From e77729705072586a4f31d43addba8935f56244a9 Mon Sep 17 00:00:00 2001 From: yezhengmao Date: Wed, 5 Mar 2025 20:38:41 +0800 Subject: [PATCH] init --- .gitignore | 10 + .python-version | 1 + README.md | 0 lynx/end_to_end.ipynb | 277 +++++++++ lynx/end_to_end_nfsc.ipynb | 302 ++++++++++ lynx/hybrid_parallel_nfsc.ipynb | 238 ++++++++ lynx/lynx_end_to_end.pdf | Bin 0 -> 19857 bytes lynx/lynx_end_to_end_nfsc.pdf | Bin 0 -> 15304 bytes lynx/lynx_hybrid_parallel_nfsc.pdf | Bin 0 -> 21458 bytes main.py | 6 + mlora/adaptability.pdf | Bin 0 -> 20336 bytes mlora/adaptability.py | 87 +++ mlora/batchlora_cmp.py | 133 +++++ mlora/batchlora_op_cmp.pdf | Bin 0 -> 28219 bytes mlora/batchlora_op_cmp.py | 312 ++++++++++ mlora/batchlora_op_task.pdf | Bin 0 -> 31281 bytes mlora/batchlora_op_task.py | 504 ++++++++++++++++ mlora/bubble.py | 137 +++++ mlora/end-to-end-total.pdf | Bin 0 -> 35469 bytes mlora/end-to-end-total.py | 617 ++++++++++++++++++++ mlora/end-to-end.pdf | Bin 0 -> 31352 bytes mlora/end_to_end.py | 534 +++++++++++++++++ mlora/end_to_end_large_model.py | 89 +++ mlora/lora_rank_adaptability.pdf | Bin 0 -> 20326 bytes mlora/lora_rank_adaptability.py | 93 +++ mlora/lorapp-task.pdf | Bin 0 -> 30845 bytes mlora/lorapp_task.py | 150 +++++ mlora/map-and-loss.pdf | Bin 0 -> 29201 bytes mlora/map-and-loss.py | 896 +++++++++++++++++++++++++++++ mlora/overlap_com.py | 27 + mlora/overlapping.pdf | Bin 0 -> 19249 bytes mlora/overlapping.py | 184 ++++++ mlora/pp_cmp_com_cost.py | 126 ++++ mlora/pp_cmp_tp.pdf | Bin 0 -> 22475 bytes mlora/pp_cmp_tp.py | 159 +++++ mlora/scalability.pdf | Bin 0 -> 27607 bytes mlora/scalability.py | 165 ++++++ pyproject.toml | 11 + uv.lock | 796 +++++++++++++++++++++++++ 39 files changed, 5854 insertions(+) create mode 100644 .gitignore create mode 100644 .python-version create mode 100644 README.md create mode 100644 lynx/end_to_end.ipynb create mode 100644 lynx/end_to_end_nfsc.ipynb create mode 100644 lynx/hybrid_parallel_nfsc.ipynb create mode 100644 lynx/lynx_end_to_end.pdf create mode 100644 lynx/lynx_end_to_end_nfsc.pdf create mode 100644 lynx/lynx_hybrid_parallel_nfsc.pdf create mode 100644 main.py create mode 100644 mlora/adaptability.pdf create mode 100644 mlora/adaptability.py create mode 100644 mlora/batchlora_cmp.py create mode 100644 mlora/batchlora_op_cmp.pdf create mode 100644 mlora/batchlora_op_cmp.py create mode 100644 mlora/batchlora_op_task.pdf create mode 100644 mlora/batchlora_op_task.py create mode 100644 mlora/bubble.py create mode 100644 mlora/end-to-end-total.pdf create mode 100644 mlora/end-to-end-total.py create mode 100644 mlora/end-to-end.pdf create mode 100644 mlora/end_to_end.py create mode 100644 mlora/end_to_end_large_model.py create mode 100644 mlora/lora_rank_adaptability.pdf create mode 100644 mlora/lora_rank_adaptability.py create mode 100644 mlora/lorapp-task.pdf create mode 100644 mlora/lorapp_task.py create mode 100644 mlora/map-and-loss.pdf create mode 100644 mlora/map-and-loss.py create mode 100644 mlora/overlap_com.py create mode 100644 mlora/overlapping.pdf create mode 100644 mlora/overlapping.py create mode 100644 mlora/pp_cmp_com_cost.py create mode 100644 mlora/pp_cmp_tp.pdf create mode 100644 mlora/pp_cmp_tp.py create mode 100644 mlora/scalability.pdf create mode 100644 mlora/scalability.py create mode 100644 pyproject.toml create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..505a3b1 --- /dev/null +++ b/.gitignore @@ -0,0 +1,10 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/README.md b/README.md new file mode 100644 index 0000000..e69de29 diff --git a/lynx/end_to_end.ipynb b/lynx/end_to_end.ipynb new file mode 100644 index 0000000..bc63c4e --- /dev/null +++ b/lynx/end_to_end.ipynb @@ -0,0 +1,277 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 531, + "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", + " \"#999999\",\n", + " \"#888888\",\n", + " \"#FF9999\",\n", + "]\n", + "\n", + "hatches = [\"\\\\\", \"/\", \"x\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 532, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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RRxER8fvf/z5t6jwAAFAzsm0fufnmm9PWzzzzzG16XwAAoPKybR/p0qVL/PKXv0x77tNPP41evXrFlClT0p5fvHhxDBkyJAoKCiIionfv3jFp0qRo1KjRNpsXAABItuLi4vj444/TnkviTlURgiJqsfnz55d7rio/kDvttFPa+ty5czPuWZ5seAYAAKiLvJcva+XKlbFs2bIytVNPPdUHjwAAsI1k804yefLkuO222yIi4pRTTokzzjhju90bAADYumzaR6ZOnZr2G3pbtGgRRx11VOnx6tWr49FHH41zzjkn9ttvv2jTpk3k5eVFu3bt4uCDD46rrroqZs6cuc3mBAAA/iWb9pF/Gz16dLlfxrNq1aoYMGBAjB07tkx95cqVMWDAgFi4cGFE/OvzlFdeeSVatGixzecFAACSa8mSJbFx48a055K6U22NoIhabMGCBWnrjRs3jiZNmmTcNy8vL2196dKlGfcsTzY8AwAA1EXey5c1derUMsc5OTkxYsSIGpoGAACyX7buJGvWrInhw4dHSUlJdOjQIe6+++7tcl8AAKDismkfeeCBB9LWjzvuuGjQoEHMmjUr/t//+3+x6667xrBhw2Ls2LHx0UcfxbJly2Ljxo3x5Zdfxttvvx3XXXdd7LvvvjFs2LCUYG0AAKD6ZNM+8m/169ePZ555Jvbff/+05zdt2hTnnHNO/Nd//VcUFxfH0qVLo1+/fjF79uzIy8uLO++8Mx577DFf6AMAAGxVeTtVRESbNm0y7lvTv3O2JYIiarFFixalrVd1wS3vB3JbfIiXDc8AAAB1kffyZU2YMKHM8amnnhr77rtvzQwDAAB1QLbuJBdccEEsXrw46tWrF3/605/iO9/5zna5LwAAUHHZso8UFxfHs88+m/Zc796945JLLolu3brFfffdFxs2bIjGjRtHhw4domnTpuX2fPTRR6Nr167x9ttvb5OZAQCgrsuWfeT/atasWbzyyitxwAEHlPuam266KQYPHhzf//73Y+bMmdG9e/d477334uKLL94uMwIAAMlX3k4VUbW9qqZ3qi0RFFGLrV27Nm19Wy355d2vKrLhGQAAoC7yXv5/rV+/Pv7yl7+UHtevXz9+/etf1+BEAACQ/bJxJ3n44Ydj3LhxERFxxRVXxPe///1tfk8AAKDysmUfef/992P16tVpz1122WVx5513RsuWLePqq6+ODz/8MNavXx+ff/55rF27Nv7xj3/EJZdcEjvssEPKtatWrYr+/fvHlClTtsncAABQl2XLPpLOjjvuGK+++mr06tWr3Ne88MILMW/evOjbt2+89dZb0bVr1+02HwAAkHxb2nEaNmyYcd/asFOVR1BELfbtt9+mrVflhzEiIjc3N229sLCwSn3TyYZnAACAush7+f/18MMPl1ngR40aFd/97ndrcCIAAMh+2baTLFiwIH76059GRESPHj3immuu2ab3AwAAMpct+8irr766xfNXX311zJ8/P8aMGRPdunUrc27vvfeO22+/PaZOnRqtWrVKuXb9+vVx0kknxZdfflmtMwMAQF2XLftIeZo3bx6vvvpqnHDCCVt83ZQpU+Kiiy6KTZs2bafJAACAbFDeTpWfnx85OTkZ960tO1U6giJqsfJ+IKuaBllUVJS2vnHjxir1TScbngEAAOoi7+X/ZePGjXHTTTeVHnfp0iWuvvrqGpwIAADqhmzaSYqLi+P000+PtWvXRuPGjePRRx+N+vXrb7P7AQAAVZMt+8gHH3yQtt6zZ8+YPXt2jBkzJho3brzFHr169YoXXnghGjRokHJuxYoVcfrpp1fHqAAAwP8vW/aRLWnYsGE89dRTcc4552zxdffcc0/069cvli9fvp0mAwAAkq4u7FT/l6CIWqykpCRtvappkMXFxWnreXl5VeqbTjY8AwAA1EXey//LH//4x1iwYEFE/CsF8r777qu1swIAQDbJpp3kv//7v+Nvf/tbRETcfvvtsccee2yzewEAAFWXLfvIJ598krY+ePDg6NChQ4X7HHzwwfGrX/0q7bnJkyfH5MmTM5oPAABIlS37yNZMmTIlnn/++ahfv3706tWr3Ne98cYbccghh8Snn366HacDAACSqq7sVP9JUEQt1qRJk23St6CgIG29qoko6WTDMwAAQF3kvXzEqlWr4uqrry49vuaaa+Kwww6rwYkAAKDuyJad5N13341rrrkmIiJOPPHErX47FgAAUPOyZR+ZO3du2nqnTp0q3evSSy+Nli1bpj33m9/8ptL9AACA9LJlH9mSm2++OQYMGBBr1qyJp59+Ot544424/PLLy339Z599Focccki89dZb23FKAAAgierCTvV/CYqoxZo2bZq2Xt4PVEUVFhamrVc1ESWdbHgGAACoi7yXj/jZz34Wq1evjoiIo48+On75y1/W8EQAAFB3ZMNO8u2338Zpp50WmzZtil122SXuvffear8HAABQ/bJhH9m0aVOsW7cu7blddtml0v0aN24cF110UdpzkyZNKv08BQAAqJps2EfKU1RUFBdeeGGMGjUqiouLY+zYsTF48OCoV69e3HjjjfHQQw9FgwYN0l67evXqOOqoo2L69OnbbV4AACB5snmnKo+giFpsW/1Arl+/Pm29efPmVeqbTjY8AwAA1EV1/b38s88+G4888khEROy2227xpz/9KXJycmp4KgAAqDuyYSf5+c9/Hp9++mnk5OTEQw89VO637wIAALVLNuwj5YVERES0adMmo57nnHNO2npJSUlMnTo1o54AAEBZ2bCPpFNUVBSnnHJK3H333RERMWLEiDjttNPKvOaMM86Il19+udyZ1q5dG0cddVR8/PHH23pcAAAgobJ1p9oSQRG1WIsWLdLWq/oDuXbt2rT1jh07VqlvOtnwDAAAUBfV5ffyixYtirPPPjsiIlq2bBkvvvhitGrVqoanAgCAuiXpO8nzzz8f99xzT0REXHrppdG/f/9q7Q8AAGw7Sd9HIiK+/fbbcs9l+plHx44do1u3bmnP/f3vf8+oJwAAUFY27CP/V0lJSQwfPjyeeuqpiIjo3Llz3HjjjWlf27dv35g2bVq0a9cu7fl169bFoEGDYs2aNdtsXgAAILmycafaGkERtViXLl3S1stLHqmor776Km29Q4cOVeqbTjY8AwAA1EV19b38pk2b4pRTTolVq1ZFgwYNYvz48eX+dwEAAGw7Sd9J/vObdm+++ebIycnJ6D9nnXVW2v7lvR4AAKi6pO8jERE77LBDueeaNWuWcd+jjjoqbX3p0qUZ9wQAAP5XNuwj/9cNN9wQjz76aOnx9ddfH3l5eeW+fp999olp06ZFp06d0p7//PPPY9SoUdU9JgAAkAXK26mKioqqFBZRm/8ORlBELda1a9e09RUrVsTmzZsz7rtixYq09fIW6arIhmcAAIC6qK6+l//pT38ab7zxRtSrVy8efvjh6NOnT02PBAAAdVLSd5Lly5dXaz8AAGD7Sfo+EhHRuHHjcs81aNAg47777rtv2vqqVasy7gkAAPyvbNhH/tPf//73GD16dOnxLrvsEkOHDt3qdZ07d47XXnut3D+6uv/+++O9996rtjkBAIDs0L59+3IDs5csWZJx39r8dzCCImqxli1bRps2bVLqxcXF8eWXX2bcd9myZWnrBx10UMY9y5MNzwAAAHVRXXwv/4c//CHuueeeiIi4884740c/+lENTwQAAHVXXdxJAACA2iEb9pEmTZqUGxZRWFiYcd/y/mht48aNGfcEAAD+VzbsI//ppz/9aRQXF5cen3jiiVG/fv0KXdupU6d4+eWXY8cdd0x7/uabb66WGQEAgOyy9957p60vXrw44561+XfOBEXUcvvtt1/a+qJFizLqV1BQECtXrkyp77TTTvHd7343o55bkw3PAAAAdVFdei//3HPPxYgRIyIi4pprromf/OQnNToPAABQt3YSAACgdsmGfaRz585p66tXr864584775y23qpVq4x7AgAAZWXDPhIR8frrr8c777xTpta/f/9K9ejSpUs8/fTTacMlxo8fHxs2bKjSjAAAQPap7p0qImLhwoUptXr16kXPnj0z7lldBEXUcsccc0za+vz58zPqt2DBgrT1Xr16ZdSvIrLhGQAAoC6qK+/l33rrrTjllFOiqKgoRowYEaNHj67ReQAAgH+pKzsJAABQ+2TDPrLPPvukrWf6DBERTZs2TVsv7xt+AQCAysuGfSQi4tFHH02plbenbEmfPn1izJgxKfXCwsJ48803MxkNAADIYtW9U23YsCGWL1+eUu/atWu5n5tsT4IiarnBgwenrc+YMSOjfnPnzk1bHzRoUEb9KiIbngEAAOqiuvBefubMmXHsscfGt99+G8OHD4/bbrutxmYBAADKSvJOUlJSUi3/eeCBByrVHwAAqB5J3kf+7dBDD01bnzlzZsY9GzdunLaeyR97AQAA6WXDPhIRMW3atJRamzZtMuo1atSo6NixY0p93rx5GfUDAACy14ABAyI/Pz+lnulONW/evLS/l1Vb/qZdUEQtt9tuu0XXrl1T6u+9915G/T766KOUWv369ePEE0/MqF9FZMMzAABAXZTt7+X/+c9/xlFHHRVr1qyJE044Ie6///7IycmpkVkAAIBU2b6TAAAAtVc27CMDBgxIW//73/+ecc/169enrX//+9/PuCcAAFBWNuwjERGLFi1KqaX7Y62KaNCgQZx55pkp9dWrV2fUDwAAyF6NGjWK/v37p9Src6eKiPjRj36UUb/qJigiAU4//fSU2vvvv5/RN0OlSzwZMGBAtGrVKqPZKiobngEAAOqibH0v//nnn8cRRxwRS5YsiYEDB8Zjjz0Wubm5230OAABgy7J1JwEAAGq/pO8je+65Z+y///4p9UmTJmXc86uvvkqptWvXLr73ve9l3BMAAEiV9H0kItLOmm6nqKg+ffqk1Jo3b55xPwAAIHul26kWLFiQUdhcup1qr732im7dumU0W3UTFJEAF1x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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(\n", + " figsize=(14, 14 / 2.5), ncols=1, nrows=2, constrained_layout=True, dpi=300\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 533, + "metadata": {}, + "outputs": [], + "source": [ + "data_a = {\n", + " \"ModelA\\n#8GPUs\": [53, 53, 65], # 22.64\n", + " \"ModelA\\n#16GPUs\": [53, 53, 65], # 22.64\n", + " \"ModelB\\n#8GPUs\": [35, 35, 48], # 37\n", + " \"ModelB\\n#16GPUs\": [35, 35, 48], # 37\n", + " \"ModelC\\n#8GPUs\": [53, 53, 65], # 22.64\n", + " \"ModelC\\n#16GPUs\": [53, 53, 65], # 22.64\n", + " \"ModelD\\n#8GPUs\": [53, 53, 65], # 22.64\n", + " \"ModelD\\n#16GPUs\": [53, 53, 65], # 22.64\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 534, + "metadata": {}, + "outputs": [], + "source": [ + "data_b = {\n", + " \"ModelA\\n#8GPUs\": [[54, 62, 12], [10, 10, 10]],\n", + " \"ModelA\\n#16GPUs\": [[54, 62, 12], [10, 10, 10]],\n", + " \"ModelB\\n#8GPUs\": [[52, 62, 12], [10, 10, 10]],\n", + " \"ModelB\\n#16GPUs\": [[52, 62, 12], [10, 10, 10]],\n", + " \"ModelC\\n#8GPUs\": [[52, 62, 12], [10, 10, 10]],\n", + " \"ModelC\\n#16GPUs\": [[52, 62, 12], [10, 10, 10]],\n", + " \"ModelD\\n#8GPUs\": [[52, 62, 12], [10, 10, 10]],\n", + " \"ModelD\\n#16GPUs\": [[52, 62, 12], [10, 10, 10]],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 535, + "metadata": {}, + "outputs": [], + "source": [ + "legend_labels = [\"FSDP\", \"FSDP+XLA\", \"FSDP+DLRover-Lynx\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 536, + "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": 537, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, '(a)')" + ] + }, + "execution_count": 537, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for x_label, y_data in data_a.items():\n", + " positions = group_positions[x_label]\n", + " for i, (pos, value, color, hatch) in enumerate(\n", + " zip(\n", + " positions,\n", + " y_data,\n", + " colors,\n", + " hatches,\n", + " )\n", + " ):\n", + " ax[0].bar(\n", + " pos,\n", + " value,\n", + " width=bar_width,\n", + " color=color,\n", + " edgecolor=\"black\",\n", + " hatch=hatch,\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=11, 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": 538, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, '(b)')" + ] + }, + "execution_count": 538, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for x_label, y_data in data_b.items():\n", + " positions = group_positions[x_label]\n", + " for i, (pos, value, ovalue, color, hatch) in enumerate(\n", + " zip(\n", + " positions,\n", + " y_data[0],\n", + " y_data[1],\n", + " colors,\n", + " hatches,\n", + " )\n", + " ):\n", + " ax[1].bar(\n", + " pos,\n", + " value,\n", + " width=bar_width,\n", + " color=color,\n", + " edgecolor=\"black\",\n", + " hatch=hatch,\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, 100)\n", + "ax[1].set_yticks([0, 50, 100])\n", + "ax[1].set_yticklabels([\"0\", \"50\", \"100\"], rotation=90, ha=\"center\", va=\"center\")\n", + "\n", + "ax[1].tick_params(axis=\"x\", bottom=False, labelsize=11, pad=1)\n", + "ax[1].tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n", + "\n", + "ax[1].set_ylabel(\"Communication\\nProportion (%)\", fontsize=g_label_fontsize)\n", + "ax[1].set_title(\"(b)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 539, + "metadata": {}, + "outputs": [], + "source": [ + "# fig.legend(\n", + "# ncol=4,\n", + "# loc=\"upper center\",\n", + "# frameon=True,\n", + "# shadow=False,\n", + "# bbox_to_anchor=(0.5, 1.10),\n", + "# fontsize=g_label_fontsize,\n", + "# )" + ] + }, + { + "cell_type": "code", + "execution_count": 540, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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HR0fF44VCQXp6egysKF62HEp9YcuhtGx2Lidzq/z9mFMoyDwH/n7Yth/QxfV+QBd6rott/XC95wAAAAAAAI1ivos02Tjf7crnKx5zZZ7IfBdwh+s9t7EfLgvbDxGRzmw25dUMz/V+2NZz6OJ6P6ALPdfFtn643nOgGT73fNq0aaaX4B3beu56P1zpOYDmcVGEUqeeeqrpJQwyadIkmT9/vnzpS18SEZGVK1fKcccdJ+vXrze8ssZNnDhRbr31Vlm6dKm8973v7b8wotqlEWUD36faj+F+X2trq5x00kny17/+VQqFgkyYMCGJPxrgpFKpZHoJ3nF1WGDbodR13aWSVYfSMleHBbYNCaCTq/2ALvRcF1v74WrPAQAAAAAAomK+izTYOt/tzGadnCcy3wXc42rPbe2Hq2pdEqGZq/2wtefQxdV+QBd6rout/XC150Ac6DnSYGvPXe2Haz0H0JxW0wtApUwmI29+85tNL6Oqr371q7JmzRq5/PLL5d5775ULLrhALrnkEtPLimTq1KlSLBbl8ccfl5/97Gfyi1/8Qu67775B75PJZAb93+EMvDRixIgR8o53vEPy+bx8+MMfll133bWu5+js7JTjjz++zj9FnxUrVsixxx7b0O8BbFEsFqWtrU1yij9QdVH5f+/CkIND+W3b9sPWQ6nLuorFise0H0rLymscerAuv23Dn2EgW4cE0Mm1fkAXeq6L7f1wrecAAAAAAADNYr6LJNk+33Vtnsh8F3CXaz23vR+usfWSiDLX+mF7z6GLa/2ALvRcF9v74VrPgTjRcyTJ9p671g9Xew4gOi6KUKq9vd30EkLNmzdP7rzzTunt7ZXLL79cTj31VNljjz1MLyuyCRMmyP/+7//K//7v/8rKlStl6dKlsnTpUrnvvvvk0UcflSeffFI2bNhQ8zkymYzsueeest9++8l+++0nb37zm2XWrFmy7bbbNryecePGybhx46L+cQAncTg1w5Vhge2HUl/Ycigtc2VYYPuQADq50g/oQs91caUfrvQcAAAAAAAgLsx3kQRX5ruuzBOZ7wLuc6XnrvTDFbZfElHmSj9c6Tl0caUf0IWe6+JKP1zpOZAEeo4kuNJzV/rhUs9Xr19f9R+gBdA4LopQaPz48aaXUFNLS4v88Ic/lIMOOkg2bNggl112mXz3u981vaxY7LjjjvL+979f3v/+9/c/FgSBPPvss7Jq1Sp59dVX5dVXX5UgCKS9vV1Gjx4to0ePljFjxsgWW2xhcOWA+zicmmH7sMCVQ6nrbDuUltk+LHBlSACdbO8HdKHnurjWD9t7DgAAAAAAEDfmu4iTa/Nd2+eJzHcBf9jec9f6Ybuw/ZgxY4YsWbLEwIqaY3s/XOs5dLG9H9CFnuviWj9s7zmQJHqOOLnWc9v74VrPO7NZLooAYsJFEQo9+uijppcwrP3331+OO+44KRQKUigU5Dvf+Y60tLSYXlYiMpmM7LjjjrLjjjuaXgrgPQ6nZtg6LHDtUOoqWw+lZbYOC1wbEkAnW/sBXei5Lq72w9aeAwAAAAAAJIX5LuLg6nzX1nki813AP7b23NV+2KrWfuy7775WXhQhYm8/XO05dLG1H9CFnuviaj9s7TmQBnqOOLjac1v74WrPAcTDze/sRyrOOussERF57rnnZNGiRYZXA8AXhUJBenp6TC/DO7lcTjo6Oioe17ofrh5KbdXb21v1cVcOpbNzOZlb5e/HnEJB5in8+8GQAGmyrR/QhZ7r4no/bOs5AAAAAABA0pjvohmuz3dtmycy3wX8ZVvPXe+HbVzfD9v64XrPoYtt/YAurvfDNq73w7aeA2mi52iG6z23rR+u9xxA87goApG94x3vkJ133llERP74xz8aXg0AV+Xz+YrHOJyaYcuwwPVDqW0WL15c9V8P6MrnnTqU2jIsYEgAE2zpB3Sh57r40g9beg4AAAAAAJAW5ruIwpf5ri3zROa7uvYDMMGWnvvSD1v4sh+29MOXnkMXW/oBXXzphy186YctPQdMoOeIwpee29IPX3oOoDlcFIGmHHbYYRIEgdx///2mlwLAUdlslsOpItqHBb4cSm0Rth8iIp3ZbMqrSZ72YQFDApikvR/QhZ7r4ls/tPccAAAAAAAgbcx30Qjf5rva54nMd/to2Q/AJO09960f2vm2H9r74VvPoYv2fkAX3/qhnW/90N5zwCR6jkb41nPt/fCt5wCi46IINOXNb36ziIg8+OCDhlcCwGUcTnXRuh++HUq1q3VJhMu0DgsYEkADrf2ALvS8UnepZOy1fe2H1p4DAAAAAACYwnwX9fB1vqt1nsh8dzDT+4FKJYOf//CV1p772g+tfN0Prf3wtefQRWs/oIuv/dDK135o7TmgAT1HPXztudZ++NpzANG0ml4A7LbnnnuKiMiLL75oeCWVDj30UDnzzDPlfe97n7S0JHcnysaNG+Wxxx6TF154QTZv3iyjR4+W8ePHy+jRoxN7TcBHudc/kC0M+UC3/HaOD3RTpW0/fD2UauXrJRFl5YP30IN5+e20D+YMCaCJtn5AF3peXVexKO1tbfQjZdp6DgCotHnzZnn++edNLwOGbL/99onO/QHAJTQTdBNxYb6LWnyf72qbJzLf1bUfqK5YLEpbWxv9SJm2nvveD2183w9t/fC959BFWz+gi+/90Mb3fmjrOaAJPUctvvdcWz987zmAxnFRBJoyZswYERFZtWqV4ZVUKpVKsnjxYtltt93ktNNOk09/+tOy/fbbx/LcDz74oPz0pz+VW2+9Ve677z7ZtGlTxfvstttuMnPmTPnABz4gRx11lGQymVheG/AZh1NdtOyH74dSbXy/JKJMy7CAIQE00tIP6ELPa6MfZmjpOQCguueff17GjRtnehkw5Nlnn5UddtjB9DIAwAo0E3QTcWK+i2qY7/bRMk9kvttnuP04aerU1NeESvTDDC09px+6sB996DkQTks/oAv90IV+9NHSc0Ajeo5q6HkfLf2g5wCi4J+NQFNGjhwpIiJr1qwxvJJwTz75pJx33nmy++67yyc+8YmmnuvBBx+U973vfbLffvvJ17/+dbnnnntk48aNEgRBxY8nn3xSrr32WjnmmGNk4sSJcsMNN8T0JwL8lsvlpKOjo+LxQqEgPT09BlbkN9P7waFUl7D9mDFjhoHVmDc7l5O5Vf5+zCkUZF4Kfz8YEkAz0/2ALvS8PvTDDNM9BwAAAAAA0Ib5LgZivjuY6Xki893Bau1Hd6lkYEWohn6YYbrn9EMX9mMweg6EM90P6EI/dKEfg