experiments/tilt-part-4-1-abstractive-approach-for-training.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/akarshu121/tilt-part-4-1-abstractive-approach-for-training?scriptVersionId=125284241\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","execution_count":1,"id":"4f8aec17","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:56:11.214877Z","iopub.status.busy":"2023-04-10T15:56:11.213752Z","iopub.status.idle":"2023-04-10T15:56:21.786043Z","shell.execute_reply":"2023-04-10T15:56:21.784728Z"},"papermill":{"duration":10.586065,"end_time":"2023-04-10T15:56:21.789229","exception":false,"start_time":"2023-04-10T15:56:11.203164","status":"completed"},"tags":[]},"outputs":[],"source":["!pip install -qqq wandb"]},{"cell_type":"code","execution_count":2,"id":"319d74ee","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:56:21.806674Z","iopub.status.busy":"2023-04-10T15:56:21.805992Z","iopub.status.idle":"2023-04-10T15:56:24.80381Z","shell.execute_reply":"2023-04-10T15:56:24.802278Z"},"papermill":{"duration":3.008937,"end_time":"2023-04-10T15:56:24.806119","exception":false,"start_time":"2023-04-10T15:56:21.797182","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: W&B API key is configured. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n","\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n","\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n","\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"]},{"data":{"text/plain":["True"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["## Logging into wandb\n","\n","import wandb\n","from kaggle_secrets import UserSecretsClient\n","user_secrets = UserSecretsClient()\n","secret_value_0 = user_secrets.get_secret(\"wandb_api\")\n","wandb.login(key=secret_value_0)"]},{"cell_type":"code","execution_count":3,"id":"e1e22577","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:56:24.823877Z","iopub.status.busy":"2023-04-10T15:56:24.823512Z","iopub.status.idle":"2023-04-10T15:56:26.56511Z","shell.execute_reply":"2023-04-10T15:56:26.56387Z"},"id":"15ohrRAbDZ4v","outputId":"35a546ba-a3d6-4b36-e54c-6b7ebd134fbb","papermill":{"duration":1.753327,"end_time":"2023-04-10T15:56:26.5678","exception":false,"start_time":"2023-04-10T15:56:24.814473","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Cloning into 'TiLT-Implementation'...\r\n","remote: Enumerating objects: 54, done.\u001b[K\r\n","remote: Counting objects: 100% (54/54), done.\u001b[K\r\n","remote: Compressing objects: 100% (43/43), done.\u001b[K\r\n","remote: Total 54 (delta 19), reused 41 (delta 9), pack-reused 0\u001b[K\r\n","Receiving objects: 100% (54/54), 270.58 KiB | 11.27 MiB/s, done.\r\n","Resolving deltas: 100% (19/19), done.\r\n"]}],"source":["!git clone https://github.com/uakarsh/TiLT-Implementation.git"]},{"cell_type":"code","execution_count":4,"id":"8dd7daa9","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:56:26.586616Z","iopub.status.busy":"2023-04-10T15:56:26.58625Z","iopub.status.idle":"2023-04-10T15:56:44.299553Z","shell.execute_reply":"2023-04-10T15:56:44.298229Z"},"id":"IlsCNhv3D0hx","outputId":"975280fa-ade1-4dac-9854-0faf7ba923e0","papermill":{"duration":17.725898,"end_time":"2023-04-10T15:56:44.302298","exception":false,"start_time":"2023-04-10T15:56:26.5764","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: transformers in /opt/conda/lib/python3.7/site-packages (from -r ./TiLT-Implementation/requirements.txt (line 1)) (4.27.4)\r\n","Requirement already satisfied: datasets in /opt/conda/lib/python3.7/site-packages (from -r 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collected packages: seqeval, evaluate\r\n","Successfully installed evaluate-0.4.0 seqeval-1.2.2\r\n","\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n","\u001b[0m"]}],"source":["!pip install -r ./TiLT-Implementation/requirements.txt"]},{"cell_type":"code","execution_count":5,"id":"6bd9e51a","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:56:44.323511Z","iopub.status.busy":"2023-04-10T15:56:44.323162Z","iopub.status.idle":"2023-04-10T15:56:44.32878Z","shell.execute_reply":"2023-04-10T15:56:44.327796Z"},"id":"1W4eZnAcD3Pg","papermill":{"duration":0.018934,"end_time":"2023-04-10T15:56:44.331041","exception":false,"start_time":"2023-04-10T15:56:44.312107","status":"completed"},"tags":[]},"outputs":[],"source":["import sys\n","sys.path.append(\"./TiLT-Implementation/src/\")"]},{"cell_type":"code","execution_count":6,"id":"48303034","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:56:44.352328Z","iopub.status.busy":"2023-04-10T15:56:44.351987Z","iopub.status.idle":"2023-04-10T15:56:50.965637Z","shell.execute_reply":"2023-04-10T15:56:50.964118Z"},"id":"hshzmmrID39p","papermill":{"duration":6.627727,"end_time":"2023-04-10T15:56:50.968245","exception":false,"start_time":"2023-04-10T15:56:44.340518","status":"completed"},"tags":[]},"outputs":[],"source":["import