transformer-pytorch-native-knowledge-distillation.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",
    "from pathlib import Path\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 torch.nn.quantized import QFunctional\n",
    "from torch.quantization import quantize_dynamic\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 19:23:24 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   33C    P0    25W / 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 = 16\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-xsmall'\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 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 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\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",
    "class ClinicModel_student(nn.Module):\n",
    "    def __init__(self, model_path):\n",
    "        super(ClinicModel_student, self).__init__()\n",
    "        self.model = AutoModel.from_pretrained(model_path)\n",
    "#         freeze((self.model).embeddings)\n",
    "        self.output = nn.Linear(384, n_classes)\n",
    "\n",
    "    def forward(self, ids, mask):\n",
    "        sequence_output = self.model(ids, mask)[0][:, 0, :]\n",
    "        logits = self.output(sequence_output)\n",
    "        return logits\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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    {
     "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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   "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"
   ]
  },
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     "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', 'mask_predictions.classifier.weight', 'mask_predictions.dense.bias', 'lm_predictions.lm_head.bias', 'mask_predictions.dense.weight', 'lm_predictions.lm_head.LayerNorm.bias', 'mask_predictions.LayerNorm.weight', 'lm_predictions.lm_head.dense.weight', 'mask_predictions.classifier.bias', 'lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.dense.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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ClinicModel(\n",
       "  (model): DebertaV2Model(\n",
       "    (embeddings): DebertaV2Embeddings(\n",
       "      (word_embeddings): Embedding(128100, 768, padding_idx=0)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-07, elementwise_affine=True)\n",
       "      (dropout): StableDropout()\n",
       "    )\n",
       "    (encoder): DebertaV2Encoder(\n",
       "      (layer): ModuleList(\n",
       "        (0): DebertaV2Layer(\n",
       "          (attention): DebertaV2Attention(\n",
       "            (self): DisentangledSelfAttention(\n",
       "              (query_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (pos_dropout): StableDropout()\n",
       "              (dropout): StableDropout()\n",
       "            )\n",
       "            (output): DebertaV2SelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-07, elementwise_affine=True)\n",
       "              (dropout): StableDropout()\n",
       "            )\n",
       "          )\n",
       "          (intermediate): DebertaV2Intermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): DebertaV2Output(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-07, elementwise_affine=True)\n",
       "            (dropout): StableDropout()\n",
       "          )\n",
       "        )\n",
       "        (1): DebertaV2Layer(\n",
       "          (attention): DebertaV2Attention(\n",
       "            (self): DisentangledSelfAttention(\n",
       "              (query_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (pos_dropout): StableDropout()\n",
       "              (dropout): StableDropout()\n",
       "            )\n",
       "            (output): DebertaV2SelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-07, elementwise_affine=True)\n",
       "              (dropout): StableDropout()\n",
       "            )\n",
       "          )\n",
       "          (intermediate): DebertaV2Intermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): DebertaV2Output(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-07, elementwise_affine=True)\n",
       "            (dropout): StableDropout()\n",
       "          )\n",
       "        )\n",
       "        (2): DebertaV2Layer(\n",
       "          (attention): DebertaV2Attention(\n",
       "            (self): DisentangledSelfAttention(\n",
       "              (query_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (pos_dropout): StableDropout()\n",
       "              (dropout): StableDropout()\n",
       "            )\n",
       "            (output): DebertaV2SelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-07, elementwise_affine=True)\n",
       "              (dropout): StableDropout()\n",
       "            )\n",
       "          )\n",
       "          (intermediate): DebertaV2Intermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): DebertaV2Output(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-07, elementwise_affine=True)\n",
       "            (dropout): StableDropout()\n",
