gentle-introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Gentle introduction to Pytorch Symbolic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "You don't need any knowledge of the inner workings of Pytorch Symbolic to use it.\n",
    "In most cases, after seeing a few examples, you're good to go.\n",
    "If you used Keras Symbolic API before, you don't even need any learning materials.\n",
    "However, this notebooks explains Pytorch Symbolic a little bit deeper, in case you just want to know more stuff.\n",
    "\n",
    "In this notebook, we'll use our built-in drawing solution designed for layered graphs.\n",
    "It will be helpful to see the exact data flow in the model.\n",
    "\n",
    "You can use this tool yourself if you have optional dependencies installed:\n",
    "* `networkx`\n",
    "* `matplotlib`\n",
    "* `scipy`.\n",
    "\n",
    "They can be installed with `pip install pytorch-symbolic[full]`.\n",
    "\n",
    "* [Link to Documentation](https://pytorch-symbolic.readthedocs.io/en/latest/quick_start/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pytorch-symbolic[full] in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (0.7.7)\n",
      "Requirement already satisfied: torchvision in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (0.13.1)\n",
      "Requirement already satisfied: tqdm in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (4.64.1)\n",
      "Requirement already satisfied: torch>=1.12.0 in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from pytorch-symbolic[full]) (1.12.1)\n",
      "Requirement already satisfied: networkx in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from pytorch-symbolic[full]) (2.8.7)\n",
      "Requirement already satisfied: scipy in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from pytorch-symbolic[full]) (1.9.2)\n",
      "Requirement already satisfied: matplotlib in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from pytorch-symbolic[full]) (3.5.2)\n",
      "Requirement already satisfied: typing_extensions in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from torchvision) (4.3.0)\n",
      "Requirement already satisfied: numpy in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from torchvision) (1.23.1)\n",
      "Requirement already satisfied: requests in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from torchvision) (2.28.1)\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from torchvision) (9.2.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from matplotlib->pytorch-symbolic[full]) (4.37.4)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from matplotlib->pytorch-symbolic[full]) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from matplotlib->pytorch-symbolic[full]) (2.8.2)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from matplotlib->pytorch-symbolic[full]) (21.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from matplotlib->pytorch-symbolic[full]) (0.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from matplotlib->pytorch-symbolic[full]) (1.4.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from requests->torchvision) (3.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from requests->torchvision) (2022.9.24)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from requests->torchvision) (2.0.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from requests->torchvision) (1.26.11)\n",
      "Requirement already satisfied: six>=1.5 in /home/gaha/miniconda3/envs/pyt/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib->pytorch-symbolic[full]) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install \"pytorch-symbolic[full]\" torchvision tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from pytorch_symbolic import SymbolicModel, Input, useful_layers, graph_algorithms\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torchvision\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Having fun with Symbolic Data\n",
    "### Simple example\n",
    "We'll first look at what happens when we interact with Symbolic Data.\n",
    "\n",
