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model.py
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804 lines (673 loc) · 33.1 KB
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import numpy as np
import torch
import pdb
from model_utils import *
import random
class NewRec(torch.nn.Module):
def __init__(self, user_num, item_num, args, second=False):
super(NewRec, self).__init__()
assert args.base_dim1 == 0 or args.input_units1 % args.base_dim1 == 0
assert args.base_dim2 == 0 or args.input_units2 % args.base_dim2 == 0
self.user_num = user_num
self.item_num = item_num
self.dev = args.device
self.model = args.model
self.no_emb = args.no_emb
self.no_fixed_emb = not args.no_emb and args.no_fixed_emb
self.num_heads = 1
dataset = args.dataset if not second else args.dataset2
self.prev_time = args.prev_time
self.lag = args.lag
self.time_embed = args.time_embed
self.time_no_fixed_embed = args.time_no_fixed_embed
self.time_embed_concat = args.time_embed_concat
self.use_week_eval = args.use_week_eval
self.maxlen = args.maxlen
# takes in item id, outputs pre-processed time-sensitive item popularity
# pdb.set_trace()
self.popularity_enc = PopularityEncoding(args, second)
# modify item popularity within most recent week in testing
self.use_week_eval = args.use_week_eval
if self.use_week_eval:
self.eval_popularity_enc = EvalPopularityEncoding(args)
# takes in item popularity feature, outputs item embedding
self.embed_layer = InitFeedForward(
args.input_units1 + args.input_units2,
args.hidden_units * 2,
args.hidden_units,
)
if args.fs_emb:
self.fs_layer = InitFeedForward(
args.hidden_units,
args.hidden_units * 2,
args.hidden_units,
)
self.fs_emb = args.fs_emb
# trainable positional embeddings
if self.no_fixed_emb:
self.pos_emb = torch.nn.Embedding(args.maxlen, args.hidden_units)
# fixed sinusoidal positional embedding
elif not self.no_emb:
self.position_enc = PositionalEncoding(args.hidden_units, args.maxlen)
# relative time difference embeddings
if self.time_embed:
# trainable
if self.time_no_fixed_embed:
self.time_pos_emb = torch.nn.Embedding(args.maxlen+1, args.hidden_units)
# fixed sinusoidal
else:
self.time_position_enc = ModPositionalEncoding(args.hidden_units, args.maxlen+1)
# second head with gate at end if item cooccurrence or user trajectory used
self.hidden_units = args.hidden_units * self.num_heads
if args.triplet_loss:
self.triplet_loss = torch.nn.TripletMarginLoss(margin=0.0, p=2)
if args.cos_loss:
self.cos_loss = (
torch.nn.CosineEmbeddingLoss()
)
self.attention_layernorms = torch.nn.ModuleList() # to be Q for self-attention
self.attention_layers = torch.nn.ModuleList()
self.forward_layernorms = torch.nn.ModuleList()
self.forward_layers = torch.nn.ModuleList()
self.last_layernorm = torch.nn.LayerNorm(self.hidden_units, eps=1e-8)
for _ in range(args.num_blocks):
new_attn_layernorm = torch.nn.LayerNorm(self.hidden_units, eps=1e-8)
self.attention_layernorms.append(new_attn_layernorm)
new_attn_layer = CausalMultiHeadAttention(
self.hidden_units, self.num_heads, args.dropout_rate, self.dev
)
self.attention_layers.append(new_attn_layer)
new_fwd_layernorm = torch.nn.LayerNorm(self.hidden_units, eps=1e-8)
self.forward_layernorms.append(new_fwd_layernorm)
new_fwd_layer = PointWiseFeedForward(self.hidden_units, args.dropout_rate)
