NumPy and PyTorch speed comparison.ipynb

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   "cell_type": "markdown",
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   "source": [
    "We want to time...\n",
    "\n",
    "* gradient computation\n",
    "* Simple arithmetic\n",
    "* Linear algebra operations\n",
    "* Functional primitives (sin, exp, etc) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-08T00:02:37.457549Z",
     "start_time": "2017-09-08T00:02:36.924829Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from speed_tests import PyTorch, NumPy\n",
    "from functools import lru_cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-08T00:02:37.545691Z",
     "start_time": "2017-09-08T00:02:37.459300Z"
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "import numpy.linalg as LA\n",
    "import torch\n",
    "import timeit\n",
    "from warnings import warn\n",
    "\n",
    "def time_fn(fn_name, class_, limit=1):\n",
    "    times = []\n",
    "    f = getattr(class_, fn_name)\n",
    "    start_time = time.time()\n",
    "    while True:\n",
    "        start = time.time()\n",
    "        r = f()\n",
    "        end = time.time()\n",
    "        times += [end - start]\n",
    "        if time.time() - start_time > limit:\n",
    "            break\n",
    "        \n",
    "    return {'result': r,\n",
    "            'time': min(times),\n",
    "            'repeats': len(times)}\n",
    "\n",
    "def get_numpy_array(in_):\n",
    "    if type(in_) == np.ndarray:\n",
    "        return in_\n",
    "    if type(in_) == torch.DoubleTensor: \n",
    "        return in_.numpy()\n",
    "    if type(in_) == torch.autograd.Variable:\n",
    "        if len(in_.data) > 1:\n",
    "            warn('len(in_.data) > 1. Concat\\'ing data')\n",
    "            return in_.data[:].numpy()\n",
    "        return in_.data[0].numpy()\n",
    "    if type(ret) != np.ndarray:\n",
    "        raise ValueError(f'{type(result)} != np.ndarray')\n",
    "\n",
    "def compare_fns(fn_name, *classes, verbose=True):\n",
    "    assert len(classes) == 2\n",
    "    \n",
    "    data = [time_fn(fn_name, c) for i, c in enumerate(classes)]\n",
    "    times = [{key: datum[key] for key in ['time']}#, 'std']}\n",
    "             for c, datum in zip(classes, data)]\n",
    "    results = [get_numpy_array(datum['result']) for datum in data]\n",
    "            \n",
    "    if verbose:\n",
    "        error = np.abs(results[0] - results[1])\n",
    "        rel_error = LA.norm(results[0] - results[1]) / LA.norm(results[0])\n",
    "        print('error.mean =', error.mean(), 'relative error =', rel_error)\n",
    "        \n",
    "    assert np.allclose(*results)\n",
    "    \n",
    "    for i, datum in enumerate(data):\n",
    "        del data[i]['result']\n",
    "    \n",
    "    return data\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-08T00:02:39.219082Z",
     "start_time": "2017-09-08T00:02:37.548487Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['add', 'complex_fn', 'cumsum', 'grad', 'matmul', 'pow', 'sin', 'svd']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy = NumPy(int(2e3))\n",
    "pytorch = PyTorch(A=numpy.A, x=numpy.x, y=numpy.y, S=numpy.S, S_svd=numpy.S_svd)\n",
    "\n",
    "numpy.__name__ = 'numpy'\n",
    "pytorch.__name__ = 'pytorch'\n",
    "fns = [f for f in dir(numpy) if f[0] != '_' and f != f.upper() and len(f) != 1 and f != 'S_svd']\n",
    "fns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-08T00:03:03.529639Z",
     "start_time": "2017-09-08T00:02:39.230254Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "add\n",
      "error.mean = 0.0 relative error = 0.0\n",
      "{'function': 'add', 'numpy_time': 0.18988633155822754, 'pytorch_time': 0.16830110549926758}\n",
      "complex_fn\n",
      "error.mean = 1.15027859819e+38 relative error = 4.63929025871e-19\n",
      "{'function': 'complex_fn', 'numpy_time': 0.0051081180572509766, 'pytorch_time': 0.005244016647338867}\n",
      "cumsum\n",
      "error.mean = 0.0 relative error = 0.0\n",
      "{'function': 'cumsum', 'numpy_time': 1.413053035736084, 'pytorch_time': 1.28731107711792}\n",
      "grad\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/scott/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:31: UserWarning: len(in_.data) > 1. Concat'ing data\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error.mean = 4.59026921431e-16 relative error = 4.17322888086e-14\n",
      "{'function': 'grad', 'numpy_time': 0.23931217193603516, 'pytorch_time': 0.03609180450439453}\n",
      "matmul\n",
      "error.mean = 3.12322168128e-14 relative error = 9.25778878186e-16\n",
      "{'function': 'matmul', 'numpy_time': 0.01742696762084961, 'pytorch_time': 0.01739025115966797}\n",
      "pow\n",
      "error.mean = 0.0 relative error = 0.0\n",
      "{'function': 'pow', 'numpy_time': 4.57763671875e-05, 'pytorch_time': 4.673004150390625e-05}\n",
      "sin\n",
      "error.mean = 0.0 relative error = 0.0\n",
      "{'function': 'sin', 'numpy_time': 0.722559928894043, 'pytorch_time': 0.719749927520752}\n",
      "svd\n",
