profiling_tscan.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fehiepsi/miniconda3/envs/pydata/lib/python3.6/site-packages/jax/lib/xla_bridge.py:144: UserWarning: No GPU/TPU found, falling back to CPU.\n",
      "  warnings.warn('No GPU/TPU found, falling back to CPU.')\n"
     ]
    }
   ],
   "source": [
    "import pytest\n",
    "from numpy.testing import assert_allclose\n",
    "\n",
    "import jax.numpy as np\n",
    "import jax.random as random\n",
    "from jax import jit, lax\n",
    "from jax.scipy.special import expit\n",
    "\n",
    "import numpyro.distributions as dist\n",
    "from numpyro.distributions.util import validation_disabled\n",
    "from numpyro.handlers import sample\n",
    "from numpyro.hmc_util import initialize_model\n",
    "from numpyro.mcmc import hmc_kernel\n",
    "from numpyro.util import tscan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "N, dim = 3000, 3\n",
    "warmup_steps, num_samples = 1000, 8000\n",
    "data = random.normal(random.PRNGKey(0), (N, dim))\n",
    "true_coefs = np.arange(1., dim + 1.)\n",
    "probs = expit(np.sum(true_coefs * data, axis=-1))\n",
    "labels = dist.bernoulli(probs).rvs(random_state=random.PRNGKey(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.62 s, sys: 35.9 ms, total: 8.66 s\n",
      "Wall time: 8.65 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with validation_disabled():\n",
    "    algo = \"HMC\"\n",
    "    def model(labels):\n",
    "        coefs = sample('coefs', dist.norm(np.zeros(dim), np.ones(dim)))\n",
    "        logits = np.sum(coefs * data, axis=-1)\n",
    "        return sample('obs', dist.bernoulli(logits, is_logits=True), obs=labels)\n",
    "\n",
    "    init_params, potential_fn = initialize_model(random.PRNGKey(2), model, (labels,), {})\n",
    "    init_kernel, sample_kernel = hmc_kernel(potential_fn, algo=algo)\n",
    "    hmc_state = init_kernel(init_params,\n",
    "                            step_size=0.1,\n",
    "                            num_steps=15,\n",
    "                            num_warmup_steps=warmup_steps)\n",
    "    sample_kernel = jit(sample_kernel)\n",
    "    hmc_states = tscan(lambda state, i: sample_kernel(state),\n",
    "                       hmc_state, np.arange(num_samples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.66 s, sys: 12 ms, total: 7.68 s\n",
      "Wall time: 7.67 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with validation_disabled():\n",
    "    algo = \"HMC\"\n",
    "    def model(labels):\n",
    "        coefs = sample('coefs', dist.norm(np.zeros(dim), np.ones(dim)))\n",
    "        logits = np.sum(coefs * data, axis=-1)\n",
    "        return sample('obs', dist.bernoulli(logits, is_logits=True), obs=labels)\n",
    "\n",
    "    init_params, potential_fn = initialize_model(random.PRNGKey(2), model, (labels,), {})\n",
    "    init_kernel, sample_kernel = hmc_kernel(potential_fn, algo=algo)\n",
    "    hmc_state = init_kernel(init_params,\n",
    "                            step_size=0.1,\n",
    "                            num_steps=15,\n",
    "                            num_warmup_steps=warmup_steps)\n",
    "    sample_kernel = jit(sample_kernel)\n",
    "    hmc_states = lax.scan(lambda state, i: sample_kernel(state),\n",
    "                          hmc_state, np.arange(num_samples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 22.7 s, sys: 71.9 ms, total: 22.7 s\n",
      "Wall time: 22.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with validation_disabled():\n",
    "    algo = \"NUTS\"\n",
    "    def model(labels):\n",
    "        coefs = sample('coefs', dist.norm(np.zeros(dim), np.ones(dim)))\n",
    "        logits = np.sum(coefs * data, axis=-1)\n",
    "        return sample('obs', dist.bernoulli(logits, is_logits=True), obs=labels)\n",
    "\n",
    "    init_params, potential_fn = initialize_model(random.PRNGKey(2), model, (labels,), {})\n",
    "    init_kernel, sample_kernel = hmc_kernel(potential_fn, algo=algo)\n",
    "    hmc_state = init_kernel(init_params,\n",
    "                            step_size=0.1,\n",
    "                            num_steps=15,\n",
    "                            num_warmup_steps=warmup_steps)\n",
    "    sample_kernel = jit(sample_kernel)\n",
    "    hmc_states = lax.scan(lambda state, i: sample_kernel(state),\n",
