def test_get_sorted_seq_config():
tensor = torch.LongTensor([
[2, 3, 1, 0, 0],
[4, 1, 0, 0, 0],
[1, 5, 2, 4, 1],
])
seq_config = f.get_sorted_seq_config({"word": tensor})
assert seq_config["seq_lengths"].tolist() == [5, 3, 2]
assert seq_config["perm_idx"].tolist() == [2, 0, 1]
assert seq_config["unperm_idx"].tolist() == [1, 2, 0]
def test_forward_rnn_with_pack():
tensor = torch.LongTensor([
[2, 3, 1, 0, 0],
[4, 1, 0, 0, 0],
[1, 5, 2, 4, 1],
])
matrix = torch.rand(10, 10)
embedded_tensor = torch.nn.functional.embedding(tensor, matrix)
seq_config = f.get_sorted_seq_config({"word": tensor})
gru = torch.nn.GRU(input_size=10, hidden_size=1, bidirectional=False, batch_first=True)
encoded_tensor = f.forward_rnn_with_pack(gru, embedded_tensor, seq_config)
assert encoded_tensor[0][3] == 0
assert encoded_tensor[0][4] == 0
assert encoded_tensor[1][2] == 0
assert encoded_tensor[1][3] == 0
assert encoded_tensor[1][4] == 0
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 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.
- data_idx: the question id, mapping with answer
- loss: A scalar loss to be optimised.
"""
context = features["context"] # aka paragraph
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
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()
context_embed = self.dropout(context_embed)
query_embed = self.dropout(query_embed)
# RNN (LSTM)
context_encoded = f.forward_rnn_with_pack(self.paragraph_rnn,
context_embed,
context_seq_config)
context_encoded = self.dropout(context_encoded)
query_encoded = f.forward_rnn_with_pack(
self.query_rnn, query_embed, query_seq_config) # (B, Q_L, H*2)
query_encoded = self.dropout(query_encoded)
query_attention = self.query_att(query_encoded, query_mask) # (B, Q_L)
query_att_sum = f.weighted_sum(query_attention,
query_encoded) # (B, H*2)
span_start_logits = self.start_attn(context_encoded, query_att_sum,
context_mask)
span_end_logits = self.end_attn(context_encoded, query_att_sum,
context_mask)
# 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)
output_dict = {
"start_logits":
span_start_logits,
"end_logits":
span_end_logits,
"best_span":
self.get_best_span(span_start_logits,
span_end_logits,
answer_maxlen=self.answer_maxlen),
}
if labels:
data_idx = labels["data_idx"]
answer_start_idx = labels["answer_start_idx"]
answer_end_idx = labels["answer_end_idx"]
output_dict["data_idx"] = data_idx
loss = self.criterion(span_start_logits, answer_start_idx)
loss += self.criterion(span_end_logits, answer_end_idx)
output_dict["loss"] = loss.unsqueeze(0)
return output_dict
def forward(self, features, labels=None):
"""
* Args:
features: feature dictionary like below.
{"sequence": [0, 3, 4, 1]}
* Kwargs:
label: label dictionary like below.
{"class_idx": 2, "data_idx": 0}
Do not calculate loss when there is no label. (inference/predict mode)
* Returns: output_dict (dict) consisting of
- sequence_embed: embedding vector of the sequence
- class_logits: representing unnormalized log probabilities of the class.
- class_idx: target class idx
- data_idx: data idx
- loss: a scalar loss to be optimized
"""
sequence = features["sequence"]
# Sorted Sequence config (seq_lengths, perm_idx, unperm_idx) for RNN pack_forward
sequence_config = f.get_sorted_seq_config(sequence)
token_embed = self.token_embedder(sequence)
token_encodings = f.forward_rnn_with_pack(
self.encoder, token_embed, sequence_config
) # [B, L, encoding_rnn_hidden_dim]
attention = self.A(token_encodings).transpose(1, 2) # [B, num_attention_heads, L]
sequence_mask = f.get_mask_from_tokens(sequence).float() # [B, L]
sequence_mask = sequence_mask.unsqueeze(1).expand_as(attention)
attention = F.softmax(f.add_masked_value(attention, sequence_mask) + 1e-13, dim=2)
attended_encodings = torch.bmm(
attention, token_encodings
) # [B, num_attention_heads, sequence_embed_dim]
sequence_embed = self.fully_connected(
attended_encodings.view(attended_encodings.size(0), -1)
) # [B, sequence_embed_dim]
class_logits = self.classifier(sequence_embed) # [B, num_classes]
output_dict = {"sequence_embed": sequence_embed, "class_logits": class_logits}
if labels:
class_idx = labels["class_idx"]
data_idx = labels["data_idx"]
output_dict["class_idx"] = class_idx
output_dict["data_idx"] = data_idx
# Loss
loss = self.criterion(class_logits, class_idx)
loss += self.penalty(attention)
output_dict["loss"] = loss.unsqueeze(0) # NOTE: DataParallel concat Error
return output_dict
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
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() # B X 1 X C_L
query_mask = f.get_mask_from_tokens(question).float() # B X 1 X Q_L
# Pre-process
context_embed = self.dropout(context_embed)
context_encoded = f.forward_rnn_with_pack(self.context_preprocess_rnn,
context_embed,
context_seq_config)
context_encoded = self.dropout(context_encoded)
query_embed = self.dropout(query_embed)
query_encoded = f.forward_rnn_with_pack(self.query_preprocess_rnn,
query_embed, query_seq_config)
query_encoded = self.dropout(query_encoded)
# Attention -> Projection
context_attnded = self.bi_attention(context_encoded, context_mask,
query_encoded, query_mask)
context_attnded = self.activation_fn(
self.attn_linear(context_attnded)) # B X C_L X dim*2
# Residual Self-Attention
context_attnded = self.dropout(context_attnded)
context_encoded = f.forward_rnn_with_pack(self.modeling_rnn,
context_attnded,
context_seq_config)
context_encoded = self.dropout(context_encoded)
context_self_attnded = self.self_attention(
context_encoded, context_mask) # B X C_L X dim*2
context_final = self.dropout(context_attnded +
context_self_attnded) # B X C_L X dim*2
# Prediction
span_start_input = f.forward_rnn_with_pack(
self.span_start_rnn, context_final,
context_seq_config) # B X C_L X dim*2
span_start_input = self.dropout(span_start_input)
span_start_logits = self.span_start_linear(span_start_input).squeeze(
-1) # B X C_L
span_end_input = torch.cat([span_start_input, context_final],
dim=-1) # B X C_L X dim*4
span_end_input = f.forward_rnn_with_pack(
self.span_end_rnn, span_end_input,
context_seq_config) # B X C_L X dim*2
span_end_input = self.dropout(span_end_input)
span_end_logits = self.span_end_linear(span_end_input).squeeze(
-1) # B X C_L
# 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)
output_dict = {
"start_logits":
span_start_logits,
"end_logits":
span_end_logits,
"best_span":
self.get_best_span(span_start_logits,
span_end_logits,
answer_maxlen=self.answer_maxlen),
}
if labels:
answer_idx = labels["answer_idx"]
answer_start_idx = labels["answer_start_idx"]
answer_end_idx = labels["answer_end_idx"]
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
