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paper_note/lynx/end_to_end.ipynb
yezhengmao 18146e8ba9 [tools]
2025-03-28 17:54:41 +08:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 331,
"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": 332,
"metadata": {},
"outputs": [
{
"data": {
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JZcuW1enMAPlULPt/kvbt28czzzwTJ5100nY/N23atPjud78bW7ZsaaDJAKitpGtYixYtoqSkJOe+hXINy0SwooAl/YWsbVp127ZtGeubN2+uVd9MimENAA3N3vmxzZs3x7XXXlv1unfv3nH55ZfncSKA+ldM14DKysoYPXp0rF+/Plq3bh333ntvNGvWrN7OB9CYFcv+/9prr2Ws9+vXL+bNmxfjx4+P1q1bb7dH//794/HHH4/mzZunvbdq1aoYPXp0XYwKUBCKZf/fnpYtW8aDDz4YZ5999nY/d+utt8agQYNi5cqVDTQZALWxM1zD/pNgRQFLpVIZ67VNq1ZWVmasJ91apTaKYQ0ADc3e+bHf/va3UVZWFhEfp1Rvu+22gp0VoK4U0zXgf/7nf+Kvf/1rRETceOONse+++9bbuQAau2LZ/998882M9eHDh0f37t2z7vP5z38+fvzjH2d87+mnn46nn346p/kACk2x7P87Mm3atHjssceiWbNm2300yMyZM+OII46It99+uwGnAyAXO8s17JMEKwpYmzZt6qVveXl5xnptE0SZFMMaABqavfPjZyh/8u4UV1xxRXzhC1/I40QADaNYrgEvv/xy1WM/Tj755B3+dhrAzq5Y9v/58+dnrPfs2bPGvS6++OLo2LFjxvd+/vOf17gfQCEqlv1/e6677roYMmRIrFu3Lh566KGYOXNm/OAHP0j8/LvvvhtHHHFEvPDCCw04JQA1tTNcw/6TYEUBa9u2bcZ60l+obCU9g6a2CaJMimENAA3N3hlx0UUXxdq1ayMi4vjjj48f/ehHeZ4IoGEUwzXgo48+im984xuxZcuW2GOPPeJ3v/tdnZ8DoNgUw/6/ZcuW2LBhQ8b39thjjxr3a926dXz3u9/N+N7UqVOrvl4AaMyKYf9Psm3btvjOd74TY8eOjcrKypg4cWIMHz48mjRpEtdcc038/ve/z/jYp4iItWvXxnHHHRezZs1qsHkBqJlivoYlEawoYPX1F3Ljxo0Z6+3bt69V30yKYQ0ADW1n3zsfeeSRuPvuuyMiYu+994577rknSkpK8jwVQMMohmvA97///Xj77bejpKQkfv/73yf+tjEA/1YM+39SqCIiomvXrjn1TLrjUSqViunTp+fUE6CQFMP+n8m2bdvi1FNPjd/85jcRETFmzJj4xje+Ue0zp59+evzlL39JnGn9+vVx3HHHxd///vf6HheAHBTrNWx7BCsKWIcOHTLWa/sXcv369RnrPXr0qFXfTIphDQANbWfeOxcvXhxnnXVWRER07NgxnnjiiejUqVOepwJoOI39GvDYY4/FrbfeGhEf38J98ODBddofoFg19v0/4uM7FiXJ9d/0PXr0iL59+2Z8729/+1tOPQEKSTHs//8plUrFGWecEQ8++GBERPTq1SuuueaajJ8dOHBgzJgxI7p165bx/Q0bNsSwYcNi3bp19TYvALkpxmvYjghWFLDevXtnrCcldbL1wQcfZKx37969Vn0zKYY1ADS0nXXv3LJlS5x66qmxZs2aaN68eUyePDnxfwuAYtXYrwGf/M3i6667LkpKSnL6c+aZZ2bsn/R5gMause//ERG77LJL4nvt2rXLue9xxx2Xsb58+fKcewIUimLY///T1VdfHffee2/V65/97GdRWlqa+PkDDjggZsyYET179sz4/nvvvRdjx46t6zEBqKWka9i2bdtqFa4o5J9jCFYUsD59+mSsr1q1KrZu3Zpz31WrVmWsJ/3DpTaKYQ0ADW1n3TsvuOCCmDlzZjRp0iTuuuuuGDBgQL5HAmhwjf0asHLlyjrtB7CzaOz7f0RE69atE99r3rx5zn0PPPDAjPU1a9bk3BOgUBTD/v9Jf/vb32LcuHFVr/fYY48YOXLkDo/r1atXPPvss4k/NLv99tvjlVdeqbM5Aai9vfbaKzFAvXTp0pz7FvLPMQQrCljHjh0zPoOysrIyli1blnPfFStWZKwfeuihOfdMUgxrAGhoO+Pe+etf/7rq1vETJkyIr3/963meCCA/dsZrAADFsf+3adMmMVxRUVGRc9+kHzpu3rw5554AhaIY9v9PuuCCC6KysrLq9cknnxzNmjXL6tiePXvGX/7yl9h1110zvn/dddfVyYwA1J39998/Y33JkiU59yzk72EJVhS4gw46KGN98eLFOfUrLy+P1atXp9V32223+PSnP51Tzx0phjUANLSdae989NFHY8yYMRERccUVV8R5552X13kA8m1nugYA8G/FsP/36tUrY33t2rU59+zSpUvGeqdOnXLuCVBIimH/j4h47rnn4qWXXqpWGzx4cI169O7dOx566KGMYYzJkyfHpk2bajUjAHWrrq9hERHvv/9+Wq1JkybRr1+/nHvWFcGKAjd06NCM9YULF+bUr6ysLGO9f//+OfXLRjGsAaCh7Sx75wsvvBCnnnpqbNu2LcaMGVPtdpEAO6ud5RoAQHXFsP8fcMABGeu5riEiom3bthnrSb/RDNDYFMP+HxFx7733ptWSrgvbM2DAgBg/fnxavaKiIp5//vlcRgOgntT1NWzTpk0ZHzPbp0+fxK8LGpJgRYEbPnx4xvqrr76aU7/58+dnrA8bNiynftkohjUANLSdYe+cM2dOnHDCCfHRRx/FGWecETfccEPeZgEoJI35GpBKperkzx133FGj/gDFoDHv//9y1FFHZazPmTMn555JjxfJ5Yd1AIWoGPb/iIgZM2ak1TI95iQbY8eOjR49eqTVFyxYkFM/AOrHkCFDokWLFmn1XK9hCxYsyPh9nkL5GbBgRYHbe++9Mz5L8pVXXsmp3xtvvJFWa9asWZx88sk59ctGMawBoKEV+975zjvvxHHHHRfr1q2Lk046KW6//fYoKSnJyywAhabYrwEAZFYM+/+QIUMy1v/2t7/l3HPjxo0Z61/84hdz7glQSIph/4/IfNv3TD9sy0bz5s3jm9/8Zlq9No+WAqDutWrVKuNjn+ryGhYR8fWvfz2nfnVNsKIRGD16dFpt9uzZOf1mVqaE0JAhQ+r9uZTFsAaAhlase+d7770XxxxzTCxdujSOPfbYuO+++6Jp06YNPgdAISvWawAA29fY9//PfOYzcfDBB6fVp06dmnPPDz74IK3WrVu3+OxnP5tzT4BC09j3/4jIOGumPTxbAwYMSKu1b98+534A1I9M17CysrKcwnCZrmH77bdf9O3bN6fZ6ppgRSNw7rnnRrt27arVPvzww3jhhRdq3GvmzJlptQsvvDDX0bJWDGsAaGjFuHe+//77ccwxx8SiRYvii1/8YkyePDlKS0tr1fPnP/95rb5RC1CIivEaAMCOFcP+f84556TV3n///cTfPtuRN998M6tzADRmxbD/Z3rsx/Lly3Put8cee6TVdt9995z7AVA/Ro4cGb169UqrP/nkkzXu9de//jWtVkjfwxKsaAQ+9alPxbnnnptWnzx5co36vPLKK7FixYpqtUMOOSSOPfbYWs2XjWJYA0BDK7a987333ouBAwfGu+++G/369YvHH388WrVqVauet956a1x66aV+6xooOsV2DQAgO8Ww/5955pmx6667ptXvuuuunPq99NJL1V43a9Ysvv3tb+fUC6BQFcP+369fv7TarFmzcu5XXl5e7XWTJk3iyCOPzLkfAPWjadOmcckll6TVa3oNW7VqVbz88svVarvvvnucccYZtZqvLglWNBIXXnhhtG7dulqtpn8hH3744bTalVdeWau5aqIY1gDQ0Ipl7ywrK4ujjz46Fi5cGIcddlg8+eST0bZt25z7VVZWxi9/+cs477zzok+fPvG5z32uDqcFKAzFcg0AoGYa+/7fqlWruPzyy9Pqd955Z2zYsKHG/R566KFqr7/zne9Et27dcp4PoFA19v3/K1/5Slrt0UcfzbnfW2+9Ve31UUcdFZ07d865H0BjtnXr1rRaZWVlHibJ7Mwzz0y709ATTzyRFpLbnilTpqSt6Sc/+Umt73hdlwQrGondd989xo0bV622YMGCmDZtWlbHl5eXx2233Vat9tWvfjVOOOGErGeYMmVKHHzwwVFaWho9evSIq6++ukb/0RbCGgAam0LYO2u7/7/99tvxxS9+McrKyqJ///7x1FNPZf1MzFQqFVu3bo2NGzfGkiVL4sUXX4zrr78+DjrooLj44oujsrIyRo0alfUsAI1JMVwDAKi5Ytj/v/Od78Shhx5arbZmzZr45S9/mXWPiIjnn38+XnvttarX3bp1i6uuuqpGPQAai8a+/2e6Ffxjjz0W8+fPz/r8nzRp0qRqr8eOHZtTH4BisGXLlrTa5s2b66z/jBkz4gtf+EK0aNEidt9997j44otrFIpo0aJF2r/1N27cGH/84x+z7nHzzTdXe3344YdnvJtTXqVoNDZv3pw6/PDDUxFR9edLX/pSVsdeddVV1Y7r0qVLasmSJVmfe9KkSdWO/9ef888/v9GsAaCxasz7/+uvv57q3Llzxh518aekpCS1aNGirNcD0Ng05mtAbdxxxx0Zzw2wsyiG/f+tt95KtW3btlqP0tLS1Jw5c7I6vqKiItW3b99q//b/05/+VKMZABqbxr7/P/TQQ2nHDxgwILV169ase6RSqdSsWbNSJSUlVT2OPfbYGh0PUGz222+/tP116NChddJ75syZqV122SWt/7Bhw2rc66STTqrW47Of/Wxqy5YtOzzunnvuqXZc69atU2+88UYuy6lXvjPVyCxcuDDVsWPHan+57rzzzu0eM23atFRpaWnV51u0aJGaNWtWjc6b6T/YiEg1adIktXz58kaxBoDGrDHu/7NmzUp16NCh3kIVEZE6+uija7QegMaoMV4DakuwAqA49v/JkyenmjZtWq1P7969UytXrtzucdu2bUudfvrp1Y677LLLanRugMaqse//5513XlqP//f//l9q27ZtWR3/7rvvprp161Z17N57713vX38AFLpPfepTaXtr375966T30KFDE7///uKLL9a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"text/plain": [
"<Figure size 2100x3500 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(\n",
" figsize=(7, 14 / 1.2), ncols=1, nrows=4, constrained_layout=True, dpi=300\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"labels_name = [\n",
" \"ModelA\\n8DP\",\n",
" \"ModelA\\n8TP\",\n",
