table-to-text-generation-training.ipynb

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   "source": [
    "import pandas as pd"
   ]
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   "source": [
    "df=pd.read_csv(\"../input/table-to-text-generation-dataset-google-totto/totto_data/tablesWithTag.csv\")\n",
    "df=df[:45000]\n",
    "df=df.sample(frac=1).reset_index()"
   ]
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    "MAXLENI=400\n",
    "MAXLENO=200"
   ]
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   "source": [
    "from transformers import T5Tokenizer\n",
    "from transformers import T5ForConditionalGeneration\n",
    "from transformers import AdamW, WarmUp, get_linear_schedule_with_warmup\n",
    "from tqdm.notebook import tqdm\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import os\n",
    "import time\n",
    "import copy\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt"
   ]
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    "tokenizer = T5Tokenizer.from_pretrained(\"t5-base\")\n",
    "model=T5ForConditionalGeneration.from_pretrained('t5-base', return_dict=True)"
   ]
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     "text": [
      "We have added 14 tokens\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Embedding(32114, 768)"
      ]
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   "source": [
    "special_tokens_dict = {'pad_token': '<pad>', 'bos_token': '<bos>', 'eos_token': '<eos>', \n",
    "                       'additional_special_tokens': ['<PAGESTART>', '<PAGEEND>', '<SECTIONSTART>', '<SECTIONEND>',\n",
    "                                                     '<TABLESTART>','<TABLEEND>','<CELLSTART>','<CELLEND>','<COLHEADERSTART>',\n",
    "                                                     '<COLHEADEREND>','<ROWHEADERSTART>','<ROWHEADEREND>']}\n",
    "\n",
    "num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)\n",
    "\n",
    "print('We have added', num_added_toks, 'tokens')\n",
    "model.encoder.resize_token_embeddings(len(tokenizer))\n",
    "model.decoder.resize_token_embeddings(len(tokenizer))"
   ]
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   "source": [
    "class tottodataset(Dataset):\n",
    "  def __init__(self,df,tokenizer):\n",
    "    self.sentence=df['sentence']\n",
    "    self.table=df['table']\n",
    "    self.tokenizer=tokenizer\n",
    "\n",
    "  def __len__(self):\n",
    "    return len(self.sentence)\n",
    "  \n",
    "  def __getitem__(self,idx):\n",
    "    inp=(self.table[idx]+'</s>').replace(\"<page_title>\", \"<PAGESTART>\").replace(\"</page_title>\", \"<PAGEEND>\") \\\n",
    "                                    .replace(\"<section_title>\", \"<SECTIONSTART>\").replace(\"</section_title>\", \"<SECTIONEND>\") \\\n",
    "                                    .replace(\"<table>\", \"<TABLESTART>\").replace(\"</table>\", \"<TABLEEND>\") \\\n",
    "                                    .replace(\"<cell>\", \"<CELLSTART>\").replace(\"</cell>\", \"<CELLEND>\") \\\n",
    "                                    .replace(\"<col_header>\", \"<COLHEADERSTART>\").replace(\"</col_header>\", \"<COLHEADEREND>\") \\\n",
    "                                    .replace(\"<row_header>\", \"<ROWHEADERSTART>\").replace(\"</row_header>\", \"<ROWHEADEREND>\")\n",
    "    out=self.sentence[idx]+'</s>'\n",
    "    inp_tokens=self.tokenizer.encode_plus(inp, padding=\"max_length\", max_length=MAXLENI, truncation=True)\n",
    "    out_tokens=self.tokenizer.encode_plus(out, padding=\"max_length\", max_length=MAXLENO, truncation=True)\n",
    "    inp_id=inp_tokens.input_ids\n",
    "    out_id=out_tokens.input_ids\n",
    "    inp_mask=inp_tokens.attention_mask\n",
    "    out_mask=out_tokens.attention_mask\n",
    "    labels=out_tokens.input_ids.copy()\n",
    "    labels=[-100  if x==self.tokenizer.pad_token_id else x for x in labels]\n",
    "\n",
    "    return {\n",
    "        \"table_text\":inp,\n",
    "        \"sentence\":out,\n",
    "        \"input_ids\":torch.tensor(inp_id, dtype=torch.long),\n",
    "        \"input_attention_mask\":torch.tensor(inp_mask, dtype=torch.long),\n",
    "        \"decoder_input_ids\":torch.tensor(out_id, dtype=torch.long),\n",
    "        \"decoder_attention_mask\":torch.tensor(out_mask, dtype=torch.long),\n",
    "        \"labels\":torch.tensor(labels, dtype=torch.long)\n",
    "    }\n",
    "\n"
   ]
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   "source": [
    "train_df=df[:41000]\n",
    "val_df=df[41000:].reset_index()"
   ]
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    "train_dataset=tottodataset(train_df,tokenizer)\n",
    "val_dataset=tottodataset(val_df,tokenizer)\n",
    "\n",
    "train_dataloader=DataLoader(train_dataset,\n",
    "                            batch_size=4,\n",
    "                            num_workers=2,\n",
    "                            shuffle=False)\n",
    "\n",
    "val_dataloader=DataLoader(val_dataset,\n",
    "                            batch_size=4,\n",
    "                            num_workers=2,\n",
    "                            shuffle=False)"
   ]
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   "source": [
    "dataloaders={'train':train_dataloader, 'eval':val_dataloader}"
   ]
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    "dataset_sizes={'train':len(train_dataset), 'eval':len(val_dataset)}"
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   "source": [
    "def train_fn(model,optimizer,scheduler,num_epochs=5):\n",
    "    since=time.time()\n",
    "    best_wts=copy.deepcopy(model.state_dict())\n",
    "    best_loss=float('inf')\n",
    "    for epoch in range(num_epochs):\n",
    "        print(f'Epoch:{epoch}/{num_epochs}')\n",
    "        print('-'*10)\n",
    "        \n",
    "        for mode in ['train','eval']:\n",
    "            if mode=='train':\n",
    "                model.train()\n",
    "            elif mode=='eval':\n",
    "                model.eval()\n",
    "            \n",
