paper_note/lynx/end_to_end_overlap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "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": 65,
   "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": 66,
   "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$2TP\",\n",
    "    \"ModelC\\n1DP$\\\\times$\\n8TP$\\\\times$8EP\",\n",
    "    \"ModelC\\n2DP$\\\\times$\\n8TP$\\\\times$8EP\",\n",
    "    \"ModelD\\n1DP$\\\\times$\\n8TP$\\\\times$8EP\",\n",
    "    \"ModelD\\n2DP$\\\\times$\\n8TP$\\\\times$8EP\",\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# comm\n",
    "data_a = {\n",
    "    labels_name_a[0]: [3.5, 10.4, 0.1],  # modela 8DP\n",
    "    labels_name_a[1]: [49.0, 26.5, 13.6],  # modela 8TP\n",
    "    labels_name_a[2]: [41.3, 25.5, 13.0],  # modelb 8TP\n",
    "    labels_name_a[3]: [33.4, 37.6, 28.2],  # modela 2DP 4TP\n",
    "    labels_name_a[4]: [31.0, 32.3, 25.7],  # modela 2DP 8TP\n",
    "    labels_name_a[5]: [31.6, 36.2, 28.2],  # modela 4DP 2TP\n",
    "    labels_name_a[6]: [-1, 42.3, 37.3],  # modelb 4DP 2TP\n",
    "    labels_name_a[7]: [52.1, 1, 1],  # modelc 1DP 8TP 8EP\n",
    "    labels_name_a[8]: [82.6, 1, 1],  # modelc 2DP 8TP 8EP\n",
    "    labels_name_a[9]: [1, 1, 1],  # modeld 1DP 8TP 8EP\n",
    "    labels_name_a[10]: [1, 1, 1],  # modeld 2DP 8TP 8EP\n",
    "}\n",
    "# total = comm/(comm+comp)\n",
    "data_a_diff = {\n",
    "    labels_name_a[0]: 38.4,  # modela 8DP\n",
    "    labels_name_a[1]: 57.6,  # modela 8TP\n",
    "    labels_name_a[2]: 52.2,  # modelb 8TP\n",
    "    labels_name_a[3]: 46.8,  # modela 2DP 4TP\n",
    "    labels_name_a[4]: 42.0,  # modela 2DP 8TP\n",
    "    labels_name_a[5]: 43.4,  # modela 4DP 2TP\n",
    "    labels_name_a[6]: 53.9,  # modelb 4DP 2TP\n",
    "    labels_name_a[7]: 53.6,  # modelc 1DP 8TP 8EP\n",
    "    labels_name_a[8]: 82.8,  # modelc 2DP 8TP 8EP\n",
    "    labels_name_a[9]: 100,  # modeld 1DP 8TP 8EP\n",
    "    labels_name_a[10]: 100,  # modeld 2DP 8TP 8EP\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"Megatron-LM\", \"XLA\", \"DLRover-Lynx\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(13.333333333333346, 0.5, 'Communication Ratio (%)')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_set = set()\n",
    "\n",
    "for idx, (x_label, y_data) in enumerate(data_a.items()):\n",
    "    diff_value = data_a_diff[x_label]\n",
    "    positions = group_positions[idx]\n",
    "\n",
    "    up_base = None\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",
    "\n",
    "        if label in label_set:\n",
    "            local_label = None\n",
    "        else:\n",
    "            local_label = label\n",
    "            label_set.add(local_label)\n",
    "\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",
    "            continue\n",
    "\n",
    "        if value == 100:\n",
    "            continue\n",
    "\n",
    "        # if up_base == None:\n",
    "        #     ax.text(\n",
    "        #         pos,\n",
    "        #         diff_value + 3,\n",
    "        #         f\"{round(value, 2)}%\",\n",
    "        #         ha=\"center\",\n",
    "        #         fontweight=\"bold\",\n",
    "        #         color=colors[1],\n",
    "        #         size=g_label_fontsize - 8,\n",
    "        #     )\n",
    "        #     up_base = value\n",
    "        # else:\n",
    "        #     ax.text(\n",
    "        #         pos,\n",
    "        #         diff_value + 3,\n",
    "        #         f\"{round((value) - up_base, 2)}%\",\n",
    "        #         ha=\"center\",\n",
    "        #         fontweight=\"bold\",\n",
    "        #         color=colors[1],\n",
    "        #         size=g_label_fontsize - 8,\n",
    "        #     )\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",
    "        total_label = None\n",
    "        if idx == 1 and i == 0:\n",
    "            total_label = \"Total Communication\"\n",
    "\n",
    "        ax.bar(\n",
    "            pos,\n",
    "            diff_value - value,\n",
    "            width=bar_width,\n",
    "            color=\"#E3E3E3\",\n",
    "            alpha=0.6,\n",
    "            label=total_label,\n",
    "            edgecolor=edgecolor,\n",
    "            bottom=value,\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(\"Communication Ratio (%)\", fontsize=g_label_fontsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0b10a63fe0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.legend(\n",
    "    ncol=4,\n",
    "    loc=\"upper center\",\n",
    "    frameon=False,\n",
    "    shadow=False,\n",
    "    bbox_to_anchor=(0.5, 0.95),\n",
    "    fontsize=g_label_fontsize,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4200x1050 with 1 Axes>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.savefig(\"end_to_end_overlap.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
}