def forward(self, c, q, c_mask, q_mask):
batch_size, c_len, _ = c.size()
q_len = q.size(1)
# Get the projected q
q = self.proj(q)
q = F.dropout(q, self.drop_prob, self.training)
q = F.relu(q) # (batch_size, q_len, hidden_size)
# Add sentinel vectors
q = torch.cat([q,self.q_cent.unsqueeze(0).repeat(batch_size,1,1)], dim=1) # (batch_size, q_len+1, hidden_size)
c = torch.cat([c,self.c_cent.unsqueeze(0).repeat(batch_size,1,1)], dim=1) # (batch_size, c_len+1, hidden_size)
q_mask = torch.cat([q_mask, torch.ones(batch_size, 1).to(self.device)], dim=1) # (batch_size, q_len+1)
c_mask = torch.cat([c_mask, torch.ones(batch_size, 1).to(self.device)], dim=1) # (batch_size, c_len+1)
# Compute affinity matrix
L = torch.bmm(c, q.transpose(1,2)) # (bs, c_len+1, hid_size) x (bs, hid_size, q_len+1) => (bs, c_len+1, q_len+1)
# Compute C2Q attention
alpha = masked_softmax(L, q_mask.view(batch_size, 1, q_len+1), dim=2) # (batch_size, c_len+1, q_len+1)
c2q_att = torch.bmm(alpha, q) # (bs, c_len+1, q_len+1) x (bs, q_len+1, hid_size) => (bs, c_len+1, hid_size)
# Compute Q2C attention
beta = masked_softmax(L, c_mask.view(batch_size, c_len+1, 1), dim=1) # (batch_size, c_len+1, q_len+1)
q2c_att = torch.bmm(beta.transpose(1,2), c) # (bs, q_len+1, c_len+1) x (bs, c_len+1, hid_size) => (bs, q_len+1, hid_size)
# Compute second level attention
att_lv2 = torch.bmm(alpha, q2c_att) # (bs, c_len+1, q_len+1) x (bs, q_len+1, hid_size) => (bs, c_len+1, hid_size)
x = torch.cat([att_lv2, c2q_att], dim=2) # (batch_size, c_len+1, hidden_size*2)
x = self.rnn(x[:,:c_len,:], c_mask[:,:c_len].sum(-1)) # (batch_size, c_len, hidden_size*4)
return x
def forward(self, c, q, c_mask, q_mask):
batch_size, c_len, _ = c.size()
q_len = q.size(1)
s = self.get_similarity_matrix(c, q) # (batch_size, c_len, q_len)
c_mask = c_mask.view(batch_size, c_len, 1) # (batch_size, c_len, 1)
q_mask = q_mask.view(batch_size, 1, q_len) # (batch_size, 1, q_len)
alpha = masked_softmax(s, q_mask,
dim=2) # (batch_size, c_len, q_len) =
beta = masked_softmax(s, c_mask, dim=1) # (batch_size, c_len, q_len)
# (bs, c_len, q_len) x (bs, q_len, hid_size) => (bs, c_len, hid_size)
A = torch.bmm(alpha, q)
# (bs, q_len, c_len) x (bs, c_len, hid_size) => (bs, q_len, hid_size)
B = torch.bmm(beta.transpose(1, 2), c)
# second level cross attention matrix N × M
# (bs, c_len, hid_size) x (bs, q_len, hid_size)^T -> (bs, c_len, q_len)
R = torch.bmm(A, torch.transpose(B, 1, 2)) # (bs, c_len, q_len)
gamma = F.softmax(R, dim=2) # (bs, c_len, q_len)
#print('gamma shape = ', gamma.shape)
#print('B shape = ', B.shape)
# (bs, q_len, c_len) x ()
D = torch.bmm(gamma, B) # (bs, q_len, hid_size)
x = torch.cat([c, A, D], dim=2)
return x
def forward(self, c, q, c_mask, q_mask, q_global, c_conv):
batch_size, c_len, _ = c.size()
q_len = q.size(1)
s = self.get_similarity_matrix(c, q) # (batch_size, c_len, q_len)
c_mask = c_mask.view(batch_size, c_len, 1) # (batch_size, c_len, 1)
q_mask = q_mask.view(batch_size, 1, q_len) # (batch_size, 1, q_len)
