def forward(self, feat_emb_value):
# square_of_sum = torch.sum(feat_emb_value, dim=1) # N * emb_dim
# square_of_sum = torch.mul(square_of_sum, square_of_sum) # N * emb_dim
# sum_of_square = torch.mul(feat_emb_value, feat_emb_value) # N * num_fields * emb_dim
# sum_of_square = torch.sum(sum_of_square, dim=1) # N * emb_dim
# bi_out = square_of_sum - sum_of_square
bi_out = bi_interaction(feat_emb_value)
return bi_out
def forward(self, feat_index):
feat_emb = self.emb_layer(feat_index) # N * num_categories * emb_dim
field_wise_emb_list = [
feat_emb[:, field_range] # N * num_categories_in_field * emb_dim
for field_range in self.field_ranges
]
field_emb_list = [
torch.sum(field_wise_emb, dim=1).unsqueeze(dim=1) # N * emb_dim
for field_wise_emb in field_wise_emb_list
]
field_emb = torch.cat(field_emb_list,
dim=1) # N * num_fields * emb_dim
# S part
y_S = self.first_order_weights(feat_index) # N * num_categories * 1
y_S = y_S.squeeze() # N * num_categories
y_S = torch.sum(y_S, dim=1) # N
y_S = torch.add(y_S, self.first_order_bias) # N
y_S = y_S.unsqueeze(dim=1) # N * 1
# MF part -> N * emb_dim
p, q = build_cross(self.num_fields,
field_emb) # N * num_pairs * emb_dim
y_MF = torch.mul(p, q) # N * num_pairs * emb_dim
y_MF = torch.mul(y_MF, self.r_mf) # N * num_pairs * emb_dim
y_MF = torch.sum(y_MF, dim=1) # N * emb_dim
# FM part
field_wise_fm = [
bi_interaction(field_wise_emb).unsqueeze(dim=1) # N * 1 * emb_dim
for field_wise_emb in field_wise_emb_list
]
field_wise_fm = torch.cat(field_wise_fm,
dim=1) # N * num_fields * emb_dim
y_FM = torch.mul(field_wise_fm, self.r_fm) # N * num_fields * emb_dim
y_FM = torch.sum(y_FM, dim=1) # N * emb_dim
# dnn
fc_in = field_emb.reshape((-1, self.num_fields * self.emb_dim))
y_dnn = self.fc_layers(fc_in)
# output
fwBI = y_MF + y_FM
fwBI = torch.cat([y_S, fwBI], dim=1) # N * (emb_dim + 1)
y = torch.cat([fwBI, y_dnn], dim=1) # N * (fc_dims[-1] + emb_dim + 1)
y = self.output_layer(y)
return y
def forward(self, feat_index, feat_value):
# With single sample, it should be expanded into 1 * F * K
# Batch_size: N
# feat_index_dim&feat_value_dim: F
# embedding_dim: K
# feat_index: N * F
# feat_value: N * F
# compute first order
feat_value = torch.unsqueeze(feat_value, dim=2) # N * F * 1
first_order_weights = self.first_order_weights(feat_index) # N * F * 1
first_order = torch.mul(feat_value, first_order_weights) # N * F * 1
first_order = torch.squeeze(first_order, dim=2) # N * F
y_first_order = torch.sum(first_order, dim=1) # N
# compute second order
# look up embedding table
feat_emb = self.emb_layer(feat_index) # N * F * K
feat_emb_value = torch.mul(feat_emb,
feat_value) # N * F * K element-wise mul
# compute sum of square
# squared_feat_emb = torch.pow(feat_emb_value, 2) # N * K
# sum_of_square = torch.sum(squared_feat_emb, dim=1) # N * K
#
# # compute square of sum
# summed_feat_emb = torch.sum(feat_emb_value, dim=1) # N * K
# square_of_sum = torch.pow(summed_feat_emb, 2) # N * K
BI = bi_interaction(feat_emb_value)
y_second_order = 0.5 * BI # N * K
y_second_order = torch.sum(y_second_order, dim=1) # N
# compute y
y = self.bias + y_first_order + y_second_order # N
y = self.output_layer(y)
return y
