transformer-pytorch-native.ipynb

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   "source": [
    "import gc\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "import pickle\n",
    "import random\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn.metrics import log_loss\n",
    "import math\n",
    "import torch\n",
    "import transformers\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.cuda.amp import GradScaler, autocast\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from transformers import AutoModel, AutoTokenizer, AdamW, get_linear_schedule_with_warmup\n",
    "os.environ['TOKENIZERS_PARALLELISM'] = 'false'\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
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     "text": [
      "Wed Feb  1 12:56:25 2023       \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| NVIDIA-SMI 470.82.01    Driver Version: 470.82.01    CUDA Version: 11.4     |\r\n",
      "|-------------------------------+----------------------+----------------------+\r\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
      "|                               |                      |               MIG M. |\r\n",
      "|===============================+======================+======================|\r\n",
      "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\r\n",
      "| N/A   36C    P0    27W / 250W |      0MiB / 16280MiB |      0%      Default |\r\n",
      "|                               |                      |                  N/A |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "                                                                               \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| Processes:                                                                  |\r\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\r\n",
      "|        ID   ID                                                   Usage      |\r\n",
      "|=============================================================================|\r\n",
      "|  No running processes found                                                 |\r\n",
      "+-----------------------------------------------------------------------------+\r\n"
     ]
    }
   ],
   "source": [
    "! nvidia-smi"
   ]
  },
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   "source": [
    "device = torch.device('cuda') if torch.cuda.is_available() else 'cpu'"
   ]
  },
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   "source": [
    "def seed_everything(seed):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True"
   ]
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   "source": [
    "EPOCHS = 12\n",
    "lr = 2e-5\n",
    "SEED = 42\n",
    "MAX_LEN = 128\n",
    "BATCH_SIZE = 24\n",
    "accumulation_steps = 4\n",
    "seed_everything(SEED)"
   ]
  },
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "      <th>Target</th>\n",
       "      <th>intent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>what expression would i use to say i love you ...</td>\n",
       "      <td>61</td>\n",
       "      <td>translate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>can you tell me how to say 'i do not speak muc...</td>\n",
       "      <td>61</td>\n",
       "      <td>translate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Text  Target     intent\n",
       "0  what expression would i use to say i love you ...      61  translate\n",
       "1  can you tell me how to say 'i do not speak muc...      61  translate"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_path = '../input/huggingfacedebertav3variants/deberta-v3-small'\n",
    "data_path = '../input/data-for-distilation' \n",
    "train = pd.read_csv('../input/data-for-distilation/Clinc_Train.csv')\n",
    "valid = pd.read_csv('../input/data-for-distilation/Clinc_valid.csv')\n",
    "n_classes = np.unique(train.Target).shape[0]\n",
    "train.head(2)"
   ]
  },
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   "source": [
    "class ArcModule(nn.Module):\n",
    "    def __init__(self, in_features=768, out_features=151, s = 10, m =0.5):\n",
    "        super().__init__()\n",
    "        self.in_features = 768\n",
    "        self.out_features = 151\n",
    "        self.s = s\n",
    "        self.m = m\n",
    "        self.weight = nn.Parameter(torch.FloatTensor(out_features, in_features))\n",
    "        nn.init.xavier_normal_(self.weight)\n",
    "\n",
    "        self.cos_m = math.cos(m)\n",
    "        self.sin_m = math.sin(m)\n",
    "        self.th = torch.tensor(math.cos(math.pi - m))\n",
    "        self.mm = torch.tensor(math.sin(math.pi - m) * m)\n",
    "\n",
    "    def forward(self, inputs, labels):\n",
    "        cos_th = F.linear(inputs, F.normalize(self.weight))\n",
