def test_masked_softmax():
tensor = torch.FloatTensor([
[2, 3, 1, 4, 5],
[4, 1, 6, 9, 10],
[1, 5, 2, 4, 1],
])
mask = torch.tensor([
[1., 1., 1., 0., 0.],
[1., 1., 0., 0., 0.],
[1., 1., 1., 1., 1.]
])
result = f.masked_softmax(tensor, mask)
assert result.argmax(dim=-1).equal(torch.LongTensor([1, 0, 1]))
def _key2x(self, S, x, x_mask):
attention = f.masked_softmax(S, x_mask) # (B, C_L)
key2x = f.weighted_sum(attention=attention, matrix=x)
return key2x.unsqueeze(1).expand(x.size()) # (B, C_L, 2d)
def forward(self, features, labels=None):
"""
* Args:
features: feature dictionary like below.
{"feature_name1": {
"token_name1": tensor,
"toekn_name2": tensor},
"feature_name2": ...}
* Kwargs:
label: label dictionary like below.
{"label_name1": tensor,
"label_name2": tensor}
Do not calculate loss when there is no label. (inference/predict mode)
* Returns: output_dict (dict) consisting of
- start_logits: representing unnormalized log probabilities of the span start position.
- end_logits: representing unnormalized log probabilities of the span end position.
- best_span: the string from the original passage that the model thinks is the best answer to the question.
- answer_idx: the question id, mapping with answer
- loss: A scalar loss to be optimised.
"""
context = features["context"]
question = features["question"]
# Sorted Sequence config (seq_lengths, perm_idx, unperm_idx) for RNN pack_forward
context_seq_config = f.get_sorted_seq_config(context)
query_seq_config = f.get_sorted_seq_config(question)
# Embedding Layer (Char + Word -> Contextual)
query_params = {"frequent_word": {"frequent_tuning": True}}
context_embed, query_embed = self.token_embedder(
context,
question,
query_params=query_params,
query_align=self.aligned_query_embedding)
context_mask = f.get_mask_from_tokens(context).float()
query_mask = f.get_mask_from_tokens(question).float()
B, C_L = context_embed.size(0), context_embed.size(1)
context_embed = self.context_highway(context_embed)
query_embed = self.query_highway(query_embed)
context_encoded = f.forward_rnn_with_pack(self.context_contextual_rnn,
context_embed,
context_seq_config)
context_encoded = self.dropout(context_encoded)
query_encoded = f.forward_rnn_with_pack(self.query_contextual_rnn,
query_embed, query_seq_config)
query_encoded = self.dropout(query_encoded)
# Attention Flow Layer
attention_context_query = self.attention(context_encoded, context_mask,
query_encoded, query_mask)
# Modeling Layer
modeled_context = f.forward_rnn_with_pack(self.modeling_rnn,
attention_context_query,
context_seq_config)
modeled_context = self.dropout(modeled_context)
M_D = modeled_context.size(-1)
# Output Layer
span_start_input = self.dropout(
torch.cat([attention_context_query, modeled_context],
dim=-1)) # (B, C_L, 10d)
span_start_logits = self.span_start_linear(span_start_input).squeeze(
-1) # (B, C_L)
span_start_probs = f.masked_softmax(span_start_logits, context_mask)
span_start_representation = f.weighted_sum(attention=span_start_probs,
matrix=modeled_context)
tiled_span_start_representation = span_start_representation.unsqueeze(
1).expand(B, C_L, M_D)
span_end_representation = torch.cat(
[
attention_context_query,
modeled_context,
tiled_span_start_representation,
modeled_context * tiled_span_start_representation,
],
dim=-1,
)
encoded_span_end = f.forward_rnn_with_pack(self.output_end_rnn,
span_end_representation,
context_seq_config)
encoded_span_end = self.dropout(encoded_span_end)
span_end_input = self.dropout(
torch.cat([attention_context_query, encoded_span_end], dim=-1))
span_end_logits = self.span_end_linear(span_end_input).squeeze(-1)
# Masked Value
span_start_logits = f.add_masked_value(span_start_logits,
context_mask,
value=-1e7)
span_end_logits = f.add_masked_value(span_end_logits,
context_mask,
value=-1e7)
# No_Answer Bias
bias = self.bias.expand(B, 1)
span_start_logits = torch.cat([span_start_logits, bias], dim=-1)
span_end_logits = torch.cat([span_end_logits, bias], dim=-1)
output_dict = {
"start_logits":
span_start_logits,
"end_logits":
span_end_logits,
"best_span":
self.get_best_span(
span_start_logits[:, :-1],
span_end_logits[:, :-1],
answer_maxlen=self.answer_maxlen, # except no_answer bias
),
}
if labels:
answer_idx = labels["answer_idx"]
answer_start_idx = labels["answer_start_idx"]
answer_end_idx = labels["answer_end_idx"]
answerable = labels["answerable"]
# No_Asnwer Case
C_L = context_mask.size(1)
answer_start_idx = answer_start_idx.masked_fill(
answerable.eq(0), C_L)
answer_end_idx = answer_end_idx.masked_fill(answerable.eq(0), C_L)
output_dict["answer_idx"] = answer_idx
# Loss
loss = self.criterion(span_start_logits, answer_start_idx)
loss += self.criterion(span_end_logits, answer_end_idx)
output_dict["loss"] = loss.unsqueeze(
0) # NOTE: DataParallel concat Error
return output_dict
def _query2context(self, S, c, c_mask):
attention = f.masked_softmax(S, c_mask) # (B, C_L)
q2c = f.weighted_sum(attention=attention, matrix=c)
return q2c.unsqueeze(1).expand(c.size()) # (B, C_L, 2d)