s1 = masked_softmax(s, q_mask, dim=2) # (batch_size, c_len, q_len)
s2 = masked_softmax(s, c_mask, dim=1) # (batch_size, c_len, q_len)
# (bs, c_len, q_len) x (bs, q_len, hid_size) => (bs, c_len, hid_size)
a = torch.bmm(s1, q)
# (bs, c_len, c_len) x (bs, c_len, hid_size) => (bs, c_len, hid_size)
b = torch.bmm(torch.bmm(s1, s2.transpose(1, 2)), c)
# make q_global the proper size (batch_size, 900) --> (batch_size, 1, 900)
q_global = q_global.unsqueeze(1) # (batch_size, 1, 900)
# elementwise product of q_clobal and c_conv
global_sim = q_global*c_conv # (batch_size, c_len, 900)
# project to size 200
global_sim = self.global_proj(global_sim) # (batch_size, c_len, 200)
# add to vector x
x = torch.cat([c, a, c * a, c * b, global_sim], dim=2) # (bs, c_len, 5 * hid_size)
return x
def forward(self, cw_idxs, qw_idxs, bert_embeddings, max_context_len,
max_question_len, device):
c_mask = torch.zeros_like(cw_idxs) != cw_idxs
q_mask = torch.zeros_like(qw_idxs) != qw_idxs
c_len, q_len = c_mask.sum(-1), q_mask.sum(-1)
#c_len, q_len = max_context_len, max_question_len
glove_c_emb = self.emb(cw_idxs) # (batch_size, c_len, 300)
glove_q_emb = self.emb(qw_idxs) # (batch_size, c_len, 300)
#print("word_vec_emb.size() before : ", word_vec_emb.size())
#word_vec_emb = self.proj_up(word_vec_emb) # (batch_size, c_len, hidden_size)
#print("word_vec_emb.size() after: ", word_vec_emb.size())
c_emb = bert_embeddings[:, 0:torch.max(
c_len), :] # (batch_size, c_len, hidden_size)
c_emb = self.proj_down(
torch.nn.functional.relu(c_emb)) # (batch_size, c_len, 300)
c_emb = c_emb + c_emb * glove_c_emb
start_logits = self.bert_start(c_emb) # (batch_size, c_len, 1)
end_logits = self.bert_end(c_emb) # (batch_size, c_len, 1)
log_p1 = masked_softmax(start_logits.squeeze(),
c_mask,
log_softmax=True) # (batch_size, c_len)
log_p2 = masked_softmax(end_logits.squeeze(), c_mask,
log_softmax=True) # (batch_size, c_len)
out = log_p1, log_p2
return out # 2 tensors, each (batch_size, c_len)
def forward(self, start, end, mask):
logits_1 = self.startFF(start)
logits_2 = self.startFF(end)
log_p1 = masked_softmax(logits_1.squeeze(), mask, log_softmax=True)
log_p2 = masked_softmax(logits_2.squeeze(), mask, log_softmax=True)
return log_p1, log_p2
def forward(self, context, question, c_masks):
"""
Single forward pass of the attention layer
:param context: (Tensor) tensor of shape (batch_size, context_len, hidden_size),
output of encoder layer on context
:param question: (Tensor) tensor of shape (batch_size, hidden_size), output of encoder layer on context
:param c_masks:
:param q_masks:
:returns: output_t (Tensor) tensor of shape (batch_size, seq_len, hidden_size)
"""
# context_hidden_proj = self.attn_proj(context) # shape (batch_size, context_len, hidden_size)
logits_1 = torch.bmm(self.attn_proj_1(context),
torch.unsqueeze(
question,
2)) # shape (batch_size, context_len, 1)
logits_1 = torch.squeeze(logits_1, -1)
logits_2 = torch.bmm(self.attn_proj_2(context),