os\n","from transformers import AutoTokenizer, AutoConfig\n","from datasets import load_dataset\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import DataLoader\n","\n","from pytorch_lightning.loggers import CSVLogger, WandbLogger\n","from pytorch_lightning.callbacks import ModelCheckpoint\n","import pytorch_lightning as pl\n","\n","from dataset import ExtFUNSDDs\n","from torchvision import transforms\n","from tqdm.auto import tqdm\n","\n","## Custom imports\n","from visual_backbone import Unet_encoder, RoIPool\n","from t5 import T5ForConditionalGenerationAbstractive, T5Stack\n","from transformers import AutoModel"]},{"cell_type":"markdown","id":"97159a8f","metadata":{"id":"uPeVZmMeEbyf","papermill":{"duration":0.009231,"end_time":"2023-04-10T15:56:50.987594","exception":false,"start_time":"2023-04-10T15:56:50.978363","status":"completed"},"tags":[]},"source":["## 1.1. Preparing the dataset"]},{"cell_type":"code","execution_count":7,"id":"a52d2c8a","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:56:51.008493Z","iopub.status.busy":"2023-04-10T15:56:51.00757Z","iopub.status.idle":"2023-04-10T15:57:06.139767Z","shell.execute_reply":"2023-04-10T15:57:06.138729Z"},"id":"qzcvnEyYD-KC","outputId":"92b00a6e-4e20-40c6-8ada-a96e87137cff","papermill":{"duration":15.145084,"end_time":"2023-04-10T15:57:06.142168","exception":false,"start_time":"2023-04-10T15:56:50.997084","status":"completed"},"tags":[]},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"88e2d50a63604917b63b70c489d07bcb","version_major":2,"version_minor":0},"text/plain":["Downloading builder script:   0%|          | 0.00/5.13k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Downloading and preparing dataset funsd/funsd to /root/.cache/huggingface/datasets/nielsr___funsd/funsd/1.0.0/0e3f4efdfd59aa1c3b4952c517894f7b1fc4d75c12ef01bcc8626a69e41c1bb9...\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"932e4f6743d54ece8653ede2e75ad539","version_major":2,"version_minor":0},"text/plain":["Downloading data:   0%|          | 0.00/16.8M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"bc693f2b6ef1414da7a20749f16b3acf","version_major":2,"version_minor":0},"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5e08b77980ef4272a906dbeb93d9920f","version_major":2,"version_minor":0},"text/plain":["Generating test split: 0 examples [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Dataset funsd downloaded and prepared to /root/.cache/huggingface/datasets/nielsr___funsd/funsd/1.0.0/0e3f4efdfd59aa1c3b4952c517894f7b1fc4d75c12ef01bcc8626a69e41c1bb9. Subsequent calls will reuse this data.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0b8f0a067259447c85e95cda7faf8f51","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/2 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"6d19fe8881524a07a9bc6a0892473206","version_major":2,"version_minor":0},"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n","\n","hf_ds = load_dataset(\"nielsr/funsd-layoutlmv3\")\n","model_name = \"t5-base\"\n","## Visual Embedding extractor's parameters\n","in_channels = 3\n","num_pool_layers = 3\n","channels = 16\n","sampling_ratio = 2\n","spatial_scale = 48 / 384\n","output_size = (3,3)\n","load_weights = True\n","max_epochs = 50\n","\n","## FUNSD Dataset specific\n","num_classes = 7\n","\n","## Tokenizer's parameter\n","model_max_length = 512\n","\n","t5_config = AutoConfig.from_pretrained(model_name)\n","## Adding new parameters\n","t5_config.update(dict(in_channels = in_channels, num_pool_layers = num_pool_layers,  channels = channels, model_max_length = model_max_length,\n","                      output_size = output_size, spatial_scale = spatial_scale, sampling_ratio = sampling_ratio, use_cache = False, load_weights = load_weights,\n","                      lr =  2e-4, num_classes = num_classes,max_epochs = max_epochs))"]},{"cell_type":"code","execution_count":8,"id":"e5364304","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:06.167138Z","iopub.status.busy":"2023-04-10T15:57:06.166134Z","iopub.status.idle":"2023-04-10T15:57:06.173004Z","shell.execute_reply":"2023-04-10T15:57:06.171963Z"},"id":"DOzw5T71EJtJ","papermill":{"duration":0.021241,"end_time":"2023-04-10T15:57:06.175376","exception":false,"start_time":"2023-04-10T15:57:06.154135","status":"completed"},"tags":[]},"outputs":[],"source":["def get_id2label_and_label2id():\n","    