    "        cos_th = cos_th.clamp(-1, 1)\n",
    "        sin_th = torch.sqrt(1.0 - torch.pow(cos_th, 2))\n",
    "        cos_th_m = cos_th * self.cos_m - sin_th * self.sin_m\n",
    "        # print(type(cos_th), type(self.th), type(cos_th_m), type(self.mm))\n",
    "        cos_th_m = torch.where(cos_th > self.th, cos_th_m, cos_th - self.mm)\n",
    "\n",
    "        cond_v = cos_th - self.th\n",
    "        cond = cond_v <= 0\n",
    "        cos_th_m[cond] = (cos_th - self.mm)[cond]\n",
    "\n",
    "        if labels.dim() == 1:\n",
    "            labels = labels.unsqueeze(-1)\n",
    "        onehot = torch.zeros(cos_th.size()).cuda()\n",
    "        labels = labels.type(torch.LongTensor).cuda()\n",
    "        onehot.scatter_(1, labels, 1.0)\n",
    "        outputs = onehot * cos_th_m + (1.0 - onehot) * cos_th\n",
    "        outputs = outputs * self.s\n",
    "        return outputs"
   ]
  },
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   "source": [
    "class callback:\n",
    "    def __init__(self):\n",
    "        self.loss = list()\n",
    "        self.model = list()\n",
    "    \n",
    "    def put(self, model, loss):\n",
    "        self.loss.append(loss)\n",
    "        self.model.append(model)\n",
    "\n",
    "    def get_model(self):\n",
    "        ind = np.argmin(self.loss)\n",
    "        return self.model[ind]\n",
    "\n",
    "def freeze(module):\n",
    "    for parameter in module.parameters():\n",
    "        parameter.requires_grad = False\n",
    "        \n",
    "def get_freezed_parameters(module):\n",
    "    freezed_parameters = []\n",
    "    for name, parameter in module.named_parameters():\n",
    "        if not parameter.requires_grad:\n",
    "            freezed_parameters.append(name)\n",
    "    return freezed_parameters\n",
    "\n",
    "class ClinicModel(nn.Module):\n",
    "    def __init__(self, model_path):\n",
    "        super(ClinicModel, self).__init__()\n",
    "        self.model = AutoModel.from_pretrained(model_path)\n",
    "#         freeze((self.model).embeddings)\n",
    "        self.output = nn.Linear(768, n_classes)\n",
    "        self.margin = ArcModule(in_features=768, out_features = n_classes)\n",
    "\n",
    "    def forward(self, ids, mask):\n",
    "        sequence_output = self.model(ids, mask)[0][:, 0, :]\n",
    "        logits = self.output(sequence_output)#self.margin(sequence_output,151)\n",
    "        return logits\n",
    "\n",
    "\n",
    "class ClinicDataset(Dataset):\n",
    "    def __init__(self, data,is_test=False):\n",
    "        self.X = data['Text'].values\n",
    "        self.Y = data['Target'].values\n",
    "        self.is_test = is_test\n",
    "        self.tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
    "        self.MAX_LEN = 128\n",
    "        \n",
    "    def __getitem__(self, idx):\n",
    "        inputs = self.tokenizer.encode_plus(self.X[idx],\n",
    "            add_special_tokens=True,\n",
    "            truncation=True,\n",
    "            max_length=self.MAX_LEN\n",
    "        )['input_ids'] \n",
    "\n",
    "        if not self.is_test:\n",
    "            target_value = self.Y[idx]\n",
    "      \n",
    "        mask = [1]*len(inputs) + [0] * (self.MAX_LEN - len(inputs)) \n",
    "        mask = torch.tensor(mask, dtype=torch.long)\n",
    "        \n",
    "        if len(inputs) != self.MAX_LEN:\n",
    "            inputs = inputs + [self.tokenizer.pad_token_id] * (self.MAX_LEN - len(inputs)) \n",
    "        ids = torch.tensor(inputs, dtype=torch.long)\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        if self.is_test:\n",
    "            return {\n",
    "                'ids': ids,\n",
    "                'mask': mask,\n",
    "            }\n",
    "        \n",
    "        else:\n",
    "            targets = torch.FloatTensor(target_value)\n",
    "            return {\n",
    "                'ids': ids,\n",
    "                'mask': mask,\n",
    "                'targets': targets\n",
    "            }\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.Y)"
   ]
  },
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
    "train_texts = train['Text'].values.tolist()\n",
    "val_texts = valid['Text'].values.tolist()\n",
    "train_labels = train['Target'].values.tolist()\n",
    "val_labels = valid['Target'].values.tolist()\n",
    "train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=128)\n",
    "val_encodings = tokenizer(val_texts, truncation=True, padding=True, max_length=128)\n",
    "\n",
    "class ClinicDatasetV2(torch.utils.data.Dataset):\n",
    "    def __init__(self, encodings, labels):\n",
    "        self.encodings = encodings\n",
    "        self.labels = labels\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
