I have trained a simple model by IWSLT'14 German to English, when I use 'paragen-export --config configs/export.yaml' to export it , but error. Could you help me? thank you.
/root/anaconda3/envs/python38/lib/python3.8/site-packages/horovod/common/util.py:258: UserWarning: Framework pytorch installed with version 1.11.0+cu102 but found version 1.11.0.
This can result in unexpected behavior including runtime errors.
Reinstall Horovod using pip install --no-cache-dir to build with the new version.
warnings.warn(get_version_mismatch_message(name, version, installed_version))
2022-06-07,03:32:52 ｜ INFO | paragen.utils.runtime | Create environment with
{
"debug": false,
"device": "cuda",
"distributed_world": 1,
"fp16": false,
"local_rank": 0,
"no_progress_bar": false,
"no_warning": false,
"pb_interval": 1,
"profiling_window": 0,
"rank": 0
}
2022-06-07,03:32:52 ｜ INFO | paragen.utils.registry | Creating TranslationTask class with configs
{
"data": {
"test": {
"class": "ParallelTextDataset",
"path": {
"de": "data/test.de",
"en": "data/test.en"
},
"sort_samples": true
}
},
"dataloader": {
"test": {
"class": "InMemoryDataLoader",
"sampler": {
"class": "SequentialSampler",
"max_tokens": 8000
}
}
},
"generator": {
"class": "SequenceGenerator",
"search": {
"class": "GreedySearch",
"maxlen_coef": "(1.2, 10)"
}
},
"mode": "evaluate",
"model": {
"class": "Seq2Seq",
"d_model": 512,
"decoder": {
"activation": "relu",
"class": "TransformerDecoder",
"d_model": 512,
"dim_feedforward": 1024,
"dropout": 0.1,
"n_head": 4,
"num_layers": 6
},
"encoder": {
"activation": "relu",
"class": "TransformerEncoder",
"d_model": 512,
"dim_feedforward": 1024,
"dropout": 0.1,
"n_head": 4,
"num_layers": 6
},
"path": "checkpoints/best.pt",
"share_embedding": "decoder-input-output"
},
"src": "de",
"tgt": "en",
"tokenizer": {
"add_bos": true,
"add_eos": true,
"class": "FastBPE",
"vocab": "resources/vocabulary/vocab"
}
}
2022-06-07,03:32:52 ｜ INFO | mosestokenizer.detokenizer.MosesDetokenizer | executing argv ['perl', '/root/anaconda3/envs/python38/lib/python3.8/site-packages/mosestokenizer/detokenizer.perl', '-q', '-b', '-l', 'de']
2022-06-07,03:32:52 ｜ INFO | mosestokenizer.detokenizer.MosesDetokenizer | spawned process 2867
2022-06-07,03:32:52 ｜ INFO | mosestokenizer.detokenizer.MosesDetokenizer | executing argv ['perl', '/root/anaconda3/envs/python38/lib/python3.8/site-packages/mosestokenizer/detokenizer.perl', '-q', '-b', '-l', 'en']
2022-06-07,03:32:52 ｜ INFO | mosestokenizer.detokenizer.MosesDetokenizer | spawned process 2868
2022-06-07,03:32:52 ｜ INFO | paragen.utils.registry | Creating FastBPE class with configs
{
"add_bos": true,
"add_eos": true,
"vocab": "resources/vocabulary/vocab"
}
2022-06-07,03:32:52 ｜ INFO | paragen.tokenizers.vocabulary | build vocab from frequency file resources/vocabulary/vocab
2022-06-07,03:32:52 ｜ INFO | paragen.utils.registry | Creating ParallelTextDataset class with configs
{
"path": {
"de": "data/test.de",
"en": "data/test.en"
},
"sort_samples": true
}
2022-06-07,03:32:59 ｜ INFO | paragen.datasets.parallel_text_dataset | Totally accept 6750 samples, discard 0 samples
2022-06-07,03:32:59 ｜ INFO | paragen.utils.registry | Creating Seq2Seq class with configs
{
"d_model": 512,
"decoder": {
"activation": "relu",
"class": "TransformerDecoder",
"d_model": 512,
"dim_feedforward": 1024,
"dropout": 0.1,
"n_head": 4,
"num_layers": 6
},
"encoder": {
"activation": "relu",
"class": "TransformerEncoder",
"d_model": 512,
"dim_feedforward": 1024,
"dropout": 0.1,
"n_head": 4,
"num_layers": 6
},
"path": "checkpoints/best.pt",
"share_embedding": "decoder-input-output"
}
2022-06-07,03:33:00 ｜ INFO | paragen.utils.registry | Creating TransformerEncoder class with configs
{
"activation": "relu",
"d_model": 512,
"dim_feedforward": 1024,
"dropout": 0.1,
"n_head": 4,
"num_layers": 6
}
2022-06-07,03:33:00 ｜ INFO | paragen.utils.registry | Creating TransformerDecoder class with configs
{
"activation": "relu",
"d_model": 512,
"dim_feedforward": 1024,
"dropout": 0.1,
"n_head": 4,
