training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fe33c8ce-c553-437d-8d1f-cdc6d00c9d4c",
   "metadata": {},
   "source": [
    "# Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e53a9e5e-0b23-40be-8039-76faa0175719",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"WANDB_CONSOLE\"] = \"off\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98028ea2-2e3d-4e14-8a58-954318ffd51c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import multiprocessing\n",
    "\n",
    "from rnn.models import IRNN\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.nn import ReLU\n",
    "from torch.nn import MSELoss\n",
    "from torch.optim import RMSprop, Adam\n",
    "from torch.utils.data import DataLoader\n",
    "import wandb\n",
    "\n",
    "from data.utils import SequenceGenerator\n",
    "from utils.config import SETTINGS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1c926bfa-77ab-4fa1-9027-fe313995d2a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytorch_lightning as pl\n",
    "from pytorch_lightning.loggers import WandbLogger"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c45014c2-78a1-4980-be27-69c64076275d",
   "metadata": {},
   "source": [
    "# Construct Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "940c8034-80e8-4654-b738-c948b2c165da",
   "metadata": {},
   "outputs": [],
   "source": [
    "CURRENT_SETTINGS = SETTINGS[\"SEQ_150\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "30d2442a-5497-44b5-af94-766bb46b3639",
   "metadata": {},
   "outputs": [],
   "source": [
    "class RNNModel(nn.Module):\n",
    "    def __init__(self, hidden_size, num_layers):\n",
    "        super().__init__()\n",
    "        embedding_size = 2\n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_layers = num_layers\n",
    "        self.rnn = IRNN(\n",
    "            embedding_size,\n",
    "            hidden_size,\n",
    "            num_layers,\n",
    "            nonlinearity=\"relu\",\n",
    "            batch_first=True,\n",
    "        )\n",
    "        self.fc = nn.Linear(hidden_size, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x.shape --> (batch_size, sequence_len, hidden_size)\n",
    "        output, hidden = self.rnn(x)\n",
    "        output = self.fc(output)\n",
    "        output = output[:, -1, :]\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f8805fe-999a-45fe-8dd7-5f25ca89b75c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PLModel(pl.LightningModule):\n",
    "    def __init__(self, hidden_size, num_layers):\n",
    "        super().__init__()\n",
    "        self.model = RNNModel(hidden_size, num_layers)\n",
    "        self.criterion = MSELoss()\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        input_, target = batch\n",
    "        output = self.model(input_)\n",
    "        loss = self.criterion(output, target)\n",
    "        self.log(\"training_loss\", loss)\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        x, y = batch\n",
    "        y_hat = self.model(x)\n",
    "        loss = self.criterion(y_hat, y)\n",
    "        self.log(\"val_loss\", loss)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        # optimizer = torch.optim.SGD(self.parameters(), lr=CURRENT_SETTINGS[\"LR\"])\n",
    "        optimizer = torch.optim.Adam(self.parameters(), lr=CURRENT_SETTINGS[\"LR\"])\n",
    "        return optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b270457-4548-4751-9cdf-ad573ed58efa",
   "metadata": {},
   "source": [
    "# Initiate Model and DataLoaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6ba878c2-9178-43a0-a292-2c82cb8f1a6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = PLModel(hidden_size=100, num_layers=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d8d4d938-a670-480a-97d5-3e4b01d38baa",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = SequenceGenerator(CURRENT_SETTINGS[\"LEN\"], 100_000)\n",
    "train_data_loader = DataLoader(\n",
    "    train_dataset, 16, num_workers=multiprocessing.cpu_count()\n",
    ")\n",
    "test_dataset = SequenceGenerator(CURRENT_SETTINGS[\"LEN\"], 10_000)\n",
    "test_data_loader = DataLoader(test_dataset, 32, num_workers=multiprocessing.cpu_count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "71157aeb-7de7-46d0-8c49-33b884e7a462",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in model.named_parameters():\n",
    "#     print(i[0], i[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90e16ba8-b01d-4eb3-9bc5-80d9f156fded",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "59152a6f-b975-4aef-b0d5-3018c397f374",
   "metadata": {},
   "outputs": [],
   "source": [
    "# wandb.finish()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6257f80f-f337-4e3d-9454-74f6255cfbad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8e647919-2525-45c4-ba31-b44dbfb3cdce",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmdmmn378\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
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       "model_id": "552115952fc84095b180475976a095be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.016668869133354747, max=1.0…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.13.5"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>./logs/wandb/run-20221109_045506-suonul63</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href=\"https://wandb.ai/mdmmn378/othoz/runs/suonul63\" target=\"_blank\">irnn-150-2022-11-08 22:55:04.535904</a></strong> to <a href=\"https://wandb.ai/mdmmn378/othoz\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "    trainer = pl.Trainer(\n",
    "        gradient_clip_val=CURRENT_SETTINGS[\"GC\"],\n",
    "        accelerator=\"gpu\",\n",
    "        devices=1,\n",
    "        max_epochs=440,\n",
    "        log_every_n_steps=10,\n",
    "        logger=WandbLogger(\n",
    "            f\"irnn-{CURRENT_SETTINGS['LEN']}-{datetime.utcnow()}\",\n",
    "            \"./logs\",\n",
    "            project=\"othoz\",\n",
    "        ),\n",
    "    )\n",
    "else:\n",
    "    trainer = pl.Trainer(\n",
    "        gradient_clip_val=CURRENT_SETTINGS[\"GC\"],\n",
    "        max_epochs=440,\n",
    "        log_every_n_steps=10,\n",
    "        logger=WandbLogger(\n",
    "            f\"irnn-{CURRENT_SETTINGS['LEN']}-{datetime.utcnow()}\",\n",
    "            \"./logs\",\n",
    "            project=\"othoz\",\n",
    "        ),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9b985874-2295-483f-a43d-f80312553009",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name      | Type     | Params\n",
      "---------------------------------------\n",
      "0 | model     | RNNModel | 10.5 K\n",
      "1 | criterion | MSELoss  | 0     \n",
      "---------------------------------------\n",
      "10.5 K    Trainable params\n",
      "0         Non-trainable params\n",
      "10.5 K    Total params\n",
      "0.042     Total estimated model params size (MB)\n"
     ]
    },
    {
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      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
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    {
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      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
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       "model_id": "f79ca1387eed47989207a1fb054e8710",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
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    {
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      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "58762b29cf654729b8552e76f883654e",
       "version_major": 2,
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      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "3ef9f8b759514e49976fb04b87d5fbbe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=440` reached.\n"
     ]
    }
   ],
   "source": [
    "trainer.fit(model, train_data_loader, test_data_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96322e5-285d-4c40-894e-a11cb49a1ca4",
   "metadata": {},
   "source": [
    "# Validate Manually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "47ae9fab-f47e-4fb3-b0fc-4377a7b2ae42",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in test_data_loader:\n",
    "    break\n",
    "\n",
    "with torch.no_grad():\n",
    "    print(model(i[0][:10].cuda()))\n",
    "print(i[1][:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c97b5cc-c096-40d3-8dc2-20eaa69300c5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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