cpu_vs_gpu.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jax.numpy as np\n",
    "from jax import jit, grad, lax\n",
    "from jax.config import config; config.update(\"jax_platform_name\", \"gpu\")\n",
    "\n",
    "import numpyro.distributions as dist\n",
    "from numpyro.examples.datasets import SP500, load_dataset\n",
    "from numpyro.handlers import sample\n",
    "from numpyro.hmc_util import initialize_model\n",
    "from numpyro.mcmc import hmc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, fetch = load_dataset(SP500, shuffle=False)\n",
    "dates, returns = fetch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def potential_fn(x):\n",
    "    return ((x - returns) ** 2).mean()\n",
    "\n",
    "@jit\n",
    "def loop(x):\n",
    "    def body_fn(i, x):\n",
    "        return x + 0.01 * grad(potential_fn)(x)\n",
    "\n",
    "    return lax.fori_loop(0, 1000, body_fn, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36.1 ms ± 2.84 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "loop(np.array(0.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeviceArray(-9989383., dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loop(np.array(0.))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the result in CPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "161 µs ± 1.81 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "loop(np.array(0.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "def loop(x):\n",
    "    def body_fn(i, x):\n",
    "        y = potential_fn(x)\n",
    "        return x + 0.01 * torch.autograd.grad(y, x)[0]\n",
    "    \n",
    "    for i in range(1000):\n",
    "        x = body_fn(i, x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = torch.from_numpy(returns).float()\n",
    "x = torch.tensor(0., requires_grad=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "344 ms ± 9.17 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "loop(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(-9989380., grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loop(x)"
   ]
  }
 ],
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