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Training script for LongGPT; Fine-tunes GPT-2 (335M) on The Pile Dataset with a context size of 8k tokens. (requires > 16GB RAM)
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| import time | |
| import random | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch.utils.data import DataLoader, IterableDataset | |
| import wandb | |
| from tqdm import tqdm | |
| from datasets import load_dataset | |
| from transformers import GPT2TokenizerFast | |
| # copy from here: https://github.com/karpathy/nanoGPT/blob/master/model.py | |
| from ngpt import GPT | |
| WANDB_STYLE = """ | |
| <style> | |
| html, body { | |
| padding: 0; | |
| margin: 0; | |
| width: 100%; | |
| height: 100%; | |
| } | |
| p { | |
| font-family: 'Verdana', sans-serif; | |
| } | |
| </style> | |
| """ | |
| def closest_power_of_2(x): | |
| return 2 ** (x - 1).bit_length() | |
| class DatasetWrapper(IterableDataset): | |
| def __init__(self, max_tokens=2**12): | |
| self.ds = load_dataset( | |
| "the_pile", | |
| name="all", | |
| split="train", | |
| streaming=True, | |
| ).shuffle(buffer_size=100_000) | |
| self.tokenizer = GPT2TokenizerFast.from_pretrained("gpt2") | |
| self.max_tokens = max_tokens | |
| def __iter__(self): | |
| buffer = [] | |
| for sample in self.ds: | |
| buffer += self.tokenizer(sample["text"])["input_ids"] | |
| buffer += [self.tokenizer.eos_token_id] | |
| while len(buffer) > self.max_tokens: | |
| yield torch.tensor(buffer[: self.max_tokens]) | |
| buffer = buffer[self.max_tokens :] | |
| class Trainer: | |
| def __init__(self): | |
| self.tokenizer: GPT2TokenizerFast = GPT2TokenizerFast.from_pretrained("gpt2") | |
| self.max_tokens = 2**12 | |
| self.grad = 1 | |
| self.step = 0 | |
| self.dataset = DatasetWrapper(self.max_tokens) | |
| self.loader = DataLoader( | |
| self.dataset, | |
| batch_size=1, | |
| num_workers=8, | |
| ) | |
| self.model = model = GPT.from_pretrained("gpt2").cuda() | |
| # model.load_state_dict(torch.load("v2.pt")) | |
| self.opt = model.configure_optimizers( | |
| weight_decay=1e-1, | |
| learning_rate=1e-6, | |
| betas=(0.9, 0.95), | |
| device_type="cuda", | |
| ) | |
| # patch model embeddings | |
| emb = model.transformer.wpe.weight.data | |
| wpe = nn.Embedding(self.max_tokens, emb.shape[1]) | |
| wpe.weight.data = emb.repeat(self.max_tokens // emb.shape[0], 1) | |
| model.transformer.wpe = wpe | |
| model.config.block_size = self.max_tokens | |
| print("Patched model embeddings:", wpe.weight.shape) | |
| self.model = torch.compile(model) | |
| def train_step(self, batch): | |
| batch = batch.cuda() | |
| x, y = batch[:, :-1], batch[:, 1:].contiguous() | |
| _, loss = self.model(x, targets=y) | |
| (loss / self.grad).backward() | |
| return loss | |
| def generate_samples(self, n_samples=8): | |
| x = torch.tensor([[self.tokenizer.eos_token_id]] * n_samples).cuda() | |
| t0 = time.time() | |
| self.model.eval() | |
| y = self.model.generate(x, max_new_tokens=1100).tolist() | |
| self.model.train() | |
| t1 = time.time() | |
| t = [self.tokenizer.decode(z) for z in y] | |
| t = "<hr>".join(f"<p>{c}</p>" for c in t) | |
| html = WANDB_STYLE + t | |
| wandb.log({"samples": wandb.Html(html)}, step=self.step) | |
| print(f"Generated in {t1-t0:.3f}s") | |
| def train(self): | |
| wandb.init( | |
| project="long-gpt", | |
| entity="...", | |
| ) | |
| prog = tqdm(self.loader) | |
| self.opt.zero_grad() | |
| for i, batch in enumerate(prog): | |
| self.step = i + 1 | |
| loss = self.train_step(batch) | |
| prog.set_description(f"loss: {loss.item():.3f}") | |
| wandb.log({"loss": loss.item(), "grad": self.grad}, step=i) | |
| if i % self.grad == 0: | |
| self.opt.step() | |
| self.opt.zero_grad() | |
| self.grad = closest_power_of_2(i + 1) | |
| if i % 100 == 0: | |
| torch.save(self.model.state_dict(), "model.pt") | |
| if i % 1000 == 0: | |
| self.generate_samples(8) | |
| if __name__ == "__main__": | |
| trainer = Trainer() | |
| trainer.train() |
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