torch.unsqueeze(
question,
2)) # shape (batch_size, context_len, 1)
logits_2 = torch.squeeze(logits_2, -1)
log_p1 = masked_softmax(logits_1, c_masks, dim=1, log_softmax=True)
log_p2 = masked_softmax(logits_2, c_masks, dim=1, log_softmax=True)
#
# alpha_t = masked_softmax(scores, c_masks, dim=1) # shape (batch_size, context_len)
# output_t = torch.mul(context, alpha_t.unsqueeze(2))
#
# output_t = torch.bmm(torch.unsqueeze(alpha_t, 1), context) # shape (batch_size, 1, hidden_size)
# output_t = output_t.squeeze(1)
return log_p1, log_p2
def forward(self, M0, M1, M2, mask):
"""
Args:
M0, M1, M2 (torch.tensor): the outputs of the three model encoders from bottom to up
See more details in Figure1: https://arxiv.org/pdf/1804.09541.pdf
Returns:
log_p1, log_p2 (torch.tensor): logits for start and end position
"""
# Shapes: (batch_size, seq_len, 1)
X1 = torch.cat([M0, M1], dim=2)
X2 = torch.cat([M0, M2], dim=2)
logits_1 = self.linear_1(X1)
logits_2 = self.linear_2(X2)
# Shapes: (batch_size, seq_len)
mask = mask.type(torch.float)
log_p1 = masked_softmax(logits_1.squeeze(), mask,
log_softmax=True) # [batch_size, seq_len]
# modified output
# logits_2 = torch.cat([logits_2, log_p1.unsqueeze(2)], dim=2) # [batch_size, seq_len, 2]
# logits_2 = self.linear_3(logits_2) # [batch_size, seq_len, 1]
log_p2 = masked_softmax(logits_2.squeeze(), mask, log_softmax=True)
return log_p1, log_p2
def forward(self, p, q, p_mask, q_mask):
batch_size, p_len, _ = p.size()
q_len = q.size(1)
# (batch_size, p_len, q_len)
s = self.get_similarity_matrix(p, q)
p_mask = p_mask.view(batch_size, p_len, 1) # (batch_size, p_len, 1)
q_mask = q_mask.view(batch_size, 1, q_len) # (batch_size, 1, q_len)
# approx eq(4)
# (batch_size, p_len, q_len)
s1 = masked_softmax(s, q_mask, dim=2)
# approx eq(5)
# (bs, p_len, q_len) x (bs, q_len, hid_size) => (bs, p_len, hid_size)
p_tilde = torch.bmm(s1, q)
# approx eq(6)
# (batch_size, p_len, q_len)
s2 = masked_softmax(s, p_mask, dim=1)
# approx eq(7)
q_tilde = torch.bmm(s2.transpose(1, 2), p) # (bs, q_len, hid_size)
x = (p_tilde, q_tilde)
return x
def forward(self, context, question, c_masks, q_masks):
"""
Single forward pass of attention layer
:param context: (Tensor) tensor of shape (batch, c_len, hidden_size)
:param question: (Tensor) tensor of shape (batch, q_len, hidden_size)
:param c_masks:
:param q_masks:
:return:
"""
batch_size, c_len, _ = context.shape
q_len = question.shape[1]
s = self.get_similarity_matrix(context,
question) # shape (batch, c_len, q_len)
c_masks = c_masks.view(batch_size, c_len, 1)
q_masks = q_masks.view(batch_size, 1, q_len)
s1 = masked_softmax(s, q_masks, dim=2) # shape (batch, c_len, q_len)
s2 = masked_softmax(s, c_masks, dim=1) # shape (batch, c_len, q_len)
a = torch.bmm(s1, question) # shape (batch, c_len, hidden_size)
ss = torch.bmm(s1, s2.transpose(1, 2)) # shape (batch, c_len, c_len)
b = torch.bmm(ss, context) # shape (batch, c_len, hidden_size)
x = torch.cat([context, a, context * a, context * b], dim=2)