" \"ModelB\\n8TP\",\n",
" \"ModelC\\n8DP\",\n",
" \"ModelC\\n8TP\",\n",
" \"ModelD\\n8TP\",\n",
" \"ModelA\\n16DP\",\n",
" \"ModelA\\n16TP\",\n",
" \"ModelB\\n16TP\",\n",
" \"ModelC\\n16DP\",\n",
" \"ModelC\\n16TP\",\n",
" \"ModelD\\n16TP\",\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 334,
"metadata": {},
"outputs": [],
"source": [
"# MFU\n",
"data_a = {\n",
" labels_name[0]: [41, 34, 44], # 22.64\n",
" labels_name[1]: [26, 36, 43], # 22.64\n",
" labels_name[2]: [30, 39, 49], # 37\n",
" labels_name[3]: [0, 0, 0], # 37\n",
" labels_name[4]: [0, 0, 0], # 22.64\n",
" labels_name[5]: [0, 0, 0], # 22.64\n",
" labels_name[6]: [0, 0, 0], # 37\n",
" labels_name[7]: [0, 0, 0], # 22.64\n",
" labels_name[8]: [0, 0, 0], # 22.64\n",
" labels_name[9]: [0, 0, 0], # 37\n",
" labels_name[10]: [0, 0, 0], # 22.64\n",
" labels_name[11]: [0, 0, 0], # 22.64\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 335,
"metadata": {},
"outputs": [],
"source": [
"# peak memory\n",
"data_d = {\n",
" labels_name[0]: [79, 41, 48],\n",
" labels_name[1]: [47, 44, 42],\n",
" labels_name[2]: [66, 60, 69],\n",
" labels_name[3]: [0, 0, 0],\n",
" labels_name[4]: [0, 0, 0],\n",
" labels_name[5]: [0, 0, 0],\n",
" labels_name[6]: [0, 0, 0], # 37\n",
" labels_name[7]: [0, 0, 0], # 22.64\n",
" labels_name[8]: [0, 0, 0], # 22.64\n",
" labels_name[9]: [0, 0, 0], # 37\n",
" labels_name[10]: [0, 0, 0], # 22.64\n",
" labels_name[11]: [0, 0, 0], # 22.64\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 336,
"metadata": {},
"outputs": [],
"source": [
"# overlapping rate\n",
"data_b = {\n",
" labels_name[0]: [0, 0, 0],\n",
" labels_name[1]: [0, 0, 0],\n",
" labels_name[2]: [0, 0, 0],\n",
" labels_name[3]: [0, 0, 0],\n",
" labels_name[4]: [0, 0, 0],\n",
" labels_name[5]: [0, 0, 0],\n",
" labels_name[6]: [0, 0, 0], # 37\n",
" labels_name[7]: [0, 0, 0], # 22.64\n",
" labels_name[8]: [0, 0, 0], # 22.64\n",
" labels_name[9]: [0, 0, 0], # 37\n",
" labels_name[10]: [0, 0, 0], # 22.64\n",
" labels_name[11]: [0, 0, 0], # 22.64\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 337,
"metadata": {},
"outputs": [],
"source": [
"# communication baseline\n",
"data_c = {\n",
" labels_name[0]: 70,\n",
" labels_name[1]: 70,\n",
" labels_name[2]: 70,\n",
" labels_name[3]: 70,\n",
" labels_name[4]: 70,\n",
" labels_name[5]: 70,\n",
" labels_name[6]: [0, 0, 0], # 37\n",
" labels_name[7]: [0, 0, 0], # 22.64\n",
" labels_name[8]: [0, 0, 0], # 22.64\n",
" labels_name[9]: [0, 0, 0], # 37\n",
" labels_name[10]: [0, 0, 0], # 22.64\n",
" labels_name[11]: [0, 0, 0], # 22.64\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 338,
"metadata": {},
"outputs": [],
"source": [
"# compilation time\n",
"data_e = {\n",
" labels_name[0]: [1, 10, 147],\n",
" labels_name[1]: [0, 10, 133],\n",
" labels_name[2]: [0, 0, 0],\n",
" labels_name[3]: [0, 0, 0],\n",