       "          )\n",
       "        )\n",
       "        (3): DebertaV2Layer(\n",
       "          (attention): DebertaV2Attention(\n",
       "            (self): DisentangledSelfAttention(\n",
       "              (query_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (pos_dropout): StableDropout()\n",
       "              (dropout): StableDropout()\n",
       "            )\n",
       "            (output): DebertaV2SelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-07, elementwise_affine=True)\n",
       "              (dropout): StableDropout()\n",
       "            )\n",
       "          )\n",
       "          (intermediate): DebertaV2Intermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): DebertaV2Output(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-07, elementwise_affine=True)\n",
       "            (dropout): StableDropout()\n",
       "          )\n",
       "        )\n",
       "        (4): DebertaV2Layer(\n",
       "          (attention): DebertaV2Attention(\n",
       "            (self): DisentangledSelfAttention(\n",
       "              (query_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (pos_dropout): StableDropout()\n",
       "              (dropout): StableDropout()\n",
       "            )\n",
       "            (output): DebertaV2SelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-07, elementwise_affine=True)\n",
       "              (dropout): StableDropout()\n",
       "            )\n",
       "          )\n",
       "          (intermediate): DebertaV2Intermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): DebertaV2Output(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-07, elementwise_affine=True)\n",
       "            (dropout): StableDropout()\n",
       "          )\n",
       "        )\n",
       "        (5): DebertaV2Layer(\n",
       "          (attention): DebertaV2Attention(\n",
       "            (self): DisentangledSelfAttention(\n",
       "              (query_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (pos_dropout): StableDropout()\n",
       "              (dropout): StableDropout()\n",
       "            )\n",
       "            (output): DebertaV2SelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-07, elementwise_affine=True)\n",
       "              (dropout): StableDropout()\n",
       "            )\n",
       "          )\n",
       "          (intermediate): DebertaV2Intermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): DebertaV2Output(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-07, elementwise_affine=True)\n",
       "            (dropout): StableDropout()\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (rel_embeddings): Embedding(512, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-07, elementwise_affine=True)\n",
       "    )\n",
       "  )\n",
       "  (output): Linear(in_features=768, out_features=151, bias=True)\n",
       "  (margin): ArcModule()\n",
       ")"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teacher = ClinicModel('../input/huggingfacedebertav3variants/deberta-v3-small')\n",
    "teacher.load_state_dict(torch.load('../input/transformer-pytorch-native/debertav3-Base_epoch__9.pth'))\n",
    "teacher.cuda()\n",
    "teacher.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "46122735",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-01T19:23:40.896649Z",
     "iopub.status.busy": "2023-02-01T19:23:40.896341Z",
     "iopub.status.idle": "2023-02-01T19:23:40.902083Z",
     "shell.execute_reply": "2023-02-01T19:23:40.901033Z"
    },
    "papermill": {
     "duration": 0.014749,
     "end_time": "2023-02-01T19:23:40.904411",
     "exception": false,
     "start_time": "2023-02-01T19:23:40.889662",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def custom_loss(logits_tea,logits_stu,loss_ce,alpha=0.5, temperature=2.0):\n",
    "    #  alpha and temperature can be tuned with optuna   \n",
    "    # Soften probabilities and compute distillation loss\n",
    "    loss_fct = nn.KLDivLoss(reduction=\"batchmean\")\n",
    "    loss_kd = temperature ** 2 * loss_fct(F.log_softmax(logits_stu / temperature, dim=-1),\n",
    "    F.softmax(logits_tea / temperature, dim=-1))\n",
    "    # Return weighted student loss\n",
    "    loss = alpha * loss_ce + (1. - alpha) * loss_kd\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5eda5aa2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-01T19:23:40.917001Z",
     "iopub.status.busy": "2023-02-01T19:23:40.916710Z",
     "iopub.status.idle": "2023-02-01T19:45:00.143215Z",
     "shell.execute_reply": "2023-02-01T19:45:00.142031Z"
    },
    "papermill": {
     "duration": 1279.234824,
     "end_time": "2023-02-01T19:45:00.145349",
     "exception": false,
