I followed all the instructions in the repo to install it from the source. Torch version - 1.10.2 torchtext - 0.11.2 cuda - 11.1

By when I run https://github.com/graph4ai/graph4nlp_demo/blob/main/AAAI2022_demo/semantic_parsing.ipynb

I get the error:

RuntimeError: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.

See the full error here:

`[ Using CUDA ] /home/synthesisproject/anaconda3/envs/g4nlp/lib/python3.8/site-packages/graph4nlp_cu111-0.5.5-py3.8.egg/graph4nlp/pytorch/modules/graph_embedding_learning/gat.py:259: UserWarning: The residual option must be False when num_heads > 1 warnings.warn("The residual option must be False when num_heads > 1") /home/synthesisproject/anaconda3/envs/g4nlp/lib/python3.8/site-packages/torch-1.10.2-py3.8-linux-x86_64.egg/torch/cuda/init.py:143: UserWarning: NVIDIA RTX A5000 with CUDA capability sm_86 is not compatible with the current PyTorch installation. The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70. If you want to use the NVIDIA RTX A5000 GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/

warnings.warn(incompatible_device_warn.format(device_name, capability, " ".join(arch_list), device_name))

RuntimeError Traceback (most recent call last) in 1 # run the model ----> 2 runner = Jobs(opt) 3 max_score = runner.train() 4 print("Train finish, best val score: {:.3f}".format(max_score)) 5 test_score = runner.translate()

in init(self, opt) 7 self._build_device(self.opt) 8 self._build_dataloader() ----> 9 self._build_model() 10 self._build_optimizer() 11 self._build_evaluation()

in _build_model(self) 75 76 def _build_model(self): ---> 77 self.model = Graph2Seq.from_args(self.opt, self.vocab).to(self.device) 78 79 def _build_optimizer(self):

~/anaconda3/envs/g4nlp/lib/python3.8/site-packages/torch-1.10.2-py3.8-linux-x86_64.egg/torch/nn/modules/module.py in to(self, *args, **kwargs) 897 return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking) 898 --> 899 return self._apply(convert) 900 901 def register_backward_hook(

~/anaconda3/envs/g4nlp/lib/python3.8/site-packages/torch-1.10.2-py3.8-linux-x86_64.egg/torch/nn/modules/module.py in _apply(self, fn) 568 def _apply(self, fn): 569 for module in self.children(): --> 570 module._apply(fn) 571 572 def compute_should_use_set_data(tensor, tensor_applied):

~/anaconda3/envs/g4nlp/lib/python3.8/site-packages/torch-1.10.2-py3.8-linux-x86_64.egg/torch/nn/modules/module.py in _apply(self, fn) 568 def _apply(self, fn): 569 for module in self.children(): --> 570 module._apply(fn) 571 572 def compute_should_use_set_data(tensor, tensor_applied):

~/anaconda3/envs/g4nlp/lib/python3.8/site-packages/torch-1.10.2-py3.8-linux-x86_64.egg/torch/nn/modules/module.py in _apply(self, fn) 568 def _apply(self, fn): 569 for module in self.children(): --> 570 module._apply(fn) 571 572 def compute_should_use_set_data(tensor, tensor_applied):

~/anaconda3/envs/g4nlp/lib/python3.8/site-packages/torch-1.10.2-py3.8-linux-x86_64.egg/torch/nn/modules/module.py in _apply(self, fn) 568 def _apply(self, fn): 569 for module in self.children(): --> 570 module._apply(fn) 571 572 def compute_should_use_set_data(tensor, tensor_applied):

~/anaconda3/envs/g4nlp/lib/python3.8/site-packages/torch-1.10.2-py3.8-linux-x86_64.egg/torch/nn/modules/rnn.py in _apply(self, fn) 187 self._flat_weights = [(lambda wn: getattr(self, wn) if hasattr(self, wn) else None)(wn) for wn in self._flat_weights_names] 188 # Flattens params (on CUDA) --> 189 self.flatten_parameters() 190 191 return ret

~/anaconda3/envs/g4nlp/lib/python3.8/site-packages/torch-1.10.2-py3.8-linux-x86_64.egg/torch/nn/modules/rnn.py in flatten_parameters(self) 173 if self.proj_size > 0: 174 num_weights += 1 --> 175 torch._cudnn_rnn_flatten_weight( 176 self._flat_weights, num_weights, 177 self.input_size, rnn.get_cudnn_mode(self.mode),

RuntimeError: CUDA error: no kernel image is available for execution on the device CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1.`

Hello, please let me know where am i going wrong. I have not made any changes and have followed the given instructions for setup. for math word problem for KG completion

Hi, I encountered some problem when I tried to build the IE graph using demo code in CLIQ-ai2021_demo classification problem. By implementation, I simply changed the config file that -- graph_name: 'ie'.