label2id = {'O': 0, 'B-HEADER': 1, 'I-HEADER': 2, 'B-QUESTION': 3, 'I-QUESTION': 4, 'B-ANSWER': 5, 'I-ANSWER': 6}\n","    id2label = {0: 'O', 1: 'B-HEADER', 2: 'I-HEADER', 3: 'B-QUESTION', 4: 'I-QUESTION', 5: 'B-ANSWER', 6: 'I-ANSWER'}\n","    return id2label, label2id\n","\n","def convert_id_to_label(list_of_label):\n","  return [id2label[x] for x in list_of_label]"]},{"cell_type":"code","execution_count":9,"id":"d7c51838","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:06.197519Z","iopub.status.busy":"2023-04-10T15:57:06.197238Z","iopub.status.idle":"2023-04-10T15:57:06.201534Z","shell.execute_reply":"2023-04-10T15:57:06.200353Z"},"id":"2x4vv43XEOKV","papermill":{"duration":0.018282,"end_time":"2023-04-10T15:57:06.20403","exception":false,"start_time":"2023-04-10T15:57:06.185748","status":"completed"},"tags":[]},"outputs":[],"source":["# train_new_tags = list(map(lambda x : convert_id_to_label(x), hf_ds['train']['ner_tags']))\n","# test_new_tags = list(map(lambda x : convert_id_to_label(x), hf_ds['test']['ner_tags']))"]},{"cell_type":"code","execution_count":10,"id":"4d6fb438","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:06.226946Z","iopub.status.busy":"2023-04-10T15:57:06.226629Z","iopub.status.idle":"2023-04-10T15:57:06.230393Z","shell.execute_reply":"2023-04-10T15:57:06.229357Z"},"id":"UcmRobRhEQ0E","papermill":{"duration":0.017941,"end_time":"2023-04-10T15:57:06.232716","exception":false,"start_time":"2023-04-10T15:57:06.214775","status":"completed"},"tags":[]},"outputs":[],"source":["# hf_ds['train'] = hf_ds['train'].remove_columns(\"ner_tags\").add_column(\"ner_tags\", train_new_tags)\n","# hf_ds['test'] = hf_ds['test'].remove_columns(\"ner_tags\").add_column(\"ner_tags\", test_new_tags)"]},{"cell_type":"markdown","id":"678aefd2","metadata":{"id":"8bHthQUuEeVD","papermill":{"duration":0.01,"end_time":"2023-04-10T15:57:06.253261","exception":false,"start_time":"2023-04-10T15:57:06.243261","status":"completed"},"tags":[]},"source":["### 1.2 Writing the `collate_fn` for custom handling of the dataloader"]},{"cell_type":"code","execution_count":11,"id":"846eca7a","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:06.275379Z","iopub.status.busy":"2023-04-10T15:57:06.274696Z","iopub.status.idle":"2023-04-10T15:57:06.281303Z","shell.execute_reply":"2023-04-10T15:57:06.280317Z"},"id":"fchZF6hxESY6","papermill":{"duration":0.02007,"end_time":"2023-04-10T15:57:06.283554","exception":false,"start_time":"2023-04-10T15:57:06.263484","status":"completed"},"tags":[]},"outputs":[],"source":["class CollateFn(object):\n","  def __init__(self, tokenizer):\n","    self.tokenizer = tokenizer\n","\n","  def __call__(self, list_of_ds):\n","    simple_keys = [\"input_ids\", \"attention_mask\", \"bboxes\", \"pixel_values\", \"labels\"]\n","    actual_batch = {}\n","    for key in simple_keys:\n","      actual_batch[key] = torch.stack([x[key] for x in list_of_ds])\n","    \n","    # actual_batch['labels'] = self.tokenizer.batch_encode_plus([x['labels'] for x in list_of_ds], return_tensors = 'pt', is_split_into_words = True,\n","    #                                                           padding='max_length', truncation = True)['input_ids']\n","    return actual_batch"]},{"cell_type":"code","execution_count":12,"id":"835fbe21","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:06.30501Z","iopub.status.busy":"2023-04-10T15:57:06.30475Z","iopub.status.idle":"2023-04-10T15:57:06.309718Z","shell.execute_reply":"2023-04-10T15:57:06.308801Z"},"id":"m26xnsfXEVFJ","papermill":{"duration":0.01817,"end_time":"2023-04-10T15:57:06.311895","exception":false,"start_time":"2023-04-10T15:57:06.293725","status":"completed"},"tags":[]},"outputs":[],"source":["# sample_batch_encoding = collate_fn([train_ds[0], train_ds[1]])\n","# for key in sample_batch_encoding:\n","#   sample_batch_encoding[key] = sample_batch_encoding[key].to(device)"]},{"cell_type":"markdown","id":"45c2444b","metadata":{"id":"u5qhZ38OGQDJ","papermill":{"duration":0.009998,"end_time":"2023-04-10T15:57:06.332342","exception":false,"start_time":"2023-04-10T15:57:06.322344","status":"completed"},"tags":[]},"source":["## 2.1 Preparing the visual model"]},{"cell_type":"code","execution_count":13,"id":"206f48c8","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:06.353964Z","iopub.status.busy":"2023-04-10T15:57:06.353697Z","iopub.status.idle":"2023-04-10T15:57:06.360541Z","shell.execute_reply":"2023-04-10T15:57:06.359529Z"},"id":"_cVQaYBUEZbv","papermill":{"duration":0.020305,"end_time":"2023-04-10T15:57:06.362854","exception":false,"start_time":"2023-04-10T15:57:06.342549","status":"completed"},"tags":[]},"outputs":[],"source":["class VisualEmbedding(nn.Module):\n","  def __init__(self, config):\n","    super().