{"cells": [{"cell_type": "markdown", "id": "3c99ec5d", "metadata": {"papermill": {"duration": 0.033967, "end_time": "2021-12-04T16:50:09.385288", "exception": false, "start_time": "2021-12-04T16:50:09.351321", "status": "completed"}, "tags": []}, "source": ["\n", "# Finetune Transformers Models with PyTorch Lightning\n", "\n", "* **Author:** PL team\n", "* **License:** CC BY-SA\n", "* **Generated:** 2021-12-04T16:53:11.286202\n", "\n", "This notebook will use HuggingFace's `datasets` library to get data, which will be wrapped in a `LightningDataModule`.\n", "Then, we write a class to perform text classification on any dataset from the [GLUE Benchmark](https://gluebenchmark.com/).\n", "(We just show CoLA and MRPC due to constraint on compute/disk)\n", "\n", "\n", "---\n", "Open in [![Open In Colab](data:image/png;base64,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){height=\"20px\" width=\"117px\"}](https://colab.research.google.com/github/PytorchLightning/lightning-tutorials/blob/publication/.notebooks/lightning_examples/text-transformers.ipynb)\n", "\n", "Give us a \u2b50 [on Github](https://www.github.com/PytorchLightning/pytorch-lightning/)\n", "| Check out [the documentation](https://pytorch-lightning.readthedocs.io/en/latest/)\n", "| Join us [on Slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-pw5v393p-qRaDgEk24~EjiZNBpSQFgQ)"]}, {"cell_type": "markdown", "id": "0e4d7209", "metadata": {"papermill": {"duration": 0.028692, "end_time": "2021-12-04T16:50:09.445776", "exception": false, "start_time": "2021-12-04T16:50:09.417084", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "49b84d9c", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2021-12-04T16:50:09.510470Z", "iopub.status.busy": "2021-12-04T16:50:09.505774Z", "iopub.status.idle": "2021-12-04T16:50:12.925383Z", "shell.execute_reply": "2021-12-04T16:50:12.924802Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 3.45077, "end_time": "2021-12-04T16:50:12.925533", "exception": false, "start_time": "2021-12-04T16:50:09.474763", "status": "completed"}, "tags": []}, "outputs": [], "source": ["! pip install --quiet \"datasets\" \"pytorch-lightning>=1.3\" \"scipy\" \"transformers\" \"torchmetrics>=0.3\" \"scikit-learn\" \"torchtext>=0.9\" \"torch>=1.6, <1.9\""]}, {"cell_type": "code", "execution_count": 2, "id": "8c24af2f", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T16:50:12.990546Z", "iopub.status.busy": "2021-12-04T16:50:12.990070Z", "iopub.status.idle": "2021-12-04T16:50:17.320862Z", "shell.execute_reply": "2021-12-04T16:50:17.320412Z"}, "papermill": {"duration": 4.364813, "end_time": "2021-12-04T16:50:17.320999", "exception": false, "start_time": "2021-12-04T16:50:12.956186", "status": "completed"}, "tags": []}, "outputs": [], "source": ["from datetime import datetime\n", "from typing import Optional\n", "\n", "import datasets\n", "import torch\n", "from pytorch_lightning import LightningDataModule, LightningModule, Trainer, seed_everything\n", "from torch.utils.data import DataLoader\n", "from transformers import (\n", "    AdamW,\n", "    AutoConfig,\n", "    AutoModelForSequenceClassification,\n", "    AutoTokenizer,\n", "    get_linear_schedule_with_warmup,\n", ")\n", "\n", "AVAIL_GPUS = min(1, torch.cuda.device_count())"]}, {"cell_type": "markdown", "id": "2134c12c", "metadata": {"papermill": {"duration": 0.029134, "end_time": "2021-12-04T16:50:17.381739", "exception": false, "start_time": "2021-12-04T16:50:17.352605", "status": "completed"}, "tags": []}, "source": ["## Training BERT with Lightning"]}, {"cell_type": "markdown", "id": "c827ca58", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.029129, "end_time": "2021-12-04T16:50:17.441085", "exception": false, "start_time": "2021-12-04T16:50:17.411956", "status": "completed"}, "tags": []}, "source": ["### Lightning DataModule for GLUE"]}, {"cell_type": "code", "execution_count": 3, "id": "5a856234", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T16:50:17.515779Z", "iopub.status.busy": "2021-12-04T16:50:17.512843Z", "iopub.status.idle": "2021-12-04T16:50:17.517417Z", "shell.execute_reply": "2021-12-04T16:50:17.517796Z"}, "papermill": {"duration": 0.047785, "end_time": "2021-12-04T16:50:17.517928", "exception": false, "start_time": "2021-12-04T16:50:17.470143", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class GLUEDataModule(LightningDataModule):\n", "\n", "    task_text_field_map = {\n", "        \"cola\": [\"sentence\"],\n", "        \"sst2\": [\"sentence\"],\n", "        \"mrpc\": [\"sentence1\", \"sentence2\"],\n", "        \"qqp\": [\"question1\", \"question2\"],\n", "        \"stsb\": [\"sentence1\", \"sentence2\"],\n", "        \"mnli\": [\"premise\", \"hypothesis\"],\n", "        \"qnli\": [\"question\", \"sentence\"],\n", "        \"rte\": [\"sentence1\", \"sentence2\"],\n", "        \"wnli\": [\"sentence1\", \"sentence2\"],\n", "        \"ax\": [\"premise\", \"hypothesis\"],\n", "    }\n", "\n", "    glue_task_num_labels = {\n", "        \"cola\": 2,\n", "        \"sst2\": 2,\n", "        \"mrpc\": 2,\n", "        \"qqp\": 2,\n", "        \"stsb\": 1,\n", "        \"mnli\": 3,\n", "        \"qnli\": 2,\n", "        \"rte\": 2,\n", "        \"wnli\": 2,\n", "        \"ax\": 3,\n", "    }\n", "\n", "    loader_columns = [\n", "        \"datasets_idx\",\n", "        \"input_ids\",\n", "        \"token_type_ids\",\n", "        \"attention_mask\",\n", "        \"start_positions\",\n", "        \"end_positions\",\n", "        \"labels\",\n", "    ]\n", "\n", "    def __init__(\n", "        self,\n", "        model_name_or_path: str,\n", "        task_name: str = \"mrpc\",\n", "        max_seq_length: int = 128,\n", "        train_batch_size: int = 32,\n", "        eval_batch_size: int = 32,\n", "        **kwargs,\n", "    ):\n", "        super().__init__()\n", "        self.model_name_or_path = model_name_or_path\n", "        self.task_name = task_name\n", "        self.max_seq_length = max_seq_length\n", "        self.train_batch_size = train_batch_size\n", "        self.eval_batch_size = eval_batch_size\n", "\n", "        self.text_fields = self.task_text_field_map[task_name]\n", "        self.num_labels = self.glue_task_num_labels[task_name]\n", "        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True)\n", "\n", "    def setup(self, stage: str):\n", "        self.dataset = datasets.load_dataset(\"glue\", self.task_name)\n", "\n", "        for split in self.dataset.keys():\n", "            self.dataset[split] = self.dataset[split].map(\n", "                self.convert_to_features,\n", "                batched=True,\n", "                remove_columns=[\"label\"],\n", "            )\n", "            self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns]\n", "            self.dataset[split].set_format(type=\"torch\", columns=self.columns)\n", "\n", "        self.eval_splits = [x for x in self.dataset.keys() if \"validation\" in x]\n", "\n", "    def prepare_data(self):\n", "        datasets.load_dataset(\"glue\", self.task_name)\n", "        AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True)\n", "\n", "    def train_dataloader(self):\n", "        return DataLoader(self.dataset[\"train\"], batch_size=self.train_batch_size)\n", "\n", "    def val_dataloader(self):\n", "        if len(self.eval_splits) == 1:\n", "            return DataLoader(self.dataset[\"validation\"], batch_size=self.eval_batch_size)\n", "        elif len(self.eval_splits) > 1:\n", "            return [DataLoader(self.dataset[x], batch_size=self.eval_batch_size) for x in self.eval_splits]\n", "\n", "    def test_dataloader(self):\n", "        if len(self.eval_splits) == 1:\n", "            return DataLoader(self.dataset[\"test\"], batch_size=self.eval_batch_size)\n", "        elif len(self.eval_splits) > 1:\n", "            return [DataLoader(self.dataset[x], batch_size=self.eval_batch_size) for x in self.eval_splits]\n", "\n", "    def convert_to_features(self, example_batch, indices=None):\n", "\n", "        # Either encode single sentence or sentence pairs\n", "        if len(self.text_fields) > 1:\n", "            texts_or_text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]]))\n", "        else:\n", "            texts_or_text_pairs = example_batch[self.text_fields[0]]\n", "\n", "        # Tokenize the text/text pairs\n", "        features = self.tokenizer.batch_encode_plus(\n", "            texts_or_text_pairs, max_length=self.max_seq_length, pad_to_max_length=True, truncation=True\n", "        )\n", "\n", "        # Rename label to labels to make it easier to pass to model forward\n", "        features[\"labels\"] = example_batch[\"label\"]\n", "\n", "        return features"]}, {"cell_type": "markdown", "id": "532d63d6", "metadata": {"papermill": {"duration": 0.029073, "end_time": "2021-12-04T16:50:17.576485", "exception": false, "start_time": "2021-12-04T16:50:17.547412", "status": "completed"}, "tags": []}, "source": ["**You could use this datamodule with standalone PyTorch if you wanted...**"]}, {"cell_type": "code", "execution_count": 4, "id": "f3f27b26", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T16:50:17.637888Z", "iopub.status.busy": "2021-12-04T16:50:17.637424Z", "iopub.status.idle": "2021-12-04T16:50:24.396830Z", "shell.execute_reply": "2021-12-04T16:50:24.396334Z"}, "papermill": {"duration": 6.791378, "end_time": "2021-12-04T16:50:24.396956", "exception": false, "start_time": "2021-12-04T16:50:17.605578", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "232400085c33454e97f802179183d1ed", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "9dce376c62274d349bd19518f0c52d1c", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading:   0%|          | 0.00/483 [00:00<?, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "99fde75ad6d84f799725a9174bcc0b8c", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "1b2e795fdc88462e8e08e155ff950008", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading:   0%|        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{"application/vnd.jupyter.widget-view+json": {"model_id": "24585ee489184bc19c28fe5f29f8ac71", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/3 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "a63585177c344603af53db4d41f998d5", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading: 0.00B [00:00, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "db15441cf1284553828fb0aa42e203a2", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading: 0.00B [00:00, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "8c7caa31e1814a54bbdfc6b6d81a55c2", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading: 0.00B [00:00, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "1a31200b1002420d8ff8d0f41c97d0e3", "version_major": 2, "version_minor": 0}, "text/plain": ["0 examples [00:00, ? examples/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "71418ba4a263483d912d664a49d33663", "version_major": 2, "version_minor": 0}, "text/plain": ["0 examples [00:00, ? examples/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "793e557cb3264027a5a8d768af7b0f03", "version_major": 2, "version_minor": 0}, "text/plain": ["0 examples [00:00, ? examples/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Dataset glue downloaded and prepared to /home/AzDevOps_azpcontainer/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "f7a7ba36868a4fbe9128417a32be55a3", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/3 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["Reusing dataset glue (/home/AzDevOps_azpcontainer/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "e26860df02a2415393589e8189ee5c36", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/3 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "fd010fb80e664bb99c2617f088cfbbb8", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/4 [00:00<?, ?ba/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:2212: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n", "  warnings.warn(\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "69477a857e104f30926fb20462b603b5", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/1 [00:00<?, ?ba/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "c6de81a20d3147c1849c878056b94e53", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/2 [00:00<?, ?ba/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/plain": ["{'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\n", "         [1, 1, 1,  ..., 0, 0, 0],\n", "         [1, 1, 1,  ..., 0, 0, 0],\n", "         ...,\n", "         [1, 1, 1,  ..., 0, 0, 0],\n", "         [1, 1, 1,  ..., 0, 0, 0],\n", "         [1, 1, 1,  ..., 0, 0, 0]]),\n", " 'input_ids': tensor([[  101,  2572,  3217,  ...,     0,     0,     0],\n", "         [  101,  9805,  3540,  ...,     0,     0,     0],\n", "         [  101,  2027,  2018,  ...,     0,     0,     0],\n", "         ...,\n", "         [  101,  1996,  2922,  ...,     0,     0,     0],\n", "         [  101,  6202,  1999,  ...,     0,     0,     0],\n", "         [  101, 16565,  2566,  ...,     0,     0,     0]]),\n", " 'labels': tensor([1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1,\n", "         1, 1, 0, 0, 1, 1, 1, 0])}"]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["dm = GLUEDataModule(\"distilbert-base-uncased\")\n", "dm.prepare_data()\n", "dm.setup(\"fit\")\n", "next(iter(dm.train_dataloader()))"]}, {"cell_type": "markdown", "id": "777a98be", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.054378, "end_time": "2021-12-04T16:50:24.507776", "exception": false, "start_time": "2021-12-04T16:50:24.453398", "status": "completed"}, "tags": []}, "source": ["### Transformer LightningModule"]}, {"cell_type": "code", "execution_count": 5, "id": "f01e93de", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T16:50:24.749268Z", "iopub.status.busy": "2021-12-04T16:50:24.742652Z", "iopub.status.idle": "2021-12-04T16:50:24.751523Z", "shell.execute_reply": "2021-12-04T16:50:24.750971Z"}, "papermill": {"duration": 0.179642, "end_time": "2021-12-04T16:50:24.751656", "exception": false, "start_time": "2021-12-04T16:50:24.572014", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class GLUETransformer(LightningModule):\n", "    def __init__(\n", "        self,\n", "        model_name_or_path: str,\n", "        num_labels: int,\n", "        task_name: str,\n", "        learning_rate: float = 2e-5,\n", "        adam_epsilon: float = 1e-8,\n", "        warmup_steps: int = 0,\n", "        weight_decay: float = 0.0,\n", "        train_batch_size: int = 32,\n", "        eval_batch_size: int = 32,\n", "        eval_splits: Optional[list] = None,\n", "        **kwargs,\n", "    ):\n", "        super().