return x
def forward(self, c, q, c_mask, q_mask):
# batch_size, c_len, _ = c.size()
# q_len = q.size(1)
# s = self.get_similarity_matrix(c, q) # (batch_size, c_len, q_len)
# c_mask = c_mask.view(batch_size, c_len, 1) # (batch_size, c_len, 1)
# q_mask = q_mask.view(batch_size, 1, q_len) # (batch_size, 1, q_len)
# s1 = masked_softmax(s, q_mask, dim=2) # (batch_size, c_len, q_len)
# s2 = masked_softmax(s, c_mask, dim=1) # (batch_size, c_len, q_len)
# # (bs, c_len, q_len) x (bs, q_len, hid_size) => (bs, c_len, hid_size)
# a = torch.bmm(s1, q)
# # (bs, c_len, c_len) x (bs, c_len, hid_size) => (bs, c_len, hid_size)
# b = torch.bmm(torch.bmm(s1, s2.transpose(1, 2)), c)
# x = torch.cat([c, a, c * a, c * b], dim=2) # (bs, c_len, 4 * hid_size)
batch_size, c_len, _ = c.size()
q_len = q.size(1)
s = self.get_similarity_matrix(c, q) # (batch_size, c_len, q_len)
c_mask = c_mask.view(batch_size, c_len, 1) # (batch_size, c_len, 1)
q_mask = q_mask.view(batch_size, 1, q_len) # (batch_size, 1, q_len)
s1 = masked_softmax(s, q_mask, dim=2) # (batch_size, c_len, q_len)
s2 = masked_softmax(s, c_mask, dim=1) # (batch_size, c_len, q_len)
# (bs, c_len, q_len) x (bs, q_len, hid_size) => (bs, c_len, hid_size)
a = torch.bmm(s1, q)
# (bs, c_len, c_len) x (bs, c_len, hid_size) => (bs, c_len, hid_size)
b = torch.bmm(torch.bmm(s1, s2.transpose(1, 2)), c)
x = torch.cat([c, a, c * a, c * b], dim=2) # (bs, c_len, 4 * hid_size)
return x
def attention_vectors(self, h, u, h_mask, u_mask):
# h_mask (batch, h_len)
# u_mask (batch, u_len)
S = self.similarity_matrix(h, u)
h_len, u_len = h.size(1), u.size(1)
# Context-to-query Attention
u_mask = u_mask.view(-1, 1, u_len)
a_t = masked_softmax(S, u_mask, dim=2) # (batch, h_len, u_len)
U_tilde = torch.bmm(a_t, u) # (batch, h_len, input_size)
STANFORD_Q2C = True
if STANFORD_Q2C:
# Query-to-context Attention (Stanford variant)
h_mask = h_mask.view(-1, h_len, 1)
b_t = masked_softmax(S, h_mask, dim=1) # (batch, h_len, u_len)
h_tt = torch.bmm(a_t, b_t.transpose(1, 2)) # (batch, h_len, h_len)
H_tilde = torch.bmm(h_tt, h) # (batch, h_len, input_size)
else:
# Query-to-context Attention (original)
b = masked_softmax(masked_max(S, u_mask, dim=2), h_mask, dim=1) # (batch, h_len)
b = b.unsqueeze(dim=1) # (batch, 1, h_len)
H_tilde = torch.bmm(b, h) # (batch, 1, input_size)
H_tilde = H_tilde.repeat(1, h_len, 1) # (batch, h_len, input_size)
return H_tilde, U_tilde
def forward(self, M0, M1, M2, mask):
p1 = self.W1(torch.cat((M0, M1), -1)).squeeze() # (batch_size, c_len)
p2 = self.W2(torch.cat((M0, M2), -1)).squeeze() # (batch_size, c_len)
log_p1 = masked_softmax(p1, mask, log_softmax=True)
log_p2 = masked_softmax(p2, mask, log_softmax=True)
return log_p1, log_p2
def forward(self, p_prime, q_prime, doc_len, query_len, doc_mask, query_mask):
batch_size, _, emb_size = p_prime.size()
d = self.bilstm1(p_prime, doc_len)
l = self.bilinearAtt(d,d)
l = masked_softmax(l, doc_mask.unsqueeze(1), dim=2)
d_tilde = torch.bmm(l,d)
