cse151b-final-project/test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\art\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import Dataset\n",
    "from transformers import BertGenerationEncoder, BertGenerationDecoder, EncoderDecoderModel, BertTokenizer\n",
    "from tqdm import tqdm as progress_bar\n",
    "import torch\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using a model of type bert to instantiate a model of type bert-generation. This is not supported for all configurations of models and can yield errors.\n",
      "You are using a model of type bert to instantiate a model of type bert-generation. This is not supported for all configurations of models and can yield errors.\n",
      "Some weights of BertGenerationDecoder were not initialized from the model checkpoint at google-bert/bert-large-uncased and are newly initialized: ['bert.encoder.layer.0.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.0.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.0.crossattention.output.dense.bias', 'bert.encoder.layer.0.crossattention.output.dense.weight', 'bert.encoder.layer.0.crossattention.self.key.bias', 'bert.encoder.layer.0.crossattention.self.key.weight', 'bert.encoder.layer.0.crossattention.self.query.bias', 'bert.encoder.layer.0.crossattention.self.query.weight', 'bert.encoder.layer.0.crossattention.self.value.bias', 'bert.encoder.layer.0.crossattention.self.value.weight', 'bert.encoder.layer.1.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.1.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.1.crossattention.output.dense.bias', 'bert.encoder.layer.1.crossattention.output.dense.weight', 'bert.encoder.layer.1.crossattention.self.key.bias', 'bert.encoder.layer.1.crossattention.self.key.weight', 'bert.encoder.layer.1.crossattention.self.query.bias', 'bert.encoder.layer.1.crossattention.self.query.weight', 'bert.encoder.layer.1.crossattention.self.value.bias', 'bert.encoder.layer.1.crossattention.self.value.weight', 'bert.encoder.layer.10.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.10.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.10.crossattention.output.dense.bias', 'bert.encoder.layer.10.crossattention.output.dense.weight', 'bert.encoder.layer.10.crossattention.self.key.bias', 'bert.encoder.layer.10.crossattention.self.key.weight', 'bert.encoder.layer.10.crossattention.self.query.bias', 'bert.encoder.layer.10.crossattention.self.query.weight', 'bert.encoder.layer.10.crossattention.self.value.bias', 'bert.encoder.layer.10.crossattention.self.value.weight', 'bert.encoder.layer.11.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.11.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.11.crossattention.output.dense.bias', 'bert.encoder.layer.11.crossattention.output.dense.weight', 'bert.encoder.layer.11.crossattention.self.key.bias', 'bert.encoder.layer.11.crossattention.self.key.weight', 'bert.encoder.layer.11.crossattention.self.query.bias', 'bert.encoder.layer.11.crossattention.self.query.weight', 'bert.encoder.layer.11.crossattention.self.value.bias', 'bert.encoder.layer.11.crossattention.self.value.weight', 'bert.encoder.layer.12.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.12.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.12.crossattention.output.dense.bias', 'bert.encoder.layer.12.crossattention.output.dense.weight', 'bert.encoder.layer.12.crossattention.self.key.bias', 'bert.encoder.layer.12.crossattention.self.key.weight', 'bert.encoder.layer.12.crossattention.self.query.bias', 'bert.encoder.layer.12.crossattention.self.query.weight', 'bert.encoder.layer.12.crossattention.self.value.bias', 'bert.encoder.layer.12.crossattention.self.value.weight', 'bert.encoder.layer.13.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.13.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.13.crossattention.output.dense.bias', 'bert.encoder.layer.13.crossattention.output.dense.weight', 'bert.encoder.layer.13.crossattention.self.key.bias', 'bert.encoder.layer.13.crossattention.self.key.weight', 'bert.encoder.layer.13.crossattention.self.query.bias', 'bert.encoder.layer.13.crossattention.self.query.weight', 'bert.encoder.layer.13.crossattention.self.value.bias', 'bert.encoder.layer.13.crossattention.self.value.weight', 'bert.encoder.layer.14.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.14.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.14.crossattention.output.dense.bias', 'bert.encoder.layer.14.crossattention.output.dense.weight', 'bert.encoder.layer.14.crossattention.self.key.bias', 'bert.encoder.layer.14.crossattention.self.key.weight', 'bert.encoder.layer.14.crossattention.self.query.bias', 'bert.encoder.layer.14.crossattention.self.query.weight', '
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "encoder = BertGenerationEncoder.from_pretrained(\"google-bert/bert-large-uncased\", bos_token_id=101, eos_token_id=102)\n",
    "# add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token\n",
    "decoder = BertGenerationDecoder.from_pretrained(\"google-bert/bert-large-uncased\", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)\n",
    "model = EncoderDecoderModel(encoder=encoder, decoder=decoder)\n",
    "\n",
    "# create tokenizer...\n",
    "tokenizer = BertTokenizer.from_pretrained(\"google-bert/bert-large-uncased\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "class CodeDataset(Dataset):\n",
    "    def __init__(self):\n",
    "        with open(\"data/conala-train.json\") as f:\n",
    "            self.data = json.load(f)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.data[idx][\"rewritten_intent\"], self.data[idx][\"snippet\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.AdamW(params=model.parameters(), lr=1e-3)\n",
    "dataloader = CodeDataset()\n",
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/2379 [00:00<?, ?it/s]C:\\Users\\art\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\transformers\\models\\encoder_decoder\\modeling_encoder_decoder.py:636: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
      "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
      "  0%|          | 3/2379 [01:18<16:16:50, 24.67s/it]"
     ]
    }
   ],
   "source": [
    "losses = []\n",
    "epochs = 10\n",
    "for i in range(epochs):\n",
    "\n",
    "    epoch_loss = 0\n",
    "\n",
    "    for idx, (question, answer) in progress_bar(enumerate(dataloader), total=len(dataloader)):\n",
    "\n",
    "        print(questions)\n",
    "        input_ids = tokenizer(question, add_special_tokens=False, return_tensors=\"pt\").input_ids.to(device)\n",
    "        label_ids = tokenizer(answer, return_tensors=\"pt\").input_ids.to(device)\n",
    "\n",
    "        loss = model(input_ids=input_ids, decoder_input_ids=label_ids, labels=label_ids).loss\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        epoch_loss += loss.item()\n",
    "\n",
    "    losses.append(epoch_loss)\n",
    "\n",
    "plt.plot(losses, color=\"green\", label=\"Training Loss\")\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}