    "Specifically, we will work on Symbolic Tensors which are a special case of Symbolic Data. They have similar API to `torch.Tensor`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x=<SymbolicTensor at 0x7f3178dd2520; 0 parents; 0 children>\n",
      "y=<SymbolicTensor at 0x7f322c366bb0; 0 parents; 0 children>\n",
      "\n",
      "After creating z = x + y:\n",
      "x=<SymbolicTensor at 0x7f3178dd2520; 0 parents; 1 children>\n",
      "y=<SymbolicTensor at 0x7f322c366bb0; 0 parents; 1 children>\n",
      "z=<SymbolicTensor at 0x7f322c3c2760; 2 parents; 0 children>\n"
     ]
    }
   ],
   "source": [
    "x = Input(shape=(5,))\n",
    "y = Input(shape=(5,))\n",
    "print(f\"x={x}\")\n",
    "print(f\"y={y}\")\n",
    "print()\n",
    "\n",
    "z = x + y\n",
    "print(\"After creating z = x + y:\")\n",
    "print(f\"x={x}\")\n",
    "print(f\"y={y}\")\n",
    "print(f\"z={z}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We created two Symbolic Tensors: `x` and `y`.\n",
    "Their sum is represented by another Symbolic Tensor: `z`, which was added to the graph of computations.\n",
    "\n",
    "When we print the tensors, we can see that `z` has two parents and both `x` and `y` have one child.\n",
    "\n",
    "If we need, we can even retrieve the instances of specific children and parents.\n",
    "\n",
    "As it is in life, you cannot change your parents, thus `.parents` are immutable.\n",
    "But `.children` will be modified when performing new operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x.children=(<SymbolicTensor at 0x7f322c3c2760; 2 parents; 0 children>,)\n",
      "y.children=(<SymbolicTensor at 0x7f322c3c2760; 2 parents; 0 children>,)\n",
      "z.parents=(<SymbolicTensor at 0x7f3178dd2520; 0 parents; 1 children>, <SymbolicTensor at 0x7f322c366bb0; 0 parents; 1 children>)\n"
     ]
    }
   ],
   "source": [
    "print(f\"x.children={x.children}\")\n",
    "print(f\"y.children={y.children}\")\n",
    "\n",
    "print(f\"z.parents={z.parents}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We are creating full fledged graphs now! Let's try to plot them..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To show you nicer plot, we prepared an option to show variable names in nodes of the graph.\n",
    "# It requires passing the namespace containing the variables, e.g. globals().\n",
    "# By default, you'll see shape of the underlying Symbolic Tensor instead.\n",
    "\n",
    "graph_algorithms.draw_graph(inputs=(x, y), outputs=z, node_text_namespace=globals(), figsize=(7, 4));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We can see that addition was converted to `AddOpLayer`.\n",
    "\n",
    "If we want to use a custom function on a symbolic tensor, we should create a `torch.nn.Module`.\n",
    "\n",
    "Simple operators like additions or object's methods will work out of the box."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### A deeper example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We will create another, more complicated model.\n",
    "\n",
    "Still no neural networks, but we'll get to them eventually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "x = Input(shape=())\n",
    "y = Input(shape=())\n",
    "\n",
    "sums = x + y\n",
    "diff = x - y\n",
    "mult = x * y\n",
    "intermediate_values = [sums, diff, mult]\n",
    "outputs = sum(intermediate_values) / 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<SymbolicTensor at 0x7f314315d5e0; 2 parents; 1 children>,\n",
       " <SymbolicTensor at 0x7f317710c7c0; 2 parents; 1 children>,\n",
       " <SymbolicTensor at 0x7f314315d1f0; 2 parents; 1 children>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intermediate_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We can see that the three intermediate values have 2 parents and 1 child."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_algorithms.draw_graph(inputs=(x, y), outputs=outputs, node_text_namespace=globals(), figsize=(10, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Moreover, we see what Python does internally, when `sum` is called. It's just a bunch of additions represented by `AddOpLayer`!\n",
