paper_note/lynx/end_to_end_mfu.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 340,
   "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",
    "    \"#5D7599\",\n",
    "    \"#233142\",\n",
    "    \"#F95959\",\n",
    "]\n",
    "\n",
    "edgecolors = [\n",
    "    \"#FFFFFF\",\n",
    "    \"#FFFFFF\",\n",
    "    \"#FFFFFF\",\n",
    "]\n",
    "\n",
    "hatches = [\"\\\\\\\\\", \"\", \"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 4200x1050 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize=(14, 14 / 4), ncols=1, nrows=1, constrained_layout=True, dpi=300\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_name_a = [\n",
    "    \"ModelA\\n8DP\",\n",
    "    \"ModelA\\n8TP\",\n",
    "    \"ModelB\\n8TP\",  \n",
    "    \"ModelA\\n2DP$\\\\times$4TP\",\n",
    "    \"ModelA\\n2DP$\\\\times$8TP\",\n",
    "    \"ModelA\\n4DP$\\\\times$2TP\",\n",
    "    \"ModelB\\n4DP$\\\\times$2TP\",\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MFU\n",
    "data_a = {\n",
    "    labels_name_a[0]: [41.0, 34.2, 44.4],  # modela 8DP\n",
    "    labels_name_a[1]: [26.2, 36.8, 43.6],  # modela 8TP\n",
    "    labels_name_a[2]: [30.1, 39.6, 49.4],  # modelb 8tp\n",
    "    labels_name_a[3]: [37.1, 30.4, 44.6],  # modela 2DP 4TP\n",
    "    labels_name_a[4]: [39.6, 41.9, 0],  # modela 2DP 8TP\n",
    "    labels_name_a[5]: [40.4, 30.5, 44.7],  # modela 4DP 2TP\n",
    "    labels_name_a[6]: [-1, 31.7, 38.0],  # modeb 4DP 2TP\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"Megatron-LM\", \"XLA\", \"DLRover-Lynx\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "bar_width = 0.2\n",
    "group_spaing = 0.15\n",
    "\n",
    "group_positions = {}\n",
    "current_pos = 0\n",
    "\n",
    "x_label_cnt = len(data_a.keys())\n",
    "x_label_group_cnt = 3\n",
    "\n",
    "for idx in range(x_label_cnt):\n",
    "    group_positions[idx] = []\n",
    "    for _ in range(x_label_group_cnt):\n",
    "        group_positions[idx].append(current_pos)\n",
    "        current_pos += bar_width\n",
    "    current_pos += group_spaing\n",
    "\n",
    "group_centers = {}\n",
    "for idx, positions in group_positions.items():\n",
    "    group_centers[idx] = sum(positions) / len(positions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(13.333333333333346, 0.5, 'MFU (%)')"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_set = set()\n",
    "for idx, (x_label, y_data) in enumerate(data_a.items()):\n",
    "    positions = group_positions[idx]\n",
    "    tmp = sorted(y_data)[-2]\n",
    "    ax.text(\n",
    "        positions[2],\n",
    "        y_data[2] + 2,\n",
    "        f\"$\\\\times${str(round(y_data[2]/tmp, 2))}\",\n",
    "        ha=\"center\",\n",
    "        fontweight=\"bold\",\n",
    "        color=colors[2],\n",
    "    )\n",
    "    for i, (pos, value, color, edgecolor, hatch, label) in enumerate(\n",
    "        zip(\n",
    "            positions,\n",
    "            y_data,\n",
    "            colors,\n",
    "            edgecolors,\n",
    "            hatches,\n",
    "            legend_labels,\n",
    "        )\n",
    "    ):\n",
    "        if label in label_set:\n",
    "            local_label = None\n",
    "        else:\n",
    "            local_label = label\n",
    "            label_set.add(local_label)\n",
    "        if value == -1:\n",
    "            ax.text(\n",
    "                pos,\n",
    "                1,\n",
    "                \"$\\\\times$OOM\",\n",
    "                rotation=90,\n",
    "                ha=\"center\",\n",
    "                va=\"bottom\",\n",
    "                color=\"red\",\n",
    "            )\n",
    "        ax.bar(\n",
    "            pos,\n",
    "            value,\n",
    "            width=bar_width,\n",
    "            color=color,\n",
    "            label=local_label,\n",
    "            hatch=hatch,\n",
    "            edgecolor=edgecolor,\n",
    "        )\n",
    "\n",
    "ax.set_xticks(list(group_centers.values()))\n",
    "ax.set_xticklabels(list(data_a.keys()))\n",
    "\n",
    "ax.set_ylim(0, 100)\n",
    "ax.set_yticks([0, 50, 100])\n",
    "ax.set_yticklabels([\"0\", \"50\", \"100\"], rotation=90, ha=\"center\", va=\"center\")\n",
    "\n",
    "ax.tick_params(axis=\"x\", bottom=False, labelsize=g_label_fontsize, pad=1)\n",
    "ax.tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
    "\n",
    "ax.set_ylabel(\"MFU (%)\", fontsize=g_label_fontsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa83bcfa6f0>"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.legend(\n",
    "    ncol=3,\n",
    "    loc=\"upper center\",\n",
    "    frameon=False,\n",
    "    shadow=False,\n",
    "    bbox_to_anchor=(0.5, 1.1),\n",
    "    fontsize=g_label_fontsize,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 4200x1050 with 1 Axes>"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.savefig(\"end_to_end_mfu.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
}