pair_02.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from forgebox.imports import *\n",
    "from forgebox.ftorch.cuda import CudaHandler\n",
    "from transformers import AutoModel,AutoTokenizer, get_cosine_schedule_with_warmup\n",
    "from typing import Dict, List\n",
    "import pytorch_lightning as pl\n",
    "from torch.utils.data.dataloader import DataLoader, default_collate\n",
    "from copy import deepcopy\n",
    "from datetime import datetime\n",
    "from tqdm.notebook import tqdm\n",
    "# from pl_bolts.callbacks.printing import PrintTableMetricsCallback\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"CUDA_LAUNCH_BLOCKING\"]=\"1\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from forgebox.config import Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "CONFIG = Config(\n",
    "    slug=\"bs\",\n",
    "    pretrain = \"rbt\",\n",
    "    learning_rate=2e-5,\n",
    "    top_lr_expansion=10,\n",
    "    max_epochs = 30,\n",
    "    frozen_years = 2,\n",
    "    drop_out_ratio=.2,\n",
    "    use_scheduler = False,\n",
    "    num_warmup_steps=0,\n",
    "    accumulate_grad_batches=1,\n",
    "    layerwise_lr_decay=1.,\n",
    "    weight_decay = 0.01,\n",
    "    adam_epsilon = 1e-6,\n",
    "    model_class = \"MeanPoolerBn\", \n",
    "    #WithAttnHead, MeanPooler， CLSReg, PairCLS, PairAttn, MeanPoolerBn\n",
    "    use_pair = False,\n",
    "    use_pretrain = True,\n",
    "    random_seed = 42,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pretrain config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-22T13:46:18.835247Z",
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     "iopub.status.idle": "2021-07-22T13:46:18.839667Z",
     "shell.execute_reply": "2021-07-22T13:46:18.838807Z",
     "shell.execute_reply.started": "2021-07-22T13:46:18.835213Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'roberta-base'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PRETRAIN = CONFIG.pretrain\n",
    "tag = dict(\n",
    "    brt=\"bert-base-uncased\",\n",
    "    rbt=\"roberta-base\"\n",
    ")[PRETRAIN]\n",
    "tag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Repeatable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(CONFIG.random_seed)\n",
    "torch.manual_seed(CONFIG.random_seed)\n",
    "np.random.seed(CONFIG.random_seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('weights/pre_rbtbs', './rbt_bs_pre')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretrained = f\"weights/pre_{PRETRAIN}{CONFIG.slug}\" if CONFIG.use_pretrain else tag\n",
    "saved = f'./{PRETRAIN}_{CONFIG.slug}_pre' if CONFIG.use_pretrain else tag\n",
    "pretrained, saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-22T13:46:18.994272Z",
     "iopub.status.busy": "2021-07-22T13:46:18.993895Z",
     "iopub.status.idle": "2021-07-22T13:46:42.849012Z",
     "shell.execute_reply": "2021-07-22T13:46:42.847886Z",
     "shell.execute_reply.started": "2021-07-22T13:46:18.994243Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at weights/pre_rbtbs were not used when initializing RobertaModel: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias']\n",
      "- This IS expected if you are initializing RobertaModel 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 RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of RobertaModel were not initialized from the model checkpoint at weights/pre_rbtbs and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(saved, use_fast=True)\n",
    "# tokenizer = AutoTokenizer.from_pretrained(tag)\n",
    "model = AutoModel.from_pretrained(pretrained)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-22T14:19:15.307014Z",
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     "iopub.status.idle": "2021-07-22T14:19:15.374632Z",
     "shell.execute_reply": "2021-07-22T14:19:15.373623Z",
     "shell.execute_reply.started": "2021-07-22T14:19:15.306980Z"
    }
   },
   "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>id</th>\n",
       "      <th>url_legal</th>\n",
       "      <th>license</th>\n",
       "      <th>excerpt</th>\n",
       "      <th>target</th>\n",
       "      <th>standard_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c12129c31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>When the young people returned to the ballroom...</td>\n",
       "      <td>-0.340259</td>\n",
       "      <td>0.464009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85aa80a4c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>All through dinner time, Mrs. Fayre was somewh...</td>\n",
       "      <td>-0.315372</td>\n",
       "      <td>0.480805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b69ac6792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>As Roger had predicted, the snow departed as q...</td>\n",
       "      <td>-0.580118</td>\n",
       "      <td>0.476676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dd1000b26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>And outside before the palace a great garden w...</td>\n",
       "      <td>-1.054013</td>\n",
       "      <td>0.450007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37c1b32fb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Once upon a time there were Three Bears who li...</td>\n",
       "      <td>0.247197</td>\n",
       "      <td>0.510845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id url_legal license  \\\n",
       "0  c12129c31       NaN     NaN   \n",
       "1  85aa80a4c       NaN     NaN   \n",
       "2  b69ac6792       NaN     NaN   \n",
       "3  dd1000b26       NaN     NaN   \n",
       "4  37c1b32fb       NaN     NaN   \n",
       "\n",
       "                                             excerpt    target  standard_error  \n",
       "0  When the young people returned to the ballroom... -0.340259        0.464009  \n",
       "1  All through dinner time, Mrs. Fayre was somewh... -0.315372        0.480805  \n",