    "\n",
    "Let us create a Symbolic Model from this graph!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________\n",
      "     Layer             Output shape   Params   Parent   \n",
      "========================================================\n",
      "1    Input_1           (None,)        0                 \n",
      "2    Input_2           (None,)        0                 \n",
      "3    AddOpLayer_1      (None,)        0        1,2      \n",
      "4    SubOpLayer_1      (None,)        0        1,2      \n",
      "5    MulOpLayer_1      (None,)        0        1,2      \n",
      "6    LambdaOpLayer_1   (None,)        0        3        \n",
      "7    AddOpLayer_2      (None,)        0        6,4      \n",
      "8    AddOpLayer_3      (None,)        0        7,5      \n",
      "9*   LambdaOpLayer_2   (None,)        0        8        \n",
      "========================================================\n",
      "Total params: 0\n",
      "Trainable params: 0\n",
      "Non-trainable params: 0\n",
      "________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = SymbolicModel(inputs=(x, y), outputs=outputs)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model has no parameters. Let us use it on random data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rx=tensor([0.7853])\n",
      "ry=tensor([0.7484])\n",
      "model(rx, ry)=tensor([0.7194])\n"
     ]
    }
   ],
   "source": [
    "rx = torch.rand(\n",
    "    1,\n",
    ")\n",
    "ry = torch.rand(\n",
    "    1,\n",
    ")\n",
    "\n",
    "print(f\"rx={rx}\")\n",
    "print(f\"ry={ry}\")\n",
    "print(f\"model(rx, ry)={model(rx, ry)}\")\n",
    "\n",
    "assert model(rx, ry) == ((rx + ry) + (rx - ry) + (rx * ry)) / 3, \"Something went wrong...\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "As we see, after defining the model on Symbolic Tensors it works perfectly fine launched on real data!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Right now, we were manipulating the data with `+`, `-` and `*` operators.\n",
    "\n",
    "Most of Python's operators are already defined for Symbolic Tensors, e.g.: `+, -, *, /, //, %, abs`.\n",
    "\n",
    "They will be converted to ``torch.nn.Module`` and registered correctly in the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Symbolic Tensors and PyTorch Layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "In deep learning, we rarely use just standard, non-parametrized operations.\n",
    "Instead, we rely on layers to transform the data.\n",
    "Let's create a small linear model with a few hidden layers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________\n",
      "     Layer      Output shape   Params   Parent   \n",
      "=================================================\n",
      "1    Input_1    (None, 4)      0                 \n",
      "2    Linear_1   (None, 10)     50       1        \n",
      "3    Linear_2   (None, 10)     110      2        \n",
      "4    Linear_3   (None, 10)     110      3        \n",
      "5*   Linear_4   (None, 1)      11       4        \n",
      "=================================================\n",
      "Total params: 281\n",
      "Trainable params: 281\n",
      "Non-trainable params: 0\n",
      "_________________________________________________\n"
     ]
    }
   ],
   "source": [
    "inputs = Input(shape=(4,))\n",
    "x1 = nn.Linear(inputs.features, 10)(inputs)\n",
    "x2 = nn.Linear(x1.features, 10)(x1)\n",
    "x3 = nn.Linear(x2.features, 10)(x2)\n",
    "outputs = nn.Linear(x3.features, 1)(x3)\n",
    "\n",
    "model = SymbolicModel(inputs, outputs)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Looks like it was a succes. Let's see how it looks like as a graph!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_algorithms.draw_graph(model=model, node_text_namespace=globals(), figsize=(4, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "It's a rather simple topology."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### An odd one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We'll now create a model that consists of a few towers - separate Symbolic Models.\n",
