roberta-base-6L-distill-pytorch.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",
    "from transformers import AutoConfig\n",
    "os.environ['TOKENIZERS_PARALLELISM'] = 'false'\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
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     "text": [
      "Sat Apr 15 11:55:18 2023       \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| NVIDIA-SMI 470.161.03   Driver Version: 470.161.03   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   38C    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 = 8\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": [
    "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": [
    "model_ckpt = 'roberta-base'\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)"
   ]
  },
  {
   "cell_type": "code",
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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",
    "class ClinicModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(ClinicModel, self).__init__()\n",
    "        self.model = AutoModel.from_pretrained('../input/transformer-distilation-pre/roberta_base_6layers_student')\n",
    "        self.output = nn.Linear(768, n_classes)\n",
    "    def forward(self, ids, mask):\n",
    "        sequence_output = self.model(ids, mask)['last_hidden_state'][:,0,:]\n",
    "        logits = self.output(sequence_output)\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": [],
   "source": [
    "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",
    "\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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     "text": [
      "Epoch--1 ### Train loss---2.7126 ### Valid_Loss---2.2395440028263973 ### Valid_Acc---0.6961290322580646\n",
      "Model Saved--epoch--1\n"
     ]
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     "text": [
      "Epoch--2 ### Train loss---1.3182 ### Valid_Loss---0.933498174066727 ### Valid_Acc---0.8516129032258064\n",
      "Model Saved--epoch--2\n"
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    {
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     "text": [
      "Epoch--3 ### Train loss---0.9175 ### Valid_Loss---0.6096371703136426 ### Valid_Acc---0.8896774193548387\n",
      "Model Saved--epoch--3\n"
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      "Epoch--4 ### Train loss---0.2812 ### Valid_Loss---0.48017742964797294 ### Valid_Acc---0.9087096774193548\n",
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      "Epoch--5 ### Train loss---0.6795 ### Valid_Loss---0.4169415351003408 ### Valid_Acc---0.9167741935483871\n",
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      "Epoch--6 ### Train loss---0.1449 ### Valid_Loss---0.37303138974194344 ### Valid_Acc---0.9238709677419354\n",
      "Model Saved--epoch--6\n"
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      "Epoch--7 ### Train loss---0.2008 ### Valid_Loss---0.3596781362492878 ### Valid_Acc---0.9261290322580645\n",
      "Model Saved--epoch--7\n"
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     "text": [
      "Epoch--8 ### Train loss---0.1486 ### Valid_Loss---0.3549013801277257 ### Valid_Acc---0.9270967741935484\n",
      "Model Saved--epoch--8\n"
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   "source": [
    "use_amp = True\n",
    "debug = False\n",
    "gc.collect()\n",
    "best_epoch_loss = np.inf\n",
    "\n",
    "net = ClinicModel()\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",
    "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",
    "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"
   ]
  },
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   "execution_count": 12,
   "id": "179f23c6",
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     "iopub.status.busy": "2023-04-15T12:00:11.313486Z",
     "iopub.status.idle": "2023-04-15T12:00:11.317953Z",
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     "exception": false,
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     "status": "completed"
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   "source": [
    "# model = AutoModel.from_pretrained('../input/transformer-distilation-pre/roberta_base_6layers_student')\n",
    "# model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "166b3a19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-15T12:00:11.335517Z",
     "iopub.status.busy": "2023-04-15T12:00:11.335208Z",
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     "status": "completed"
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    "# x = model(input_ids,input_masks)"
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  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3aa08e69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-15T12:00:11.358579Z",
     "iopub.status.busy": "2023-04-15T12:00:11.357746Z",
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     "end_time": "2023-04-15T12:00:11.364358",
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     "status": "completed"
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   "outputs": [],
   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2225e856",
   "metadata": {
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     "end_time": "2023-04-15T12:00:11.378574",
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