self.forward_layers.append(new_fwd_layer)
def log2feats(self, users, log_seqs, time1_seqs, time2_seqs, time_embed):
# obtain popularity-based feature vectors for sequence history, apply embedding layer, add positional encoding
seqs = self.popularity_enc(log_seqs, time1_seqs, time2_seqs)
seqs = self.embed_layer(seqs)
if self.fs_emb:
seqs = self.fs_layer(seqs)
if self.no_fixed_emb:
positions = np.tile(
np.array(range(log_seqs.shape[1])), [log_seqs.shape[0], 1]
)
seqs += self.pos_emb(torch.LongTensor(positions).to(self.dev))
elif not self.no_emb:
seqs += self.position_enc(seqs)
# apply relative time encoding/embedding
if self.time_embed:
if self.time_no_fixed_embed:
timeres = self.time_pos_emb(torch.LongTensor(time_embed).to(self.dev))
else:
timeres = self.time_position_enc(time_embed)
if self.time_embed_concat:
seqs = torch.stack((seqs, timeres), dim=2).view(seqs.shape[0], -1, seqs.shape[2])
else:
seqs += timeres
# apply relative time concatenated
if self.time_embed and self.time_embed_concat:
timeline_mask = torch.repeat_interleave(torch.BoolTensor(log_seqs == 0), 2, dim=1).to(self.dev)
else:
timeline_mask = torch.BoolTensor(log_seqs == 0).to(self.dev)
seqs *= ~timeline_mask.unsqueeze(-1) # broadcast in last dim
tl = seqs.shape[1] # time dim len for enforce causality
attention_mask = ~torch.tril(
torch.ones((tl, tl), dtype=torch.bool, device=self.dev)
)
# run attention
for i in range(len(self.attention_layers)):
Q = self.attention_layernorms[i](seqs)
mha_outputs = self.attention_layers[i](
Q, seqs, time_mask=timeline_mask, attn_mask=attention_mask
)
seqs = Q + mha_outputs
seqs = self.forward_layernorms[i](seqs)
seqs = self.forward_layers[i](seqs)
seqs *= ~timeline_mask.unsqueeze(-1)
# final layer to get user feature at each sequence position
log_feats = self.last_layernorm(seqs) # (U, T, C) -> (U, -1, C)
if self.num_heads == 2:
log_feats = self.gate(log_feats[:, :, :self.hidden_units//self.num_heads], log_feats[:, :, self.hidden_units//self.num_heads:])
if self.time_embed_concat:
log_feats = log_feats[:, np.arange(2*self.maxlen, step=2)]
return log_feats
def forward(
self,
users,
log_seqs,
time1_seqs,
time2_seqs,
time_embed,
pos_seqs,
neg_seqs,
pos_user,
neg_user,
):
# for training
# avoid information leakage with lag >= 1
time1_seqs, time2_seqs = np.maximum(0, time1_seqs - 1 - self.lag//4), np.maximum(0, time2_seqs - self.lag)
# obtain user feature at each position
log_feats = self.log2feats(users, log_seqs, time1_seqs[:,:-1], time2_seqs[:,:-1], time_embed)
full_feats = log_feats
# if regularization get last position positive and negative user representations across the batch
pos_embed = log_feats[:, -1, :][pos_user]
neg_embed = log_feats[:, -1, :][neg_user]
# use previous or current interaction time (lag is also applied)
if self.prev_time:
mod_time1, mod_time2 = time1_seqs[:,:-1], time2_seqs[:,:-1]
else:
mod_time1, mod_time2 = time1_seqs[:,1:], time1_seqs[:,1:]
# obtain popularity-based embeddings for positive and negative item sequences
pos_embs = self.embed_layer(
self.popularity_enc(pos_seqs, mod_time1, mod_time2)
)
neg_embs = self.embed_layer(
self.popularity_enc(neg_seqs, mod_time1, mod_time2)
)
pos_logits = (full_feats * pos_embs).sum(dim=-1)
neg_logits = (full_feats * neg_embs).sum(dim=-1)
return pos_logits, neg_logits, full_feats[:, -1, :], pos_embed, neg_embed
