dmmiller612 / bert-extractive-summarizer

Easy to use extractive text summarization with BERT
MIT License
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Error when running xlnet for individual paragraphs on linux using gpu #135

Open gaurav-95 opened 2 years ago

gaurav-95 commented 2 years ago

I am running bertxl summarization on a GPU Ubuntu instance in AWS ec2 and it runs fine on my windows machine using CPU. But throws an error running there. Any assistance would be helpful. I am ready to give any other extra information if this was not sufficient.

File "/home/ubuntu/neus/cron_summary_desc.py", line 282, in store_summaries sum_df = extract_summaries(df, min_desc_length) File "/home/ubuntu/neus/cron_summary_desc.py", line 344, in extract_summaries mini_sum_xl = ''.join(model_xl(para, num_sentences = sent_count)) File "/home/ubuntu/neus/neusenv/lib/python3.8/site-packages/summarizer/summary_processor.py", line 234, in call return self.run(body, ratio, min_length, max_length, File "/home/ubuntu/neus/neusenv/lib/python3.8/site-packages/summarizer/summaryprocessor.py", line 202, in run sentences, = self.cluster_runner(sentences, ratio, algorithm, use_first, num_sentences) File "/home/ubuntu/neus/neusenv/lib/python3.8/site-packages/summarizer/summary_processor.py", line 108, in cluster_runner hidden = self.model(sentences) File "/home/ubuntu/neus/neusenv/lib/python3.8/site-packages/summarizer/transformer_embeddings/bert_embedding.py", line 173, in call return self.create_matrix(content, hidden, reduce_option, hidden_concat) File "/home/ubuntu/neus/neusenv/lib/python3.8/site-packages/summarizer/transformer_embeddings/bert_embedding.py", line 151, in create_matrix return np.asarray([ File "/home/ubuntu/neus/neusenv/lib/python3.8/site-packages/summarizer/transformer_embeddings/bert_embedding.py", line 152, in np.squeeze(self.extract_embeddings( File "/home/ubuntu/neus/neusenv/lib/python3.8/site-packages/summarizer/transformer_embeddings/bert_embedding.py", line 108, in extract_embeddings pooled, hidden_states = self.model(tokens_tensor)[-2:] File "/home/ubuntu/neus/neusenv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl return forward_call(*input, *kwargs) File "/home/ubuntu/neus/neusenv/lib/python3.8/site-packages/transformers/models/xlnet/modeling_xlnet.py", line 1180, in forward word_emb_k = self.word_embedding(input_ids) File "/home/ubuntu/neus/neusenv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl return forward_call(input, **kwargs) File "/home/ubuntu/neus/neusenv/lib/python3.8/site-packages/torch/nn/modules/sparse.py", line 158, in forward return F.embedding( File "/home/ubuntu/neus/neusenv/lib/python3.8/site-packages/torch/nn/functional.py", line 2183, in embedding return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) RuntimeError: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got torch.cuda.FloatTensor instead (while checking arguments for embedding)