fine_tune_test.ipynb

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   "source": [
    "<a href=\"https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/T5/Fine_tune_CodeT5_for_generating_docstrings_from_Ruby_code.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h-7bq0hWR3ny"
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   "source": [
    "## Set-up environment\n",
    "\n",
    "Let's first install the required libraries:\n",
    "* HuggingFace Transformers (for the CodeT5 model)\n",
    "* HuggingFace Datasets (for loading the dataset + preprocessing it)\n",
    "* PyTorch Lightning (for training)\n",
    "* Weights and Biases (for logging training metrics)."
   ]
  },
  {
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    "outputId": "05a2c493-1f1c-4dc9-b8f5-cc765053e0f4"
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   "outputs": [],
   "source": [
    "!pip install -q transformers datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
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    "outputId": "04643557-c4a8-4302-8778-b2807e1afb9b"
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   "outputs": [],
   "source": [
    "!pip install -q pytorch-lightning wandb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8x-xYVZfR5ji"
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   "source": [
    "## Preprocess data\n",
    "\n",
    "Here, we load the \"code_to_text\" portion of the [CodeXGLUE](https://microsoft.github.io/CodeXGLUE/) dataset. As you may know, GLUE [(Wang et al., 2018)](https://arxiv.org/abs/1804.07461) is a famous benchmark in NLP, which led to a lot of progress (see the leaderboard [here](https://gluebenchmark.com/)). Microsoft has now created a similar benchmark called CodeXGLUE [(Lu et al., 2021)](https://arxiv.org/abs/2102.04664), but for code + natural language instead of just natural language. It consits of several subtasks (similar to GLUE).\n",
    "\n",
    "Let's only load the examples of the Ruby programming language. This is a fairly small dataset, which is ideally suited for demonstration purposes in Google Colab. The Python split has way more training examples (250,000), but training this in Google Colab isn't ideal."
   ]
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     "text": [
      "Found cached dataset code_x_glue_tc_text_to_code (/home/sberman/.cache/huggingface/datasets/code_x_glue_tc_text_to_code/default/0.0.0/059898ce5bb35e72c699c69af37020002b38b251734ddaeedef30ae7e6292717)\n"
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       "  0%|          | 0/3 [00:00<?, ?it/s]"
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     "text": [
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['id', 'nl', 'code'],\n",
      "        num_rows: 100000\n",
      "    })\n",
      "    validation: Dataset({\n",
      "        features: ['id', 'nl', 'code'],\n",
      "        num_rows: 2000\n",
      "    })\n",
      "    test: Dataset({\n",
      "        features: ['id', 'nl', 'code'],\n",
      "        num_rows: 2000\n",
      "    })\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset( \"code_x_glue_tc_text_to_code\", \"default\")\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "1UAZ7UPCU-uk"
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   "source": [
    "As you can see, the \"code-to-text/ruby\" split consists of a training, validation and test set. Let's look at one particular example:"
   ]
  },
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    {
     "name": "stdout",
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     "text": [
      "Code: void function ( Directory arg0 , Collection < SnapshotMetaData > arg1 , long arg2 ) { List < IndexCommit > loc0 = DirectoryReader . listCommits ( arg0 ) ; Map < String , Integer > loc1 = buildRefCounts ( arg1 , loc0 ) ; for ( IndexCommit loc2 : loc0 ) { if ( loc2 . getGeneration ( ) == arg2 ) { deleteIndexFiles ( arg0 , loc1 , loc2 ) ; break ; } } }\n",
      "Docstring: this method deletes index files of the @linkplain indexcommit for the specified generation number\n"
     ]
    }
   ],
   "source": [
    "example = dataset['train'][2]\n",
    "\n",
    "print(\"Code:\", example[\"code\"])\n",
    "print(\"Docstring:\", example[\"nl\"].split(\" .\")[0])"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "The goal for the model is to generate a docstring based on the provided code. \n",
    "\n",
    "Let's now prepare the examples (i.e. code-docstring pairs) for the model. As you might know, Transformer models like BERT, BART, T5 etc. don't expect text as direct input, but rather integers which are called `input_ids` in HuggingFace Transformers. These represent tokens of a certain vocabulary. The model will learn rich contextual embedding vectors for each token, allowing it to get good results.\n",
    "\n",
    "In other words, we need to turn the \"Code\" input from above into `input_ids`, and similarly, we need to turn the \"Docstring\" output from above into `input_ids`, which will serve as the `labels` for the model.\n",
