GPT2 LM#

This tutorial is available as an IPython notebook at Malaya/example/gpt2-lm.

[1]:

import os

os.environ['CUDA_VISIBLE_DEVICES'] = ''

[2]:

import malaya

/home/husein/.local/lib/python3.8/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
  warn("The installed version of bitsandbytes was compiled without GPU support. "

/home/husein/.local/lib/python3.8/site-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32

/home/husein/dev/malaya/malaya/tokenizer.py:214: FutureWarning: Possible nested set at position 3397
  self.tok = re.compile(r'({})'.format('|'.join(pipeline)))
/home/husein/dev/malaya/malaya/tokenizer.py:214: FutureWarning: Possible nested set at position 3927
  self.tok = re.compile(r'({})'.format('|'.join(pipeline)))

List available GPT2 models#

[4]:

malaya.language_model.available_gpt2

[4]:

{'mesolitica/gpt2-117m-bahasa-cased': {'Size (MB)': 454}}

Load GPT2 LM model#

def gpt2(
    model: str = 'mesolitica/gpt2-117m-bahasa-cased',
    force_check: bool = True,
    **kwargs,
):
    """
    Load GPT2 language model.

    Parameters
    ----------
    model: str, optional (default='mesolitica/gpt2-117m-bahasa-cased')
        Check available models at `malaya.language_model.available_gpt2`.
    force_check: bool, optional (default=True)
        Force check model one of malaya model.
        Set to False if you have your own huggingface model.

    Returns
    -------
    result: malaya.torch_model.gpt2_lm.LM class
    """

[5]:

model = malaya.language_model.gpt2()

Loading the tokenizer from the `special_tokens_map.json` and the `added_tokens.json` will be removed in `transformers 5`,  it is kept for forward compatibility, but it is recommended to update your `tokenizer_config.json` by uploading it again. You will see the new `added_tokens_decoder` attribute that will store the relevant information.
You are resizing the embedding layer without providing a `pad_to_multiple_of` parameter. This means that the new embedding dimension will be 32001. This might induce some performance reduction as *Tensor Cores* will not be available. For more details about this, or help on choosing the correct value for resizing, refer to this guide: https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc

[6]:

model.score('saya suke awak')

[6]:

-51.3840389251709

[7]:

model.score('saya suka awak')

[7]:

-46.20505905151367

[8]:

model.score('najib razak')

[8]:

-48.355825901031494

[9]:

model.score('najib comel')

[9]:

-52.79338455200195