       "2  As Roger had predicted, the snow departed as q... -0.580118        0.476676  \n",
       "3  And outside before the palace a great garden w... -1.054013        0.450007  \n",
       "4  Once upon a time there were Three Bears who li...  0.247197        0.510845  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df = pd.read_csv(\"train.csv\")\n",
    "test_df = pd.read_csv(\"train.csv\").head(20)\n",
    "display(train_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-22T13:51:57.676060Z",
     "iopub.status.busy": "2021-07-22T13:51:57.675670Z",
     "iopub.status.idle": "2021-07-22T13:51:57.840797Z",
     "shell.execute_reply": "2021-07-22T13:51:57.839727Z",
     "shell.execute_reply.started": "2021-07-22T13:51:57.676030Z"
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py:405: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8cc61176d0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_df.target.groupby(pd.cut(list(train_df.target),\n",
    "                                        bins = list(i*.2-4 for i in  range(40)))).count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-22T14:14:57.247786Z",
     "iopub.status.busy": "2021-07-22T14:14:57.247300Z",
     "iopub.status.idle": "2021-07-22T14:14:57.257541Z",
     "shell.execute_reply": "2021-07-22T14:14:57.256295Z",
     "shell.execute_reply.started": "2021-07-22T14:14:57.247749Z"
    }
   },
   "outputs": [],
   "source": [
    "class SingleDataset(Dataset):\n",
    "    def __init__(self, df):\n",
    "        self.df = df\n",
    "        self.indices = df.index\n",
    "        \n",
    "    def __len__(self,): return len(self.df)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        row_ = dict(self.df.loc[self.indices[idx]])\n",
    "        row = dict((k,v) for k,v in row_.items() if k in [\"excerpt\",\"id\",\"target\"])\n",
    "        return row\n",
    "    \n",
    "    def collate(self, batch):\n",
    "        batch = default_collate(batch)\n",
    "        batch[\"excerpt\"] = tokenizer(batch[\"excerpt\"], \n",
    "                                     truncation=True,\n",
    "                                     return_tensors='pt', \n",
    "                                     return_token_type_ids=False, \n",
    "                                     padding=True,\n",
    "                                     max_length=tokenizer.max_len_sentences_pair\n",
    "                                    )\n",
    "        if 'target' in batch:\n",
    "            batch['target'] = batch['target'].float()\n",
    "        return batch\n",
    "    \n",
    "class PairDataset(SingleDataset):\n",
    "    def __init__(self, df):\n",
    "        super().__init__(df)\n",
    "        \n",
    "    def __getitem__(self, idx):\n",
    "        r1 = dict(self.df.loc[self.indices[idx]])\n",
    "        r2 = dict(self.df.loc[random.choice(self.indices)])\n",
    "        t1 = r1[\"excerpt\"]\n",
    "        t2 = r2[\"excerpt\"]\n",
    "        row = {\"excerpt\":f\"{t1}[SEP]{t2}\",\n",
    "               \"target\":torch.FloatTensor([\n",
    "                   r1[\"target\"],\n",
    "                   r2[\"target\"],\n",
    "                   1. if r1[\"target\"]>r2[\"target\"] else 0.\n",
    "               ])\n",
    "              }\n",
    "        return row\n",
    "    \n",
    "    def get_row(self, row_):\n",
    "        row = dict((k,v) for k,v in row_.items() if k in [\"excerpt\",\"id\",\"target\"])\n",
    "        return row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-22T14:14:38.751413Z",
     "iopub.status.busy": "2021-07-22T14:14:38.750966Z",
     "iopub.status.idle": "2021-07-22T14:14:38.757133Z",
     "shell.execute_reply": "2021-07-22T14:14:38.755698Z",
     "shell.execute_reply.started": "2021-07-22T14:14:38.751378Z"
    }
   },
   "outputs": [],
   "source": [
    "LitDataset = PairDataset if CONFIG.use_pair else SingleDataset\n",
    "train_ds = LitDataset(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-22T14:45:51.607175Z",
     "iopub.status.busy": "2021-07-22T14:45:51.606811Z",
     "iopub.status.idle": "2021-07-22T14:45:51.613373Z",
     "shell.execute_reply": "2021-07-22T14:45:51.612144Z",
     "shell.execute_reply.started": "2021-07-22T14:45:51.607145Z"
    }
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 20\n",
    "def create_dl(df, istrain = True):\n",
    "    \"\"\"\n",
    "    Create dataloader from a dataframe\n",
    "    \"\"\"\n",
    "    ds = LitDataset(df)\n",
    "    dl = DataLoader(ds, shuffle=istrain,\n",
    "                    batch_size=BATCH_SIZE if istrain else BATCH_SIZE*4,\n",
    "                    num_workers =4 if istrain else 8,\n",
    "                    collate_fn = ds.collate)\n",
    "    return dl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-22T14:46:35.020204Z",
     "iopub.status.busy": "2021-07-22T14:46:35.019834Z",
     "iopub.status.idle": "2021-07-22T14:46:35.074674Z",
     "shell.execute_reply": "2021-07-22T14:46:35.073908Z",
     "shell.execute_reply.started": "2021-07-22T14:46:35.020174Z"
    }
   },
   "outputs": [],
   "source": [
    "a_dl = create_dl(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-22T14:46:35.020204Z",
     "iopub.status.busy": "2021-07-22T14:46:35.019834Z",
     "iopub.status.idle": "2021-07-22T14:46:35.074674Z",
     "shell.execute_reply": "2021-07-22T14:46:35.073908Z",
     "shell.execute_reply.started": "2021-07-22T14:46:35.020174Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': ['b56a17586',\n",
       "  'cf014ad6c',\n",
       "  'cc445e16d',\n",
       "  '25268f8d2',\n",
       "  '3138a5d15',\n",
       "  '50e970a38',\n",
       "  '55f425cd8',\n",
       "  '031f30122',\n",
       "  'ba96ff079',\n",
       "  'e2093875b',\n",
       "  '4f9cb89a9',\n",
       "  '04fe69def',\n",
       "  '884c4771b',\n",
       "  '8b851c890',\n",
       "  'a0283cf3f',\n",
       "  '1b0a2c763',\n",