    "\n",
    "In addition, as these models are trained on batches of examples rather than one example at a time, we'll need to pad/truncate both the inputs and labels, such that they are all of the same length. That's why we also will add an `attention_mask` input to the model, such that it knows not to take into account padding tokens when computing attention scores.\n",
    "\n",
    "To summarize: \n",
    "* input: code, which is turned into `input_ids` + `attention_mask`\n",
    "* output: docstrings, which are turned into `labels` (which are the `input_ids` of the docstrings).\n",
    "\n",
    "Below, we define a `preprocess_examples` function, which we can apply on the entire dataset. "
   ]
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   "source": [
    "from transformers import RobertaTokenizer\n",
    "\n",
    "tokenizer = RobertaTokenizer.from_pretrained(\"Salesforce/codet5-small\")\n",
    "\n",
    "prefix = \"Summarize Ruby: \"\n",
    "max_input_length = 256\n",
    "max_target_length = 128\n",
    "\n",
    "def preprocess_examples(examples):\n",
    "  # encode the code-docstring pairs\n",
    "  codes = examples['code']\n",
    "  docstrings = example[\"nl\"].split(\" .\")[0]\n",
    "  \n",
    "  inputs = [prefix + code for code in codes]\n",
    "  model_inputs = tokenizer(inputs, max_length=max_input_length, padding=\"max_length\", truncation=True)\n",
    "\n",
    "  # encode the summaries\n",
    "  labels = tokenizer(codes, max_length=max_target_length, padding=\"max_length\", truncation=True).input_ids\n",
    "\n",
    "  # important: we need to replace the index of the padding tokens by -100\n",
    "  # such that they are not taken into account by the CrossEntropyLoss\n",
    "  labels_with_ignore_index = []\n",
    "  for labels_example in labels:\n",
    "    labels_example = [label if label != 0 else -100 for label in labels_example]\n",
    "    labels_with_ignore_index.append(labels_example)\n",
    "  \n",
    "  model_inputs[\"labels\"] = labels_with_ignore_index\n",
    "\n",
    "  return model_inputs"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Now that we have defined the function, let's call `.map()` on the HuggingFace Dataset object, which allows us to apply this function in batches (by default a batch size of 1,000 is used!) - hence super fast."
   ]
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     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /home/sberman/.cache/huggingface/datasets/code_x_glue_tc_text_to_code/default/0.0.0/059898ce5bb35e72c699c69af37020002b38b251734ddaeedef30ae7e6292717/cache-0acd8121fc0bd515.arrow\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a3fc38ad79994a2582e6be17013d5a3e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6abc029f26a4130af38f99524327e5c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = dataset.map(preprocess_examples, batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nevKo-Sob21L",
    "outputId": "21c559aa-256b-416d-f173-ffd3ea9d9696"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['id', 'nl', 'code', 'input_ids', 'attention_mask', 'labels'],\n",
       "        num_rows: 100000\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['id', 'nl', 'code', 'input_ids', 'attention_mask', 'labels'],\n",
       "        num_rows: 2000\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['id', 'nl', 'code', 'input_ids', 'attention_mask', 'labels'],\n",
       "        num_rows: 2000\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9mSM2_eDalx-"
   },
   "source": [
    "Next, let's set the format to \"torch\" and create PyTorch dataloaders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "gq2-xLG_alXH"
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "dataset.set_format(type=\"torch\", columns=['input_ids', 'attention_mask', 'labels'])\n",
    "train_dataloader = DataLoader(dataset['train'], shuffle=True, batch_size=8)\n",
    "valid_dataloader = DataLoader(dataset['validation'], batch_size=4)\n",
    "test_dataloader = DataLoader(dataset['test'], batch_size=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6y0l7v8obmml",
    "outputId": "19f19119-ac23-4213-fa21-ccd398dca61f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['input_ids', 'attention_mask', 'labels'])\n"
     ]
    }
   ],
   "source": [
    "batch = next(iter(train_dataloader))\n",
    "print(batch.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YH4MIhxFc2BR"
   },
   "source": [
    "Let's verify an example, by decoding it back into text:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 171
    },
    "id": "jsqjA1OHcn5V",
    "outputId": "f869a827-4b24-46d5-ccd8-1c7978225933"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<s>Summarize Ruby: String function ( ) { return this. sizeConstraintSetId ; }</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.decode(batch['input_ids'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 103
    },