      "error.mean = 2.86063707444e-14 relative error = 2.28221959828e-15\n",
      "{'function': 'svd', 'numpy_time': 0.08397579193115234, 'pytorch_time': 0.08576488494873047}\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "for fn in fns:\n",
    "    print(fn)\n",
    "    t_np, t_pyt = compare_fns(fn, numpy, pytorch)\n",
    "    \n",
    "    data += [{'function': fn,\n",
    "              'numpy_time': t_np['time'], 'pytorch_time': t_pyt['time']}]\n",
    "    print(data[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-08T00:03:28.873093Z",
     "start_time": "2017-09-08T00:03:28.326485Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>function</th>\n",
       "      <th>numpy_time</th>\n",
       "      <th>pytorch_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>function</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>add</th>\n",
       "      <td>add</td>\n",
       "      <td>0.189886</td>\n",
       "      <td>0.168301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>complex_fn</th>\n",
       "      <td>complex_fn</td>\n",
       "      <td>0.005108</td>\n",
       "      <td>0.005244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cumsum</th>\n",
       "      <td>cumsum</td>\n",
       "      <td>1.413053</td>\n",
       "      <td>1.287311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grad</th>\n",
       "      <td>grad</td>\n",
       "      <td>0.239312</td>\n",
       "      <td>0.036092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>matmul</th>\n",
       "      <td>matmul</td>\n",
       "      <td>0.017427</td>\n",
       "      <td>0.017390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pow</th>\n",
       "      <td>pow</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sin</th>\n",
       "      <td>sin</td>\n",
       "      <td>0.722560</td>\n",
       "      <td>0.719750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>svd</th>\n",
       "      <td>svd</td>\n",
       "      <td>0.083976</td>\n",
       "      <td>0.085765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              function  numpy_time  pytorch_time\n",
       "function                                        \n",
       "add                add    0.189886      0.168301\n",
       "complex_fn  complex_fn    0.005108      0.005244\n",
       "cumsum          cumsum    1.413053      1.287311\n",
       "grad              grad    0.239312      0.036092\n",
       "matmul          matmul    0.017427      0.017390\n",
       "pow                pow    0.000046      0.000047\n",
       "sin                sin    0.722560      0.719750\n",
       "svd                svd    0.083976      0.085765"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from altair import Chart\n",
    "import pandas as pd\n",
    "df = pd.DataFrame(data)\n",
    "df.index = df.function\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-08T00:03:30.564754Z",
     "start_time": "2017-09-08T00:03:30.557258Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "df['pytorch_time_norm'] = df['pytorch_time'] / df['numpy_time']\n",
    "df['numpy_time_norm'] = df['numpy_time'] /   df['numpy_time']\n",
    "df['pytorch_speedup'] = df['numpy_time'] /   df['pytorch_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-08T00:03:31.648188Z",
     "start_time": "2017-09-08T00:03:31.288021Z"
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   },
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    {
     "data": {
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DXsoGAAAAwBBhAQAAAIAhwgIAAAAAQ4QFAAAAAIYICwAAAAAMERYAAAAAGCIs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13725bc18>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 314,
       "width": 389
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(x='function', y=['numpy_time', 'pytorch_time'],\n",
    "#             yerr=[df['numpy_time_std'], df['pytorch_time_std']],\n",
    "            logy=False)\n",
    "plt.ylabel('Time (s)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-08T00:03:50.331130Z",
     "start_time": "2017-09-08T00:03:49.967985Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1.4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3Otc4T5o0SZL08ssva8CAAZoxY4bWr1+v6OhoHT16VCtWrJCvr69ef/11BQYG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\nzzGzC1W22GcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1375f1f60>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 316,
       "width": 373
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(x='function', y=['numpy_time_norm', 'pytorch_time_norm'])\n",
    "plt.ylim(0, 1.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-08T00:05:28.699470Z",
     "start_time": "2017-09-08T00:05:28.683054Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>function</th>\n",
       "      <th>numpy_time</th>\n",
       "      <th>pytorch_time</th>\n",
       "      <th>pytorch_time_norm</th>\n",
       "      <th>numpy_time_norm</th>\n",
       "      <th>pytorch_speedup</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>function</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>add</th>\n",
       "      <td>add</td>\n",
       "      <td>0.189886</td>\n",
       "      <td>0.168301</td>\n",
       "      <td>0.886326</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.128254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>complex_fn</th>\n",
       "      <td>complex_fn</td>\n",
       "      <td>0.005108</td>\n",
       "      <td>0.005244</td>\n",
       "      <td>1.026604</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.974085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cumsum</th>\n",
       "      <td>cumsum</td>\n",
       "      <td>1.413053</td>\n",
       "      <td>1.287311</td>\n",
       "      <td>0.911014</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.097678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grad</th>\n",
       "      <td>grad</td>\n",
       "      <td>0.239312</td>\n",
       "      <td>0.036092</td>\n",
       "      <td>0.150815</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.630651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>matmul</th>\n",
       "      <td>matmul</td>\n",
       "      <td>0.017427</td>\n",
       "      <td>0.017390</td>\n",
       "      <td>0.997893</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.002111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pow</th>\n",
       "      <td>pow</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>1.020833</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.979592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sin</th>\n",
       "      <td>sin</td>\n",
       "      <td>0.722560</td>\n",
       "      <td>0.719750</td>\n",
       "      <td>0.996111</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.003904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>svd</th>\n",
       "      <td>svd</td>\n",
       "      <td>0.083976</td>\n",
       "      <td>0.085765</td>\n",
       "      <td>1.021305</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.979140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              function  numpy_time  pytorch_time  pytorch_time_norm  \\\n",
       "function                                                              \n",
       "add                add    0.189886      0.168301           0.886326   \n",
       "complex_fn  complex_fn    0.005108      0.005244           1.026604   \n",
       "cumsum          cumsum    1.413053      1.287311           0.911014   \n",
       "grad              grad    0.239312      0.036092           0.150815   \n",
       "matmul          matmul    0.017427      0.017390           0.997893   \n",
       "pow                pow    0.000046      0.000047           1.020833   \n",
       "sin                sin    0.722560      0.719750           0.996111   \n",
       "svd                svd    0.083976      0.085765           1.021305   \n",
       "\n",
       "            numpy_time_norm  pytorch_speedup  \n",
       "function                                      \n",
       "add                     1.0         1.128254  \n",
       "complex_fn              1.0         0.974085  \n",
       "cumsum                  1.0         1.097678  \n",
       "grad                    1.0         6.630651  \n",
       "matmul                  1.0         1.002111  \n",
       "pow                     1.0         0.979592  \n",
       "sin                     1.0         1.003904  \n",
       "svd                     1.0         0.979140  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-08T00:06:18.220670Z",
     "start_time": "2017-09-08T00:06:18.215838Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['add', 'complex_fn', 'cumsum', 'grad', 'matmul', 'pow', 'sin', 'svd'], dtype='object', name='function')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-08T00:08:53.095421Z",
     "start_time": "2017-09-08T00:08:53.085350Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>function</th>\n",
       "      <th>grad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>function</th>\n",
       "      <td>grad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numpy_time</th>\n",
       "      <td>0.239312</td>\n",
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       "    <tr>\n",
       "      <th>pytorch_time</th>\n",
       "      <td>0.0360918</td>\n",
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       "    <tr>\n",
       "      <th>pytorch_time_norm</th>\n",
       "      <td>0.150815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numpy_time_norm</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>pytorch_speedup</th>\n",
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      "text/plain": [
       "function                grad\n",
       "function                grad\n",
       "numpy_time          0.239312\n",
       "pytorch_time       0.0360918\n",
       "pytorch_time_norm   0.150815\n",
       "numpy_time_norm            1\n",
       "pytorch_speedup      6.63065"
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    }
   ],
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    "grad_times = df[df.function == 'grad'].T\n",
    "grad_times"
   ]
  },
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