paper_note/lynx/end_to_end.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2100x3230.77 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize=(7, 14 / 1.3), ncols=1, nrows=4, constrained_layout=True, dpi=300\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "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": 22,
   "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": 23,
   "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": 24,
   "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": 25,
   "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": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"Megatron-LM\", \"XLA\", \"DLRover-Lynx\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '(a)')"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '(b)')"
      ]
     },
     "execution_count": 29,
     "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '(c)')"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '(d)')"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa20a998b00>"
      ]
     },
     "execution_count": 32,
     "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2100x3230.77 with 4 Axes>"
      ]
     },
     "execution_count": 33,
     "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": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}