    "id": "sYQWyCDzcqzh",
    "outputId": "5b98b1b8-edb3-4269-fa1e-6770999f5921"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<s>String function ( ) { return this. sizeConstraintSetId ; }</s>'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = batch['labels'][0]\n",
    "tokenizer.decode([label for label in labels if label != -100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MOxapqxzS0PO"
   },
   "source": [
    "## Fine-tune using PyTorch Lightning\n",
    "\n",
    "As we will train the model using PyTorch Lightning, we first need to define a `LightningModule`, which is an `nn.Module` with some additional functionalities. We just need to define the `forward` pass, `training_step` (and optionally `validation_step` and `test_step`), and the corresponding dataloaders. PyTorch Lightning will then automate the training for us, handling device placement (i.e. we don't need to type `.to(device)` anywhere), etc. It also comes with support for loggers (such as Tensorboard, Weights and Biases) and callbacks.\n",
    "\n",
    "Of course, you could also train the model in other ways:\n",
    "* using regular PyTorch\n",
    "* using the HuggingFace Trainer (in this case, the Seq2SeqTrainer)\n",
    "* using HuggingFace Accelerate\n",
    "* etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "RuHlP1MuR_tJ"
   },
   "outputs": [],
   "source": [
    "from transformers import T5ForConditionalGeneration, AdamW, get_linear_schedule_with_warmup\n",
    "import pytorch_lightning as pl\n",
    "\n",
    "class CodeT5(pl.LightningModule):\n",
    "    def __init__(self, lr=5e-5, num_train_epochs=15, warmup_steps=1000):\n",
    "        super().__init__()\n",
    "        self.model = T5ForConditionalGeneration.from_pretrained(\"Salesforce/codet5-small\")\n",
    "        self.save_hyperparameters()\n",
    "\n",
    "    def forward(self, input_ids, attention_mask, labels=None):     \n",
    "        outputs = self.model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
    "        return outputs\n",
    "    \n",
    "    def common_step(self, batch, batch_idx):\n",
    "        outputs = self(**batch)\n",
    "        loss = outputs.loss\n",
    "\n",
    "        return loss\n",
    "      \n",
    "    def training_step(self, batch, batch_idx):\n",
    "        loss = self.common_step(batch, batch_idx)     \n",
    "        # logs metrics for each training_step,\n",
    "        # and the average across the epoch\n",
    "        self.log(\"training_loss\", loss)\n",
    "\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        loss = self.common_step(batch, batch_idx)     \n",
    "        self.log(\"validation_loss\", loss, on_epoch=True)\n",
    "\n",
    "        return loss\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "        loss = self.common_step(batch, batch_idx)     \n",
    "\n",
    "        return loss\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        # create optimizer\n",
    "        optimizer = AdamW(self.parameters(), lr=self.hparams.lr)\n",
    "        # create learning rate scheduler\n",
    "        num_train_optimization_steps = self.hparams.num_train_epochs * len(train_dataloader)\n",
    "        lr_scheduler = {'scheduler': get_linear_schedule_with_warmup(optimizer,\n",
    "                                                    num_warmup_steps=self.hparams.warmup_steps,\n",
    "                                                    num_training_steps=num_train_optimization_steps),\n",
    "                        'name': 'learning_rate',\n",
    "                        'interval':'step',\n",
    "                        'frequency': 1}\n",
    "        \n",
    "        return {\"optimizer\": optimizer, \"lr_scheduler\": lr_scheduler}\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        return train_dataloader\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        return valid_dataloader\n",
    "\n",
    "    def test_dataloader(self):\n",
    "        return test_dataloader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CuYF6-tYZuuN"
   },
   "source": [
    "Let's start up Weights and Biases!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "id": "HKDavW52ZuKv",
    "outputId": "631ad4c6-d320-43e9-f15c-e6aaa8b643f0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mshmublu\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wandb\n",
    "\n",
    "wandb.login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sAxLx7_Td1G-"
   },
   "source": [
    "Next, we initialize the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
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      "6f57502431d34979acc781ab11cfb7b1",
      "b738914e73c54766b549bde562db8020",
      "815b7b62c2da446f87c07bdb440a399d",
      "665f37a818da4e898ca0bd9a26ec0edf",
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      "1e78db819c04445cbb10d0a662c8ea5b",
      "1e46fd23daee406e96efd41f12952fe2"
     ]
    },
    "id": "ZtDmLYzEc9uP",
    "outputId": "339714e5-47c0-44c3-dba6-33bd726c569d"