def raw(
self,
log_seqs,
time1_seqs,
time2_seqs,
):
# for training
# avoid information leakage with lag >= 1
time1_seqs, time2_seqs = np.maximum(0, time1_seqs - 1 - self.lag // 4), np.maximum(0, time2_seqs - self.lag)
pop_enc = self.popularity_enc(log_seqs, time1_seqs, time2_seqs).cpu()
return pop_enc.numpy()
def user_score(self, log_seqs, time1_seqs, time2_seqs, time_embed, user):
log_feats = self.log2feats(user, log_seqs, time1_seqs, time2_seqs, time_embed)
return log_feats[:, -1, :]
def handle_inference(self):
del self.popularity_enc
return
def predict(
self, log_seqs, time1_seqs, time2_seqs, time_embed, item_indices, time1_pred, time2_pred, user
):
# for inference
# obtain user feature at each position
log_feats = self.log2feats(user, log_seqs, time1_seqs, time2_seqs, time_embed)
full_feats = log_feats
final_feat = full_feats[:, -1, :]
# apply most recent week popularity adjustment
if self.use_week_eval:
item_embs = self.embed_layer(
self.eval_popularity_enc(
item_indices, time1_pred, time2_pred, user
)
)
else:
item_embs = self.embed_layer(
self.popularity_enc(
item_indices, time1_pred, time2_pred
)
)
logits = item_embs.matmul(final_feat.unsqueeze(-1)).squeeze(-1)
return logits
def regloss(self, users, pos_users, neg_users, triplet_loss, cos_loss):
if not triplet_loss and not cos_loss:
return 0
users, pos_users, neg_users = (
users.to(self.dev),
pos_users.to(self.dev),
neg_users.to(self.dev),
)
loss = 0
if triplet_loss:
loss += self.triplet_loss(torch.unsqueeze(users, 1), pos_users, neg_users)
if cos_loss:
loss += self.cos_loss(
torch.repeat_interleave(users, repeats=10, dim=0),
torch.reshape(
pos_users,
(pos_users.shape[0] * pos_users.shape[1], pos_users.shape[2]),
),
torch.Tensor([1]).to(self.dev),
)
loss += self.cos_loss(
torch.repeat_interleave(users, repeats=10, dim=0),
torch.reshape(
neg_users,
(neg_users.shape[0] * neg_users.shape[1], neg_users.shape[2]),
),
torch.Tensor([-1]).to(self.dev),
)
return loss
class NewB4Rec(torch.nn.Module):
def __init__(self, itemnum, compare_size, args):
super(NewB4Rec, self).__init__()
assert args.input_units1 % args.base_dim1 == 0
assert args.input_units2 % args.base_dim2 == 0
self.maxlen = args.maxlen
self.item_num = itemnum
self.dev = args.device
self.no_fixed_emb = args.no_fixed_emb
self.compare_size = compare_size
self.popularity_enc = PopularityEncoding(args)
self.embed_layer = InitFeedForward(
args.input_units1 + args.input_units2,
args.hidden_units * 2,
args.hidden_units,
)
if self.no_fixed_emb:
self.pos_emb = torch.nn.Embedding(args.maxlen, args.hidden_units)
else:
self.position_enc = PositionalEncoding(args.hidden_units, args.maxlen)
self.logsoftmax = torch.nn.LogSoftmax(dim=1)
if args.triplet_loss:
self.triplet_loss = torch.nn.TripletMarginLoss(margin=0.0, p=2)
if args.cos_loss:
self.cos_loss = (
torch.nn.CosineEmbeddingLoss()
) # torch.nn.CosineSimilarity()
# multi-layers transformer blocks, deep network
self.attention_layernorms = torch.nn.ModuleList() # to be Q for self-attention
self.attention_layers = torch.nn.ModuleList()
self.forward_layernorms = torch.nn.ModuleList()
self.forward_layers = torch.nn.ModuleList()
for _ in range(args.num_blocks):
new_attn_layernorm = torch.nn.LayerNorm(args.hidden_units, eps=1e-8)
self.attention_layernorms.append(new_attn_layernorm)
new_attn_layer = MultiHeadAttention(
