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fine-tune.py
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fine-tune.py
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from datasets import load_dataset
dataset = load_dataset("code_x_glue_ct_code_to_text", "ruby")
print(dataset)
example = dataset['train'][0]
print("Code:", example["code"])
print("Docstring:", example["docstring"])
from transformers import RobertaTokenizer
tokenizer = RobertaTokenizer.from_pretrained("Salesforce/codet5-small")
prefix = "Summarize Ruby: "
max_input_length = 256
max_target_length = 128
def preprocess_examples(examples):
# encode the code-docstring pairs
codes = examples['code']
docstrings = examples['docstring']
inputs = [prefix + code for code in codes]
model_inputs = tokenizer(inputs, max_length=max_input_length, padding="max_length", truncation=True)
# encode the summaries
labels = tokenizer(docstrings, max_length=max_target_length, padding="max_length", truncation=True).input_ids
# important: we need to replace the index of the padding tokens by -100
# such that they are not taken into account by the CrossEntropyLoss
labels_with_ignore_index = []
for labels_example in labels:
labels_example = [label if label != 0 else -100 for label in labels_example]
labels_with_ignore_index.append(labels_example)
model_inputs["labels"] = labels_with_ignore_index
return model_inputs
dataset = dataset.map(preprocess_examples, batched=True)
#print(dataset)
from torch.utils.data import DataLoader
dataset.set_format(type="torch", columns=['input_ids', 'attention_mask', 'labels'])
train_dataloader = DataLoader(dataset['train'], shuffle=True, batch_size=8)
valid_dataloader = DataLoader(dataset['validation'], batch_size=4)
test_dataloader = DataLoader(dataset['test'], batch_size=4)
batch = next(iter(train_dataloader))
print(batch.keys())
"""
#all
out = [tokenizer.decode(outtoken) for outtoken in batch['input_ids']]
print(out)
print('-------------------------')
#labels = batch['labels']
out = [tokenizer.decode([label for label in labels if label != -100]) for labels in batch['labels']]
print(out)
"""
out = tokenizer.decode(batch['input_ids'][0])
#print(out)
#print('-------------------------')
labels = batch['labels'][0]
out = tokenizer.decode([label for label in labels if label != -100])
#print(out)
from transformers import T5ForConditionalGeneration, AdamW, get_linear_schedule_with_warmup
import pytorch_lightning as pl
class CodeT5(pl.LightningModule):
def __init__(self, lr=5e-5, num_train_epochs=15, warmup_steps=1000):
super().__init__()
self.model = T5ForConditionalGeneration.from_pretrained("Salesforce/codet5-small")
self.save_hyperparameters()
def forward(self, input_ids, attention_mask, labels=None):
outputs = self.model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
return outputs
def common_step(self, batch, batch_idx):
outputs = self(**batch)
loss = outputs.loss
return loss
def training_step(self, batch, batch_idx):
loss = self.common_step(batch, batch_idx)
# logs metrics for each training_step,
# and the average across the epoch
self.log("training_loss", loss)
return loss
def validation_step(self, batch, batch_idx):
loss = self.common_step(batch, batch_idx)
self.log("validation_loss", loss, on_epoch=True)
return loss
def test_step(self, batch, batch_idx):
loss = self.common_step(batch, batch_idx)
return loss
def configure_optimizers(self):
# create optimizer
optimizer = AdamW(self.parameters(), lr=self.hparams.lr)
# create learning rate scheduler
num_train_optimization_steps = self.hparams.num_train_epochs * len(train_dataloader)
lr_scheduler = {'scheduler': get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=self.hparams.warmup_steps,
num_training_steps=num_train_optimization_steps),
'name': 'learning_rate',
'interval':'step',
'frequency': 1}
return {"optimizer": optimizer, "lr_scheduler": lr_scheduler}
def train_dataloader(self):
return train_dataloader
def val_dataloader(self):
return valid_dataloader
def test_dataloader(self):
return test_dataloader
import wandb
wandb.login()
model = CodeT5()
from pytorch_lightning import Trainer
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
wandb_logger = WandbLogger(name='codet5-finetune-code-summarization-ruby-shuffle', project='CodeT5')
# for early stopping, see https://pytorch-lightning.readthedocs.io/en/1.0.0/early_stopping.html?highlight=early%20stopping
early_stop_callback = EarlyStopping(
monitor='validation_loss',
patience=3,
strict=False,
verbose=False,
mode='min'
)
lr_monitor = LearningRateMonitor(logging_interval='step')
trainer = Trainer(accelerator='gpu',
devices = 1,
default_root_dir="/content/drive/MyDrive/CodeT5/Notebooks/Checkpoints",
logger=wandb_logger,
callbacks=[early_stop_callback, lr_monitor])
trainer.fit(model)
save_directory = "." # save in the current working directory, you can change this of course
model.model.save_pretrained(save_directory)
repo_url = "https://huggingface.co/mltrev23/codet5-small-code-summarization-ruby"
from huggingface_hub import Repository
repo = Repository(local_dir="checkpoint", # note that this directory must not exist already
clone_from=repo_url,
git_user="mltrev23",
git_email="[email protected]",
use_auth_token=True,
)
model.model.save_pretrained("/content/checkpoint")
tokenizer.save_pretrained("/content/checkpoint")
repo.push_to_hub(commit_message="First commit")