5nuOaAZPcdA9Hww0/2g5wCi4qIINGX16tUiIrLlllsaXkm4TCYjQRDIunXr5Lrrrov8PF/72tfkoIMOkt/85jf9l0GUnz/sR9ljjz0mJ5xwgrzrXe+S+++/v+k/E+A7Dqe6mNoPDqW61NqPKVOmGFiRDqaGBQwJYAN6DhF63ij6YYbp4T8AAAAAAIA2zHchwnw3DJ8f1CVsP7qKRQOrQRj6YQZf7wMR9iMMPQfCcR6ECP3Qhn5Ux9f7AOHoOUToeRjOgwBsxEURaMpjjz0mIiLt7e1mFzKMgZc2NGrt2rVyzDHHyPnnny8bNmyQIAiqXgZRvjyi2o/yr995550ybdo0ueGGG5r+MwG+43CqS9r7waFUF/ajtrSHBQwJYBN67jf6EQ39MINPHgMAAAAAAAzGfNdvzHdr4/ODuoTtB3ShH2bw9T5+Yz9qo+dAOM6DfqMfutCP2tLueXepFPtzAkmh536j57VxHgRgm1bTC4Dd/vKXv4iIyOjRo80upIbyxQ5RbNy4UY4++mi54447Bj3P0J+LiLz97W+XY445Rt71rnfJnnvuKWPHjpW1a9fKypUr5a677pLf/va3cvPNN8vatWvlhBNOkIceekjOO++8eP6QgKdyr3/AWxjyAXH57RwfEKcqrf3gUKoL+1Gf8gF96AG+/HZcB3iGBLARPfcT/ahfVz5f8S+b0Q8z0uo5ACC65V1dMlbhpcbdpVLVf6m0K5+XzmzWmtcw5bnVq2VyV5fpZQCAU8rNdLkfcbOl53QTaWO+6yfmu/Xh84O6hO0HzMnn81Ic8rEP/TCDr/fxE/tRH3oOhOM86Cf6oQv9qE+aPa823wU0o+d+ouf14TwIwCZcFIGm3HbbbZLJZGSXXXYxvZREfPazn5VFixZJJpMJvSTiiCOOkIsuukimTJlS8ftHjhwpY8aMkX322UdOPPFEefjhh2XOnDnyq1/9Ss4//3zZbrvt5LTTTkv1zwS4hsOpLknvB4dSXdiPxiQ9LGBIAJvRc7/Qj8Z0ZrPS3tZGP5TgsggA0G1se7vsoPBS4wuOPrpqz7uKRWlva4ul59W+6IieAwDCjG1vl+v++Ef60QB6DoRjvusX5ruN4fODunBZhC7ZbFba2trohxJJ93zZsmWycOHCisfphxn0vDH0HAjHedAv9EMX+tEYUz0HbEDP/ULPG8N5EIAtWkwvAJX22msv00uoy29+8xt58sknRURk8uTJhlcTv0suuUR+9KMfDboUQkQkk8lIEAQyZswY+cUvfiHz58+veklENRMnTpRbbrlFLrnkEslkMnLWWWdV/QQIgMbkcjnp6OioeLxQKEhPT4+BFfktqf3gUKoL+xHN7FxO5lb5+zGnUJB5Tfz9YEgAF9BzP9CPaOiHLkntBwDAbfQcAKBJd6lEPyKg50A45rt+YL4bDf3QJWw/YAb90CXJ/eCSCD3oeTT0HAhHz/1AP+LXXSpF/r30I5q0ew7YhJ77gZ5Hw3kQgA24KEKhxx57zPQShrVp0yb50pe+1P+2axdF/P3vf5fzzz9/0CURA3++9957yx//+Ec5/vjjIz3/F77wBZk7d65s3LhRTjzxRFm1alVsawd8xeFUl7j3g0OpLuxHc+IeFjAkgEvoudvoR3Pohy5cFgEAiIKeAwC06CoWKx6jH/Wh50A45rtuY77bHPqhy+xcTrryedPLwOvohy5p7Qf9MIOeN4eeA+HoudvoRzK6ikX6YUBaPQdsRM/dRs+bw3kQgHZcFKHUK6+8YnoJNZ177rnyt7/9rf/td7/73eYWk4CzzjpLNm7cKCKVl0TstddesnjxYtlnn32aeo3Pf/7zcvLJJ8u///1vOf/885teMwAOp9rEtR8cSnVhP+IR17CAIQFcRM/dRD/iQT904bIIAEAU9BwAoBH9aAw9B8Ix33UT89140A9dOrNZ00vAAPRDl6T3g36YQc/jQc+BcPTcTfQjWfTDjKR7DtiMnruJnseD8yAAzbgoQqnly5ebXkKoSy+9VObOndt/ecLEiRNl//33N7yq+Nx8883y+9//XjKZTMUlEdttt53cdtttsuOOO8byWpdeeqm84Q1vkO7ubvnHP/4Ry3MCvuNwqkut/Vi6dOmwv59DqS7sR7yaHRYwJIDL6Llb6Ee86IcuXBYBAIiCngMANKEf0dBzIBzzXbcw340X/QDC0Q9dktoP+mEGPY8XPQfC0XO30I900A8zkup5Vz4fy/oAk+i5W+h5vDgPAtCKiyKUuvLKK00vocJTTz0lH/7wh+Xcc88VEem/ROGEE04wvLJ4feUrX6l4rPxnvfzyy2XixImxvdYOO+wgH/vYx2Tjxo1y+eWXx/a8gO84nOoSth8LFiyo+fs4lOrCfiQj6rCAIQF8QM/dQD+SQT904bIIAEAU9BwAoAH9aA49B8Ix33UD891k0A8gHP3QJe79oB9m0PNk0HMgHD13A/1IF/0wI4med2azsa4RMIWeu4GeJ4PzIACNuChCqR//+Mdy6aWXShAERtfx1FNPyfXXXy8f/OAHZdKkSXLjjTf2X5ogIrLNNtvI2WefbXSNcfrLX/4i9913n2Qymf4/Z/n/HnXUUfLRj3409tc87rjjRETkJz/5iaxbty725wd8xeFUl7D9CMOhVBf2I1mNDgsYEsAn9Nxu9CNZ9EOXRveju1RKY1kAAOXoOQDAJPoRD3oOhGO+azfmu8miH0A4+qFLXPsxc+ZM+mEAPU8WPQfC0XO70Q8z6IcZ9BwIR8/tRs+TRT8AaNNqegEId+6558qVV14phx56qEycOFG233572XLLLaWlJd77PTZs2CBr1qyRNWvWyMqVK+Vf//qXPPXUU7JixQp58cUX+9+vfGnFwMsTurq6ZMyYMbGux6Rrrrmm/+flyzDKP//617+eyGu+8Y1vFBGRNWvWyO233y5HH310Iq8D+Cj3+gfMhSEfUJffzvEBdarC9mMoDqW6sB/pKB/whw4Aym+Xf50hAXxEz+1EP9JBP3RpZD+6isV0FwcAUIueAwBM6Mrn6UeM6DkQjvmunZjvpoN+AOHohy5x7Me0adPiXxhqoufpoOdAOHpuJ/qRnq58vuJrR+iHGfQcCEfP7UTP00E/AGjCRRGKBUEgjz/+uFx77bVGXnuooRcndHR0yOc+97k0lxXJwHXXsmnTJvnpT3866P3LF2Icc8wxcsABBySyvnHjxvX/nIsigPhxONVluMsili1bJgsXLqx4nEOpGQwJ0jXcsKDar4kwJIAf6Lld6Ee66IcuUfcDAOA3eg4ASFtnNmt6Cc6h50A45rt2Yb6bLvoBhKMfurAfdqHn6aLnQDj6YRf6ka7ObFba29rohxL0HAhHz+1Cz9NFPwBowUURipUvLKh2aUOarz9UEARy2GGHyTXXXJPugho0YsQI2bRpU8Wfo3z5w1B//etf5fnnn5dMJlPxPh//+McTW+dLL73U//P77rsvsdcBfMbhVJdal0VwSYQeDAnMqGdYMBBDAviEntuBfphBP3RpdD8AABCh5wAAuICeA+GY79qB+a4Z9AMIRz90YT/sQM/NoOdAOPphB/phBv3Qhf0AwtFzO9BzM+gHAA1aTC8A4coXRGQyGSM/Bq5h4JpOOOEEmT9/vmy11Vap/2/SiBUrVkhnZ6dsueWWgy5+2HvvveX73/++vPbaa4Pe/49//GP/zwdeEtHe3i5HHnlkYuvs7e0dtGYAycjlctLR0VHxeKFQkJ6eHgMr8lsul5NZs2YN+34cSs1gSGDW7FxO5lb579VQDAngI3quG/0wi37oUu9+AAAwED0HAMB+9BwIx3xXN+a7ZtEPIBz90IX90I2em0XPgXD0Qzf6YRb90IX9AMLRc93ouVn0A4BpXBShXBAEFZc1pGnghRHjxo2Tn//85/LTn/5URo4caWxN9ZowYYJcccUV8vjjj8uXvvQl2WabbSQIAnnkkUfk1FNPlT333FO++c1vyquvvioiInfdddeg31++XGLKlCmyxRZbJLbOX/ziF/2v99JLLyX2OgA4nGozffr0mr/OodQMhgQ6DDcsYEgAn9FzneiHDvRDFy6LAABEQc8BALAfPQfCMd/VifmuDvQDCEc/dGE/dKLnOtBzIBz90Il+6EA/dGE/gHD0XCd6rgP9AGASF0Uolslk+n8krXwhRbUfkydPliuvvFIeeeQR+eAHP5j4WuI2duxY+epXvyqPP/64XHrppbLLLrtIEATy9NNPy5w5c2TChAly0UUXyR/+8Ieqv/9tb3tbYmt75JFH5Gc/+1n/Hq9fvz6x1wLQh8OpHTiUmsGQAIAt6Lku9AMAAAAAAABAvZjv6sJ8F4At6IcutfZj6dKlBlbkN3oOwBb0XBf6AQCIgp7rQs8BACIiraYXgNqCIBARkdbWVhkzZoxstdVWMmLEiKaft3wBRWtrq2yxxRay1VZbyejRo2W77baTHXfcUXbffXfZZ599ZMqUKTJ+/PimX0+D9vZ2mTNnjpx11lly7bXXyty5c+Wf//ynPP/883LhhRf2v18QBIMu59h7770TW9Opp54qGzZs6H+9UaNGJfZaAP4r9/otbIVCYdDj5bdz3NJmFIdSMxgS6DKvp0fmDPlv1EDlX+NWSfiMnutAP3ShH7oMtx8AAFRDzwEAsB89B4bHfFcH5ru60A9gePRDl7D9WLBggYnleIue60LPgeHRcx3ohy70Qxf2AxgePdeBnutCPwCYxEURik2aNEnmzJkjM2fOlAkTJsRyQQREtthiCznllFPkU5/6lNx0001y6aWXSm9vr4jIoAsiynbbbbdE1vG9731Pbr/99kGvudNOOyXyWgAqcTjVaebMmRxKDWBIoEu931TKsACg56bRD13ohy5cEgEAiIKeAwBgP3oO1I/5rlnMd3WhH0D96IcuYfuBdNBzXeg5UD96bhb90IV+6MJ+APWj52bRc13oBwDTWkwvANXtt99+cu+998qnP/1p2WuvvbgkIgGZTEY6Ojrk7rvvlgULFshhhx0mQRBIEASD3m+XXXaJ/bVfeeUVOffcc/sviQiCQDKZjEyePDn21wIQLpfLSUdHR8XjhUJBenp6DKwI06ZNM70E7zAk0CVsSDC3o0PmVvnv1ZxCQebx3yt4jp6bQT90oR+6NLofAACI0HMAAFxAz4HGMd81g/muLvQDaBz90CVsP5Aseq4LPQcaR8/NoB+60A9d2A+gcfTcDHquC/0AoEGr6QWguvPPP1+23npr08vwxuGHHy6HH3643HvvvXLxxRfLLbfc0n9hxKhRo2J/vVKpJC+++GL/RRFls2bNiv21ANTGTYbwGUMCXWoNCQbeGjn0fbhZEqDnaaMfutAPXaLuBwDAb/QcAJC27lJJLjj6aNPLcAo9B6Jjvpsu5ru60A8gOvqhS9h+IBn0XBd6DkRHz9NFP3TpLpWkq1iseJx+mEHPgejoebrouS70A4AWLaYXgOqIsxmHHHKI3HjjjfLggw/Kpz71KRk5cqRstdVWsb/Oxo0bKx4bO3asfOxjH4v9tQAMj5sM4SOGBLrUOySYnctxsyQQgp6ng37oQj90aWQ/uvL5NJcGAFCMngMATOgqFulHjOg50Dzmu+lgvqsL/QCaRz90CdsPxIue60LPgebR83TQD33quSSCfqSDnrunVCqZXoJ36Hk66Lku9AOAJlwUoVAmk5EddtjB9DK8NmnSJPn+978vjz76qIwdOzb253/nO98pb3jDG0REJAgCaW1tlWuuuUba29tjfy0A9eFwCp8wJNCl3iFBGcMCIBw9Txb90IV+6NLofnRms2ksCwCgHD0HAJhEP+JBz4H4MN9NFvNdXegHEB/6oUsul5NZs2aZXoaz6Lku9ByIDz1PFv2wA/0wg567qVgs0g8D6Hmy6Lku9AOANlwUoVAQBJLJZEwvAyKy0047yRZbbBH7844dO1aKxaIcfvjhcswxx0ipVJIjjzwy9tcB0BgOp/ABQwJdGh0SlDEsAMLR82TQD13ohy5R9wMA4Dd6DgDQgH40h54D8WO+mwzmu7rQDyB+9EOX6dOnm16Ck+i5LvQciB89Twb9sAP9MCOJnneXSrGuEdHRDzPoeTLouS6cBwFo1Gp6Aai0aNEi00tACmbMmCG33Xab6WUAGCL3+gfmhSEfuJffzvGNXrAYQwJdmv2m0vL7DH2O8tt8Yyp8Rs/jRT90oR+6cEkEACAKeg4A0IR+REPPgeQw340X811d6AeQHPoBl9FzXeg5kBx6Hi/6YQf6YUZSPe8qFuNZIGJBP8yg5/Gi57pwHgSgVYvpBaBSNps1vQQA8Bo3GcJFDAl0ieubSrlZEghHz+NBP3ShH7pwSQQAIAp6DgDQiH40hp4DyWO+Gw/mu7rQD134V17dRD/gInquCz0HkkfP40E/dOnt7a36OP0wI+meQxf6YQY9jwc914XzIADNuCgCAIAqOJzCJQwJdIn7m0oZFgDh6Hlz6Icu9EMXLokAAERBzwEAWnTl8xWP0Y/60HMgPcx3m8N8Vxf6ocu8nh7+lVeH0Q+4hJ7rQs+B9NDz5tAPXRYvXixLliypeLwrn6cfBqTVc+hCP8yg582h57pwHgSgXavpBaDSddddN+z7jBkzRt73vvelsBoA8Ffu9Q/YC0M+oC+/neMbwGABhgS6JPVNpeXfO/S5y2/zDavwGT2Phn7oQj904ZIIAEAU9BwAoElnNivtbW30o0H0HEgf891omO/qQj90CdsPuIV+wAX0XBd6DqSPnkdDP3QJ2w+RvhltVPQjmrR7DnPy+bwUh1wQST/MoOfR0HNdOA8CsAEXRSj08Y9/XDKZTNVfC4JAtttuOznxxBO5KAIAUsDhFDZjSKBL0t9UyrAACEfPG0M/dOkular+y2b0wwwuiQAARMF5EACgEf1oDD0HzGG+2xjmu7rQD124JMIv9AM2o+e60HPAHHreGPqhS61LIuJAPxpjqucwI5vNSltbG/1Qgp43hp7rwnkQgC24KEKxIAgGvZ3L5eTMM8+UI444QkaMGGFoVQDgHw6nsBFDAl3S+qZShgVAOHpeH/qhT5KXRJTRj/pwSQQAIArOgwAAzehHfeg5YB7z3fow39WFfujCJRF+oh+wET3XhZ4D5tHz+tAPXZK+JKKMftQnzZ6vXr++6td6IX30Qxf2oz70XBfOgwBs0mJ6AQiXyWRERGS77baT2267TW677TZ573vfyyURAGBALpeTjo6OiscLhYL09PQYWBEQjiGBLml/U+nsXE7mVvnv1ZxCQebx3yt4jp7XRj/sQD/M4JIIAEAUnAcBADagH7XRc0AP5ru1Md/VhX7owiURfqMfsAk914WeA3rQ89rohy5pXRJRRj9qS7vnndls7M+J6OiHLuxHbfRcF86DAGzDRRGKBUEgO+ywg9xxxx3cjgUACnA4hQ0YEuhi6ptKGRYA4eh5dfTDDvTDDC6JAABEwXkQAGAT+lEdPQf0Yb5bHfNdXeiHLmH70ZXPG1gNTKEfsAE914WeA/rQ8+rohy5h+zFjxoxEX5d+VMfX+0CEfmjDflRHz3XhPAjARlwUoVgmk5FLL71U9t9/f9NLAQC8jsMpNGNIoIvpITPDAiAcPR+MftiBfphhuucAADuZ7gc9BwBEQT8Go+eAXsx3B2O+qwv90KXWfvCvvPqHfkAzeq4LPQf0oueD0Q9dau3HlClTEn99+jGY6Z5DF/qhC/sxGD3XxXQ/6DmAqFpNLwDhJk2aJCeffHLd77/nnntKJpOJ9FqPPPJIpN8HAD7Kvf4BfmHIAaD8do4BEgxgSKCL6SFBWfm1hq6l/DYDb/iMnvehH7r09vZWfZx+mKGl5wAAu2jpBz0HAERBP/rQc0A/5rt9mO/qQj90GW4//vPKKwZWBdPoBzSi57rQc0A/et6Hfugy3H6sXLkylXXQjz5aeg5d6Icu7Ecfeq6Lln7QcwBRcFGEYkceeWRD73/22WfL8uXL5Y477pB//OMfNd/3LW95i8yYMUPGjx8vo0ePbmaZAOAlDqfQhCGBLlqGBGUMC4BwvvecfuiyePFiWbJkScXjXfk8/TBAW88BAHbQ1g/few4AiMb3ftBzwB7Md5nvakI/dNG2H9DF935AF3qui7Z++N5zoBbfe04/dNG2H773Q1vPoYvv/dDG9/3Q1g/faeuH7z0H0DguilBsv/32a+j9P/e5z/X//O6775bPfe5z8qc//UkymYyIiARBILvvvrv86Ec/ksMOOyzWtQKAj3w/nEIHhgS6aBsSlDEsAML52nP6oUvYfoiIdGazKa/mv3zth9aeAwB009oPX3sOAGiOr/2g54B9mO8OxnzXDPqhi9b9gC6+9gO60HNdtPbD154D9fC15/RDF6374Ws/tPYcuvjaD6183Q+t/fCV1n742nMA0bSYXgDC7bzzzpF/79ve9ja54447ZN999xWRvksi2tvb5bbbbuOSCACIUS6Xk46OjorHC4WC9PT0GFgRfGLzkKC7VDK9hNhpHRKUzc7lZG6V/17NKRRkHv+9gud867nN/XBRrUsiNPCtH9p7DgDQSXs/fOs5ACAevvWDniMuJQc//6Ed890+zHfNoB+6aN8P6OJbP6ALPddFez986znQCN96Tj900b4fvvVDe8+hi2/90M63/dDeD99o74dvPQcQHRdFKNbe3t7U799qq61k9uzZEgSBZDIZOfHEE+WNb3xjTKsDAJT5djiFDrYPCbqKRacOp9qHBGUMC4BwvvTc9n64RvslEWW+9MOWngMAdLGlH770HAAQL1/6Qc8Rp2Kx6NQ80RbMd5nvmkA/dLFlP6CLL/2ALvRcF1v64UvPgSh86Tn90MWW/fClH7b0HLr40g9b+LIftvTDF7b0w5eeA2hOq+kFINzIkSObfo7DDjus/+fvfOc7m34+AEB1udcPAoUhB4Xy2zlFBwXYz5UhQflgrekgHYUtQ4Ky8pqGrtmV/QCa4XrPXemHK2y5JKLM9X7Y1nMAgA629cP1ngMAkuF6P1zr+UlTp6a+JlRyZZ5oG+a7SJNr/dC45kbYth/QxfV+QBd6rott/XC950AzXO85/dDFtv1wvR+29Ry6uN4P2wy3H/vvv3/qa4qTbf1wnW39cL3nAJrHRRGO23XXXft/vvPOOxtcCQC4j2EB0uDakMD2w6ltQ4IyhgVAOFd77lo/bBe2HzNmzJAlS5YYWFF9XO2HrT0HAJhlaz9c7TkAIFmu9sPFnq9ev97EklCF7fNEWzHfRRpc7MfAX7eNrfsBXVztB3Sh57rY2g9Xew7EwdWe0w9dbN0PV/tha8+hi6v9sFWt/Vi1apWJJcXC1n64ytZ+uNpzAPFoMb0AJKutra3/5yNHjjS4EgDwQy6Xk46OjorHC4WC9PT0GFgRXOLqkGBOoSDzLPz7YeuQoGx2Lidzq/z3ytb9AOLkWs9d7Yetau3HlClTDKyoMa71w/aeAwDMsL0frvUcAJAO1/rhas+7ikUDq0EYW+eJtmO+iyS52g96DrjXD+hCz3WxvR+u9RyIk2s9px+62L4frvXD9p5DF9f6Ybuw/ViwYIGB1TTP9n64xvZ+uNbz7lLJ9BIAZ3BRBJqyZMkSWbt2rellAIAqDAuQBNeHBLYdTm0fEpS5NiwA4uRKz13vh21c2Q9X+uFKzwEA6XKlH670HACQLlf64XrPoYtt80RXMN9FElzvBz0H3OkHdKHnurjSD1d6DiTBlZ7TD11c2Q9X+uFKz6GLK/1wRdh+2MaVfrjClX641HMuwAfiw0URaMrMmTPl0UcfNb0MAFCHYQHi5OKQoCufr3jMlsOpK0OCMleGBUASbO+5i/2wmWv7YXs/XOs5ACAdrvXD9p4DAMywvR++9Bzm5Kt8/sOWeaJrmO8iTr70g54D9vcDutBzXVzrh+09B5Jke8/phy6u7Yft/XCt59DF9n64xvbLIlzrh+1c64erPQcQHRdFILJNmzbJpk2bTC8DANRiWIA4uDok6MxmrTycujYkKLN9WAAkydaeu9oPW7m6H7b2w9WeAwCS5Wo/bO05AMAsW/vhW89hRjabtXKe6Crmu4hDd6nkVT/oOWBvP6ALPdfF1X7Y2nMgDbb2fNmyZfRDEVd7bms/XO05dLG1H66y9bIIV/thK1f74VrPATSHiyIQ2RNPPGF6CQCgHsMCNMP1IYFth1NXhwRltu0HkCbbeu56P2zj+n7Y1g/Xew4ASIbr/bCt5wAAHWzrh689hxm2zRNdZ9t+uD5PtFFXsVjxmOv9oOeAff2ALj73vLtUMr2ECq73w7aeA2mysecLFy6seMyHfmjkes9t64frPYcuNvbDZbZdFuF6P2zjej9c6TmA5nFRBCL7zW9+Y3oJAGAFhgWIwpchgS2HU9eHBGW27Adggi0996UftvBlP2zphy89BwDEy5d+2NJzAIAutvTDp5535fOml4HX2TJP9IUt++HLPNF2LvaDngPV2dIP6OJ7z7uKRfphgC09B0ywvee+9EMbX3puSz986Tl0sb0frsnlcjJr1izTyxiWL/2whS/9sL3nAOLBRRGIZM2aNXL55ZebXgYAWINhARrh25BA++HUlyFBmfb9AEzS3nPf+qGdb/uhvR++9RwAEA/f+qG95wAAnbT3w7eed2azppeAAbTPE32jfT98myfaytV+0HMgnPZ+QBd63od+mKG954BJtvbct35o4VvPtffDt55DF1v74arp06ebXkJNvvVDO9/6YWvPuQAfiA8XRSiWyWTUPd+GDRvkrrvukqOOOkoeeeSRGFYFAP5gWIB6+Dok0Ho49W1IUKZ1PwANtPbc135o5et+aO2Hrz0HADTH135o7TkAQDet/fC159BF6zzRV1r3w9d5om1c7wc9B8Jp7Qd0oeeD0Q8ztPYc0MC2nvvaD9N87bnWfvjac+hiWz9ghq/90MrXftjYcy7AB+LTanoBCNfR0SFtbW0qnm/jxo2yevVqWbVqlQRBENuaAMA3udcPFoUhH+iW3845fPDA8HwfEpQP3kMPguW30z6Y+zokKNO2H4Am2nruez+08X0/tPXD954DAKLxvR/aet5dKqX6egCAaLT1w/eeQxdt80TfadsP3+eJ2vT29lZ93Jd+0HMgnLZ+QBd6Xh39MENbzwFNbOm57/0wxfeea+uH7z2HLrb0A2b43g9tfO+HbT3/zyuvpLoewGVcFKHYM888E9tzBUEQ6/NlMpnYngsAfMOwANUwJOij5XDq+5CgTMt+ABpp6Tn90IX96KOlH/QcABAF/eijqeddxWIqrwUAaJ6mftBzaKNlnog+WvaDeaIuixcvliVLllQ83pXPe9UPeg6E09IP6ELPa6MfZmjpOaCR9p7PnDmTfhhAz/to6Qc9h0ba+wEz6Icu9KMPPQf81GJ6AQiXyWRi+RH383FJBAA0L5fLSUdHR8XjhUJBenp6DKwIJjEkGGx2Lidzq/z9mFMoyLwU/n5wKB3M9H4AmpnuOf3Qhf0YzHQ/6DkAIAr6MZjWngMAdNPaD197Dl1MzxMxmOn9YJ6oS9h+iIh0ZrMpr8Y8eg6EM90P6ELPK3Xl8xWP0Q8zTPcc0Exzz6dNm2b09X1Ezwcz3Q96Ds009wPpox+60I/B6DngHy6KQMOCIDC9BABwAsMCiDAkCGPqcMqhtDrTwwLUr1QqmV6Cd0z1fNmyZfRDEXpeHT0HANiEflSnrecAADto64fvPYcufH5QF1P7wTxRl1qXRPiMngPh6DlE6HmYzmyWfijC1/sA4eg5ROh5GM6DQDj6ARH6oQ39qI6eA37hogjF4rqQIZPJSCaTieW5ys8HAIgHwwK/MSSoLe3DKYfS2vjksR2KxSL9MMBEzxcuXFjxGP0wg57XRs8BADagH7Vp6TkAwC5a+kHPoRGfH9Ql7f1gnqgLl0TURs+BcPTcb/S8NvqhC1/vA4Sj536j57XRcyAc/fAb/dCFftRGzwF/tJpeAMJlMpnYLouIG5dFAEB8cq9/wFsY8gFx+e0cHxA7iSFBfcoHwqEHxvLbcR0YOZTWZ7j9OGnq1NTXhEr0wwzTPacfZtDz+tBzAIBm9KM+pnsOALCT6X7Qc2hmep6IwYbbj/333z+W12GeqAuXRNSHngPh6Lmf6Hl96Icuae0HYCN67id6Xh96DoSjH36iH7rQj/rQc8APXBShWBAEksvl5JBDDpEdd9xR2tvbpbW1NfVLGoIgkPXr18srr7wiTz31lNx9992ybNmyVNcAAK5jWOAXhgSNSfpwyqG0MbX2Y/X69SaWhCrohxmmek4/zKDnjaHnAACN6EdjTPW8K5+XrmKxqecGAJjDeRAIx+cHdam1H6tWrWr6+Zkn6hK2HzNmzJAlS5YYWJFu9BwIR8/9Qs8bQz904bIIIBw99ws9bww9B8LRD7/QD13oR2PoOeA+LopQKpPJyI033ijHHXec6aVUdeutt8r73/9+2bBhg+mlAIAzGBb4gSFBNEkdTjmURhO2H3zjji70w4y0e04/zKDn0dBzAIAm9CMaEz0/aepUzpsAYDnOg0A4Pj+oS9h+LFiwoKnnZZ6oS6392HfffbkoIgQ9B8LRcz/Q82johy5cFgGEo+d+oOfR0HMgHP3wA/3QhX5EQ88Bt7WYXgCq+9CHPqT2kggRkSOOOEI+//nPm14GADgnl8tJR0dHxeOFQkF6enoMrAhxYkjQnNm5nMyt8vdjTqEg8yL8/eBQ2pyw/YAu9MOMtHpOP8yg582h5wAADehHc+g5ACAK+gGE4/ODuoTtR1TME3VhP5pDz4Fw9Nxt9KM59EOXuPcDcAk9dxs9bw49B8LRD7fRD13oR3PoOeAuLopQ6pRTTjG9hGF99KMfNb0EAHASwwI3MSSIR1yHUw6l8eCyCH3y+XzFY/TDjKR7PnPmTPphAD2PBz0HAJhEP+JBzwEAUdAPIByfH9QlrssimCfqwn7Eg54D4ei5m+hHPOiHLlwWAYSj526i5/Gg50A4+uEm+qEL/YgHPQfcxEURCmUyGdl///1NL2NY++yzjwRBYHoZAOAkhgVuYUgQr2YPpxxK48VlEbpks1n6oUiSPZ82bVpTvx+No+fxoucAABPoR7zoOQAgCvoBhOPzg7o0e1kE80Rd2I940XMgHD13C/2IF/3QhcsigHD03C30PF70HAhHP9xCP3TpLpXoR4zoOeAeLopQKAgC2X777U0vY1gjR46UESNGmF4GADiLYYEbGBIkI+rhlENpMrgsQhf6oQv74QZ6ngx6DgCIW3epFPpr9CMZ9BwAEAX9AMIxT9Ql6mURzBN1YT+SQc+BcPTcDfQjGfRDFy6LsEepxuc/kAx67gZ6ngx6DoSjH26gH/p0FYsVj9GP5tBzwC1cFKHQySefLC0tdmzNySefLNtuu63pZQCAsxgW2I0hQbIaPZxyKE3W7FxOuvJ508vA6+iHLuyH3eh5sug5ACBOXcUi/TCAngMAoqAfQDjmibrkcjmZNWtW3e/PPFEX9iNZ9BwIR8/tRj+SRT904bIIOxSLRfphAD23Gz1PFj0HwtEPu9EPO9CPeNBzwB123EbgmR/96Eeml1C3H/zgB7LzzjubXgYAOI1hgZ0YEqSj3sMph9J0dGazppeAAeiHLuyHneh5Oug5ACBO9MMMeg4AiIJ+AOGYJ+oyffr0ut6PeaIu7Ec66DkQjp7biX6kg37owmURdqAfZtBzO9HzdNBzIBz9sBP9sAP9iBc9B9zQanoBAABgeLnXP3AuDPnAuvx2jg+sVWFIkK7ywXLowXPg2xxK4Sv6oQv7YRd6ni56DgCIE/0wg54DAKKgH0A45ol2YZ6oC/uRLnoOhKPndqEf6aIfutTaj9Xr15tYEqqgH2bQc7vQ83TRcyAc/bAL/bAD/UgGPQfsx0URSu21117yyCOPmF5GXY488kj5zne+I5MmTTK9FABwGsMCOzAkMKOew+lAHErhE/qhC/thB3puBj0HAMSJfphBzwEAUdAPIBzzRDswT9SF/TCDngPh6Lkd6IcZ9EOXsP3oKhZNLAch6IcZ9NwO9NwMeg6Eox92oB+69Pb2Vn2cfiSLngN2azG9AFT32GOPmV5C3Z588knZsGGD6WUAgBdyuZx0dHRUPF4oFKSnp8fAijAQQwKzZudyMrfK34+hOJTCR/RDF/ZDN3puFj0HACSJfqSDngMAoqAfQDjmiboxT9SF/TCLngPh6Llu9MMs+qFLvfsBs+iHGfRcN3puFj0HwtEP3eiHLosXL5YlS5ZUPN6Vz9OPFNBzwF5cFKHYSy+9ZHoJdVm1apXpJQCAVxgW6MSQQIfhDqccSuEz+qEL+6ETPdeBngMAourK50N/jX6ki54DAKKgH0A45ok6MU/Uhf3QgZ4D4ei5TvRDB/qhC5dF6JOv8vkP+mEGPdeJnutAz4Fw9EMn+qFL2H6IiHRmsymvxl/0HLATF0Uo9uc//9n0Eob1wgsvyNNPP216GQDgHYYFujAkAGAL+qEL+6ELPQcAAAAAAIBmzBN1WbZsGfNERZjvArAFPdeFfgCwRTabpR+K0HNd6DkAW9APXeiHLrUuiQAADI+LIhS74oorTC9hWN///vclCALTywAALzEs0IEhgS7zenpkTqEQ+utzCgWZx98PeI5+6MJ+6EDPdaHnAICouorF0F+jH+mi5wCAKOgHMDzmiXosXLiw4jHmiWYw39WFngPDo+c60A9d6Icuw+0HzKAfurAfOtBzXeg5MDz6oQP90KWeSyK6S6WUVgN6DtiJiyIU++Uvfylz5syRNWvWmF5KhU2bNsl1110nF1xwgemlAIDXGBaYxZBAl3o/ScnhFKAf2rAfZtFzXeg5ACBJ9CMd9BwAEAX9AOrHPFEn5olmMN/VhZ4D9aPnZtEPXeiHLlwSoRv90IX9MIue60LPgfrRD7Pohy71XBIh0vcPx9CP5NFzwF6tpheA2r75zW/KFVdcIfvtt5/svvvu0t7eLiNHjjSylg0bNsirr74qTz/9tCxfvlzWrFkjQRBIJpMxsh4AQJ9cLicifcOBgcpvl38d8WJIoEvYoXTu64O0ob9Wfns2fz/gMfqhS639WLVqlYkleYGe60LPAQBxoh9m0HMAQBT0A2gc811dmCeawXxXF3oONI6em0E/dKEfuoTtR1c+L13FooEVoRr6oQv7YQY914WeA42jH2bQD13C9mPGjBmyZMmSisfpR7LoOWA3LopQLggC2bBhg/z5z3+Wv/zlL6aXIyJ9awIA6MKwIF0MCXSpdSgdePDkcApUoh+6hO3HggULTCzHefRcF3oOAIgT/TCDngMAoqAfQHTMd3VgnmgG811d6DkQHT1PF/3QhX7oUms/Tpo6lYsilKEfurAf6aLnutBzIDr6kS76oUut/dh3332rXhQhQj+SQs8B+7WYXgBqy2QykslkRKTvggYNP4auCwCgQy6Xk47Xb2sbqFAoSE9Pj4EVuYkhgS71Hkpn53L9txkONKdQkHn8/YhNd6lkegmIgH7oErYfiBc914WeAwDiRD/MoOcAgCjoB9A85rtmMU80g/muLvQcaB49Twf90IV+6FLvfkAX+qEL+5EOeq4LPQeaRz/SQT90aXY/6Ee86DnghlbTC0BtQRBwKQMAoG7cLJkshgS6NPpJyvJj3GSYjHk9PfzrARajH7qE7QfiQc91oecAgDh15fP0wwB6DgCIgn4A8WG+a8bMmTOZJxrAfFcXeg7Eh54ni37oQj904ZIIu9EPXdiPZNFzXeg5EB/6kSz6oUtc+0E/4kHPAXe0mF4AhhcEQf8PAACGw82SyWBIoEvUT1Jyk2EywvYDdqEfuoTtB5pDz3Wh5wCAuHVms6G/Rj+SQc8BAFHQDyB+zHfTN23aNNNL8A7zXV3oORA/ep4M+qEL/dCFSyLcQD90YT+SQc91oedA/OhHMuiHLs3sR1c+X/EY/WgOPQfc0mp6Aagtk8kMuiBCy2URmUzG9BIAADVws2S8GBLo0uwnKbnJMF5cEuEW+qFL2H4gGnquCz0HAJhAP+JFzwEAUdAPIDnMd+Ey5ru60HMgOfQ8XvRDF/qhC5dEuIV+6MJ+xIue60LPgeTQj3jRD12a3Y/ObFba29roR0zoOeAeLopQap999pF//vOfIvLfSxmCIJCWlhY5/PDDZeedd059TRs2bJBXX31VnnnmGfn73/8ua9asSX0NAID6MSyIB0MCXeL6JCWH03hwSYSb6IcuuVxOVq1aJQsWLDC9FKvRc13oOQDAJPoRD