"num_layers": 6
}
2022-06-07,03:33:00 ｜ INFO | paragen.models.abstract_model | neural network architecture
[TransformerEncoder(
(_embed): Embedding(10004, 512, padding_idx=3)
(_pos_embed): SinusoidalPositionalEmbedding(
(embedding): __SinusoidalPositionalEmbedding()
)
(_embed_dropout): Dropout(p=0.1, inplace=False)
(_layers): ModuleList(
(0): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ffn): FFN(
(_fc1): Linear(in_features=512, out_features=1024, bias=True)
(_fc2): Linear(in_features=1024, out_features=512, bias=True)
)
(self_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(ffn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
)
(1): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ffn): FFN(
(_fc1): Linear(in_features=512, out_features=1024, bias=True)
(_fc2): Linear(in_features=1024, out_features=512, bias=True)
)
(self_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(ffn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
)
(2): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ffn): FFN(
(_fc1): Linear(in_features=512, out_features=1024, bias=True)
(_fc2): Linear(in_features=1024, out_features=512, bias=True)
)
(self_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(ffn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
)
(3): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ffn): FFN(
(_fc1): Linear(in_features=512, out_features=1024, bias=True)
(_fc2): Linear(in_features=1024, out_features=512, bias=True)
)
(self_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(ffn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
)
(4): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ffn): FFN(
(_fc1): Linear(in_features=512, out_features=1024, bias=True)
(_fc2): Linear(in_features=1024, out_features=512, bias=True)
)
(self_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(ffn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
)
(5): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ffn): FFN(
(_fc1): Linear(in_features=512, out_features=1024, bias=True)
(_fc2): Linear(in_features=1024, out_features=512, bias=True)
)
(self_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(ffn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
)
)
), TransformerDecoder(
(_embed): Embedding(10004, 512, padding_idx=3)
(_pos_embed): SinusoidalPositionalEmbedding(
(embedding): __SinusoidalPositionalEmbedding()
)
(_embed_dropout): Dropout(p=0.1, inplace=False)
(_layers): ModuleList(
(0): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ffn): FFN(
(_fc1): Linear(in_features=512, out_features=1024, bias=True)
(_fc2): Linear(in_features=1024, out_features=512, bias=True)
)
(self_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(multihead_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(ffn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
)
(1): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ffn): FFN(
(_fc1): Linear(in_features=512, out_features=1024, bias=True)
(_fc2): Linear(in_features=1024, out_features=512, bias=True)
)
(self_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(multihead_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(ffn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
)
(2): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ffn): FFN(
(_fc1): Linear(in_features=512, out_features=1024, bias=True)
(_fc2): Linear(in_features=1024, out_features=512, bias=True)
)
(self_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(multihead_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(ffn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
)
(3): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ffn): FFN(
(_fc1): Linear(in_features=512, out_features=1024, bias=True)
(_fc2): Linear(in_features=1024, out_features=512, bias=True)
)
(self_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(multihead_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(ffn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
)
(4): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ffn): FFN(