    "\n",
    "It means we will use Symbolic Models inside another Symbolic Model. Cool, isn't it?\n",
    "\n",
    "There'll be:\n",
    "* 3 separate inputs\n",
    "* 2 separate outputs\n",
    "\n",
    "After a few layers of separated computations, the results will be concatenated, then transformed some more and returned.\n",
    "\n",
    "On the drawing we will display the shapes of Symbolic Tensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________\n",
      "     Layer             Output shape   Params   Parent   \n",
      "========================================================\n",
      "1    Input_1           (None, 5)      0                 \n",
      "2    Input_2           (None, 5)      0                 \n",
      "3    Input_3           (None, 5)      0                 \n",
      "4    SymbolicModel_1   (None, 10)     280      1        \n",
      "5    SymbolicModel_2   (None, 10)     390      2        \n",
      "6    SymbolicModel_3   (None, 10)     500      3        \n",
      "7    ConcatLayer_1     (None, 30)     0        4,5,6    \n",
      "8*   Linear_1          (None, 5)      155      7        \n",
      "9*   Linear_2          (None, 3)      93       7        \n",
      "========================================================\n",
      "Total params: 1418\n",
      "Trainable params: 1418\n",
      "Non-trainable params: 0\n",
      "________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def get_tower(height):\n",
    "    inputs = x = Input((5,))\n",
    "    for _ in range(height):\n",
    "        x = nn.Linear(x.features, 10)(x)\n",
    "    model = SymbolicModel(inputs, x)\n",
    "    return model\n",
    "\n",
    "\n",
    "towers = [get_tower(height=3 + i) for i in range(3)]\n",
    "inputs = [Input((5,)) for _ in range(3)]\n",
    "tower_outputs = []\n",
    "\n",
    "for x, tower in zip(inputs, towers):\n",
    "    tower_outputs.append(tower(x))\n",
    "\n",
    "x = useful_layers.ConcatLayer(dim=1)(*tower_outputs)\n",
    "\n",
    "out1 = nn.Linear(x.features, 5)(x)\n",
    "out2 = nn.Linear(x.features, 3)(x)\n",
    "\n",
    "model = SymbolicModel(inputs, (out1, out2))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_algorithms.draw_graph(model=model, figsize=(8, 5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "If we want, we can even \"zoom in\" to the inner Symbolic Model. Let's try it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________\n",
      "     Layer      Output shape   Params   Parent   \n",
      "=================================================\n",
      "1    Input_1    (None, 5)      0                 \n",
      "2    Linear_1   (None, 10)     60       1        \n",
      "3    Linear_2   (None, 10)     110      2        \n",
      "4*   Linear_3   (None, 10)     110      3        \n",
      "=================================================\n",
      "Total params: 280\n",
      "Trainable params: 280\n",
      "Non-trainable params: 0\n",
      "_________________________________________________\n"
     ]
    }
   ],
   "source": [
    "towers[0].summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_algorithms.draw_graph(model=towers[0], figsize=(4, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "This can be useful if the model is complex and has many submodels!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Subgraphs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We don't need to use all the created nodes. We can use just a subset of them: a subgraph.\n",
    "\n",
    "Your models can take arbitrary Symbolic Data as input, even if it doesn't come from `Input`.\n",
    "\n",
    "One thing that is forbidden though is using incomplete inputs, for example when a node depends on a node that is not in its subgraph. Example for this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node <SymbolicTensor at 0x7f3142fc4160; 2 parents; 0 children> depends on the output of a foreign node! Perhaps you set the wrong inputs?\n"
     ]
    }
   ],
   "source": [
    "a = Input((1,))\n",
    "b = Input((1,))\n",
    "c = a + b\n",