# FUSE
concat_input_fuse = torch.cat((d,d_tilde,d*d_tilde,d-d_tilde),2)
d_prime = self.tanh(self.linear1(concat_input_fuse))
d_double_prime = self.bilstm2(d_prime, doc_len)
# Get q_double_prime
q_double_prime = self.bilstm3(q_prime, query_len)
# Get gamma
gamma = self.linear2(q_double_prime).permute(0,2,1)
gamma = masked_softmax(gamma, query_mask.unsqueeze(1), dim=2)
q_bold = torch.bmm(gamma, q_double_prime)#.squeeze(1)
#==============================compute 1-dim self attention for P =====================
p_double_prime = d
gamma_p = self.linear3(p_double_prime).permute(0,2,1)
gamma_p = masked_softmax(gamma_p, doc_mask.unsqueeze(1), dim=2)
p_bold = torch.bmm(gamma_p, p_double_prime)
#=======================================================================================
return d_double_prime, q_bold, p_bold
def forward(self, cw_idxs, qw_idxs):
# cw_idxs (batch_size, c_len)
# qw_idxs (batch_size, q_len)
c_mask = torch.zeros_like(cw_idxs) != cw_idxs
q_mask = torch.zeros_like(qw_idxs) != qw_idxs
c_len, q_len = c_mask.sum(-1), q_mask.sum(-1)
c_emb = self.embed(cw_idxs) # (batch_size, c_len, input_size)
q_emb = self.embed(qw_idxs) # (batch_size, q_len, input_size)
c_emb = F.dropout(c_emb, self.drop_prob, self.training)
q_emb = F.dropout(q_emb, self.drop_prob, self.training)
p_i = self.passage_rnn(c_emb, c_len) # (batch_size, c_len, h)
q = self.query_rnn(q_emb, q_len) # (batch_size, 1, h)
q_t = q.permute(0, 2, 1) # (batch_size, h, 1)
logits_start = self.att_start(p_i, q_t)
logits_end = self.att_end(p_i, q_t)
probs_start = masked_softmax(logits_start, c_mask,
log_softmax=True) # (batch_size, c_len)
probs_end = masked_softmax(logits_end, c_mask,
log_softmax=True) # (batch_size, c_len)
return probs_start, probs_end
def forward(self, c, q, c_mask, q_mask):
batch_size, c_len, _ = c.size()
"""
print("batch_size: ", batch_size)
print("c_len: ", c_len)
"""
q_len = q.size(1)
"""
print("q_len: ", q_len)
print("c.size(): ", c.size())
print("q.size(): ", q.size())
"""
s = self.get_similarity_matrix(c, q) # (batch_size, c_len, q_len)
"""
print("s.size() ", s.size())
print("c_mask.size() :", c_mask.size())
print("q_mask.size() :", q_mask.size())
"""
c_mask = c_mask.view(batch_size, c_len, 1) # (batch_size, c_len, 1)
q_mask = q_mask.view(batch_size, 1, q_len) # (batch_size, 1, q_len)
s1 = masked_softmax(s, q_mask, dim=2) # (batch_size, c_len, q_len)
s2 = masked_softmax(s, c_mask, dim=1) # (batch_size, c_len, q_len)
# (bs, c_len, q_len) x (bs, q_len, hid_size) => (bs, c_len, hid_size)
a = torch.bmm(s1, q)
# (bs, c_len, c_len) x (bs, c_len, hid_size) => (bs, c_len, hid_size)
b = torch.bmm(torch.bmm(s1, s2.transpose(1, 2)), c)
x = torch.cat([c, a, c * a, c * b], dim=2) # (bs, c_len, 4 * hid_size)
return x
def forward(self, context_hiddens, context_mask, question_hiddens,
question_mask):
batch_size, c_len, _ = context_hiddens.size()
q_len = question_hiddens.size(1)
s = self.get_similarity_matrix(
context_hiddens, question_hiddens) # (batch_size, c_len, q_len)