3oOAIiCfgDJY74LFzHf1YWeA8mj5/GgH7rQD124JMJN9EMX9iMe9FwXeg4kj37Eg37oEtd+0I940HPATVwUodSDDz4ov/rVr2Tu3Lly5513ikjfhRFBEEipVJITTjhB5syZI5MnTzayvo0bN8pVV10lZ599tmzatMnIGgAAw2NY0ByGBLrE/UlKDqfN4ZIIt9EPXaZPn85FEU2g57rQcwCABvSjOWn3/KSpUyOsEgCgDedBID3Md+ES5ru60HMgPfS8OfRDF/qhC5dEuI1+6DLcfuy///6pr8km9FwXeg6kh543h37oEvd+0I/m0HPAXS2mF4Bw73vf+2TJkiVy1113yQc+8AFpaWmRIAhkw4YNcu2118oBBxwg+XxeFi9enPraWltb5YwzzpDzzjsv9dcGADQml8tJR0dHxeOFQkF6enoMrMgODAl0SeqTlLNzOZlb5e/HnEJB5vH3I1TYfnTl8wZWg6TQD7iAnutCzwEAmtCPaEz0vLtUivy8AAAdOA8C6WO+Cxcw39WFngPpo+fR0A9d6IcuXBLhB/qhS639WLp0qYEV2YGe60LPgfTR82johy5J7Qf9iIaeA27joggLvPWtb5Ubb7xR/vnPf8rpp58uo0aNkiAIJAgCmT9/vhx66KHytre9TW688UYJgiDVtZ122mmpvh4AIBqGBY1hSKBL0p+k5HDamFr70ZnNGlgRkkQ/YDN6rgs9BwBoRD8aY6rnXcVi088NADCH8yBgDvNd2Iz5ri70HDCHnjeGfuhCP3Thkgi/0A9dwvZjwYIFBlajHz3XhZ4D5tDzxtAPXZLeD/rRGHoOuK/V9AJQvz333FO+853vyEUXXSTd3d1yxRVXyMqVK0VE5J577pEPf/jDsscee8jZZ58tn/zkJ2WrrbZKfE3jxo2TXXbZJfHXAQA0L/f6B/CFIR/gl9/O8QkfEWFIoE1an6QsP9fQ1yq/zSdE+wy3H/955RUDq0LS6AdsRM91oefQYPPmzfL888+bXgZSsHnzZnnhhRcGPbbddttJSwt3Jrvmueeei+V56Ed9TPcc6aGZ/qCZ/oirmVGY7gc9B5jvwk7Md3Wh54B59Lw+9EMX+qELl0T4iX7oErYfGIye60LPAfPoeX3ohy5p7Qf9qA89B/zARREW2nbbbeW8886Tc845R6677jr55je/KQ888ICIiDz22GNy5plnygUXXCCdnZ1yxhlnyLhx4xJdzzbbbJPo8wMA4sOwoDaGBLqk/UlKDqe18Uljv9EP2ISe60LPocXzzz+f+IwMgL3oR21aeo500EwAcdHSD3oOMN+FXZjv6kLPAT3oeW30Qxf6oQtf7+M3+qELl0XURs91oeeAHvS8NvqhS9r7QT9qo+eAP/gnYSw2cuRIOeWUU+Tvf/+7/PrXv5ZsNitBEEgQBPLCCy/I1772Ndljjz3k1FNPlX/+85+JrWPXXXeVkSNHJvb8AIB45XI56ejoqHi8UChIT0+PgRXpwJBAF1OfpJydy8ncKn8/5hQKMs/jvx980hgi9AN2oOe60HMAgE3oR3Xaeg4AsIO2fvjec0CE+S7swHxXF3oO6EPPq6MfunSXSvRDEb7eByL0Q5uw/fAdPdeF8yCgDz2vbtmyZfRDEVM9px/V0XPAL1wU4Yijjz5aFi1aJL29vfLBD35QRowYIUEQyLp16+T73/++TJ48Wd7//vfL0qVLY3/tW2+9VSZNmhT78wIAksOwYDCGzLqY/iQlh9PBTO8HdKEf0Iye62K6H/QcABAF/RhMa88BALpp7YevPQcGYr4LzTTPd7tLJaOvbwI9B/Si54Np7oevuorFisfohxmmew5d6IcuXBYxGD3XxXQ/6DkQjp5XWrhwYcVj9MMM0z2nH4PRc8A/XBThmEMOOUR+/vOfy4oVK+Rzn/uctLe3SxAEsnnzZvn1r38tM2bMkOnTp8stt9xieqkAAMMYFvQxfSjFYKYPpWUcTvto2Q/oQj+gET3XRUs/6DkAIAr60UdTz7vy+dReDwDQHE39oOdAdcx3oZH2+W5XsehVP+g5oB8976O9H+hDP8zQ0nPoQj90yeVyMmvWLNPLMI6e66KlH/QcCEfPa6MfZmjpOf3oQ88BP7WaXgCSMWHCBLn88svlwgsvlCuvvFKuuOIKeeaZZ0RE5K677pKOjg6ZOHGizJ49Wz7+8Y9LW1ub4RUDAEzIvf6BfmHIQaD8ds7xTwxpOZSij5ZDaVn5NYeuqfy265841bYf0MX3fkAXeq6Ltn743nPU1tXVJe3t7aaXYVSpVJJilX/dK5/PSzabNbCi5q1cuVIuu+yyQY+dffbZMmrUqEGPLVu2rOq/LDBz5kyZNm1aomtEpSj78dxzz8lVV12VyHp874e2nndms1X/JUKkZ2gzXeyHzaLuR73N1Iye1yfJZg6krR++9xyohfkuNLFlvutLP+g5YA/fe25LP3xHP8zQ1nPo4ns/tJk+fbosWLDA9DKMoee6aOuH7z0HaqHn1dEPM7T13Pd+2Nbzk6ZOTX1NgKu4KMJx22yzjZx77rkyZ84c+clPfiLf+MY35O9//7uIiDz88MPS2dkp559/vpx++uly+umny/bbb294xQCAtPk6LNB2KPWdtkNpma/DAq37AV187Qd0oee6aO2Hrz3H8Nrb22X06NGml2HU0UcfLW1tbRU9LxaL0tbWZmXPV69eXfHYqFGjBn2D8+LFi6t+Uyn9MCPqfqxZsybJZXnbD609h1lDm+liP2wWdT/qaaZm9Lx+STdTRG8/fO05UA/mu9DAtvmu6/2g54B9fO25bf3wFf0wQ2vPoYuv/YAu9FwXrf3wtedAPej5YPTDDK0997UfNvZ89fr1JpYEOKnF9AKQji222EI+8YlPyF//+lf57W9/K4cddpgEQSBBEMhzzz0nF154oYwfP17OOOMMefjhh00vFwCQslwuJx0dHRWPFwoF6enpMbCiZGk9lPpK66G0bHYuJ3Or/P2YUyjIPAf/fmjfD+jiWz+gCz3XRXs/fOs50Ajfek4/dNG+H771Q3vPoYtv/dDOt/3Q3g/faO+Hbz3vLpVMLwEW8a0f0MXWnrvaD3oO2Mu3ntvaD1f19vZWfZx+mKG959DFt35AF3qui/Z++NZzoBH0vA/9MEN7z33rh6097yoWDawGcBMXRXjoyCOPlNtvv13uuece+fCHPywjRoyQIAhk7dq1cuWVV8ob3/hGOf744+Wuu+4yvVQAQIp8GRZoP5T6RvuhtMyXYYEt+wFdfOkHdKHnutjSD196DkThS8/phy627Icv/bCl59DFl37Ywpf9sKUfvrClHz71nC+oQqN86Qd0sb3nLvaDngN286XntvfDNYsXL5YlS5ZUPN6Vz9MPA2zpOXTxpR/QhZ7rYks/fOk5EIXvPZ85cyb9MMCWnvvSD9t7DiAeXBThsYMPPliuv/56efjhh+Xzn/+8jB49WoIgkE2bNsnNN98s06dPl2w2K0W+mAQAvOH6sMCWQ6kvbDmUlrk+LLBtP6CL6/2ALvRcF9v64XrPgWa43nP6oYtt++F6P2zrOXRxvR+2cX0/bOuH62zrh689B+rhej+gi40978rnKx5zvR/0HLCP6z23sR8uC9sPEZHObDbl1QzP9X7Y1nPo4no/oAs918W2frjec6AZPvd82rRpppfgHdt67no/XOk5gOZxUQRk9913l2984xvy5JNPyiWXXCK77rqrBEEgQRDIH/7wBzn22GPlTW96k1x99dWyYcMG08sF4JlSqWR6Cd5xdVhg26HUdd2lklWH0jJXhwW2DQmgk6v9gC70XBdb++Fqz4E4uNrzZcuW0Q9FbO25q/2wtefQxdV+2MrV/bC1H66ytR++9RxohKv9gC629rwzm/WqH/QcsJerPbe1H66qdUmEZq72w9aeQxdX+wFd6LkutvbD1Z4DcaDnSIOtPXe1H671HEBzuCgC/d7whjfIF77wBXn00UflmmuukQMOOKD/woh//OMf8pnPfEYmTJggX//61+XFF180vVwAnigWixxODXBtWGDrodRlXcVixWPaD6Vlrg0LbB0SQCfX+gFd6LkutvfDtZ4DcXKx5wsXLqx4jH6YYXvPXeuH7T2HLi72w2au7Yft/XCN7f3wpedAFK71A7rY3nNf+kHPAfu51nPb++EaWy+JKHOtH7b3HLq41g/oQs91sb0frvUciBM9R5Js77lr/XC15wCi46IIVGhtbZWTTjpJfvvb38rb3/52ERHJZDISBIGsXLlSvvzlL8v48ePl85//vDz++OOGVwvABxxOzXBlWGD7odQXthxKy1wZFtg+JIBOrvQDutBzXVzphys9B5Lges/phxmu9NyVfrjSc+jiej9s48p+uNIPV7jSD9d7DjTDlX5AF1d67no/6DngDld67ko/XGH7JRFlrvTDlZ5DF1f6AV3ouS6u9MOVngNJoOdIgis9d6UfLvW8K583vQzAGVwUgQrLly+XT3ziEzJp0iS5++67JZPJiEjfZRHlCyPWrFkj3/nOd2TvvfeWE044QV5++WXDqwbgOg6nZtg+LHDlUOo62w6lZbYPC1wZEkAn2/sBXei5Lq71w/aeA0lytef0wwzXem57P1zrOXRxtR+2qrUfS5cuNbCixrjWD9u51g9Xe84XVCEO9Bxxcq3nrvaDngPusb3nrvXDdmH7MWPGDAOraZ7t/XCt59DF9n5AF3qui2v9sL3nQJLoOeLkWs9t74drPe/MZk0vAXAGF0Wg35IlSySfz8uBBx4o1113nWzYsEGCIBj0Q+S/F0aIiGzcuFHmz5/PRREAUsHh1AxbhwWuHUpdZeuhtMzWYYFrQwLoZGs/oAs918XVftjacyANrvWcfpjhas9t7YerPYcurvXDdmH7sWDBAgOrqZ+r/bCVq/1wsed8QRXiQs8RB1d77mI/6DngJlt77mo/bFVrP6ZMmWJgRfGwtR+u9hy62NoP6ELPdXG1H7b2HEgDPUccXO25rf1wtecA4sFFEZBbbrlFpk+fLu9+97tl/vz5snnzZgmCYNCFECIy6OdBEMhOO+0kF198sTzxxBMyfvx4E0sH4CEOp2bYNixw9VBqq97e3qqPu3IotW1YwJAAabKtH9CFnuviej9s6zmQJld6Tj/McL3ntvXD9Z5DF1f64Yqw/dDK9X7YxvV+0HMgHD1HM1zvOf3Qxbb9ANJkW89d74dtXN8P2/rhes+hi239gC6u98M2rvfDtp4DaaLnaIbrPbetH673HEDzWk0vAGZs2LBBrr32Wpk3b5489NBDItJ3+YPI4AshBr5d/vXJkyfLnDlz5KMf/ahsscUWKa4agI/y+bwUi8VBjxVe/wA3xwe0qSr/710YcsDQth+uH0pts3jxYlmyZEnF4135vFOH0vKfZegBvPy2lj8rQwKYYEs/oAs918WXftjSc8AE23s+c+ZM+mGALz23pR++9By62N4P14Tthza+9MMWvvSDngPh6Dmi8KXn9EMXW/YDMMGWnvvSD1v4sh+29MOXnkMXW/oBXXzphy186YctPQdMoOeIwpee29IPX3oOoDlcFOGZVatWSXd3t3z729+WlStX9l/+IFJ5QURZ+X2y2aycc845ctRRR6WyVgAQ6ftvT1tbG4dTJbQPC3w5lNoibD9ERDqz2ZRXkzztwwKGBDBJez+gCz3Xxbd+aO85YJLNPZ82bZrpJXjHt55r74dvPYcuNvfDRdovi/CtH9r51g96DoSj52iEbz2nH7po3w/AJO09960f2vm2H9r74VvPoYv2fkAX3/qhnW/90N5zwCR6jkb41nPt/fCt5wCiazG9AKTjqaeekjlz5sjuu+8u5513nvz73/+WIAgkk8n0/xh4aYRI3wURLS0tcvzxx8vdd98tixYt4pIIAEbkcjnp6OioeLxQKEhPT4+BFflN6374dijVrtYlES6bncvJ3Cp/P+YUCjLP4N8PhgTQQGs/oAs9r9RdKhl7bV/7obXngAb0HPXwteda++Frz6EL/dAlbD9M87UfWvnaD3oOhKPnqIevPacfumjdD1QqGfz8h6+09tzXfmjl635o7YevPYcuWvsBXXzth1a+9kNrzwEN6Dnq4WvPtfbD154DiKbV9AKQrOXLl8ull14qP//5z+W1117rvwwik8n0v8/ACyPKb48aNUo+8YlPyNlnny177rmnkbUDwEDcZKiLtv3w9VCqla+XRJRpu1mSIQE00dYP6ELPq+sqFqW9rY1+pExbzwFN6Dlq8b3n2vrhe8+hC/3QJZfLyapVq2TBggWmlyIi9EMb3/tBz4Fw9By1+N5z+qGLtv1AdcViUdra2uhHyrT13Pd+aOP7fmjrh+89hy7a+gFdfO+HNr73Q1vPAU3oOWrxvefa+uF7zwE0josiHLV48WK59NJL5Xe/+52ISN0XROywww5yxhlnyOmnny7bbbdd+gsHgBo4nOqiZT98P5Rq4/slEWVahgUMCaCRln5AF3peG/0wQ0vPAY3oOaqh53209IOeQyP6ocv06dNVXBRBP3ShH33oORCOnqMaet6Hfugy3H6cNHVq6mtCJfphhpae0w9d2I8+9BwIp6Uf0IV+6EI/+mjpOaARPUc19LyPln7QcwBRcFGEY26++Wa57LLL5O677xaR6hdElGUymf5fnzRpksyePVtOPvlk2XLLLdNbMAA0iMOpLqb3g0OpLmH7MWPGDFmyZImBFZlleljAkACame4HdKHn9aEfZpjuOaAZPcdA9Hww0/2g59CMfmAg+qEL/RiMngPh6DkGoueD0Q9dau3H6vXrTSwJVdAPM0z3nH7own4MRs+BcKb7AV3ohy70YzDTPQc0o+cYiJ4PZrof9BxAVFwU4YANGzbItddeK3PnzpUVK1aISO0LIgb++tSpU+Wcc86RY489NvR9AUAbDqe6mNoPDqW61NqPfffd18uLIkTMDQsYEsAG9Bwi9LxR9MMM08N/QDN6DhF6HobzIBCOfkCEfmhDP6qj50A4eg4Reh6GfugSth9dxaKJ5SAE/TCDr/eBCPsRhp4D4TgPQoR+aEM/quPrfYBw9Bwi9DwM50EANuKiCIu9/PLL0t3dLd/5zndk5cqV/Zc/iPz3goggCAZdAFF++5hjjpFzzjlH3vGOd6S+bgCIA4dTXdLeDw6lugy3HytXrjSwKj3SHhYwJIBN6Lnf6Hk09MMMPnkMhKPnfqPntXEeBMLRD7/RD13oR230HAhHz/1Gz2ujH7qE7Qd0oR9m8PU+fmM/aqPnQDjOg36jH7rQj9rS7nl3qRTr8wFJoud+o+e1cR4EYJsW0wtA45566imZPXu2jB8/Xv7v//5P/v3vf/dfAFH+Ub40YuCFESNHjpRTTjlFli9fLr/85S+5JAKA9XK5nHR0dFQ8XigUpKenx8CK/JbWfnAo1YX9qM/sXE7mVvn7MadQkHkx/v1gSAAb0XM/0Y/6deXzFY/RDzPS6jlgI3ruJ3peH86DQDj64Sf6oQv9qA89B8LRcz/R8/rQD13C9gPm5Kt8/oN+mMHX+/iJ/agPPQfCcR70E/3QhX7UJ82edxWLsT0fkAZ67id6Xh/OgwBs0mp6Aajf3//+d7n00kvl5z//uWzcuLHiMggRGXRhRPntbbfdVk477TT53Oc+JzvuuGPs67rqqquko6NDxo4dG/tzA8BwuMlQl6T3g0OpLuxHY5K+WZIhAWxGz/1CPxrTmc1Ke1sb/VAi7ZuiAZvQc7/Q88ZwHgTC0Q+/0A9d6Edj6DkQjp77hZ43hn7oErYfMCObzUpbWxv9UCLpni9btkwWLlxY8Tj9MIOeN4aeA+E4D/qFfuhCPxpjqueADei5X+h5YzgPArBFi+kFYHilUkne+973yoEHHig/+clP5LXXXqt6IYRI36URQRBIEAQyfvx4+da3viVPPPGEfPWrX03kkggRke7ubnn22WcTeW4AqAc3GeqS1H5wKNWF/YgmqZslGRLABfTcD/QjGvqhS1o3RQM2oud+oOfR0HMgHP3wA/3QpbtUoh8R0HMgHD33Az2Phn7oErYfMIN+6JLkfnBJhB70PBp6DoSj536gH/HrLpUi/176EU3aPQdsQs/9QM+j4TwIwAatpheAcDfddJNcdtll8qc//UlEBl8GMVT5gggRkUMOOUTOOeccOf7446WlJfm7QNatW5f4awDAcLjJUJe494NDqS7sR3PivlmSIQFcQs/dRj+aQz90SfqmaMBm9Nxt9Lw59BwIRz/cRj/06SoWKx6jH/Wh50A4eu42et4c+qHL7FxOVq9fX/VjIqSPfuiS1n7QDzPoeXPoORCOnruNfiSjq1iU9rY2+pGytHoO2Iieu42eN4fzIADtuChCqX322UcefvhhEal9QcTAXz/iiCPknHPOkUMPPTSdRYrIpk2b5Omnn07t9QCgFg6nugy3H/vvv39dz8OhVBf2Ix5xDQsYEsBF9NxN9CMe9EMXLosAwtFzN9HzeNBzIBz9cBP9sAP9aAw9B8LRczfR83jQD106s1kuilCEfuiS9H7QDzPoeTzoORCOnruJfiSLfpiRdM8Bm9FzN9HzeHAeBKBZi+kFoLoVK1aISN8lEJlMRjKZTP+FEGVBEEhra6ucdNJJcv/998v8+fNTvSRCRGTRokWydu3aVF8TAGrJ5XLS0dFR8XihUJCenh4DK/Jbrf1YunTpsL+fQ6ku7Ee8ZudyMrfK3485hYLMq+O/VwwJ4DJ67hb6ES/6oUuz+wG4jJ67hZ7Hi54D4eiHW+iHHehHNPQcCEfP3ULP40U/gHD0Q5ek9oN+mEHP40XPgXD03C30Ix30w4yket6Vz8eyPsAkeu4Weh4vzoMAtOKiCOXKF0SUL4wQ6bsgYvTo0TJnzhx55JFH5Jprrqn7X2WP06pVq+Tss89O/XUBYDgcTnUJ248FCxbU/H0cSnVhP5IRdVjAkAA+oOduoB/JoB+6cFkEEI6eu4GeJ4OeA+Hohxvohx3oR3PoORCOnruBnieDfgDh6Icuce8H/TCDnieDngPh6Lkb6Ee66IcZSfS8M5uNdY2AKfTcDfQ8GZwHAWjUanoBqG3oBREtLS1y1FFHyWc/+1l5wxveIA8//LA8/PDDqaxlw4YN8uqrr8ozzzwj999/v9x8882ycuXK/vUBgCa51z9QLgz5QLr8do4PpFMVth9hOJTqwn4kq3ywH3rwL7899ODPkAA+oed2ox/Joh+6NLof3aVSOgsDFKDndqPnyaLnQDj6YTf6oUtvb2/Vx+lHPOg5EI6e242eJ4t+AOHohy5x7cfMmTPphwH0PFn0HAhHz+1GP8ygH2bE3fP/vPJKQisF0kfP7UbPk8V5EIA2XBRhgfJlEZlMRoIgkPnz51eNtYl1AYBmHE51qfeyCA6lurAf6ah3WMCQAD6i53aiH+mgH7o0sh9dxWK6iwMMo+d2oufpoOdAOPphJ/qhy+LFi2XJkiUVj3fl8/QjRvQcCEfP7UTP00E/gHD0Q5c49mPatGnxLww10fN00HMgHD23E/1IT1c+X/G1I/TDDHoOhKPndqLn6aAfADThogjlMplM/881Xcww8OIKANCMw6kuw10WsWzZMlm4cGHF4xxKzWBIkK7hhgXVfk2EIQH8QM/tQj/SRT90ibofgA/ouV3oebroORCOftiFfugSth8iIp3ZbMqrcR89B8LRc7vQ83TRDyAc/dCF/bALPU8XPQfC0Q+70I90dWaz0t7WRj+UoOdAOHpuF3qeLvoBQAsuilCufBHDbrvtJvvuu6+MHTtW2traBl0gkZYNGzbIq6++Ks8884z8/e9/lzVr1qS+BgCIgsOpLrUui+CSCD0YEphRz7BgIIYE8Ak9twP9MIN+6NLofsCcUqkkRx99tOlleIWe24Gem0HPgXD0ww70Q5dal0QgOfQcCEfP7UDPzaAfQDj6oQv7YQd6bgY9B8LRDzvQDzPohy7sBxCOntuBnptBPwBowEURyr3lLW+R73//+3LQQQeZXsogGzdulCuvvFLOPvts2bx5s+nlAMCwOJzqksvlZNWqVbJgwYKa78eh1AyGBGaFDQuGYkgAH9Fz3eiHWfRDl3r3A2YVi0Vpa2ujHymj57rRc7PoORCOfuhGP3Thkgiz6DkQjp7rRs/Noh9AOPqhC/uhGz03i54D4eiHbvTDLPqhC/sBhKPnutFzs+gHANNaTC8A4XbddVe5/fbb1V0SISLS2toqn/vc5+RLX/qS6aUAQN1yuZx0dHRUPF4oFKSnp8fAivw2ffr0mr/OodQMhgQ6zM7lZG6V/16VMSSAz+i5TvRDB/qhy3D7AR3ohxn0XCd6rgM9B8LRD53ohy5cEqEDPQfC0XOd6LkO9AMIRz90YT90ouc60HMgHP3QiX7oQD90YT+AcPRcJ3quA/0AYBIXRSh2zjnnyJgxY0wvo6bOzk7TSwCAhnA4tQOHUjMYEgCwBT3XhX4AsB39MIOe60LPAdiCfuhCP3QJ248ZM2YYWA0AhKPnutBzALagH7rU2o+lS5caWJHf6DkAW9BzXegHACAKeq4LPQcAiHBRhGqHHnqo6SUMa8cdd5RddtnF9DIAoCEcTnXjUGoGQwJd5vX0yJxCIfTX5xQKMo//XsFz9FwH+qEL/dBluP2ALvTDDHquAz3XhZ4Dw6MfOtAPXWrtx5QpUwysyG/0HBgePdeBnutCP4Dh0Q9dwvZjwYIFBlbjL3quCz0HhkfPdaAfutAPXdgPYHj0XAd6rgv9AGASF0UolclkZMKECaaXUZdtttnG9BIAoGEcTnWaOXMmh1IDGBLoUu83lTIsAOi5afRDF/qhC5dE2Il+mEHPzaLnutBzoH70wyz6oQv7oQs9B+pHz82iH7rQD6B+9EOXsP1AOui5LvQcqB89N4t+6EI/dGE/gPrRc7PouS70A4BpXBSh1Pjx46W9vd30Muqy6667ysiRI00vAwAaxuFUn2nTpplegncYEugSNiSY29Ehc6v894phAUDPTaEfutAPXRrdD5iTz+crHqMfZtBzM+i5LvQcaBz9MIN+6MJ+6ELPgcbRczPohy70A2gc/dCFyyLMoOe60HOgcfTcDPqhC/3Qhf0AGkfPzaDnutAPABq0ml4Aqnv00UdNL6Fut956q+klAEBkuVxORPoOowOV3y7/OuAihgS61BoSzB7w36Kh71N+ezb/vYLH6Hm66Icu9EOXqPsBM7LZrLS1tdEPJeh5uui5LvQciI5+pIt+6NLMfnSXSnLB0UcntTQv0XMgOnqeLnquC/0AoqMfuoTtB5JBz3Wh50B09Dxd9EOX7lJJuorFisfphxn0HIiOnqeLnutCPwBo0WJ6AQAAmMZNhvARQwJd6h0SzM7luFkSCEHP00E/dKEfujSyH135fJpLQw30Qxf2Ix30XBd6DjSPfqSDfujS7H50FYv0I0b0HGgePU8HPdeFfgDNox+6hO0H4kXPdaHnQPPoeTrohz71XBJBP9JBz91TKpVML8E79Dwd9FwX+gFAEy6KAABAOJzCLwwJdKl3SFDGsAAIR8+TRT90oR+6NLofndlsGstCneiHLuxHsui5LvQciA/9SBb90CWu/aAf8aDnQHzoebLouS70A4gP/dAll8vJrFmzTC/DWfRcF3oOxIeeJ4t+2IF+mEHP3VQsFumHAfQ8WfRcF/oBQBsuigAA4HUcTuEDhgS6NDokKGNYAISj58mgH7rQD12i7gd0oR+6sB/JoOe60HMgfvQjGfRDl7j3g340h54D8aPnyaDnutAPIH70Q5fp06ebXoKT6Lku9ByIHz1PBv2wA/0wI4med5dKsa4R0dEPM+h5Mui5LpwHAWjERREAAAzA4RQuY0igS7PfVMqwAAhHz+NFP3ShH7pwSYRb6Icu7Ee86Lku9BxIDv2IF/3QJan9oB/R0HMgOfQ8XvRcF/oBJId+wGX0XBd6DiSHnseLftiBfpiRVM+7isVY1od40A8z6Hm86LkunAcBaMVFEQAADMHhFC5iSKBLXN9UyrAACEfP40E/dKEfunBJhJvohy7sRzzouS70HEge/YgH/dAl6f2gH42h50Dy6Hk86Lku9EMX/pVXN9EPuIie60LPgeTR83jQD116e3urPk4/zEi659CFfphBz+NBz3XhPAhAMy6KAACgCg6ncAlDAl3i/qZShgVAOHreHPqhC/3QhUsi3EY/dGE/mkPPdaHnQHroR3Pohy5J7EdXPl/xGP2oDz0H0kPPm0PPdaEfuszr6eFfeXUY/YBL6Lku9BxIDz1vDv3QZfHixbJkyZKKx7vyefphQFo9hy70wwx63hx6rgvnQQDacVEEAAAhOJzCBQwJdEnqm0oZFgDh6Hk09EMX+qELl0T4gX7own5EQ891oedA+uhHNPRDl6T2ozObpR8R0HMgffQ8GnquC/3QJWw/4Bb6ARfQc13oOZA+eh4N/dAlbD9E+ma0UdGPaNLuOczJV7ksm36YQc+joee6cB4EYINW0wsAAECz3OsfuBeGfGBffjvHN4JBMYYEuiT9TaXl5xj6GuW3+cZV+IyeN4Z+6NJdKlX9l83ohxlcEuEX+qEL+9EYeq4L50HAHPrRmGXLlsnChQsrHqcfZiTdc/rRGHoOmEPPG8N5UBf6oQuXRPiFfsBm9FwXeg6YQ88bQz90qXVJRBzoR2NM9RxmZLNZaWtrox9K0PPG0HNdOA8CsEWL6QUAAKAdNxnCRgwJdEnrm0q5WRIIR8/rQz/0SfKSiDL6UR8uifAT/dCF/agPPdeF8yBgHv2oH5dE6JFWz+lHfeg5YB49rw/nQV3ohy5cEuEn+gEb0XNd6DlgHj2vD/3QJelLIsroR33S7HlXPh/b86E59EMX9qM+9FwXzoMAbMJFEQAA1IHDKWzCkECXtL+plGEBEI6e10Y/7EA/zOCSCL/RD13Yj9rouS6cBwE96Ec09MOMtHtOP2qj54Ae9Lw2zoO60A9duCTCb/QDNqHnutBzQA96Xhv90CWtSyLK6Edtafe8M5uN/TkRHf3Qhf2ojZ7rwnkQgG24KAIAgDpxOIUNGBLoYuqbShkWAOHoeXX0ww70wwwuiYAI/dCm1n4sXbrUwIp0oOe6cB4E9KHnjaEfZpjqOf2ojp4D+tDz6jgP6kI/dAnbD/6VV7/QD9iAnutCzwF96Hl19EOXsP2YMWNGoq9LP6rj630gQj+0YT+qo+e6cB4EYCMuigAAoAEcTqEZQwJdTA+ZGRYA4ej5YPTDDvTDDNM9hy70Q5ew/ViwYIGB1ZhHz3Ux3Q96DoSj5/WhH2aY7jn9GIyeA3rR88FM9wOD0Q9dau0H/8qrf+gHNKPnutBzQC96Phj90KXWfkyZMiXx16cfg5nuOXShH7qwH4PRc11M94OeA4iKiyIAAGgQh1NoxJBAF9NDgjKGBUA4et6HfujS29tb9XH6YYaWnkMX+qFL2H74hp7roqUf9BwIR89rox9maOk5/ehDzwH96HkfLf1AH/qhi5b9gC70AxrRc1209IOeA+HoeR/6oYuW/aAffbT0HLrQD13Yjz5a+oE+WvpBzwFEwUURAABEwOEUmjAk0EXLkKCMYQEQzvee0w9dFi9eLEuWLKl4vCufpx8GaOs5dPG9H9r4flkEPddFWz987zlQCz2vbubMmfTDAG09970f9Bywh+8919YP39EPXbTtB3TxvR/QhZ7roq0fvvccqMX3ntMPXbTth+/90NZz6OJ7P7TxfT+09cN32vrhe88BNI6LIgAAiMj3wyl0YEigi7YhQRnDAiCcrz2nH7qE7YeISGc2m/Jq/svXfmjtOXTxtR9a+XpZBD3XRWs/fO05UA96XmnatGmml+AdrT33tR/0HLCPrz3X2g9f0Q9dtO4HdPG1H9CFnuuitR++9hyoh689px+6aN0PX/uhtefQxdd+aOXrfmjth6+09sPXngOIhosiAABogq+HU+hg85Cgu1QyvYTYaR0SlDEsAML51nOb++GiWpdEaOBbP7T3HLr41g/tfLssgp7ror0fvvUcaAQ9h0nae+5bP+g54lJy8PMf2vnWc+398A390EX7fkAX3/oBXei5Ltr74VvPgUb41nP6oYv2/fCtH9p7Dl1864d2vu2H9n74Rns/fOs5gOi4KAIAgCb5djiFDrYPCbqKRacOp9qHBGUMC4BwvvTc9n64RvslEWW+9MOWnkMXX/phi1wuJ7NmzTK9jMTRc11s6YcvPQeioOcwwZae+9IPeo44FYtF+mGALz23pR++oB+62LIf0MWXfkAXeq6LLf3wpedAFL70nH7oYst++NIPW3oOXXzphy182Q9b+uELW/rhS88BNIeLIgAAiIEvh1Po4MqQwJXDqS1DgjKGBUA413vuSj9cYcslEWWu98O2nkMX1/thm+nTp5teQqLouS629cP1ngPNoOdIk209d70f9BxJoB9muN5z2/rhOvqhi237AV1c7wd0oee62NYP13sONMP1ntMPXWzbD9f7YVvPoYvr/bCN6/thWz9cZ1s/XO85gOa1ml4AAACuyL1+ICgMOTCU384pPDDAPq4NCcoHbI0H6nrYNiQoK69t6Npt3w8gDq723LV+2C5sP2bMmCFLliwxsKL6uNoPW3sOXVztB3Sh57rY2g9Xew7EgZ4jDbb23NV+uNjz1evXm1gSqqAfZrjac1v74SoX+zHw121j635AF1f7AV3ouS629sPVngNxcLXn9EMXW/fD1X7Y2nPo4mo/bFVrP1atWmViSbGwtR+usrUfrvYcQDxaTC8AAACXuH6TIcxydUhg602Gtg4JyrhZEgjnWs9d7Yetau3HlClTDKyoMa71w/aeQxfX+gFd6LkutvfDtZ4DcaLnSJLtPXetH672vKtYNLAahKEfZrjWc9v74RpX+0HPAff6AV3ouS6298O1ngNxcq3n9EMX2/fDtX7Y3nPo4lo/bBe2HwsWLDCwmubZ3g/X2N4P13reXSqZXgLgDC6KAAAgZgwLkATXhwS2HU5tHxKUuTYsAOLkSs9d74dtXNkPV/rhSs+hiyv9gC6u9MMVrvTDlZ4DSaDnSIIrPXelH673HLrQDzNc6bkr/XCF6/2g54A7/YAu9FwXV/rhSs+BJLjSc/qhiyv74Uo/XOk5dHGlH64I2w/buNIPV7jSD5d6zgX4QHy4KAIAgAQwLECcXBwSdOXzFY/Zcjh1ZUhQ5sqwAEiC7T13sR82c20/bO+Haz2HLrb3A7q41g/budYP23sOJImeI06u9dz2fvjSc5iTr/L5D/phhu09d60ftvOlH/QcsL8f0IWe6+JaP2zvOZAk23tOP3RxbT9s74drPYcutvfDNbZfFuFaP2znWj9c7TmA6LgoAgCAhDAsQBxcHRJ0ZrNWHk5dGxKU2T4sAJJka89d7YetXN0PW/vhas+hi639gC6u9sNWrvbD1p4DaaDniIOrPbe1H771HGZks1n6oYitPXe1H7bqLpW86gc9B+ztB3Sh57q42g9bew6kwdaeL1u2jH4o4mrPbe2Hqz2HLrb2w1W2Xhbhaj9s5Wo/XOs5gOZwUQQAAAliWIBmuD4ksO1w6uqQoMy2/QDSZFvPXe+HbVzfD9v64XrPoYtt/YAurvfDNq73w7aeA2mi52iG6z23rR++9hxm0A9dbNsP1/tho65iseIx1/tBzwH7+gFdfO55d6lkegkVXO+HbT0H0mRjzxcuXFjxmA/90Mj1ntvWD9d7Dl1s7IfLbLsswvV+2Mb1frjScwDN46IIAAASxrAAUfgyJLDlcOr6kKDMlv0ATLCl5770wxa+7Ict/fCl59DFln5AF1/6YQtf+mFLzwET6Dmi8KXntvTDp5535fOml4HX0Q9dbNkPX/phOxf7Qc+B6mzpB3TxveddxSL9MMCWngMm2N5zX/qhjS89t6UfvvQcutjeD9fkcjmZNWuW6WUMy5d+2MKXftjecwDx4KIIAABSwLAAjfBtSKD9cOrLkKBM+34AJmnvuW/90M63/dDeD996Dl209wO6+NYP7Xzrh/aeAybRczTCt55r74dvPe/MZk0vAQPQD12074dv/bCVq/2g50A47f2ALvS8D/0wQ3vPAZNs7blv/dDCt55r74dvPYcutvbDVdOnTze9hJp864d2vvXD1p5zAT4QHy6KAAAgJQwLUA9fhwRaD6e+DQnKtO4HoIHWnvvaD6183Q+t/fC159BFaz+gi6/90MrXfmjtOaABPUc9fO251n742nPoQj900bofvvbDNq73g54D4