    "\n",
    "try:\n",
    "    # c depends on a and b, but we don't put b in the inputs\n",
    "    SymbolicModel(inputs=(a,), outputs=(c,))\n",
    "except AssertionError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Let's use previous graph as an example for using subgraphs to create new models.\n",
    "\n",
    "Let's say we want a new model that shares weights with the existing one.\n",
    "We want to omit the tower 1 and tower 3 computations and we'll put their outputs in `inputs`.\n",
    "\n",
    "This way we will be able to provide them by hand and the computation will be omitted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "input1 = tower_outputs[0]\n",
    "input2 = inputs[1]\n",
    "input3 = tower_outputs[2]\n",
    "\n",
    "graph_algorithms.draw_graph(inputs=(input1, input2, input3), outputs=(out1, out2), figsize=(8, 5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________\n",
      "     Layer             Output shape   Params   Parent   \n",
      "========================================================\n",
      "1    Input_1           (None, 10)     0                 \n",
      "2    Input_2           (None, 5)      0                 \n",
      "3    Input_3           (None, 10)     0                 \n",
      "4    SymbolicModel_1   (None, 10)     390      2        \n",
      "5    ConcatLayer_1     (None, 30)     0        1,4,3    \n",
      "6*   Linear_1          (None, 5)      155      5        \n",
      "7*   Linear_2          (None, 3)      93       5        \n",
      "========================================================\n",
      "Total params: 638\n",
      "Trainable params: 638\n",
      "Non-trainable params: 0\n",
      "________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = SymbolicModel(inputs=(input1, input2, input3), outputs=(out1, out2))\n",
    "model.summary()\n",
    "\n",
    "real_outputs = model(torch.rand(1, 10), torch.rand(1, 5), torch.rand(1, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Symbolic API in real models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Let us create and train a small ResNet example that will work on MNIST dataset.\n",
    "\n",
    "To train the model quickly, you need to have CUDA capable device. On Colab, be sure to set \"Hardware accelerator\" to \"GPU\".\n",
    "\n",
    "In this notebook, if CUDA is not detected, we'll automatically use a smaller neural network and a shorter training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Creating dataset\n",
    "\n",
    "ds = torchvision.datasets.MNIST(root=\".\", download=True, transform=torchvision.transforms.ToTensor())\n",
    "train, valid = torch.utils.data.random_split(ds, [50000, 10000])\n",
    "\n",
    "train_dl = torch.utils.data.DataLoader(\n",
    "    train, batch_size=100, shuffle=True, drop_last=True, pin_memory=True, num_workers=1\n",
    ")\n",
    "valid_dl = torch.utils.data.DataLoader(\n",
    "    valid, batch_size=100, shuffle=False, drop_last=True, pin_memory=True, num_workers=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def create_model_gpu():\n",
    "    def shortcut_func(x, channels, stride):\n",
    "        if x.channels != channels or stride != 1:\n",
    "            return x(nn.Conv2d(x.channels, channels, kernel_size=1, bias=False, stride=stride))\n",
    "        else:\n",
    "            return nn.Identity()(x)\n",
    "\n",
    "    inputs = Input(batch_shape=(100, 1, 28, 28))\n",
    "    flow = inputs(nn.Conv2d(inputs.channels, 16, 3, padding=1))\n",
    "\n",
    "    for group_size, width, stride in [(2, 16, 1), (2, 32, 2), (2, 64, 2)]:\n",
    "        for _ in range(group_size):\n",
    "            shortcut = shortcut_func(flow, width, stride)\n",
    "\n",
    "            flow = nn.BatchNorm2d(flow.channels)(flow)(nn.ReLU())\n",
    "            flow = nn.Conv2d(flow.channels, width, 3, stride, 1)(flow)\n",
    "            flow = nn.BatchNorm2d(flow.channels)(flow)(nn.ReLU())\n",
    "            flow = nn.Conv2d(flow.channels, width, 3, 1, 1)(flow)\n",
    "\n",
    "            flow = flow + shortcut\n",
    "            stride = 1\n",