args.hidden_units, args.num_heads, args.dropout_rate
)
self.attention_layers.append(new_attn_layer)
new_fwd_layernorm = torch.nn.LayerNorm(args.hidden_units, eps=1e-8)
self.forward_layernorms.append(new_fwd_layernorm)
new_fwd_layer = PointWiseFeedForward2(
args.hidden_units, args.hidden_units * 4, args.dropout_rate
)
self.forward_layers.append(new_fwd_layer)
self.out = torch.nn.Linear(args.hidden_units, args.hidden_units)
def GELU(self, x):
return (
0.5
* x
* (1 + torch.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x, 3))))
)
def log2feats(self, log_seqs, time1_seqs, time2_seqs):
tensor_seqs = torch.LongTensor(log_seqs)
mask = (
(tensor_seqs > 0)
.unsqueeze(1)
.repeat(1, tensor_seqs.size(1), 1)
.unsqueeze(1)
.to(self.dev)
)
seqs = self.popularity_enc(log_seqs, time1_seqs, time2_seqs)
seqs = self.embed_layer(seqs)
if self.no_fixed_emb:
positions = np.tile(
np.array(range(log_seqs.shape[1])), [log_seqs.shape[0], 1]
)
seqs += self.pos_emb(torch.LongTensor(positions).to(self.dev))
else:
seqs = self.position_enc(seqs)
for i in range(len(self.attention_layers)):
# seqs = torch.transpose(seqs, 0, 1)
Q = self.attention_layernorms[i](seqs)
mha_outputs = self.attention_layers[i](Q, mask)
seqs = Q + mha_outputs
# seqs = torch.transpose(seqs, 0, 1)
seqs = self.forward_layernorms[i](seqs)
seqs = self.forward_layers[i](seqs)
return self.out(seqs)
def forward(self, seqs, time1_seqs, time2_seqs, candidates=None):
final_feat = self.log2feats(seqs, time1_seqs, time2_seqs) # B x T x V
final_feat = self.GELU(final_feat)
if candidates is not None:
items = candidates
t1 = np.repeat(time1_seqs.flatten()[-1], candidates.shape)
t2 = np.repeat(time2_seqs.flatten()[-1], candidates.shape)
items_, t1_, t2_ = (
np.expand_dims(items, -1),
np.expand_dims(t1, -1),
np.expand_dims(t2, -1),
)
item_embs = self.embed_layer(self.popularity_enc(items_, t1_, t2_))
return item_embs.squeeze(1).matmul(final_feat.squeeze(0).T)[:, -1]
# randomly choose group to rank and obtain loss from, all items is too large, appending actual labels to end of random ones
items = np.append(
np.random.choice(
np.arange(1, self.item_num + 1),
size=(seqs.shape[0], seqs.shape[1], self.compare_size),
),
np.expand_dims(seqs, axis=-1),
axis=2,
)
t1 = np.tile(np.expand_dims(time1_seqs, -1), (1, 1, self.compare_size + 1))
t2 = np.tile(np.expand_dims(time2_seqs, -1), (1, 1, self.compare_size + 1))
items_, t1_, t2_ = (
items.reshape((items.shape[0], items.shape[1] * items.shape[2])),
t1.reshape((t1.shape[0], t1.shape[1] * t1.shape[2])),
t2.reshape((t2.shape[0], t2.shape[1] * t2.shape[2])),
)
item_embs = self.embed_layer(self.popularity_enc(items_, t1_, t2_))
item_embs = item_embs.reshape(
(item_embs.shape[0], seqs.shape[1], -1, item_embs.shape[-1])
)
logits = item_embs.matmul(final_feat.unsqueeze(-1)).squeeze(-1)
logits = self.logsoftmax(logits)
logits = logits.view(-1, logits.size(-1)) # (B*T) x V
return logits
def predict(self, seqs, time1_seqs, time2_seqs, candidates):
scores = self.forward(seqs, time1_seqs, time2_seqs, candidates) # T x V
return scores
# taken from https://github.com/guoyang9/BPR-pytorch/tree/master
class BPRMF(torch.nn.Module):
def __init__(self, user_num, item_num, args):
super(BPRMF, self).__init__()
self.user_emb = torch.nn.Embedding(user_num + 1, args.hidden_units)