    "        item['labels'] = torch.tensor(self.labels[idx])\n",
    "        return {'ids':item.get('input_ids'),'mask':item.get('attention_mask'),'labels':item.get('labels')}\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(ClinicDatasetV2(train_encodings, train_labels),batch_size=BATCH_SIZE, shuffle=True, num_workers=2)\n",
    "val_loader = torch.utils.data.DataLoader(ClinicDatasetV2(val_encodings, val_labels),batch_size=BATCH_SIZE, shuffle=False, num_workers=2)\n",
    "# for data in train_loader:\n",
    "#     print(data)\n",
    "#     break\n"
   ]
  },
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   "outputs": [],
   "source": [
    "# train_loader = torch.utils.data.DataLoader(ClinicDataset(train), batch_size=BATCH_SIZE, shuffle=True, num_workers=2)\n",
    "# val_loader = torch.utils.data.DataLoader(ClinicDataset(valid), batch_size=BATCH_SIZE, shuffle=False, num_workers=2)"
   ]
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   "source": [
    "# for data in train_loader:\n",
    "#     print(data)\n",
    "#     break"
   ]
  },
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   "source": [
    "def valid_func(model,valid_loader):\n",
    "    model.eval()\n",
    "    bar = tqdm(valid_loader,file=sys.stdout)\n",
    "    loss_fn = torch.nn.CrossEntropyLoss()\n",
    "    PROB = []\n",
    "    TARGETS = []\n",
    "    losses = []\n",
    "    PREDS = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for batch_idx, (data) in enumerate(bar):\n",
    "            input_ids = data['ids'].cuda()\n",
    "            input_masks = data['mask'].cuda()\n",
    "            targets = data['labels'].long().view(-1).cuda()\n",
    "\n",
    "            logits = model(input_ids,input_masks)\n",
    "\n",
    "            PREDS += [torch.argmax(logits, 1).detach().cpu()]\n",
    "            TARGETS += [targets.detach().cpu()]\n",
    "\n",
    "            loss = loss_fn (logits, targets)\n",
    "            losses.append(loss.item())\n",
    "           \n",
    "            bar.set_description(f'loss: {loss.item():.5f}')\n",
    "\n",
    "    PREDS = torch.cat(PREDS).cpu().numpy()\n",
    "    TARGETS = torch.cat(TARGETS).cpu().numpy()\n",
    "    accuracy = (PREDS==TARGETS).mean()\n",
    "   \n",
    "    loss_valid = np.mean(losses)\n",
    "    return loss_valid, accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8a6464e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-01T12:56:28.274858Z",
     "iopub.status.busy": "2023-02-01T12:56:28.274586Z",
     "iopub.status.idle": "2023-02-01T13:06:35.198898Z",
     "shell.execute_reply": "2023-02-01T13:06:35.196808Z"
    },
    "papermill": {
     "duration": 606.932206,
     "end_time": "2023-02-01T13:06:35.201991",
     "exception": false,
     "start_time": "2023-02-01T12:56:28.269785",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at ../input/huggingfacedebertav3variants/deberta-v3-small were not used when initializing DebertaV2Model: ['mask_predictions.LayerNorm.bias', 'lm_predictions.lm_head.dense.bias', 'mask_predictions.classifier.weight', 'lm_predictions.lm_head.bias', 'mask_predictions.dense.bias', 'mask_predictions.dense.weight', 'lm_predictions.lm_head.dense.weight', 'mask_predictions.LayerNorm.weight', 'lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.LayerNorm.bias', 'mask_predictions.classifier.bias']\n",
      "- This IS expected if you are initializing DebertaV2Model 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 DebertaV2Model from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
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       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
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    },
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--1 ### Train loss---3.633 ### Valid_Loss---3.4455231758264393 ### Valid_Acc---0.42935483870967744\n",
      "Model Saved--epoch--1\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e6dd04467bd44be8bc8ba286a71f8dc",
       "version_major": 2,
       "version_minor": 0
      },
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       "  0%|          | 0/636 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
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      "text/plain": [
       "  0%|          | 0/130 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--2 ### Train loss---0.8469 ### Valid_Loss---0.9511075984973174 ### Valid_Acc---0.8738709677419355\n",
      "Model Saved--epoch--2\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f746850593dc40a2a4eec9d56f4c4dd8",
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      "text/plain": [
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      ]
     },
     "metadata": {},