__init__()\n","    self.unet_encoder = Unet_encoder(in_channels = config.in_channels, channels = config.channels, num_pool_layers = config.num_pool_layers)\n","    self.roi_pool = RoIPool(output_size = config.output_size, spatial_scale = config.spatial_scale)\n","    self.proj = nn.Linear(in_features = 128 * 3 * 3, out_features = config.d_model)\n","    self.config = config\n","\n","  def forward(self, pixel_values, bboxes):\n","    image_embedding = self.unet_encoder(pixel_values)\n","    feature_maps_bboxes = self.roi_pool(image_embedding, bboxes).flatten(2)\n","    projection = self.proj(feature_maps_bboxes)\n","    return projection"]},{"cell_type":"markdown","id":"a3df542e","metadata":{"id":"jdJzjR8elcoj","papermill":{"duration":0.010203,"end_time":"2023-04-10T15:57:06.383712","exception":false,"start_time":"2023-04-10T15:57:06.373509","status":"completed"},"tags":[]},"source":["## 2.2 Preparing the semantic model"]},{"cell_type":"code","execution_count":14,"id":"8a561a3c","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:06.405772Z","iopub.status.busy":"2023-04-10T15:57:06.405473Z","iopub.status.idle":"2023-04-10T15:57:06.413279Z","shell.execute_reply":"2023-04-10T15:57:06.412134Z"},"id":"SvWFMOIMDoaR","papermill":{"duration":0.021233,"end_time":"2023-04-10T15:57:06.415353","exception":false,"start_time":"2023-04-10T15:57:06.39412","status":"completed"},"tags":[]},"outputs":[],"source":["class TiLTTransformer(nn.Module):\n","  def __init__(self, config):\n","    super().__init__()\n","    self.config = config\n","    self.visual_embedding_extractor = VisualEmbedding(config)\n","    self.t5_model = T5ForConditionalGenerationAbstractive(config)\n","    \n","\n","  def generate(self, batch):\n","    total_embedding = self.common_step(batch)\n","    return self.t5_model.generate(input_embeds = total_embedding)\n","\n","  def common_step(self, batch):\n","    ## Visual embedding\n","    visual_embedding = self.visual_embedding_extractor(pixel_values = batch['pixel_values'], bboxes = batch['bboxes'])\n","\n","    ## Semantic embedding from t5_model's embedding layer\n","    semantic_embedding = self.t5_model.shared(batch['input_ids'])\n","\n","    ## Net embedding is addition of both the embeddings\n","    total_embedding = visual_embedding + semantic_embedding\n","\n","    return total_embedding\n","\n","  def forward(self, batch):\n","\n","    total_embedding = self.common_step(batch)\n","\n","    ## This is then fed to t5_model\n","    final_output = self.t5_model(attention_mask = batch['attention_mask'], inputs_embeds = total_embedding,\n","                            labels = batch['labels'])\n","    \n","    return final_output"]},{"cell_type":"code","execution_count":15,"id":"5052dc0b","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:06.438872Z","iopub.status.busy":"2023-04-10T15:57:06.437776Z","iopub.status.idle":"2023-04-10T15:57:06.442586Z","shell.execute_reply":"2023-04-10T15:57:06.44155Z"},"id":"sZXlIJcmypem","papermill":{"duration":0.018557,"end_time":"2023-04-10T15:57:06.444719","exception":false,"start_time":"2023-04-10T15:57:06.426162","status":"completed"},"tags":[]},"outputs":[],"source":["# tilt_model = TiLTTransformer(t5_config).to(device)\n","# output = tilt_model(sample_batch_encoding)"]},{"cell_type":"markdown","id":"6912d013","metadata":{"id":"y0KzA2Icys9y","papermill":{"duration":0.010127,"end_time":"2023-04-10T15:57:06.466065","exception":false,"start_time":"2023-04-10T15:57:06.455938","status":"completed"},"tags":[]},"source":["## 3.1 Preparing the metrics to evaluate the predictions"]},{"cell_type":"code","execution_count":16,"id":"ee2e80cf","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:06.489251Z","iopub.status.busy":"2023-04-10T15:57:06.488157Z","iopub.status.idle":"2023-04-10T15:57:14.552932Z","shell.execute_reply":"2023-04-10T15:57:14.551655Z"},"id":"MKSWfgh-ywOk","papermill":{"duration":8.078698,"end_time":"2023-04-10T15:57:14.555596","exception":false,"start_time":"2023-04-10T15:57:06.476898","status":"completed"},"tags":[]},"outputs":[],"source":["import evaluate\n","\n","def get_labels(predictions, references):\n","\n","    # Transform predictions and references tensors to numpy arrays\n","    if predictions.device.type == \"cpu\":\n","        y_pred = predictions.detach().clone().numpy()\n","        y_true = references.detach().clone().numpy()\n","\n","    else:\n","        y_pred = predictions.detach().cpu().clone().numpy()\n","        y_true = references.detach().cpu().clone().numpy()\n","\n","    # Remove ignored index (special tokens)\n","    true_predictions = [\n","        [id2label[p] for (p, l) in zip(pred, gold_label) if l != -100]\n","        for pred, gold_label in zip(y_pred, y_true)\n","    ]\n","    true_labels = [\n","        [id2label[l] for (p, l) in zip(pred, gold_label) if l != -100]\n","        for pred, gold_label in zip(y_pred, y_true)\n","    ]\n","    return true_predictions, true_labels"]},{"cell_type":"code","execution_count":17,"id":"b8fa6ecd","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:14.578085Z","iopub.status.busy":"2023-04-10T15:57:14.577779Z","iopub.status.idle":"2023-04-10T15:57:14.582434Z","shell.execute_reply":"2023-04-10T15:57:14.581312Z"},"id":"mNIJk9Vnz-JG","papermill":{"duration":0.018497,"end_time":"2023-04-10T15:57:14.584655","exception":false,"start_time":"2023-04-10T15:57:14.566158","status":"completed"},"tags":[]},"outputs":[],"source":["# labels = sample_batch_encoding['labels']"]},{"cell_type":"code","execution_count":18,"id":"3e571969","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:14.606729Z","iopub.status.busy":"2023-04-10T15:57:14.606407Z","iopub.status.idle":"2023-04-10T15:57:14.610794Z","shell.execute_reply":"2023-04-10T15:57:14.609741Z"},"id":"L4bldNSl0FUG","papermill":{"duration":0.017892,"end_time":"2023-04-10T15:57:14.613139","exception":false,"start_time":"2023-04-10T15:57:14.595247","status":"completed"},"tags":[]},"outputs":[],"source":["# true_predictions, true_labels = get_labels(predictions = output.logits.argmax(axis = -1), references = labels)"]},{"cell_type":"code","execution_count":19,"id":"751370fc","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:14.635658Z","iopub.status.busy":"2023-04-10T15:57:14.63493Z","iopub.status.idle":"2023-04-10T15:57:14.639367Z","shell.execute_reply":"2023-04-10T15:57:14.638357Z"},"id":"c5tLEdWw1ywr","papermill":{"duration":0.017963,"end_time":"2023-04-10T15:57:14.641464","exception":false,"start_time":"2023-04-10T15:57:14.623501","status":"completed"},"tags":[]},"outputs":[],"source":["# eval_metric = evaluate.load(\"seqeval\")\n","# metric = eval_metric.compute(predictions = true_predictions, references = true_labels)"]},{"cell_type":"markdown","id":"edf2bcc0","metadata":{"id":"Q0X5UKG_2idi","papermill":{"duration":0.010734,"end_time":"2023-04-10T15:57:14.662765","exception":false,"start_time":"2023-04-10T15:57:14.652031","status":"completed"},"tags":[]},"source":["## Part: 4 Writing the `pytorch_lightning` code for training on FUNSD"]},{"cell_type":"code","execution_count":20,"id":"37c5e365","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:14.686209Z","iopub.status.busy":"2023-04-10T15:57:14.685137Z","iopub.status.idle":"2023-04-10T15:57:16.427694Z","shell.execute_reply":"2023-04-10T15:57:16.42647Z"},"id":"XhBnjeKbjTcG","outputId":"9c63cfa5-a051-4092-d31c-97f4a8b302d8","papermill":{"duration":1.757115,"end_time":"2023-04-10T15:57:16.430381","exception":false,"start_time":"2023-04-10T15:57:14.673266","status":"completed"},"tags":[]},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8c55c82e10664ff1954ade49d8a85972","version_major":2,"version_minor":0},"text/plain":["Downloading (…)ve/main/spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0a0dff3233034db58e13bda3956951f5","version_major":2,"version_minor":0},"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["id2label, label2id = get_id2label_and_label2id()\n","transform = transforms.Compose([transforms.ToTensor(), \n","                                transforms.Lambda(lambda x : 2 * x - 1)])\n","\n","## Tokenizer\n","tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast = True, model_max_length = model_max_length)\n","\n","train_ds = ExtFUNSDDs(hf_ds['train'],tokenizer = tokenizer, transform = transform)\n","val_ds = ExtFUNSDDs(hf_ds['test'],tokenizer = tokenizer, transform = transform)\n","\n","collate_fn = CollateFn(tokenizer)"]},{"cell_type":"code","execution_count":21,"id":"95c19cf4","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:16.453983Z","iopub.status.busy":"2023-04-10T15:57:16.453666Z","iopub.status.idle":"2023-04-10T15:57:16.461444Z","shell.execute_reply":"2023-04-10T15:57:16.46051Z"},"id":"vT6i27HDjdKd","papermill":{"duration":0.022022,"end_time":"2023-04-10T15:57:16.463736","exception":false,"start_time":"2023-04-10T15:57:16.441714","status":"completed"},"tags":[]},"outputs":[],"source":["class DataModule(pl.LightningDataModule):\n","\n","  def __init__(self, train_dataset, eval_dataset, batch_size: int = 2):\n","    super(DataModule, self).