   },
   "outputs": [],
   "source": [
    "model = CodeT5()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import torch\n",
    "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "# model = model.to(device)\n",
    "\n",
    "#torch.multiprocessing.set_start_method('spawn')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# for epoch in range(10):\n",
    "#     model.train()\n",
    "#     train_loss = 0.0\n",
    "#     for batch in train_dataloader:\n",
    "#         # put batch on device\n",
    "#         batch = {k:v.to(device) for k,v in batch.items()}\n",
    "        \n",
    "#         # forward pass\n",
    "#         outputs = model(**batch)\n",
    "#         loss = outputs.loss\n",
    "        \n",
    "#         train_loss += loss.item()\n",
    "        \n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "#         optimizer.zero_grad()\n",
    "\n",
    "#     print(\"Loss after epoch {epoch}:\", train_loss/len(train_dataloader))\n",
    "    \n",
    "#     model.eval()\n",
    "#     val_loss = 0.0\n",
    "#     with torch.no_grad():\n",
    "#         for batch in eval_dataloader:\n",
    "#             # put batch on device\n",
    "#             batch = {k:v.to(device) for k,v in batch.items()}\n",
    "            \n",
    "#             # forward pass\n",
    "#             outputs = model(**batch)\n",
    "#             loss = outputs.logits\n",
    "            \n",
    "#             val_loss += loss.item()\n",
    "                  \n",
    "#     print(\"Validation loss after epoch {epoch}:\", val_loss/len(eval_dataloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \n",
    "# torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KPzgguodd2uS"
   },
   "source": [
    "We can now simply start training on Colab's GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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      "aa0f3ba3281b42a795fcf7cb40fcedc5",
      "5b3ba494dbdf4ef283f8fec59eb757bf",
      "7588263041e94db5a76f3d639fa3f83d",
      "0cdc6c0c4653446387ea36dca5e2d56c",
      "359908f6803b4c15b359dc5b6e2f27d1",
      "8b5998d5e4424dd8962ea3cbc4dcfd64",
      "df800cd117b946c88137a1c46c427216",
      "d0f4f94047774e1aa4ea2200fe732b6b",
      "ad7e17e4626b4c6c9b0dd47deb19dcdd",
      "a3916440cfba4999b3a1874f6d59de94",
      "09ce1ccdf1704d739e888a87717a920f",
      "38568309247a48ab9315dfdd1fcafd4b"
     ]
    },
    "id": "WroP64V8dWuO",
    "outputId": "5ef87e53-16df-4ed7-cf4b-f2b400aea7ea"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/2\n",
      "Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/2\n",
      "----------------------------------------------------------------------------------------------------\n",
      "distributed_backend=nccl\n",
      "All distributed processes registered. Starting with 2 processes\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "/home/sberman/.local/lib/python3.8/site-packages/pytorch_lightning/loops/utilities.py:89: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.\n",
      "  rank_zero_warn(\n",
      "Missing logger folder: /home/sberman/notebook-saved/T5-doc-to-code/lightning_logs\n",
      "Missing logger folder: /home/sberman/notebook-saved/T5-doc-to-code/lightning_logs\n",
      "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1]\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
      "/home/sberman/.local/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "/home/sberman/.local/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "\n",
      "  | Name  | Type                       | Params\n",
      "-----------------------------------------------------\n",
      "0 | model | T5ForConditionalGeneration | 60.5 M\n",
      "-----------------------------------------------------\n",
      "60.5 M    Trainable params\n",
      "0         Non-trainable params\n",
      "60.5 M    Total params\n",
      "241.969   Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sberman/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:229: UserWarning: strategy=ddp_spawn and num_workers=0 may result in data loading bottlenecks. Consider setting num_workers>0 and persistent_workers=True\n",
      "  rank_zero_warn(\n",
      "/home/sberman/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:535: PossibleUserWarning: It is recommended to use `self.log('validation_loss', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.\n",
      "  warning_cache.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a5446593156148e1a693f83660c91bc6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ProcessExitedException",