     "start_time": "2023-02-01T19:23:40.910525",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at ../input/huggingfacedebertav3variants/deberta-v3-xsmall were not used when initializing DebertaV2Model: ['mask_predictions.LayerNorm.bias', 'mask_predictions.classifier.weight', 'deberta.embeddings.word_embeddings._weight', 'mask_predictions.dense.bias', 'lm_predictions.lm_head.bias', 'mask_predictions.dense.weight', 'lm_predictions.lm_head.LayerNorm.bias', 'mask_predictions.LayerNorm.weight', 'lm_predictions.lm_head.dense.weight', 'mask_predictions.classifier.bias', 'lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.dense.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"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4636830a27584a619808df0f41da26e8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/954 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14dbf85ba25e49d4ba366157fbc3b386",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/194 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--1 ### Train loss---4.0887 ### Valid_Loss---4.126749318899567 ### Valid_Acc---0.18161290322580645\n",
      "Model Saved--epoch--1\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e4766093da14143a29e5d9791930679",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/954 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "99499da570884aaca76a4a80395f02cd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/194 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--2 ### Train loss---3.6775 ### Valid_Loss---2.3666920603550587 ### Valid_Acc---0.6225806451612903\n",
      "Model Saved--epoch--2\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bcc82d2df2ef406e8993c190f29f81b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/954 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "691872ecb3ec43b6b3e7c6e2f4ef55ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/194 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--3 ### Train loss---2.8576 ### Valid_Loss---1.402904796692514 ### Valid_Acc---0.7893548387096774\n",
      "Model Saved--epoch--3\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77fd8946019d426e923bef0b9fd6d642",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/954 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/194 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--4 ### Train loss---2.4139 ### Valid_Loss---0.8950232924490246 ### Valid_Acc---0.8616129032258064\n",
      "Model Saved--epoch--4\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec5e696f9ff343c788cb9b7d00ae0f73",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/954 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "63745adccff649dd8ae33814557da885",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--5 ### Train loss---1.0885 ### Valid_Loss---0.638222834537017 ### Valid_Acc---0.8909677419354839\n",
      "Model Saved--epoch--5\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "457f0841eec243029f3baf683288a924",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/954 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--6 ### Train loss---1.0825 ### Valid_Loss---0.5050947372849608 ### Valid_Acc---0.9029032258064517\n",
      "Model Saved--epoch--6\n"
     ]
    },
    {
     "data": {
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       "version_minor": 0
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     },
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    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--7 ### Train loss---1.4461 ### Valid_Loss---0.4333716676925722 ### Valid_Acc---0.9119354838709678\n",
      "Model Saved--epoch--7\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
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     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--8 ### Train loss---0.9241 ### Valid_Loss---0.391169246583793 ### Valid_Acc---0.9193548387096774\n",
      "Model Saved--epoch--8\n"
     ]
    },
    {
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       "model_id": "332bb1b55b124d82af9adf40e778ba93",
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      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e787429bacfc4fff921154c6f2163ca8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/194 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--9 ### Train loss---0.5999 ### Valid_Loss---0.3639204547375671 ### Valid_Acc---0.9216129032258065\n",
      "Model Saved--epoch--9\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1ae4fadad0846579a2d779a1131165e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/954 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8caa543036e4eba9457e912c9a5d59c",
       "version_major": 2,
       "version_minor": 0
      },
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       "  0%|          | 0/194 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--10 ### Train loss---0.9345 ### Valid_Loss---0.35315192842222365 ### Valid_Acc---0.922258064516129\n",
      "Model Saved--epoch--10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b277d625aefc4d63a57ba1d378b82226",
       "version_major": 2,
       "version_minor": 0
      },
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       "  0%|          | 0/954 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ae4e9081609408e8443f477e10c4511",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/194 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--11 ### Train loss---0.893 ### Valid_Loss---0.33739771050660267 ### Valid_Acc---0.925483870967742\n",
