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| """ | |
| Sample from the trained model with PyTorch | |
| """ | |
| import os | |
| import pickle | |
| from contextlib import nullcontext | |
| import torch | |
| from model import ModelArgs, Transformer | |
| from tokenizer import Tokenizer | |
| from tinystories import get_tokenizer_model_path | |
| # ----------------------------------------------------------------------------- | |
| checkpoint = 'out/ckpt.pt' | |
| start = "" # or "<|endoftext|>" or etc. Can also specify a file, use as: "FILE:prompt.txt" | |
| num_samples = 1 # number of samples to draw | |
| max_new_tokens = 100 # number of tokens generated in each sample | |
| temperature = 1.0 # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions | |
| top_k = 300 # retain only the top_k most likely tokens, clamp others to have 0 probability | |
| tokenizer = "" # override the tokenizer model path | |
| seed = 1337 | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1', etc. | |
| #dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16' # 'float32' or 'bfloat16' or 'float16' | |
| dtype = "float32" | |
| compile = False # use PyTorch 2.0 to compile the model to be faster | |
| exec(open('configurator.py').read()) # overrides from command line or config file | |
| # ----------------------------------------------------------------------------- | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed(seed) | |
| torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul | |
| torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn | |
| device_type = 'cuda' if 'cuda' in device else 'cpu' # for later use in torch.autocast | |
| 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) | |
| # init from a model saved in a specific directory | |
| checkpoint_dict = torch.load(checkpoint, map_location=device) | |
| gptconf = ModelArgs(**checkpoint_dict['model_args']) | |
| model = Transformer(gptconf) | |
| state_dict = checkpoint_dict['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, strict=False) | |
| model.eval() | |
| model.to(device) | |
| if compile: | |
| print("Compiling the model...") | |
| model = torch.compile(model) # requires PyTorch 2.0 (optional) | |
| # load the tokenizer | |
| vocab_source = checkpoint_dict["config"].get("vocab_source", "llama2") | |
| vocab_size = gptconf.vocab_size | |
| if tokenizer: | |
| # a specific tokenizer is provided, use it | |
| tokenizer_model = tokenizer | |
| else: | |
| # let's try to find the tokenizer model automatically. bit gross here... | |
| query_vocab_size = 0 if vocab_source == "llama2" else vocab_size | |
| tokenizer_model = get_tokenizer_model_path(vocab_size=query_vocab_size) | |
| enc = Tokenizer(tokenizer_model=tokenizer_model) | |
| # encode the beginning of the prompt | |
| if start.startswith('FILE:'): | |
| with open(start[5:], 'r', encoding='utf-8') as f: | |
| start = f.read() | |
| start_ids = enc.encode(start, bos=True, eos=False) | |
| x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...]) | |
| # run generation | |
| 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(enc.decode(y[0].tolist())) | |
| print('---------------') | |