remove unnecessary example

examples/run_gpt2_generate_unconditional_samples.py

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