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ltcptgeneral 2024-03-12 21:42:59 -07:00
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from torch.utils.data import Dataset
from transformers import BertGenerationEncoder, BertGenerationDecoder, EncoderDecoderModel, BertTokenizer
from tqdm import tqdm as progress_bar
import torch
import matplotlib
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
encoder = BertGenerationEncoder.from_pretrained("google-bert/bert-base-uncased", bos_token_id=101, eos_token_id=102)
# add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
decoder = BertGenerationDecoder.from_pretrained("google-bert/bert-base-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)
model = EncoderDecoderModel(encoder=encoder, decoder=decoder)
# create tokenizer...
tokenizer = BertTokenizer.from_pretrained("google-bert/bert-large-uncased")
import json
class CodeDataset(Dataset):
def __init__(self):
with open("data/conala-train.json") as f:
self.data = json.load(f)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
intent = self.data[idx]["rewritten_intent"] if self.data[idx]["rewritten_intent"] else self.data[idx]["intent"]
return intent, self.data[idx]["snippet"]
optimizer = torch.optim.AdamW(params=model.parameters(), lr=1e-3)
dataloader = CodeDataset()
model = model.to(device)
losses = []
epochs = 10
for i in range(epochs):
epoch_loss = 0
for idx, (question, answer) in progress_bar(enumerate(dataloader), total=len(dataloader)):
input_ids = tokenizer(question, add_special_tokens=False, return_tensors="pt").input_ids.to(device)
label_ids = tokenizer(answer, return_tensors="pt").input_ids.to(device)
loss = model(input_ids=input_ids, decoder_input_ids=label_ids, labels=label_ids).loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch_loss += loss.item()
losses.append(epoch_loss)
plt.plot(losses, color="green", label="Training Loss")
plt.legend(loc = 'upper left')
plt.savefig("plot.png")