HuggingFace Transformers For Text Generation with CTRL with Google Colab's free GPU
I wanted to test TextGeneration with CTRL using PyTorch-Transformers, before using it for fine-tuning. But it doesn't prompt anything like it does with GPT-2 and other similar language generation models. I'm very new for this and am stuck and can't figure out what's going on. 

This is the procedure I followed in my Colab notebook,

!pip install transformers


!git clone https://github.com/huggingface/pytorch-transformers.git


!python pytorch-transformers/examples/run_generation.py \
    --model_type=ctrl \
    --length=100 \
    --model_name_or_path=ctrl \
    --temperature=0.2 \
    --repetition_penalty=1.2 \


And this is what I get after running the script

02/10/2020 01:02:31 - INFO - transformers.tokenization_utils -   loading file https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json from cache at /root/.cache/torch/transformers/a858ad854d3847b02da3aac63555142de6a05f2a26d928bb49e881970514e186.285c96a541cf6719677cfb634929022b56b76a0c9a540186ba3d8bbdf02bca42
02/10/2020 01:02:31 - INFO - transformers.tokenization_utils -   loading file https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt from cache at /root/.cache/torch/transformers/aa2c569e6648690484ade28535a8157aa415f15202e84a62e82cc36ea0c20fa9.26153bf569b71aaf15ae54be4c1b9254dbeff58ca6fc3e29468c4eed078ac142
02/10/2020 01:02:31 - INFO - transformers.configuration_utils -   loading configuration file https://storage.googleapis.com/sf-ctrl/pytorch/ctrl-config.json from cache at /root/.cache/torch/transformers/d6492ca334c2a4e079f43df30956acf935134081b2b3844dc97457be69b623d0.1ebc47eb44e70492e0c20494a084f108332d20fea7fe5ad408ef5e7a8f2baef4
02/10/2020 01:02:31 - INFO - transformers.configuration_utils -   Model config CTRLConfig {
  "architectures": null,
  "attn_pdrop": 0.1,
  "bos_token_id": 0,
  "dff": 8192,
  "do_sample": false,
  "embd_pdrop": 0.1,
  "eos_token_ids": 0,
  "finetuning_task": null,
  "from_tf": false,
  "id2label": {
    "0": "LABEL_0"
  },
  "initializer_range": 0.02,
  "is_decoder": false,
  "label2id": {
    "LABEL_0": 0
  },
  "layer_norm_epsilon": 1e-06,
  "length_penalty": 1.0,
  "max_length": 20,
  "model_type": "ctrl",
  "n_ctx": 512,
  "n_embd": 1280,
  "n_head": 16,
  "n_layer": 48,
  "n_positions": 50000,
  "num_beams": 1,
  "num_labels": 1,
  "num_return_sequences": 1,
  "output_attentions": false,
  "output_hidden_states": false,
  "output_past": true,
  "pad_token_id": 0,
  "pruned_heads": {},
  "repetition_penalty": 1.0,
  "resid_pdrop": 0.1,
  "summary_activation": null,
  "summary_first_dropout": 0.1,
  "summary_proj_to_labels": true,
  "summary_type": "cls_index",
  "summary_use_proj": true,
  "temperature": 1.0,
  "top_k": 50,
  "top_p": 1.0,
  "torchscript": false,
  "use_bfloat16": false,
  "vocab_size": 246534
}

02/10/2020 01:02:31 - INFO - transformers.modeling_utils -   loading weights file https://storage.googleapis.com/sf-ctrl/pytorch/seqlen256_v1.bin from cache at /root/.cache/torch/transformers/c146cc96724f27295a0c3ada1fbb3632074adf87e9aef8269e44c9208787f8c8.b986347cbab65fa276683efbb9c2f7ee22552277bcf6e1f1166557ed0852fdf0
tcmalloc: large alloc 1262256128 bytes == 0x38b92000 @  0x7fe1900bdb6b 0x7fe1900dd379 0x7fe139843b4a 0x7fe1398455fa 0x7fe13bb7578a 0x7fe13bdbe30b 0x7fe13be05b37 0x7fe184c8cad5 0x7fe184c8d17b 0x7fe184c91160 0x7fe184ade496 0x551b15 0x5aa6ec 0x50abb3 0x50c5b9 0x508245 0x5096b7 0x595311 0x54a6ff 0x551b81 0x5aa6ec 0x50abb3 0x50c5b9 0x508245 0x509642 0x595311 0x54a6ff 0x551b81 0x5aa6ec 0x50abb3 0x50c5b9
tcmalloc: large alloc 1262256128 bytes == 0x19fdda000 @  0x7fe1900bdb6b 0x7fe1900dd379 0x7fe139843b4a 0x7fe1398455fa 0x7fe13bb7578a 0x7fe13bdbe30b 0x7fe13be05b37 0x7fe184c8cad5 0x7fe184c8d17b 0x7fe184c91160 0x7fe184ade496 0x551b15 0x5aa6ec 0x50abb3 0x50c5b9 0x508245 0x509642 0x595311 0x54a6ff 0x551b81 0x5aa6ec 0x50abb3 0x50d390 0x508245 0x509642 0x595311 0x54a6ff 0x551b81 0x5a067e 0x50d966 0x508245
^C


and then terminates. Could this be because of a GPU problem?

Any sort of help is appreciated.

The solution was to increase the RAM. Since I was using the Google Colab's free GPU, I was going through this: GitHub issue
and found this useful: Solution

The following piece of code will crash the session in Colab and select 'Get more RAM', which will increase the RAM up to 25.51GB

d=[]
while(1):
  d.append('1')