     "evalue": "process 1 terminated with signal SIGSEGV",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mProcessExitedException\u001b[0m                    Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-9826e0f31112>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m trainer = Trainer(accelerator='gpu', default_root_dir=\"/home/sberman/notebook-saved/T5-doc-to-code\", \n\u001b[1;32m     17\u001b[0m                   callbacks=[early_stop_callback, lr_monitor])\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m    694\u001b[0m         \"\"\"\n\u001b[1;32m    695\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrategy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 696\u001b[0;31m         self._call_and_handle_interrupt(\n\u001b[0m\u001b[1;32m    697\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_impl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_dataloaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_dataloaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatamodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mckpt_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    698\u001b[0m         )\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36m_call_and_handle_interrupt\u001b[0;34m(self, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    646\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrategy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlauncher\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrategy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlauncher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlaunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainer_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    649\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    650\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mtrainer_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pytorch_lightning/strategies/launchers/multiprocessing.py\u001b[0m in \u001b[0;36mlaunch\u001b[0;34m(self, function, trainer, *args, **kwargs)\u001b[0m\n\u001b[1;32m    105\u001b[0m             \u001b[0mprocess_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_queue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m         mp.start_processes(\n\u001b[0m\u001b[1;32m    108\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrapping_function\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m             \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprocess_args\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/torch/multiprocessing/spawn.py\u001b[0m in \u001b[0;36mstart_processes\u001b[0;34m(fn, args, nprocs, join, daemon, start_method)\u001b[0m\n\u001b[1;32m    196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m     \u001b[0;31m# Loop on join until it returns True or raises an exception.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m     \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    199\u001b[0m         \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/torch/multiprocessing/spawn.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    138\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mexitcode\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m                 \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSignals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mexitcode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m                 raise ProcessExitedException(\n\u001b[0m\u001b[1;32m    141\u001b[0m                     \u001b[0;34m\"process %d terminated with signal %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m                     \u001b[0;34m(\u001b[0m\u001b[0merror_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mProcessExitedException\u001b[0m: process 1 terminated with signal SIGSEGV"
     ]
    }
   ],
   "source": [
    "from pytorch_lightning import Trainer\n",
    "from pytorch_lightning.loggers import WandbLogger\n",
    "from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor\n",
    "\n",
    "#wandb_logger = WandbLogger(name='codet5-finetune-code-summarization-ruby-shuffle', project='CodeT5')\n",
    "# for early stopping, see https://pytorch-lightning.readthedocs.io/en/1.0.0/early_stopping.html?highlight=early%20stopping\n",
    "early_stop_callback = EarlyStopping(\n",
    "    monitor='validation_loss',\n",
    "    patience=3,\n",
    "    strict=False,\n",
    "    verbose=False,\n",
    "    mode='min'\n",
    ")\n",
    "lr_monitor = LearningRateMonitor(logging_interval='step')\n",
    "\n",
    "trainer = Trainer(accelerator='gpu', default_root_dir=\"/home/sberman/notebook-saved/T5-doc-to-code\", \n",
    "                  callbacks=[early_stop_callback, lr_monitor])\n",
    "trainer.fit(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oMxAo19KIo9j"
   },
   "source": [
    "Once we're done training, we can also save the HuggingFace model as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EhfLMOAmhIG6"
   },
   "outputs": [],
   "source": [
    "save_directory = \".\" # save in the current working directory, you can change this of course\n",
    "model.model.save_pretrained(save_directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jr0O3iN5IsdW"
   },
   "source": [
    "This allows us to easily load the trained model again using the `from_pretrained()` method, as shown below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BzhasngvAwX3"