    "\n",
    "    flow = nn.BatchNorm2d(flow.channels)(flow)(nn.ReLU())\n",
    "    flow = nn.MaxPool2d(kernel_size=flow.HW)(flow)(nn.Flatten())\n",
    "    outs = nn.Linear(flow.features, 10)(flow)\n",
    "    return inputs, outs\n",
    "\n",
    "\n",
    "def create_model_cpu():\n",
    "    def shortcut_func(x, channels, stride):\n",
    "        if x.channels != channels or stride != 1:\n",
    "            return x(nn.Conv2d(x.channels, channels, kernel_size=1, bias=False, stride=stride))\n",
    "        else:\n",
    "            return nn.Identity()(x)\n",
    "\n",
    "    inputs = Input(batch_shape=(100, 1, 28, 28))\n",
    "    flow = inputs(nn.Conv2d(inputs.channels, 4, 3, padding=1))\n",
    "\n",
    "    for group_size, width, stride in [(2, 4, 2), (2, 16, 2), (2, 32, 2)]:\n",
    "        for _ in range(group_size):\n",
    "            shortcut = shortcut_func(flow, width, stride)\n",
    "\n",
    "            flow = nn.BatchNorm2d(flow.channels)(flow)(nn.ReLU())\n",
    "            flow = nn.Conv2d(flow.channels, width, 3, stride, 1)(flow)\n",
    "            flow = nn.BatchNorm2d(flow.channels)(flow)(nn.ReLU())\n",
    "            flow = nn.Conv2d(flow.channels, width, 3, 1, 1)(flow)\n",
    "\n",
    "            flow = flow + shortcut\n",
    "            stride = 1\n",
    "\n",
    "    flow = nn.BatchNorm2d(flow.channels)(flow)(nn.ReLU())\n",
    "    flow = nn.MaxPool2d(kernel_size=flow.HW)(flow)(nn.Flatten())\n",
    "    outs = nn.Linear(flow.features, 10)(flow)\n",
    "    return inputs, outs\n",
    "\n",
    "\n",
    "if device.type == \"cuda\":\n",
    "    inputs, outputs = create_model_gpu()\n",
    "else:\n",
    "    inputs, outputs = create_model_cpu()\n",
    "\n",
    "model = SymbolicModel(inputs=inputs, outputs=outputs).to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "It is a large graph to draw! But let's try it anyway..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 3500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_algorithms.draw_graph(\n",
    "    model=model,\n",
    "    edge_text_func=lambda x: \"\",\n",
    "    node_text_namespace=globals(),\n",
    "    rotate_graph=True,\n",
    "    figsize=(35, 4),\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a949e7494c840959f8b4f194c16a729",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average training accuracy in epoch 1: 95.486%\n",
      "Average training accuracy in epoch 2: 99.012%\n",
      "Average training accuracy in epoch 3: 99.230%\n",
      "Average training accuracy in epoch 4: 99.376%\n",
      "Average training accuracy in epoch 5: 99.466%\n"
     ]
    }
   ],
   "source": [
    "def traininig(model, optimizer, epochs):\n",
    "    model.train()\n",
    "\n",
    "    loss_fn = nn.CrossEntropyLoss()\n",
    "\n",
    "    for epoch in tqdm(range(epochs)):\n",
    "        mean_acc = 0\n",
    "        mean_acc_n = 0\n",
    "\n",
    "        for x, y in train_dl:\n",
    "            x = x.to(device)\n",
    "            y = y.to(device)\n",
    "\n",
    "            model.zero_grad()\n",
    "            outs = model(x)\n",
    "\n",
    "            loss = loss_fn(outs, y)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            accuracy = torch.mean((outs.argmax(1) == y).to(torch.float)).detach().cpu()\n",
    "            mean_acc += accuracy * x.shape[0]\n",
    "            mean_acc_n += x.shape[0]\n",
    "\n",
    "        print(f\"Average training accuracy in epoch {epoch + 1}: {mean_acc / mean_acc_n * 100 : <6.3f}%\")\n",
    "\n",
    "\n",
    "traininig(model, optimizer, epochs=5 if device.type == \"cuda\" else 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average validation accuracy: 99.030%\n"
     ]
    }
   ],
   "source": [
    "def evaluation(model):\n",
    "    model.eval()\n",
    "\n",
    "    mean_acc = 0\n",
    "    mean_acc_n = 0\n",
    "\n",
    "    loss_fn = nn.CrossEntropyLoss()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for x, y in valid_dl:\n",
    "            x = x.to(device)\n",
    "            y = y.to(device)\n",
    "            outs = model(x)\n",