self.item_emb = torch.nn.Embedding(item_num + 1, args.hidden_units)
self.dev = args.device
def forward(self, user, pos_item, neg_item):
user = self.user_emb(torch.LongTensor(user).to(self.dev))
item_i = self.item_emb(torch.LongTensor(pos_item).to(self.dev))
item_j = self.item_emb(torch.LongTensor(neg_item).to(self.dev))
prediction_i = item_i.matmul(user.unsqueeze(-1)).squeeze(-1)
prediction_j = item_j.matmul(user.unsqueeze(-1)).squeeze(-1)
return prediction_i, prediction_j
def predict(self, user, item_indices):
user = self.user_emb(torch.LongTensor(user).to(self.dev))
items = self.item_emb(torch.LongTensor(item_indices).to(self.dev))
logits = (user * items).sum(dim=-1)
return logits
# taken from https://github.com/pmixer/SASRec.pytorch/blob/master/model.py
class SASRec(torch.nn.Module):
def __init__(self, user_num, item_num, args):
super(SASRec, self).__init__()
self.user_num = user_num
self.item_num = item_num
self.dev = args.device
self.item_emb = torch.nn.Embedding(
self.item_num + 1, args.hidden_units, padding_idx=0
)
self.pos_emb = torch.nn.Embedding(args.maxlen, args.hidden_units)
self.emb_dropout = torch.nn.Dropout(p=args.dropout_rate)
self.attention_layernorms = torch.nn.ModuleList() # to be Q for self-attention
self.attention_layers = torch.nn.ModuleList()
self.forward_layernorms = torch.nn.ModuleList()
self.forward_layers = torch.nn.ModuleList()
self.last_layernorm = torch.nn.LayerNorm(args.hidden_units, eps=1e-8)
for _ in range(args.num_blocks):
new_attn_layernorm = torch.nn.LayerNorm(args.hidden_units, eps=1e-8)
self.attention_layernorms.append(new_attn_layernorm)
new_attn_layer = CausalMultiHeadAttention(
args.hidden_units, args.num_heads, args.dropout_rate, self.dev
)
self.attention_layers.append(new_attn_layer)
new_fwd_layernorm = torch.nn.LayerNorm(args.hidden_units, eps=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)
def log2feats(self, log_seqs):
seqs = self.item_emb(torch.LongTensor(log_seqs).to(self.dev))
seqs *= self.item_emb.embedding_dim**0.5
positions = np.tile(np.array(range(log_seqs.shape[1])), [log_seqs.shape[0], 1])
seqs += self.pos_emb(torch.LongTensor(positions).to(self.dev))
seqs = self.emb_dropout(seqs)
timeline_mask = torch.BoolTensor(log_seqs == 0).to(self.dev)
seqs *= ~timeline_mask.unsqueeze(-1) # broadcast in last dim
tl = seqs.shape[1] # time dim len for enforce causality
attention_mask = ~torch.tril(
torch.ones((tl, tl), dtype=torch.bool, device=self.dev)
)
for i in range(len(self.attention_layers)):
Q = self.attention_layernorms[i](seqs)
mha_outputs = self.attention_layers[i](
Q, seqs, time_mask=timeline_mask, attn_mask=attention_mask
)
seqs = Q + mha_outputs
seqs = self.forward_layernorms[i](seqs)
seqs = self.forward_layers[i](seqs)
seqs *= ~timeline_mask.unsqueeze(-1)
log_feats = self.last_layernorm(seqs) # (U, T, C) -> (U, -1, C)
return log_feats
def forward(self, log_seqs, pos_seqs, neg_seqs): # for training
log_feats = self.log2feats(log_seqs) # user_ids hasn't been used yet
pos_embs = self.item_emb(torch.LongTensor(pos_seqs).to(self.dev))
neg_embs = self.item_emb(torch.LongTensor(neg_seqs).to(self.dev))
pos_logits = (log_feats * pos_embs).sum(dim=-1)
neg_logits = (log_feats * neg_embs).sum(dim=-1)
return pos_logits, neg_logits
def predict(self, log_seqs, item_indices): # for inference
log_feats = self.log2feats(log_seqs) # user_ids hasn't been used yet
