def forward(self, question_embed, question_mask, column_embed,
column_name_mask, column_mask):
B, C_L, N_L, embed_D = list(column_embed.size())
# Column Encoder
encoded_column = utils.encode_column(column_embed, column_name_mask,
self.column_rnn)
encoded_question, _ = self.question_rnn(question_embed)
if self.column_attention:
attn_matrix = torch.bmm(
encoded_column,
self.linear_attn(encoded_question).transpose(1, 2))
attn_matrix = f.add_masked_value(attn_matrix,
question_mask.unsqueeze(1),
value=-1e7)
attn_matrix = F.softmax(attn_matrix, dim=-1)
attn_question = (encoded_question.unsqueeze(1) *
attn_matrix.unsqueeze(3)).sum(2)
else:
attn_matrix = self.seq_attn(encoded_question, question_mask)
attn_question = f.weighted_sum(attn_matrix, encoded_question)
attn_question = attn_question.unsqueeze(1)
logits = self.mlp(
self.linear_question(attn_question) +
self.linear_column(encoded_column)).squeeze()
logits = f.add_masked_value(logits, column_mask, value=-1e7)
return logits
def forward(self,
context_embed,
question_embed,
context_mask=None,
question_mask=None):
C, Q = context_embed, question_embed
B, C_L, Q_L, D = C.size(0), C.size(1), Q.size(1), Q.size(2)
similarity_matrix_shape = torch.zeros(B, C_L, Q_L,
D) # (B, C_L, Q_L, D)
C_ = C.unsqueeze(2).expand_as(similarity_matrix_shape)
Q_ = Q.unsqueeze(1).expand_as(similarity_matrix_shape)
C_Q = torch.mul(C_, Q_)
S = self.W_0(torch.cat([C_, Q_, C_Q], 3)).squeeze(3) # (B, C_L, Q_L)
S_question = S
if question_mask is not None:
S_question = f.add_masked_value(S_question,
question_mask.unsqueeze(1),
value=-1e7)
S_q = F.softmax(S_question, 2) # (B, C_L, Q_L)
S_context = S.transpose(1, 2)
if context_mask is not None:
S_context = f.add_masked_value(S_context,
context_mask.unsqueeze(1),
value=-1e7)
S_c = F.softmax(S_context, 2) # (B, Q_L, C_L)
A = torch.bmm(S_q, Q) # context2query (B, C_L, D)
B = torch.bmm(S_q, S_c).bmm(C) # query2context (B, Q_L, D)
out = torch.cat([C, A, C * A, C * B], dim=-1)
return out
def decode_then_output(
self,
encoded_used_column,
encoded_question,
question_mask,
decoder_input,
decoder_hidden=None,
):
B = encoded_used_column.size(0)
decoder_output, decoder_hidden = self.decoder(
decoder_input.view(B * self.column_maxlen, -1, self.token_maxlen),
decoder_hidden)
decoder_output = decoder_output.contiguous().view(
B, self.column_maxlen, -1, self.model_dim)
decoder_output = decoder_output.unsqueeze(3)
logits = self.mlp(
self.linear_column(encoded_used_column) +
self.linear_conds(decoder_output) +
self.linear_question(encoded_question)).squeeze()
logits = f.add_masked_value(logits,
question_mask.unsqueeze(1).unsqueeze(1),
value=-1e7)
return logits, decoder_hidden
def forward(self, question_embed, question_mask, column_embed,
column_name_mask, col_idx):
B, C_L, N_L, embed_D = list(column_embed.size())
# Column Encoder
encoded_column = utils.encode_column(column_embed, column_name_mask,
self.column_rnn)
encoded_used_column = utils.filter_used_column(
encoded_column, col_idx, padding_count=self.column_maxlen)
encoded_question, _ = self.question_rnn(question_embed)
if self.column_attention:
attn_matrix = torch.matmul(
self.linear_attn(encoded_question).unsqueeze(1),
encoded_used_column.unsqueeze(3)).squeeze()
attn_matrix = f.add_masked_value(attn_matrix,
question_mask.unsqueeze(1),
value=-1e7)
attn_matrix = F.softmax(attn_matrix, dim=-1)
attn_question = (encoded_question.unsqueeze(1) *
attn_matrix.unsqueeze(3)).sum(2)
else:
attn_matrix = self.seq_attn(encoded_question, question_mask)
attn_question = f.weighted_sum(attn_matrix, encoded_question)
attn_question = attn_question.unsqueeze(1)
return self.mlp(
self.linear_question(attn_question) +
self.linear_column(encoded_used_column)).squeeze()
def _scaled_dot_product(self, query, key, value, mask=None):
K_D = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(K_D)
if mask is not None:
