def main(model: GPT2LMHeadModel, enc: GPT2Tokenizer, phrase: str = ''):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
nsamples = 1
length = 40
temperature = 1.2
top_k = 0
top_p = 0.9
batch_size = 1
stop_token = [enc.encoder[x] for x in ('<|endoftext|>', '.', '?', '!')]
assert nsamples % batch_size == 0
if length == -1:
length = model.config.n_ctx // 2
elif length > model.config.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
model.config.n_ctx)
context_tokens = enc.encode(phrase) if phrase else [
enc.encoder['<|endoftext|>']
]
generated = 0
out = sample_sequence(model=model,
length=length,
context=context_tokens,
start_token=None,
batch_size=batch_size,
temperature=temperature,
top_k=top_k,
device=device,
top_p=top_p,
stop_token=stop_token)
out = out[:, len(context_tokens):].tolist()
return enc.decode(out[0])
def encode_many_texts(tokenizer: GPT2Tokenizer, texts: Iterable[str]) \
-> torch.Tensor:
"""Uses -1 as padding."""
encoded_texts = [tokenizer.encode(text) for text in texts]
max_len = max(len(text) for text in encoded_texts)
padded_encoded_texts = [
text + [-1] * (max_len - len(text)) for text in encoded_texts
]
return torch.tensor(padded_encoded_texts)