context_mask = context_mask.view(batch_size, c_len,
1) # (batch_size, c_len, 1)
question_mask = question_mask.view(batch_size, 1,
q_len) # (batch_size, 1, q_len)
s1 = masked_softmax(s, question_mask,
dim=2) # (batch_size, c_len, q_len)
s2 = masked_softmax(s, context_mask,
dim=1) # (batch_size, c_len, q_len)
# (bs, c_len, q_len) x (bs, q_len, hid_size) => (bs, c_len, hid_size)
a = torch.bmm(s1, question_hiddens)
# (bs, c_len, c_len) x (bs, c_len, hid_size) => (bs, c_len, hid_size)
b = torch.bmm(torch.bmm(s1, s2.transpose(1, 2)), context_hiddens)
x = torch.cat(
[context_hiddens, a, context_hiddens * a, context_hiddens * b],
dim=2) # (bs, c_len, 4 * hid_size)
return x
def __forward(self, query, passage):
# 0.
passage, passage_length = passage
batch_size = passage.size(0)
passage_length = passage.size(1)
passage_mask = passage.eq(0)
query_mask = query.eq(0)
# 0.1 Encoding
embedded_query = self.embeddings_tgt(query) # (N, W, D)
embedded_passage = self.embeddings_src(passage)
# 1. Separately encoding.
encoded_passage = self.passage_encoder(embedded_passage, passage_mask)
encoded_query = self.query_encoder(embedded_query, query_mask)
encoding_dim = encoded_query.size(-1)
# 2. Interaction.
# Shape: (batch_size, passage_length, query_length)
passage_query_similarity = self._matrix_attention(
encoded_passage, encoded_query)
# Shape: (batch_size, passage_length, query_length)
passage_query_attention = util.masked_softmax(passage_query_similarity,
query_mask)
# Shape: (batch_size, passage_length, encoding_dim)
passage_query_vectors = util.weighted_sum(encoded_query,
passage_query_attention)
# We replace masked values with something really negative here, so they don't affect the
# max below.
masked_similarity = util.replace_masked_values(
passage_query_similarity, query_mask.unsqueeze(1), -1e7)
# Shape: (batch_size, passage_length)
query_passage_similarity = masked_similarity.max(dim=-1)[0].squeeze(-1)
# Shape: (batch_size, passage_length)
query_passage_attention = util.masked_softmax(query_passage_similarity,
passage_mask)
# Shape: (batch_size, encoding_dim)
query_passage_vector = util.weighted_sum(encoded_passage,
query_passage_attention)
# Shape: (batch_size, passage_length, encoding_dim)
tiled_query_passage_vector = query_passage_vector.unsqueeze(1).expand(
batch_size, passage_length, encoding_dim)
# Shape: (batch_size, passage_length, encoding_dim * 4)
final_merged_passage = torch.cat([
encoded_passage, passage_query_vectors, encoded_passage *
passage_query_vectors, encoded_passage * tiled_query_passage_vector
],
dim=-1)
# 3. Compress Composition Mix ... ? or just max_pooling or mean
# output = self.combine(final_merged_passage, passage_mask)
output = torch.mean(final_merged_passage, 1)
prob = self.sigmoid(self.linear(output))
return prob
def forward(self, m0, m1, m2, mask):
logits_1 = self.linear1(torch.cat((m0, m1),
2)) # (batch_size, n_context, 1)
logits_2 = self.linear2(torch.cat((m0, m2),
2)) # (batch_size, n_context, 1)