The error says

"ValueError: num_samples should be a positive integer value, but got num_samples=0"

I checked the built graph in data.pt and label.py, only to find they are null.

Could someone explain why I encountered this error? Thank you!

🐛 Bug

When trying KDD2021_demo semantic_parsing in jupyter notebook, I got the following error upon "Run the model"

[ Using CUDA ]
Loading pre-built vocab model stored in ../data/jobs\processed\node_emb_graph\vocab.pt
---------------------------------------------------------------------------
UnpicklingError                           Traceback (most recent call last)
<ipython-input-7-8ce32756f002> in <module>
      1 # run the model
----> 2 runner = Jobs(opt)
      3 max_score = runner.train()
      4 print("Train finish, best val score: {:.3f}".format(max_score))
      5 runner.load_checkpoint("best.pth")

<ipython-input-2-a40dcc751a5f> in __init__(self, opt)
      6         self.use_coverage = self.opt["decoder_args"]["rnn_decoder_share"]["use_coverage"]
      7         self._build_device(self.opt)
----> 8         self._build_dataloader()
      9         self._build_model()
     10         self._build_loss_function()

<ipython-input-2-a40dcc751a5f> in _build_dataloader(self)
     58 
     59         # Call the TREC dataset API
---> 60         dataset = JobsDataset(root_dir=self.opt["graph_construction_args"]["graph_construction_share"]["root_dir"],
     61                               pretrained_word_emb_name=self.opt["pretrained_word_emb_name"],
     62                               pretrained_word_emb_cache_dir=self.opt["pretrained_word_emb_cache_dir"],

c:\users\chris\dev\pyvenv_gnn\lib\site-packages\graph4nlp_cu111-0.4.0-py3.8.egg\graph4nlp\pytorch\datasets\jobs.py in __init__(self, root_dir, topology_builder, topology_subdir, pretrained_word_emb_name, pretrained_word_emb_url, pretrained_word_emb_cache_dir, graph_type, merge_strategy, edge_strategy, seed, word_emb_size, share_vocab, lower_case, thread_number, port, dynamic_graph_type, dynamic_init_topology_builder, dynamic_init_topology_aux_args)
     68         """
     69         # Initialize the dataset. If the preprocessed files are not found, then do the preprocessing and save them.
---> 70         super(JobsDataset, self).__init__(root_dir=root_dir, topology_builder=topology_builder,
     71                                           topology_subdir=topology_subdir, graph_type=graph_type,
     72                                           edge_strategy=edge_strategy, merge_strategy=merge_strategy,

c:\users\chris\dev\pyvenv_gnn\lib\site-packages\graph4nlp_cu111-0.4.0-py3.8.egg\graph4nlp\pytorch\data\dataset.py in __init__(self, root_dir, topology_builder, topology_subdir, share_vocab, **kwargs)
    692         self.data_item_type = Text2TextDataItem
    693         self.share_vocab = share_vocab
--> 694         super(Text2TextDataset, self).__init__(root_dir, topology_builder, topology_subdir, **kwargs)
    695 
    696     def parse_file(self, file_path) -> list:

c:\users\chris\dev\pyvenv_gnn\lib\site-packages\graph4nlp_cu111-0.4.0-py3.8.egg\graph4nlp\pytorch\data\dataset.py in __init__(self, root, topology_builder, topology_subdir, tokenizer, lower_case, pretrained_word_emb_name, pretrained_word_emb_url, target_pretrained_word_emb_name, target_pretrained_word_emb_url, pretrained_word_emb_cache_dir, max_word_vocab_size, min_word_vocab_freq, use_val_for_vocab, seed, thread_number, port, timeout, **kwargs)
    370             self.val = data['val']
    371 
--> 372         self.build_vocab()
    373 
    374     @property