    "            running_loss=0.0\n",
    "            \n",
    "            for data in tqdm(dataloaders[mode]):\n",
    "                input_ids = data[\"input_ids\"].to(device, dtype=torch.long)\n",
    "                labels = data['labels'].to(device, dtype=torch.long)\n",
    "            \n",
    "                optimizer.zero_grad()\n",
    "                with torch.set_grad_enabled(mode=='train'):\n",
    "                    outputs=model(\n",
    "                                input_ids =input_ids,\n",
    "                                labels = labels\n",
    "                            )\n",
    "                    loss, logits=outputs[:2]\n",
    "                    \n",
    "                    if mode=='train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()                    \n",
    "                    running_loss += loss.item()\n",
    "\n",
    "            if mode == 'train':\n",
    "                scheduler.step()\n",
    "                \n",
    "            epoch_loss=running_loss/dataset_sizes[mode]\n",
    "            \n",
    "            print('{} Loss: {:.4f} '.format(\n",
    "                mode, epoch_loss))\n",
    "            \n",
    "            if mode=='eval' and epoch_loss<best_loss:\n",
    "                best_wts=copy.deepcopy(model.state_dict())\n",
    "                best_loss=epoch_loss\n",
    "            \n",
    "            print()\n",
    "\n",
    "        time_elapsed = time.time() - since\n",
    "        print('Training complete in {:.0f}m {:.0f}s'.format(\n",
    "            time_elapsed // 60, time_elapsed % 60))\n",
    "        print('Best val loss: {:4f}'.format(best_loss))\n",
    "    \n",
    "        model.load_state_dict(best_wts)\n",
    "    return model\n",
    "        "
   ]
  },
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   "id": "8cd24bf0",
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     "status": "completed"
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    {
     "name": "stdout",
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     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "device=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9faf7754",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "model=model.to(device)\n",
    "optimizer=AdamW(model.parameters(), lr=1e-4)\n",
    "scheduler=torch.optim.lr_scheduler.StepLR(optimizer, step_size=2, gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "385cdeef",
   "metadata": {
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     "status": "completed"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sun Aug 22 18:29:00 2021       \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| NVIDIA-SMI 450.119.04   Driver Version: 450.119.04   CUDA Version: 11.0     |\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    34W / 250W |   1981MiB / 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",
      "+-----------------------------------------------------------------------------+\r\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
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     "text": [
      "Epoch:0/7\n",
      "----------\n"
     ]
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       "  0%|          | 0/10250 [00:00<?, ?it/s]"
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      "train Loss: 0.2954 \n",
      "\n"
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      "eval Loss: 0.2411 \n",
      "\n",
      "Training complete in 71m 8s\n",
      "Best val loss: 0.241144\n",
      "Epoch:1/7\n",
      "----------\n"
     ]
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      "train Loss: 0.2369 \n",
      "\n"
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     "text": [
      "eval Loss: 0.2306 \n",
      "\n",
      "Training complete in 142m 28s\n",
      "Best val loss: 0.230629\n",
      "Epoch:2/7\n",
      "----------\n"
     ]
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      "train Loss: 0.1969 \n",
      "\n"
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     "text": [
      "eval Loss: 0.2289 \n",
      "\n",
      "Training complete in 213m 44s\n",
      "Best val loss: 0.228897\n",
      "Epoch:3/7\n",
      "----------\n"
     ]
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     "text": [
      "train Loss: 0.1890 \n",
      "\n"
     ]
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     "text": [
      "eval Loss: 0.2278 \n",
      "\n",
      "Training complete in 284m 57s\n",
      "Best val loss: 0.227825\n",
      "Epoch:4/7\n",
      "----------\n"
     ]
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     "output_type": "stream",
     "text": [
      "train Loss: 0.1833 \n",
      "\n"
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     "text": [
      "eval Loss: 0.2271 \n",
      "\n",
      "Training complete in 356m 10s\n",
      "Best val loss: 0.227144\n",
      "Epoch:5/7\n",
      "----------\n"
     ]
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     "metadata": {},
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.1825 \n",
      "\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "eval Loss: 0.2269 \n",
      "\n",
      "Training complete in 427m 23s\n",
      "Best val loss: 0.226886\n",
      "Epoch:6/7\n",
      "----------\n"
     ]
    },
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.1819 \n",
      "\n"
     ]
    },
    {
     "data": {
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     "output_type": "stream",
     "text": [
      "eval Loss: 0.2267 \n",
      "\n",
      "Training complete in 498m 42s\n",
      "Best val loss: 0.226709\n"
     ]
    }
   ],
   "source": [
    "history=train_fn(model,optimizer,scheduler,num_epochs=7)"
   ]
  },
  {
   "cell_type": "code",
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   "id": "7d8c4fa9",
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   },
   "outputs": [],
   "source": [
    "torch.save(model, \"T5Epoch:7\")"
   ]
  }
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