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    },
    {
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       "  0%|          | 0/130 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--3 ### Train loss---0.3941 ### Valid_Loss---0.45812941732314916 ### Valid_Acc---0.9283870967741935\n",
      "Model Saved--epoch--3\n"
     ]
    },
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--4 ### Train loss---0.3807 ### Valid_Loss---0.32332655870570587 ### Valid_Acc---0.9409677419354838\n",
      "Model Saved--epoch--4\n"
     ]
    },
    {
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       "version_minor": 0
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     },
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     "output_type": "display_data"
    },
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--5 ### Train loss---0.1838 ### Valid_Loss---0.2732638687468492 ### Valid_Acc---0.9454838709677419\n",
      "Model Saved--epoch--5\n"
     ]
    },
    {
     "data": {
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       "model_id": "7fab9702d8a74dc8a578a4c44cbe52f5",
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     },
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       "  0%|          | 0/130 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--6 ### Train loss---0.0858 ### Valid_Loss---0.24106482359079215 ### Valid_Acc---0.9487096774193549\n",
      "Model Saved--epoch--6\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81581e7b250842d38c2227acefab0468",
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--7 ### Train loss---0.2253 ### Valid_Loss---0.22488338154955553 ### Valid_Acc---0.9509677419354838\n",
      "Model Saved--epoch--7\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--8 ### Train loss---0.0717 ### Valid_Loss---0.22035079693708282 ### Valid_Acc---0.9516129032258065\n",
      "Model Saved--epoch--8\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--9 ### Train loss---0.0471 ### Valid_Loss---0.20861863942387013 ### Valid_Acc---0.9564516129032258\n",
      "Model Saved--epoch--9\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--10 ### Train loss---0.0738 ### Valid_Loss---0.20790134782974537 ### Valid_Acc---0.9551612903225807\n",
      "Model Saved--epoch--10\n"
     ]
    },
    {
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    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--11 ### Train loss---0.0895 ### Valid_Loss---0.2068163343776877 ### Valid_Acc---0.9548387096774194\n",
      "Model Saved--epoch--11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     },
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    },
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--12 ### Train loss---0.1723 ### Valid_Loss---0.206066120419508 ### Valid_Acc---0.954516129032258\n",
      "Model Saved--epoch--12\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "257"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "use_amp = True\n",
    "debug = False\n",
    "gc.collect()\n",
    "best_epoch_loss = np.inf\n",
    "# train_loader = torch.utils.data.DataLoader(ClinicDataset(train), batch_size=BATCH_SIZE, shuffle=True, num_workers=2)\n",
    "# val_loader = torch.utils.data.DataLoader(ClinicDataset(valid), batch_size=BATCH_SIZE, shuffle=False, num_workers=2)\n",
    "\n",
    "net = ClinicModel(model_path)\n",
    "net.cuda()\n",
    "log_df = pd.DataFrame(columns = ['Epoch','Train_Loss','Valid_Loss','Valid_Accuracy'])\n",
    "\n",
    "loss_fn = torch.nn.CrossEntropyLoss()\n",
    "optimizer = AdamW(net.parameters(), lr = lr)    \n",
    "param_optimizer = list(net.named_parameters())\n",
    "no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n",
    "optimizer_grouped_parameters = [\n",
    "{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},\n",
    "{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
    "]\n",
    "num_train_optimization_steps = int(EPOCHS * len(train_loader) / accumulation_steps)\n",
    "scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0.05 * num_train_optimization_steps,\n",
    "                                    num_training_steps=num_train_optimization_steps)# PyTorch scheduler\n",
    "if use_amp:\n",
    "        scaler = torch.cuda.amp.GradScaler()\n",
    "\n",
    "for epoch in range(EPOCHS):\n",
    "    start_time = time.time()\n",
    "    avg_loss = 0.0\n",
    "    net.train()\n",
    "    tbar = tqdm(train_loader, file=sys.stdout)\n",
    "    loss_list = []\n",
    "    val_loss_list = []\n",
    "    for step, data in enumerate(tbar):\n",
    "        if debug and step == 10:\n",
    "            print('Debug Mode. Only train on first 100 batches.')\n",
    "            break\n",
    "        input_ids = data['ids'].cuda()\n",
