Code Issues

[theGirlwithMoon]

Hello！Can this code run? I have reported some errors running on pytorch and can not find a solution. Is there any special requirement other than the environment you specified?

[zhengbw0324]

@theGirlwithMoon
Hello, can you give me some more detailed information? Such as configuration and error log information.

[theGirlwithMoon]

Here is my configuration：
pytorch 2.0.1
cpu
python3.8
And here is my error log:

D:\ProgramFiles\Anaconda\envs\pytorch\python.exe D:/code/python/ReSeq-main/ReSeq-main/main.py
25 Dec 18:10 INFO Parameters:
gpu_id=0
worker=10
use_gpu=True
seed=2020
state=INFO
reproducibility=True
data_path=./run/datasets\ask
checkpoint_dir=saved
show_progress=True
save_dataset=True
dataset_save_path=./saved/ask-dataset.pth
save_dataloaders=False
dataloaders_save_path=None
shuffle=True
epochs=300
train_batch_size=1024
learner=adam
learning_rate=0.001
train_neg_sample_num=1
eval_step=1
stopping_step=10
clip_grad_norm=None
weight_decay=1e-05
loss_decimal_place=4
require_pow=False
enable_amp=False
enable_scaler=False
transform=None
metrics=['Recall', 'Precision', 'MRR', 'NDCG']
topk=[5]
valid_metric=NDCG@5
valid_metric_bigger=True
eval_batch_size=20480
metric_decimal_place=4
n_layers=2
n_heads=2
embedding_size=64
n_factors=64
inner_size=256
hidden_dropout_prob=0.5
attn_dropout_prob=0.5
hidden_act=gelu
layer_norm_eps=1e-12
initializer_range=0.02
t_weight=2.0
kd_loss_weight=0.003
field_separator=
seq_separator=
USER_ID_FIELD=q_id
ITEM_ID_FIELD=a_id
RATING_FIELD=label
TIME_FIELD=timestamp
seq_len=None
LABEL_FIELD=label
threshold={'label': 0.5}
NEG_PREFIX=neg_
USER_LIST_LENGTH_FIELD=q_list_length
ITEM_LIST_LENGTH_FIELD=a_list_length
LIST_SUFFIX=_list
MAX_LIST_LENGTH=50
POSITION_FIELD=position_id
NEG_USERS_FIELD=neg_q
NEG_ITEMS_FIELD=neg_a
eval_neg_num=100
dataset=ask
model=ReSeq
eval_type=EvaluatorType.RANKING
eval_candidate_num=50
device=cpu
25 Dec 18:10 INFO Load filtered dataset from: [./saved/ask-dataset.pth]
25 Dec 18:10 INFO [Training]: train_batch_size = [1024]
25 Dec 18:10 INFO [Evaluation]: eval_batch_size = [20480]
25 Dec 18:10 INFO ReSeq(
(user_f_embedding): Embedding(6032, 64, padding_idx=0)
(item_f_embedding): Embedding(3417, 64, padding_idx=0)
(user_p_embedding): Embedding(6032, 64, padding_idx=0)
(item_p_embedding): Embedding(3417, 64, padding_idx=0)
(user_preference): Transformer(
(position_embedding): Embedding(51, 64)
(trm_encoder): TransformerEncoder(
(layer): ModuleList(
(0-1): 2 x TransformerLayer(
(multi_head_attention): MultiHeadAttention(
(query): Linear(in_features=64, out_features=64, bias=True)
(key): Linear(in_features=64, out_features=64, bias=True)
(value): Linear(in_features=64, out_features=64, bias=True)
(softmax): Softmax(dim=-1)
(attn_dropout): Dropout(p=0.5, inplace=False)
(dense): Linear(in_features=64, out_features=64, bias=True)
(LayerNorm): LayerNorm((64,), eps=1e-12, elementwise_affine=True)
(out_dropout): Dropout(p=0.5, inplace=False)
)
(feed_forward): FeedForward(
(dense_1): Linear(in_features=64, out_features=256, bias=True)
(dense_2): Linear(in_features=256, out_features=64, bias=True)
(LayerNorm): LayerNorm((64,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.5, inplace=False)
)
)
)
)
(LayerNorm): LayerNorm((64,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.5, inplace=False)
)
(item_preference): Transformer(
(position_embedding): Embedding(51, 64)
(trm_encoder): TransformerEncoder(
(layer): ModuleList(
(0-1): 2 x TransformerLayer(
(multi_head_attention): MultiHeadAttention(
(query): Linear(in_features=64, out_features=64, bias=True)
(key): Linear(in_features=64, out_features=64, bias=True)
(value): Linear(in_features=64, out_features=64, bias=True)
(softmax): Softmax(dim=-1)
(attn_dropout): Dropout(p=0.5, inplace=False)
(dense): Linear(in_features=64, out_features=64, bias=True)
(LayerNorm): LayerNorm((64,), eps=1e-12, elementwise_affine=True)
(out_dropout): Dropout(p=0.5, inplace=False)
)
(feed_forward): FeedForward(
(dense_1): Linear(in_features=64, out_features=256, bias=True)
(dense_2): Linear(in_features=256, out_features=64, bias=True)
(LayerNorm): LayerNorm((64,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.5, inplace=False)
)
)
)
)
(LayerNorm): LayerNorm((64,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.5, inplace=False)
)
(user_feature): Transformer(
(position_embedding): Embedding(51, 64)
(trm_encoder): TransformerEncoder(
