sample.py

import os
import pickle
from contextlib import nullcontext
import torch
from model import GPTConfig, GPT

import re
out_dir = 'out'
dataset = ''
start = "\n"
num_samples = 10
max_new_tokens = 500
temperature = 0.8
top_k = 200
seed = 1337
device = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
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)
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)

ckpt_path = os.path.join(out_dir, 'agm_3.pt')
checkpoint = torch.load(ckpt_path, map_location=device)
model = GPT(GPTConfig(**checkpoint['model_args']))
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)
model.eval()
model.to(device)
if compile: model = torch.compile(model)

meta_path = os.path.join('data', checkpoint['config']['dataset'], 'meta.pkl')
with open(meta_path, 'rb') as f:
    meta = pickle.load(f)
stoi, itos = meta['stoi'], meta['itos']
is_word_level = ' ' in itos.get(0, '') or ' ' in itos.get(1, '') or len(stoi) > 500
def encode(s):
    if is_word_level:
        return [stoi[w] for w in re.findall(r"[\w']+|[^\w\s]", s)]
    return [stoi[c] for c in s]

def decode(l):
    if is_word_level:
        return ' '.join([itos[i] for i in l])
    return ''.join([itos[i] for i in l])

if start.startswith('FILE:'):
    with open(start[5:], 'r', encoding='utf-8') as f:
        start = f.read()
x = torch.tensor(encode(start), 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('---------------')