    "        input_masks = data['mask'].cuda()\n",
    "        targets = data['labels'].long().view(-1).cuda()\n",
    "        if use_amp:\n",
    "                with torch.cuda.amp.autocast():\n",
    "                     pred = net(input_ids,input_masks)\n",
    "                     loss = loss_fn(pred, targets)\n",
    "                scaler.scale(loss).backward()\n",
    "                if step % accumulation_steps == 0 or step == len(tbar) - 1:\n",
    "                    scaler.step(optimizer)\n",
    "                    scaler.update()\n",
    "                    optimizer.zero_grad()\n",
    "                    scheduler.step()\n",
    "        else:\n",
    "            pred = net(input_ids,input_masks)\n",
    "            loss = loss_fn(pred, targets)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            optimizer.zero_grad()\n",
    "       \n",
    "\n",
    "    loss_list.append(loss.detach().cpu().item())\n",
    "    avg_loss = np.round(np.mean(loss_list), 4)\n",
    "    tbar.set_description(f\"Epoch {epoch + 1} Loss: {avg_loss} lr: {scheduler.get_last_lr()}\")\n",
    "    vloss,vaccuracy = valid_func(net,val_loader )\n",
    "    log_df.loc[len(log_df.index)] = [epoch+1,avg_loss,vloss,vaccuracy]\n",
    "    print(f'Epoch--{epoch+1} ### Train loss---{avg_loss} ### Valid_Loss---{vloss} ### Valid_Acc---{vaccuracy}')\n",
    "    if vloss<best_epoch_loss:\n",
    "        best_epoch_loss = vloss\n",
    "        PATH = f\"debertav3-Base_epoch__{epoch}.pth\"\n",
    "        torch.save(net.state_dict(), PATH)\n",
    "        print(f'Model Saved--epoch--{epoch+1}')\n",
    "        \n",
    "    \n",
    "del train_loader\n",
    "del net\n",
    "del val_loader\n",
    "gc.collect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eff4b51c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-01T13:06:35.220010Z",
     "iopub.status.busy": "2023-02-01T13:06:35.219535Z",
     "iopub.status.idle": "2023-02-01T13:06:35.235554Z",
     "shell.execute_reply": "2023-02-01T13:06:35.234670Z"
    },
    "papermill": {
     "duration": 0.027257,
     "end_time": "2023-02-01T13:06:35.237697",
     "exception": false,
     "start_time": "2023-02-01T13:06:35.210440",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Epoch</th>\n",
       "      <th>Train_Loss</th>\n",
       "      <th>Valid_Loss</th>\n",
       "      <th>Valid_Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.6330</td>\n",
       "      <td>3.445523</td>\n",
       "      <td>0.429355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8469</td>\n",
       "      <td>0.951108</td>\n",
       "      <td>0.873871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.3941</td>\n",
       "      <td>0.458129</td>\n",
       "      <td>0.928387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.3807</td>\n",
       "      <td>0.323327</td>\n",
       "      <td>0.940968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.1838</td>\n",
       "      <td>0.273264</td>\n",
       "      <td>0.945484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0858</td>\n",
       "      <td>0.241065</td>\n",
       "      <td>0.948710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.2253</td>\n",
       "      <td>0.224883</td>\n",
       "      <td>0.950968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0717</td>\n",
       "      <td>0.220351</td>\n",
       "      <td>0.951613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0471</td>\n",
       "      <td>0.208619</td>\n",
       "      <td>0.956452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0738</td>\n",
       "      <td>0.207901</td>\n",
       "      <td>0.955161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.206816</td>\n",
       "      <td>0.954839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.1723</td>\n",
       "      <td>0.206066</td>\n",
       "      <td>0.954516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Epoch  Train_Loss  Valid_Loss  Valid_Accuracy\n",
       "0     1.0      3.6330    3.445523        0.429355\n",
       "1     2.0      0.8469    0.951108        0.873871\n",
       "2     3.0      0.3941    0.458129        0.928387\n",
       "3     4.0      0.3807    0.323327        0.940968\n",
       "4     5.0      0.1838    0.273264        0.945484\n",
       "5     6.0      0.0858    0.241065        0.948710\n",
       "6     7.0      0.2253    0.224883        0.950968\n",
       "7     8.0      0.0717    0.220351        0.951613\n",
       "8     9.0      0.0471    0.208619        0.956452\n",
       "9    10.0      0.0738    0.207901        0.955161\n",
       "10   11.0      0.0895    0.206816        0.954839\n",
       "11   12.0      0.1723    0.206066        0.954516"
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