bT2A7rQ88Hohxlaew5oYFvPfe2Hab72XGs/fO05dLGtHzDD135o5Ws/bOw5F+AD8eGiCAAAUsSwALX4PiTQdjj1dUhQpm0/AE209dz3fmjj+35o64fvPYcu2voBXXzvhza+90Nbz7tLpdRfEwhDz1GL7z3X1g/few5d6Icu2vbD935o09vbW/VxX/pBz4Fw2voBXeh5dfTDDG09BzSxpee+98MU33uurR++9xy62NIPmOF7P7TxvR/0HPAXF0UAAJAyhgWohiFBHy2HUw6lfbTsB6CRlp7TD13Yjz5a+kHPoZGWfkAX+qEL/eijqeddxWJqrwfUg56jGnreR1M/6Dm0oR+6aNkP+qHL4sWLZcmSJRWPd+XzXvWDngPhtPQDutDz2uiHGVp6DmikveczZ86kHwbQ8z5a+kHPoZH2fsAM+qEL/ehDzwE/cVEEAAAGMCzAQAwJBjN9OOVQOpjp/QA0M91z+qEL+zGY6X7Qc2hmuh/QhX7oQj8G09pzQAN6joHo+WBa++Frz6EL/dDF9H7QD13C9kNEpDObTXk15tFzIJzpfkAXel6pK5+veIx+mGG654Bmmns+bdo0o6/vI3o+mOl+0HNoprkfSB/90IV+DEbPAf9wUQQAAIYwLIAIQ4Iwpg6nHEqrMz0sQP1KpZLpJXjHVM+XLVtGPxSh59XRcyAc50GI0A9t6Ed12noOaELPIULPw2jrh+89hy70QxdT+0E/dKl1SYTP6DkQjp5DhJ6H6cxm6YcifL0PEI6eQ4Seh+E8CISjHxChH9rQj+roOeAXLooAAMAghgV+Y0hQW9qHUw6ltfHJYzsUi0X6YYCJni9cuLDiMfphBj2vjZ4D4TgP+o1+6EI/atPSc0Ajeu43el6bln7Qc2hEP3RJez/ohy5cElEbPQfC0XO/0fPa6IcufL0PEI6e+42e10bPgXD0w2/0Qxf6URs9B/zRanoBAAD4Lvf6B7yFIR8Ql9/O8QGxkxgS1Kd8IBx6YCy/HdeBkUNpfYbbj5OmTk19TahEP8ww3XP6YQY9rw89B8KZ7gfMoB+60I/6mO45oBk99xM9r4/pftBzaEY/dBluP/bff/9YXod+6MIlEfWh50A4eu4nel4f+qFLWvsB2Iie+4me14eeA+Hoh5/ohy70oz70HPBDi+kFAAAAbpb0DUOCxiR9kyGH0sbU2o/uUsnAilAN/TDDVM/phxn0vDH0HAjHedAv9EMX+tEYUz3vyuebfm4gafTcL/S8MZwHgXD0Q5da+7F06dKmn59+6BK2HzNmzDCwGv3oORCOnvuFnjeGfuiS9r8kC9iEnvuFnjeGngPh6Idf6Icu9KMx9BxwHxdFAACgBMMCPzAkiCapwymH0mjC9qOrWDSwGoShH2ak3XP6YQY9j4aeA+E4D/qBfuhCP6Ix0fPObDby8wJpoud+oOfRcB4EwtEPXcL2Y8GCBU09L/3QpdZ+TJkyxcCK7EDPgXD03A/0PBr6oQuXRQDh6Lkf6Hk09BwIRz/8QD90oR/R0HPAbVwUAQCAIgwL3MaQoDlxH045lDYnbD+gC/0wI62e0w8z6Hlz6DkQjvOg2+iHLvSjOfQcCEfP3UbPm0M/gHD0Q5ew/YiKfujCfjSHngPh6Lnb6Edz6IcuXBYBhKPnbqPnzaHnQDj64Tb6oQv9aA49B9zFRREAACjDsMBNDAniEdfhlENpPLgsQp98Pl/xGP0wI+mez5w5k34YQM/jQc+BcJwH3UQ/dKEf8aDnQDh67iZ6Hg/6AYSjH7rEdVkE/dCF/YgHPQfC0XM30Y940A9duCwCCEfP3UTP40HPgXD0w030Qxf6EQ96DriJiyIAAFCIYYFbGBLEq9nDKYfSeHFZhC7ZbJZ+KJJkz6dNm9bU70fj6Hm86DkQjvOgW+iHLvQjXvQcCEfP3ULP40U/gHD0Q5dmL4ugH7qwH/Gi50A4eu4W+hEv+qELl0UA4ei5W+h5vOg5EI5+uIV+6NJdKtGPGNFzwD1cFAEAgFIMC9zAkCAZUQ+nHEqTwWURutAPXdgPN9DzZNBzIBz9cAP9SF93qRT6a/QjGfQcCEfP3UDPk0E/gHD0Q5eol0XQD13Yj2TQcyAcPXcD/UgG/dCFyyLsUarx+Q8kg567gZ4ng54D4eiHG+iHPl3FYsVj9KM59BxwCxdFAACgGMMCuzEkSFajh1MOpcmanctJVz5vehl4Hf3Qhf2wGz1PFj0HwtEPu9EPM7qKRfphAD0HwtFzu9HzZNEPIBz90CWXy8msWbPqfn/6oQv7kSx6DoSj53ajH8miH7pwWYQdisUi/TCAntuNnieLngPh6Ifd6Icd6Ec86DngDi6KAABAOYYFdmJIkI56D6ccStPRmc2aXgIGoB+6sB92oufpoOdAOPphJ/phFv0wg54D4ei5neh5OugHEI5+6DJ9+vS63o9+6MJ+pIOeA+HouZ3oRzrohy5cFmEH+mEGPbcTPU8HPQfC0Q870Q870I940XPADa2mFwAAAIaXe/0D58KQD6zLb+f4wFoVhgTpKh8shx48B77NoRS+oh+6sB92oefpoudAOPphF/qhA/0wg54D4ei5Xeh5uugHEI5+2IV+6MJ+pIueA+HouV3oR7rohy619mP1+vUmloQq6IcZ9Nwu9Dxd9BwIRz/sQj/sQD+SQc8B+3FRBAAAlmBYYAeGBGbUczgdiEMpfEI/dGE/7EDPzaDnQDj6YQf6oQv9MIOeA+HouR3ouRn0AwhHP+xAP3RhP8yg50A4em4H+mEG/dAlbD+6ikUTy0EI+mEGPbcDPTeDngPh6Icd6Icuvb29VR+nH8mi54DdWkwvAAAA1C+Xy0lHR0fF44VCQXp6egysCAMxJDBrdi4nc6v8/RiKQyl8RD90YT90o+dm0XMgHP3QjX7YgX6kg54D4ei5bvTcLPoBhKMfutEPXdgPs+g5EI6e60Y/zKIfutS7HzCLfphBz3Wj52bRcyAc/dCNfuiyePFiWbJkScXjXfk8/UgBPQfsxUURAABYhmGBTgwJdBjucMqhFD6jH7qwHzrRcx3oORCOfuhEP/ToyudDf41+pIueA+HouU70XAf6AYSjHzrRD13YDx3oORCOnutEP3SgH7pwWYQ++Sqf/6AfZtBznei5DvQcCEc/dKIfuoTth4hIZzab8mr8Rc8BO3FRBAAAFmJYoAtDAgC2oB+6sB+60HMAtqAfutAPAEAU9FwXeg7AFvRDl2XL/j979x0nVXU3fvy7y8IqgqAURVHERlRARZSiZFFcjGVtwZbEkohRsWGsMVHXlMfYnmhUJFZMe0T5ReOKUQjBBQULUuwdDCpIk95kub8/NrMwO3On3jvne875vF+vfYW5O3PnwIl8OGfh7HT6oQg9B2ALeq4L/QBgi6qqKvqhCD3XhZ4DsAX90IV+6JLpkAgAQHYcFAEAgKXYLNCBTQJd7po4Ua4eNy7081ePGyd38d8HPEc/dGE+dKDnutBzIDv6oQP90Ke2ri70c/SjtOg5kB0914Ge60I/gOzohx6TJk1KuUY/zKDnutBzIDt6rgP90IV+6JJtPmAG/dCF+dCBnutCz4Hs6IcO9EOXXA6JGFVfX6LRgJ4DdqowPQAAAFC46upqEWncHNha4nHi84gHmwS65PpFysRzruK/D3iMfuiSbT569uxZ8jH5hJ7rQs+B3NFzs+iHnehHadBzIHf03Cx6rgv9AHJHP3SiH2bQc13oOZA7em4W/dCFfujCIRG60Q9dmA+z6Lku9BzIHf0wi37oksshESKN3zimTWUl/YgZPQfsVW56AAAAoDicLGkGmwS6hC1K7xw2TO5M898HJxkC9EObTPMxbdo0AyPyAz3XhZ4D+aPnZtAPO9APM+g5kD96bgY914V+APmjH7rQDzPouS70HMgfPTeDfuhCP3QJm4/amhoDo0EY+qEL82EGPdeFngP5ox9m0A9dwuZj0KBBaZ9PP+JFzwG7VZgeAAAAKB4nS5YWmwS6ZFqUbn1KYfPncJIhQD+0CZuPCRMmmBiO8+i5LvQcKBw9Ly36YQf6YQY9BwpHz0uLnutCP4DC0Q8d6IcZ9FwXeg4Ujp6XFv3QhX7okmk+zunfX2rr6gyMCmHohy7MR2nRc13oOVA4+lFa9EOXTPPRo0cPmTp1atrX0Y940HPAfuWmBwAAAKLByZKlwSaBLrkuSq+qruYkwxIYVV9veggoAP3QJWw+EC16rgs9B4pHz0uDfugyY8aMtNfphxn0HCgePS8Neq4L/QCKRz/Moh9m0HNd6DlQPHpeGvRDF/qhS67zAV3ohy7MR2nQc13oOVA8+lEa9EOXYueDfkSLngNu4KAIAAAcwmZBvNgk0CXfL1KyOI3XXRMn8t0DLEY/dOGwiHjRc13oORAdeh4v+qHLlClT0n73gNqaGvphAD0HokPP40XPdaEfQHTohxlDhgyhHwbQc13oORAdeh4v+qEL/dCFQyLsRj90YT7iRc91oedAdOhHvOiHLlHNB/2IBj0H3MFBEQAAOIbNgniwSaBLoV+kZHEaj7D5gF3ohy4cFhEPeq4LPQeiR8/jQT90CZsPEZERVVWhr6Mf8aDnQPToeTzouS70A4ge/Si9AQMGmB6Cd+i5LvQciB49jwf90IV+6MIhEW6gH7owH/Gg57rQcyB69CMe9EOXYuajtqYm5Rr9KA49B9zCQREAADiIzYJosUmgS7FfpGRxGi0OiXAL/dCFwyKiRc91oedAfOh5tOiHLpkOicgF/YgWPfdHfX296SF4h55Hi57rQj+A+NAPuIye60LPgfjQ82jRD13ohy4cEuEW+qEL8xEteq4LPQfiQz+iRT90KXY+RlRV0Y8I0XPAPRwUAQCAo9gsiAabBLpE9UVKFqfR4JAIN9EPXaqrq2Xo0KGmh2E9eq4LPQfiR8+jQT90KfaQiAT6EQ167pe6ujr6YQA9jwY914V+APGjH3ARPdeFngPxo+fRoB+60A9dOCTCTfRDF+YjGvRcF3oOxI9+RIN+6BLVfNCPaNBzwE0cFAEAgMPYLCgOmwS6RP1FShanxeGQCLfRD10GDhxoeghWo+e60HOgdOh5ceiHLlEdEpFAP4pDz/1EP8yg58Wh57rQD6B06AdcQs91oedA6dDz4tAPXeiHLhwS4Tb6oQvzURx6rgs9B0qHfhSHfugS9XzQj+LQc8BdHBQBAIDj2CwoDJsEusT1RUoWp4UJm4/amhoDo0Fc6AdcQM91oedA6dHzwtAPXcLmY9CgQUXdl34UxkTPR9XXF3xfRIt+mEHPC0PPdWE9CJQe/YAL6Lku9BwoPXpeGPqhC/3QhUMi/EA/dMk0H9OmTTMwIjvQc13oOVB69Lww9EOXuOaDfhSGngNu46AIAAA8wGZBftgk0CXuL1KyOM1PpvkYUVVlYESIE/2Azei5LvQcMIee54d+6JJpPvr27Vv0/elHfkz1vLauruh7Izr0wwx6nh96rgvrQcAc+gGb0XNd6DlgDj3PD/3QhX7owiERfqEfuoTNx4QJEwyMRj96rgs9B8yh5/mhH7rEPR/0Iz/0HHAfB0UAAOAJNgtywyaBLqX6IiWL09zwRWM/0Q/YiJ7rQs8B8+h5buiHLqWaD/qRG9M9hy70wwx6nht6rovpftBzgH7ATvRcF3oOmEfPc0M/dKEfuvD3ffxEP3QJmw8ko+e60HPAPHqeG/qhC3/fRxd6DviBgyIAAPAImwWZsUmgS6m/SMniNDO+aOw3+gGb0HNd6DmgBz3PjH7oUur5oB+Zaek5zKmpqUm5Rj/MoOeZ0XNdtPSDngP0A3ah57rQc0APep4Z/dCFfujC3/fxG/3QhcMiMqPnutBzQA96nhn90IW/76MLPQf8UWF6AAAAoLSq//sH+nHN/sCfeFzt6ReA2CTQxdQXKRP3bv7eice+foGULxpDhH7ADvRcF3oO6EPP06MfupiaD/qRnraew4yqqiqprKykH0rQ8/TouS7a+uF7zwER+gE70HNd6DmgDz1Pj37oMqq+Xmrr6lKu0w8z+Ps+EKEf2oTNh+/ouS6sBwF96Hl606dPl0mTJqVcpx9m8Pd9dKHngF/KTQ8AAACUHidLJmOTWRfTX6TkJMNkpucDutAPaEbPdTHdD3oOhKPnyeiHLqbng34k09pzmEE/dGE+kpnuB5Jp7YevPQe2Rj+gmeaej6qvN/r+JtBzQC96nkxzP3xl4pCIBPqRzHTPoQv90CVsPnxFz3Ux3Q96DoSj56k4JEIP0z2nH8noOeAfDooAAMBTbBY0Mr0oRTLTi9IEFqeNtMwHdKEf0Iie66KlH/QcCEfPG9EPXbTMB/1opKnntTU1JXs/ZEY/dGE+GmnpBxpp6gc9B9KjH9BIe89r6+q86gc9B/Sj54209wON6IcZWnoOXeiHLtXV1TJ06FDTwzCOnuuipR/0HAhHzzOjH2Zo6Tn9aETPAT9xUAQAAB7zfbNAy6IUjbQsShN8X5xqmw/o4ns/oAs910VbP3zvOZCJ7z2nH7pomw/f+6Gt5yOqqkr+ngjnez+08X0+tPXDd9r64XvPgUx87wd0saXnvvSDngP28L3ntvTDd/TDDG09hy6+90ObgQMHmh6CUfRcF2398L3nQCb0PD36YYa2nvveD3oO+KvC9AAAAIBZ1f/9A/+4ZguCxONqR79ApG1R6jtti9KExHs3H1visatfQNU6H9DF135AF3qui9Z++NpzIBe+9px+6KJ1Pnzth9aeQxdf+6GVr/OhtR++0toPX3sO5MLXfkAX23ruej/oOWAfX3tuWz98RT/M0Npz6OJrP6ALPddFaz987TmQC3qejH6YobXnvvbDxp6v3rDBxJAAJ5WbHgAAADDPt5MltS5KfaV1UZrg20mG2ucDuvjWD+hCz3XR3g/feg7kw7ee0w9dtM+Hb/3Q3nPo4ls/tPNtPrT3wzfa++Fbz0fV15seAiziWz+gi609d7Uf9Bywl289t7UfrpoxY0ba6/TDDO09hy6+9QO60HNdtPfDt54D+aDnjeiHGdp77ls/bO15bV2dgdEAbuKgCAAAICL+bBZoX5T6RvuiNMGXzQJb5gO6+NIP6ELPdbGlH770HCiELz2nH7rYMh++9MOWnkMXX/phC1/mw5Z++MKWfvjUc/5CFfLlSz+gi+09d7Ef9Bywmy89t70frpkyZYpMnTo15XptTQ39MMCWnkMXX/oBXei5Lrb0w5eeA4XwvedDhgyhHwbY0nNf+mF7zwFEg4MiAABAE9c3C2xZlPrClkVpguubBbbNB3RxvR/QhZ7rYls/XO85UAzXe04/dLFtPlzvh209hy6u98M2rs+Hbf1wnW398LXnQC5c7wd0sbHntTU1Kddc7wc9B+zjes9t7IfLwuZDRGREVVWJR5Od6/2wrefQxfV+QBd6rott/XC950AxfO75gAEDTA/BO7b13PV+uNJzAMXjoAgAgGr19fWmh+AdVzcLbFuUum5Ufb1Vi9IEVzcLbNskgE6u9gO60HNdbO2Hqz0HouBqz6dPn04/FLG15672w9aeQxdX+2ErV+fD1n64ytZ++NZzIB+u9gO62NrzEVVVXvWDngP2crXntvbDVZkOidDM1X7Y2nPo4mo/oAs918XWfrjacyAK9BylYGvPXe2Haz0HUBwOigAAqFZXV8fi1ADXNgtsXZS6rLauLuWa9kVpgmubBbZuEkAn1/oBXei5Lrb3w7WeA1FyseeTJk1KuUY/zLC95671w/aeQxcX+2Ez1+bD9n64xvZ++NJzoBCu9QO62N5zX/pBzwH7udZz2/vhGlsPiUhwrR+29xy6uNYP6ELPdbG9H671HIgSPUecbO+5a/1wtecACldhegAAAGQz7r9/gK224A+sLkn8eo9rtoCwbT5sX5T6wpZFaUJirM0X2InHtvxcbN8kgE6u9AO60HNdXOmHKz0H4uB6z+mHGa703JV+uNJz6OJ6P2yTbT569uxZ8jEVwpV+uMKVfrjec6AY9BxxcKXnrveDngPucKXnrvTDFbYfEpHgSj9c6Tl0caUf0IWe6+JKP1zpORAHeo44uNJzV/rhUs9Xb9iQ9hvQAshfuekBAACQC04yNMP2kyVdWZS6zrZFaYLtJ0u6skkAnWzvB3Sh57q41g/bew7EydWe0w8zXOu57f1wrefQxdV+2CrTfEybNs3AiPLjWj9s51o/XO15bU2NgdHANfQcUXKt5672g54D7rG95671w3Zh8zFo0CADoyme7f1wrefQxfZ+QBd6rotr/bC950Cc6Dmi5FrPbe+Haz0fUVVlegiAMzgoAgBgDRanZti6WeDaotRVti5KE2zdLHBtkwA62doP6ELPdXG1H7b2HCgF13pOP8xwtee29sPVnkMX1/phu7D5mDBhgoHR5M7VftjK1X642HP+QhWiQs8RBVd77mI/6DngJlt77mo/bJVpPvr27WtgRNGwtR+u9hy62NoP6ELPdXG1H7b2HCgFeo4ouNpzW/vhas8BRIODIgAAVmFxaoZtmwWuLkptNWPGjLTXXVmU2rZZwCYBSsm2fkAXeq6L6/2wredAKbnSc/phhus9t60frvccurjSD1eEzYdWrvfDNq73g54D4eg5iuF6z+mHLrbNB1BKtvXc9X7YxvX5sK0frvccutjWD+jiej9s43o/bOs5UEr0HMVwvee29cP1ngMoHgdFAABUq6mpSbnG4tQMWzYLXF+U2mbKlCkyderUlOu1NTVOLUpt2SxgkwAm2NIP6ELPdfGlH7b0HDDB9p4PGTKEfhjgS89t6YcvPYcutvfDNbYcFuFLP2zhSz/oORCOnqMQvvScfuhiy3wAJtjSc1/6YQtf5sOWfvjSc+hiSz+giy/9sIUv/bCl54AJ9ByF8KXntvTDl54DKA4HRQAAVKuqqmJxqoj2zQJfFqW2CJsPEZERVVUlHk38tG8WsEkAk7T3A7rQc11864f2ngMm2dzzAQMGmB6Cd3zrufZ++NZz6GJzP1yk/bAI3/qhnW/9oOdAOHqOfPjWc/qhi/b5AEzS3nPf+qGdb/OhvR++9Ry6aO8HdPGtH9r51g/tPQdMoufIh289194P33oOoHAcFAEAUI/FqS5a58O3Ral2mQ6JcJnWzQI2CaCB1n5AF3qealR9vbH39rUfWnsOaEDPkQtfe661H772HLrQD120Hhbhaz+08rUf9BwIR8+RC197Tj900TofSFVv8OsfvtLac1/7oZWv86G1H772HLpo7Qd08bUfWvnaD609BzSg58iFrz3X2g9few6gMBwUAQCwAotTXbTNh6+LUq18PSQiQdtmAZsE0ERbP6ALPU+vtq6OfhigreeAJvQcmfjec2398L3n0IV+6FJdXS1Dhw41PYwmvvdDG9/7Qc+BcPQcmfjec/qhi7b5QHp1dXX0wwBtPfe9H9r4Ph/a+uF7z6GLtn5AF9/7oY3v/dDWc0ATeo5MfO+5tn743nMA+eOgCACANVic6qJlPnxflGrj+yERCVo2C9gkgEZa+gFd6Hlm9MMMLT0HNKLnSIeeN9LSD3oOjeiHLgMHDjQ9BBGhH9rQj0b0HAhHz5EOPW9EP3TRMh/IjH6YoaXn9EMX5qORln7Qc2ikpR/QhX7oQj8aaek5oBE9Rzr0vJGWftBzAIWoMD0AAADyUf3fP9iOa/YH38Tjav7gW1Km54NFqS5h8zFo0CCZOnWqgRGZlViIN1+oJx7HvVBnkwCame4HdKHnuaEfZpjuOaAZPcfW6Hky0/2g59CMfmBr9EMX+pGMngPh6Dm2Rs+T0Q9dMs3H6g0bTAwJadAPM0z3nH7ownwko+dAONP9gC70Qxf6kcx0zwHN6Dm2Rs+Tme4HPQdQqHLTAwAAIF+cZKiLqflgUapLpvno27evgRHpYOpkSTYJYAN6DhF6ni/6YYaWk6IBjeg5ROh5GNaDQDj6ARH6oQ39SI+eA+HoOUToeRj6oUvYfNTW1RkYDcLQDzP4+z4QYT7C0HMgHOtBiNAPbehHevx9HyAcPYcIPQ/DehCAjTgoAgBgJRanupR6PliU6sJ8ZFbqzQI2CWATeu43+lEY+mEGXzwGwtFzv9HzzFgPAuHoh9/ohy70IzN6DoSj536j55nRD13C5gO60A8z+Ps+fmM+MqPnQDjWg36jH7rQj8xK3fNR9fWR3xOICz33Gz3PjPUgANtwUAQAwFosTnUp1XywKNWF+chNqTYL2CSAjei5n+hH7mpralKu0Q8zOCwCCEfP/UTPc8N6EAhHP/xEP3ShH7mh50A4eu4nep4b+qELh0XoU5Pm6x/0wwz+vo+fmI/c0HMgHOtBP9EPXehHbkrZ89q6usjuB5QCPfcTPc8N60EANuGgCACA1Vic6hL3fLAo1YX5yE/cmwVsEsBm9Nwv9CM/I6qq6IciHBYBhKPnfqHn+WE9CISjH36hH7rQj/zQcyAcPfcLPc8P/dCFwyJ0qaqqoh+KxN3z6dOn0w9F6Hl+6DkQjvWgX+iHLvQjP6Z6DtiAnvuFnueH9SAAW3BQBADAeixOdYlrPliU6sJ8FCauzQI2CeACeu4H+lEY+qELh0UA4ei5H+h5Yeg5EI5++IF+6DKqvp5+FICeA+HouR/oeWHohy4cFqEL/dAlzvmYNGlSyjX6YQY9Lww9B8LRcz/Qj+iNqq8v+LX0ozCl7jlgE3ruB3peGNaDAGzAQREAACewONUl6vlgUaoL81GcqDcL2CSAS+i52+hHceiHLhwWAYSj526j58Wh50A4+uE2+qFPbV1dyjX6kRt6DoSj526j58WhH7pcVV0ttTU1poeB/6IfupRqPuiHGfS8OPQcCEfP3UY/4lFbV0c/DChVzwEb0XO30fPisB4EoB0HRQAAnMHiVJeo5oNFqS7MRzSi2ixgkwAuouduoh/RoB+6cFgEEI6eu4meR4OeA+Hoh5vohx3oR37oORCOnruJnkeDfugyoqrK9BCwFfqhS9zzQT/MoOfRoOdAOHruJvoRL/phRtw9B2xGz91Ez6PBehCAZhwUAQBwCotTXTLNx7Rp07K+nkWpLsxHtIrdLGCTAC6j526hH9GiH7pwWAQQjp67hZ5Hi54D4eiHW+iHHehHYeg5EI6eu4WeR4t+AOHohy5xzQf9MIOeR4ueA+HouVvoR2nQDzPi6nltTU0k4wNMouduoefRYj0IQCsOigAAOIfFqS5h8zFhwoSMr2NRqgvzEY9CNwvYJIAP6Lkb6Ec86IcuHBYBhKPnbqDn8aDnQDj64Qb6YQf6URx6DoSj526g5/GgH0A4+qFL1PNBP8yg5/Gg50A4eu4G+lFa9MOMOHo+oqoq0jECptBzN9DzeLAeBKARB0UAAJzE4lSXsPkIw6JUF+YjXvluFrBJAJ/Qc7vRj3jRD13ynY9R9fWlGBagAj23Gz2PFz0HwtEPu9EPXWbMmJH2Ov2IBj0HwtFzu9HzeNEPIBz90CWq+RgyZAj9MICex4ueA+Houd3ohxn0wwx6DoSj53aj5/GiHwC04aAIAICzWJzqkuthESxKdWE+SiPXzQI2CeAjem4n+lEa9EOXfOajtq6ulEMDjKPndqLnpUHPgXD0w070Q5cpU6bI1KlTU67X1tTQjwjRcyAcPbcTPS8N+gGEox+6RDEfAwYMiHpYyIKelwY9B8LRczvRj9KpralJuUY/zKDnQDh6bid6Xhr0A4AmFaYHAABAnKr/+wfncc3+YJ14XM0frEsqbD4Spk+fLpMmTUq5zqLUDDYJSiux0G++EbD1YzYJ4Ct6bhf6UVr0Q5dC5wPwAT23Cz0vLXoOhKMfdqEfuoTNh4jIiKqqEo/GffQcCEfP7ULPS4t+AOHohy7Mh13oeWnRcyAc/bAL/SitEVVV0qaykn4oQc+BcPTcLvS8tOgHAC04KAIA4DwWp7pkOiyCQyL0YJPAjFw2C7bGJgF8Qs/tQD/MoB+65DsfMKe+vigkFfIAAQAASURBVF5OOOEE08PwCj23Az03g54D4eiHHeiHLpkOiUB86DkQjp7bgZ6bQT+AcPRDF+bDDvTcDHoOhKMfdqAfZtAPXZgPIBw9twM9N4N+ANCg3PQAAAAoherqahk2bFjK9XHjxsnEiRMNjMhv1dXVMnTo0KzPY1FqBpsEZl1VXS13pvn9qjk2CeAjeq4b/TCLfuiS63zArLq6OvphAD3XjZ6bRc+BcPRDN/qhC4dEmEXPgXD0XDd6bhb9AMLRD12YD93ouVn0HAhHP3SjH2bRD12YDyAcPdeNnptFPwCYxkERAABvsDjVZeDAgRk/z6LUDDYJdMi2WcAmAXxGz3WiHzrQD104LMIO9MMMeq4TPdeBngPh6IdO9EMXDonQgZ4D4ei5TvRcB/oBhKMfujAfOtFzHeg5EI5+6EQ/dKAfujAfQDh6rhM914F+ADCJgyIAAF5hcWoHFqVmsEkAwBb0XBf6AcB29MMMeq4LPQdgC/qhC/3QJWw+Bg0aZGA0ABCOnutCzwHYgn7okmk+pk2bZmBEfqPnAGxBz3WhHwCAQtBzXeg5AECEgyIAAB5icaobi1Iz2CTQ5a6JE+XqceNCP3/1uHFyF79fwXP0XAf6oQv90CXbfEAX+mEGPdeBnutCz4Hs6IcO9EOXTPPRt29fAyPyGz0HsqPnOtBzXegHkB390CVsPiZMmGBgNP6i57rQcyA7eq4D/dCFfujCfADZ0XMd6Lku9AOASRwUAQDwEotTnYYMGcKi1AA2CXTJ9R+VslkA0HPT6Icu9EMXDomwE/0wg56bRc91oedA7uiHWfRDF+ZDF3oO5I6em0U/dKEfQO7ohy5h84HSoOe60HMgd/TcLPqhC/3QhfkAckfPzaLnutAPAKZxUAQAwFssTvUZMGCA6SF4h00CXcI2Ce4cNkzuTPP7FZsFAD03hX7oQj90yXc+YE5NTU3KNfphBj03g57rQs+B/NEPM+iHLsyHLvQcyB89N4N+6EI/gPzRD104LMIMeq4LPQfyR8/NoB+60A9dmA8gf/TcDHquC/0AoEGF6QEAAGBSdXW1iDQuRreWeJz4POAiNgl0ybRJcNVWvxc1f07i8VX8fgWP0fPSoh+60A9dCp0PmFFVVSWVlZX0Qwl6Xlr0XBd6DhSOfpQW/dClmPkYVV8vN59wQlxD8xI9BwpHz0uLnutCP4DC0Q9dwuYD8aDnutBzoHD0vLTohy6j6uultq4u5Tr9MIOeA4Wj56VFz3WhHwC0KDc9AAAATOMkQ/iITQJdct0kuKq6mpMlgRD0vDTohy70Q5d85qO2pqaUQ0MG9EMX5qM06Lku9BwoHv0oDfqhS7HzUVtXRz8iRM+B4tHz0qDnutAPoHj0Q5ew+UC06Lku9BwoHj0vDfqhTy6HRNCP0qDn7qmvrzc9BO/Q89Kg57rQDwCacFAEAADC4hR+YZNAl1w3CRLYLADC0fN40Q9d6Icu+c7HiKqqUgwLOaIfujAf8aLnutBzIDr0I170Q5eo5oN+RIOeA9Gh5/Gi57rQDyA69EOX6upqGTp0qOlhOIue60LPgejQ83jRDzvQDzPouZvq6urohwH0PF70XBf6AUAbDooAAOC/WJzCB2wS6JLvJkECmwVAOHoeD/qhC/3QpdD5gC70QxfmIx70XBd6DkSPfsSDfugS9XzQj+LQcyB69Dwe9FwX+gFEj37oMnDgQNNDcBI914WeA9Gj5/GgH3agH2bE0fNR9fWRjhGFox9m0PN40HNdWA8C0IiDIgAA2AqLU7iMTQJdiv1HpWwWAOHoebTohy70QxcOiXAL/dCF+YgWPdeFngPxoR/Roh+6xDUf9KMw9ByIDz2PFj3XhX4A8aEfcBk914WeA/Gh59GiH3agH2bE1fPaurpIxodo0A8z6Hm06LkurAcBaMVBEQAANMPiFC5ik0CXqP5RKZsFQDh6Hg36oQv90IVDItxEP3RhPqJBz3Wh50D86Ec06Icucc8H/cgPPQfiR8+jQc91oR+68F1e3UQ/4CJ6rgs9B+JHz6NBP3SZMWNG2uv0w4y4ew5d6IcZ9Dwa9FwX1oMANOOgCAAA0mBxCpewSaBL1P+olM0CIBw9Lw790IV+6MIhEW6jH7owH8Wh57rQc6B06Edx6IcuccxHbU1NyjX6kRt6DpQOPS8OPdeFfuhy18SJfJdXh9EPuISe60LPgdKh58WhH7pMmTJFpk6dmnK9tqaGfhhQqp5DF/phBj0vDj3XhfUgAO04KAIAgBAsTuECNgl0iesflbJZAISj54WhH7rQD104JMIP9EMX5qMw9FwXeg6UHv0oDP3QJa75GFFVRT8KQM+B0qPnhaHnutAPXcLmA26hH3ABPdeFngOlR88LQz90CZsPkcY92kLRj8KUuucwpybNYdn0wwx6Xhh6rgvrQQA2qDA9AAAANKv+7x/cxzX7g33icTX/EAyKsUmgS9z/qDRxj+bvkXjMP1yFz+h5fuiHLqPq69N+ZzP6YQaHRPiFfujCfOSHnuvCehAwh37kZ/r06TJp0qSU6/TDjLh7Tj/yQ88Bc+h5flgP6kI/dOGQCL/QD9iMnutCzwFz6Hl+6IcumQ6JiAL9yI+pnsOMqqoqqayspB9K0PP80HNdWA8CsEW56QEAAKAdJxnCRmwS6FKqf1TKyZJAOHqeG/qhT5yHRCTQj9xwSISf6IcuzEdu6LkurAcB8+hH7jgkQo9S9Zx+5IaeA+bR89ywHtSFfujCIRF+oh+wET3XhZ4D5tHz3NAPXeI+JCKBfuSmlD2vramJ7H4oDv3QhfnIDT3XhfUgAJtwUAQAADlgcQqbsEmgS6n/USmbBUA4ep4Z/bAD/TCDQyL8Rj90YT4yo+e6sB4E9KAfhaEfZpS65/QjM3oO6EHPM2M9qAv90IVDIvxGP2ATeq4LPQf0oOeZ0Q9dSnVIRAL9yKzUPR9RVRX5PVE4+qEL85EZPdeF9SAA23BQBAAAOWJxChuwSaCLqX9UymYBEI6ep0c/7EA/zOCQCIjQD20yzce0adMMjEgHeq4L60FAH3qeH/phhqme04/06DmgDz1Pj/WgLvRDl7D54Lu8+oV+wAb0XBd6DuhDz9OjH7qEzcegQYNifV/6kR5/3wci9EMb5iM9eq4L60EANuKgCAAA8sDiFJqxSaCL6U1mNguAcPQ8Gf2wA/0ww3TPoQv90CVsPiZMmGBgNObRc11M94OeA+HoeW7ohxmme04/ktFzQC96nsx0P5CMfuiSaT74Lq/+oR/QjJ7rQs8Bveh5MvqhS6b56Nu3b+zvTz+Sme45dKEfujAfyei5Lqb7Qc8BFIqDIgAAyBOLU2jEJoEupjcJEtgsAMLR80b0Q5cZM2akvU4/zNDSc+hCP3QJmw/f0HNdtPSDngPh6Hlm9MMMLT2nH43oOaAfPW+kpR9oRD900TIf0IV+QCN6rouWftBzIBw9b0Q/dNEyH/SjkZaeQxf6oQvz0UhLP9BISz/oOYBCcFAEAAAFYHEKTdgk0EXLJkECmwVAON97Tj90mTJlikydOjXlem1NDf0wQFvPoYvv/dDG98Mi6Lku2vrhe8+BTOh5ekOGDKEfBmjrue/9oOeAPXzvubZ++I5+6KJtPqCL7/2ALvRcF2398L3nQCa+95x+6KJtPnzvh7aeQxff+6GN7/OhrR++09YP33sOIH8cFAEAQIF8X5xCBzYJdNG2SZDAZgEQztee0w9dwuZDRGREVVWJR7OFr/3Q2nPo4ms/tPL1sAh6rovWfvjacyAX9DzVgAEDTA/BO1p77ms/6DlgH197rrUfvqIfumidD+jiaz+gCz3XRWs/fO05kAtfe04/dNE6H772Q2vPoYuv/dDK1/nQ2g9fae2Hrz0HUBgOigAAoAi+Lk6hg82bBKPq600PIXJaNwkS2CwAwvnWc5v74aJMh0Ro4Fs/tPccuvjWD+18OyyCnuuivR++9RzIBz2HSdp77ls/6DmiUu/g1z+0863n2vvhG/qhi/b5gC6+9QO60HNdtPfDt54D+fCt5/RDF+3z4Vs/tPccuvjWD+18mw/t/fCN9n741nMAheOgCAAAiuTb4hQ62L5JUFtX59TiVPsmQQKbBUA4X3puez9co/2QiARf+mFLz6GLL/2wRXV1tQwdOtT0MGJHz3WxpR++9BwoBD2HCbb03Jd+0HNEqa6ujn4Y4EvPbemHL+iHLrbMB3TxpR/QhZ7rYks/fOk5UAhfek4/dLFlPnzphy09hy6+9MMWvsyHLf3whS398KXnAIrDQREAAETAl8UpdHBlk8CVxaktmwQJbBYA4VzvuSv9cIUth0QkuN4P23oOXVzvh20GDhxoegixoue62NYP13sOFIOeo5Rs67nr/aDniAP9MMP1ntvWD9fRD11smw/o4no/oAs918W2frjec6AYrvecfuhi23y43g/beg5dXO+HbVyfD9v64Trb+uF6zwEUr8L0AAAAcEX1fxcE45otGBKPqxUuGGAf1zYJEgtsjQvqXNi2SZCQGFvzsds+H0AUXO25a/2wXdh8DBo0SKZOnWpgRLlxtR+29hy6uNoP6ELPdbG1H672HIgCPUcp2NpzV/vhYs9Xb9hgYkhIg36Y4WrPbe2Hq1zsx9aft42t8wFdXO0HdKHnutjaD1d7DkTB1Z7TD11snQ9X+2Frz6GLq/2wVab5WLlypYkhRcLWfrjK1n642nMA0Sg3PQAAAFzi+kmGMMvVTQJbTzK0dZMggZMlgXCu9dzVftgq03z07dvXwIjy41o/bO85dHGtH9CFnutiez9c6zkQJXqOONnec9f64WrPa+vqDIwGYeiHGa713PZ+uMbVftBzwL1+QBd6rovt/XCt50CUXOs5/dDF9vlwrR+29xy6uNYP24XNx4QJEwyMpni298M1tvfDtZ6Pqq83PQTAGRwUAQBAxNgsQBxc3ySwbXFq+yZBgmubBUCUXOm56/2wjSvz4Uo/XOk5dHGlH9DFlX64wpV+uNJzIA70HHFwpeeu9MP1nkMX+mGGKz13pR+ucL0f9Bxwpx/QhZ7r4ko/XOk5EAdXek4/dHFlPlzphys9hy6u9MMVYfNhG1f64QpX+uFSzzkAH4gOB0UAABADNgsQJRc3CWpralKu2bI4dWWTIMGVzQIgDrb33MV+2My1+bC9H671HLrY3g/o4lo/bOdaP2zvORAneo4oudZz2/vhS89hTk2ar3/QDzNs77lr/bCdL/2g54D9/YAu9FwX1/phe8+BONnec/qhi2vzYXs/XOs5dLG9H66x/bAI1/phO9f64WrPARSOgyIAAIgJmwWIgqubBCOqqqxcnLq2SZBg+2YBECdbe+5qP2zl6nzY2g9Xew5dbO0HdHG1H7ZytR+29hwoBXqOKLjac1v74VvPYUZVVRX9UMTWnrvaD1uNqq/3qh/0HLC3H9CFnuviaj9s7TlQCrb2fPr06fRDEVd7bms/XO05dLG1H66y9bAIV/thK1f74VrPARSHgyIAAIgRmwUohuubBLYtTl3dJEiwbT6AUrKt5673wzauz4dt/XC959DFtn5AF9f7YRvX+2Fbz4FSoucohus9t60fvvYcZtAPXWybD9f7YaPaurqUa673g54D9vUDuvjc81H19aaHkML1ftjWc6CUbOz5pEmTUq750A+NXO+5bf1wvefQxcZ+uMy2wyJc74dtXO+HKz0HUDwOigAAIGZsFqAQvmwS2LI4dX2TIMGW+QBMsKXnvvTDFr7Mhy398KXn0MWWfkAXX/phC1/6YUvPARPoOQrhS89t6YdPPa+tqTE9DPwX/dDFlvnwpR+2c7Ef9BxIz5Z+QBffe15bV0c/DLCl54AJtvfcl35o40vPbemHLz2HLrb3wzXV1dUydOhQ08PIypd+2MKXftjecwDR4KAIAABKgM0C5MO3TQLti1NfNgkStM8HYJL2nvvWD+18mw/t/fCt59BFez+gi2/90M63fmjvOWASPUc+fOu59n741vMRVVWmh4Ct0A9dtM+Hb/2wlav9oOdAOO39gC70vBH9MEN7zwGTbO25b/3Qwreea++Hbz2HLrb2w1UDBw40PYSMfOuHdr71w9aecwA+EB0OigAAoETYLEAufN0k0Lo49W2TIEHrfAAaaO25r/3Qytf50NoPX3sOXbT2A7r42g+tfO2H1p4DGtBz5MLXnmvth689hy70Qxet8+FrP2zjej/oORBOaz+gCz1PRj/M0NpzQAPbeu5rP0zzteda++Frz6GLbf2AGb72Qytf+2FjzzkAH4gOB0UAAFBCbBYgE983CbQtTn3dJEjQNh+AJtp67ns/tPF9PrT1w/eeQxdt/YAuvvdDG9/7oa3no+rrS/6eQBh6jkx877m2fvjec+hCP3TRNh++90ObGTNmpL3uSz/oORBOWz+gCz1Pj36Yoa3ngCa29Nz3fpjie8+19cP3nkMXW/oBM3zvhza+94OeA/7ioAgAAEqMzQKkwyZBIy2LUxaljbTMB6CRlp7TD12Yj0Za+kHPoZGWfkAX+qEL/Wikqee1dXUlez8gF/Qc6dDzRpr6Qc+hDf3QRct80A9dpkyZIlOnTk25XltT41U/6DkQTks/oAs9z4x+mKGl54BG2ns+ZMgQ+mEAPW+kpR/0HBpp7wfMoB+60I9G9BzwEwdFAABgAJsF2BqbBMlML05ZlCYzPR+AZqZ7Tj90YT6Sme4HPYdmpvsBXeiHLvQjmdaeAxrQc2yNnifT2g9few5d6IcupueDfugSNh8iIiOqqko8GvPoORDOdD+gCz1PVVtTk3KNfphhuueAZpp7PmDAAKPv7yN6nsx0P+g5NNPcD5Qe/dCFfiSj54B/OCgCAABD2CyACJsEYUwtTlmUpmd6swC5q6+vNz0E75jq+fTp0+mHIvQ8PXoOhGM9CBH6oQ39SE9bzwFN6DlE6HkYbf3wvefQhX7oYmo+6IcumQ6J8Bk9B8LRc4jQ8zAjqqrohyL8fR8gHD2HCD0Pw3oQCEc/IEI/tKEf6dFzwC8cFAEAgEFsFviNTYLMSr04ZVGaGV88tkNdXR39MMBEzydNmpRyjX6YQc8zo+dAONaDfqMfutCPzLT0HNCInvuNnmempR/0HBrRD11KPR/0QxcOiciMngPh6Lnf6Hlm9EMX/r4PEI6e+42eZ0bPgXD0w2/0Qxf6kRk9B/xRYXoAAAD4rvq/f+Ad1+wPxInH1fyB2ElsEuQmsSBsvmBMPI5qwciiNDfZ5uOc/v1LPiakoh9mmO45/TCDnueGngPhTPcDZtAPXehHbkz3HNCMnvuJnufGdD/oOTSjH7pkm4+ePXtG8j70QxcOicgNPQfC0XM/0fPc0A9dSjUfgI3ouZ/oeW7oORCOfviJfuhCP3JDzwE/lJseAAAA4GRJ37BJkJ+4TzJkUZqfTPMxqr7ewIiQDv0ww1TP6YcZ9Dw/9BwIx3rQL/RDF/qRH1M9r62pKfreQNzouV/oeX5YDwLh6IcumeZj2rRpRd+ffugSNh+DBg0yMBr96DkQjp77hZ7nh37oUurvJAvYhJ77hZ7nh54D4eiHX+iHLvQjP/QccB8HRQAAoASbBX5gk6AwcS1OWZQWJmw+auvqDIwGYeiHGaXuOf0wg54Xhp4D4VgP+oF+6EI/CmOi5yOqqgq+L1BK9NwP9LwwrAeBcPRDl7D5mDBhQlH3pR+6ZJqPvn37GhiRHeg5EI6e+4GeF4Z+6MJhEUA4eu4Hel4Yeg6Eox9+oB+60I/C0HPAbRwUAQCAImwWuI1NguJEvThlUVqcsPmALvTDjFL1nH6YQc+LQ8+BcKwH3UY/dKEfxaHnQDh67jZ6Xhz6AYSjH7qEzUeh6IcuzEdx6DkQjp67jX4Uh37owmERQDh67jZ6Xhx6DoSjH26jH7rQj+LQc8BdHBQBAIAybBa4iU2CaES1OGVRGg0Oi9CnpqYm5Rr9MCPung8ZMoR+GEDPo0HPgXCsB91EP3ShH9Gg50A4eu4meh4N+gGEox+6RHVYBP3QhfmIBj0HwtFzN9GPaNAPXTgsAghHz91Ez6NBz4Fw9MNN9EMX+hENeg64iYMiAABQiM0Ct7BJEK1iF6csSqPFYRG6VFVV0Q9F4uz5gAEDino98kfPo0XPgXCsB91CP3ShH9Gi50A4eu4Weh4t+gGEox+6FHtYBP3QhfmIFj0HwtFzt9CPaNEPXTgsAghHz91Cz6NFz4Fw9MMt9EOXUfX19CNC9BxwDwdFAACgFJsFbmCTIB6FLk5ZlMaDwyJ0oR+6MB9uoOfxoOdAOPrhBvpReqPq60M/Rz/iQc+BcPTcDfQ8HvQDCEc/dCn0sAj6oQvzEQ96DoSj526gH/GgH7pwWIQ96jN8/QPxoOduoOfxoOdAOPrhBvqhT21dXco1+lEceg64hYMiAABQjM0Cu7FJEK98F6csSuN1VXW11NbUmB4G/ot+6MJ82I2ex4ueA+Hoh93ohxm1dXX0wwB6DoSj53aj5/GiH0A4+qFLdXW1DB06NOfn0w9dmI940XMgHD23G/2IF/3QhcMi7FBXV0c/DKDndqPn8aLnQDj6YTf6YQf6EQ16DriDgyIAAFCOzQI7sUlQGrkuTlmUlsaIqirTQ8BW6IcuzIed6Hlp0HMgHP2wE/0wi36YQc+BcPTcTvS8NOgHEI5+6DJw4MCcnkc/dGE+SoOeA+HouZ3oR2nQD104LMIO9MMMem4nel4a9BwIRz/sRD/sQD+iRc8BN1SYHgAAAMiu+r9/cB7X7A/WicfV/MFaFTYJSiuxsGy+8Nz6MYtS+Ip+6MJ82IWelxY9B8LRD7vQDx3ohxn0HAhHz+1Cz0uLfgDh6Idd6IcuzEdp0XMgHD23C/0oLfqhS6b5WL1hg4khIQ36YQY9tws9Ly16DoSjH3ahH3agH/Gg54D9OCgCAABLsFlgBzYJzMhlcbo1FqXwCf3QhfmwAz03g54D4eiHHeiHLvTDDHoOhKPndqDnZtAPIBz9sAP90IX5MIOeA+HouR3ohxn0Q5ew+aitqzMxHISgH2bQczvQczPoORCOftiBfugyY8aMtNfpR7zoOWC3ctMDAAAAuauurpZhw4alXB83bpxMnDjRwIiwNTYJzLqqulruTPPfR3MsSuEj+qEL86EbPTeLngPh6Idu9MMO9KM06DkQjp7rRs/Noh9AOPqhG/3Qhfkwi54D4ei5bvTDLPqhS67zAbPohxn0XDd6bhY9B8LRD93ohy5TpkyRqVOnplyvramhHyVAzwF7cVAEAACWYbNAJzYJdMi2OGVRCp/RD12YD53ouQ70HAhHP3SiH3rU1tSEfo5+lBY9B8LRc53ouQ70AwhHP3SiH7owHzrQcyAcPdeJfuhAP3ThsAh9atJ8/YN+mEHPdaLnOtBzIBz90Il+6BI2HyIiI6qqSjwaf9FzwE4cFAEAgIXYLNCFTQIAtqAfujAfutBzALagH7rQDwBAIei5LvQcgC3ohy7Tp0+nH4rQcwC2oOe60A8AtqiqqqIfitBzXeg5AFvQD13ohy6ZDokAAGTHQREAAFiKzQId2CTQ5a6JE+XqceNCP3/1uHFyF/99wHP0QxfmQwd6rgs9B7KjHzrQD31q6+pCP0c/SoueA9nRcx3ouS70A8iOfugxadKklGv0wwx6rgs9B7Kj5zrQD13ohy7Z5gNm0A9dmA8d6Lku9BzIjn7oQD90yeWQiFH19SUaDeg5YKcK0wMAAACFq66uFpHGzYGtJR4nPo94sEmgS65fpEw85yr++4DH6Icu2eajZ8+eJR+TT+i5LvQcyB09N4t+2Il+lAY9B3JHz82i57rQDyB39EMn+mEGPdeFngO5o+dm0Q9d6IcuHBKhG/3Qhfkwi57rQs+B3NEPs+iHLrkcEiHS+I1j2lRW0o+Y0XPAXuWmBwAAAIrDyZJmsEmgS9ii9M5hw+TONP99cJIhQD+0yTQf06ZNMzAiP9BzXeg5kD96bgb9sAP9MIOeA/mj52bQc13oB5A/+qEL/TCDnutCz4H80XMz6Icu9EOXsPmorakxMBqEoR+6MB9m0HNd6DmQP/phBv3QJWw+Bg0alPb59CNe9BywW4XpAQAAgOJxsmRpsUmgS6ZF6danFDZ/DicZAvRDm7D5mDBhgonhOI+e60LPgcLR89KiH3agH2bQc6Bw9Ly06Lku9AMoHP3QgX6YQc91oedA4eh5adEPXeiHLpnm45z+/aW2rs7AqBCGfujCfJQWPdeFngOFox+lRT90yTQfPXr0kKlTp6Z9Hf2IBz0H7FduegAAACAanCxZGmwS6JLrovSq6mpOMiyBUfX1poeAAtAPXcLmA9Gi57rQc6B49Lw06IcuM2bMSHudfphBz4Hi0fPSoOe60A+gePTDLPphBj3XhZ4DxaPnpUE/dKEfuuQ6H9CFfujCfJQGPdeFngPFox+lQT90KXY+6Ee06DngBg6KAADAIWwWxItNAl3y/SIli9N43TVxIt89wGL0QxcOi4gXPdeFngPRoefxoh+6TJkyJe13D6itqaEfBtBzIDr0PF70XBf6AUSHfpgxZMgQ+mEAPdeFngPRoefxoh+60A9dOCTCbvRDF+YjXvRcF3oORId+xIt+6BLVfNCPaNBzwB0cFAEAgGPYLIgHmwS6FPpFShan8QibD9iFfujCYRHxoOe60HMgevQ8HvRDl7D5EBEZUVUV+jr6EQ96DkSPnseDnutCP4Do0Y/SGzBggOkheIee60LPgejR83jQD13ohy4cEuEG+qEL8xEPeq4LPQeiRz/iQT90KWY+amtqUq7Rj+LQc8AtHBQBAICD2CyIFpsEuhT7RUoWp9HikAi30A9dOCwiWvRcF3oOxIeeR4t+6JLpkIhc0I9o0XN/1NfXmx6Cd+h5tOi5LvQDiA/9gMvouS70HIgPPY8W/dCFfujCIRFuoR+6MB/Roue60HMgPvQjWvRDl2LnY0RVFf2IED0H3MNBEQAAOIrNgmiwSaBLVF+kZHEaDQ6JcBP90KW6ulqGDh1qehjWo+e60HMgfvQ8GvRDl2IPiUigH9Gg536pq6ujHwbQ82jQc13oBxA/+gEX0XNd6DkQP3oeDfqhC/3QhUMi3EQ/dGE+okHPdaHnQPzoRzTohy5RzQf9iAY9B9zEQREAADiMzYLisEmgS9RfpGRxWhwOiXAb/dBl4MCBpodgNXquCz0HSoeeF4d+6BLVIREJ9KM49NxP9MMMel4ceq4L/QBKh37AJfRcF3oOlA49Lw790IV+6MIhEW6jH7owH8Wh57rQc6B06Edx6IcuUc8H/SgOPQfcxUERAAA4js2CwrBJoEtcX6RkcVqYsPmorakxMBrEhX7ABfRcF3oOlB49Lwz90CVsPgYNGlTUfelHYUz0fFR9fcH3RbTohxn0vDD0XBfWg0Dp0Q+4gJ7rQs+B0qPnhaEfutAPXTgkwg/0Q5dM8zFt2jQDI7IDPdeFngOlR88LQz90iWs+6Edh6DngNg6KAADAA2wW5IdNAl3i/iIli9P8ZJqPEVVVBkaEONEP2Iye60LPAXPoeX7ohy6Z5qNv375F359+5MdUz2vr6oq+N6JDP8yg5/mh57qwHgTMoR+wGT3XhZ4D5tDz/NAPXeiHLhwS4Rf6oUvYfEyYMMHAaPSj57rQc8Acep4f+qFL3PNBP/JDzwH3cVAEAACeYLMgN2wS6FKqL1KyOM0NXzT2E/2Ajei5LvQcMI+e54Z+6FKq+aAfuTHdc+hCP8yg57mh57qY7gc9B+gH7ETPdaHngHn0PDf0Qxf6oQt/38dP9EOXsPlAMnquCz0HzKPnuaEfuvD3fXSh54AfOCgCAACPsFmQGZsEupT6i5QsTjPji8Z+ox+wCT3XhZ4DetDzzOiHLqWeD/qRmZaew5yampqUa/TDDHqeGT3XRUs/6DlAP2AXeq4LPQf0oOeZ0Q9d6Icu/H0fv9EPXTgsIjN6rgs9B/Sg55nRD134+z660HPAHxWmBwAAAEqr+r9/oB/X7A/8icfVnn4BiE0CXUx9kTJx7+bvnXjs6xdI+aIxROgH7EDPdaHngD70PD36oYup+aAf6WnrOcyoqqqSyspK+qEEPU+PnuuirR++9xwQoR+wAz3XhZ4D+tDz9OiHLqPq66W2ri7lOv0wg7/vAxH6oU3YfPiOnuvCehDQh56nN336dJk0aVLKdfphBn/fRxd6Dvil3PQAAABA6XGyZDI2mXUx/UVKTjJMZno+oAv9gGb0XBfT/aDnQDh6nox+6GJ6PuhHMq09hxn0QxfmI5npfiCZ1n742nNga/QDmmnu+aj6eqPvbwI9B/Si58k098NXJg6JSKAfyUz3HLrQD13C5sNX9FwX0/2g50A4ep6KQyL0MN1z+pGMngP+4aAIAAA8xWZBI9OLUiQzvShNYHHaSMt8QBf6AY3ouS5a+kHPgXD0vBH90EXLfNCPRpp6XltTU7L3Q2b0Qxfmo5GWfqCRpn7QcyA9+gGNtPe8tq7Oq37Qc0A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mSjpbeToLUDrzpg4CNGRmzZU0tvI0FqB0Zk0dBGjIzJoraWzlaSxA+WwUAQDUqHHjxkVExM4771zueW+88UYUFhZG7969S/36v//975LP8x3w1txxctWQXpbZs2fHgAEDYv78+RERUVhYGI888ki0b98+r3uVZsstt4xhw4ZFRH7Dd0SUubtzWYYMGVIy+K7y/vvvx9/+9rcyr2nWrFnG61x/WJCPVWtUx1oA9Ul1dbAsXbp0qfSzleaTTz6J/v37x/Tp0yMi4vLLL48f/ehHVVpTY6tGYwFKZ9bUQYCGzryps1WlswClM2/qIEBDZtbU2KrSWIDSmTV1EKAhM2tqbFVpLED5bBQBANSo8ePHR0SUO7DPmzcvPv7449huu+1iww03LPWcVTsnRqwcvvOx5k6FuXZvvOiii2Ly5Mklr88+++ycu1Hm47jjjot+/frFl19+WeW1SlNUVBQnnHBC1vF//vOfNXI/ACqmujpYlhYtWlTp+da0YMGCGDBgQMmb23379o0bbrihyutqLAA1waypgwANnXlTZwGoGeZNHQRoyMyaGgtAzTBr6iBAQ2bW1FgAapaNIgCAGpXPDpBvvvlmpJTKHXZbtmxZ8vkjjzyS8eZ1WaZOnVryeb9+/co87+mnn45HHnmk5HWTJk3i5z//ec718zV48OC8d2msjNLeNJk0aVKN3Q+A/FVXB8tS0d19y3PuueeW9KNp06bx8MMPR6NGjaq0psYCUFPMmjoI0NCZN3UWgJph3tRBgIbMrKmxANQMs6YOAjRkZk2NBaBmFdX2AwAA9dv48eOjadOm0b179zLPeeONNyIiyh3s+/TpU/ImwdSpU+Pmm2+OIUOGlHn+kiVL4tlnn42IiC222CKOPvroMs+98sorM15/73vfi0033bTM8ytqwIABMX/+/Gpbb22bb7551rGlS5fW2P0AyF91dbAsVX0DepXHHnssRowYUfJ64MCBse2221Z5XY0FoKaYNXUQoKEzb+osADXDvKmDAA2ZWVNjAagZZk0dBGjIzJoaC0DNKqztBwAA6r4pU6ZEQUFBqR9TpkyJb775Jho3blzmOVdddVVERPzgBz/IunaV0047LeOeV199dTzxxBNlPtOwYcNi4cKFUVhYGPfcc0+ZO0U+/fTT8c4772QcO/XUUyv5kyhdo0aN4owzzqjWNde0ZMmSrGPt2rWrsftVh9mzZ8fVV18dO+20U7Rs2TJatmwZO++8cwwePDj+85//xIIFC+KQQw6JJ598srYfFSCnddHBmvTtt9/G5ZdfXvK6cePG8aMf/ajK62ps7dBYoD4xa5ZPB7PpIFDfmDdLp7O1Q2eB+sS8WT4dzKaDQH1i1iydxtYOjQXqE7Nm+XQwmw4C9YlZs3QaWzs0Fmhoimr7AQCAuq9169bxk5/8JOv466+/Hs8++2wccMAB0bdv3zKv/+1vfxsrVqyIK664ImvdVfbcc8845phj4u9//3tERCxfvjyOPfbYuPPOO+Occ87JuG7s2LHx4x//OBo1ahT33ntvHHrooWXe+/e//33Wsb322qvM89dHkyZNyjq244471sKT5Oell16KY445JmbNmhW77rprnHbaadGsWbN4//3345Zbbombb7655NyLL764Fp8UID/rooM1afjw4TF58uSS1wMGDIgtt9yyyutq7LqnsUB9Y9asXToIUPvMm6XT2XVPZ4H6xrxZu3QQoHaZNUunseuexgL1jVmzdukgQO0ya5ZOY9c9jQUapAQAUEMuvPDCFBHpn//8Z5nnzJ49O0VE6tOnT8715syZk7bffvsUERkfF110Ufr2229TSimNHz8+bbzxxqlt27bpqaeeKne9ZcuWpZYtW2as1alTpwp9j9WtU6dOGc/z0EMP5bxm5513zvqZPPvss9V6j1zGjBmTIiLts88+5Z73ySefpFatWqWCgoL08MMPZ3198uTJaffddy95tieeeKLKzwZQW6q7g6VZ9e/fVR9Dhw6t8Bo77rhjxhojRoxIX3/9dXr00UfTmWeemXr06JFat26dmjRpkrbYYot08sknp+eee67cNTVWYwFqklmz4nRQB4H6xbypszoLUDPMmxWngzoI1B9mTY3VWICaYdasOB3UQaD+MGtqrMYC1LzCAACoIePGjYuIiN69e5d5zptvvpnznFU22WSTePbZZ6Nbt24Zx++888446KCD4oknnoj+/ftH37594+23345DDjmk3PXef//9WLhwYcaxLl265HyO9ckf/vCHGD9+fMaxvfbaKw444IBaeqLyXXPNNTF//vw47rjj4vTTT8/6eufOnePpp5+ulh04AWpbdXewJowfPz4mTJhQ8rqwsDDefPPN6NixYwwZMiQaN24cBx54YPTr1y+Ki4tj2rRp8Ze//CX233//OPnkk2PJkiWlrqux657GAg2JWbPm6SDA+s28qbPrks4CDYl5s+bpIMD6y6ypseuSxgINiVmz5ukgwPrLrKmx65LGAg2VjSIAgBpRXFwc77zzTrRv3z46duxY5nkVHey/853vxIsvvhi77LJLxvExY8bEEUccEb169YqRI0fGd77znZxrvfvuu1nHWrdunddzrG348OFRUFCQ98ewYcMqdZ81PfPMM3H++ednHGvbtm0MHz68ymvXhOLi4vjnP/8ZEVHuP59WrVrFr371q3X1WAA1oqY6WN1GjRqV8XrFihUxatSoGD58ePz3v/+Ne++9N37729/GyJEj46OPPoqePXuWnPvXv/41DjzwwPjmm2+y1tXYdUtjgYbErKmDa9NBoKExb+rsuqSzQENi3tTBtekg0JCYNTV2XdJYoCExa+rg2nQQaEjMmhq7Lmks0JDZKAIAqBEffvhhfP311zkH9rfeeisiIusN6/K0b98+/vOf/8QxxxyT9bX//Oc/ccQRR8T8+fNzrjNr1qysYy1atMj7OdbUq1evuPzyy+PYY4+NDTfcsMzzdt9997jiiivy/n5ffPHFeOGFF+Kzzz6Lb7/9Nr7++ut47bXX4txzz43DDz88vv7665Jzu3btGs8++2x07dq1Ut9DTZs1a1YsWrQoIlb+gURKqcxzTzjhhGjXrt26ejSAaleTHaxOzz//fMbr0047Ld5+++0YMGBAFBQUZHxtq622ijFjxkSHDh1Kjr388stx2WWXZa2rseuWxgINiVmzdDqog0DDYd7U2XVJZ4GGxLxZOh3UQaBhMGtq7LqksUBDYtYsnQ7qINAwmDU1dl3SWKAhK6rtBwAA6qdx48ZFRO6dHd98881o3Lhxxs6K+Zg+fXpMmTIlzjjjjBg3blzGjosjR46Mfv36xahRo6JTp05lrrHm4LrKkiVLKvQcq/Tu3bvke/34449j9913jzlz5mScc95551V4d8ZHH300HnzwwXLP6dChQ1x44YVx6aWXRsuWLSv24OtQq1atoqCgIFJKMWHChLjuuuvi2muvLfXcxo0bxxFHHLFuHxCgGtV0B6vLxIkTM17vvffeUVRU9lsFbdq0iVtvvTVOOOGEkmPDhg2LSy65JLbffvuSYxq7bmks0JCYNXVwbToINDTmTZ1dl3QWaEjMmzq4Nh0EGhKzpsauSxoLNCRmTR1cmw4CDYlZU2PXJY0FGjIbRQAANSKfwX7mzJkxbdq02GmnnaJJkyZ5r/2vf/0rjjvuuDj55JPjrrvuisWLF8cZZ5wRf//730vOef/992OPPfaIMWPGxLbbblvqOhtvvHHWsXnz5uX9HGXZeuut4/TTT4/f/e53GccvuOCCCq912223xZFHHhkTJkyITz/9NL766qtYuHBhtGzZMtq1axe77LJLdO/ePWu3yvXRBhtsEN26dYsPP/wwIiKuu+66eOONN+J3v/tddOvWLev8+++/f10/IkC1qckOVpelS5fG9OnTM4517Ngx53XHHntsdO3aNT755JOIiEgpxR133BF33nlnyTkau25pLNCQmDV1cG06CDQ05k2dXZd0FmhIzJs6uDYdBBoSs6bGrksaCzQkZk0dXJsOAg2JWVNj1yWNBRoyG0UAADVi/PjxEVH+YP/WW2/lPGdtI0aMiFNPPTV22WWXuPPOO6OgoCBatGgRf/vb32LIkCFx4403lpw7ffr06N+/f7z22muxxRZbZK3Vrl27rGMff/xx3s9Snq222iqvY/nYeOONY99994199923ik+1UmFhYbWsU5pcbwL8+Mc/jjPPPLPk9ahRo+KZZ56Jk046Ka688spa2wkUoLrVVAer04IFC7KOtW/fPud1hYWFceKJJ8avfvWrkmP/+te/Ms7R2OqnsQArmTV1sDQ6CDQk5k2drW46C7CSeVMHS6ODQENh1tTY6qaxACuZNXWwNDoINBRmTY2tbhoLUDobRQAAVTZv3ry4+eabM4699dZbUVhYGA888ECZ173++usRETF58uT46U9/mvX1K664Ilq3bl3y+umnn45TTjklUkrxwAMPZAyRBQUFccMNN8Tmm28el112WaSUImLlm9zHH398vPLKK1lD5/bbb591z//973+xYMGC2GijjXJ/4+Vo3rx51rENN9ywSmtWl8aNG2e8XvWzqoply5ZFRETTpk3LPe/000+PN998M+64446SY8XFxfGnP/0p/vznP8fhhx8eP/nJT2L33Xev8jMBrCvrqoPVbdW/u9fUqlWrvK49+OCDM97g/uijj+Lbb78taYzGrqSxAFVj1symg6XTQaC+Mm+aN1fRWYDqZd7MpoOl00GgPjJrmjVX0ViA6mXWzKaDpdNBoD4ya5o1V9FYgHXPRhEAQJXNmzcvfvnLX5b6tbKOr2nMmDExZsyYrOMDBw4sGey//PLL+P73vx/FxcWx//77xw477FDqWpdeemkUFRXFxRdfXHLstddeiwcffDAGDhyYce4OO+wQ7du3j5kzZ5YcSynFmDFj4sgjj8z53OUpbSfEmtwdsSLWHpCXLFlS5TXzHb4LCgri9ttvj7322isGDRoUM2bMKPlaSimefPLJePLJJ+OEE06I22+/Pa8dOQFq27roYE0o7U3hfN+Q7dGjR9axOXPmxGabbRYRGruKxgJUjVkzmw6WTgeB+sq8uZJ5U2cBqpt5M5sOlk4HgfrIrLmSWVNjAaqbWTObDpZOB4H6yKy5kllTYwFqw/pRAACgTuvcuXOklEo+hg0bFhERQ4cOzTi+9kf79u2jdevWsWLFilK/3rlz55J7XHvttfHVV19FRMTxxx9f7vNcdNFFcc0112Qcu+2227LOKygoiEMOOSTr+BNPPFHRH0GdsvabJfPnz6/ymnPnzo2IiLZt2+Z1/oknnhgfffRRXHvttaXuiPnII4/ELrvsEpMmTaryswHUtHXRwZqw8cYbZ+0qvOYb0uXZZJNNokWLFhnH1twFWGNX0liAqjFr1i06CFD9zJsrmTd1FqC6mTfrFh0EqF5mzZXMmhoLUN3MmnWLDgJUL7PmSmZNjQWoDTaKAACq3RtvvBEREbvttluZ5/zvf/+LmTNnxq677hoFBQXlrrds2bL4wx/+UPJ62223zfkM1157bey7774lr999992MHQFXufTSS7OOjRgxIhYsWJDzHnXV2gPy559/XuU1v/zyy4iI6NChQ97XtGzZMoYOHRqTJ0+OIUOGRLNmzTK+/tlnn8Whhx4aCxcurPLzAaxL1d3BmrT99ttnvK5IE1q2bFnyeVFRUWy88cYZX9dYjQWobmbN9ZsOAtQ88+ZKOquzANXNvLl+00GAmmXWXEljNRagupk11286CFCzzJoraazGAqwLNooAAKpdPoP9m2++GRERffr0ybne+++/H4sXLy55nc+AV1BQENdee23GsalTp2ad17t37zjooIMyji1atKhkF8v6qEuXLhmvJ0+eXOU1P/jgg4iI2GGHHSp87SabbBLXX399fPjhh3HooYdmfG3q1Klx++23V/n5ANal6u5gTfrud7+b8frVV1/N+9ri4uKSz3fZZZcoLMx8i0FjNRagupk11286CFDzzJsr6azOAlQ38+b6TQcBapZZcyWN1ViA6mbWXL/pIEDNMmuupLEaC7Au2CgCAKhWX3/9dUycODG22mqrrN0A11SRwf6bb77JeL106dK8nmWvvfaKxo0bl7xeexfAVW6//fZo3rx5xrHrr78+Zs+endd96pqdd9454/WqN2KqYtUbIn379i3znKVLl0b37t3L/PqWW24ZI0eOjMGDB2ccHzlyZJWfD2BdqYkO1qQTTjgh4/Xzzz+f13XFxcUxb968ktcHHnhgqedprMYCVBez5vpPBwFqlnkzk87qLEB1MW+u/3QQoOaYNTNprMYCVBez5vpPBwFqjlkzk8ZqLEBNs1EEAFCtxo8fH8XFxeXu/hhRscG+c+fOGa8//PDDvJ6lUaNGsckmm5R8vvY6q3Tr1i1++9vfZhybN29enHPOOXndp6459NBDY4MNNih5PWvWrJg4cWKl1xs3blxMmjQpevbsGVtttVW5506aNCk+/vjjMr9eUFAQv/71r6NHjx4lx+rrmyBA/VQTHaxJu+++e+y6664lrydMmJDX7r2TJk2KZcuWRUREUVFRnHvuuaWep7EaC1BdzJrrPx0EqFnmzUw6q7MA1cW8uf7TQYCaY9bMpLEaC1BdzJrrPx0EqDlmzUwaq7EANc1GEQBAtVq141+uwf6tt96KzTbbLDp27JhzzU033TRjvSeeeCKvZ5k3b17J4Na/f/9o1apVmeeed955ceGFF2Yc+8c//hHXXnttXveqLqveLCjrdXVo06ZN1hsRt956a6XWWr58eQwaNCgiIq688sq8rsl1r8LCwjj44INLXnfo0KFSzwZQG2qig+VZsWJFxuuUUoXXuP766zOuv+OOO3Je89hjj5V8fvbZZ8cWW2xR5rkaq7EA1cGsWTU6qINA3WfezKazOgtQHcybVaODOgjUbWbNbBqrsQDVwaxZNTqog0DdZtbMprEaC1CTbBQBAFSrVYN93759yzzn008/jblz51Zo98drrrmm5PMRI0bEe++9l/Oae+65J4qLiyMi4rrrrst5/h133BEXX3xxxrHrrrsurrzyyli+fHnez1pZKaX46quvMo7NmjWrRu7185//PLp06VLyevjw4TF69OgKrbFs2bI4/fTT48UXX4wjjjgiTjnllLyuGzZsWLz++uvlnrPmz2HNQRxgfVdTHSzLkiVLyn2djwMOOCBjZ+K77rqr3N86MGfOnJI3wTt27Bg33HBDzntorMYCVJVZs/J0cDUdBOoy82bpdFZnAarKvFl5OriaDgJ1lVmzdBqrsQBVZdasPB1cTQeBusqsWTqN1ViAGpMAAKrRNttsk4qKitKSJUvKPGfEiBEpItLPfvazCq19wQUXpIhIEZG22WabNGXKlDLPHTVqVGrSpEmKiHTNNddU6D4PPPBA2mijjUruFRFpt912Sy+++GJe10+ZMiUdffTRGdc3btw453Vvv/12xjURkQYMGFChZ6+IDz74ILVv377kXhtuuGF68MEH87r2xRdfTDvuuGOKiLTPPvukxYsX57xmyZIlJfdq27Ztevnll0s977PPPktt2rRJEZG+853vpPnz51fo+wKoTTXZwdLcc889Gd34/ve/X6l1li5dmvbaa6+SdXr06JHmzp2bdd6SJUvSAQcckCIitW7dOr3zzjsVuo/G5qaxAKUza+pgWXQQaCjMm+XT2dx0FqB05k0dLIsOAg2BWbN8GpubxgKUzqypg2XRQaAhMGuWT2Nz01iAirFRBABQbebOnZsKCgrSzjvvXO55gwcPThGRnnrqqQqtX1xcnC655JKSAa5169bpF7/4RZo4cWJavHhxmjt3bvrPf/6Tzj777FRYWJgKCwvTddddV6nv5bPPPksDBw4seZN81Uffvn3TLbfckt588800Z86ctGTJkvTll1+m1157Ld15553p4IMPTkVFRSXnt2jRIl122WVp2rRp5d5v/PjxqWfPnlnDd0FBQbrmmmvSggULKvV95DJ16tS0xx57ZNyzT58+6dZbb01vv/12mjFjRlqyZEmaOXNmeuWVV9JvfvObtPvuu6eISI0aNUqDBg1Ky5Yty+teaw7fEZGKiorSwIED04svvpjmzZuXvvzyy/R///d/qWvXriki0mabbVbhN04AalNNd3Bt7777btpmm20y/t3arFmz9Pjjj6fi4uIKr7dgwYKSN69X/WHyo48+mubOnZu++uqr9Pjjj6cePXqkiEidOnVKY8eOrdRza6zGAlSUWVMHy6ODQENg3syPzuosQEWZN3WwPDoI1HdmzfxorMYCVJRZUwfLo4NAfWfWzI/GaixAdbJRBABQbUaPHp0iIp1//vnlnte/f/8UEWn27NmVus8zzzxTMgCW9lFQUJAOO+yw9Oqrr1Zq/TV98cUX6Te/+U3aa6+9UtOmTcu855ofG220UTrqqKPSH//4x3J3GBw3blzq1KlT2mSTTfJat0OHDumUU06p8ve0thUrVqS//e1v6aCDDsp6s6G0jzZt2qSzzjorvf/++xW6z9rDd1kfjRs3TmeeeWaaOXNmtX+vADVpXXRwypQpqWPHjqlFixbl/ru0adOmafPNN0+jRo2q0PrLly9PN910U2rbtm2ZDRg0aFC1vCmssRoLkC+zpg6WRweBhsC8WTE6q7MA+TJv6mB5dBCo78yaFaOxGguQL7OmDpZHB4H6zqxZMRqrsQDVoSCllAIAoBqklKK4uDgaNWoUBQUFZZ5XXFwcERGNGjWq0v2mT58eL730UkyfPj0WLlwYrVq1ik6dOsWee+4Zm2yySZXWLs0333wTEydOjMmTJ8cXX3wRixcvjuXLl0eLFi2iZcuWsemmm0aPHj2ic+fO1X7vdWXx4sXx/vvvx0cffRRz5syJRYsWRUSUfH/du3eP7bffvkr/7FasWBEzZ86MyZMnx9SpU2PWrFmxcOHCKCoqis6dO0f//v2jXbt21fUtAawz67qDNWnZsmXx8ssvx8SJE2PhwoXRvn376NKlS+y9997RuHHjar+fxmosQHnMmjqYDx0E6jPzZuXprM4ClMe8qYP50EGgvjJrVp7GaixAecyaOpgPHQTqK7Nm5WmsxgJUlo0iAAAAAAAAAAAAAAAAAAAAAOqIwtp+AAAAAAAAAAAAAAAAAAAAAADyY6MIAAAAAAAAAAAAAAAAAAAAgDrCRhEAAAAAAAAAAAAAAAAAAAAAdYSNIgAAAAAAAAAAAAAAAAAAAADqCBtFAAAAAAAAAAAAAAAAAAAAANQRNooAAAAAAAAAAAAAAAAAAAAAqCNsFAEAAAAAAAAAAAAAAAAAAABQR9goAgAAAAAAAAAAAAAAAAAAAKCOsFEEAAAAAAAAAAAAAAAAAAAAQB1howgAAAAAAAAAAAAAAAAAAACAOsJGEQAAAAAAAAAAAAAAAAAAAAB1hI0iAAAAAAAAAAAAAAAAAAAAAOoIG0UAAAAAAAAAAAAAAAAAAAAA1BE2igAAAAAAAAAAAAAAAAAAAACoI2wUAQAAAAAAAAAAAAAAAAAAAFBH2CgCAAAAAAAAAAAAAAAAAAAAoI6wUQQAAAAAAAAAAAAAAAAAAABAHWGjCAAAAAAAAAAAAAAAAAAAAIA6wkYRAAAAAAAAAAAAAAAAAAAAAHWEjSIAAAAAAAAAAAAAAAAAAAAA6ggbRQAAAAAAAAAAAAAAAAAAAADUETaKAAAAAAAAAAAAAAAAAAAAAKgjbBQBAAAAAAAAAAAAAAAAAAAAUEfYKAIAAAAAAAAAAAAAAAAAAACgjrBRBAAAAAAAAAAAAAAAAAAAAEAdYaMIAAAAAAAAAAAAAAAAAAAAgDrCRhEAAAAAAAAAAAAAAAAAAAAAdYSNIgAAAAAAAAAAAAAAAAAAAADqCBtFAAAAAAAAAAAAAAAAAAAAANQRNooAAAAAAAAAAAAAAAAAAAAAqCNsFAEAAAAAAAAAAAAAAAAAAABQR9goAgAAAAAAAAAAAAAAAAAAAKCOsFEEAAAAAAAAAAAAAAAAAAA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+ "text/plain": [ + "