    "                          hmc_state, np.arange(num_samples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 21.8 s, sys: 48 ms, total: 21.9 s\n",
      "Wall time: 21.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with validation_disabled():\n",
    "    algo = \"NUTS\"\n",
    "    def model(labels):\n",
    "        coefs = sample('coefs', dist.norm(np.zeros(dim), np.ones(dim)))\n",
    "        logits = np.sum(coefs * data, axis=-1)\n",
    "        return sample('obs', dist.bernoulli(logits, is_logits=True), obs=labels)\n",
    "\n",
    "    init_params, potential_fn = initialize_model(random.PRNGKey(2), model, (labels,), {})\n",
    "    init_kernel, sample_kernel = hmc_kernel(potential_fn, algo=algo)\n",
    "    hmc_state = init_kernel(init_params,\n",
    "                            step_size=0.1,\n",
    "                            num_steps=15,\n",
    "                            num_warmup_steps=warmup_steps)\n",
    "    sample_kernel = jit(sample_kernel)\n",
    "    hmc_states = tscan(lambda state, i: sample_kernel(state),\n",
    "                       hmc_state, np.arange(num_samples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.68 s, sys: 16 ms, total: 7.7 s\n",
      "Wall time: 7.69 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with validation_disabled():\n",
    "    algo = \"HMC\"\n",
    "    def model(labels):\n",
    "        coefs = sample('coefs', dist.norm(np.zeros(dim), np.ones(dim)))\n",
    "        logits = np.sum(coefs * data, axis=-1)\n",
    "        return sample('obs', dist.bernoulli(logits, is_logits=True), obs=labels)\n",
    "\n",
    "    init_params, potential_fn = initialize_model(random.PRNGKey(2), model, (labels,), {})\n",
    "    init_kernel, sample_kernel = hmc_kernel(potential_fn, algo=algo)\n",
    "    hmc_state = init_kernel(init_params,\n",
    "                            step_size=0.1,\n",
    "                            num_steps=15,\n",
    "                            num_warmup_steps=warmup_steps)\n",
    "    sample_kernel = jit(sample_kernel)\n",
    "    hmc_states = tscan(lambda state, i: sample_kernel(state),\n",
    "                       hmc_state, np.arange(num_samples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 21.9 s, sys: 36 ms, total: 22 s\n",
      "Wall time: 21.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with validation_disabled():\n",
    "    algo = \"NUTS\"\n",
    "    def model(labels):\n",
    "        coefs = sample('coefs', dist.norm(np.zeros(dim), np.ones(dim)))\n",
    "        logits = np.sum(coefs * data, axis=-1)\n",
    "        return sample('obs', dist.bernoulli(logits, is_logits=True), obs=labels)\n",
    "\n",
    "    init_params, potential_fn = initialize_model(random.PRNGKey(2), model, (labels,), {})\n",
    "    init_kernel, sample_kernel = hmc_kernel(potential_fn, algo=algo)\n",
    "    hmc_state = init_kernel(init_params,\n",
    "                            step_size=0.1,\n",
    "                            num_steps=15,\n",
    "                            num_warmup_steps=warmup_steps)\n",
    "    sample_kernel = jit(sample_kernel)\n",
    "    hmc_states = tscan(lambda state, i: sample_kernel(state),\n",
    "                       hmc_state, np.arange(num_samples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 22.6 s, sys: 28 ms, total: 22.6 s\n",
      "Wall time: 22.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with validation_disabled():\n",
    "    algo = \"NUTS\"\n",
    "    def model(labels):\n",
    "        coefs = sample('coefs', dist.norm(np.zeros(dim), np.ones(dim)))\n",
    "        logits = np.sum(coefs * data, axis=-1)\n",
    "        return sample('obs', dist.bernoulli(logits, is_logits=True), obs=labels)\n",
    "\n",
    "    init_params, potential_fn = initialize_model(random.PRNGKey(2), model, (labels,), {})\n",
    "    init_kernel, sample_kernel = hmc_kernel(potential_fn, algo=algo)\n",
    "    hmc_state = init_kernel(init_params,\n",
    "                            step_size=0.1,\n",
    "                            num_steps=15,\n",
    "                            num_warmup_steps=warmup_steps)\n",
    "    sample_kernel = jit(sample_kernel)\n",
    "    hmc_states = lax.scan(lambda state, i: sample_kernel(state),\n",
    "                          hmc_state, np.arange(num_samples))"
   ]
  }
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