__init__()\n","    self.batch_size = batch_size\n","    self.train_dataset = train_dataset\n","    self.eval_dataset = eval_dataset\n","\n","  def train_dataloader(self):\n","    return DataLoader(self.train_dataset, batch_size=self.batch_size,\n","                      shuffle=True, collate_fn = collate_fn)\n","\n","  def val_dataloader(self):\n","    return DataLoader(self.eval_dataset, batch_size=self.batch_size,\n","                      shuffle=False, collate_fn = collate_fn)"]},{"cell_type":"code","execution_count":22,"id":"617fda92","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:16.490739Z","iopub.status.busy":"2023-04-10T15:57:16.49037Z","iopub.status.idle":"2023-04-10T15:57:16.51247Z","shell.execute_reply":"2023-04-10T15:57:16.510705Z"},"id":"aqrzxUcb12Ov","papermill":{"duration":0.040537,"end_time":"2023-04-10T15:57:16.515094","exception":false,"start_time":"2023-04-10T15:57:16.474557","status":"completed"},"tags":[]},"outputs":[],"source":["class TiltModel(pl.LightningModule):\n","    def __init__(self, config):\n","        super().__init__()\n","        self.config = config\n","        self.tilt_model = TiLTTransformer(config)\n","        self.eval_metric = evaluate.load(\"seqeval\")\n","\n","    def forward(self, batch):\n","        return self.tilt_model(batch)\n","\n","    def training_step(self, batch, batch_idx):\n","        output = self(batch)\n","        loss = output.loss\n","\n","        predictions = output.logits.argmax(dim=-1)        \n","        true_predictions, true_labels = get_labels(predictions = predictions, references =  batch['labels'])\n","\n","        results = self.eval_metric.compute(predictions=true_predictions, references=true_labels)\n","        self.log(\"train_loss\", output.loss.item(),\n","                 prog_bar=True, on_epoch=True, logger=True)\n","        self.log(\"train_overall_fl\",\n","                 results[\"overall_f1\"], prog_bar=True, on_epoch=True, logger=True)\n","        self.log(\"train_overall_recall\",\n","                 results[\"overall_recall\"], prog_bar=True, on_epoch=True, logger=True)\n","        self.log(\"train_overall_precision\",\n","                 results[\"overall_precision\"], prog_bar=True, on_epoch=True, logger=True)\n","\n","        return loss\n","\n","    def validation_step(self, batch, batch_idx):\n","        output = self(batch)\n","        loss = output.loss\n","\n","        predictions = output.logits.argmax(dim=-1)        \n","        true_predictions, true_labels = get_labels(predictions = predictions, references =  batch['labels'])\n","        results = self.eval_metric.compute(predictions=true_predictions, references=true_labels)\n","\n","        self.log(\"val_loss\", output.loss.item(),\n","                 prog_bar=True, on_epoch=True, logger=True)\n","        self.log(\"val_overall_fl\", results[\"overall_f1\"],\n","                 prog_bar=True, on_epoch=True, logger=True)\n","        self.log(\"val_overall_recall\",\n","                 results[\"overall_recall\"], prog_bar=True, on_epoch=True, logger=True)\n","        self.log(\"val_overall_precision\",\n","                 results[\"overall_precision\"], prog_bar=True, on_epoch=True, logger=True)\n","\n","        return loss\n","\n","    def configure_optimizers(self):\n","        optimizer = torch.optim.AdamW(self.parameters(), lr=self.config.lr)\n","        return optimizer"]},{"cell_type":"code","execution_count":23,"id":"d938ef68","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:16.553593Z","iopub.status.busy":"2023-04-10T15:57:16.553164Z","iopub.status.idle":"2023-04-10T15:57:16.562366Z","shell.execute_reply":"2023-04-10T15:57:16.561207Z"},"id":"T5S_yW3Hmvjf","papermill":{"duration":0.026317,"end_time":"2023-04-10T15:57:16.564479","exception":false,"start_time":"2023-04-10T15:57:16.538162","status":"completed"},"tags":[]},"outputs":[],"source":["def perform_evaluation(path: str=None, pl_model=None, pl_dl=None):\n","    print(\"Evaluating the model\")\n","    if path is not None:\n","      pl_model = pl_model.load_from_checkpoint(path, config = t5_config)\n","\n","    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","    eval_metric = evaluate.load(\"seqeval\")\n","    pl_model = pl_model.to(device)\n","    pl_model.eval();\n","\n","    for idx, batch in enumerate(tqdm(pl_dl.val_dataloader())):\n","      # move batch to device\n","      batch = {k: v.to(device) for k, v in batch.items()}\n","      \n","      with torch.no_grad():\n","        outputs = pl_model(batch)\n","\n","        predictions = outputs.logits.argmax(-1)\n","        true_predictions, true_labels = get_labels(predictions, batch[\"labels\"])\n","        