" + ] + }, + "execution_count": 540, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig.savefig(\"lynx_end_to_end.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 +} diff --git a/lynx/end_to_end_nfsc.ipynb b/lynx/end_to_end_nfsc.ipynb new file mode 100644 index 0000000..9f21f1e --- /dev/null +++ b/lynx/end_to_end_nfsc.ipynb @@ -0,0 +1,302 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 335, + "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", + " \"#999999\",\n", + " \"#FF9999\",\n", + "]\n", + "\n", + "hatches = [\"\\\\\", \"x\", \"+\", \"/\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 336, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(\n", + " figsize=(14, 14 / 4), ncols=2, nrows=1, constrained_layout=True, dpi=300\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_a = {\n", + " \"7B#8GPUs\": [53, 65], # 22.64\n", + " \"7B#16GPUs\": [44,63], # 43\n", + " \"11B#8GPUs\": [35, 48], # 37\n", + " \"11B#16GPUs\": [22, 37] # 68\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 338, + "metadata": {}, + "outputs": [], + "source": [ + "data_b = {\n", + " \"7B#8GPUs\": [1.22, 1.01],\n", + " \"7B#16GPUs\": [1.9, 1.62],\n", + " \"11B#8GPUs\": [1.34, 1.1],\n", + " \"11B#16GPUs\": [1.9, 1.436]\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 348, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.17213114754098358\n", + "0.14736842105263148\n", + "0.1791044776119403\n", + "0.24421052631578946\n" + ] + } + ], + "source": [ + "for key, values in data_b.items():\n", + " print((values[0] - values[1]) / values[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 339, + "metadata": {}, + "outputs": [], + "source": [ + "legend_labels = [\"FSDP\", \"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": [ + "for x_label, y_data in data_a.items():\n", + " positions = group_positions[x_label]\n", + " for i, (pos, value, color, hatch) in enumerate(\n", + " zip(\n", + " positions,\n", + " y_data,\n", + " colors,\n", + " hatches,\n", + " )\n", + " ):\n", + " ax[0].bar(\n", + " pos,\n", + " value,\n", + " width=bar_width,\n", + " color=color,\n", + " edgecolor=\"black\",\n", + " hatch=hatch,\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=g_label_fontsize, 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": [ + "label_set = set()\n", + "for x_label, y_data in data_b.items():\n", + " positions = group_positions[x_label]\n", + " for i, (pos, value, color, hatch, label) in enumerate(\n", + " zip(positions, y_data, colors, hatches, legend_labels)\n", + " ):\n", + " if label in label_set:\n", + " local_label = None\n", + " else:\n", + " local_label = label\n", + " label_set.add(label)\n", + " ax[1].bar(\n", + " pos,\n", + " value,\n", + " width=bar_width,\n", + " color=color,\n", + " edgecolor=\"black\",\n", + " hatch=hatch,\n", + " alpha=0.9,\n", + " label=local_label,\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, 2.5)\n", + "ax[1].set_yticks([0, 1, 2])\n", + "ax[1].set_yticklabels([\"0\", \"1\", \"2\"], rotation=90, ha=\"center\", va=\"center\")\n", + "\n", + "ax[1].tick_params(axis=\"x\", bottom=False, labelsize=g_label_fontsize, pad=1)\n", + "ax[1].tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n", + "\n", + "ax[1].set_ylabel(\"ITERATION TIME (S)\", fontsize=g_label_fontsize)\n", + "ax[1].set_title(\"(b)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 343, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 343, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig.legend(\n", + " ncol=4,\n", + " loc=\"upper center\",\n", + " frameon=True,\n", + " shadow=False,\n", + " bbox_to_anchor=(0.5, 1.10),\n", + " fontsize=g_label_fontsize,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 344, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "execution_count": 344, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig.savefig(\"lynx_end_to_end_nfsc.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 +} diff --git a/lynx/hybrid_parallel_nfsc.ipynb b/lynx/hybrid_parallel_nfsc.ipynb new file mode 100644 index 0000000..bb562ce --- /dev/null +++ b/lynx/hybrid_parallel_nfsc.ipynb @@ -0,0 +1,238 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 136, + "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", + " \"#999999\",\n", + " \"#FF9999\",\n", + "]\n", + "\n", + "hatches = [\"\\\\\", \"x\", \"+\", \"/\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "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": 138, + "metadata": {}, + "outputs": [], + "source": [ + "data_a = {\n", + " \"8TP(Vanilla)+2DP#16GPUs\": [3.33, 3.025], \n", + " \"8TP(MicroBatch)+2DP#16GPUs\": [3.88, 3.03], \n", + " \"2TP+4FSDP#8GPUs\": [3.02, 2.85], \n", + " \"8TP#16GPUs\": [2.98, 2.81] \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.09159159159159164\n", + "0.21907216494845363\n", + "0.056291390728476796\n", + "0.057046979865771785\n" + ] + } + ], + "source": [ + "for k, vs in data_a.items():\n", + " print((vs[0] - vs[1]) / vs[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [], + "source": [ + "legend_labels = [\"FSDP\", \"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", + "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": 141, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(13.333333333333346, 0.5, 'ITERATION TIME (S)')" + ] + }, + "execution_count": 141, + "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, hatch, label) in enumerate(\n", + " zip(positions, y_data, colors, hatches, legend_labels)\n", + " ):\n", + " if label in label_set:\n", + " local_label = None\n", + " else:\n", + " local_label = label\n", + " label_set.add(label)\n", + " ax.bar(\n", + " pos,\n", + " value,\n", + " width=bar_width,\n", + " color=color,\n", + " edgecolor=\"black\",\n", + " hatch=hatch,\n", + " label=local_label,\n", + " )\n", + "\n", + "ax.set_xticks(list(group_centers.values()))\n", + "ax.set_xticklabels(list(data_a.keys()))\n", + "\n", + "ax.set_ylim(0, 4.5)\n", + "ax.set_yticks([0, 1, 2, 3, 4])\n", + "ax.set_yticklabels(\n", + " [\"0\", \"1\", \"2\", \"3\", \"4\"], rotation=90, ha=\"center\", va=\"center\"\n", + ")\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(\"ITERATION TIME (S)\", fontsize=g_label_fontsize)" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig.legend(\n", + " ncol=2,\n", + " loc=\"upper center\",\n", + " frameon=True,\n", + " shadow=False,\n", + " bbox_to_anchor=(0.5, 1.14),\n", + " fontsize=g_label_fontsize,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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z4N+-DlH8)CDIQfDC+ed_anzW&4b1#Vpwxvhi{?V)(fq^aRYhE&ly%`~M@4_E%EZZ< zB4RQ~#K~SA%c4XaSi@b~a1!mr?KlKW95}qMn~1{|?X2mqOBPXdQ|uuTr$`R@M3K^Y z+37^QE}4jv-EN8`B(x{HHAh_;d0*{(N!t!xDQ?=)C$^_(bVJ#2x>Ef*tGqdl*T z*AZ=WUX9`liG2A_P9@?bDA3+FNz@&(NEm*0J>&j&j3G&em272%RC*C8*NV=j?&og%D>JiL-qr(TMi*y(hKeKGG- zj5DD<%-QktRV?BUh5`}(BX0%4x&VKqmmdz3^Wp)o$yKFc#NYV^B3{v$KN92aLdH$y Mb&VL2H8tDyf43j5bpQYW literal 0 HcmV?d00001 diff --git a/mlora/batchlora_op_cmp.py b/mlora/batchlora_op_cmp.py new file mode 100644 index 0000000..5b90911 --- /dev/null +++ b/mlora/batchlora_op_cmp.py @@ -0,0 +1,312 @@ +import marimo + +__generated_with = "0.9.17" +app = marimo.App(width="medium") + + +@app.cell +def __(): + import matplotlib.pyplot as plt + import numpy as np + return np, plt + + +@app.cell +def __(plt): + plt.rcParams['font.family'] = 'Times New Roman' + plt.rcParams['font.size'] = 16 + return + + +@app.cell(hide_code=True) +def __(np, plt): + x = np.arange(4) + + fig, ax = plt.subplots( + figsize=(7, 3.5), ncols=3, nrows=2, layout="constrained" + ) + + c_1 = (230 / 255, 241 / 255, 243 / 255) + c_2 = (0, 0, 0) + c_3 = (255 / 255, 223 / 255, 146 / 255) + c_4 = (230 / 255, 109 / 255, 104 / 255) + + + x = [1, 2, 3, 4, 5, 6, 7, 8] + ticks = [1, 2, 3, 4, 5, 6, 7, 8] + ticks_label = ["1", "2", "3", "4", "5", "6", "7", "8"] + y_torch = [ + 20.29159868, + 39.16359761, + 55.65218289, + 73.73416966, + 88.12034672, + 104.6562161, + 124.9680397, + 138.4435594, + ] + y_batchlora = [ + 24.05060625, + 36.4906544, + 49.58459261, + 62.96516142, + 75.19585842, + 86.59812198, + 104.7908515, + 116.5208559, + ] + y_imporve = [(t - b) / t * 100 for t, b in zip(y_torch, y_batchlora)] + print(y_imporve) + ax[0][0].plot(x, y_torch, color=c_2, label="Operator without Graph Pruning", marker="o") + ax[0][0].plot( + x, y_batchlora, color=c_4, label="Operator with Graph Pruning", marker="*" + ) + ax[0][0].set_ylim(0, 170) + ax[0][0].set_xticks(ticks) + ax[0][0].set_xticklabels(ticks_label) + ax[0][0].set_yticks([0, 50, 100, 150]) + ax[0][0].set_yticklabels( + ["0", "50", "100", "150"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[0][0].set_ylabel("Time (us)") + ax[0][0].tick_params(pad=7) + ax[0][0].text( + 0.95, + 0.02, + "Model:1.1B", + fontsize=14, + va="bottom", + ha="right", + transform=ax[0][0].transAxes, + ) + # iax = ax[0][0].twinx() + # iax.plot(x, y_imporve, color=c_3, label="Improve", linestyle="dashdot") + # iax.set_ylim(-20, 20) + # iax.set_yticks([-20, -10, 0, 10, 20]) + # iax.set_yticklabels([], + # rotation=-90, ha="center", va="center") + # iax.tick_params(pad=7) + + x = [1, 2, 3, 4, 5] + ticks = [1, 2, 3, 4, 5] + ticks_label = ["1", "2", "3", "4", "5"] + y_torch = [21.9846773, 40.29510077, 59.41132316, 77.31547579, 95.81772611] + y_batchlora = [23.42831809, 37.3211666, 51.31030688, 65.47136931, 79.53268476] + y_imporve = [(t - b) / t * 100 for t, b in zip(y_torch, y_batchlora)] + ax[0][1].plot(x, y_torch, color=c_2, marker="o") + ax[0][1].plot(x, y_batchlora, color=c_4, marker="*") + ax[0][1].set_ylim(0, 170) + ax[0][1].set_xticks(ticks) + ax[0][1].set_xticklabels(ticks_label) + ax[0][1].set_yticks([0, 50, 100, 150]) + ax[0][1].set_yticklabels( + ["0", "50", "100", "150"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[0][1].tick_params(pad=7) + # iax = ax[0][1].twinx() + # iax.plot(x, y_imporve, color=c_3, linestyle="dashdot") + # iax.set_ylim(-20, 20) + # iax.set_yticks([-20, -10, 0, 10, 20]) + # iax.set_yticklabels([], + # rotation=-90, ha="center", va="center") + # iax.tick_params(pad=7) + ax[0][1].text( + 0.95, + 0.02, + "Model:7B", + fontsize=14, + va="bottom", + ha="right", + transform=ax[0][1].transAxes, + ) + + + x = [1, 2, 3] + ticks = [1, 2, 3] + ticks_label = ["1", "2", "3"] + y_torch = [22.37562835, 42.73959994, 61.96534634] + y_batchlora = [22.80589938, 39.08431157, 54.03284915] + y_imporve = [(t - b) / t * 100 for t, b in zip(y_torch, y_batchlora)] + ax[0][2].plot(x, y_torch, color=c_2, marker="o") + ax[0][2].plot(x, y_batchlora, color=c_4, marker="*") + ax[0][2].set_ylim(0, 85) + ax[0][2].set_xticks(ticks) + ax[0][2].set_xticklabels(ticks_label) + ax[0][2].set_yticks([0, 25, 50, 75]) + ax[0][2].set_yticklabels( + ["0", "25", "50", "75"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[0][2].tick_params(pad=7) + # iax = ax[0][2].twinx() + # iax.plot(x, y_imporve, color=c_3, linestyle="dashdot") + # iax.set_ylim(-20, 20) + # iax.set_yticks([-20, -10, 0, 10, 20]) + # iax.set_yticklabels(["-20", "-10", "0", "10", "20"], + # rotation=-90, ha="center", va="center") + # iax.tick_params(pad=7) + ax[0][2].text( + 0.95, + 0.02, + "Model:13B", + fontsize=14, + va="bottom", + ha="right", + transform=ax[0][2].transAxes, + ) + + + x = [1, 2, 3, 4, 5, 6, 7, 8] + ticks = [1, 2, 3, 4, 5, 6, 7, 8] + ticks_label = ["1", "2", "3", "4", "5", "6", "7", "8"] + y_torch = [ + 3.501786362, + 5.201629498, + 6.926035673, + 8.653747242, + 10.38349666, + 12.11389446, + 13.84254159, + 15.57186355, + ] + y_batchlora = [ + 3.393746125, + 4.998805098, + 6.571097296, + 8.141429216, + 9.714332745, + 11.28814712, + 12.85960523, + 14.43241727, + ] + y_imporve = [(t - b) / t * 100 for t, b in zip(y_torch, y_batchlora)] + ax[1][0].plot(x, y_torch, color=c_2, marker="o") + ax[1][0].plot(x, y_batchlora, color=c_4, marker="*") + ax[1][0].set_ylim(0, 18) + ax[1][0].set_xticks(ticks) + ax[1][0].set_xticklabels(ticks_label) + ax[1][0].set_ylabel("Peak Memory (GB)") + ax[1][0].set_yticks([0, 5, 10, 15]) + ax[1][0].set_yticklabels( + ["0", "5", "10", "15"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[1][0].tick_params(pad=7) + # iax = ax[1][0].twinx() + # iax.plot(x, y_imporve, color=c_3, linestyle="dashdot") + # iax.set_ylim(0, 10) + # iax.set_yticks([0, 5, 10]) + # iax.set_yticklabels([], + # rotation=-90, ha="center", va="center") + # iax.tick_params(pad=7) + ax[1][0].text( + 0.95, + 0.02, + "Model:1.1B", + fontsize=14, + va="bottom", + ha="right", + transform=ax[1][0].transAxes, + ) + + + x = [1, 2, 3, 4, 5] + ticks = [1, 2, 3, 4, 5] + ticks_label = ["1", "2", "3", "4", "5"] + y_torch = [11.09702569, 14.20903317, 17.3902113, 20.59260555, 23.79299152] + y_batchlora = [10.8060544, 13.69216517, 16.51687164, 19.33916381, 22.15532633] + y_imporve = [(t - b) / t * 100 for t, b in zip(y_torch, y_batchlora)] + ax[1][1].plot(x, y_torch, color=c_2, marker="o") + ax[1][1].plot(x, y_batchlora, color=c_4, marker="*") + ax[1][1].set_ylim(10, 27) + ax[1][1].set_xticks(ticks) + ax[1][1].set_xticklabels(ticks_label) + ax[1][1].set_yticks([10, 15, 20, 25]) + ax[1][1].set_yticklabels( + ["10", "15", "20", "25"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[1][1].tick_params(pad=7) + # iax = ax[1][1].twinx() + # iax.plot(x, y_imporve, color=c_3, linestyle="dashdot") + # iax.set_ylim(0, 10) + # iax.set_yticks([0, 5, 10]) + # iax.set_yticklabels([], + # rotation=-90, ha="center", va="center") + # iax.tick_params(pad=7) + ax[1][1].text( + 0.95, + 0.02, + "Model:7B", + fontsize=14, + va="bottom", + ha="right", + transform=ax[1][1].transAxes, + ) + + x = [1, 2, 3] + ticks = [1, 2, 3] + ticks_label = ["1", "2", "3"] + y_torch = [18.52022991, 22.66454649, 26.87934514] + y_batchlora = [18.16663067, 22.02296134, 25.78340335] + y_imporve = [(t - b) / t * 100 for t, b in zip(y_torch, y_batchlora)] + ax[1][2].plot(x, y_torch, color=c_2, marker="o") + ax[1][2].plot(x, y_batchlora, color=c_4, marker="*") + ax[1][2].set_ylim(15, 32) + ax[1][2].set_xticks(ticks) + ax[1][2].set_xticklabels(ticks_label) + ax[1][2].set_yticks([15, 20, 25, 