| import os | |
| import pickle | |
| from contextlib import nullcontext | |
| import torch | |
| import tiktoken | |
| from model import GPTConfig, GPT | |
| init_from = 'resume' | |
| out_dir = 'out' | |
| start = "\n" | |
| num_samples = 10 | |
| max_new_tokens = 500 | |
| temperature = 0.8 | |
| top_k = 200 | |
| seed = 1337 | |
| device = 'cuda' | |
| dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16' | |
| compile = False | |
| exec(open('configurator.py').read()) | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed(seed) | |
| torch.backends.cuda.matmul.allow_tf32 = True | |
| torch.backends.cudnn.allow_tf32 = True | |
| device_type = 'cuda' if 'cuda' in device else 'cpu' | |
| ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype] | |
| ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=device_type, dtype=ptdtype) | |
| if init_from == 'resume': | |
| ckpt_path = os.path.join(out_dir, 'ckpt.pt') | |
| checkpoint = torch.load(ckpt_path, map_location=device) | |
| gptconf = GPTConfig(**checkpoint['model_args']) | |
| model = GPT(gptconf) | |
| state_dict = checkpoint['model'] | |
| unwanted_prefix = '_orig_mod.' | |
| for k,v in list(state_dict.items()): | |
| if k.startswith(unwanted_prefix): | |
| state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k) | |
| model.load_state_dict(state_dict) | |
| elif init_from.startswith('gpt2'): | |
| model = GPT.from_pretrained(init_from, dict(dropout=0.0)) | |
| model.eval() | |
| model.to(device) | |
| if compile: | |
| model = torch.compile(model) | |
| load_meta = False | |
| if init_from == 'resume' and 'config' in checkpoint and 'dataset' in checkpoint['config']: | |
| meta_path = os.path.join('data', checkpoint['config']['dataset'], 'meta.pkl') | |
| load_meta = os.path.exists(meta_path) | |
| if load_meta: | |
| print(f"Loading meta from {meta_path}...") | |
| with open(meta_path, 'rb') as f: | |
| meta = pickle.load(f) | |
| stoi, itos = meta['stoi'], meta['itos'] | |
| encode = lambda s: [stoi[c] for c in s] | |
| decode = lambda l: ''.join([itos[i] for i in l]) | |
| else: | |
| print("No meta.pkl found, assuming GPT-2 encodings...") | |
| enc = tiktoken.get_encoding("gpt2") | |
| encode = lambda s: enc.encode(s, allowed_special={"<|endoftext|>"}) | |
| decode = lambda l: enc.decode(l) | |
| if start.startswith('FILE:'): | |
| with open(start[5:], 'r', encoding='utf-8') as f: | |
| start = f.read() | |
| start_ids = encode(start) | |
| x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...]) | |
| with torch.no_grad(): | |
| with ctx: | |
| for k in range(num_samples): | |
| y = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k) | |
| print(decode(y[0].tolist())) | |
| print('---------------') | |