paper-figures/appendix_figure_10_relaysgd_vs_relaysgdgrad.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from algorithms import ChainTopology, RingTopology, QuadraticsTask, relaysum_model, relaysum_grad, relaysum_mix, all_reduce, gossip, d2, gradient_tracking, BinaryTreeTopology, relaysum_grad_overlap\n",
    "from random_quadratics import RandomQuadraticsTask\n",
    "import torch\n",
    "import math\n",
    "import tuning\n",
    "import pandas as pd\n",
    "from warehouse import Warehouse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.set_default_dtype(torch.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reach a fixed plateau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_workers = 32\n",
    "d = 10\n",
    "# noise = 0.5\n",
    "noise = 0\n",
    "mu = 0.5\n",
    "zetas = [0, .01, 1]\n",
    "max_steps = [200, 500, 2000]\n",
    "eval_intervals = [2, 5, 20]\n",
    "plateau = 1e-6\n",
    "seed = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "warehouse = Warehouse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning for 0\n",
      "5.027451938842201e-13\n",
      "Tuning for 0.01\n",
      "8.776453727932232e-07\n",
      "Tuning for 1\n",
      "0.0014456269036839942\n"
     ]
    }
   ],
   "source": [
    "algorithm_name = \"Gossip\"\n",
    "algorithm = gossip\n",
    "topology_name = \"ring\"\n",
    "topology = RingTopology(num_workers)\n",
    "\n",
    "warehouse.clear(\"error\", {\"algorithm\": algorithm_name})\n",
    "\n",
    "for zeta2, eval_interval, num_steps in zip(zetas, eval_intervals, max_steps):\n",
    "    print(f\"Tuning for {zeta2}\")\n",
    "    task = RandomQuadraticsTask(num_workers, d=d, heterogeneity=zeta2, sgd_noise=noise, mu=mu, seed=seed)\n",
    "    steps, learning_rate = tuning.tune_plateau(start_lr=10, desired_plateau=plateau, task=task, algorithm=algorithm, topology=topology, max_steps=70000, num_test_points=1000)\n",
    "    if learning_rate is None:\n",
    "        continue\n",
    "    tags = {\"algorithm\": algorithm_name, \"learning_rate\": learning_rate, \"zeta2\": zeta2}\n",
    "    warehouse.clear(\"error\", tags)\n",
    "    errors = {}\n",
    "    for iterate in algorithm(task, topology, learning_rate, num_steps):\n",
    "        if iterate.step % eval_interval == 0:\n",
    "            error = task.error(iterate.state).item() #/ initial_error\n",
    "            errors[iterate.step] = error\n",
    "#             if error - errors.get(iterate.step - eval_interval * 10, 1000000) > -1e-6:\n",
    "#                 print(\"no improvement after\", iterate.step)\n",
    "#                 break\n",
    "            if error < 1e-12:\n",
    "                break\n",
    "            warehouse.log_metric(\n",
    "                \"error\", {\"value\": error, \"step\": iterate.step}, tags\n",
    "            )\n",
    "    print(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning for 0\n",
      "7.337735926317597e-11\n",
      "Tuning for 0.01\n",
      "2.0265183017786903e-08\n",
      "Tuning for 1\n",
      "8.653164309535555e-07\n"
     ]
    }
   ],
   "source": [
    "algorithm_name = \"RelaySGD/Grad\"\n",
    "algorithm = relaysum_grad\n",
    "topology_name = \"chain\"\n",
    "topology = ChainTopology(num_workers)\n",
    "\n",
    "warehouse.clear(\"error\", {\"algorithm\": algorithm_name})\n",
    "\n",
    "for zeta2, eval_interval, num_steps in zip(zetas, eval_intervals, max_steps):\n",
    "    print(f\"Tuning for {zeta2}\")\n",
    "    task = RandomQuadraticsTask(num_workers, d=d, heterogeneity=zeta2, sgd_noise=noise, mu=mu, seed=seed)\n",
    "    steps, learning_rate = tuning.tune_plateau(start_lr=10, desired_plateau=plateau, task=task, algorithm=algorithm, topology=topology, max_steps=70000, num_test_points=1000)\n",
    "    if learning_rate is None:\n",
    "        continue\n",
    "    tags = {\"algorithm\": algorithm_name, \"learning_rate\": learning_rate, \"zeta2\": zeta2}\n",
    "    warehouse.clear(\"error\", tags)\n",
    "    errors = {}\n",