log_p1 = masked_softmax(logits_1.squeeze(), mask, log_softmax=True)
log_p2 = masked_softmax(logits_2.squeeze(), mask, log_softmax=True)
return log_p1, log_p2
def forward(self, M1, M2, M3, mask):
X1 = torch.cat([M1, M2], dim=2)
X2 = torch.cat([M1, M3], dim=2)
logits_1 = self.linear_1(X1)
logits_2 = self.linear_2(X2)
log_p1 = masked_softmax(logits_1.squeeze(), mask, log_softmax=True)
log_p2 = masked_softmax(logits_2.squeeze(), mask, log_softmax=True)
return log_p1, log_p2
def forward(self, att, mod, mask):
logits_1 = self.mod_linear_1(mod)
mod_2 = self.rnn(mod, mask)
logits_2 = self.mod_linear_2(mod_2)
log_p1 = masked_softmax(logits_1.squeeze(), mask, log_softmax=True)
log_p2 = masked_softmax(logits_2.squeeze(), mask, log_softmax=True)
return log_p1, log_p2
def forward(self, pw_idxs: torch.Tensor, pc_idxs: Optional[torch.Tensor],
qw_idxs: torch.Tensor, qc_idxs: Optional[torch.Tensor]):
""" Run a forward step
pw_idxs: word indices in the paragraph 64, 254
pc_idxs: char indices in the paragraph 64, 254, 16
qw_idxs: word indices in the question 64, 20
qc_idx: char indices in the question 64, 20, 16
"""
p_mask = torch.zeros_like(pw_idxs) != pw_idxs
q_mask = torch.zeros_like(qw_idxs) != qw_idxs
p_len, q_len = p_mask.sum(-1), q_mask.sum(-1)
p_emb = self.emb(pw_idxs, pc_idxs) # (batch_size, p_len, hidden_size)
q_emb = self.emb(qw_idxs, qc_idxs) # (batch_size, q_len, hidden_size)
# eq (1)
# (batch_size, p_len, 2 * hidden_size)
p_enc = self.enc(p_emb, p_len)
# approx eq (2)
# (batch_size, q_len, 2 * hidden_size)
q_enc = self.enc(q_emb, q_len)
# approx eq (3)-(7)
(p_tilde,
q_tilde) = self.att(p_enc, q_enc, p_mask,
q_mask) # 2 x (batch_size, p_len, 2*hidden_size)
# eq (8) + (11)
p_fused1 = self.p_fusion1(p_enc, p_tilde)
# eq (9) + (12)
q_fused1 = self.q_fusion1(q_enc, q_tilde)
# eq (13)
p_enc_13 = self.p_enc_eq_13(p_fused1, p_len)
q_enc_13 = self.q_enc_eq_13(q_fused1, q_len)
p_fused_16 = self.self_attention(p_enc_13)
# more steps missing in here
contextual_p = self.p_enc_eq_17(p_fused_16, p_len)
# question partial processing
# eq (19)
q_enc_17 = self.q_enc_eq_17(q_enc_13, q_len)
weighted_q = self.q_linear_align_18(q_enc_17)
logits_start = self.bilinear_start(weighted_q, contextual_p)
logits_end = self.bilinear_end(weighted_q, contextual_p)
log_start = masked_softmax(logits_start, p_mask, log_softmax=True)
log_end = masked_softmax(logits_end, p_mask, log_softmax=True)
out = (log_start, log_end)
return out
def forward(self, att, mod, mask):
# Shapes: (batch_size, seq_len, 1)
logits_1 = self.att_linear_1(att) + self.mod_linear_1(mod)
mod_2 = self.rnn(mod, mask.sum(-1))
logits_2 = self.att_linear_2(att) + self.mod_linear_2(mod_2)
# Shapes: (batch_size, seq_len)
log_p1 = masked_softmax(logits_1.squeeze(), mask, log_softmax=True)
log_p2 = masked_softmax(logits_2.squeeze(), mask, log_softmax=True)
return log_p1, log_p2
def forward(self, M_1, M_2, M_3, mask):
begin = torch.cat([M_1, M_2], dim=2)
begin = self.W1(begin)