       "  '47b6010d9',\n",
       "  '4900e5f25',\n",
       "  'd35944194',\n",
       "  '2b10b4e23'],\n",
       " 'excerpt': {'input_ids': tensor([[    0,  1121,   502,  ...,     1,     1,     1],\n",
       "         [    0,   133,   920,  ...,  1816,     4,     2],\n",
       "         [    0, 28750,   383,  ...,     2,     1,     1],\n",
       "         ...,\n",
       "         [    0,   133,  1086,  ...,     1,     1,     1],\n",
       "         [    0,   133,  5402,  ...,     1,     1,     1],\n",
       "         [    0,  3762,     9,  ...,     1,     1,     1]]), 'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\n",
       "         [1, 1, 1,  ..., 1, 1, 1],\n",
       "         [1, 1, 1,  ..., 1, 0, 0],\n",
       "         ...,\n",
       "         [1, 1, 1,  ..., 0, 0, 0],\n",
       "         [1, 1, 1,  ..., 0, 0, 0],\n",
       "         [1, 1, 1,  ..., 0, 0, 0]])},\n",
       " 'target': tensor([-1.5247,  0.9442, -0.1016, -0.0147, -1.1151, -3.4778, -0.5281, -0.0039,\n",
       "         -0.6317, -0.3405, -0.5070, -2.7445,  0.7848, -1.8453, -0.0384, -2.2179,\n",
       "         -1.2201, -1.5355, -1.6386, -2.3860])}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch = next(iter(a_dl))\n",
    "batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-22T14:37:38.474444Z",
     "iopub.status.busy": "2021-07-22T14:37:38.473793Z",
     "iopub.status.idle": "2021-07-22T14:37:38.481405Z",
     "shell.execute_reply": "2021-07-22T14:37:38.480502Z",
     "shell.execute_reply.started": "2021-07-22T14:37:38.474392Z"
    }
   },
   "outputs": [],
   "source": [
    "def split_k(df, k: int=5):\n",
    "    kmap = np.random.choice(range(k), size=len(df))\n",
    "    return dict((ki, dict(train=(kmap!=ki), valid=(kmap==ki))) for ki in range(5))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-22T14:38:20.650125Z",
     "iopub.status.busy": "2021-07-22T14:38:20.649649Z",
     "iopub.status.idle": "2021-07-22T14:38:20.660139Z",
     "shell.execute_reply": "2021-07-22T14:38:20.658951Z",
     "shell.execute_reply.started": "2021-07-22T14:38:20.650082Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: {'train': array([ True,  True,  True, ...,  True,  True,  True]),\n",
       "  'valid': array([False, False, False, ..., False, False, False])},\n",
       " 1: {'train': array([ True,  True,  True, ...,  True,  True,  True]),\n",
       "  'valid': array([False, False, False, ..., False, False, False])},\n",
       " 2: {'train': array([ True,  True, False, ...,  True,  True, False]),\n",
       "  'valid': array([False, False,  True, ..., False, False,  True])},\n",
       " 3: {'train': array([False,  True,  True, ..., False,  True,  True]),\n",
       "  'valid': array([ True, False, False, ...,  True, False, False])},\n",
       " 4: {'train': array([ True, False,  True, ...,  True, False,  True]),\n",
       "  'valid': array([False,  True, False, ..., False,  True, False])}}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split_5 = split_k(train_df, 5)\n",
    "split_5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataModule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-22T14:43:55.738413Z",
     "iopub.status.busy": "2021-07-22T14:43:55.738038Z",
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     "shell.execute_reply": "2021-07-22T14:43:55.745359Z",
     "shell.execute_reply.started": "2021-07-22T14:43:55.738381Z"
    }
   },
   "outputs": [],
   "source": [
    "class LitDataMod(pl.LightningDataModule):\n",
    "    def __init__(self, train_df, test_df, split_map: Dict[str, np.ndarray]):\n",
    "        super().__init__()\n",
    "        self.train_df = train_df\n",
    "        self.test_df = test_df\n",
    "        self.split_map = split_map\n",
    "        \n",
    "    def train_dataloader(self):\n",
    "        df = self.train_df[self.split_map[\"train\"]]\n",
    "        return create_dl(df, istrain=True)\n",
    "    \n",
    "    def val_dataloader(self):\n",
    "        df = self.train_df[self.split_map[\"valid\"]]\n",
    "        return create_dl(df, istrain=False)\n",
    "    \n",
    "    def test_dataloader(self):\n",
    "        df = self.test_df\n",
    "        return create_dl(df, istrain=False)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-22T14:51:10.351444Z",
     "iopub.status.busy": "2021-07-22T14:51:10.350980Z",
     "iopub.status.idle": "2021-07-22T14:51:10.359417Z",
     "shell.execute_reply": "2021-07-22T14:51:10.358095Z",
     "shell.execute_reply.started": "2021-07-22T14:51:10.351410Z"
    }
   },
   "outputs": [],
   "source": [
    "model.pooler = nn.Linear(model.config.hidden_size, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Freeze/ unfreeze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====================== GRADUAL UNFREEZE ======================\n",
    "def unfreeze(self):\n",
    "    \"\"\"unfreeze this module, and its sub modules\"\"\"\n",
    "    for p in self.parameters():\n",
    "        p.requires_grad = True\n",
    "        \n",
    "def freeze(self):\n",
    "    \"\"\"freeze this module, and its sub modules\"\"\"\n",
    "    for p in self.parameters():\n",
    "        p.requires_grad = False\n",
    "        \n",
    "def measure_freeze(m:nn.Module)->\"a describtion about how many submodules are unfreezed\":\n",
    "    total = 0\n",
    "    trainable = 0\n",
    "    for param in m.parameters():\n",
    "        total+=1\n",
    "        if param.requires_grad: trainable+=1\n",
    "    return f\"☀️ {trainable} trainables/{total} total\"\n",
    "        \n",
    "nn.Module.unfreeze = unfreeze\n",
    "nn.Module.freeze = freeze\n",
    "\n",
    "class GradualUnfreeze(pl.callbacks.Callback):\n",
    "    \"\"\"\n",
    "    A Callback that will deploy ULMFit's gradual unfreeze trick\n",
    "    \"\"\"\n",
    "    def on_validation_epoch_start(self, trainer, pl_module):\n",
    "        epoch = trainer.current_epoch\n",
    "        \n",
    "        if epoch==0:\n",
    "            pl_module.freeze()\n",
    "        \n",
    "        # layers from top to bottom\n",
    "        glist = pl_module.gradual_list()\n",
    "        \n",
    "        if epoch<len(glist):\n",
    "            print(f\"🔥 unfreezing [{type(glist[epoch])}] on [epoch {epoch}]\")\n",
    "            glist[epoch].unfreeze()\n",
    "                \n",
    "            # print out statistics for current unfreezing\n",
    "            print(f\"{measure_freeze(pl_module)}\")\n",
    "\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finetune Scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FineTuneScheduler(pl.callbacks.Callback):\n",
    "    \"\"\"\n",
    "    Train the top layer for 2 epoch\n",
    "    then un freeze all\n",
    "    \"\"\"\n",
    "\n",
    "    def on_epoch_start(self, trainer, pl_module):\n",
    "        epoch = trainer.current_epoch\n",
    "\n",
    "        if epoch == 0:\n",
    "            pl_module.freeze()\n",
    "            for tl in pl_module.top_layers:\n",
    "                tl.unfreeze()\n",
    "        if epoch == CONFIG.frozen_years:\n",
    "            print(f\"🐺 unfreeze\")\n",
    "            pl_module.unfreeze()\n",
    "            pl_module.base.embeddings.freeze()\n",
    "\n",
    "# ====================== OPTIMIZER CONFIGURATION ======================\n",
    "\n",
    "\n",
    "def modules_to_opt_conf(modules, **opt_kwargs) -> \"a list of PyTorch optimizer config\":\n",
    "    \"\"\"\n",
    "    put in a sequence of pytorch modules, \n",
    "    return optimizer param groups of configs\n",
    "    \"\"\"\n",
    "    param_list = []\n",
    "    param_list_nd = []\n",
    "    no_decay = [\"bias\", \"LayerNorm.weight\"]\n",
    "    for m in modules:\n",
    "        for n, p in m.named_parameters():\n",
    "            if any(nd in n for nd in no_decay):\n",
    "                param_list_nd.append(p)\n",
    "            else:\n",
    "                param_list.append(p)\n",
    "\n",
    "    opt_kwargs_nd = deepcopy(opt_kwargs)\n",
    "    opt_kwargs_nd[\"weight_decay\"] = 0.\n",
    "    return [dict(params=param_list, **opt_kwargs),  # param_group with weight decay\n",
    "            # param_group without weight decay\n",
    "            dict(params=param_list_nd, **opt_kwargs_nd),\n",
    "            ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_config(config, **kwargs):\n",
    "    for k, v in kwargs.items():\n",
    "        setattr(model.config, k, v)\n",
    "    return config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# inf_dl = LitDataMod(train_df, test_df, split_5[0]).test_dataloader()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-22T14:57:37.042125Z",
     "iopub.status.busy": "2021-07-22T14:57:37.041746Z",
     "iopub.status.idle": "2021-07-22T14:57:37.054399Z",
     "shell.execute_reply": "2021-07-22T14:57:37.053098Z",
     "shell.execute_reply.started": "2021-07-22T14:57:37.042094Z"
    }
   },
   "outputs": [],
   "source": [
    "class BaseModel(pl.LightningModule):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.learning_rate = CONFIG.learning_rate\n",
    "        self.use_scheduler = CONFIG.learning_rate\n",
    "        self.crit = nn.MSELoss()\n",
    "#         self.crit = nn.KLDivLoss()\n",
    "\n",
    "    def training_step(self, batch: Dict[str, torch.Tensor], batch_idx: int):\n",
    "        x = batch[\"excerpt\"]\n",
    "        y = batch[\"target\"]\n",
    "\n",
    "        y_ = self(input_ids=x[\"input_ids\"], attention_mask=x[\"attention_mask\"])\n",
    "        loss = self.crit(y_[:, 0], y)\n",
    "        self.log(\"loss\", loss)\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch: Dict[str, torch.Tensor], batch_idx: int):\n",
    "        x = batch[\"excerpt\"]\n",
    "        y = batch[\"target\"]\n",
    "\n",
    "        y_ = self(input_ids=x[\"input_ids\"], attention_mask=x[\"attention_mask\"])\n",
    "        loss = self.crit(y_[:, 0], y)\n",
    "        self.log(\"val_loss\", loss)\n",
    "        return loss\n",
    "\n",
    "    def test_step(self, batch: Dict[str, torch.Tensor], batch_idx: int):\n",
    "        x = batch[\"excerpt\"]\n",
    "        ids = batch[\"id\"]\n",
    "\n",
    "        y_ = self(input_ids=x[\"input_ids\"], attention_mask=x[\"attention_mask\"])\n",
    "\n",
    "        target = y_.detach().cpu().numpy().reshape(-1)\n",
    "\n",
    "        return pd.DataFrame({\"id\": ids, \"target\": target})\n",
    "\n",
    "    @property\n",
    "    def top_layers(self):\n",
    "        return [self.reg, ]\n",
    "\n",
    "    def gradual_list(self) -> List[nn.Module]:\n",
    "        \"\"\"\n",
    "        Return a list of list of nn.Modules, \n",
    "        for the modules to be unfreezed by the start of each epoch\n",
    "        \"\"\"\n",
    "        # top layer\n",
    "        rt = self.top_layers\n",
    "\n",
    "        # encoder layers\n",
    "        rt += list(self.base.encoder.layer)[::-1]\n",
    "\n",
    "        # embedding\n",
    "        rt += [model.embeddings, ]\n",
    "        return rt\n",
    "\n",
    "    def configure_optimizers(self,):\n",
    "        \"\"\"\n",
    "        Create the optimizer\n",
    "        \"\"\"\n",
    "        gradual = self.gradual_list()\n",
    "\n",
    "        no_decay = [\"bias\", \"LayerNorm.weight\"]\n",
    "        h = self.config\n",
    "        # top layer param group\n",
    "        opt_configs = modules_to_opt_conf(self.top_layers,\n",
    "                                          lr=self.learning_rate*CONFIG.top_lr_expansion,\n",
    "                                          eps=CONFIG.adam_epsilon,\n",
    "                                          weight_decay=CONFIG.weight_decay)\n",
    "\n",
    "        delay = len(self.top_layers)\n",
    "        # encoder layer one by one from top to bottom param group\n",
    "        for i in range(len(gradual)-delay):\n",
    "            opt_configs += modules_to_opt_conf([gradual[i+delay], ],\n",
    "                                               lr=self.learning_rate *\n",
    "                                               (h.layerwise_lr_decay**(i+1)),\n",
    "                                               eps=CONFIG.adam_epsilon,\n",
    "                                               weight_decay=CONFIG.weight_decay)\n",
    "\n",
    "        optimizer = torch.optim.AdamW(opt_configs)\n",
    "        self.opt = optimizer\n",
    "        if self.use_scheduler:\n",
    "            scheduler = get_cosine_schedule_with_warmup(optimizer,\n",
    "                                                        num_warmup_steps=CONFIG.num_warmup_steps,\n",
    "                                                        num_training_steps=2000, )\n",
    "            return dict(optimizer=optimizer, lr_scheduler=scheduler)\n",
    "        return optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Headers & Poolers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AttentionHead(nn.Module):\n",
    "    def __init__(self, in_features, hidden_dim):\n",
    "        super().__init__()\n",
    "        self.in_features = in_features\n",
    "        self.middle_features = hidden_dim\n",
    "\n",
    "        self.W = nn.Linear(in_features, hidden_dim)\n",
    "        self.V = nn.Linear(hidden_dim, 1)\n",
    "        self.out_features = hidden_dim\n",
    "\n",
    "    def forward(self, features):\n",
    "        att = torch.tanh(self.W(features))\n",
    "        score = self.V(att)\n",
    "        attention_weights = torch.softmax(score, dim=1)\n",
    "        context_vector = attention_weights * features\n",
    "        context_vector = torch.sum(context_vector, dim=1)\n",
    "\n",
    "        return context_vector\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-22T14:57:37.042125Z",
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     "iopub.status.idle": "2021-07-22T14:57:37.054399Z",
     "shell.execute_reply": "2021-07-22T14:57:37.053098Z",
     "shell.execute_reply.started": "2021-07-22T14:57:37.042094Z"
    }
   },
   "outputs": [],
   "source": [
    "class PairBase(BaseModel):\n",
    "    def __init__(self, base):\n",
    "        super().__init__()\n",
    "        self.base = base\n",
    "        self.config = self.base.config\n",
    "        self.bce = nn.BCEWithLogitsLoss()\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "        self.accuracy = pl.metrics.Accuracy()\n",
    "\n",
    "    def training_step(self, batch: Dict[str, torch.Tensor], batch_idx: int):\n",
    "        x = batch[\"excerpt\"]\n",
    "        y = batch[\"target\"]\n",
    "\n",
    "        y_ = self(input_ids=x[\"input_ids\"], attention_mask=x[\"attention_mask\"])\n",
    "        loss_mse = self.crit(y_[:, :2], y[:, :2])\n",
    "        loss_bce = self.bce(y_[:, 2], y[:, 2])\n",
    "        acc = self.accuracy(self.sigmoid(y_[:, 2]), y[:, 2].long())\n",
    "        loss_both = loss_mse + loss_bce\n",
    "        mse_single = self.crit(y_[:, 0], y[:, 0])\n",
    "        \n",
    "        self.log(\"loss_both\", loss_both)\n",
    "        self.log(\"loss\", mse_single)\n",
    "        self.log(\"loss_bce\", loss_bce)\n",
    "        self.log(\"acc\",acc)\n",
    "        return loss_both\n",
    "\n",
    "\n",
    "    def validation_step(self, batch: Dict[str, torch.Tensor], batch_idx: int):\n",
    "        x = batch[\"excerpt\"]\n",
    "        y = batch[\"target\"]\n",
    "\n",
    "        y_ = self(input_ids=x[\"input_ids\"], attention_mask=x[\"attention_mask\"])\n",
    "        loss_mse = self.crit(y_[:, :2], y[:, :2])\n",
    "        loss_bce = self.bce(y_[:, 2], y[:, 2])\n",
    "        loss_both = loss_mse + loss_bce\n",
    "        mse_single = self.crit(y_[:, 0], y[:, 0])\n",
    "        \n",
    "        acc = self.accuracy(self.sigmoid(y_[:, 2]), y[:, 2].long())\n",
    "        \n",
    "        self.log(\"val_loss_both\", loss_both)\n",
    "        self.log(\"val_loss\", mse_single)\n",
    "        self.log(\"val_loss_bce\", loss_bce)\n",
    "        self.log(\"val_acc\",acc)\n",
    "        return loss_both\n",
    "\n",
    "    def test_step(self, batch: Dict[str, torch.Tensor], batch_idx: int):\n",
    "        x = batch[\"excerpt\"]\n",
    "        ids = batch[\"id\"]\n",
    "\n",
    "        y_ = self(input_ids=x[\"input_ids\"], attention_mask=x[\"attention_mask\"])\n",
    "\n",
    "        target = y_[:,:1].detach().cpu().numpy().reshape(-1)\n",
    "\n",
    "        return pd.DataFrame({\"id\": ids, \"target\": target})\n",
    "    \n",
    "    \n",
    "class PairCLS(PairBase):\n",
    "    def __init__(self, base):\n",
    "        super().__init__(base)\n",
    "        self.reg = nn.Linear(base.config.hidden_size, 3)\n",
    "        \n",
    "    def forward(self, **excerpt: torch.Tensor) -> torch.Tensor:\n",
    "        vec = self.base(**excerpt).last_hidden_state[:, 0, :]\n",
    "        return self.reg(vec)\n",
    "    \n",
    "class PairAttn(PairBase):\n",
    "    def __init__(self, base):\n",
    "        super().__init__(base)\n",
    "        self.head = AttentionHead(\n",
    "            self.config.hidden_size, self.config.hidden_size)\n",
    "        self.dout = nn.Dropout(.1)\n",
    "        self.reg = nn.Linear(base.config.hidden_size, 3)\n",
    "\n",
    "    def forward(self, **excerpt: torch.Tensor) -> torch.Tensor:\n",
    "        attention_mask = excerpt[\"attention_mask\"]\n",
    "        vec = self.base(**excerpt).last_hidden_state\n",
    "        vec *= (attention_mask[..., None])\n",
    "        return self.reg(self.dout(self.head(vec)))\n",
    "\n",
    "    @property\n",
    "    def top_layers(self):\n",
    "        return [self.reg, self.head]\n",
    "\n",
    "class CLSReg(BaseModel):\n",
    "    def __init__(self, base):\n",
    "        super().__init__()\n",
    "        self.base = base\n",
    "        self.config = self.base.config\n",
    "        self.reg = nn.Linear(base.config.hidden_size, 1)\n",
    "\n",
    "    def forward(self, **excerpt: torch.Tensor) -> torch.Tensor:\n",
    "        vec = self.base(**excerpt).last_hidden_state[:, 0, :]\n",
    "        return self.reg(vec)\n",
    "\n",
    "\n",
    "class CLSnn(BaseModel):\n",
    "    def __init__(self, base):\n",
    "        super().__init__()\n",
    "        self.base = base\n",
    "        self.config = self.base.config\n",
    "        self.reg = nn.Sequential(\n",
    "            nn.Linear(base.config.hidden_size,\n",
    "                      base.config.hidden_size, bias=False),\n",
    "            nn.BatchNorm1d(base.config.hidden_size),\n",
    "            nn.LeakyReLU(),\n",
    "            nn.Linear(base.config.hidden_size, 1),\n",
    "        )\n",
    "\n",
    "    def forward(self, **excerpt: torch.Tensor) -> torch.Tensor:\n",
    "        x = excerpt[\"input_ids\"]\n",
    "        vec = self.base(**excerpt).last_hidden_state[:, 0, :]\n",
    "        return self.reg(vec)\n",
    "\n",
    "\n",
    "class MeanPooler(BaseModel):\n",
    "    def __init__(self, base):\n",
    "        super().__init__()\n",
    "        self.base = base\n",
    "        self.config = self.base.config\n",
    "        self.reg = nn.Linear(base.config.hidden_size, 1)\n",
    "        self.dropout = nn.Dropout(CONFIG.drop_out_ratio)\n",
    "\n",
    "    def reduce_hidden_state(self, vec, attention_mask):\n",
    "        vec = vec*(attention_mask[..., None])\n",
    "        return vec.sum(dim=1)/(attention_mask.sum(-1)[..., None])\n",
    "\n",
    "    def forward(self, **excerpt: torch.Tensor) -> torch.Tensor:\n",
    "        vec = self.base(**excerpt).last_hidden_state\n",
    "        vec = self.reduce_hidden_state(vec, excerpt[\"attention_mask\"])\n",
    "        return self.reg(self.dropout(vec))\n",
    "    \n",
    "class MeanPoolerBn(MeanPooler):\n",
    "    def __init__(self, base):\n",
    "        super().__init__(base)\n",
    "        self.bn = nn.BatchNorm1d(base.config.hidden_size)\n",
    "\n",
    "    def forward(self, **excerpt: torch.Tensor) -> torch.Tensor:\n",
    "        vec = self.base(**excerpt).last_hidden_state\n",
    "        vec = self.reduce_hidden_state(vec, excerpt[\"attention_mask\"])\n",
    "        vec = self.bn(vec)\n",
    "        return self.reg(self.dropout(vec))\n",
    "\n",
    "\n",
    "# class MeanPooler(BaseModel):\n",
    "#     def __init__(self, base):\n",
    "#         super().__init__()\n",
    "#         self.base = base\n",
    "#         self.config = self.base.config\n",
    "#         self.reg = nn.Linear(base.config.hidden_size, 1)\n",
    "\n",
    "#     def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "#         vec = self.base(x).last_hidden_state.mean(dim=1)\n",
    "#         return self.reg(vec)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "class WithAttnHead(BaseModel):\n",
    "    def __init__(self, base):\n",
    "        super().__init__()\n",
    "        self.base = base\n",
    "        self.config = self.base.config\n",
    "        self.head = AttentionHead(\n",
    "            self.config.hidden_size, self.config.hidden_size)\n",
    "        self.dout = nn.Dropout(.1)\n",
    "        self.reg = nn.Linear(base.config.hidden_size, 1)\n",
    "\n",
    "    def forward(self, **excerpt: torch.Tensor) -> torch.Tensor:\n",
    "        x = excerpt[\"input_ids\"]\n",
    "        attention_mask = excerpt[\"attention_mask\"]\n",
    "        vec = self.base(**excerpt).last_hidden_state\n",
    "        vec *= (attention_mask[..., None])\n",
    "        return self.reg(self.dout(self.head(vec)))\n",
    "\n",
    "    @property\n",
    "    def top_layers(self):\n",
    "        return [self.reg, self.head]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import Output\n",
    "\n",
    "import copy\n",
    "\n",
    "class PrintTableMetricsCallback(pl.Callback):\n",
    "    \"\"\"\n",
    "    A metrics callback keep showing pandas dataframe\n",
    "    \"\"\"\n",
    "    def __init__(self) -> None:\n",
    "        self.metrics: List = []\n",
    "\n",
    "    def on_fit_start(\n",
    "        self, trainer: pl.Trainer,\n",
    "        pl_module: pl.LightningModule\n",
    "    ) -> None:\n",
    "        pl_module.output = Output()\n",
    "        display(pl_module.output)\n",
    "\n",
    "    def on_validation_epoch_end(\n",
    "        self, trainer: pl.Trainer,\n",
    "        pl_module: pl.LightningModule\n",
    "    ) -> None:\n",
    "        metrics_dict = copy.copy(trainer.callback_metrics)\n",
    "        self.metrics.append(dict((k, v.item())\n",
    "                                 for k, v in metrics_dict.items()))\n",
    "        pl_module.output.clear_output()\n",
    "        with pl_module.output:\n",
    "            display(pd.DataFrame(self.metrics).tail(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Device and Save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> 2 cuda devices found >>>\n",
      "Device 0: \n",
      "\tname:Tesla V100-PCIE-32GB\n",
      "\tused:4MB\tfree:32506MB\n",
      "Device 1: \n",
      "\tname:Tesla V100-PCIE-32GB\n",
      "\tused:4MB\tfree:32506MB\n",
      "cuda stats refreshed\n",
      "Found the most idle GPU: cuda:0, 32506 MB Mem remained\n"
     ]
    }
   ],
   "source": [
    "dev = CudaHandler().idle().device.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('weights/MeanPoolerBn_bs_31_000355')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelClass = dict(\n",
    "    MeanPooler=MeanPooler,\n",
    "    WithAttnHead=WithAttnHead,\n",
    "    CLSReg=CLSReg,\n",
    "    PairCLS=PairCLS,\n",
    "    PairAttn=PairAttn,\n",
    "    MeanPoolerBn=MeanPoolerBn,\n",
    ")[CONFIG.model_class]\n",
    "\n",
    "train_time = datetime.now().strftime(\"%d_%H%M%S\")\n",
    "WEIGHTS = Path(f\"weights/{ModelClass.__name__}_{CONFIG.slug}_{train_time}/\")\n",
    "WEIGHTS.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "WEIGHTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run folds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-22T15:07:07.774250Z",
     "iopub.status.busy": "2021-07-22T15:07:07.773521Z",
     "iopub.status.idle": "2021-07-22T15:07:07.782826Z",
     "shell.execute_reply": "2021-07-22T15:07:07.781437Z",
     "shell.execute_reply.started": "2021-07-22T15:07:07.774195Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at weights/pre_rbtbs were not used when initializing RobertaModel: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias']\n",
      "- This IS expected if you are initializing RobertaModel 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 RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of RobertaModel were not initialized from the model checkpoint at weights/pre_rbtbs and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LEARNING RATE]2e-05\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a950a43fdbd43899a2c658310a933d8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  | Name    | Type         | Params\n",
      "-----------------------------------------\n",
      "0 | crit    | MSELoss      | 0     \n",
      "1 | base    | RobertaModel | 124 M \n",
      "2 | reg     | Linear       | 769   \n",
      "3 | dropout | Dropout      | 0     \n",
      "4 | bn      | BatchNorm1d  | 1.5 K \n",
      "-----------------------------------------\n",
      "124 M     Trainable params\n",
      "0         Non-trainable params\n",
      "124 M     Total params\n",
      "498.592   Total estimated model params size (MB)\n"
     ]
    },
    {
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       "model_id": "",
       "version_major": 2,
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      "text/plain": [
       "Validation sanity check: 0it [00:00, ?it/s]"
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      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
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     "text": [
      "/anaconda3/lib/python3.7/site-packages/pytorch_lightning/trainer/callback_hook.py:101: LightningDeprecationWarning: The signature of `Callback.on_train_epoch_end` has changed in v1.3. `outputs` parameter has been removed. Support for the old signature will be removed in v1.5\n",
      "  \"The signature of `Callback.on_train_epoch_end` has changed in v1.3.\"\n"
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     "text": [
      "🐺 unfreeze\n"
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      "Some weights of the model checkpoint at weights/pre_rbtbs were not used when initializing RobertaModel: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias']\n",
      "- This IS expected if you are initializing RobertaModel 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 RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of RobertaModel were not initialized from the model checkpoint at weights/pre_rbtbs and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "/anaconda3/lib/python3.7/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:360: UserWarning: Checkpoint directory weights/MeanPoolerBn_bs_31_000355 exists and is not empty.\n",
      "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"
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      "[LEARNING RATE]2e-05\n"
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      "\n",
      "  | Name    | Type         | Params\n",
      "-----------------------------------------\n",
      "0 | crit    | MSELoss      | 0     \n",
      "1 | base    | RobertaModel | 124 M \n",
      "2 | reg     | Linear       | 769   \n",
      "3 | dropout | Dropout      | 0     \n",
      "4 | bn      | BatchNorm1d  | 1.5 K \n",
      "-----------------------------------------\n",
      "124 M     Trainable params\n",
      "0         Non-trainable params\n",
      "124 M     Total params\n",
      "498.592   Total estimated model params size (MB)\n"
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      "[SAVE]weights/MeanPoolerBn_bs_31_000355\n"
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      "Some weights of the model checkpoint at weights/pre_rbtbs were not used when initializing RobertaModel: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias']\n",
      "- This IS expected if you are initializing RobertaModel 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 RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of RobertaModel were not initialized from the model checkpoint at weights/pre_rbtbs and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "/anaconda3/lib/python3.7/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:360: UserWarning: Checkpoint directory weights/MeanPoolerBn_bs_31_000355 exists and is not empty.\n",
      "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
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      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"
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      "\n",
      "  | Name    | Type         | Params\n",
      "-----------------------------------------\n",
      "0 | crit    | MSELoss      | 0     \n",
      "1 | base    | RobertaModel | 124 M \n",
      "2 | reg     | Linear       | 769   \n",
      "3 | dropout | Dropout      | 0     \n",
      "4 | bn      | BatchNorm1d  | 1.5 K \n",
      "-----------------------------------------\n",
      "124 M     Trainable params\n",
      "0         Non-trainable params\n",
      "124 M     Total params\n",
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      "[SAVE]weights/MeanPoolerBn_bs_31_000355\n"
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     "text": [
      "Some weights of the model checkpoint at weights/pre_rbtbs were not used when initializing RobertaModel: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias']\n",
      "- This IS expected if you are initializing RobertaModel 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 RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of RobertaModel were not initialized from the model checkpoint at weights/pre_rbtbs and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "/anaconda3/lib/python3.7/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:360: UserWarning: Checkpoint directory weights/MeanPoolerBn_bs_31_000355 exists and is not empty.\n",
      "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"
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      "[LEARNING RATE]2e-05\n"
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       "Output()"
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     "text": [
      "\n",
      "  | Name    | Type         | Params\n",
      "-----------------------------------------\n",
      "0 | crit    | MSELoss      | 0     \n",
      "1 | base    | RobertaModel | 124 M \n",
      "2 | reg     | Linear       | 769   \n",
      "3 | dropout | Dropout      | 0     \n",
      "4 | bn      | BatchNorm1d  | 1.5 K \n",
      "-----------------------------------------\n",
      "124 M     Trainable params\n",
      "0         Non-trainable params\n",
      "124 M     Total params\n",
      "498.592   Total estimated model params size (MB)\n"
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     "text": [
      "[SAVE]weights/MeanPoolerBn_bs_31_000355\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at weights/pre_rbtbs were not used when initializing RobertaModel: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias']\n",
      "- This IS expected if you are initializing RobertaModel 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 RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of RobertaModel were not initialized from the model checkpoint at weights/pre_rbtbs and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "/anaconda3/lib/python3.7/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:360: UserWarning: Checkpoint directory weights/MeanPoolerBn_bs_31_000355 exists and is not empty.\n",
      "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LEARNING RATE]2e-05\n"
     ]
    },
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       "Output()"
      ]
     },
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  | Name    | Type         | Params\n",
      "-----------------------------------------\n",
      "0 | crit    | MSELoss      | 0     \n",
      "1 | base    | RobertaModel | 124 M \n",
      "2 | reg     | Linear       | 769   \n",
      "3 | dropout | Dropout      | 0     \n",
      "4 | bn      | BatchNorm1d  | 1.5 K \n",
      "-----------------------------------------\n",
      "124 M     Trainable params\n",
      "0         Non-trainable params\n",
      "124 M     Total params\n",
      "498.592   Total estimated model params size (MB)\n"
     ]
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       "Training: 0it [00:00, ?it/s]"
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      "🐺 unfreeze\n"
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     "text": [
      "[SAVE]weights/MeanPoolerBn_bs_31_000355\n"
     ]
    }
   ],
   "source": [
    "for fold in tqdm(range(len(split_5)), leave=False):\n",
    "    model = AutoModel.from_pretrained(pretrained)\n",
    "    lit_model = ModelClass(model)\n",
    "    lit_model.base.config = init_config(lit_model.base.config,\n",
    "                                        layerwise_lr_decay=CONFIG.layerwise_lr_decay,\n",
    "                                        adam_epsilon=1e-6,\n",
    "                                        weight_decay=0.01\n",
    "                                        )\n",
    "#     lit_model.freeze()\n",
    "\n",
    "    saver = pl.callbacks.ModelCheckpoint(dirpath=WEIGHTS,\n",
    "                                         monitor=\"val_loss\",\n",
    "                                         mode=\"min\",\n",
    "                                         save_top_k=1,\n",
    "                                         save_weights_only=True,\n",
    "                                         filename='{epoch}-{val_loss:.2f}'+f\"fd{fold}\")\n",
    "\n",
    "    early = pl.callbacks.EarlyStopping(\n",
    "        \"val_loss\", mode=\"min\", patience=12)\n",
    "\n",
    "    trainer = pl.Trainer(\n",
    "        max_epochs=CONFIG.max_epochs,\n",
    "        callbacks=[saver,\n",
    "                   early, \n",
    "                   FineTuneScheduler(),\n",
    "                   pl.callbacks.StochasticWeightAveraging(),\n",
    "                   PrintTableMetricsCallback(),\n",
    "                  ],\n",
    "        logger=False,\n",
    "        fast_dev_run=False,\n",
    "        gpus=[dev, ],\n",
    "        stochastic_weight_avg=True,\n",
    "        accumulate_grad_batches=CONFIG.accumulate_grad_batches,\n",
    "        val_check_interval=0.25,\n",
    "        #         auto_lr_find = True,\n",
    "    )\n",
    "#     trainer.tune(lit_model, datamodule=LitDataMod(\n",
    "#         train_df, test_df, split_5[fold]))\n",
    "    print(f\"[LEARNING RATE]{lit_model.learning_rate}\")\n",
    "    trainer.fit(lit_model, datamodule=LitDataMod(\n",
    "        train_df, test_df, split_5[fold]))\n",
    "    lit_model = lit_model.cpu()\n",
    "\n",
    "    print(f\"[SAVE]{WEIGHTS}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !CUDA_LAUNCH_BLOCKING=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deliver empety weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "if len(WEIGHTS.ls()) ==0:\n",
    "    print(f\"[REMOVE EMPETY]{WEIGHTS}\")\n",
    "    WEIGHTS.rmdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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