__init__()\n", "\n", "        self.save_hyperparameters()\n", "\n", "        self.config = AutoConfig.from_pretrained(model_name_or_path, num_labels=num_labels)\n", "        self.model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, config=self.config)\n", "        self.metric = datasets.load_metric(\n", "            \"glue\", self.hparams.task_name, experiment_id=datetime.now().strftime(\"%d-%m-%Y_%H-%M-%S\")\n", "        )\n", "\n", "    def forward(self, **inputs):\n", "        return self.model(**inputs)\n", "\n", "    def training_step(self, batch, batch_idx):\n", "        outputs = self(**batch)\n", "        loss = outputs[0]\n", "        return loss\n", "\n", "    def validation_step(self, batch, batch_idx, dataloader_idx=0):\n", "        outputs = self(**batch)\n", "        val_loss, logits = outputs[:2]\n", "\n", "        if self.hparams.num_labels >= 1:\n", "            preds = torch.argmax(logits, axis=1)\n", "        elif self.hparams.num_labels == 1:\n", "            preds = logits.squeeze()\n", "\n", "        labels = batch[\"labels\"]\n", "\n", "        return {\"loss\": val_loss, \"preds\": preds, \"labels\": labels}\n", "\n", "    def validation_epoch_end(self, outputs):\n", "        if self.hparams.task_name == \"mnli\":\n", "            for i, output in enumerate(outputs):\n", "                # matched or mismatched\n", "                split = self.hparams.eval_splits[i].split(\"_\")[-1]\n", "                preds = torch.cat([x[\"preds\"] for x in output]).detach().cpu().numpy()\n", "                labels = torch.cat([x[\"labels\"] for x in output]).detach().cpu().numpy()\n", "                loss = torch.stack([x[\"loss\"] for x in output]).mean()\n", "                self.log(f\"val_loss_{split}\", loss, prog_bar=True)\n", "                split_metrics = {\n", "                    f\"{k}_{split}\": v for k, v in self.metric.compute(predictions=preds, references=labels).items()\n", "                }\n", "                self.log_dict(split_metrics, prog_bar=True)\n", "            return loss\n", "\n", "        preds = torch.cat([x[\"preds\"] for x in outputs]).detach().cpu().numpy()\n", "        labels = torch.cat([x[\"labels\"] for x in outputs]).detach().cpu().numpy()\n", "        loss = torch.stack([x[\"loss\"] for x in outputs]).mean()\n", "        self.log(\"val_loss\", loss, prog_bar=True)\n", "        self.log_dict(self.metric.compute(predictions=preds, references=labels), prog_bar=True)\n", "        return loss\n", "\n", "    def setup(self, stage=None) -> None:\n", "        if stage != \"fit\":\n", "            return\n", "        # Get dataloader by calling it - train_dataloader() is called after setup() by default\n", "        train_loader = self.trainer.datamodule.train_dataloader()\n", "\n", "        # Calculate total steps\n", "        tb_size = self.hparams.train_batch_size * max(1, self.trainer.gpus)\n", "        ab_size = self.trainer.accumulate_grad_batches * float(self.trainer.max_epochs)\n", "        self.total_steps = (len(train_loader.dataset) // tb_size) // ab_size\n", "\n", "    def configure_optimizers(self):\n", "        \"\"\"Prepare optimizer and schedule (linear warmup and decay)\"\"\"\n", "        model = self.model\n", "        no_decay = [\"bias\", \"LayerNorm.weight\"]\n", "        optimizer_grouped_parameters = [\n", "            {\n", "                \"params\": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n", "                \"weight_decay\": self.hparams.weight_decay,\n", "            },\n", "            {\n", "                \"params\": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],\n", "                \"weight_decay\": 0.0,\n", "            },\n", "        ]\n", "        optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)\n", "\n", "        scheduler = get_linear_schedule_with_warmup(\n", "            optimizer,\n", "            num_warmup_steps=self.hparams.warmup_steps,\n", "            num_training_steps=self.total_steps,\n", "        )\n", "        scheduler = {\"scheduler\": scheduler, \"interval\": \"step\", \"frequency\": 1}\n", "        return [optimizer], [scheduler]"]}, {"cell_type": "markdown", "id": "be59b890", "metadata": {"papermill": {"duration": 0.055801, "end_time": "2021-12-04T16:50:24.863114", "exception": false, "start_time": "2021-12-04T16:50:24.807313", "status": "completed"}, "tags": []}, "source": ["## Training"]}, {"cell_type": "markdown", "id": "c1a5d61a", "metadata": {"papermill": {"duration": 0.054576, "end_time": "2021-12-04T16:50:24.972560", "exception": false, "start_time": "2021-12-04T16:50:24.917984", "status": "completed"}, "tags": []}, "source": ["### CoLA\n", "\n", "See an interactive view of the\n", "CoLA dataset in [NLP Viewer](https://huggingface.co/nlp/viewer/?dataset=glue&config=cola)"]}, {"cell_type": "code", "execution_count": 