(_fc1): Linear(in_features=512, out_features=1024, bias=True)
(_fc2): Linear(in_features=1024, out_features=512, bias=True)
)
(self_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(multihead_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(ffn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
)
(5): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)
)
(ffn): FFN(
(_fc1): Linear(in_features=512, out_features=1024, bias=True)
(_fc2): Linear(in_features=1024, out_features=512, bias=True)
)
(self_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(multihead_attn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(ffn_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
)
)
(_out_proj): Linear(in_features=512, out_features=10004, bias=False)
)]
2022-06-07,03:33:00 ｜ INFO | paragen.models.abstract_model | parameter size: 41787392
2022-06-07,03:33:00 ｜ INFO | paragen.models.abstract_model | load model from checkpoints/best.pt
2022-06-07,03:33:14 ｜ INFO | paragen.models.abstract_model | keys IN this model but NOT IN loaded model >>>
2022-06-07,03:33:14 ｜ INFO | paragen.models.abstract_model | - None
2022-06-07,03:33:14 ｜ INFO | paragen.models.abstract_model | keys NOT IN this model but IN loaded model >>>
2022-06-07,03:33:14 ｜ INFO | paragen.models.abstract_model | - None
2022-06-07,03:33:14 ｜ INFO | paragen.models.abstract_model | move model to cuda
2022-06-07,03:33:14 ｜ INFO | paragen.utils.registry | Creating SequenceGenerator class with configs
{
"search": {
"class": "GreedySearch",
"maxlen_coef": "(1.2, 10)"
}
}
2022-06-07,03:33:14 ｜ INFO | paragen.utils.registry | Creating GreedySearch class with configs
{
"maxlen_coef": "(1.2, 10)"
}
2022-06-07,03:33:14 ｜ INFO | paragen.generators.abstract_generator | move model to cuda
2022-06-07,03:33:14 ｜ INFO | paragen.utils.registry | Creating SequentialSampler class with configs
{
"max_tokens": 8000
}
2022-06-07,03:33:14 ｜ INFO | paragen.utils.registry | Creating InMemoryDataLoader class with configs
{
"post_collate_fn": null
}
2022-06-07,03:33:14 ｜ INFO | paragen.samplers.abstract_sampler | Deriving total 1 batches
Traceback (most recent call last):
File "/root/anaconda3/envs/python38/bin/paragen-export", line 33, in
sys.exit(load_entry_point('paragen', 'console_scripts', 'paragen-export')())
File "/root/ParaGen/paragen/entries/export.py", line 14, in main
task.export(path, **export_conf)
File "/root/ParaGen/paragen/tasks/translation_task.py", line 263, in export
_fetch_first_sample(),
File "/root/ParaGen/paragen/tasks/translation_task.py", line 259, in _fetch_first_sample
for sample in dataloader:
File "/root/ParaGen/paragen/dataloaders/in_memory_dataloader.py", line 116, in iter
for samples in super().iter():
File "/root/anaconda3/envs/python38/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 530, in next
data = self._next_data()
File "/root/anaconda3/envs/python38/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 570, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/root/anaconda3/envs/python38/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
return self.collate_fn(data)
File "/root/ParaGen/paragen/tasks/translation_task.py", line 180, in _collate
src = convert_idx_to_tensor(src,
File "/root/ParaGen/paragen/utils/tensor.py", line 49, in convert_idx_to_tensor
idx = create_tensor(idx, tensor_type)
File "/root/ParaGen/paragen/utils/tensor.py", line 134, in create_tensor
T = torch.LongTensor(idx)
ValueError: expected sequence of length 4 at dim 2 (got 5)
2022-06-07,03:33:15 ｜ INFO | mosestokenizer.detokenizer.MosesDetokenizer | killing process 2867
2022-06-07,03:33:15 ｜ INFO | mosestokenizer.detokenizer.MosesDetokenizer | killing process 2868
77 Dec 21, 2022
348 Jan 8, 2023
7.1k Jan 3, 2023
153 Jan 7, 2023
2.5k Jan 6, 2023
282 Jan 1, 2023
55 Nov 29, 2022
2 Dec 20, 2022
2.2k Jan 8, 2023
708 Dec 19, 2022
19.4k Jan 4, 2023
22 Nov 25, 2022
185 Dec 21, 2022
161 Dec 8, 2022
112 Dec 16, 2022
897 Jan 5, 2023
490 Dec 15, 2022
195 Dec 17, 2022
24 Oct 26, 2022