C2_W3_Lab_1_autograph-basics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hrXv0rU9sIma"
   },
   "source": [
    "# Autograph: Basic\n",
    "In this ungraded lab, you will go through  some of the basics of autograph so you can explore what the generated code looks like."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NiolgWMPgpwI"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Addition in autograph\n",
    "You can use the `@tf.function` decorator to automatically generate the graph-style code as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sim_add(a,b):\n",
    "    return a+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[5. 2.]\n",
      " [4. 9.]], shape=(2, 2), dtype=float32)\n",
      "def tf__add_1(a, b):\n",
      "    with ag__.FunctionScope('add', 'fscope', ag__.ConversionOptions(recursive=True, user_requested=True, optional_features=(), internal_convert_user_code=True)) as fscope:\n",
      "        do_return = False\n",
      "        retval_ = ag__.UndefinedReturnValue()\n",
      "        try:\n",
      "            do_return = True\n",
      "            retval_ = (ag__.ld(a) + ag__.ld(b))\n",
      "        except:\n",
      "            do_return = False\n",
      "            raise\n",
      "        return fscope.ret(retval_, do_return)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "@tf.function\n",
    "def add(a, b):\n",
    "    return a + b\n",
    "\n",
    "\n",
    "a = tf.Variable([[1.,2.],[3.,4.]])\n",
    "b = tf.Variable([[4.,0.],[1.,5.]])\n",
    "print(tf.add(a, b))\n",
    "\n",
    "# See what the generated code looks like\n",
    "print(tf.autograph.to_code(add.python_function))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tf__add(a, b):\n",
    "    with ag__.FunctionScope('add', 'fscope', ag__.ConversionOptions(recursive=True, user_requested=True, optional_features=(), internal_convert_user_code=True)) as fscope:\n",
    "        do_return = False\n",
    "        retval_ = ag__.UndefinedReturnValue()\n",
    "        try:\n",
    "            do_return = True\n",
    "            retval_ = (ag__.ld(a) + ag__.ld(b))\n",
    "        except:\n",
    "            do_return = False\n",
    "            raise\n",
    "        return fscope.ret(retval_, do_return)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 48s, sys: 22.4 s, total: 2min 11s\n",
      "Wall time: 1min 36s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for i in range(999):\n",
    "    for j in range(999):\n",
    "        add(99,99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 97.5 ms, sys: 1.6 ms, total: 99.1 ms\n",
      "Wall time: 98 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for i in range(999):\n",
    "    for j in range(999):\n",
    "        sim_add(99,99)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**wtf**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## if-statements in autograph\n",
    "Control flow statements which are very intuitive to write in eager mode can look very complex in graph mode. You can see that in the next examples: first a simple function, then a more complicated one that involves lots of ops and conditionals (fizzbuzz)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "def tf__f_1(x):\n",
      "    with ag__.FunctionScope('f', 'fscope', ag__.ConversionOptions(recursive=True, user_requested=True, optional_features=(), internal_convert_user_code=True)) as fscope:\n",
      "        do_return = False\n",
      "        retval_ = ag__.UndefinedReturnValue()\n",
      "\n",
      "        def get_state():\n",
      "            return (x,)\n",
      "\n",
      "        def set_state(vars_):\n",
      "            nonlocal x\n",
      "            (x,) = vars_\n",
      "\n",
      "        def if_body():\n",
      "            nonlocal x\n",
      "            x = (ag__.ld(x) * ag__.ld(x))\n",
      "\n",
      "        def else_body():\n",
      "            nonlocal x\n",
      "            pass\n",
      "        ag__.if_stmt((ag__.ld(x) > 0), if_body, else_body, get_state, set_state, ('x',), 1)\n",
      "        try:\n",
      "            do_return = True\n",
      "            retval_ = ag__.ld(x)\n",
      "        except:\n",
      "            do_return = False\n",
      "            raise\n",
      "        return fscope.ret(retval_, do_return)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# simple function that returns the square if the input is greater than zero\n",
    "@tf.function\n",
    "def f(x):\n",
    "    if x>0:\n",
    "        x = x * x\n",
    "    return x\n",
    "\n",
    "print(tf.autograph.to_code(f.python_function))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fizzbuzz in autograph\n",
    "\n",
    "You may remember implementing [fizzbuzz](http://wiki.c2.com/?FizzBuzzTest) in preparation for a coding interview.  \n",
    "- Imagine how much fun it would be if you were asked to impement the graph mode version of that code!  \n",
    "\n",
    "Fortunately, you can just use `@tf.function` and then call `tf.autograph.to_code`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def fizzbuzz(max_num):\n",
    "    counter = 0\n",
    "    for num in range(max_num):\n",
    "        if num % 3 == 0 and num % 5 == 0:\n",
    "            y = num*num\n",
    "        elif num % 3 == 0:\n",
    "            y = num*num\n",
    "        elif num % 5 == 0:\n",
    "            y = num*num\n",
    "        else:\n",
    "            y = num*num\n",
    "        counter += 1\n",
    "    return counter\n",
    "\n",
    "\n",
    "def simple_fizzbuzz(max_num):\n",
    "    counter = 0\n",
    "    for num in range(max_num):\n",
    "        if num % 3 == 0 and num % 5 == 0:\n",
    "            y = num*num\n",
    "        elif num % 3 == 0:\n",
    "            y = num*num\n",
    "        elif num % 5 == 0:\n",
    "            y = num*num\n",
    "        else:\n",
    "            y = num*num\n",
    "        counter += 1\n",
    "    return counter\n",
    "\n",
    "# print(tf.autograph.to_code(fizzbuzz.python_function))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 16.4 s, sys: 0 ns, total: 16.4 s\n",
      "Wall time: 16.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for i in range(999):\n",
    "    for j in range(999):\n",
    "        simple_fizzbuzz(99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "for i in range(999):\n",
    "    for j in range(999):\n",
    "        fizzbuzz(99)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**wtf**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "Autograph Basics.ipynb",
   "private_outputs": true,
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}