paper_note/lynx/compile_time_fsdp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "plt.rcParams[\"font.size\"] = 16\n",
    "\n",
    "g_label_fontsize = 16\n",
    "\n",
    "colors = [\n",
    "    \"#455D7A\",\n",
    "    \"#233142\",\n",
    "    \"#F95959\",\n",
    "]\n",
    "\n",
    "edgecolors = [\n",
    "    \"#FFFFFF\",\n",
    "    \"#FFFFFF\",\n",
    "    \"#FFFFFF\",\n",
    "]\n",
    "\n",
    "hatches = [\"\\\\\\\\\", \"\", \"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2100x840 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize=(7, 14 / 5), ncols=1, nrows=1, constrained_layout=True, dpi=300\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_name = [\n",
    "    \"ModelA\\n8FSDP\",\n",
    "    \"ModelA\\n16FSDP\",\n",
    "    \"ModelB\\n8FSDP\",\n",
    "    \"ModelB\\n16FSDP\",\n",
    "    \"ModelC\\n8FSDP\",\n",
    "    \"ModelC\\n16FSDP\",\n",
    "    \"ModelD\\n8FSDP\",\n",
    "    \"ModelD\\n16FSDP\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# memory cost\n",
    "data_a = {\n",
    "    labels_name[0]: [0, 53, 1200],  # 22.64\n",
    "    labels_name[1]: [0, 53, 1200],  # 22.64\n",
    "    labels_name[2]: [0, 35, 1200],  # 37\n",
    "    labels_name[3]: [0, 35, 1200],  # 37\n",
    "    labels_name[4]: [0, 53, 1200],  # 22.64\n",
    "    labels_name[5]: [0, 53, 1200],  # 22.64\n",
    "    labels_name[6]: [0, 53, 1200],  # 22.64\n",
    "    labels_name[7]: [0, 53, 1200],  # 22.64\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"FSDP\", \"XLA\", \"DLRover-Lynx\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "bar_width = 0.2\n",
    "group_spaing = 0.15\n",
    "\n",
    "group_positions = {}\n",
    "current_pos = 0\n",
    "\n",
    "for x_label, y_data in data_a.items():\n",
    "    group_positions[x_label] = []\n",
    "    for i in range(len(y_data)):\n",
    "        group_positions[x_label].append(current_pos)\n",
    "        current_pos += bar_width\n",
    "    current_pos += group_spaing\n",
    "\n",
    "group_centers = {}\n",
    "for x_label, positions in group_positions.items():\n",
    "    group_centers[x_label] = sum(positions) / len(positions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '(a)')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_set = set()\n",
    "\n",
    "for x_label, y_data in data_a.items():\n",
    "    positions = group_positions[x_label]\n",
    "    for i, (pos, value, color, edgecolor, label, hatch) in enumerate(\n",
    "        zip(\n",
    "            positions,\n",
    "            y_data,\n",
    "            colors,\n",
    "            edgecolors,\n",
    "            legend_labels,\n",
    "            hatches,\n",
    "        )\n",
    "    ):\n",
    "        if label in label_set:\n",
    "            local_label = None\n",
    "        else:\n",
    "            local_label = label\n",
    "            label_set.add(local_label)\n",
    "\n",
    "        ax.bar(\n",
    "            pos,\n",
    "            value,\n",
    "            width=bar_width,\n",
    "            color=color,\n",
    "            edgecolor=edgecolor,\n",
    "            hatch=hatch,\n",
    "            label=local_label,\n",
    "        )\n",
    "\n",
    "\n",
    "ax.set_xticks(list(group_centers.values()))\n",
    "ax.set_xticklabels(list(data_a.keys()))\n",
    "\n",
    "ax.set_ylim(0, 1500)\n",
    "ax.set_yticks([0, 500, 1000, 1500])\n",
    "ax.set_yticklabels([\"0\", \"500\", \"1000\", \"1500\"], rotation=90, ha=\"center\", va=\"center\")\n",
    "\n",
    "ax.tick_params(axis=\"x\", bottom=False, labelsize=11, pad=1)\n",
    "ax.tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
    "\n",
    "ax.set_ylabel(\"Time (s)\", fontsize=g_label_fontsize)\n",
    "ax.set_title(\"(a)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f491076ad20>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.legend(\n",
    "    ncol=3,\n",
    "    loc=\"upper center\",\n",
    "    frameon=False,\n",
    "    shadow=False,\n",
    "    fontsize=g_label_fontsize,\n",
    "    bbox_to_anchor=(0.5, 1)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2100x840 with 1 Axes>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.savefig(\"compile_time_fsdp.pdf\", bbox_inches=\"tight\", dpi=1000)\n",
    "fig"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}