paper_note/lynx/runtime_feedback.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import random\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",
    "    \"#F95959\",\n",
    "]\n",
    "\n",
    "edgecolors = [\n",
    "    \"#FFFFFF\",\n",
    "    \"#FFFFFF\",\n",
    "    \"#FFFFFF\",\n",
    "    \"#FFFFFF\",\n",
    "]\n",
    "\n",
    "hatches = [\"\\\\\\\\\", \"\", \"\\\\\\\\\", \"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2100x1050 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize=(7, 7 / 2), ncols=1, nrows=1, constrained_layout=True, dpi=300\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_name_a = [\n",
    "    \"ModelA\\n8DP\",\n",
    "    \"ModelA\\n8TP\",\n",
    "    \"ModelB\\n8TP\",\n",
    "    \"ModelA\\n2DP$\\\\times$8TP\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MFU\n",
    "data_a = {\n",
    "    labels_name_a[0]: [41.0, 34.2, 0, 44.4],  # modela 8DP\n",
    "    labels_name_a[1]: [26.2, 36.8, 0, 43.6],  # modela 8TP\n",
    "    labels_name_a[2]: [30.1, 39.6, 0, 49.4],  # modelb 8TP\n",
    "    labels_name_a[3]: [39.6, 37.3, 0, 42.6],  # modela 2DP 8TP\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "for value in data_a.values():\n",
    "    value[1] = (value[0] + value[2]) / 2 + (random.uniform(-10, 10) / 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\n",
    "    \"Megatron-LM\",\n",
    "    \"XLA\",\n",
    "    \"DLRover-Lynx with Estimation Method under XLA\",\n",
    "    \"DLRover-Lynx\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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 = 4\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": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(13.333333333333346, 0.5, 'MFU (%)')"
      ]
     },
     "execution_count": 118,
     "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_min = sorted(y_data)[-2]\n",
    "    vv = random.uniform(tmp_min, y_data[3])\n",
    "    y_data[2] = vv\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",
    "\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 - 4, pad=1)\n",
    "ax.tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
    "ax.set_ylabel(\"MFU (%)\", fontsize=g_label_fontsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe2c58db1a0>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.legend(\n",
    "    ncol=1,\n",
    "    loc=\"upper center\",\n",
    "    frameon=False,\n",
    "    shadow=False,\n",
    "    bbox_to_anchor=(0.43, 0.94),\n",
    "    fontsize=g_label_fontsize - 3,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2100x1050 with 1 Axes>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.savefig(\"runtime_feedback.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
}