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model.py
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model.py
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import numpy as np
import paddle
import sys
FLOAT_MIN = -sys.float_info.max
class PointWiseFeedForward(paddle.nn.Layer):
def __init__(self, hidden_units, dropout_rate): # wried, why fusion X 2?
super(PointWiseFeedForward, self).__init__()
self.conv1 = paddle.nn.Conv1D(hidden_units, hidden_units, kernel_size=1,
weight_attr=paddle.ParamAttr(initializer=paddle.nn.initializer.XavierUniform()))
self.dropout1 = paddle.nn.Dropout(p=dropout_rate)
self.relu = paddle.nn.ReLU()
self.conv2 = paddle.nn.Conv1D(hidden_units, hidden_units, kernel_size=1,
weight_attr=paddle.ParamAttr(initializer=paddle.nn.initializer.XavierUniform()))
self.dropout2 = paddle.nn.Dropout(p=dropout_rate)
def forward(self, inputs):
outputs = self.dropout2(self.conv2(self.relu(self.dropout1(self.conv1(inputs.transpose([0, 2, 1]))))))
outputs = outputs.transpose([0, 2, 1]) # as Conv1D requires (N, C, Length)
outputs += inputs
return outputs
class TimeAwareMultiHeadAttention(paddle.nn.Layer):
# required homebrewed mha layer for Ti/SASRec experiments
def __init__(self, hidden_size, head_num, dropout_rate, dev):
super(TimeAwareMultiHeadAttention, self).__init__()
self.Q_w = paddle.nn.Linear(hidden_size, hidden_size,
weight_attr=paddle.ParamAttr(initializer=paddle.nn.initializer.XavierUniform()))
self.K_w = paddle.nn.Linear(hidden_size, hidden_size,
weight_attr=paddle.ParamAttr(initializer=paddle.nn.initializer.XavierUniform()))
self.V_w = paddle.nn.Linear(hidden_size, hidden_size,
weight_attr=paddle.ParamAttr(initializer=paddle.nn.initializer.XavierUniform()))
self.dropout = paddle.nn.Dropout(p=dropout_rate)
self.softmax = paddle.nn.Softmax(-1)
self.hidden_size = hidden_size
self.head_num = head_num
self.head_size = hidden_size // head_num
self.dropout_rate = dropout_rate
self.dev = dev
def forward(self, queries, keys, time_mask, attn_mask, time_matrix_K, time_matrix_V, abs_pos_K, abs_pos_V):
Q, K, V = self.Q_w(queries), self.K_w(keys), self.V_w(keys)
# head dim * batch dim for parallelization (h*N, T, C/h)
if self.head_num > 1:
Q_ = paddle.concat(paddle.split(Q, self.head_num, axis=2), axis=0)
K_ = paddle.concat(paddle.split(K, self.head_num, axis=2), axis=0)
V_ = paddle.concat(paddle.split(V, self.head_num, axis=2), axis=0)
time_matrix_K_ = paddle.concat(paddle.split(time_matrix_K, self.head_num, axis=3), axis=0)
time_matrix_V_ = paddle.concat(paddle.split(time_matrix_V, self.head_num, axis=3), axis=0)
abs_pos_K_ = paddle.concat(paddle.split(abs_pos_K, self.head_num, axis=2), axis=0)
abs_pos_V_ = paddle.concat(paddle.split(abs_pos_V, self.head_num, axis=2), axis=0)
else:
Q_ = Q
K_ = K
V_ = V
time_matrix_K_ = time_matrix_K
time_matrix_V_ = time_matrix_V
abs_pos_K_ = abs_pos_K
abs_pos_V_ = abs_pos_V
# print(Q_.shape, time_matrix_K_.shape, abs_pos_K_.shape)
# batched channel wise matmul to gen attention weights
attn_weights = paddle.matmul(Q_, K_, transpose_y=True)
attn_weights += paddle.matmul(Q_, abs_pos_K_, transpose_y=True)
# print(time_matrix_K_.shape, Q_.shape)
attn_weights += paddle.matmul(time_matrix_K_, Q_.unsqueeze(-1)).squeeze(-1)
# seq length adaptive scaling
attn_weights = attn_weights / (K_.shape[-1] ** 0.5)