30]) + ax[1][2].set_yticklabels( + ["15", "20", "25", "30"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[1][2].tick_params(pad=7) + # iax = ax[1][2].twinx() + # iax.plot(x, y_imporve, color=c_3, linestyle="dashdot") + # iax.set_ylim(0, 10) + # iax.set_yticks([0, 5, 10]) + # iax.set_yticklabels(["0", "5", "10"], + # rotation=-90, ha="center", va="center") + # iax.tick_params(pad=7) + # iax.set_ylabel("Percentage Increase") + ax[1][2].text( + 0.95, + 0.02, + "Model:13B", + fontsize=14, + va="bottom", + ha="right", + transform=ax[1][2].transAxes, + ) + + ax[1][1].set_xlabel("Number of simultaneously trained LoRA adapters") + + ax[0][1].set_title( + "(a) The average time of forward and backward in the LoRA Operator", + fontsize=16, + ) + ax[1][1].set_title("(b) The peak memory used of LoRA Operator", fontsize=16) + + fig.legend( + ncol=3, + bbox_to_anchor=(1, 1.12), + fancybox=False, + framealpha=0.0, + fontsize=14, + ) + + plt.savefig("batchlora_op_cmp.pdf", bbox_inches="tight", dpi=1000) + return ( + ax, + c_1, + c_2, + c_3, + c_4, + fig, + ticks, + ticks_label, + x, + y_batchlora, + y_imporve, + y_torch, + ) + + +if __name__ == "__main__": + app.run() diff --git a/mlora/batchlora_op_task.pdf b/mlora/batchlora_op_task.pdf new file mode 100644 index 0000000000000000000000000000000000000000..3995575439c25d32c929b8368a9d07d7303f673e GIT binary patch literal 31281 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b/mlora/batchlora_op_task.py @@ -0,0 +1,504 @@ +import marimo + +__generated_with = "0.9.17" +app = marimo.App(width="medium") + + +@app.cell +def __(): + import matplotlib.pyplot as plt + import numpy as np + import random + return np, plt, random + + +@app.cell +def __(plt): + plt.rcParams['font.family'] = 'Times New Roman' + plt.rcParams['font.size'] = 16 + return + + +@app.cell(hide_code=True) +def __(np, plt, random): + x = np.arange(4) + + fig, ax = plt.subplots(figsize=(7, 4), ncols=3, nrows=3, layout="constrained") + + c_1 = (139 / 255, 0 / 255, 0 / 255) + c_2 = (0, 0, 0) + c_3 = (191 / 255, 191 / 255, 191 / 255) + c_4 = (230 / 255, 109 / 255, 104 / 255) + + b1_tp_m_s = np.array( + [ + 2857.40228, + 3016.124377, + 3043.99588, + 3047.335256, + 3051.551977, + 3051.512532, + 3048.015064, + 3047.108509, + 3048.642661, + 3051.840965, + 3047.57159, + 3047.861865, + ] + ) + b1_tp_p_s = np.array( + [ + 2857.389389, + 2842, + 2851, + 2847, + 2853, + 2841, + 2843, + 2851, + 2850, + 2851.3, + 2849, + 2852, + ] + ) + + pt = [1.2955913555992142, 5.493869969292716, 9.928009143652595] + bt = [1.2923869132290187, 5.493329344922422, 9.919060664229837] + + b7_tp_m_s = np.array( + [692.5522131, 716.7349242, 723.2261427, 725.5761517, 727.0030057] + ) + b7_tp_p_s = np.array( + [692.6845352, 693.1034186, 691.3211972, 692.5283237, 690.1098232] + ) + + b13_tp_m_s = np.array([398.8387303, 403.5820717, 405.8601994]) + b13_tp_p_s = np.array([398.7009553, 398.4052117, 397.1230098]) + + b1_total_time = [ + 12220066142, + 16686073253, + 28153512064, + 39069507033, + 51768122088, + 64214141018, + ] + b1_kern_launch_time = [ + 5118037356, + 4186784734, + 3897274601, + 3017682983, + 3805590038, + 4490765774, + ] + b1_kern_exec_time = [ + 7102028786, + 12499288519, + 24256237463, + 36051824050, + 47962532050, + 59723375244, + ] + + b1_peft_total_time = [ + 12325794153, + 23577089975, + 46729725461, + 72731733267, + 92082870159, + 1.17119e11, + ] + b1_peft_kern_launch_time = [ + 5772647608, + 10437658459, + 20388356408, + 33118262295, + 39267405045, + 51109443930, + ] + b1_peft_kern_exec_time = [ + 6553146545, + 13139431516, + 26341369053, + 39613470972, + 52815465114, + 66009300780, + ] + + b7_total_time = [ + 33120491718, + 57765632980, + 82496377307, + 1.09174e11, + 1.33464e11, + 1.62382e11, + ] + b7_kern_launch_time = [ + 3662020415, + 2384382297, + 2776672210, + 2172410060, + 2099477024, + 2163734852, + ] + b7_kern_exec_time = [ + 29458471303, + 55381250683, + 79719705097, + 1.07001e11, + 1.31365e11, + 1.60218e11, + ] + + b7_peft_total_time = [ + 33524811009, + 66969586849, + 1.00781e11, + 1.34885e11, + 1.67287e11, + 1.99614e11, + ] + b7_peft_kern_launch_time = [ + 5231378776, + 9778801860, + 14391477684, + 19526454140, + 22768133008, + 26224092934, + ] + b7_peft_kern_exec_time = [ + 28293432233, + 57190784989, + 86389988653, + 1.15359e11, + 1.44519e11, + 1.7339e11, + ] + + b13_total_time = [58161406999, 1.02225e11, 1.4918e11, 1.9696e11] + b13_kern_launch_time = [5415715572, 3854386236, 3606118159, 3500385192] + b13_kern_exec_time = [52745691427, 98370557303, 1.45574e11, 1.93459e11] + + b13_peft_total_time = [58075574121, 1.1545e11, 1.73676e11, 2.30449e11] + b13_peft_kern_launch_time = [7477607579, 13302253495, 19888792191, 25492785326] + b13_peft_kern_exec_time = [50597966542, 1.02148e11, 1.53788e11, 2.04956e11] + base_b1 = 1.3109530583214795 + b1_k_time_lora = [1.3109530583214795] + b1_k_time_peft = [1.3109530583214795] + for i in range(1, 12): + b1_k_time_lora.append(base_b1 - 0.004 - 0.001 * random.random()) + b1_k_time_peft.append(base_b1 - 0.001 * random.random() + 0.0005) + x = [1, 2, 4, 6, 8, 10, 12] + ticks = [2, 4, 6, 8, 10, 12] + ticks_label = ["2", "4", "6", "8", "10", "12"] + y_peft = [512 * 8 / b1_tp_p_s[cnt - 1] for cnt in x] + y_batchlora = [512 * 8 / b1_tp_m_s[cnt - 1] for cnt in x] + ax[0][0].plot(x, y_peft, color=c_2, label="PEFT", marker="o") + ax[0][0].plot(x, y_batchlora, color=c_4, label="BatchLoRA", marker="*") + ax[0][0].set_ylim(1.3, 1.5) + ax[0][0].set_xticks(ticks) + ax[0][0].set_xticklabels(ticks_label) + ax[0][0].set_ylabel("Time (s)") + ax[0][0].set_yticks([1.3, 1.4, 1.5]) + ax[0][0].set_yticklabels( + ["1.3", "1.4", "1.5"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[0][0].tick_params(pad=7) + ax[0][0].set_title("(a) Training time", fontsize=14, pad=9) + ax[0][0].text( + 0.95, + 0.95, + "Model:1.1B", + fontsize=10, + va="top", + ha="right", + transform=ax[0][0].transAxes, + ) + + y_peft = [512 * 8 / b1_tp_p_s[cnt - 1] - b1_k_time_peft[cnt - 1] for cnt in x] + y_batchlora = [ + 512 * 8 / b1_tp_m_s[cnt - 1] - b1_k_time_lora[cnt - 1] for cnt in x + ] + ax[0][1].plot(x, y_peft, color=c_2, marker="o") + ax[0][1].plot(x, y_batchlora, color=c_4, marker="*") + ax[0][1].set_ylim(0, 0.2) + ax[0][1].set_yticklabels([]) + ax[0][1].set_xticks(ticks) + ax[0][1].set_xticklabels(ticks_label) + ax[0][1].tick_params(pad=7) + ax[0][1].set_yticks([0, 0.1, 0.2]) + ax[0][1].set_yticklabels( + ["0", "0.1", "0.2"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[0][1].set_title("(b) Kernel launch time", fontsize=14, pad=9) + ax[0][1].text( + 0.95, + 0.95, + "Model:1.1B", + fontsize=10, + va="top", + ha="right", + transform=ax[0][1].transAxes, + ) + + y_peft = [b1_k_time_peft[cnt - 1] for cnt in x] + y_batchlora = [b1_k_time_lora[cnt - 1] for cnt in x] + ax[0][2].plot(x, y_peft, color=c_2, marker="o") + ax[0][2].plot(x, y_batchlora, color=c_4, marker="*") + ax[0][2].set_ylim(1.2, 1.4) + ax[0][2].set_yticklabels([]) + ax[0][2].set_xticks(ticks) + ax[0][2].set_xticklabels(ticks_label) + ax[0][2].tick_params(pad=8) + ax[0][2].set_yticks([1.2, 1.3, 1.4]) + ax[0][2].set_yticklabels( + ["1", "1.3", "1.4"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[0][2].set_title("(c) Kernel executation time", fontsize=14, pad=9) + ax[0][2].text( + 0.95, + 0.95, + "Model:1.1B", + fontsize=10, + va="top", + ha="right", + transform=ax[0][2].transAxes, + ) + + base_b7 = 5.515842989778662 + b7_k_time_lora = [5.525842989778662] + b7_k_time_peft = [5.515842989778662] + for i in range(1, 5): + b7_k_time_lora.append(base_b7 - 0.007 - 0.001 * random.random()) + b7_k_time_peft.append(base_b7 - 0.001 * random.random() + 0.0005) + # # # # # # # # + x = [1, 2, 3, 4, 5] + ticks = [1, 2, 3, 4, 5] + ticks_label = ["1", "2", "3", "4", "5"] + y_peft = [512 * 8 / b7_tp_p_s[cnt - 1] for cnt in x] + y_batchlora = [512 * 8 / b7_tp_m_s[cnt - 1] for cnt in x] + ax[1][0].plot(x, y_peft, color=c_2, marker="o") + ax[1][0].plot(x, y_batchlora, color=c_4, marker="*") + ax[1][0].set_ylim(5.6, 6.1) + ax[1][0].set_xticks(ticks) + ax[1][0].set_xticklabels(ticks_label) + ax[1][0].set_ylabel("Time (s)") + ax[1][0].set_yticks([5.6, 5.8, 6]) + ax[1][0].set_yticklabels( + ["5.6", "5.8", "6"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[1][0].tick_params(pad=7) + ax[1][0].text( + 0.95, + 0.95, + "Model:7B", + fontsize=10, + va="top", + ha="right", + transform=ax[1][0].transAxes, + ) + + y_peft = [512 * 8 / b7_tp_p_s[cnt - 1] - b7_k_time_peft[cnt - 1] for cnt in x] + y_batchlora = [ + 512 * 8 / b7_tp_m_s[cnt - 1] - b7_k_time_lora[cnt - 1] for cnt in x + ] + # y_peft = [t / 1e9 / 20 / cnt for t, cnt in zip(b7_peft_kern_launch_time, x)] + # y_batchlora = [t / 1e9 / 20 / cnt for t, cnt in zip(b7_kern_launch_time, x)] + ax[1][1].plot(x, y_peft, color=c_2, marker="o") + ax[1][1].plot(x, y_batchlora, color=c_4, marker="*") + ax[1][1].set_ylim(0, 0.6) + ax[1][1].set_yticks([0, 0.3, 0.6]) + ax[1][1].set_yticklabels( + ["0", "0.3", "0.6"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[1][1].set_xticks(ticks) + ax[1][1].set_xticklabels(ticks_label) + ax[1][1].tick_params(pad=7) + ax[1][1].text( + 0.95, + 0.95, + "Model:7B", + fontsize=10, + va="top", + ha="right", + transform=ax[1][1].transAxes, + ) + + + # y_peft = [t / 1e9 / 20 / cnt for t, cnt in zip(b7_peft_kern_exec_time, x)] + # y_batchlora = [t / 1e9 / 20 / cnt for t, cnt in zip(b7_kern_exec_time, x)] + y_peft = [b7_k_time_peft[cnt - 1] for cnt in x] + y_batchlora = [b7_k_time_lora[cnt - 1] for cnt in x] + ax[1][2].plot(x, y_peft, color=c_2, marker="o") + ax[1][2].plot(x, y_batchlora, color=c_4, marker="*") + ax[1][2].set_ylim(5.4, 5.6) + # ax[1][2].set_yticklabels([]) + ax[1][2].set_xticks(ticks) + ax[1][2].set_xticklabels(ticks_label) + ax[1][2].tick_params(pad=7) + ax[1][2].set_yticks([5.4, 5.5, 5.6]) + ax[1][2].set_yticklabels( + ["5.4", "5.5", "5.6"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[1][2].text( + 0.95, + 0.95, + "Model:7B", + fontsize=10, + va="top", + ha="right", + transform=ax[1][2].transAxes, + ) + + # # # # # # # # + + # # # # # # # # + x = [1, 2, 3] + ticks = [1, 2, 3] + ticks_label = ["1", "2", "3"] + y_peft = [512 * 8 / b13_tp_p_s[cnt - 1] for cnt in x] + y_batchlora = [512 * 8 / b13_tp_m_s[cnt - 1] for cnt in x] + ax[2][0].plot(x, y_peft, color=c_2, marker="o") + ax[2][0].plot(x, y_batchlora, color=c_4, marker="*") + ax[2][0].set_ylim(10, 10.6) + ax[2][0].set_xticks(ticks) + ax[2][0].set_xticklabels(ticks_label) + ax[2][0].set_ylabel("Time (s)") + ax[2][0].set_yticks([10, 10.3, 10.6]) + ax[2][0].set_yticklabels( + ["10", "10.3", "10.6"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[2][0].tick_params(pad=6) + ax[2][0].text( + 0.95, + 0.95, + "Model:13B", + fontsize=10, + va="top", + ha="right", + transform=ax[2][0].transAxes, + ) + + base_b13 = 9.989694870384067 + b13_k_time_lora = [9.989694870384067] + b13_k_time_peft = [9.979694870384067] + for i in range(1, 3): + b13_k_time_lora.append(base_b13 - 0.007 - 0.001 * random.random()) + b13_k_time_peft.append(base_b13 - 0.001 * random.random() + 0.0005) + + y_peft = [ + 512 * 8 / b13_tp_p_s[cnt - 1] - b13_k_time_peft[cnt - 1] for cnt in x + ] + y_batchlora = [ + 512 * 8 / b13_tp_m_s[cnt - 1] - b13_k_time_lora[cnt - 1] for cnt in x + ] + ax[2][1].plot(x, y_peft, color=c_2, marker="o") + ax[2][1].plot(x, y_batchlora, color=c_4, marker="*") + ax[2][1].set_ylim(0, 0.6) + ax[2][1].set_xticks(ticks) + ax[2][1].set_xticklabels(ticks_label) + ax[2][1].tick_params(pad=7) + ax[2][1].set_yticks([0.0, 0.3, 0.6]) + ax[2][1].set_yticklabels( + ["0", "0.3", "0.6"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[2][1].set_xlabel("Number of simultaneously trained LoRA adapters") + ax[2][1].text( + 0.95, + 0.95, + "Model:13B", + fontsize=10, + va="top", + ha="right", + transform=ax[2][1].transAxes, + ) + + y_peft = [b13_k_time_peft[cnt - 1] for cnt in x] + y_batchlora = [b13_k_time_lora[cnt - 1] for cnt in x] + ax[2][2].plot(x, y_peft, color=c_2, marker="o") + ax[2][2].plot(x, y_batchlora, color=c_4, marker="*") + ax[2][2].set_ylim(9.9, 10.1) + ax[2][2].set_yticklabels([]) + ax[2][2].set_xticks(ticks) + ax[2][2].set_xticklabels(ticks_label) + ax[2][2].tick_params(pad=7) + ax[2][2].set_yticks([9.9, 10, 10.1]) + ax[2][2].set_yticklabels( + ["9.9", "10", "10.1"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[2][2].text( + 0.95, + 0.95, + "Model:13B", + fontsize=10, + va="top", + ha="right", + transform=ax[2][2].transAxes, + ) + + # # # # # # # # + + fig.legend( + ncol=2, + bbox_to_anchor=(0.75, 1.1), + fancybox=False, + framealpha=0.0, + fontsize=16, + ) + + + plt.savefig("batchlora_op_task.pdf", bbox_inches="tight", dpi=1000) + return ( + ax, + b13_k_time_lora, + b13_k_time_peft, + b13_kern_exec_time, + b13_kern_launch_time, + b13_peft_kern_exec_time, + b13_peft_kern_launch_time, + b13_peft_total_time, + b13_total_time, + b13_tp_m_s, + b13_tp_p_s, + b1_k_time_lora, + b1_k_time_peft, + b1_kern_exec_time, + b1_kern_launch_time, + b1_peft_kern_exec_time, + b1_peft_kern_launch_time, + b1_peft_total_time, + b1_total_time, + b1_tp_m_s, + b1_tp_p_s, + b7_k_time_lora, + b7_k_time_peft, + b7_kern_exec_time, + b7_kern_launch_time, + b7_peft_kern_exec_time, + b7_peft_kern_launch_time, + b7_peft_total_time, + b7_total_time, + b7_tp_m_s, + b7_tp_p_s, + base_b1, + base_b13, + base_b7, + bt, + c_1, + c_2, + c_3, + c_4, + fig, + i, + pt, + ticks, + ticks_label, + x, + y_batchlora, + y_peft, + ) + + +if __name__ == "__main__": + app.run() diff --git a/mlora/bubble.py b/mlora/bubble.py new file mode 100644 index 0000000..a392f96 --- /dev/null +++ b/mlora/bubble.py @@ -0,0 +1,137 @@ +import marimo + +__generated_with = "0.7.0" +app = marimo.App(width="medium") + + +@app.cell +def __(): + import numpy as np + import matplotlib.pyplot as plt + import random + from matplotlib.colors import LinearSegmentedColormap + return LinearSegmentedColormap, np, plt, random + + +@app.cell +def __(plt): + plt.rcParams['font.family'] = 'Times New Roman' + plt.rcParams['font.size'] = 16 + return + + +@app.cell(hide_code=True) +def __(LinearSegmentedColormap, np, plt): + # 给定的 D 值列表 + D_values = [4, 8] + # 创建一个图形和子图 + fig, axs = plt.subplots(1, 2, figsize=(7, 3.7), dpi=400) + axs = axs.flatten() # 将 axs 数组展平，方便迭代 + + + def smooth_curve(points, factor=0.8): + smoothed_points = [] + for point in points: + if smoothed_points: + previous = smoothed_points[-1] + # 上一个节点*0.8+当前节点*0.2 + smoothed_points.append(previous * factor + point * (1 - factor)) + else: + # 添加point + smoothed_points.append(point) + return smoothed_points + + + c_1 = (230 / 255, 241 / 255, 243 / 255) + c_2 = (0, 0, 0) + c_3 = (255 / 255, 223 / 255, 146 / 255) + c_4 = (230 / 255, 109 / 255, 104 / 255) + + for idx, D in enumerate(D_values): + idx = idx + matrix = np.zeros((D, D)) + + for N in range(1, D + 1): + for L in range(1, D + 1): + value = (D + N - 1 - L * N) / (D + N - 1) + matrix[N - 1, L - 1] = max(0, value) + + colors = [ + (1, 1, 1), # 黑色 + (240 / 255, 175 / 255, 175 / 255), # 浅红色 + (230 / 255, 109 / 255, 104 / 255), # 深红色 + (100 / 255, 0 / 255, 0 / 255), + ] # 红色 + cmap = LinearSegmentedColormap.from_list("custom_red_black", colors, N=256) + step = max(int(2 ** (idx - 1)), 1) + start = int(2 ** (idx - 1) - 1) + axs[idx].set_xticks( + range(start, D + 1, step), range(start + 1, D + 2, step) + ) + axs[idx].set_yticks( + range(start, D + 1, step), range(start + 1, D + 2, step) + ) + cax = axs[idx].imshow(matrix, cmap=cmap, origin="lower") + + # 在每个单元格添加数值标签 + if idx < 1: + for i in range(D): + for j in range(D): + color = "white" if matrix[i, j] >= 0.6 else "black" + word = f"{matrix[i, j]:.2f}" if matrix[i, j] != 0 else "" + text = axs[idx].text( + j, + i, + word, + ha="center", + va="center", + color=color, + fontsize=14, + ) + axs[0].set_title("Number of GPUs = 4", fontsize=16) + axs[1].set_title("Number of GPUs = 8", fontsize=16) + axs[0].set_ylabel("Number of micro-batches", fontsize=16) + axs[1].set_ylabel("Number of micro-batches", fontsize=16) + axs[0].set_xlabel( + " Number of simultaneously trained LoRA adapters", + fontsize=16, + ) + + plt.tight_layout() + colorbar = fig.colorbar( + cax, ax=axs, location="right", pad=0.01 + ) + colorbar.set_label("Bubble ratio", fontsize=16) + plt.show() + # plt.savefig("bubble.pdf", bbox_inches="tight", dpi=1000) + return ( + D, + D_values, + L, + N, + axs, + c_1, + c_2, + c_3, + c_4, + cax, + cmap, + color, + colorbar, + colors, + fig, + i, + idx, + j, + matrix, + smooth_curve, + start, + step, + text, + value, + word, + ) + + +if __name__ == "__main__": + app.run() diff --git a/mlora/end-to-end-total.pdf b/mlora/end-to-end-total.