end = torch.cat([M_1, M_3], dim=2)
end = self.W2(end)
log_p1 = masked_softmax(begin.squeeze(), mask, log_softmax=True)
log_p2 = masked_softmax(end.squeeze(), mask, log_softmax=True)
return log_p1, log_p2
def forward(self, M_1, M_2, M_3, mask):
y_i = self.ifv(M_1, M_2, M_3, mask)
# y_i = None
logits_1 = self.Ws(torch.cat((M_1, M_2), dim=1)).squeeze()
logits_2 = self.We(torch.cat((M_1, M_3), dim=1)).squeeze()
log_p1 = masked_softmax(logits_1, mask, dim=1, log_softmax=True)
log_p2 = masked_softmax(logits_2, mask, dim=1, log_softmax=True)
return y_i, log_p1, log_p2
def forward(self, att, mask, c_len):
mod = self.modeling(att, c_len) # (batch_size, c_len, 2 * hidden_size)
# Shapes: (batch_size, seq_len, 1)
logits_1 = self.att_linear_1(mod)
logits_2 = self.att_linear_2(mod)
# Shapes: (batch_size, seq_len)
log_p1 = masked_softmax(logits_1.squeeze(), mask, log_softmax=True)
log_p2 = masked_softmax(logits_2.squeeze(), mask, log_softmax=True)
return log_p1, log_p2
def forward(self, M0, M1, M2, mask):
# 08/10: mod: (batch_size, seq_len, h)
# mask: (batch_size, seq_len, 1)
logits_1 = self.w1(torch.cat([M0, M1], dim=-1))
logits_2 = self.w1(torch.cat([M0, M2], dim=-1))
# Shapes: (batch_size, seq_len)
log_p1 = masked_softmax(logits_1.squeeze(), mask, log_softmax=True)
log_p2 = masked_softmax(logits_2.squeeze(), mask, log_softmax=True)
return log_p1, log_p2
def forward(self, m0, m1, m2, mask):
x1 = torch.cat([m0, m1], dim=-1)
x2 = torch.cat([m0, m2], dim=-1)
from util import masked_softmax
log_p1 = masked_softmax(self.span_start_linear(x1).squeeze(),
mask,
log_softmax=True)
log_p2 = masked_softmax(self.span_end_linear(x2).squeeze(),
mask,
log_softmax=True)
return log_p1, log_p2
def forward(self, att, mod, mask):
# Shapes: (batch_size, seq_len, 1)
logits_1 = self.att_linear_1(att) + self.mod_linear_1(mod)
if not self.use_transformer:
mod_2 = self.enc(mod, mask.sum(-1))
else:
mod_2 = self.enc(mod, mask)
logits_2 = self.att_linear_2(att) + self.mod_linear_2(mod_2)
# Shapes: (batch_size, seq_len)
log_p1 = masked_softmax(logits_1.squeeze(), mask, log_softmax=True)
log_p2 = masked_softmax(logits_2.squeeze(), mask, log_softmax=True)
return log_p1, log_p2
def forward(self, M1, M2, M3, mask):
X1 = torch.cat([M1, M2], dim=1)
X2 = torch.cat([M1, M3], dim=1)
L1 = self.w1(X1)
L2 = self.w2(X2)
Y1 = mask_logits(L1.squeeze(), mask)
Y2 = mask_logits(L2.squeeze(), mask)
from util import masked_softmax
log_p1 = masked_softmax(Y1.squeeze(), mask, log_softmax=True)
log_p2 = masked_softmax(Y2.squeeze(), mask, log_softmax=True)
return log_p1, log_p2
def forward(self, att, masks):
"""
:param att: output of the attention layer, shape (batch_size, seq_len, hidden_size)
:param masks:
:return:
"""
logits1 = F.dropout(self.output_proj1(att), self.drop_prob)
logits2 = F.dropout(self.output_proj2(att), self.drop_prob)
log_p1 = masked_softmax(logits1.squeeze(-1), masks, log_softmax=True)
log_p2 = masked_softmax(logits2.squeeze(-1), masks, log_softmax=True)
return log_p1, log_p2