eval_metric.add_batch(references=true_labels,predictions=true_predictions)\n","\n","    results = eval_metric.compute()\n","\n","    metrics = {}\n","    for key in ['overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']:\n","      print_statement = '{0: <30}'.format(str(key) + \" has value:\")\n","      print(print_statement, results[key])\n","      metrics[key] = results[key]\n","\n","    return metrics"]},{"cell_type":"code","execution_count":24,"id":"bc3379eb","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:16.599934Z","iopub.status.busy":"2023-04-10T15:57:16.599513Z","iopub.status.idle":"2023-04-10T15:57:16.618259Z","shell.execute_reply":"2023-04-10T15:57:16.61695Z"},"id":"I23YafqBnxda","papermill":{"duration":0.045936,"end_time":"2023-04-10T15:57:16.6209","exception":false,"start_time":"2023-04-10T15:57:16.574964","status":"completed"},"tags":[]},"outputs":[],"source":["checkpoint_callback = ModelCheckpoint(dirpath=\"./tilt/models\",monitor=\"val_overall_fl\", mode=\"max\", filename='tilt_best_ckpt', save_top_k = 1)\n","logger = CSVLogger(\"./tilt/logs\", name=\"funsd_dataset\")"]},{"cell_type":"code","execution_count":25,"id":"108679da","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:16.647754Z","iopub.status.busy":"2023-04-10T15:57:16.646791Z","iopub.status.idle":"2023-04-10T15:57:47.757443Z","shell.execute_reply":"2023-04-10T15:57:47.756361Z"},"papermill":{"duration":31.125317,"end_time":"2023-04-10T15:57:47.760569","exception":false,"start_time":"2023-04-10T15:57:16.635252","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33miakarshu\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"]},{"data":{"text/html":["wandb version 0.14.2 is available!  To upgrade, please run:\n"," $ pip install wandb --upgrade"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Tracking run with wandb version 0.14.0"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Run data is saved locally in <code>/kaggle/working/wandb/run-20230410_155716-efoj1grb</code>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Syncing run <strong><a href='https://wandb.ai/iakarshu/TiLT%20on%20FUNSD/runs/efoj1grb' target=\"_blank\">spring-plasma-4</a></strong> to <a href='https://wandb.ai/iakarshu/TiLT%20on%20FUNSD' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":[" View project at <a href='https://wandb.ai/iakarshu/TiLT%20on%20FUNSD' target=\"_blank\">https://wandb.ai/iakarshu/TiLT%20on%20FUNSD</a>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":[" View run at <a href='https://wandb.ai/iakarshu/TiLT%20on%20FUNSD/runs/efoj1grb' target=\"_blank\">https://wandb.ai/iakarshu/TiLT%20on%20FUNSD/runs/efoj1grb</a>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["/opt/conda/lib/python3.7/site-packages/pytorch_lightning/loggers/wandb.py:396: UserWarning: There is a wandb run already in progress and newly created instances of `WandbLogger` will reuse this run. If this is not desired, call `wandb.finish()` before instantiating `WandbLogger`.\n","  \"There is a wandb run already in progress and newly created instances of `WandbLogger` will reuse\"\n"]}],"source":["wandb.init(config=t5_config, project=\"TiLT on FUNSD\")\n","wandb_logger = WandbLogger(project=\"TiLT on FUNSD\", log_model = False, entity=\"iakarshu\")"]},{"cell_type":"code","execution_count":26,"id":"2cefd18a","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:47.797794Z","iopub.status.busy":"2023-04-10T15:57:47.797423Z","iopub.status.idle":"2023-04-10T15:57:47.87625Z","shell.execute_reply":"2023-04-10T15:57:47.875248Z"},"id":"rXvP5KoBoHK6","outputId":"d605b3af-62e1-41e4-b582-6f11c1f1bb20","papermill":{"duration":0.100701,"end_time":"2023-04-10T15:57:47.879117","exception":false,"start_time":"2023-04-10T15:57:47.778416","status":"completed"},"tags":[]},"outputs":[],"source":["trainer = pl.Trainer(default_root_dir=\"./tilt/logs\", devices=\"auto\",\n","                     accelerator=\"auto\",max_epochs= t5_config.max_epochs, \n","                     logger=wandb_logger,callbacks=[checkpoint_callback],\n","                     log_every_n_steps = 5)"]},{"cell_type":"code","execution_count":27,"id":"b55768aa","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:57:47.915791Z","iopub.status.busy":"2023-04-10T15:57:47.915408Z","iopub.status.idle":"2023-04-10T15:58:08.452674Z","shell.execute_reply":"2023-04-10T15:58:08.448751Z"},"id":"Til_O0zfocpN","outputId":"0b6ee30e-9ac2-40ab-841f-9f04fe53b853","papermill":{"duration":20.559413,"end_time":"2023-04-10T15:58:08.456269","exception":false,"start_time":"2023-04-10T15:57:47.896856","status":"completed"},"tags":[]},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fb95ff3f65ae43f5961f64fe2040f7ee","version_major":2,"version_minor":0},"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/892M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Weights loaded successfully!