" labels_name[4]: [0, 0, 0],\n",
" labels_name[5]: [0, 0, 0],\n",
" labels_name[6]: [0, 0, 0], # 37\n",
" labels_name[7]: [0, 0, 0], # 22.64\n",
" labels_name[8]: [0, 0, 0], # 22.64\n",
" labels_name[9]: [0, 0, 0], # 37\n",
" labels_name[10]: [0, 0, 0], # 22.64\n",
" labels_name[11]: [0, 0, 0], # 22.64\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 339,
"metadata": {},
"outputs": [],
"source": [
"legend_labels = [\"Megatron-LM\", \"XLA\", \"DLRover-Lynx\"]"
]
},
{
"cell_type": "code",
"execution_count": 340,
"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": 341,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, '(a)')"
]
},
"execution_count": 341,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"label_set = set()\n",
"for x_label, y_data in data_a.items():\n",
" positions = group_positions[x_label]\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",
" ax[0].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[0].set_xticks(list(group_centers.values()))\n",
"ax[0].set_xticklabels(list(data_a.keys()))\n",
"\n",
"ax[0].set_ylim(0, 100)\n",
"ax[0].set_yticks([0, 50, 100])\n",
"ax[0].set_yticklabels([\"0\", \"50\", \"100\"], rotation=90, ha=\"center\", va=\"center\")\n",
"\n",
"ax[0].tick_params(axis=\"x\", bottom=False, labelsize=8, pad=1)\n",
"ax[0].tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
"\n",
"ax[0].set_ylabel(\"MFU (%)\", fontsize=g_label_fontsize)\n",
"ax[0].set_title(\"(a)\")"
]
},
{
"cell_type": "code",
"execution_count": 342,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, '(b)')"
]
},
"execution_count": 342,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for x_label, y_data in data_d.items():\n",
" positions = group_positions[x_label]\n",
" for i, (pos, value, color, edgecolor, hatch) in enumerate(\n",
" zip(\n",
" positions,\n",
" y_data,\n",
" colors,\n",
" edgecolors,\n",
" hatches,\n",
" )\n",
" ):\n",
" ax[1].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[1].set_xticks(list(group_centers.values()))\n",
"ax[1].set_xticklabels(list(data_a.keys()))\n",
"\n",
"ax[1].set_ylim(0, 90)\n",
"ax[1].set_yticks([0, 40, 80])\n",
"ax[1].set_yticklabels([\"0\", \"40\", \"80\"], rotation=90, ha=\"center\", va=\"center\")\n",
"\n",
"ax[1].tick_params(axis=\"x\", bottom=False, labelsize=8, pad=1)\n",
"ax[1].tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
"\n",
"ax[1].set_ylabel(\"Peak Memory (GB)\", fontsize=g_label_fontsize)\n",
"ax[1].set_title(\"(b)\")"
]
},
{
"cell_type": "code",
"execution_count": 343,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, '(c)')"
]
},
"execution_count": 343,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"label_set = set()\n",
"total_label = \"Non-Overlap Communication\"\n",
"\n",
"for x_label, y_data in data_b.items():\n",
" positions = group_positions[x_label]\n",
" for i, (pos, value, diff_value, color, edgecolor, label, hatch) in enumerate(\n",