    "            accuracy = torch.mean((outs.argmax(1) == y).to(torch.float)).detach().cpu()\n",
    "            mean_acc += accuracy\n",
    "            mean_acc_n += 1\n",
    "\n",
    "    print(f\"Average validation accuracy: {mean_acc / mean_acc_n * 100 : <6.3f}%\")\n",
    "\n",
    "\n",
    "evaluation(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Acceleration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Hurray! We've trained a network that is pretty accurate. What else can we improve?\n",
    "\n",
    "Maybe performance? With Pytorch Symbolic you can enable CUDA Graphs using just one argument.\n",
    "\n",
    "This will work especially well for models with many small layers or where CPU calls are slow in comparison to GPU kernel execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:CUDA Graphs can result in undefined behaviour! Please read https://pytorch.org/docs/stable/notes/cuda.html#constraints.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd2105200b014e2eb05a584ba37af74e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average training accuracy in epoch 1: 95.528%\n",
      "Average training accuracy in epoch 2: 99.016%\n",
      "Average training accuracy in epoch 3: 99.230%\n",
      "Average training accuracy in epoch 4: 99.430%\n",
      "Average training accuracy in epoch 5: 99.450%\n",
      "Average validation accuracy: 99.110%\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "    inputs, outputs = create_model_gpu()\n",
    "    model = SymbolicModel(inputs=inputs, outputs=outputs, enable_cuda_graphs=True)\n",
    "    optimizer = torch.optim.Adam(model.parameters())\n",
    "\n",
    "    traininig(model, optimizer, epochs=5)\n",
    "    evaluation(model)\n",
    "else:\n",
    "    print(\"CUDA Graphs require CUDA capable device!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Automatic code generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Pytorch Symbolic generates forward function source automatically. We are able too peek what was generated.\n",
    "\n",
    "We tweak one of the constants of code generator, just for the sake of presentation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from pytorch_symbolic import config, add_to_graph\n",
    "\n",
    "config.CODEGEN_MIN_LOOP_LENGTH = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Now we define the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "x1 = Input(shape=(3, 32, 32))\n",
    "\n",
    "x2 = nn.Conv2d(x1.C, 16, 3, padding=1)(x1)\n",
    "x3 = nn.Conv2d(x2.C, 16, 3, padding=1)(x2)\n",
    "x4 = nn.Conv2d(x3.C, 16, 3, padding=1)(x3)\n",
    "\n",
    "x11 = nn.Conv2d(x4.C, 16, 3, padding=1)(x4)\n",
    "x12 = nn.Conv2d(x11.C, 16, 3, padding=1)(x11)\n",
    "x13 = nn.Conv2d(x12.C, 16, 3, padding=1)(x12)\n",
    "x14 = nn.Conv2d(x13.C, 16, 3, padding=1)(x13)\n",
    "x15 = nn.Conv2d(x14.C, 16, 3, padding=1)(x14)\n",
    "\n",
    "x21 = nn.Conv2d(x4.C, 16, 3, padding=1)(x4)\n",
    "x22 = nn.Conv2d(x21.C, 16, 3, padding=1)(x21)\n",
    "x23 = nn.Conv2d(x22.C, 16, 3, padding=1)(x22)\n",
    "\n",
    "x5 = add_to_graph(torch.concat, [x15, x23], dim=1)\n",
    "x6 = nn.Flatten()(x5)\n",
    "x7 = nn.Linear(x6.features, 1)(x6)\n",
    "\n",
    "model = SymbolicModel(inputs=x1, outputs=x7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "def forward(self,y00):\n",
      "    l = self._execution_order_layers\n",
      "    x02 = y00\n",
      "    for layer in l[0:3]:\n",
      "        x02 = layer(x02)\n",
      "    x07 = x02\n",
      "    for layer in l[3:8]:\n",
      "        x07 = layer(x07)\n",
      "    x10 = x02\n",
      "    for layer in l[8:11]:\n",
      "        x10 = layer(x10)\n",
      "    x11 = l[11](x07, x10)\n",
      "    o00 = x11\n",
      "    for layer in l[12:14]:\n",
      "        o00 = layer(o00)\n",
      "    return o00\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(model._generated_forward_source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_algorithms.draw_graph(model=model, node_text_namespace=globals(), figsize=(8, 12));"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}