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View Code? Open in Web Editor NEW[NeurIPS 2022] Zero-Shot Video Question Answering via Frozen Bidirectional Language Models
Home Page: https://arxiv.org/abs/2206.08155
License: Apache License 2.0
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ziyang412 ricklentz sharkwyf ycsun1972 otakbeku makual kenn-san laudrup21 michael-x-0 harithproietti zhaoyue-zephyrus sangyeop-lee ngthanhtin jinuhwang danielflaherty jisonz jeanpark96 vhzy mohammedmahdiali inesriahi trananhdat holdenhuan9 yuzuriha-inori-xfrozenbilm's Issues
Bad zero-shot results on TVQA
Hi, I ran the zero-shot result on TVQA dataset with the given zero-shot checkpoint frozenbilm.pth and the given TVQA video features clipvitl14.pth. I also used the microsoft/deberta-v2-xlarge checkpoint. However, I got the val acc 31.59 instead of the reported 59.7.
About the stage of pre-training
Hi, I noticed that you only trained on webvid10m for two epochs, is it converging already?
Verification code is need for downloading the data features
Hi @antoyang ,
Thanks a lot for open-sourcing the project! I just got in trouble when trying to download the pre-computed features, since it requires a verification code that seems not provided in the repo.
Best,
Junting
Error on zero-shot VQA
Hi. Thanks for providing code! I'm having the same issue as #3 on the VQA demo. I have the Microsoft deberta-v2-xlarge ( https://huggingface.co/microsoft/deberta-v2-xlarge ) downloaded from huggingface in a folder called transformers_cache. I've set the TRANSFORMERS_CACHE environment variable to point at it (if I remove this, it complains that deberta is missing, so I assume this part is correct). Do you have any idea why it might be failing?
The command I'm running is:
python demo_videoqa.py --combine_datasets msrvtt --combine_datasets_val msrvtt \ --suffix="." --max_tokens=256 --ds_factor_ff=8 --ds_factor_attn=8 \ --load=models/frozenbilm.pth --msrvtt_vocab_path=data/MSRVTT-QA/vocab.json \ --question_example question --video_example test.mp4 --device='cpu'
And the error is:
Traceback (most recent call last):
File "demo_videoqa.py", line 170, in
main(args)
File "/user/work/tp8961/conda_envs/frozenbilm_env/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "demo_videoqa.py", line 32, in main
tokenizer = get_tokenizer(args)
File "/user/work/tp8961/FrozenBiLM/model/init.py", line 96, in get_tokenizer
tokenizer = DebertaV2Tokenizer.from_pretrained(
File "/user/work/tp8961/conda_envs/frozenbilm_env/lib/python3.8/site-packages/transformers/tokenization_utils_base.py", line 1788, in from_pretrained
return cls._from_pretrained(
File "/user/work/tp8961/conda_envs/frozenbilm_env/lib/python3.8/site-packages/transformers/tokenization_utils_base.py", line 1923, in _from_pretrained
tokenizer = cls(*init_inputs, **init_kwargs)
File "/user/work/tp8961/conda_envs/frozenbilm_env/lib/python3.8/site-packages/transformers/models/deberta_v2/tokenization_deberta_v2.py", line 145, in init
self._tokenizer = SPMTokenizer(vocab_file, split_by_punct=split_by_punct, sp_model_kwargs=self.sp_model_kwargs)
File "/user/work/tp8961/conda_envs/frozenbilm_env/lib/python3.8/site-packages/transformers/models/deberta_v2/tokenization_deberta_v2.py", line 296, in init
spm.load(vocab_file)
File "/user/work/tp8961/conda_envs/frozenbilm_env/lib/python3.8/site-packages/sentencepiece/init.py", line 367, in Load
return self.LoadFromFile(model_file)
File "/user/work/tp8961/conda_envs/frozenbilm_env/lib/python3.8/site-packages/sentencepiece/init.py", line 171, in LoadFromFile
return _sentencepiece.SentencePieceProcessor_LoadFromFile(self, arg)
RuntimeError: Internal: src/sentencepiece_processor.cc(890) [model_proto->ParseFromArray(serialized.data(), serialized.size())]
Conda Environment Setting
Hi.
Instruction says to run "pip install requirements.txt", but it is "pip install -r requirements.txt", right?
And my question is about this error;
$ pip install -r requirements.txt
ERROR: Could not find a version that satisfies the requirement clip==1.0 (from versions: 0.0.1, 0.1.0, 0.2.0)
ERROR: No matching distribution found for clip==1.0
How can I download clip==1.0?