    "    for iterate in algorithm(task, topology, learning_rate, num_steps):\n",
    "        if iterate.step % eval_interval == 0:\n",
    "            error = task.error(iterate.state).item() #/ initial_error\n",
    "            errors[iterate.step] = error\n",
    "#             if error - errors.get(iterate.step - eval_interval * 10, 1000000) > -1e-6:\n",
    "#                 print(\"no improvement after\", iterate.step)\n",
    "#                 break\n",
    "            if error < 1e-12:\n",
    "                break\n",
    "            warehouse.log_metric(\n",
    "                \"error\", {\"value\": error, \"step\": iterate.step}, tags\n",
    "            )\n",
    "    print(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51 0.869140625\n",
      "5.317038806262599e-10\n",
      "51 0.869140625\n",
      "9.848753756980244e-13\n",
      "50 0.8837890625\n",
      "9.590106486712102e-13\n"
     ]
    }
   ],
   "source": [
    "algorithm_name = \"RelaySGD\"\n",
    "algorithm = relaysum_model\n",
    "topology_name = \"chain\"\n",
    "topology = ChainTopology(num_workers)\n",
    "\n",
    "warehouse.clear(\"error\", {\"algorithm\": algorithm_name})\n",
    "\n",
    "for zeta2, eval_interval, num_steps in zip(zetas, eval_intervals, max_steps):\n",
    "    eval_interval = 1\n",
    "    task = RandomQuadraticsTask(num_workers, d=d, heterogeneity=zeta2, sgd_noise=noise, mu=mu, seed=seed)\n",
    "    steps, learning_rate = tuning.tune_fastest(start_lr=10, target_quality=plateau, task=task, algorithm=algorithm, topology=topology, max_steps=10000, num_test_points=1000)\n",
    "    print(steps, learning_rate)\n",
    "    tags = {\"algorithm\": algorithm_name, \"learning_rate\": learning_rate, \"zeta2\": zeta2}\n",
    "    warehouse.clear(\"error\", tags)\n",
    "    errors = {}\n",
    "    for iterate in algorithm(task, topology, learning_rate, num_steps):\n",
    "        if iterate.step % eval_interval == 0:\n",
    "            error = task.error(iterate.state).item() #/ initial_error\n",
    "            errors[iterate.step] = error\n",
    "#             if error - errors.get(iterate.step - eval_interval * 10, 1000000) > -1e-6:\n",
    "#                 print(\"no improvement after\", iterate.step)\n",
    "#                 break\n",
    "            if error < 1e-12:\n",
    "                break\n",
    "            warehouse.log_metric(\n",
    "                \"error\", {\"value\": error, \"step\": iterate.step}, tags\n",
    "            )\n",
    "    print(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 641.852x180 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 172,
       "width": 626
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rcParams['text.usetex'] = True\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"serif\",\n",
    "    \"font.serif\": [\"Times\"],\n",
    "    'text.latex.preamble' : r'\\usepackage{amsmath}\\usepackage{amssymb}\\usepackage{newtxmath}'\n",
    "})\n",
    "\n",
    "colors = [sns.color_palette(\"tab10\")[i] for i in [7, 0, 1, 9, 3]]\n",
    "sns.set_palette(colors)\n",
    "\n",
    "df = warehouse.query(\"error\")\n",
    "df[\"zeta2name\"] = df[\"zeta2\"].replace({0: \"0 (equal optimum)\", .01: \"0.01 (heterogeneous)\", 1: \"1 (very heterogeneous)\"})\n",
    "df[\"algoname\"] = df.algorithm.replace({\"D2\": r\"$\\text{D}^2$\", \"RelaySum/Model\": \"RelaySGD\"})\n",
    "g = sns.FacetGrid(hue=\"algoname\", col=\"zeta2name\", data = df, height=2.5, sharex=False);\n",
    "g.map(plt.plot, \"step\", \"value\")\n",
    "\n",
    "for ax in g.axes[0]:\n",
    "    ax.axhline(1e-6, ls='--', c='gray', alpha=0.3)\n",
    "\n",
    "g.set_titles(r\"$\\zeta^2 =$ {col_name}\")\n",
    "g.set(yscale=\"log\");\n",
    "# g.set_ylabels(r\"Mean sq. distance to optimum\")\n",
    "g.set_ylabels(r\"Suboptimality $f(\\bar{\\mathbf{x}}) - f(\\mathbf{x}^\\star)$\")\n",
    "g.set(xlabel=\"Steps\")\n",
    "g.tight_layout()\n",
    "g.set(ylim=[1e-9, 1])\n",
    "g.add_legend(title=\"\");"
   ]
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