# key masking, -2^32 lead to leaking, inf lead to nan
# 0 * inf = nan, then reduce_sum([nan,...]) = nan
# time_mask = time_mask.unsqueeze(-1).expand(attn_weights.shape[0], -1, attn_weights.shape[-1])
time_mask = time_mask.astype('int32').unsqueeze(-1).tile([self.head_num, 1, 1])
time_mask = time_mask.expand([-1, -1, attn_weights.shape[-1]]).astype(paddle.bool)
# print(attn_mask.shape)
attn_mask = attn_mask.astype('int32').unsqueeze(0).expand([attn_weights.shape[0], -1, -1]).astype(paddle.bool)
# print(attn_mask.shape)
paddings = paddle.ones(attn_weights.shape) * (-2 ** 32 + 1) # -1e23 # float('-inf')
# print(attn_mask.shape, paddings.shape, attn_weights.shape)
attn_weights = paddle.where(time_mask, paddings, attn_weights) # True:pick padding
attn_weights = paddle.where(attn_mask, paddings, attn_weights) # enforcing causality
attn_weights = self.softmax(attn_weights)
# attn_weights = paddle.where(time_mask, paddings, attn_weights) # weird query mask in tf impl
# attn_weights[attn_weights != attn_weights] = 0 # rm nan for -inf into softmax case
attn_weights = self.dropout(attn_weights)
outputs = attn_weights.matmul(V_)
outputs += attn_weights.matmul(abs_pos_V_)
outputs += attn_weights.unsqueeze(2).matmul(time_matrix_V_).reshape(outputs.shape).squeeze(2)
# (num_head * N, T, C / num_head) -> (N, T, C)
if self.head_num > 1:
outputs = paddle.concat(paddle.split(outputs, self.head_num, axis=0), axis=2) # div batch_size
return outputs
class TiSASRec(paddle.nn.Layer):
def __init__(self, user_num, item_num, time_num, args):
super(TiSASRec, self).__init__()
self.user_num = user_num
self.item_num = item_num
self.dev = args.device
# TODO: loss += args.l2_emb for regularizing embedding vectors during training
self.item_emb = paddle.nn.Embedding(self.item_num + 1, args.hidden_units, padding_idx=0,
weight_attr=paddle.ParamAttr(
initializer=paddle.nn.initializer.XavierUniform()))
self.item_emb_dropout = paddle.nn.Dropout(p=args.dropout_rate)
self.abs_pos_K_emb = paddle.nn.Embedding(args.maxlen, args.hidden_units,
weight_attr=paddle.ParamAttr(
initializer=paddle.nn.initializer.XavierUniform()))
self.abs_pos_V_emb = paddle.nn.Embedding(args.maxlen, args.hidden_units,
weight_attr=paddle.ParamAttr(
initializer=paddle.nn.initializer.XavierUniform()))
self.time_matrix_K_emb = paddle.nn.Embedding(args.time_span + 1, args.hidden_units,
weight_attr=paddle.ParamAttr(
initializer=paddle.nn.initializer.XavierUniform()))
self.time_matrix_V_emb = paddle.nn.Embedding(args.time_span + 1, args.hidden_units,
weight_attr=paddle.ParamAttr(
initializer=paddle.nn.initializer.XavierUniform()))
self.item_emb_dropout = paddle.nn.Dropout(p=args.dropout_rate)
self.abs_pos_K_emb_dropout = paddle.nn.Dropout(p=args.dropout_rate)
self.abs_pos_V_emb_dropout = paddle.nn.Dropout(p=args.dropout_rate)
self.time_matrix_K_dropout = paddle.nn.Dropout(p=args.dropout_rate)
self.time_matrix_V_dropout = paddle.nn.Dropout(p=args.dropout_rate)
self.attention_layernorms = paddle.nn.LayerList() # to be Q for self-attention
self.attention_layers = paddle.nn.LayerList()
self.forward_layernorms = paddle.nn.LayerList()
self.forward_layers = paddle.nn.LayerList()
self.last_layernorm = paddle.nn.LayerNorm(args.hidden_units, epsilon=1e-8)
for _ in range(args.num_blocks):
new_attn_layernorm = paddle.nn.LayerNorm(args.hidden_units, epsilon=1e-8)
self.attention_layernorms.append(new_attn_layernorm)
new_attn_layer = TimeAwareMultiHeadAttention(args.hidden_units,
