paper_note/lynx/runtime_feedback.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 134,
   "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",
    "]\n",
    "\n",
    "edgecolors = [\n",
    "    \"#FFFFFF\",\n",
    "    \"#FFFFFF\",\n",
    "    \"#FFFFFF\",\n",
    "]\n",
    "\n",
    "hatches = [\"\\\\\\\\\", \"\", \"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
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NpBjWANDY7J2f2rp1a9x4441Vr/v06RPXXnttDicCaHjFdA2orKyMMWPGxMaNG6N169Zx3333RbNmzRrsfACFrFj2/zfeeCNj/bDDDov58+fHhAkTonXr1jvsMXjw4HjiiSeiefPmae+tWbMmxowZUx+jAuSFYtn/d6Rly5bx0EMPxbnnnrvDz912220xdOjQWL16dSNNBkBd7ArXsH8nWJHHUqlUxnpd06qVlZUZ60m3VqmLYlgDQGOzd37q9ttvj7Kysoj4NKV6xx135O2sAPWlmK4B//Vf/xV//vOfIyLilltuiX333bfBzgVQ6Ipl/1+wYEHG+siRI6Nnz5417vP5z38+vve972V875lnnolnnnkmq/kA8k2x7P87M2PGjPjDH/4QzZo12+GjQWbOnBmHH354vPXWW404HQDZ2FWuYf9KsCKPtWnTpkH6lpeXZ6zXNUGUSTGsAaCx2Ts/fYbyv96dYuLEifGFL3whhxMBNI5iuQa8+uqrVY/9OPnkk3f612kAu7pi2f8XLVqUsd67d+9a97ryyiujY8eOGd/78Y9/XOt+APmoWPb/HfnpT38aw4cPjw0bNsTDDz8cM2fOjP/8z/9M/Pw777wThx9+eMyePbsRpwSgtnaFa9i/E6zIY23bts1YT/qCqqmkZ9DUNUGUSTGsAaCx2TsjLr/88li/fn1ERHz5y1+O7373uzmeCKBxFMM14JNPPolvfOMbsW3btvjsZz8bv/71r+v9HADFphj2/23btsVHH32U8b3Pfvazte7XunXruOiiizK+N3369KrvFwAKWTHs/0m2b98eF1xwQYwbNy4qKyvjrrvuipEjR0aTJk3iRz/6UfzmN7/J+NiniIj169fHcccdF7NmzWq0eQGonWK+hiURrMhjDfUFuWnTpoz19u3b16lvJsWwBoDGtqvvnY899ljcc889ERGx1157xb333hslJSU5ngqgcRTDNeCKK66It956K0pKSuI3v/lN4l8bA/BPxbD/J4UqIiK6deuWVc+kOx6lUql44YUXsuoJkE+KYf/PZPv27XHaaafFr371q4iIGDt2bHzjG9+o9pkzzzwz/vSnPyXOtHHjxjjuuOPib3/7W0OPC0AWivUatiOCFXmsQ4cOGet1/YLcuHFjxnqvXr3q1DeTYlgDQGPblffOZcuWxTnnnBMRER07downn3wyOnXqlOOpABpPoV8D/vCHP8Rtt90WEZ/ewn3YsGH12h+gWBX6/h/x6R2LkmT7b/pevXpF//79M773l7/8JaueAPmkGPb/f5dKpeKss86Khx56KCIi9t577/jRj36U8bNHH310vPjii9GjR4+M73/00UcxYsSI2LBhQ4PNC0B2ivEatjOCFXmsT58+GetJSZ2a+uCDDzLWe/bsWae+mRTDGgAa2666d27bti1OO+20WLduXTRv3jymTp2a+L8FQLEq9GvAv/5l8U9/+tMoKSnJ6r+zzz47Y/+kzwMUukLf/yMidtttt8T32rVrl3Xf4447LmN95cqVWfcEyBfFsP//uxtuuCHuu+++qtc//OEPo7S0NPHzBx54YLz44ovRu3fvjO+/++67MW7cuPoeE4A6SrqGbd++vU7hinz+PYZgRR7r27dvxvqaNWuioqIi675r1qzJWE/6h0tdFMMaABrbrrp3XnLJJTFz5sxo0qRJ3H333TFkyJBcjwTQ6Ar9GrB69ep67Qewqyj0/T8ionXr1onvNW/ePOu+/fr1y1hft25d1j0B8kUx7P//6i9/+UuMHz++6vVnP/vZGD169E6P23vvveO5555L/KXZnXfeGa+99lq9zQlA3e25556JAeoVK1Zk3Teff48hWJHHOnbsmPEZlJWVlfH+++9n3XfVqlUZ64MGDcq6Z5JiWANAY9sV985f/vKXVbeOnzRpUnz961/P8UQAubErXgMAKI79v02bNonhii1btmTdN+mXjlu3bs26J0C+KIb9/19dcsklUVlZWfX65JNPjmbNmtXo2N69e8ef/vSn6Ny5c8b3f/rTn9bLjADUnwMOOCBjffny5Vn3zOefYQlW5LkBAwZkrC9btiyrfuXl5bF27dq0+u677x6f+9znsuq5M8WwBoDGtivtnY8//niMHTs2IiImTpwYF154YU7nAci1XekaAMA/FcP+v/fee2esr1+/PuueXbt2zVjv1KlT1j0B8kkx7P8REc8//3y88sor1WrDhg2rVY8+ffrEww8/nDGMMXXq1Ni8eXOdZgSgftX3NSwi4r333kurNWnSJA477LCse9YXwYo8d/zxx2esL1myJKt+ZWVlGeuDBw/Oql9NFMMaABrbrrJ3zp49O0477bTYvn17jB07ttrtIgF2VbvKNQCA6oph/z/wwAMz1rNdQ0RE27ZtM9aT/qIZoNAUw/4fEXHfffel1ZKuCzsyZMiQmDBhQlp9y5Yt8dJLL2UzGgANpL6vYZs3b874mNm+ffsmfl/QmAQr8tzIkSMz1l9//fWs+i1atChjfcSIEVn1q4liWANAY9sV9s65c+fGCSecEJ988kmcddZZcfPNN+dsFoB8UsjXgFQqVS//TZ48uVb9AYpBIe///3DkkUdmrM+dOzfrnkmPF8nml3UA+agY9v+IiBdffDGtlukxJzUxbty46NWrV1p98eLFWfUDoGEMHz48WrRokVbP9hq2ePHijD/nyZffAQtW5Lm99tor47MkX3vttaz6vfnmm2m1Zs2axcknn5xVv5oohjUANLZi3zvffvvtOO6442LDhg3x1a9+Ne68884oKSnJySwA+abYrwEAZFYM+//w4cMz1v/yl79k3XPTpk0Z60cddVTWPQHySTHs/xGZb/ue6ZdtNdG8efP45je/mVavy6OlAKh/rVq1yvjYp/q8hkVEfP3rX8+qX30TrCgAY8aMSavNmTMnq7/MypQQGj58eIM/l7IY1gDQ2Ip173z33XfjmGOOiRUrVsSxxx4b999/fzRt2rTR5wDIZ8V6DQBgxwp9//8//+f/xMCBA9Pq06dPz7rnBx98kFbr0aNH7Lfffln3BMg3hb7/R0TGWTPt4TU1ZMiQtFr79u2z7gdAw8h0DSsrK8sqDJfpGrb//vtH//79s5qtvglWFIDzzz8/2rVrV6324YcfxuzZs2vda+bMmWm1yy67LNvRaqwY1gDQ2Ipx73zvvffimGOOiaVLl8ZRRx0VU6dOjdLS0jr1/PGPf1ynH9QC5KNivAYAsHPFsP+fd955abX33nsv8a/PdmbBggU1OgdAISuG/T/TYz9WrlyZdb/PfvazabXu3btn3Q+AhjF69OjYe++90+pPPfVUrXv9+c9/Tqvl08+wBCsKwGc+85k4//zz0+pTp06tVZ/XXnstVq1aVa128MEHx7HHHlun+WqiGNYA0NiKbe9899134+ijj4533nknDjvssHjiiSeiVatWdep52223xdVXX+2vroGiU2zXAABqphj2/7PPPjs6d+6cVr/77ruz6vfKK69Ue92sWbP49re/nVUvgHxVDPv/YYcdllabNWtW1v3Ky8urvW7SpEkcccQRWfcDoGE0bdo0rrrqqrR6ba9ha9asiVdffbVarXv37nHWWWfVab76JFhRIC677LJo3bp1tVptvyAfeeSRtNp1111Xp7lqoxjWANDYimXvLCsriy9+8YuxZMmSOOSQQ+Kpp56Ktm3bZt2vsrIyfvazn8WFF14Yffv2jYMOOqgepwXID8VyDQCgdgp9/2/VqlVce+21afUpU6bERx99VOt+Dz/8cLXXF1xwQfTo0SPr+QDyVaHv/1/5ylfSao8//njW/RYuXFjt9ZFHHhldunTJuh9AIauoqEirVVZW5mCSzM4+++y0Ow09+eSTaSG5HZk2bVramr7//e/X+Y7X9UmwokB07949xo8fX622ePHimDFjRo2OLy8vjzvuuKNa7ZRTTokTTjihxjNMmzYtBg4cGKWlpdGrV6+44YYbavX/tPmwBoBCkw97Z133/7feeiuOOuqoKCsri8GDB8fTTz9d42diplKpqKioiE2bNsXy5cvj5ZdfjptuuikGDBgQV155ZVRWVsYZZ5xR41kACkkxXAMAqL1i2P8vuOCCGDRoULXaunXr4mc/+1mNe0REvPTSS/HGG29Uve7Ro0dcf/31teoBUCgKff/PdCv4P/zhD7Fo0aIan/9fPfDAA9Vejxs3Lqs+AMVg27ZtabWtW7fWW/8XX3wxvvCFL0SLFi2ie/fuceWVV9YqFNGiRYu0f+tv2rQpfv/739e4xy9+8Ytqrw899NCMd3PKqRQFY+vWralDDz00FRFV/33pS1+q0bHXX399teO6du2aWr58eY3P/cADD1Q7/h//XXzxxQWzBoBCVcj7/1//+tdUly5dMvaoj/9KSkpSS5curfF6AApNIV8D6mLy5MkZzw2wqyiG/X/hwoWptm3bVutRWlqamjt3bo2O37JlS6p///7V/u3/6KOP1moGgEJT6Pv/ww8/nHb8kCFDUhUVFTXukUqlUrNmzUqVlJRU9Tj22GNrdTxAsdl///3T9tfjjz++XnrPnDkztdtuu6X1HzFiRK17ffWrX63WY7/99ktt27Ztp8fde++91Y5r3bp16s0338xmOQ3KT6YKzJIlS1IdO3as9sU1ZcqUHR4zY8aMVGlpadXnW7RokZo1a1atzpvp/2EjItWkSZPUypUrC2INAIWsEPf/WbNmpTp06NBgoYqISH3xi1+s1XoAClEhXgPqSrACoDj2/6lTp6aaNm1arU+fPn1Sq1ev3uFx27dvT5155pnVjrvmmmtqdW6AQlXo+/+FF16Y1uM//uM/Utu3b6/R8e+8806qR48eVcfutddeDf79B0C++8xnPpO2t/bv379eeh9//PGJP39/+eWXa9Vrw4YNqb333rtajwkTJuzwmHnz5lW77pWUlKQeeeSRuiypwfjJVAGaNWtWqk2bNlVfYKWlpYlfYL/73e+qfbZt27apJ598stbnzJRU+sd/L730UkGsAaDQFdL+/8c//jHVqlWrBg1VRETqzjvvrPWaAApRIV0D6oNgBcCnimH/nzx5crW/Oo6IVL9+/VJvvfVWxs+vWbMm7S/dxo4dW+vzAhSyQt7/t23blhozZkxaj+OPPz71zjvvJB5XWVmZevDBB1OdOnWqOqZ79+6phQsX1notAMXkySefTAy+/fWvf61z/3333Tdx///tb39b636LFi1KdevWrVpQ4he/+EXGzz777LOprl27Vn12t912S02ePLmOK2o4JalUKhUUnJdffjlGjRoV77//flVt1KhRMXr06OjRo0eUlZXF3XffHc8//3zV+3369IkHHngg+vfvX+vz9e3bN+bNm5dWb9KkSaxYsSK6du2a92sAKAaFsP9PnTo1vv71r2d87lt9atGiRaxatSratWvXoOcByBeFcA2oL1OmTImzzz47re7bV2BXVAz7/7Rp0+LMM8+Mjz76qKrWvHnzOP300+PYY4+N7t27x7p16+LFF1+Me+65JzZs2FD1mZtuuikuvvjiWp8ToNAV8v6fSqXiO9/5Ttx4441RWVlZVS8tLY3jjz8+vvzlL8eee+4ZpaWlsW7dunj99dfjsccei/nz51d9dujQoXH//fc36PcdAPlq06ZNsXz58vjTn/4U3/ve96r9O/pfdenSJW644YYYMmRIfPazn41WrVrV+lwnnnhi/O///m/G915++eU49NBDa91z0aJFMXLkyFiwYEFVbejQoXHGGWfEXnvtFe+//3488MAD8eijj1a936NHj7j33ntj6NChtT5fo8ltroO6WLlyZer000/f6V/ztmvXLjVhwoRUeXl51ud68MEHM/au6/OVG3MNAMUi3/f/s846q8HvVBERqVNPPTXrdQEUqny/BtQXd6wAqK4Y9v8FCxakRowYUaN/65eUlKRGjhzpr5SBXV6h7/9vvPFG6rjjjqvVz3v233//1JQpU1IVFRVZnxegUP3mN7+p88/Nn3/++Vqd86WXXsp416IRI0bUaS0bN25MXXLJJWmPBvz3/1q0aJG69NJLUx9++GGdztcY3LGiCMyfPz8mT54czz33XLz99tuxadOm6Ny5cwwcODBOPPHEGDNmTL38Ne+0adNiwoQJMW/evOjWrVucf/75cfXVV0eTJk0KZg0AxaQY9n8AsuMaALBrKob9/4033oiHH344pk+fHkuXLo21a9dG06ZNY/fdd48DDjggjj766Bg9enTss88+dT4XQLEo9P1/8eLF8eSTT8af/vSnKCsrizVr1sT69eujdevW0alTp9hzzz1jyJAhccwxx8SQIUOipKSkzmsBKEQffPBBLFu2rE499tprr2jdunWtjnnxxRfj6quvjldffTU6dOgQZ5xxRlx//fXRokWLOs0SEbF06dK46667Yvr06bFgwYLYuHFjdOjQIQ444IA4/vjj45vf/GZ06dKlzudpDIIVAAAAAAAAAAAJ/JkRAAAAAAAAAEACwQoAAAAAAAAAgASCFQAAAAAAAAAACQQrAAAAAAAAAAASCFYAAAAAAAAAACQQrAAAAAAAAAAASCBYAQAAAAAAAACQQLACAAAAAAAAACCBYAUAAAAAAAAAQALBCgAAAAAAAACABIIVAAAAAAAAAAAJBCsAAAAAAAAAABIIVgAAAAAAAAAAJBCsAAAAAAAAAABIIFgBAAAAAAAAAJBAsAIAAAAAAAAAIIFgBQAAAAAAAABAAsEKAAAAAAAAAIAEghUAAAAAAAAAAAkEKwAAAAAAAAAAEghWAAAAAAAAAAAkEKwAAAAAAAAAAEggWAEAAAAAAAAAkECwAgAAAAAAAAAggWAFAAAAAAAAAEACwQoAAAAAAAAAgASCFQAAAAAAAAAACQQrAAAAAAAAAAASCFYAAAAAAAAAACQQrAAAAAAAAAAASCBYAQAAAAAAAACQQLACAAAAAAAAACCBYAUAAAAAAAAAQALBCgAAAAAAAACABIIVAAAAAAAAAAAJBCsAAAAAAAAAABIIVgAAAAAAAAAAJBCsAAAAAAAAAABIIFgBAAAAAAAAAJBAsAIAAAAAAAAAIIFgBQAAAAAAAABAgv8P23iyDsigTPsAAAAASUVORK5CYII=",
      "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": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_name_a = [\n",
    "    \"ModelA\\n8DP\",\n",
    "    \"ModelA\\n8TP\",\n",
    "    \"ModelA\\n2DP$\\\\times$4TP\",\n",
    "    \"ModelA\\n2DP$\\\\times$8TP\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MFU\n",
    "data_a = {\n",
    "    labels_name_a[0]: [34.2 + 1, 0, 44.4],  # modela 8DP\n",
    "    labels_name_a[1]: [36.8 + 1, 0, 43.6],  # modela 8TP\n",
    "    labels_name_a[2]: [39.6 + 1, 0, 44.6],  # modela 2DP 4TP\n",
    "    labels_name_a[3]: [30.4 + 1, 0, 42.6],  # modela 2DP 8TP\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"Lynx with XLA\", \"Lynx with Limited Feedback\", \"DLRover-Lynx\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(13.333333333333346, 0.5, 'MFU (%)')"
      ]
     },
     "execution_count": 141,
     "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",
    "\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": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4966d8d0d0>"
      ]
     },
     "execution_count": 142,
     "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, 1.12),\n",
    "    fontsize=g_label_fontsize - 3,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2100x1050 with 1 Axes>"
      ]
     },
     "execution_count": 143,
     "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
}