(layer): ModuleList(
(0-1): 2 x TransformerLayer(
(multi_head_attention): MultiHeadAttention(
(query): Linear(in_features=64, out_features=64, bias=True)
(key): Linear(in_features=64, out_features=64, bias=True)
(value): Linear(in_features=64, out_features=64, bias=True)
(softmax): Softmax(dim=-1)
(attn_dropout): Dropout(p=0.5, inplace=False)
(dense): Linear(in_features=64, out_features=64, bias=True)
(LayerNorm): LayerNorm((64,), eps=1e-12, elementwise_affine=True)
(out_dropout): Dropout(p=0.5, inplace=False)
)
(feed_forward): FeedForward(
(dense_1): Linear(in_features=64, out_features=256, bias=True)
(dense_2): Linear(in_features=256, out_features=64, bias=True)
(LayerNorm): LayerNorm((64,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.5, inplace=False)
)
)
)
)
(LayerNorm): LayerNorm((64,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.5, inplace=False)
)
(item_feature): Transformer(
(position_embedding): Embedding(51, 64)
(trm_encoder): TransformerEncoder(
(layer): ModuleList(
(0-1): 2 x TransformerLayer(
(multi_head_attention): MultiHeadAttention(
(query): Linear(in_features=64, out_features=64, bias=True)
(key): Linear(in_features=64, out_features=64, bias=True)
(value): Linear(in_features=64, out_features=64, bias=True)
(softmax): Softmax(dim=-1)
(attn_dropout): Dropout(p=0.5, inplace=False)
(dense): Linear(in_features=64, out_features=64, bias=True)
(LayerNorm): LayerNorm((64,), eps=1e-12, elementwise_affine=True)
(out_dropout): Dropout(p=0.5, inplace=False)
)
(feed_forward): FeedForward(
(dense_1): Linear(in_features=64, out_features=256, bias=True)
(dense_2): Linear(in_features=256, out_features=64, bias=True)
(LayerNorm): LayerNorm((64,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.5, inplace=False)
)
)
)
)
(LayerNorm): LayerNorm((64,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.5, inplace=False)
)
(rec_loss): BPRLoss()
(kd_loss): MSELoss()
)
Trainable parameters: 1631154
Train 0: 0%| | 0/53 [00:03<?, ?it/s]
Traceback (most recent call last):
File "D:/code/python/ReSeq-main/ReSeq-main/main.py", line 125, in
main_process(model=args.model, dataset=args.dataset, config_file_list=config_file_list)
File "D:/code/python/ReSeq-main/ReSeq-main/main.py", line 95, in main_process
best_valid_score, best_valid_result = trainer.fit(
File "D:\code\python\ReSeq-main\ReSeq-main\trainer.py", line 292, in fit
train_loss = self._train_epoch(
File "D:\code\python\ReSeq-main\ReSeq-main\trainer.py", line 141, in _train_epoch
for batch_idx, interaction in enumerate(iter_data):
File "D:\ProgramFiles\Anaconda\Anaconda3\envs\pytorch\lib\site-packages\tqdm\std.py", line 1178, in iter
for obj in iterable:
File "D:\code\python\ReSeq-main\ReSeq-main\data\dataloader.py", line 67, in iter
res = super().iter()
File "D:\ProgramFiles\Anaconda\Anaconda3\envs\pytorch\lib\site-packages\torch\utils\data\dataloader.py", line 441, in iter
return self._get_iterator()
File "D:\ProgramFiles\Anaconda\Anaconda3\envs\pytorch\lib\site-packages\torch\utils\data\dataloader.py", line 388, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "D:\ProgramFiles\Anaconda\Anaconda3\envs\pytorch\lib\site-packages\torch\utils\data\dataloader.py", line 1042, in init
w.start()
File "D:\ProgramFiles\Anaconda\Anaconda3\envs\pytorch\lib\multiprocessing\process.py", line 121, in start
self._popen = self._Popen(self)
File "D:\ProgramFiles\Anaconda\Anaconda3\envs\pytorch\lib\multiprocessing\context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "D:\ProgramFiles\Anaconda\Anaconda3\envs\pytorch\lib\multiprocessing\context.py", line 327, in _Popen
return Popen(process_obj)
File "D:\ProgramFiles\Anaconda\Anaconda3\envs\pytorch\lib\multiprocessing\popen_spawn_win32.py", line 93, in init
reduction.dump(process_obj, to_child)
File "D:\ProgramFiles\Anaconda\Anaconda3\envs\pytorch\lib\multiprocessing\reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
TypeError: cannot pickle 'torch._C.Generator' object
Traceback (most recent call last):
File "", line 1, in
File "D:\ProgramFiles\Anaconda\Anaconda3\envs\pytorch\lib\multiprocessing\spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "D:\ProgramFiles\Anaconda\Anaconda3\envs\pytorch\lib\multiprocessing\spawn.py", line 126, in _main
self = reduction.pickle.load(from_parent)
EOFError: Ran out of input

Process finished with exit code 1

[zhengbw0324]

@theGirlwithMoon
Please try reducing the worker parameter