   },
   "source": [
    "## Inference\n",
    "\n",
    "Now that we've trained a model, let's test it on some examples from the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iPXM9tH8AyCN",
    "outputId": "b2bd427d-dfcd-405c-b22c-cfa6abe54421"
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"code_x_glue_ct_code_to_text\", \"ruby\")\n",
    "print(dataset['test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "spbNHeE5ETpG",
    "outputId": "8fce8a4b-95e5-474d-a73b-2d9b71c9e1cc"
   },
   "outputs": [],
   "source": [
    "test_example_1 = dataset['test'][2]\n",
    "test_example = {}\n",
    "test_example['code'] = \"\"\"def confirm_if_ejson_keys_prunable\n",
    "      hidden_key = ejson_provisioner.ejson_keys_secret\n",
    "      return unless secret.dig(\"metadata\", \"annotations\", KubernetesResource::First_APPLIED_ANNOTATION)\"\"\"\n",
    "print(\"Code:\", test_example['code'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4XwCaigDI622"
   },
   "source": [
    "We can load our trained model as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Vu68VMzTEXek"
   },
   "outputs": [],
   "source": [
    "from transformers import T5ForConditionalGeneration\n",
    "\n",
    "model = T5ForConditionalGeneration.from_pretrained(save_directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2w7JC9CxJJ_F"
   },
   "source": [
    "We can prepare the example using `RobertaTokenizer`, and generate using the `.generate()` method. Note that there are several ways of doing generation (greedy decoding/beam search/top k sampling/etc.), for that I refer to Patrick's blog post which you can find [here](https://huggingface.co/blog/how-to-generate). Here we will just use the default settings (i.e. greedy decoding)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NhpiAr6UE7vG",
    "outputId": "833a8244-7324-4113-dada-d4b7103cfbfe"
   },
   "outputs": [],
   "source": [
    "# prepare for the model\n",
    "input_ids = tokenizer(test_example['code'], return_tensors='pt').input_ids\n",
    "# generate\n",
    "outputs = model.generate(input_ids)\n",
    "print(\"Generated docstring:\", tokenizer.decode(outputs[0], skip_special_tokens=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cjxvMu0rKDzL"
   },
   "source": [
    "Let's compare this to the ground-truth docstring:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gLy5iUqHKHmy",
    "outputId": "582be69e-0594-4194-d337-01e530b1223e"
   },
   "outputs": [],
   "source": [
    "print(\"Ground truth:\", test_example['docstring'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_pretrained(\"/home/sberman/notebook-saved/T5-doc-to-code\")\n",
    "tokenizer.save_pretrained(\"/home/sberman/notebook-saved/T5-doc-to-code\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nC_9q7BDF_cU"
   },
   "source": [
    "## Upload trained model to the hub\n",
    "\n",
    "Cool! We can also share our model with the world, by uploading it to [hf.co](https://hf.co). For that, we need to install Git-LFS, which is used for using git with large files (note that each model on the hub = a git repository!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I_qTWbVoFKUi",
    "outputId": "9924bcad-efcf-49f5-a6e0-1ddcc55c1bfc"
   },
   "outputs": [],
   "source": [
    "!sudo apt-get install git-lfs\n",
    "!git config --global user.email \"niels.rogge1@gmail.com\"\n",
    "!git config --global user.name \"Niels Rogge\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7zKZKbHrJ05q"
   },
   "source": [
    "Next, we can login with the credentials of our HuggingFace account (you can sign up on [hf.co](https://hf.co) if you haven't already!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JSVClVYvGBBn",
    "outputId": "af77f7d0-d2a3-4d7e-d196-4ad87883a3d9"
   },
   "outputs": [],
   "source": [
    "!huggingface-cli login"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qmWFxFncHA8l"
   },
   "outputs": [],
   "source": [
    "repo_url = \"https://huggingface.co/nielsr/codet5-small-code-summarization-ruby\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q_9dhtuoHBnr",
    "outputId": "9741c2d3-6daa-4cb3-94ae-011dbbb79bb4"
   },
   "outputs": [],
   "source": [
    "from huggingface_hub import Repository\n",
    "\n",
    "repo = Repository(local_dir=\"checkpoint\", # note that this directory must not exist already\n",
    "                  clone_from=repo_url,\n",
    "                  git_user=\"Niels Rogge\",\n",
    "                  git_email=\"niels.rogge1@gmail.com\",\n",
    "                  use_auth_token=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "W3aKp8-JHNom"
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    "model.save_pretrained(\"/home/sberman/notebook-saved/T5-doc-to-code\")\n",
    "tokenizer.save_pretrained(\"/home/sberman/notebook-saved/T5-doc-to-code\")"
   ]
  },
  {
   "cell_type": "code",
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    "colab": {
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