6, "id": "774115a0", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T16:50:25.086490Z", "iopub.status.busy": "2021-12-04T16:50:25.086020Z", "iopub.status.idle": "2021-12-04T16:51:46.067948Z", "shell.execute_reply": "2021-12-04T16:51:46.068322Z"}, "papermill": {"duration": 81.04138, "end_time": "2021-12-04T16:51:46.068501", "exception": false, "start_time": "2021-12-04T16:50:25.027121", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Global seed set to 42\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "0071178f4db54b2a9c7a0c792adbfde9", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading:   0%|          | 0.00/684 [00:00<?, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "30ba7a6dd2474decba96c5f0bf2bce3a", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading:   0%|          | 0.00/742k [00:00<?, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "d7dae0ee2e2646e1978146fa0de58868", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading:   0%|          | 0.00/1.25M [00:00<?, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Downloading and preparing dataset glue/cola (download: 368.14 KiB, generated: 596.73 KiB, post-processed: Unknown size, total: 964.86 KiB) to /home/AzDevOps_azpcontainer/.cache/huggingface/datasets/glue/cola/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "b914533a226d4d61a3210f832b9ebc2f", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading:   0%|          | 0.00/377k [00:00<?, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "a0cf9e0337ca4591a8fd2b4a18798ed6", "version_major": 2, "version_minor": 0}, "text/plain": ["0 examples [00:00, ? examples/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "9f3dc3170efe4d58b049d797351cbf2b", "version_major": 2, "version_minor": 0}, "text/plain": ["0 examples [00:00, ? examples/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "ce292548b4264a999f3391cc9f9c9116", "version_major": 2, "version_minor": 0}, "text/plain": ["0 examples [00:00, ? examples/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Dataset glue downloaded and prepared to /home/AzDevOps_azpcontainer/.cache/huggingface/datasets/glue/cola/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "0e56c8fa2e6949fa92c82ae590c0c102", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/3 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "fc3711945d9e4919812ed60c813fd1ce", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/9 [00:00<?, ?ba/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "0ef73cefa80f45fe905e11c4e69428de", "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": "a498a133f31744148a6511162b4f2006", "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": "7576f432b63142dc9109122a8cc5169f", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading:   0%|          | 0.00/45.2M [00:00<?, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["Some weights of the model checkpoint at albert-base-v2 were not used when initializing AlbertForSequenceClassification: ['predictions.dense.weight', 'predictions.LayerNorm.bias', 'predictions.decoder.weight', 'predictions.dense.bias', 'predictions.LayerNorm.weight', 'predictions.decoder.bias', 'predictions.bias']\n", "- This IS expected if you are initializing AlbertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing AlbertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['classifier.bias', 'classifier.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "16c2a4832e3a401ea68d32abaf964d49", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading:   0%|          | 0.00/1.86k [00:00<?, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["GPU available: True, used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Reusing dataset glue (/home/AzDevOps_azpcontainer/.cache/huggingface/datasets/glue/cola/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "2419c9ecb7684056b818360707e6d132", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/3 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py:469: LightningDeprecationWarning: DataModule.setup has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.setup.\n", "  rank_zero_deprecation(\n", "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\n", "  | Name  | Type                            | Params\n", "----------------------------------------------------------\n", "0 | model | AlbertForSequenceClassification | 11.7 M\n", "----------------------------------------------------------\n", "11.7 M    Trainable params\n", "0         Non-trainable params\n", "11.7 M    Total params\n", "46.740    Total estimated model params size (MB)\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "6749ba501a614790944905389b6bfa32", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation sanity check: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:111: UserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", "  