Created
December 4, 2023 19:58
-
-
Save tchaton/7c21adc4e0441e8639dcbbadcf027065 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import lightning as L | |
import torch | |
import torch.nn.functional as F | |
from lightning.pytorch.demos import Transformer, WikiText2 | |
from torch.utils.data import DataLoader, random_split | |
import os | |
from time import sleep | |
class LanguageModel(L.LightningModule): | |
def __init__(self): | |
super().__init__() | |
self.model = Transformer(vocab_size=33278) | |
def prepare_data(self): | |
dataset = WikiText2() | |
def setup(self, *args, **kwargs): | |
dataset = WikiText2() | |
n = len(dataset) | |
self.train_dataset, self.val_dataset, self.test_dataset = random_split(dataset, [n - 4000, 2000, 2000]) | |
def training_step(self, batch, batch_idx): | |
input, target = batch | |
output = self.model(input, target) | |
loss = F.nll_loss(output, target.view(-1)) | |
self.log("train_loss", loss, prog_bar=True) | |
return loss | |
def validation_step(self, batch, batch_idx): | |
input, target = batch | |
output = self.model(input, target) | |
loss = F.nll_loss(output, target.view(-1)) | |
self.log("val_loss", loss, prog_bar=True) | |
return loss | |
def test_step(self, batch, batch_idx): | |
input, target = batch | |
output = self.model(input, target) | |
loss = F.nll_loss(output, target.view(-1)) | |
self.log("test_loss", loss, prog_bar=True) | |
return loss | |
def configure_optimizers(self): | |
return torch.optim.SGD(self.parameters(), lr=0.1) | |
def train_dataloader(self): | |
return DataLoader(self.train_dataset, batch_size=20, shuffle=True) | |
def val_dataloader(self): | |
return DataLoader(self.val_dataset, batch_size=20, shuffle=True) | |
def test_dataloader(self): | |
return DataLoader(self.test_dataset, batch_size=20, shuffle=True) | |
def main(): | |
L.seed_everything(42) | |
# Model | |
model = LanguageModel() | |
# Trainer | |
trainer = L.Trainer(gradient_clip_val=0.25, max_epochs=8) | |
trainer.fit(model) | |
trainer.test(model) | |
if __name__ == "__main__": | |
main() |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment