sandbox-RL-matchpyramid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import argparse\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from importlib import reload "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from deeprank.dataset import DataLoader, PairGenerator, ListGenerator\n",
    "from deeprank import utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7fcd38708b10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed = 1234\n",
    "torch.manual_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[./data/letor/r5w/word_dict.txt]\n",
      "\tWord dict size: 193367\n",
      "[./data/letor/r5w/qid_query.txt]\n",
      "\tData size: 1692\n",
      "[./data/letor/r5w/docid_doc.txt]\n",
      "\tData size: 65323\n",
      "[./data/letor/r5w/embed_wiki-pdc_d50_norm]\n",
      "\tEmbedding size: 109282\n",
      "Generate numpy embed: (193368, 50)\n"
     ]
    }
   ],
   "source": [
    "loader = DataLoader('./config/letor07_mp_fold1.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "letor_config = json.loads(open('./config/letor07_mp_fold1.model').read())\n",
    "#device = torch.device(\"cuda\")\n",
    "device = torch.device(\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[./data/letor/r5w/relation.train.fold1.txt]\n",
      "\tInstance size: 47828\n",
      "Pair Instance Count: 325439\n"
     ]
    }
   ],
   "source": [
    "Letor07Path = letor_config['data_dir']\n",
    "\n",
    "letor_config['fill_word'] = loader._PAD_\n",
    "letor_config['embedding'] = loader.embedding\n",
    "letor_config['feat_size'] = loader.feat_size\n",
    "letor_config['vocab_size'] = loader.embedding.shape[0]\n",
    "letor_config['embed_dim'] = loader.embedding.shape[1]\n",
    "letor_config['pad_value'] = loader._PAD_\n",
    "\n",
    "pair_gen = PairGenerator(rel_file=Letor07Path + '/relation.train.fold%d.txt'%(letor_config['fold']), \n",
    "                         config=letor_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from deeprank import select_module\n",
    "from deeprank import rank_module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "letor_config['q_limit'] = 20\n",
    "letor_config['d_limit'] = 2000\n",
    "letor_config['max_match'] = 20\n",
    "letor_config['win_size'] = 5\n",
    "letor_config['finetune_embed'] = True\n",
    "letor_config['lr'] = 0.0001\n",
    "select_net = select_module.PointerNet(config=letor_config)\n",
    "select_net = select_net.to(device)\n",
    "select_net.embedding.weight.data.copy_(torch.from_numpy(loader.embedding))\n",
    "select_net.train()\n",
    "select_optimizer = optim.RMSprop(select_net.parameters(), lr=letor_config['lr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "letor_config['simmat_channel'] = 1\n",
    "letor_config['conv_params'] = [(8, 5, 5)]\n",
    "letor_config['fc_params'] = []\n",
    "letor_config['dpool_size'] = [3, 10]\n",
    "letor_config['lr'] = 0.005\n",
    "letor_config['finetune_embed'] = False\n",
    "rank_net = rank_module.MatchPyramidNet(config=letor_config)\n",
    "rank_net = rank_net.to(device)\n",
    "rank_net.embedding.weight.data.copy_(torch.from_numpy(loader.embedding))\n",
    "rank_net.train()\n",
    "rank_optimizer = optim.Adam(rank_net.parameters(), lr=letor_config['lr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_device(*variables):\n",
    "    return (torch.from_numpy(variable).to(device) for variable in variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_text(x):\n",
    "    print(' '.join([loader.word_dict[w.item()] for w in x]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X1, X1_len, X1_id, X2, X2_len, X2_id, Y, F = \\\n",
    "#         pair_gen.get_batch(data1=loader.query_data, data2=loader.doc_data)\n",
    "# X1, X1_len, X2, X2_len, Y, F = \\\n",
    "#         to_device(X1, X1_len, X2, X2_len, Y, F)\n",
    "# show_text(X2[0])\n",
    "# X1, X2_new, X1_len, X2_len_new = select_net(X1, X2, X1_len, X2_len, X1_id, X2_id)\n",
    "# show_text(X1[0])\n",
    "# for i in range(5):\n",
    "#     print(i, end=' ')\n",
    "#     show_text(X2_new[0][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X1 = X1[:1]\n",
    "# X1_len = X1_len[:1]\n",
    "# X2 = X2[:1]\n",
    "# X2_len = X2_len[:1]\n",
    "# X1_id = X1_id[:1]\n",
    "# X2_id = X2_id[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show_text(X2[0])\n",
    "# X1, X2_new, X1_len, X2_len_new = select_net(X1, X2, X1_len, X2_len, X1_id, X2_id)\n",
    "# show_text(X1[0])\n",
    "# for i in range(5):\n",
    "#     print(i, end=' ')\n",
    "#     show_text(X2_new[0][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
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      "select loss: -96.22090911865234\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "select loss: -93.91915893554688\n",
      "rank loss: 0.5992094874382019\n",
      "select loss: -107.67223358154297\n",
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      "rank loss: 0.8584585785865784\n",
      "select loss: -51.682193756103516\n",
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      "select loss: -51.64109802246094\n",
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      "select loss: -89.10066223144531\n",
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      "select loss: -131.12059020996094\n",
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      "select loss: -87.29681396484375\n",
      "rank loss: 0.7574819326400757\n",
      "select loss: -79.4306411743164\n",
      "rank loss: 0.6800760626792908\n",
      "select loss: -77.4550552368164\n",
      "rank loss: 0.6202412247657776\n",
      "select loss: -107.96597290039062\n",
      "rank loss: 0.6444135904312134\n",
      "select loss: -103.300537109375\n",
      "rank loss: 0.6809501051902771\n",
      "select loss: -105.48197174072266\n",
      "rank loss: 0.7577294111251831\n",
      "select loss: -70.65491485595703\n",
      "rank loss: 0.802255392074585\n",
      "select loss: -70.52487182617188\n",
      "rank loss: 0.8033855557441711\n",
      "select loss: -63.217681884765625\n",
      "rank loss: 0.8669212460517883\n",
      "select loss: -54.064659118652344\n",
      "rank loss: 0.7495759725570679\n",
      "select loss: -75.19174194335938\n",
      "rank loss: 0.6306124925613403\n",
      "select loss: -119.3406753540039\n",
      "rank loss: 0.6154310703277588\n",
      "select loss: -113.041259765625\n",
      "Time Cost: 1881.577339887619 s\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start_t = time.time()\n",
    "for i in range(450):\n",
    "    # One Step Forward\n",
    "    X1, X1_len, X1_id, X2, X2_len, X2_id, Y, F = \\\n",
    "        pair_gen.get_batch(data1=loader.query_data, data2=loader.doc_data)\n",
    "    X1, X1_len, X2, X2_len, Y, F = \\\n",
    "        to_device(X1, X1_len, X2, X2_len, Y, F)\n",
    "    X1, X2, X1_len, X2_len = select_net(X1, X2, X1_len, X2_len, X1_id, X2_id)\n",
    "    X2, X2_len = utils.data_adaptor(X2, X2_len, select_net, rank_net, letor_config)\n",
    "    output = rank_net(X1, X2, X1_len, X2_len, 0)\n",
    "    reward = rank_net.pair_reward(output, mode=0)\n",
    "    \n",
    "    # Update Rank Net\n",
    "    rank_loss = rank_net.pair_loss(output, Y)\n",
    "    print('rank loss:', rank_loss.item())\n",
    "    rank_optimizer.zero_grad()\n",
    "    rank_loss.backward()\n",
    "    rank_optimizer.step()\n",
    "    \n",
    "    # Update Select Net\n",
    "    select_loss = select_net.loss(reward)\n",
    "    print('select loss:', select_loss.item())\n",
    "    select_optimizer.zero_grad()\n",
    "    select_loss.backward()\n",
    "    select_optimizer.step()\n",
    "    \n",
    "end_t = time.time()\n",
    "print('Time Cost: %s s' % (end_t-start_t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(select_net.state_dict(), \"identity.ckpt\")\n",
    "torch.save(rank_net.state_dict(), \"matchpyramid.ckpt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(select_net, \"identity.model\")\n",
    "torch.save(rank_net, \"matchpyramid.model\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "GPU v0: Time Cost: 93.18958330154419 s\n",
    "GPU v1: Time Cost: 70.9958758354187 s\n",
    "---\n",
    "CPU v0: Time Cost: 22.77282667160034 s\n",
    "CPU v0 v1: Time Cost: 34.77551507949829 s\n",
    "CPU v1: Time Cost: 74.07408261299133 s\n",
    "CPU v2: Time Cost: 51.10329461097717 s"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "GPU Time Cost: 105.99338483810425 s\n",
    "CPU Time Cost: 28.412545680999756 s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MatchPyramidNet(\n",
       "  (embedding): Embedding(193368, 50, padding_idx=193367)\n",
       "  (conv_sequential): Sequential(\n",
       "    (0): Conv2d(1, 8, kernel_size=[5, 5], stride=(1, 1), padding=[2, 2])\n",
       "  )\n",
       "  (dpool_layer): AdaptiveMaxPool2d(output_size=[3, 10])\n",
       "  (fc_sequential): Sequential()\n",
       "  (out_layer): Linear(in_features=240, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[./data/letor/r5w/relation.test.fold1.txt]\n",
      "\tInstance size: 13652\n",
      "List Instance Count: 336\n",
      "[Test] 0.4359386539000405\n"
     ]
    }
   ],
   "source": [
    "select_net_e = torch.load(f='identity.model')\n",
    "rank_net_e = torch.load(f='matchpyramid.model')\n",
    "\n",
    "list_gen = ListGenerator(rel_file=Letor07Path+'/relation.test.fold%d.txt'%(letor_config['fold']),\n",
    "                         config=letor_config)\n",
    "map_v = 0.0\n",
    "map_c = 0.0\n",
    "\n",
    "with torch.no_grad():\n",
    "    for X1, X1_len, X1_id, X2, X2_len, X2_id, Y, F in \\\n",
    "        list_gen.get_batch(data1=loader.query_data, data2=loader.doc_data):\n",
    "        #print(X1.shape, X2.shape, Y.shape)\n",
    "        X1, X1_len, X2, X2_len, Y, F = to_device(X1, X1_len, X2, X2_len, Y, F)\n",
    "        X1, X2, X1_len, X2_len = select_net_e(X1, X2, X1_len, X2_len, X1_id, X2_id)\n",
    "        X2, X2_len = utils.data_adaptor(X2, X2_len, select_net, rank_net, letor_config)\n",
    "        #print(X1.shape, X2.shape, Y.shape)\n",
    "        pred = rank_net_e(X1, X2, X1_len, X2_len, 0)\n",
    "        map_o = utils.eval_MAP(pred.tolist(), Y.tolist())\n",
    "        #print(pred.shape, Y.shape)\n",
    "        map_v += map_o\n",
    "        map_c += 1.0\n",
    "    map_v /= map_c\n",
    "\n",
    "print('[Test]', map_v)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Reward v0: 0.4359386539000405\n",
    "Reward v1: 0.42572616969349864\n",
    "Reward v2: 0.4245778777643799"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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