mirror of
https://github.com/huggingface/transformers.git
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89 lines
3.3 KiB
Python
89 lines
3.3 KiB
Python
#!/usr/bin/env python3
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import argparse
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import logging
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import torch
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import torch.nn.functional as F
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import numpy as np
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from tqdm import trange
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from pytorch_pretrained_bert import GPT2LMHeadModel, GPT2Tokenizer
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logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
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datefmt = '%m/%d/%Y %H:%M:%S',
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level = logging.INFO)
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logger = logging.getLogger(__name__)
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def top_k_logits(logits, k):
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if k == 0:
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return logits
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values, _ = torch.topk(logits, k)
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min_values = values[:, -1]
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return torch.where(logits < min_values, torch.ones_like(logits, dtype=logits.dtype) * -1e10, logits)
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def sample_sequence(model, length, start_token=None, batch_size=None, context=None, temperature=1, top_k=0, device='cuda'):
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if start_token is None:
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assert context is not None, 'Specify exactly one of start_token and context!'
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context = torch.tensor(context, device=device, dtype=torch.long)
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else:
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assert context is None, 'Specify exactly one of start_token and context!'
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context = torch.full((batch_size, 1), start_token, device=device, dtype=torch.long)
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prev = context
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output = context
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past = None
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with torch.no_grad():
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for i in trange(length):
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logits, past = model(prev, past=past)
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logits = logits[:, -1, :] / temperature
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logits = top_k_logits(logits, k=top_k)
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log_probs = F.softmax(logits, dim=-1)
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prev = torch.multinomial(log_probs, num_samples=1)
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output = torch.cat((output, prev), dim=1)
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return output
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def sample_model():
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parser = argparse.ArgumentParser()
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parser.add_argument('--model_name_or_path', type=str, default='gpt2', help='pretrained model name or path to local checkpoint')
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parser.add_argument("--seed", type=int, default=0)
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parser.add_argument("--nsamples", type=int, default=0)
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parser.add_argument("--batch_size", type=int, default=1)
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parser.add_argument("--length", type=int, default=-1)
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parser.add_argument("--temperature", type=int, default=1)
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parser.add_argument("--top_k", type=int, default=0)
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args = parser.parse_args()
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print(args)
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np.random.seed(args.seed)
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torch.random.manual_seed(args.seed)
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torch.cuda.manual_seed(args.seed)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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enc = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
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model = GPT2LMHeadModel.from_pretrained(args.model_name_or_path)
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model.to(device)
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model.eval()
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if args.length == -1:
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args.length = model.config.n_ctx
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elif args.length > model.config.n_ctx:
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raise ValueError("Can't get samples longer than window size: %s" % model.config.n_ctx)
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generated = 0
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while args.nsamples == 0 or generated < args.nsamples:
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out = sample_sequence(
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model=model, length=args.length,
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start_token=enc.encoder['<|endoftext|>'],
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batch_size=args.batch_size,
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temperature=args.temperature, top_k=args.top_k, device=device
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)
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out = out.tolist()
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for i in range(args.batch_size):
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generated += args.batch_size
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text = enc.decode(out[i])
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print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
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print(text)
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if __name__ == '__main__':
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sample_model()
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