Errors in MSRVTT-QA test set
Hi, I have found some spelling errors in the test set of MSRVTT. For example, "badmitten", "peson", "tenni". How did you handle such ground truth errors during the testing?
How2qa
What is the difference between the folowing checkpoints.
frozenbilm_how2qa.pth
frozenbilm_how2qa1p.pth
frozenbilm_how2qa10p.pth
Do you L2 Normalize the CLIP Features?
Hi @antoyang,
After extracting the features from CLIP-L/14, do you L2 normalize the features before passing it to the subsequent pipeline? Thanks
webvid_clipvitl14_features
Hi Antoine,
Thanks for your great and open work! I was failed to find the video features of WebVid in your provided files. Could you please provide me with the download link?
[Import Error] with demo_videoqa.py
python demo_videoqa.py --combine_datasets msrvtt --combine_datasets_val msrvtt --suffix="." --max_tokens=256 --ds_factor_ff=8 --ds_factor_attn=8 --load=checkpoints/frozenbilm_msrvtt10p.pth --msrvtt_vocab_path=data/MSRVTT-QA/vocab.json --question_example "what is that dog doing?" --video_example ./angry_cute_dog.mp4
I downloaded all the data and checkpoints files. Also i downloaded transformers library from hugging face. But... plz.. check my error message..
ImportError: cannot import name 'GreedySearchOutput' from 'transformers.generation_utils'(FrozenBiLM/transformers/src/transformers/generation_utils.py)
what version of transformers library are u using?
Few-shot VideoQA training details
Hi,
Thanks for the great work and publicly available code.
Could you please share the few-shot training parameters (batch size, learning rate, etc.)? I could not reproduce the results.
Thanks in advance.
Autoregressive Model Checkpoint
Hi,
Thank you for the great work!
Is it possible for you to upload the checkpoint for GPT-J-6B as well?
Unexpected Zero-shot Results
Hi,
I tried to evaluate the fine-tuned checkpoints provided in the repo. My environment has been correctly configured and I followed all steps up to Zero-shot VideoQA section. As I only have one GPU, I didn't use distributed inference.
Here is what I used to run the evaluation:
python videoqa.py --test --eval --combine_datasets <dataset> --combine_datasets_val <dataset> --save_dir=zs<dataset> --ds_factor_ff=8 --ds_factor_attn=8 --suffix="." --batch_size_val=32 --max_tokens=256 --load=checkpoints/frozenbilm_<dataset>.pth --<dataset>_vocab_path <data_folder>/vocab1000.json
I tried with ActivityNet-VQA and iVQA and couldn't get any expected results.
For instance, here is what got by testing on ActivityNet-VQA:
number of params: 29735424
loading from checkpoints/frozenbilm_activitynet.pth
test: [ 0/250] eta: 0:07:27 acc: 0.0000 (0.0000) time: 1.7891 data: 0.3052 max mem: 6485
test: [100/250] eta: 0:03:35 acc: 0.0000 (0.0006) time: 1.4358 data: 0.0020 max mem: 7765
test: [200/250] eta: 0:01:11 acc: 0.0000 (0.0005) time: 1.4355 data: 0.0021 max mem: 7765
test: [249/250] eta: 0:00:01 acc: 0.0000 (0.0006) time: 1.4344 data: 0.0020 max mem: 7765
test: Total time: 0:05:59 (1.4361 s / it)
activitynet
test acc1: 0.06%
test acc10: 0.55%
acc motion: 0.00%
acc spatial: 0.12%
acc temporal: 0.00%
acc yesno: 0.00%
acc color: 0.57%
acc object: 0.00%
acc location: 0.00%
acc number: 0.00%
acc other: 0.00%
acc sub: 0.10%; proportion 25.25%
And results on iVQA:
number of params: 29735424
loading from checkpoints/frozenbilm_ivqa.pth
test: [ 0/63] eta: 0:02:40 acc: 0.0000 (0.0000) time: 2.5405 data: 0.2846 max mem: 6485
test: [62/63] eta: 0:00:01 acc: 0.0000 (0.0000) time: 1.1953 data: 0.0018 max mem: 7766
test: Total time: 0:01:16 (1.2169 s / it)
ivqa
test acc1: 0.00%
test acc10: 0.95%
acc sub: 0.00%; proportion 14.20%
Do you have any ideas on this issue?
Cheers
Problems in reproducing the code process
Hello, thank you very much for being able to share your work,！I've run into a couple of problems in trying to reproduce your work:
- running main.py, videoqa.py gives an error:“ERROR:root:No token file found. Also make sure that a [prod] section with a 'token = value' assignment exists.”