final_feat = log_feats[:, -1, :] # only use last QKV classifier, a waste
item_embs = self.item_emb(
torch.LongTensor(item_indices).to(self.dev)
) # (U, I, C)
logits = item_embs.matmul(final_feat.unsqueeze(-1)).squeeze(-1)
return logits
# adapted from https://github.com/jaywonchung/BERT4Rec-VAE-Pytorch/tree/master
class BERT4Rec(torch.nn.Module):
def __init__(self, itemnum, args):
super(BERT4Rec, self).__init__()
self.maxlen = args.maxlen
self.item_num = itemnum
self.dev = args.device
self.item_emb = torch.nn.Embedding(
self.item_num + 1, args.hidden_units, padding_idx=0
)
self.pos_emb = torch.nn.Embedding(args.maxlen, args.hidden_units)
self.emb_dropout = torch.nn.Dropout(p=args.dropout_rate)
self.logsoftmax = torch.nn.LogSoftmax(dim=1)
self.pause = args.pause
# multi-layers transformer blocks, deep network
self.attention_layernorms = torch.nn.ModuleList() # to be Q for self-attention
self.attention_layers = torch.nn.ModuleList()
self.forward_layernorms = torch.nn.ModuleList()
self.forward_layers = torch.nn.ModuleList()
for _ in range(args.num_blocks):
new_attn_layernorm = torch.nn.LayerNorm(args.hidden_units, eps=1e-8)
self.attention_layernorms.append(new_attn_layernorm)
new_attn_layer = MultiHeadAttention(
args.hidden_units, args.num_heads, args.dropout_rate
)
self.attention_layers.append(new_attn_layer)
new_fwd_layernorm = torch.nn.LayerNorm(args.hidden_units, eps=1e-8)
self.forward_layernorms.append(new_fwd_layernorm)
new_fwd_layer = PointWiseFeedForward2(
args.hidden_units, args.hidden_units * 4, args.dropout_rate
)
self.forward_layers.append(new_fwd_layer)
self.out = torch.nn.Linear(
args.hidden_units, args.hidden_units) #, self.item_num+1
def GELU(self, x):
return (
0.5
* x
* (1 + torch.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * torch.pow(x, 3))))
)
def log2feats(self, log_seqs):
mask = (
(log_seqs > 0)
.unsqueeze(1)
.repeat(1, log_seqs.size(1), 1)
.unsqueeze(1)
.to(self.dev)
)
# embedding the indexed sequence to sequence of vectors
seqs = self.item_emb(log_seqs.to(self.dev))
seqs *= self.item_emb.embedding_dim**0.5
positions = np.tile(np.array(range(log_seqs.shape[1])), [log_seqs.shape[0], 1])
seqs += self.pos_emb(torch.LongTensor(positions).to(self.dev))
seqs = self.emb_dropout(seqs)
for i in range(len(self.attention_layers)):
# seqs = torch.transpose(seqs, 0, 1)
Q = self.attention_layernorms[i](seqs)
mha_outputs = self.attention_layers[i](Q, mask)
seqs = Q + mha_outputs
# seqs = torch.transpose(seqs, 0, 1)
seqs = self.forward_layernorms[i](seqs)
seqs = self.forward_layers[i](seqs)
return self.out(seqs)
def forward(self, seqs):
final_feat = self.log2feats(seqs)
# final_feat = self.GELU(final_feat)
item_embs = self.item_emb(torch.arange(0, self.item_num + 1).to(self.dev))
logits = item_embs.matmul(final_feat.unsqueeze(-1)).squeeze(-1)
logits = logits.view(-1, logits.size(-1)) # (B*T) x V
# logits = self.logsoftmax(logits)
return logits
def predict(self, seqs, candidates):
scores = self.forward(seqs) # T x V
candidates = candidates.to(self.dev)
scores = torch.reshape(scores, (seqs.shape[0], seqs.shape[1], -1))[:,-1,:]
if len(candidates.shape) == 1:
candidates = torch.unsqueeze(candidates, 0)
scores = scores.gather(1, candidates)
# else:
# scores = scores[-1, :]
# scores = scores.gather(0, candidates)
return scores