args.num_heads,
args.dropout_rate,
args.device)
self.attention_layers.append(new_attn_layer)
new_fwd_layernorm = paddle.nn.LayerNorm(args.hidden_units, epsilon=1e-8)
self.forward_layernorms.append(new_fwd_layernorm)
new_fwd_layer = PointWiseFeedForward(args.hidden_units, args.dropout_rate)
self.forward_layers.append(new_fwd_layer)
# self.pos_sigmoid = paddle.nn.Sigmoid()
# self.neg_sigmoid = paddle.nn.Sigmoid()
def seq2feats(self, log_seqs, time_matrices):
seqs = self.item_emb(paddle.to_tensor(log_seqs).astype(paddle.int64))
seqs *= self.item_emb._embedding_dim ** 0.5
seqs = self.item_emb_dropout(seqs)
positions = np.tile(np.array(range(log_seqs.shape[1])), [log_seqs.shape[0], 1])
positions = paddle.to_tensor(positions).astype(paddle.int64)
abs_pos_K = self.abs_pos_K_emb(positions)
abs_pos_V = self.abs_pos_V_emb(positions)
abs_pos_K = self.abs_pos_K_emb_dropout(abs_pos_K)
abs_pos_V = self.abs_pos_V_emb_dropout(abs_pos_V)
time_matrices = paddle.to_tensor(time_matrices).astype(paddle.int64)
time_matrix_K = self.time_matrix_K_emb(time_matrices)
time_matrix_V = self.time_matrix_V_emb(time_matrices)
time_matrix_K = self.time_matrix_K_dropout(time_matrix_K)
time_matrix_V = self.time_matrix_V_dropout(time_matrix_V)
# mask 0th items(placeholder for dry-run) in log_seqs
# would be easier if 0th item could be an exception for training
timeline_mask = paddle.to_tensor(log_seqs == 0)
seqs *= paddle.to_tensor(log_seqs != 0).astype(paddle.get_default_dtype()).unsqueeze(
-1) # broadcast in last dim
tl = seqs.shape[1] # time dim len for enforce causality
attention_mask = (paddle.tril(paddle.ones([tl, tl])) == 0).astype(paddle.bool)
for i in range(len(self.attention_layers)):
# Self-attention, Q=layernorm(seqs), K=V=seqs
# seqs = paddle.transpose(seqs, 0, 1) # (N, T, C) -> (T, N, C)
Q = self.attention_layernorms[i](seqs)
mha_outputs = self.attention_layers[i](Q, seqs,
timeline_mask, attention_mask,
time_matrix_K, time_matrix_V,
abs_pos_K, abs_pos_V)
seqs = Q + mha_outputs
# seqs = paddle.transpose(seqs, 0, 1) # (T, N, C) -> (N, T, C)
# Point-wise Feed-forward, actually 2 Conv1D for channel wise fusion
seqs = self.forward_layernorms[i](seqs)
seqs = self.forward_layers[i](seqs)
# print(print(seqs.reshape([-1])[:10]))
# print(timeline_mask)
seqs *= (timeline_mask.astype(int) == 0).astype(paddle.get_default_dtype()).unsqueeze(-1)
log_feats = self.last_layernorm(seqs)
return log_feats
def forward(self, log_seqs, time_matrices, pos_seqs, neg_seqs): # for training
log_feats = self.seq2feats( log_seqs, time_matrices)
pos_embs = self.item_emb(paddle.to_tensor(pos_seqs).astype(paddle.int64))
neg_embs = self.item_emb(paddle.to_tensor(neg_seqs).astype(paddle.int64))
pos_logits = (log_feats * pos_embs).sum(-1)
neg_logits = (log_feats * neg_embs).sum(-1)
# pos_pred = self.pos_sigmoid(pos_logits)
# neg_pred = self.neg_sigmoid(neg_logits)
return pos_logits, neg_logits # pos_pred, neg_pred
def predict(self, log_seqs, time_matrices, item_indices): # for inference
log_feats = self.seq2feats(log_seqs, time_matrices)
final_feat = log_feats[:, -1, :] # only use last QKV classifier, a waste
item_embs = self.item_emb(paddle.to_tensor(item_indices).astype(paddle.int64)) # (U, I, C)
logits = item_embs.matmul(final_feat.unsqueeze(-1)).squeeze(-1)
# preds = self.pos_sigmoid(logits) # rank same item list for different users
return logits # preds # (U, I)