\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ee10f48eeec2416588bee030900378d6","version_major":2,"version_minor":0},"text/plain":["Downloading builder script:   0%|          | 0.00/6.34k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["pl_model = TiltModel(t5_config)\n","pl_dl = DataModule(train_ds, val_ds, batch_size = 2)"]},{"cell_type":"code","execution_count":28,"id":"5e632ae0","metadata":{"execution":{"iopub.execute_input":"2023-04-10T15:58:08.482045Z","iopub.status.busy":"2023-04-10T15:58:08.481714Z","iopub.status.idle":"2023-04-10T16:32:13.251448Z","shell.execute_reply":"2023-04-10T16:32:13.250281Z"},"id":"vNe5cvo_oolk","papermill":{"duration":2044.785567,"end_time":"2023-04-10T16:32:13.254833","exception":false,"start_time":"2023-04-10T15:58:08.469266","status":"completed"},"tags":[]},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"cfab09b5890e4fcca2d09b1086333e4f","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":["/opt/conda/lib/python3.7/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"03d32b68ae9f4715ae4991086b258946","version_major":2,"version_minor":0},"text/plain":["Training: 0it [00:00, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["Token indices sequence length is longer than the specified maximum sequence length for this model (533 > 512). Running this sequence through the model will result in indexing errors\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2a8ced892be542469728218b4582f2b7","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"366070843d61455b8db9c58f85f6c54a","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c0d58c1e844646e1a66bca2afcb13dba","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3765a8dbcf99438690c544ce0b5f11ad","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, 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Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d1944184c25b4bcabc78bf5018fe0d81","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"55cabaf9b36549249d446f46771e892e","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b728197a94be4ed4a09ef36c99c38e66","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c168dfaf65eb4ce0a31b8fe83b3ce402","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, 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?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8d10ba8a00c840e4b57a89ea8d6d8df0","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"601876b489ed4807828aedd0ddd0cfd1","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4a0df187bca645b2ac15e1e140036390","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d3217e5d7718486aa5829dab85f988ca","version_major":2,"version_minor":0},"text/plain":["Validation: 0it [00:00, ?it/s]"]},"metadata":{},"output_type":"display_data"}],"source":["trainer.fit(pl_model, pl_dl)"]},{"cell_type":"code","execution_count":29,"id":"0101e58a","metadata":{"execution":{"iopub.execute_input":"2023-04-10T16:32:13.300426Z","iopub.status.busy":"2023-04-10T16:32:13.300096Z","iopub.status.idle":"2023-04-10T16:32:13.30774Z","shell.execute_reply":"2023-04-10T16:32:13.306786Z"},"id":"NY_PxjFRpDav","papermill":{"duration":0.029194,"end_time":"2023-04-10T16:32:13.309866","exception":false,"start_time":"2023-04-10T16:32:13.280672","status":"completed"},"tags":[]},"outputs":[],"source":["ckpt_folder = \"./tilt/models\"\n","if os.path.exists(ckpt_folder):\n","  ckpt_path = os.path.join(ckpt_folder, os.listdir(ckpt_folder)[0])\n","else:\n","  ckpt_path = None"]},{"cell_type":"code","execution_count":30,"id":"e706f1e1","metadata":{"execution":{"iopub.execute_input":"2023-04-10T16:32:13.347768Z","iopub.status.busy":"2023-04-10T16:32:13.347495Z","iopub.status.idle":"2023-04-10T16:32:40.321702Z","shell.execute_reply":"2023-04-10T16:32:40.319638Z"},"id":"5WC_qC5UoxBK","outputId":"1a3d8fe2-06fb-4d3a-b7ce-6da31bac55b4","papermill":{"duration":26.997033,"end_time":"2023-04-10T16:32:40.325308","exception":false,"start_time":"2023-04-10T16:32:13.328275","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Evaluating the model\n","Weights loaded successfully!\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"7b25cae2de754231b2dd164c3a7697eb","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/25 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["overall_precision has value:   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