# adapted from https://github.com/RuihongQiu/DuoRec/recbole/model/sequential_recommender/cl4srec.py
class CL4SRec(torch.nn.Module):
def __init__(self, item_num, args):
super(CL4SRec, self).__init__()
self.n_items = item_num
self.dev = args.device
self.batch_size = args.batch_size
self.mask_default = self.mask_correlated_samples(batch_size=self.batch_size)
self.item_emb = torch.nn.Embedding(
self.n_items + 1, args.hidden_units, padding_idx=0
)
self.pos_emb = torch.nn.Embedding(args.maxlen, args.hidden_units)
self.emb_dropout = torch.nn.Dropout(p=args.dropout_rate)
self.ce = torch.nn.CrossEntropyLoss()
self.attention_layernorms = torch.nn.ModuleList() # to be Q for self-attention
self.attention_layers = torch.nn.ModuleList()
self.forward_layernorms = torch.nn.ModuleList()
self.forward_layers = torch.nn.ModuleList()
self.last_layernorm = torch.nn.LayerNorm(args.hidden_units, eps=1e-8)
for _ in range(args.num_blocks):
new_attn_layernorm = torch.nn.LayerNorm(args.hidden_units, eps=1e-8)
self.attention_layernorms.append(new_attn_layernorm)
new_attn_layer = CausalMultiHeadAttention(
args.hidden_units, args.num_heads, args.dropout_rate, self.dev
)
self.attention_layers.append(new_attn_layer)
new_fwd_layernorm = torch.nn.LayerNorm(args.hidden_units, eps=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)
def log2feats(self, log_seqs, skip_dev=False):
if not skip_dev:
seqs = self.item_emb(torch.LongTensor(log_seqs).to(self.dev))
else:
seqs = self.item_emb(log_seqs)
seqs *= self.item_emb.embedding_dim**0.5
positions = np.tile(np.array(range(log_seqs.shape[1])), [log_seqs.shape[0], 1])
seqs += self.pos_emb(torch.LongTensor(positions).to(self.dev))
seqs = self.emb_dropout(seqs)
if not skip_dev:
timeline_mask = torch.BoolTensor(log_seqs == 0).to(self.dev)
else:
timeline_mask = (log_seqs == 0).bool().to(log_seqs.device)
seqs *= ~timeline_mask.unsqueeze(-1) # broadcast in last dim
tl = seqs.shape[1] # time dim len for enforce causality
attention_mask = ~torch.tril(
torch.ones((tl, tl), dtype=torch.bool, device=self.dev)
)
for i in range(len(self.attention_layers)):
Q = self.attention_layernorms[i](seqs)
mha_outputs = self.attention_layers[i](
Q, seqs, time_mask=timeline_mask, attn_mask=attention_mask
)
seqs = Q + mha_outputs
seqs = self.forward_layernorms[i](seqs)
seqs = self.forward_layers[i](seqs)
seqs *= ~timeline_mask.unsqueeze(-1)
log_feats = self.last_layernorm(seqs) # (U, T, C) -> (U, -1, C)
return log_feats
def item_crop(self, item_seq, item_seq_len, eta=0.6):
num_left = math.floor(item_seq_len * eta)
if item_seq_len.cpu() - num_left <= 1:
return item_seq
crop_begin = random.randint(1, item_seq_len.cpu() - num_left)
croped_item_seq = np.zeros(item_seq.shape)
croped_item_seq[-num_left:] = item_seq.cpu().detach().numpy()[-num_left - crop_begin:-crop_begin]
return torch.tensor(croped_item_seq, dtype=torch.long, device=item_seq.device)
def item_mask(self, item_seq, item_seq_len, gamma=0.3):
num_mask = math.floor(item_seq_len * gamma)
mask_index = np.random.randint(1, item_seq_len.cpu()+1, num_mask)
# mask_index = random.sample(range(1, item_seq_len+1), k=num_mask)
masked_item_seq = item_seq.cpu().detach().numpy().copy()
masked_item_seq[-mask_index] = 0 # token 0 has been used for semantic masking
return torch.tensor(masked_item_seq, dtype=torch.long, device=item_seq.device)
