weighted average over encoder outputs, will be
# concatenated to the input vector of the decoder at each time
# step.
self._decoder_input_dim = input_dim + target_embedding_dim
elif attention == "none":
self._decoder_attention = None
self._decoder_input_dim = target_embedding_dim
else:
raise Exception("attention not implemented {}".format(attention))
self._decoder_cell = LSTMCell(self._decoder_input_dim,
self._decoder_hidden_dim)
# Allow for a bottleneck layer between encoder outputs and distribution over vocab
# The bottleneck layer consists of a linear transform and helps to reduce
# number of parameters
if self._output_proj_input_dim != self._decoder_output_dim:
self._projection_bottleneck = Linear(self._decoder_output_dim,
self._output_proj_input_dim)
else:
self._projection_bottleneck = lambda x: x
self._output_projection_layer = Linear(self._output_proj_input_dim,
num_classes)
self._dropout = torch.nn.Dropout(p=dropout)
def build_image_sent_module(task, d_inp, params):
pooler = Pooler(project=True, d_inp=d_inp, d_proj=params["d_proj"])
return pooler