rank_zero_warn(\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/sklearn/metrics/_classification.py:873: RuntimeWarning: invalid value encountered in double_scalars\n", "  mcc = cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)\n", "Global seed set to 42\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:111: UserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", "  rank_zero_warn(\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "c7b3a99b038c43f1a91e4eeb823d3bdc", "version_major": 2, "version_minor": 0}, "text/plain": ["Training: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "c093a7fa06d44eecacca09142c6cb10f", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}], "source": ["seed_everything(42)\n", "\n", "dm = GLUEDataModule(model_name_or_path=\"albert-base-v2\", task_name=\"cola\")\n", "dm.setup(\"fit\")\n", "model = GLUETransformer(\n", "    model_name_or_path=\"albert-base-v2\",\n", "    num_labels=dm.num_labels,\n", "    eval_splits=dm.eval_splits,\n", "    task_name=dm.task_name,\n", ")\n", "\n", "trainer = Trainer(max_epochs=1, gpus=AVAIL_GPUS)\n", "trainer.fit(model, datamodule=dm)"]}, {"cell_type": "markdown", "id": "558c5c04", "metadata": {"papermill": {"duration": 0.085746, "end_time": "2021-12-04T16:51:46.240709", "exception": false, "start_time": "2021-12-04T16:51:46.154963", "status": "completed"}, "tags": []}, "source": ["### MRPC\n", "\n", "See an interactive view of the\n", "MRPC dataset in [NLP Viewer](https://huggingface.co/nlp/viewer/?dataset=glue&config=mrpc)"]}, {"cell_type": "code", "execution_count": 7, "id": "d36defa4", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T16:51:46.416178Z", "iopub.status.busy": "2021-12-04T16:51:46.415711Z", "iopub.status.idle": "2021-12-04T16:52:47.506459Z", "shell.execute_reply": "2021-12-04T16:52:47.505998Z"}, "papermill": {"duration": 61.180662, "end_time": "2021-12-04T16:52:47.506592", "exception": false, "start_time": "2021-12-04T16:51:46.325930", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Global seed set to 42\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "3a17a3eb85534b74ac1eefa26defad16", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "0fa015ede53d4050aa7e6e876d34c41f", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading:   0%|          | 0.00/411 [00:00<?, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "6170d79172bc4539a0135cb0497f661a", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading:   0%|          | 0.00/208k [00:00<?, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "d2e32195e3f84a6fa2002d3d3e678026", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading:   0%|          | 0.00/426k [00:00<?, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["Reusing dataset glue (/home/AzDevOps_azpcontainer/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "9f099745e78040218dbf66e885b2a072", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/3 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "dc508382ec9d4f61aa20a3f07da16b58", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/4 [00:00<?, ?ba/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:2212: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n", "  warnings.warn(\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "040ef14810244097b3ac610bb64d13c2", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/1 [00:00<?, ?ba/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "99293785be53419f8f499597f2ce9a0e", "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": "6a5f4154a82a4995b6adbddaccdbac58", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading:   0%|          | 0.00/251M [00:00<?, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["Some weights of the model checkpoint at distilbert-base-cased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_transform.weight', 'vocab_projector.weight']\n", "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-cased and are newly initialized: ['pre_classifier.weight', 'classifier.bias', 'pre_classifier.bias', 'classifier.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}, {"name": "stderr", "output_type": "stream", "text": ["GPU available: True, used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Reusing dataset glue (/home/AzDevOps_azpcontainer/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "07bc41d189304d9db550e85567c0b98f", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/3 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py:469: LightningDeprecationWarning: DataModule.setup has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.setup.