c:\users\chris\dev\pyvenv_gnn\lib\site-packages\graph4nlp_cu111-0.4.0-py3.8.egg\graph4nlp\pytorch\data\dataset.py in build_vocab(self)
    640             data_for_vocab = self.val + data_for_vocab
    641 
--> 642         vocab_model = VocabModel.build(saved_vocab_file=self.processed_file_paths['vocab'],
    643                                        data_set=data_for_vocab,
    644                                        tokenizer=self.tokenizer,

c:\users\chris\dev\pyvenv_gnn\lib\site-packages\graph4nlp_cu111-0.4.0-py3.8.egg\graph4nlp\pytorch\modules\utils\vocab_utils.py in build(cls, saved_vocab_file, data_set, tokenizer, lower_case, max_word_vocab_size, min_word_vocab_freq, pretrained_word_emb_name, pretrained_word_emb_url, target_pretrained_word_emb_name, target_pretrained_word_emb_url, pretrained_word_emb_cache_dir, word_emb_size, share_vocab)
    184             print('Loading pre-built vocab model stored in {}'.format(saved_vocab_file))
    185             with open(saved_vocab_file, 'rb') as f:
--> 186                 vocab_model = pickle.load(f)
    187 
    188         else:

UnpicklingError: A load persistent id instruction was encountered,
but no persistent_load function was specified.

To Reproduce

Steps to reproduce the behavior:

1. Following the instructions by the site to open semantic_parsing.
2. Running through code segments.
3. The error occurred when running "Run the model" code segment.

Expected behavior

Don't expect the error.

Environment

• Graph4NLP Version (e.g., 0.4.1): v0.4.1
• Backend Library & Version (e.g., PyTorch 1.6.0): 1.8.0
• OS (e.g., Linux): Windows 10
• How you installed Graph4NLP (pip, source): followed "Installation via source code"
• Build command you used (if compiling from source): As listed instructions
• Python version: 3.8.10
• CUDA/cuDNN version (if applicable): 11.1
• GPU models and configuration (e.g. 2080Ti): RTX2060
• Any other relevant information:
• • torchtext==0.9.0
• • pickle.compatible_formats ['1.0', '1.1', '1.2', '1.3', '2.0', '3.0', '4.0', '5.0']

Additional context

1. Started jupyter notebook in python virtual environment.
2. Also tried setting config_file (in Set up the config segment) to the absolute path.
3. text_classification example faced the same issue.

Thanks for this wonderful repo!!

There is a small typo in the below highlighted word and also a link is missing for graph4nlp repository page, You will need to install our graph4nlp library in order to run the demo code. Please follow the following environment setup instructinos.

I followed the instructions in the repo: created environment (python 3.8)
git clone -b v0.5.5 https://github.com/graph4ai/graph4nlp.git

While this created a graph4nlp folder, it does not have a graph4nlp_demo folder. I had to separately clone to the graph4nlp_demo repo

Hello! Your NAACL2021 tutorial about gnn4nlp is so amazing. I am eager to see the video about it. Do you have a plan when to release it? Thanks so much

TypeError                                 Traceback (most recent call last)
/tmp/ipykernel_23/1251835385.py in <module>
      7 print('\n' + config['out_dir'])
      8 
----> 9 runner = ModelHandler(config)
     10 t0 = time.time()
     11 

/tmp/ipykernel_23/370220804.py in __init__(self, config)
      6         self.logger.write(self.config['out_dir'])
      7         self._build_device()
----> 8         self._build_dataloader()
      9         self._build_model()
     10         self._build_optimizer()

/tmp/ipykernel_23/370220804.py in _build_dataloader(self)
     77                                   self.config['graph_type'] in ('node_emb', 'node_emb_refined') else None,
     78                               dynamic_init_topology_builder=dynamic_init_topology_builder,
---> 79                               dynamic_init_topology_aux_args={'dummy_param': 0})
     80 
     81         self.train_dataloader = DataLoader(dataset.train, batch_size=self.config['batch_size'], shuffle=True,

TypeError: __init__() missing 1 required positional argument: 'graph_name'

https://github.com/graph4ai/graph4nlp_demo/blob/main/SIGIR2021_demo/text_classification.ipynb in this notebook, in line 16 of class TextClassifier when i initialize DependencyBasedGraphConstruction, it raise this error

self.graph_topology = DependencyBasedGraphConstruction( TypeError: init() got an unexpected keyword argument 'embedding_style'