- how to set combine_datasets and combine_datasets_val
I hope you can take time out of your busy schedule to help me out!
Problematic Tokennizer?
Hi! I am trying zeroshot inference with the code below
DATA_DIR=data
DATASET=activitynet
DATASET_FILE=ActivityNet-QA
CKPT_PATH=checkpoints/frozenbilm_activitynet.pth
TRANSFORMERS_CACHE=/root/.cache/huggingface/transformers \
CUDA_VISIBLE_DEVICES=4,5,6,7 \
CUDA_LAUNCH_BLOCKING=1 \
python -m torch.distributed.run --nproc_per_node 4 videoqa.py --test --eval \
--combine_datasets $DATASET --combine_datasets_val $DATASET --save_dir=zs${DATASET} \
--ds_factor_ff=8 --ds_factor_attn=8 --suffix="." \
--batch_size_val=32 --max_tokens=256 --load=$CKPT_PATH \
"--${DATASET}_vocab_path"=$DATA_DIR/$DATASET_FILE/vocab1000.json \
"--${DATASET}_train_csv_path"=$DATA_DIR/$DATASET_FILE/train.json "--${DATASET}_test_csv_path"=$DATA_DIR/$DATASET_FILE/test.csvWhile I encountered the issue of sentencepiece
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
*****************************************
ERROR:root:No token file found. Also make sure that a [prod] section with a 'token = value' assignment exists.
ERROR:root:No token file found. Also make sure that a [prod] section with a 'token = value' assignment exists.
ERROR:root:No token file found. Also make sure that a [prod] section with a 'token = value' assignment exists.
ERROR:root:No token file found. Also make sure that a [prod] section with a 'token = value' assignment exists.
| distributed init (rank 0): env://
| distributed init (rank 3): env://
| distributed init (rank 1): env://
| distributed init (rank 2): env://
Namespace(combine_datasets=['activitynet'], combine_datasets_val=['activitynet'], webvid_features_path='webvid_clipvitl14_features', webvid_train_csv_path='data/WebVid/train_captions.csv', webvid_val_csv_path='data/WebVid/val_captions.csv', lsmdc_features_path='data/LSMDC/clipvitl14.pth', lsmdc_train_csv_path='data/LSMDC/training.csv', lsmdc_val_csv_path='data/LSMDC/val.csv', lsmdc_test_csv_path='data/LSMDC/test.csv', lsmdc_vocab_path='data/LSMDC/vocab.json', lsmdc_subtitles_path='data/LSMDC/subtitles.pkl', ivqa_features_path='data/iVQA/clipvitl14.pth', ivqa_train_csv_path='data/iVQA/train.csv', ivqa_val_csv_path='data/iVQA/val.csv', ivqa_test_csv_path='data/iVQA/test.csv', ivqa_vocab_path='data/iVQA/vocab.json', ivqa_subtitles_path='data/iVQA/subtitles.pkl', msrvtt_features_path='data/MSRVTT-QA/clipvitl14.pth', msrvtt_train_csv_path='data/MSRVTT-QA/train.csv', msrvtt_val_csv_path='data/MSRVTT-QA/val.csv', msrvtt_test_csv_path='data/MSRVTT-QA/test.csv', msrvtt_vocab_path='data/MSRVTT-QA/vocab.json', msrvtt_subtitles_path='data/MSRVTT-QA/subtitles.pkl', msvd_features_path='data/MSVD-QA/clipvitl14.pth', msvd_train_csv_path='data/MSVD-QA/train.csv', msvd_val_csv_path='data/MSVD-QA/val.csv', msvd_test_csv_path='data/MSVD-QA/test.csv', msvd_vocab_path='data/MSVD-QA/vocab.json', msvd_subtitles_path='data/MSVD-QA/subtitles.pkl', activitynet_features_path='data/ActivityNet-QA/clipvitl14.pth', activitynet_train_csv_path='data/ActivityNet-QA/train.json', activitynet_val_csv_path='data/ActivityNet-QA/val.csv', activitynet_test_csv_path='data/ActivityNet-QA/test.csv', activitynet_vocab_path='data/ActivityNet-QA/vocab1000.json', activitynet_subtitles_path='data/ActivityNet-QA/subtitles.pkl', tgif_features_path='data/TGIF-QA/clipvitl14.pth', tgif_frameqa_train_csv_path='data/TGIF-QA/train_frameqa.csv', tgif_frameqa_test_csv_path='data/TGIF-QA/test_frameqa.csv', tgif_vocab_path='data/TGIF-QA/vocab.json', how2qa_features_path='data/How2QA/clipvitl14_split.pth', how2qa_train_csv_path='data/How2QA/train.csv', how2qa_val_csv_path='data/How2QA/public_val.csv', how2qa_subtitles_path='data/How2QA/subtitles.pkl', tvqa_features_path='data/TVQA/clipvitl14.pth', tvqa_train_csv_path='data/TVQA/train.csv', tvqa_val_csv_path='data/TVQA/val.csv', tvqa_test_csv_path='data/TVQA/test_public.csv', tvqa_subtitles_path='data/TVQA/subtitles.pkl', vqa_features_path='data/VQA/clipvitl14.pth', vqa_train_pkl_path='data/VQA/train_list.pkl', vqa_val_pkl_path='data/VQA/val_list.csv', vqa_vocab_path='data/VQA/vocab.json', mlm_prob=0.15, lr=0.0003, beta1=0.9, beta2=0.95, batch_size=32, batch_size_val=32, weight_decay=0, epochs=10, lr_drop=10, optimizer='adam', clip_max_norm=0.1, schedule='', fraction_warmup_steps=0.1, eval_skip=1, print_freq=100, freeze_lm=True, model_name='/root/.cache/huggingface/transformers/deberta-v2-xlarge', ds_factor_attn=8, ds_factor_ff=8, ft_ln=True, freeze_mlm=True, dropout=0.1, scratch=False, n_ans=0, freeze_last=True, test=True, save_dir='zsactivitynet', presave_dir='', device='cuda', seed=42, load='checkpoints/frozenbilm_activitynet.pth', resume=False, start_epoch=0, eval=True, num_workers=3, world_size=4, dist_url='env://', max_feats=10, features_dim=768, use_video=True, use_context=True, max_tokens=256, max_atokens=5, prefix='', suffix='.', rank=0, gpu=0, distributed=True, dist_backend='nccl')
Traceback (most recent call last):
File "/mnt/lustre/lychen/code/sm/FrozenBiLM/videoqa.py", line 530, in <module>
main(args)
Traceback (most recent call last):
File "/mnt/lustre/lychen/code/sm/FrozenBiLM/videoqa.py", line 266, in main
File "/mnt/lustre/lychen/code/sm/FrozenBiLM/videoqa.py", line 530, in <module>
tokenizer = get_tokenizer(args)
File "/mnt/lustre/lychen/code/sm/FrozenBiLM/model/__init__.py", line 96, in get_tokenizer
tokenizer = DebertaV2Tokenizer.from_pretrained(
main(args)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 1777, in from_pretrained
File "/mnt/lustre/lychen/code/sm/FrozenBiLM/videoqa.py", line 266, in main
tokenizer = get_tokenizer(args)
File "/mnt/lustre/lychen/code/sm/FrozenBiLM/model/__init__.py", line 96, in get_tokenizer
tokenizer = DebertaV2Tokenizer.from_pretrained(
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 1777, in from_pretrained
return cls._from_pretrained(
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 1932, in _from_pretrained
return cls._from_pretrained(
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 1932, in _from_pretrained
tokenizer = cls(*init_inputs, **init_kwargs)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/transformers/models/deberta_v2/tokenization_deberta_v2.py", line 149, in __init__
self._tokenizer = SPMTokenizer(vocab_file, split_by_punct=split_by_punct, sp_model_kwargs=self.sp_model_kwargs)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/transformers/models/deberta_v2/tokenization_deberta_v2.py", line 301, in __init__
tokenizer = cls(*init_inputs, **init_kwargs)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/transformers/models/deberta_v2/tokenization_deberta_v2.py", line 149, in __init__
self._tokenizer = SPMTokenizer(vocab_file, split_by_punct=split_by_punct, sp_model_kwargs=self.sp_model_kwargs)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/transformers/models/deberta_v2/tokenization_deberta_v2.py", line 301, in __init__
spm.load(vocab_file)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/sentencepiece/__init__.py", line 905, in Load
spm.load(vocab_file)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/sentencepiece/__init__.py", line 905, in Load
return self.LoadFromFile(model_file)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/sentencepiece/__init__.py", line 310, in LoadFromFile
return self.LoadFromFile(model_file)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/sentencepiece/__init__.py", line 310, in LoadFromFile
return _sentencepiece.SentencePieceProcessor_LoadFromFile(self, arg)
RuntimeError return _sentencepiece.SentencePieceProcessor_LoadFromFile(self, arg):
Internal: src/sentencepiece_processor.cc(1101) [model_proto->ParseFromArray(serialized.data(), serialized.size())]
RuntimeError: Internal: src/sentencepiece_processor.cc(1101) [model_proto->ParseFromArray(serialized.data(), serialized.size())]
Traceback (most recent call last):
File "/mnt/lustre/lychen/code/sm/FrozenBiLM/videoqa.py", line 530, in <module>
main(args)
File "/mnt/lustre/lychen/code/sm/FrozenBiLM/videoqa.py", line 266, in main
tokenizer = get_tokenizer(args)
File "/mnt/lustre/lychen/code/sm/FrozenBiLM/model/__init__.py", line 96, in get_tokenizer
tokenizer = DebertaV2Tokenizer.from_pretrained(
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 1777, in from_pretrained
return cls._from_pretrained(
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 1932, in _from_pretrained