" zip(\n",
" positions,\n",
" y_data,\n",
" list(data_c.values()),\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[2].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",
" ax[2].bar(\n",
" pos,\n",
" diff_value - value,\n",
" bottom=value,\n",
" color=\"#E3E3E3\",\n",
" edgecolor=edgecolor,\n",
" width=bar_width,\n",
" label=total_label,\n",
" )\n",
" if total_label is not None:\n",
" total_label = None\n",
"\n",
"ax[2].set_xticks(list(group_centers.values()))\n",
"ax[2].set_xticklabels(list(data_a.keys()))\n",
"\n",
"ax[2].set_ylim(0, 100)\n",
"ax[2].set_yticks([0, 50, 100])\n",
"ax[2].set_yticklabels([\"0\", \"50\", \"100\"], rotation=90, ha=\"center\", va=\"center\")\n",
"\n",
"ax[2].tick_params(axis=\"x\", bottom=False, labelsize=8, pad=1)\n",
"ax[2].tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
"\n",
"ax[2].set_ylabel(\"Comm-Comp Overlap Ratio (%)\", fontsize=g_label_fontsize)\n",
"ax[2].set_title(\"(c)\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, '(d)')"
]
},
"execution_count": 344,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for x_label, y_data in data_e.items():\n",
" positions = group_positions[x_label]\n",
" for i, (pos, value, color, edgecolor, hatch) in enumerate(\n",
" zip(\n",
" positions,\n",
" y_data,\n",
" colors,\n",
" edgecolors,\n",
" hatches,\n",
" )\n",
" ):\n",
" ax[3].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[3].set_xticks(list(group_centers.values()))\n",
"ax[3].set_xticklabels(list(data_a.keys()))\n",
"\n",
"ax[3].set_ylim(0, 210)\n",
"ax[3].set_yticks([0, 50, 100, 150, 200])\n",
"ax[3].set_yticklabels([\"0\", \"50\", \"100\", \"150\", \"200\"], rotation=90, ha=\"center\", va=\"center\")\n",
"\n",
"ax[3].tick_params(axis=\"x\", bottom=False, labelsize=8, pad=1)\n",
"ax[3].tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
"\n",
"ax[3].set_ylabel(\"Compile Time (s)\", fontsize=g_label_fontsize)\n",
"ax[3].set_title(\"(d)\")"
]
},
{
"cell_type": "code",
"execution_count": 345,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f1f6193d5e0>"
]
},
"execution_count": 345,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ax[0].legend(\n",
" ncol=3,\n",
" loc=\"upper center\",\n",
" frameon=False,\n",
" shadow=False,\n",
" fontsize=g_label_fontsize,\n",
")\n",
"\n",
"ax[2].legend(\n",
" ncol=1,\n",
" loc=\"upper center\",\n",
" frameon=False,\n",
" shadow=False,\n",
" fontsize=g_label_fontsize,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 346,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 2100x3500 with 4 Axes>"
]
},
"execution_count": 346,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fig.savefig(\"end_to_end_single.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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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