      "Model Saved--epoch--11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "607c6ec04796458fba9dc0f5b9453d5d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/954 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee925b8bd2934aa1849de7d499fa33d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/194 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--12 ### Train loss---0.5462 ### Valid_Loss---0.3350447088950458 ### Valid_Acc---0.9258064516129032\n",
      "Model Saved--epoch--12\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "282"
      ]
     },
     "execution_count": 12,
     "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_student(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",
    "                     pred_tea = teacher(input_ids,input_masks)\n",
    "                     loss_ce = loss_fn(pred, targets)\n",
    "                     loss = custom_loss(pred_tea,pred,loss_ce)\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-xsmall_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",
    "gc.collect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "52bcd9b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-01T19:45:00.166361Z",
     "iopub.status.busy": "2023-02-01T19:45:00.165366Z",
     "iopub.status.idle": "2023-02-01T19:45:00.178945Z",
     "shell.execute_reply": "2023-02-01T19:45:00.177891Z"
    },
    "papermill": {
     "duration": 0.026737,
     "end_time": "2023-02-01T19:45:00.181584",
     "exception": false,
     "start_time": "2023-02-01T19:45:00.154847",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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>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>4.0887</td>\n",
       "      <td>4.126749</td>\n",
       "      <td>0.181613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.6775</td>\n",
       "      <td>2.366692</td>\n",
       "      <td>0.622581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.8576</td>\n",
       "      <td>1.402905</td>\n",
       "      <td>0.789355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.4139</td>\n",
       "      <td>0.895023</td>\n",
       "      <td>0.861613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0885</td>\n",
       "      <td>0.638223</td>\n",
       "      <td>0.890968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0825</td>\n",
       "      <td>0.505095</td>\n",
       "      <td>0.902903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1.4461</td>\n",
       "      <td>0.433372</td>\n",
       "      <td>0.911935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.9241</td>\n",
       "      <td>0.391169</td>\n",
       "      <td>0.919355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.5999</td>\n",
       "      <td>0.363920</td>\n",
       "      <td>0.921613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.9345</td>\n",
       "      <td>0.353152</td>\n",
       "      <td>0.922258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.8930</td>\n",
       "      <td>0.337398</td>\n",
       "      <td>0.925484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.5462</td>\n",
       "      <td>0.335045</td>\n",
       "      <td>0.925806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Epoch  Train_Loss  Valid_Loss  Valid_Accuracy\n",
       "0     1.0      4.0887    4.126749        0.181613\n",
       "1     2.0      3.6775    2.366692        0.622581\n",
       "2     3.0      2.8576    1.402905        0.789355\n",
       "3     4.0      2.4139    0.895023        0.861613\n",
       "4     5.0      1.0885    0.638223        0.890968\n",
       "5     6.0      1.0825    0.505095        0.902903\n",
       "6     7.0      1.4461    0.433372        0.911935\n",
       "7     8.0      0.9241    0.391169        0.919355\n",
       "8     9.0      0.5999    0.363920        0.921613\n",
       "9    10.0      0.9345    0.353152        0.922258\n",
       "10   11.0      0.8930    0.337398        0.925484\n",
       "11   12.0      0.5462    0.335045        0.925806"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e8a8b18a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-01T19:45:00.202444Z",
     "iopub.status.busy": "2023-02-01T19:45:00.202142Z",
     "iopub.status.idle": "2023-02-01T19:45:00.207481Z",
     "shell.execute_reply": "2023-02-01T19:45:00.206449Z"
    },
    "papermill": {
     "duration": 0.01784,
     "end_time": "2023-02-01T19:45:00.209726",
     "exception": false,
     "start_time": "2023-02-01T19:45:00.191886",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def compute_size(model):\n",
    "    state_dict = model.state_dict()\n",
    "    tmp_path = Path(\"model.pt\")\n",
    "    torch.save(state_dict, tmp_path)\n",
    "    # Calculate size in megabytes\n",
    "    size_mb = Path(tmp_path).stat().st_size / (1024 * 1024)\n",
    "    # Delete temporary file\n",
    "    tmp_path.unlink()\n",
    "    print(f\"Model size (MB) - {size_mb:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "baedecdd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-01T19:45:00.229587Z",