Traceback (most recent call last):
File "/mnt/lustre/lychen/code/sm/FrozenBiLM/videoqa.py", line 530, in <module>
tokenizer = cls(*init_inputs, **init_kwargs)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/transformers/models/deberta_v2/tokenization_deberta_v2.py", line 149, in __init__
self._tokenizer = SPMTokenizer(vocab_file, split_by_punct=split_by_punct, sp_model_kwargs=self.sp_model_kwargs)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/transformers/models/deberta_v2/tokenization_deberta_v2.py", line 301, in __init__
spm.load(vocab_file)main(args)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/sentencepiece/__init__.py", line 905, in Load
File "/mnt/lustre/lychen/code/sm/FrozenBiLM/videoqa.py", line 266, in main
return self.LoadFromFile(model_file)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/sentencepiece/__init__.py", line 310, in LoadFromFile
tokenizer = get_tokenizer(args)
File "/mnt/lustre/lychen/code/sm/FrozenBiLM/model/__init__.py", line 96, in get_tokenizer
return _sentencepiece.SentencePieceProcessor_LoadFromFile(self, arg)
RuntimeError: Internal: src/sentencepiece_processor.cc(1101) [model_proto->ParseFromArray(serialized.data(), serialized.size())]
tokenizer = DebertaV2Tokenizer.from_pretrained(
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 1777, in from_pretrained
return cls._from_pretrained(
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 1932, in _from_pretrained
tokenizer = cls(*init_inputs, **init_kwargs)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/transformers/models/deberta_v2/tokenization_deberta_v2.py", line 149, in __init__
self._tokenizer = SPMTokenizer(vocab_file, split_by_punct=split_by_punct, sp_model_kwargs=self.sp_model_kwargs)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/transformers/models/deberta_v2/tokenization_deberta_v2.py", line 301, in __init__
spm.load(vocab_file)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/sentencepiece/__init__.py", line 905, in Load
return self.LoadFromFile(model_file)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/sentencepiece/__init__.py", line 310, in LoadFromFile
return _sentencepiece.SentencePieceProcessor_LoadFromFile(self, arg)
RuntimeError: Internal: src/sentencepiece_processor.cc(1101) [model_proto->ParseFromArray(serialized.data(), serialized.size())]
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 1066196) of binary: /mnt/lustre/anaconda3/envs/dream/bin/python
Traceback (most recent call last):
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/torch/distributed/run.py", line 766, in <module>
main()
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
return f(*args, **kwargs)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/torch/distributed/run.py", line 762, in main
run(args)
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/torch/distributed/run.py", line 753, in run
elastic_launch(
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/mnt/lustre/anaconda3/envs/dream/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 246, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
videoqa.py FAILED
------------------------------------------------------------
Failures:
[1]:
time : 2022-11-07_10:48:31
host : localhost.vm
rank : 1 (local_rank: 1)
exitcode : 1 (pid: 1066197)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[2]:
time : 2022-11-07_10:48:31
host : localhost.vm
rank : 2 (local_rank: 2)
exitcode : 1 (pid: 1066198)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[3]:
time : 2022-11-07_10:48:31
host : localhost.vm
rank : 3 (local_rank: 3)
exitcode : 1 (pid: 1066199)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2022-11-07_10:48:31
host : localhost.vm
rank : 0 (local_rank: 0)
exitcode : 1 (pid: 1066196)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================This isssue is the same as the one below. It looks like some prblem from vocab. How can we fix it?
sentencepiece\sentencepiece\src\sentencepiece_processor.cc(1102) [model_proto->ParseFromArray(serialized.data(), serialized.size())] · Issue #20011 · huggingface/transformers
huggingface/transformers#20011
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