mask = mask.unsqueeze(1).unsqueeze(1) # [B, #H, C_L, D]
scores = f.add_masked_value(scores, mask, value=-1e7)
attn = F.softmax(scores, dim=-1)
attn = self.dropout(attn)
return torch.matmul(attn, value)
def forward(self, context, context_mask, query, query_mask):
c, c_mask, q, q_mask = context, context_mask, query, query_mask
S = self._make_similiarity_matrix(c, q) # (B, C_L, Q_L)
masked_S = f.add_masked_value(S, query_mask.unsqueeze(1), value=-1e7)
c2q = self._context2query(S, q, q_mask)
q2c = self._query2context(masked_S.max(dim=-1)[0], c, c_mask)
# [h; u˜; h◦u˜; h◦h˜] ~ (B, C_L, 8d)
G = torch.cat((c, c2q, c * c2q, c * q2c), dim=-1)
return G
def test_add_masked_value():
a = torch.rand(3, 5)
a_mask = torch.FloatTensor([
[1, 1, 1, 0, 0],
[1, 1, 0, 0, 0],
[1, 1, 1, 1, 1],
])
tensor = f.add_masked_value(a, a_mask, value=100)
assert tensor[0][3] == 100
assert tensor[0][4] == 100
assert tensor[1][2] == 100
assert tensor[1][3] == 100
assert tensor[1][4] == 100
def forward(self, x, x_mask, key, key_mask):
S = self._trilinear(x, key)
if self.self_attn:
seq_length = x.size(1)
diag_mask = self.diag_mask.narrow(0, 0, seq_length).narrow(
1, 0, seq_length)
joint_mask = 1 - self._compute_attention_mask(x_mask, key_mask)
mask = torch.clamp(diag_mask + joint_mask, 0, 1)
masked_S = S + mask * (-1e7)
x2key = self._x2key(masked_S, key, key_mask)
return torch.cat((x, x2key, x * x2key), dim=-1)
else:
joint_mask = 1 - self._compute_attention_mask(x_mask, key_mask)
masked_S = S + joint_mask * (-1e7)
x2key = self._x2key(masked_S, key, key_mask)
masked_S = f.add_masked_value(S, key_mask.unsqueeze(1), value=-1e7)
key2x = self._key2x(masked_S.max(dim=-1)[0], x, x_mask)
return torch.cat((x, x2key, x * x2key, x * key2x), dim=-1)
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
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"]
question = features["question"]
# 1. Input Embedding Layer
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.context_highway(context_embed)
context_embed = self.dropout(context_embed)
context_embed = self.context_embed_pointwise_conv(context_embed)
query_embed = self.query_highway(query_embed)
query_embed = self.dropout(query_embed)
query_embed = self.query_embed_pointwise_conv(query_embed)
# 2. Embedding Encoder Layer
for encoder_block in self.embed_encoder_blocks:
context = encoder_block(context_embed)
context_embed = context
query = encoder_block(query_embed)
query_embed = query
# 3. Context-Query Attention Layer
context_query_attention = self.co_attention(context, query,
context_mask, query_mask)
# Projection (memory issue)
context_query_attention = self.pointwise_conv(context_query_attention)
context_query_attention = self.dropout(context_query_attention)
# 4. Model Encoder Layer
model_encoder_block_inputs = context_query_attention
# Stacked Model Encoder Block
stacked_model_encoder_blocks = []
for i in range(3):
for _, model_encoder_block in enumerate(self.model_encoder_blocks):
output = model_encoder_block(model_encoder_block_inputs,
context_mask)
model_encoder_block_inputs = output
stacked_model_encoder_blocks.append(output)
# 5. Output Layer
span_start_inputs = torch.cat(
[stacked_model_encoder_blocks[0], stacked_model_encoder_blocks[1]],
dim=-1)
span_start_inputs = self.dropout(span_start_inputs)
span_start_logits = self.span_start_linear(span_start_inputs).squeeze(
-1)
span_end_inputs = torch.cat(
[stacked_model_encoder_blocks[0], stacked_model_encoder_blocks[2]],
dim=-1)
span_end_inputs = self.dropout(span_end_inputs)
span_end_logits = self.span_end_linear(span_end_inputs).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)
output_dict = {
"start_logits": span_start_logits,
"end_logits": span_end_logits,
"best_span": self.get_best_span(span_start_logits,
span_end_logits),
}
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
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