\n", "  rank_zero_deprecation(\n", "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\n", "  | Name  | Type                                | Params\n", "--------------------------------------------------------------\n", "0 | model | DistilBertForSequenceClassification | 65.8 M\n", "--------------------------------------------------------------\n", "65.8 M    Trainable params\n", "0         Non-trainable params\n", "65.8 M    Total params\n", "263.132   Total estimated model params size (MB)\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "4d1e30b18d0543d9b7173fd3d73dda4b", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation sanity check: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:111: UserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", "  rank_zero_warn(\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Global seed set to 42\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:111: UserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", "  rank_zero_warn(\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "1ff6d90e8bec43c9ad14a884885bb58b", "version_major": 2, "version_minor": 0}, "text/plain": ["Training: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "28389f7839834a03967b8e510423d606", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "b38a414c2403412cb2937dad234b2404", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "944f3de537e94766a3bb59814eb0e73c", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}], "source": ["seed_everything(42)\n", "\n", "dm = GLUEDataModule(\n", "    model_name_or_path=\"distilbert-base-cased\",\n", "    task_name=\"mrpc\",\n", ")\n", "dm.setup(\"fit\")\n", "model = GLUETransformer(\n", "    model_name_or_path=\"distilbert-base-cased\",\n", "    num_labels=dm.num_labels,\n", "    eval_splits=dm.eval_splits,\n", "    task_name=dm.task_name,\n", ")\n", "\n", "trainer = Trainer(max_epochs=3, gpus=AVAIL_GPUS)\n", "trainer.fit(model, datamodule=dm)"]}, {"cell_type": "markdown", "id": "438416f1", "metadata": {"papermill": {"duration": 0.118137, "end_time": "2021-12-04T16:52:47.739400", "exception": false, "start_time": "2021-12-04T16:52:47.621263", "status": "completed"}, "tags": []}, "source": ["### MNLI\n", "\n", " - The MNLI dataset is huge, so we aren't going to bother trying to train on it here.\n", " - We will skip over training and go straight to validation.\n", "\n", "See an interactive view of the\n", "MRPC dataset in [NLP Viewer](https://huggingface.co/nlp/viewer/?dataset=glue&config=mnli)"]}, {"cell_type": "code", "execution_count": 8, "id": "6d6e666a", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T16:52:47.979300Z", "iopub.status.busy": "2021-12-04T16:52:47.978827Z", "iopub.status.idle": "2021-12-04T16:54:15.685042Z", "shell.execute_reply": "2021-12-04T16:54:15.684623Z"}, "papermill": {"duration": 87.831933, "end_time": "2021-12-04T16:54:15.685167", "exception": false, "start_time": "2021-12-04T16:52:47.853234", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading and preparing dataset glue/mnli (download: 298.29 MiB, generated: 78.65 MiB, post-processed: Unknown size, total: 376.95 MiB) to /home/AzDevOps_azpcontainer/.cache/huggingface/datasets/glue/mnli/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "2d34ec42f81048b28c25c692c86ffd6a", "version_major": 2, "version_minor": 0}, "text/plain": ["Downloading:   0%|          | 0.00/313M [00:00<?, ?B/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "3328bd4bb4344ab8adc67d30679e5259", "version_major": 2, "version_minor": 0}, "text/plain": ["0 examples [00:00, ? examples/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "42347815b7504fa8bc1579fe2f3cc26c", "version_major": 2, "version_minor": 0}, "text/plain": ["0 examples [00:00, ? examples/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "5a90a7a196434ca6a4203779d531366b", "version_major": 2, "version_minor": 0}, "text/plain": ["0 examples [00:00, ? examples/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "de049b4c0c0f4f9cb8f4d6bd164bcf03", "version_major": 2, "version_minor": 0}, "text/plain": ["0 examples [00:00, ? examples/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "4584f523c0ea4b759d59634396812939", "version_major": 2, "version_minor": 0}, "text/plain": ["0 examples [00:00, ? examples/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Dataset glue downloaded and prepared to /home/AzDevOps_azpcontainer/.cache/huggingface/datasets/glue/mnli/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "6825b5f3d1134155a08cf6468329a32c", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/5 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "1ed356d6622d463b8d15d3571a839dca", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/393 [00:00<?, ?ba/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:2212: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n", "  warnings.warn(\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "27b46c5fc533481e91df42433f0488e6", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/10 [00:00<?, ?ba/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "a31baeb38e144a16afcc2b9443c0dbc3", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/10 [00:00<?, ?ba/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "203bf9bae98c4c10a87648996620d0ea", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/10 [00:00<?, ?ba/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "43ec4fc112ca434dbcce16e22069c0ed", "version_major": 2, "version_minor": 0}, "text/plain": ["  0%|          | 0/10 [00:00<?, ?ba/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["Some weights of the model checkpoint at distilbert-base-cased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_transform.weight', 'vocab_projector.weight']\n", "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-cased and are newly initialized: ['pre_classifier.weight', 'classifier.bias', 'pre_classifier.bias', 'classifier.