     "iopub.status.busy": "2023-02-01T19:45:00.229320Z",
     "iopub.status.idle": "2023-02-01T19:45:00.234805Z",
     "shell.execute_reply": "2023-02-01T19:45:00.233905Z"
    },
    "papermill": {
     "duration": 0.017579,
     "end_time": "2023-02-01T19:45:00.236804",
     "exception": false,
     "start_time": "2023-02-01T19:45:00.219225",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def Benchmark(model,val_loader):\n",
    "    loss,acc = valid_func(model,val_loader)\n",
    "    compute_size(model)\n",
    "    print(f'Loss--{loss}---Acc---{acc}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5382609f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-01T19:45:00.256823Z",
     "iopub.status.busy": "2023-02-01T19:45:00.256139Z",
     "iopub.status.idle": "2023-02-01T19:45:05.414965Z",
     "shell.execute_reply": "2023-02-01T19:45:05.413584Z"
    },
    "papermill": {
     "duration": 5.171712,
     "end_time": "2023-02-01T19:45:05.417741",
     "exception": false,
     "start_time": "2023-02-01T19:45:00.246029",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "548f16b23b7145a890aaeecbd2f9e9aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/194 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model size (MB) - 539.96\n",
      "Loss--0.20858218174274126---Acc---0.9551612903225807\n"
     ]
    }
   ],
   "source": [
    "Benchmark(teacher,val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "77efb80c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-01T19:45:05.438706Z",
     "iopub.status.busy": "2023-02-01T19:45:05.438381Z",
     "iopub.status.idle": "2023-02-01T19:45:10.463047Z",
     "shell.execute_reply": "2023-02-01T19:45:10.461389Z"
    },
    "papermill": {
     "duration": 5.038172,
     "end_time": "2023-02-01T19:45:10.465826",
     "exception": false,
     "start_time": "2023-02-01T19:45:05.427654",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "39865f7bb1234d7792830b7ee544c416",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/194 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model size (MB) - 269.93\n",
      "Loss--0.3350447088950458---Acc---0.9258064516129032\n"
     ]
    }
   ],
   "source": [
    "Benchmark(net,val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6a19292e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-01T19:45:10.487469Z",
     "iopub.status.busy": "2023-02-01T19:45:10.487126Z",
     "iopub.status.idle": "2023-02-01T19:45:10.497406Z",
     "shell.execute_reply": "2023-02-01T19:45:10.496555Z"
    },
    "papermill": {
     "duration": 0.023527,
     "end_time": "2023-02-01T19:45:10.499433",
     "exception": false,
     "start_time": "2023-02-01T19:45:10.475906",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "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",
    "    start = time.time()\n",
    "    with torch.no_grad():\n",
    "        for batch_idx, (data) in enumerate(bar):\n",
    "            input_ids = data['ids']\n",
    "            input_masks = data['mask']\n",
    "            targets = data['labels'].long().view(-1)\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",
    "    end = time.time()\n",
    "\n",
    "    PREDS = torch.cat(PREDS).cpu().numpy()\n",
    "    TARGETS = torch.cat(TARGETS).cpu().numpy()\n",
    "    accuracy = (PREDS==TARGETS).mean()\n",
    "#     print(f'avg_time forsample{}')\n",
    "    loss_valid = np.mean(losses)\n",
    "    return loss_valid, accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
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   "source": [
    "del val_loader \n",
    "val_loader = torch.utils.data.DataLoader(ClinicDatasetV2(val_encodings, val_labels),batch_size=BATCH_SIZE, shuffle=False, num_workers=2)"
   ]
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     "text": [
      "Some weights of the model checkpoint at ../input/huggingfacedebertav3variants/deberta-v3-small were not used when initializing DebertaV2Model: ['mask_predictions.LayerNorm.bias', 'mask_predictions.classifier.weight', 'mask_predictions.dense.bias', 'lm_predictions.lm_head.bias', 'mask_predictions.dense.weight', 'lm_predictions.lm_head.LayerNorm.bias', 'mask_predictions.LayerNorm.weight', 'lm_predictions.lm_head.dense.weight', 'mask_predictions.classifier.bias', 'lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.dense.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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      "Model size (MB) - 418.16\n",
      "Loss--2.0788149902040196---Acc---0.49483870967741933\n"
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   ],
   "source": [
    "del teacher\n",
    "teacher = ClinicModel('../input/huggingfacedebertav3variants/deberta-v3-small')\n",
    "teacher.load_state_dict(torch.load('../input/transformer-pytorch-native/debertav3-Base_epoch__9.pth'))\n",
    "model_quantized = quantize_dynamic(teacher, {nn.Linear}, dtype=torch.qint8)\n",
    "Benchmark(model_quantized,val_loader)"
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   "source": [
    "# Try with optuna to get optimal alpha and temperature "
   ]
  }
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