fori+vmap2.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "from jax import random\n",
    "import jax.numpy as np\n",
    "from jax.config import config; config.update('jax_platform_name', 'gpu')\n",
    "\n",
    "import numpyro\n",
    "import numpyro.distributions as dist\n",
    "from numpyro.mcmc import MCMC, NUTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "N, dim = 10000, 100\n",
    "data = random.normal(random.PRNGKey(0), (N, dim))\n",
    "true_coefs = random.normal(random.PRNGKey(1), (dim,))\n",
    "logits = np.dot(data, true_coefs)\n",
    "labels = dist.Bernoulli(logits=logits).sample(random.PRNGKey(2))\n",
    "\n",
    "def model(labels):\n",
    "    coefs = numpyro.sample('coefs', dist.Normal(np.zeros(dim), np.ones(dim)))\n",
    "    logits = np.dot(data, coefs)\n",
    "    return numpyro.sample('obs', dist.Bernoulli(logits=logits), obs=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sample: 100%|██████████| 2000/2000 [02:11<00:00, 15.17it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DeviceArray(127698.93, dtype=float32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tic = time.time()\n",
    "mcmc = MCMC(NUTS(model), num_warmup=1000, num_samples=1000, num_chains=1000, chain_method='vectorized')\n",
    "mcmc.run(random.PRNGKey(0), labels, collect_fields=('z', 'num_steps'), collect_warmup=True)\n",
    "toc = time.time()\n",
    "# leapfrogs / s\n",
    "mcmc.get_samples()[1].sum() / (toc - tic)"
   ]
  }
 ],
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