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=20)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\n", "  rank_zero_deprecation(\n", "GPU available: True, used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:111: UserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", "  rank_zero_warn(\n", "/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:111: UserWarning: The dataloader, val_dataloader 1, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", "  rank_zero_warn(\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "a690db0f7e5b4c22a70cec51295c6e2e", "version_major": 2, "version_minor": 0}, "text/plain": ["Validating: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["--------------------------------------------------------------------------------\n", "DATALOADER:0 VALIDATE RESULTS\n", "{'accuracy_matched': 0.3237901031970978,\n", " 'accuracy_mismatched': 0.31794142723083496,\n", " 'val_loss_matched': 1.104950189590454,\n", " 'val_loss_mismatched': 1.1043992042541504}\n", "--------------------------------------------------------------------------------\n", "DATALOADER:1 VALIDATE RESULTS\n", "{'accuracy_matched': 0.3237901031970978,\n", " 'accuracy_mismatched': 0.31794142723083496,\n", " 'val_loss_matched': 1.104950189590454,\n", " 'val_loss_mismatched': 1.1043992042541504}\n", "--------------------------------------------------------------------------------\n"]}, {"data": {"text/plain": ["[{'val_loss_matched': 1.104950189590454,\n", "  'accuracy_matched': 0.3237901031970978,\n", "  'val_loss_mismatched': 1.1043992042541504,\n", "  'accuracy_mismatched': 0.31794142723083496},\n", " {'val_loss_matched': 1.104950189590454,\n", "  'accuracy_matched': 0.3237901031970978,\n", "  'val_loss_mismatched': 1.1043992042541504,\n", "  'accuracy_mismatched': 0.31794142723083496}]"]}, "execution_count": 8, "metadata": {}, "output_type": "execute_result"}], "source": ["dm = GLUEDataModule(\n", "    model_name_or_path=\"distilbert-base-cased\",\n", "    task_name=\"mnli\",\n", ")\n", "dm.setup(\"fit\")\n", "model = GLUETransformer(\n", "    model_name_or_path=\"distilbert-base-cased\",\n", "    num_labels=dm.num_labels,\n", "    eval_splits=dm.eval_splits,\n", "    task_name=dm.task_name,\n", ")\n", "\n", "trainer = Trainer(gpus=AVAIL_GPUS, progress_bar_refresh_rate=20)\n", "trainer.validate(model, dm.val_dataloader())"]}, {"cell_type": "markdown", "id": "bf582092", "metadata": {"papermill": {"duration": 0.135707, "end_time": "2021-12-04T16:54:15.957607", "exception": false, "start_time": "2021-12-04T16:54:15.821900", "status": "completed"}, "tags": []}, "source": ["## Congratulations - Time to Join the Community!\n", "\n", "Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the Lightning\n", "movement, you can do so in the following ways!\n", "\n", "### Star [Lightning](https://github.com/PyTorchLightning/pytorch-lightning) on GitHub\n", "The easiest way to help our community is just by starring the GitHub repos! This helps raise awareness of the cool\n", "tools we're building.\n", "\n", "### Join our [Slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-pw5v393p-qRaDgEk24~EjiZNBpSQFgQ)!\n", "The best way to keep up to date on the latest advancements is to join our community! Make sure to introduce yourself\n", "and share your interests in `#general` channel\n", "\n", "\n", "### Contributions !\n", "The best way to contribute to our community is to become a code contributor! At any time you can go to\n", "[Lightning](https://github.com/PyTorchLightning/pytorch-lightning) or [Bolt](https://github.com/PyTorchLightning/lightning-bolts)\n", "GitHub Issues page and filter for \"good first issue\".\n", "\n", "* [Lightning good first issue](https://github.com/PyTorchLightning/pytorch-lightning/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* [Bolt good first issue](https://github.com/PyTorchLightning/lightning-bolts/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* You can also contribute your own notebooks with useful examples !\n", "\n", "### Great thanks from the entire Pytorch Lightning Team for your interest !\n", "\n", "[![Pytorch 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