def item_reorder(self, item_seq, item_seq_len, beta=0.6):
num_reorder = math.floor(item_seq_len * beta)
if item_seq_len.cpu() - num_reorder <= 1:
return item_seq
reorder_begin = np.random.randint(1, item_seq_len.cpu() - num_reorder)
reordered_item_seq = item_seq.cpu().detach().numpy().copy()
shuffle_index = np.arange(-reorder_begin - num_reorder, -reorder_begin)
np.random.shuffle(shuffle_index)
reordered_item_seq[shuffle_index] = reordered_item_seq[-reorder_begin - num_reorder:-reorder_begin]
return torch.tensor(reordered_item_seq, dtype=torch.long, device=item_seq.device)
def augment(self, log_seq_np, log_seq_len):
log_seq = torch.LongTensor(log_seq_np).to(self.dev)
aug_seq1 = torch.clone(log_seq)
aug_seq2 = torch.clone(log_seq)
for i in range(log_seq.shape[0]):
switch = random.sample(range(3), k=2)
if switch[0] == 0:
aug_seq1[i]= self.item_crop(log_seq[i], log_seq_len[i])
elif switch[0] == 1:
aug_seq1[i] = self.item_mask(log_seq[i], log_seq_len[i])
elif switch[0] == 2:
aug_seq1[i] = self.item_reorder(log_seq[i], log_seq_len[i])
if switch[1] == 0:
aug_seq2[i] = self.item_crop(log_seq[i], log_seq_len[i])
elif switch[1] == 1:
aug_seq2[i] = self.item_mask(log_seq[i], log_seq_len[i])
elif switch[1] == 2:
aug_seq2[i] = self.item_reorder(log_seq[i], log_seq_len[i])
return aug_seq1, aug_seq2
def mask_correlated_samples(self, batch_size):
N = 2 * batch_size
mask = torch.ones((N, N), dtype=bool)
mask = mask.fill_diagonal_(0)
for i in range(batch_size):
mask[i, batch_size + i] = 0
mask[batch_size + i, i] = 0
return mask
def info_nce(self, z_i, z_j, batch_size, temp=1, sim='dot'):
N = 2 * batch_size
z = torch.cat((z_i, z_j), dim=0)
if sim == 'cos':
sim = torch.nn.functional.cosine_similarity(z.unsqueeze(1), z.unsqueeze(0), dim=2) / temp
elif sim == 'dot':
sim = torch.mm(z, z.T) / temp
sim_i_j = torch.diag(sim, batch_size)
sim_j_i = torch.diag(sim, -batch_size)
positive_samples = torch.cat((sim_i_j, sim_j_i), dim=0).reshape(N, 1)
if batch_size != self.batch_size:
mask = self.mask_correlated_samples(batch_size)
else:
mask = self.mask_default
negative_samples = sim[mask].reshape(N, -1)
labels = torch.zeros(N).to(positive_samples.device).long()
logits = torch.cat((positive_samples, negative_samples), dim=1)
return self.ce(logits, labels)
def forward(self, log_seqs, item_seq_len, pos_seqs, neg_seqs): # for training
log_feats = self.log2feats(log_seqs) # user_ids hasn't been used yet
aug_seqs1, aug_seqs2 = self.augment(log_seqs, item_seq_len)
pos_embs = self.item_emb(torch.LongTensor(pos_seqs).to(self.dev))
neg_embs = self.item_emb(torch.LongTensor(neg_seqs).to(self.dev))
pos_logits = (log_feats * pos_embs).sum(dim=-1)
neg_logits = (log_feats * neg_embs).sum(dim=-1)
log_aug_feats1 = self.log2feats(aug_seqs1, skip_dev=True)[:, -1, :]
log_aug_feats2 = self.log2feats(aug_seqs2, skip_dev=True)[:, -1, :]
aug_loss = self.info_nce(log_aug_feats1, log_aug_feats2, self.batch_size)
return pos_logits, neg_logits, aug_loss
def predict(self, log_seqs, item_indices): # for inference
log_feats = self.log2feats(log_seqs) # user_ids hasn't been used yet
final_feat = log_feats[:, -1, :] # only use last QKV classifier, a waste
item_embs = self.item_emb(
torch.LongTensor(item_indices).to(self.dev)
) # (U, I, C)
logits = item_embs.matmul(final_feat.unsqueeze(-1)).squeeze(-1)
return logits