Traceback (most recent call last):
  File "train_summary_loop.py", line 59, in <module>
    summarizer = GeneTransformer(max_output_length=args.max_output_length, device=args.device, tokenizer_type='gpt2', starter_model=summarizer_model_start)
  File "/home/tait-dev-0/summary_loop/summary_loop/model_generator.py", line 30, in __init__
    self.reload(starter_model)
  File "/home/tait-dev-0/summary_loop/summary_loop/model_generator.py", line 39, in reload
    print(self.model.load_state_dict(torch.load(from_file)))
  File "/home/tait-dev-0/anaconda2/envs/summary_loop/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1045, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for GPT2LMHeadModel:
	Missing key(s) in state_dict: "transformer.h.0.attn.masked_bias", "transformer.h.1.attn.masked_bias", "transformer.h.2.attn.masked_bias", "transformer.h.3.attn.masked_bias", "transformer.h.4.attn.masked_bias", "transformer.h.5.attn.masked_bias", "transformer.h.6.attn.masked_bias", "transformer.h.7.attn.masked_bias", "transformer.h.8.attn.masked_bias", "transformer.h.9.attn.masked_bias", "transformer.h.10.attn.masked_bias", "transformer.h.11.attn.masked_bias". 

Dear author, I tried to load the fluency_news_model_file models but failed. It seems that the "news_gpt2_bs32.bin" is not provided in the release.

I tried to replace it with "fluency_news_bs32.bin", but it does not seem to match the GeneTransformer. I.e. when I tried to load the fluency model using modelf=GeneTransformer(max_output_length=args.max_output_length, device=args.device, starter_model=fluency_news_model_file) it shows "IncompatibleKeys(missing_keys=['transformer.h.0.attn.masked_bias', 'transformer.h.1.attn.masked_bias', 'transformer.h.2.attn.masked_bias', 'transformer.h.3.attn.masked_bias', 'transformer.h.4.attn.masked_bias', 'transformer.h.5.attn.masked_bias', 'transformer.h.6.attn.masked_bias', 'transformer.h.7.attn.masked_bias', 'transformer.h.8.attn.masked_bias', 'transformer.h.9.attn.masked_bias', 'transformer.h.10.attn.masked_bias', 'transformer.h.11.attn.masked_bias'], unexpected_keys=[]) "

Is this fine?

In addition, when I tried to load the key word coverage model, the keys do not match either I.e. When running modelc = KeywordCoverage(args.device, keyword_model_file=coverage_keyword_model_file, model_file=coverage_model_file)} It shows IncompatibleKeys(missing_keys=['bert.embeddings.position_ids', 'cls.predictions.decoder.bias'], unexpected_keys=[])

Wondering how I could deal with this situation

Followed the instruction from here and changed the beam_size to more than 1. IndexError occur:

~/summary_loop/model_generator.py in decode(self, bodies, max_output_length, max_batch_size, beam_size, return_scores, sample, progress)
    232             with torch.no_grad():
    233                 if beam_size > 1:
--> 234                     batch_outputs = self.decode_beam_batch(batch_bodies, beam_size=beam_size, max_output_length=max_output_length, sample=sample)
    235                 else:
    236                     batch_outputs = self.decode_batch(batch_bodies, max_output_length=max_output_length, sample=sample, return_scores=return_scores)

~/summary_loop/model_generator.py in decode_beam_batch(self, bodies, beam_size, max_output_length, sample)
    200             if build_up is not None:
    201                 build_up = build_up[tracks, :]
--> 202             past = [p[:, tracks, :] for p in past]
    203 
    204             # Update the latest scores, and the current_build

~/summary_loop/model_generator.py in <listcomp>(.0)
    200             if build_up is not None:
    201                 build_up = build_up[tracks, :]
--> 202             past = [p[:, tracks, :] for p in past]
    203 
    204             # Update the latest scores, and the current_build

IndexError: tensors used as indices must be long, byte or bool tensors

I followed the instruction training model with the provided example wikinews.db: python train_summary_loop.py --experiment wikinews_test --dataset_file data/wikinews.db

It did start training but later stop due to Runtimeerror:

Traceback (most recent call last):
  File "train_summary_loop.py", line 138, in <module>
    sampled_summaries, sampled_logprobs, sampled_tokens, input_past, sampled_end_idxs = summarizer.decode_batch(bodies, max_output_length=args.max_output_length, return_logprobs=True, sample=True)
  File "/home/robin/TrySomethingNew/summary_loop/model_generator.py", line 100, in decode_batch
    _, input_past = self.model(input_ids=inputs, past_key_values=None)
  File "/home/robin/virtual-env/summary-loop/lib/python3.6/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/robin/virtual-env/summary-loop/lib/python3.6/site-packages/transformers/modeling_gpt2.py", line 731, in forward
    return_dict=return_dict,
  File "/home/robin/virtual-env/summary-loop/lib/python3.6/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/robin/virtual-env/summary-loop/lib/python3.6/site-packages/transformers/modeling_gpt2.py", line 533, in forward
    input_ids = input_ids.view(-1, input_shape[-1])
RuntimeError: cannot reshape tensor of 0 elements into shape [-1, 0] because the unspecified
dimension size -1 can be any value and is ambiguous

You mentioned "Releasing the 11,490 summaries generated by the Summary Loop model (summary_loop_length46.bin) on the CNN/DM test set." and provided the json file "cnndm_test_summary_loop.json". Is there any code to get the json file(summaries) from the given model(.bin). If you have such code, then please share.

Is resuming training simply starting from the checkpoint instead of the gpt3 copier bin?

For example:

#summarizer_model_start = os.path.join(models_folder, "gpt2_copier23.bin")
summarizer_model_start = os.path.join(models_folder, "summarizer_wikinews_test_0_ckpt.bin")

(dlenv) D:\summary loop\summary_loop-0.1>python summary_loop_length10.bin --experiment wikinews_test --dataset_file data/wikinews.db File "summary_loop_length10.bin", line 1 SyntaxError: Non-UTF-8 code starting with '\x80' in file summary_loop_length10.bin on line 1, but no encoding declared; see http://python.org/dev/peps/pep-0263/ for details

Anyone else get this issue? Currently debugging

Hi,

I'm trying to train the models from scratch since I'd like to use them on a different language. It seems that one needs a dataset in hdf5 format instead of SQL to do that. Can you please release a sample of data in hdf5 format?

Thanks

I'm trying to run the example:

python train_summary_loop.py --experiment wikinews_test --dataset_file ../data/wikinews.db --root_folder ../ --device cuda

but it seems I'm missing the ../models/fluency_news_bs32.bin

it doesn't seem to be in the list of downloadable models. Mistake??

python train_summary_loop.py --experiment wikinews_test --dataset_file ../data/wikinews.db

Traceback (most recent call last):
  File "train_summary_loop.py", line 56, in <module>
    bert_tokenizer = utils_tokenizer.BERTCacheTokenizer()
  File "/home/tait-dev-0/summary_loop/summary_loop/utils_tokenizer.py", line 88, in __init__
    self.tokenizer.max_len = 10000
AttributeError: can't set attribute

transformers 3.0.2 py_0 conda-forge

I created a separate conda environment. Is this a transformer version issue?

Updated occurrences of torch.load to include map_location parameter. When attempting to train with --device set to cpu, torch.load may attempt to load a file with GPU tensors, which will lead to loading to GPU by default (see: https://pytorch.org/docs/stable/generated/torch.load.html). If --device is set to cpu, this will error on a cpu-only machine. Otherwise, it will go against desired functionality. This pull request resolves this issue.