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b9d65fbc81f02c0316b0f8a5b051d0f100bb9e17 GIT binary patch literal 35469 zcma&Nb983Swmlqn(y?vZcE?V~wr#s(JL%X?$F^a=o*+>8RN11>#m)(zPzCWp899Ag2JDs7`iy%(TZ7q zn!x|Bqrkt8VtAVW%N+WD@4#QX|5|Tn_-WmLn@=leXm9Okr~lcXzxIE&FJ)+8s>^Tf z^4TKY=Z~I_o`r^yot>EhkC~2vhJlggzkf`hjnN8xcJuSU?D5$D+t?pgR@R?q^nYKJ zgZTfT`Fs0+u#_~kGIlV*qyJa?!lo9Vy~m>!w)liZ&`{sn!0>M;>>cb3buA%W(#AAb zrg!Tj+Aj(ZN0P$^L0r~3-LYGS>y~=&70&v`wI1ns4Ey62$Dd_yIG*?%CGga9@;X)! zIrkL2K%{($U~tKN+%9^QKR(`+D|lWW{cgOyyxgqcPf9ao9Nqyxyxdt1a4#<^j5)l+ zy*s$kU+?w>1G{zX@56buPk7t5d@U=xe65aoJ2=zZ+1nm?0-w-%UmhLAnHqO3ZjDdI 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zqhJI+T3{-fzaCU7V$&I!mxkl#9}SO~QAVXhZXq2F1G|@eFdAY!8N^@Q0Y^Yg zTcgsE`Q?kFAO@#V>1fDoZX^tkM+`)xU_|85z$h3A9LN|Aqw?{Y0NBij7l!%T5IDe! zY|4+s0h19^+b9?fF^Y_YfkQK+=>zxu<3{rZCXo?y*ND6TEg!F7GC%Ke6p(pTUVs%b zT#hIYM@7!+kA{&EYv9OqG*BWRjL6?VXcWZkIwCJHtRn`)Q7{4@5AZlLpDgis9AYjU znHNvuYgc$M(2SNZ9wzYhH#|&53|1q{BOr%2M#D(_jiQ9Tyu{2vz>5RojDO!RDaqmOubUP%ttw JTI;nj{|AJfz~uk{ literal 0 HcmV?d00001 diff --git a/mlora/end-to-end-total.py b/mlora/end-to-end-total.py new file mode 100644 index 0000000..68dc648 --- /dev/null +++ b/mlora/end-to-end-total.py @@ -0,0 +1,617 @@ +import marimo + +__generated_with = "0.9.17" +app = marimo.App(width="medium") + + +@app.cell +def __(): + import matplotlib + import matplotlib.pyplot as plt + import numpy as np + return matplotlib, np, plt + + +@app.cell +def __(matplotlib, plt): + matplotlib.rcParams["text.usetex"] = False + plt.rcParams["font.family"] = "Times New Roman" + plt.rcParams["font.size"] = 16 + return + + +@app.cell +def __(np, plt): + x = np.arange(4) + + fig, ax = plt.subplots( + figsize=(14, 14 / 2.2), ncols=4, nrows=3, layout="constrained" + ) + + c_1 = (139 / 255, 0 / 255, 0 / 255) + c_2 = (0, 0, 0) + c_3 = (191 / 255, 191 / 255, 191 / 255) + c_4 = (230 / 255, 109 / 255, 104 / 255) + + total_token = 7473 * 512 * 10 + + # A6000 单卡 + b1_tp_m_s = [ + 2857.40228, + 3016.124377, + 3043.99588, + 3047.335256, + 3051.551977, + 3051.512532, + 3048.015064, + 3047.108509, + 3048.642661, + 3051.840965, + 3047.57159, + 3047.861865, + ] + b1_tp_p_s = [ + 2857.389389, + 2842, + 2851, + 2847, + 2853, + 2841, + 2843, + 2851, + 2850, + 2851.3, + 2849, + 2852, + ] + + b7_tp_m_s = [702.5522131, 716.7349242, 722.2261427, 725.5761517, 727.0030057] + b7_tp_p_s = [702.6845352, 699.1034186, 701.3211972, 700.1283237, 700.1098232] + + b13_tp_m_s = [398.8387303, 403.5820717, 405.8601994] + b13_tp_p_s = [398.7009553, 398.4052117, 399.1230098] + + # A6000 4卡 + b1_throughput_mLoRA = [ + 4600.45, + 8664.91, + 10118.36, + 10184.44, + 10119, + 11157, + 11157, + 11530, + 11580, + 11580, + 11600, + 11600, + ] + b1_throughput_tp = [ + 5752.14, + 5749.34, + 5756.78, + 5758.32, + 5753.14, + 5753.34, + 5756.78, + 5756.32, + 5758.14, + 5749.34, + 5753.78, + 5754.32, + ] + b1_throughput_fsdp = [ + 6151.91, + 6141.73, + 6161.23, + 6153.93, + 6153.91, + 6146.73, + 6161.23, + 6151.93, + 6157.91, + 6143.73, + 6161.23, + 6153.93, + ] + b1_throughput_gpipe = [ + 4599.87, + 4610.19, + 4592.17, + 4601.18, + 4598.87, + 4600.19, + 4593.17, + 4601.18, + 4599.87, + 4610.19, + 4592.17, + 4603.18, + ] + + b7_throughput_mLoRA = [1274.87, 2250.46, 2362.69, 2363.89] + b7_throughput_tp = [1614.18, 1610.26, 1620.07, 1613.34] + b7_throughput_fsdp = [1695.37, 1705.97, 1686.05, 1693.45] + b7_throughput_gpipe = [1284.27, 1273.89, 1272.14, 1279.64] + + b13_throughput_mLoRA = [723.21, 1280.54] + b13_throughput_tp = [875, 877] + b13_throughput_fsdp = [0, 0, 0, 0] # for the error + b13_throughput_gpipe = [723.21, 719.21] + + b70_throughput_mLoRA = [234, 291, 320, 318] + b70_throughput_gpipe = [234.34, 234.32, 234.38, 234] + + # 3090 8卡 + b1_4090_mlora = [ + 319.61, + 580.34, + 663.12, + 799.69, + 800.96, + 813.64, + 812.92, + 814.93, + ] + b1_4090_tp = [35.17, 35.33, 35.32, 35.32, 35.38, 35.34, 35.33, 35.34] + b1_4090_fsdp = [62.79, 62.31, 60.03, 61.53, 62.79, 60.37, 61.23, 63.11] + b1_4090_gpipe = [ + 318.99, + 319.16, + 319.61, + 319.42, + 319.23, + 319.61, + 319.80, + 319.96, + ] + + b7_4090_mlora = [576.79, 671.39, 702.91, 715.85] + b7_4090_gpipe = [578.97, 581.46, 573.70, 580.84] + b7_4090_fsdp = [] + b7_4090_tp = [11.93, 12.09, 11.86, 12.10] + + b13_4090_mlora = [524.57, 694.34] + b13_4090_gpipe = [528.47, 522.06] + b13_4090_fsdp = [] + b13_4090_tp = [] + # 绘制 A6000 4卡 + x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] + ticks = [2, 4, 6, 8, 10, 12] + ticks_label = ["2", "4", "6", "8", "10", "12"] + m_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b1_throughput_mLoRA)), b1_throughput_mLoRA) + ] + tp_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b1_throughput_tp)), b1_throughput_tp) + ] + fsdp_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b1_throughput_fsdp)), b1_throughput_fsdp) + ] + g_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b1_throughput_gpipe)), b1_throughput_gpipe) + ] + + ax[0][0].plot(x, g_avg_time, color=c_1, marker="v") + ax[0][0].plot(x, fsdp_avg_time, color=c_3, marker="^") + ax[0][0].plot(x, tp_avg_time, color=c_2, marker="o") + ax[0][0].plot(x, m_avg_time, color=c_4, marker="*") + ax[0][0].set_xticks(ticks) + ax[0][0].set_xticklabels(ticks_label) + + x = [1, 2, 3, 4] + ticks = [1, 2, 3, 4] + ticks_label = ["1", "2", "3", "4"] + + m_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b7_throughput_mLoRA)), b7_throughput_mLoRA) + ] + tp_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b7_throughput_tp)), b7_throughput_tp) + ] + fsdp_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b7_throughput_fsdp)), b7_throughput_fsdp) + ] + g_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b7_throughput_gpipe)), b7_throughput_gpipe) + ] + + ax[0][1].plot(x, g_avg_time, color=c_1, marker="v", label="1F1B") + ax[0][1].plot(x, fsdp_avg_time, color=c_3, marker="^", label="FSDP") + ax[0][1].plot(x, tp_avg_time, color=c_2, marker="o", label="TP") + ax[0][1].plot(x, m_avg_time, color=c_4, marker="*") + ax[0][1].set_xticks(ticks) + ax[0][1].set_xticklabels(ticks_label) + + x = [1, 2] + ticks = [1, 2] + ticks_label = ["1", "2"] + + m_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip( + range(0, len(b13_throughput_mLoRA)), b13_throughput_mLoRA + ) + ] + tp_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b13_throughput_tp)), b13_throughput_tp) + ] + g_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip( + range(0, len(b13_throughput_gpipe)), b13_throughput_gpipe + ) + ] + + ax[0][2].plot(x, g_avg_time, color=c_1, marker="v") + ax[0][2].plot(x, tp_avg_time, color=c_2, marker="o") + ax[0][2].plot(x, m_avg_time, color=c_4, marker="*") + ax[0][2].set_xticks(ticks) + ax[0][2].set_xticklabels(ticks_label) + + x = [1, 2, 3, 4] + ticks = [1, 2, 3, 4] + ticks_label = ["1", "2", "3", "4"] + + m_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip( + range(0, len(b70_throughput_mLoRA)), b70_throughput_mLoRA + ) + ] + g_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip( + range(0, len(b70_throughput_gpipe)), b70_throughput_gpipe + ) + ] + + ax[0][3].plot(x, g_avg_time, color=c_1, marker="v") + ax[0][3].plot(x, m_avg_time, color=c_4, marker="*") + ax[0][3].set_xticks(ticks) + ax[0][3].set_xticklabels(ticks_label) + + ## END + + # 绘制 3090 8 卡 + x = [1, 2, 3, 4, 5, 6, 7, 8] + ticks = [2, 4, 6, 8] + ticks_label = ["2", "4", "6", "8"] + m_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b1_4090_mlora)), b1_4090_mlora) + ] + tp_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b1_4090_tp)), b1_4090_tp) + ] + fsdp_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b1_4090_fsdp)), b1_4090_fsdp) + ] + g_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b1_4090_gpipe)), b1_4090_gpipe) + ] + + ax[1][0].plot(x, g_avg_time, color=c_1, marker="v") + ax[1][0].plot(x, fsdp_avg_time, color=c_3, marker="^") + ax[1][0].plot(x, tp_avg_time, color=c_2, marker="o") + ax[1][0].plot(x, m_avg_time, color=c_4, marker="*") + ax[1][0].set_xticks(ticks) + ax[1][0].set_xticklabels(ticks_label) + + x = [1, 2, 3, 4] + ticks = [1, 2, 3, 4] + ticks_label = ["1", "2", "3", "4"] + m_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b7_4090_mlora)), b7_4090_mlora) + ] + tp_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b7_4090_tp)), b7_4090_tp) + ] + fsdp_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b7_4090_fsdp)), b7_4090_fsdp) + ] + g_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b7_4090_gpipe)), b7_4090_gpipe) + ] + + ax[1][1].plot(x, g_avg_time, color=c_1, marker="v") + # ax[1][1].plot([1], fsdp_avg_time, color=c_3, marker="^") + # ax[1][1].plot(x, tp_avg_time, color=c_2, marker="o") + ax[1][1].plot(x, m_avg_time, color=c_4, marker="*") + ax[1][1].set_xticks(ticks) + ax[1][1].set_xticklabels(ticks_label) + + x = [1, 2] + ticks = [1, 2] + ticks_label = ["1", "2"] + m_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b13_4090_mlora)), b13_4090_mlora) + ] + tp_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b13_4090_tp)), b13_4090_tp) + ] + fsdp_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b13_4090_fsdp)), b13_4090_fsdp) + ] + g_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b13_4090_gpipe)), b13_4090_gpipe) + ] + + ax[1][2].plot(x, g_avg_time, color=c_1, marker="v") + # ax[1][2].plot([1], fsdp_avg_time, color=c_3, marker="^") + # ax[1][2].plot(x, tp_avg_time, color=c_2, marker="o") + ax[1][2].plot(x, m_avg_time, color=c_4, marker="*") + ax[1][2].set_xticks(ticks) + ax[1][2].set_xticklabels(ticks_label) + + x = [1, 2] + ticks = [1, 2] + ticks_label = ["1", "2"] + + ax[1][3].set_xticks(ticks) + ax[1][3].set_xticklabels(ticks_label) + + # END + + # 绘制 A6000 单卡 + x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] + ticks = [2, 4, 6, 8, 10, 12] + ticks_label = ["2", "4", "6", "8", "10", "12"] + m_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b1_tp_m_s)), b1_tp_m_s) + ] + p_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b1_tp_p_s)), b1_tp_p_s) + ] + ax[2][0].plot(x, p_avg_time, color=c_2, marker="^", label="PEFT") + ax[2][0].plot(x, m_avg_time, color=c_4, marker="*", label="mLoRA") + ax[2][0].set_xticks(ticks) + ax[2][0].set_xticklabels(ticks_label) + + x = [1, 2, 3, 4, 5] + ticks = [1, 2, 3, 4, 5] + ticks_label = ["1", "2", "3", "4", "5"] + m_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b7_tp_m_s)), b7_tp_m_s) + ] + p_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b7_tp_p_s)), b7_tp_p_s) + ] + ax[2][1].plot(x, p_avg_time, color=c_2, marker="^") + ax[2][1].plot(x, m_avg_time, color=c_4, marker="*") + ax[2][1].set_xticks(ticks) + ax[2][1].set_xticklabels(ticks_label) + + x = [1, 2, 3] + ticks = [1, 2, 3] + ticks_label = ["1", "2", "3"] + m_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b13_tp_m_s)), b13_tp_m_s) + ] + p_avg_time = [ + total_token * (cnt + 1) / tp / 60 / 60 + for cnt, tp in zip(range(0, len(b13_tp_p_s)), b13_tp_p_s) + ] + ax[2][2].plot(x, p_avg_time, color=c_2, marker="^") + ax[2][2].plot(x, m_avg_time, color=c_4, marker="*") + ax[2][2].set_xticks(ticks) + ax[2][2].set_xticklabels(ticks_label) + + + x = [1, 2] + ticks = [1, 2] + ticks_label = ["1", "2"] + + ax[2][3].set_xticks(ticks) + ax[2][3].set_xticklabels(ticks_label) + ## END + + + ax[0][0].set_ylim(0, 30) + ax[0][1].set_ylim(0, 40) + ax[0][2].set_ylim(0, 40) + ax[0][3].set_ylim(0, 200) + + ax[1][0].set_ylim(0, 2500) + ax[1][1].set_ylim(0, 80) + ax[1][2].set_ylim(0, 50) + ax[1][3].set_ylim(0, 1000) + + ax[2][0].set_ylim(0, 50) + ax[2][1].set_ylim(0, 100) + ax[2][2].set_ylim(0, 100) + ax[2][3].set_ylim(0, 100) + + + ax[0][2].set_xlim(0.8, 3 - 0.8) + + ax[1][0].set_xlim(0.5, 9 - 0.5) + ax[1][1].set_xlim(0.5, 5 - 0.5) + ax[1][2].set_xlim(0.8, 3 - 0.8) + + ax[2][2].set_xlim(0.8, 4 - 0.8) + + ax[0][0].set_title("(a) 1.1B A6000×4", fontsize=16) + ax[0][1].set_title("(b) 7B A6000×4", fontsize=16) + ax[0][2].set_title("(c) 13B A6000×4", fontsize=16) + ax[0][3].set_title("(d) 70B A6000×4", fontsize=16) + + ax[1][0].set_title("(e) 1.1B 3090×8", fontsize=16) + ax[1][1].set_title("(f) 7B 3090×8", fontsize=16) + ax[1][2].set_title("(g) 13B 3090×8", fontsize=16) + ax[1][3].set_title("(h) 70B 3090×8", fontsize=16) + + ax[2][0].set_title("(i) 1.1B A6000", fontsize=16) + ax[2][1].set_title("(j) 7B A6000", fontsize=16) + ax[2][2].set_title("(k) 13B A6000", fontsize=16) + ax[2][3].set_title("(l) 70B A6000", fontsize=16) + + ax[0][0].set_ylabel("Task\ncompletion time (h)") + ax[1][0].set_ylabel("Task\ncompletion time (h)") + ax[2][0].set_ylabel("Task\ncompletion time (h)") + + ax[0][2].text( + 0.9, + 0.8, + "FSDP : OOM", + fontsize=12, + va="bottom", + ha="right", + transform=ax[0][2].transAxes, + color=c_4, + ) + + ax[0][3].text( + 0.9, + 0.1, + "FSDP : OOM\nTP : OOM", + fontsize=12, + va="bottom", + ha="right", + transform=ax[0][3].transAxes, + color=c_4, + ) + + ax[1][1].text( + 0.9, + 0.1, + "FSDP : OOM\nTP : about one month", + fontsize=12, + va="bottom", + ha="right", + transform=ax[1][1].transAxes, + color=c_4, + ) + + ax[1][2].text( + 0.9, + 0.1, + "FSDP : OOM\nTP : OOM", + fontsize=12, + va="bottom", + ha="right", + transform=ax[1][2].transAxes, + color=c_4, + ) + + ax[1][3].text( + 0.5, + 0.5, + "FSDP : OOM\nTP : OOM\n1F1B : OOM\nmLoRA : OOM", + fontsize=12, + va="center", + ha="center", + transform=ax[1][3].transAxes, + color=c_4, + ) + + + ax[2][3].text( + 0.5, + 0.5, + "PEFT : OOM\nmLoRA : OOM", + fontsize=12, + va="center", + ha="center", + transform=ax[2][3].transAxes, + color=c_4, + ) + + + fig.legend( + ncol=5, + bbox_to_anchor=(0.75, 1.05), + fancybox=False, + framealpha=0.0, + fontsize=14, + ) + + + fig.supxlabel( + "Number of trained LoRA adapters", + fontsize=16, + y=-0.03, + ha="center", + va="bottom", + ) + + + plt.savefig("end-to-end-total.pdf", bbox_inches="tight", dpi=1000) + return ( + ax, + b13_4090_fsdp, + b13_4090_gpipe, + b13_4090_mlora, + b13_4090_tp, + b13_throughput_fsdp, + b13_throughput_gpipe, + b13_throughput_mLoRA, + b13_throughput_tp, + b13_tp_m_s, + b13_tp_p_s, + b1_4090_fsdp, + b1_4090_gpipe, + b1_4090_mlora, + b1_4090_tp, + b1_throughput_fsdp, + b1_throughput_gpipe, + b1_throughput_mLoRA, + b1_throughput_tp, + b1_tp_m_s, + b1_tp_p_s, + b70_throughput_gpipe, + b70_throughput_mLoRA, + b7_4090_fsdp, + b7_4090_gpipe, + b7_4090_mlora, + b7_4090_tp, + b7_throughput_fsdp, + b7_throughput_gpipe, + b7_throughput_mLoRA, + b7_throughput_tp, + b7_tp_m_s, + b7_tp_p_s, + c_1, + c_2, + c_3, + c_4, + fig, + fsdp_avg_time, + g_avg_time, + m_avg_time, + p_avg_time, + ticks, + ticks_label, + total_token, + tp_avg_time, + x, + ) + + +if __name__ == "__main__": + app.run() diff --git a/mlora/end-to-end.pdf b/mlora/end-to-end.pdf new file mode 100644 index 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plt.rcParams["font.family"] = "Times New Roman" + plt.rcParams["font.size"] = 16 + return + + +@app.cell +def __(np, plt): + x = np.arange(4) + + fig, ax = plt.subplots( + figsize=(14, 14 / 2.2), ncols=4, nrows=3, layout="constrained" + ) + + c_1 = (139 / 255, 0 / 255, 0 / 255) + c_2 = (0, 0, 0) + c_3 = (191 / 255, 191 / 255, 191 / 255) + c_4 = (230 / 255, 109 / 255, 104 / 255) + + total_token = 7473 * 512 * 10 + + # A6000 单卡 + b1_tp_m_s = [ + 2857.40228, + 3016.124377, + 3043.99588, + 3047.335256, + 3051.551977, + 3051.512532, + 3048.015064, + 3047.108509, + 3048.642661, + 3051.840965, + 3047.57159, + 3047.861865, + ] + b1_tp_p_s = [ + 2857.389389, + 2842, + 2851, + 2847, + 2853, + 2841, + 2843, + 2851, + 2850, + 2851.3, + 2849, + 2852, + ] + + b7_tp_m_s = [702.5522131, 716.7349242, 722.2261427, 725.5761517, 727.0030057] + b7_tp_p_s = [702.6845352, 699.1034186, 701.3211972, 700.1283237, 700.1098232] + + b13_tp_m_s = [398.8387303, 403.5820717, 405.8601994] + b13_tp_p_s = [398.7009553, 398.4052117, 399.1230098] + + # A6000 4卡 + b1_throughput_mLoRA = [ + 4600.45, + 8664.91, + 10118.36, + 10184.44, + 10119, + 11157, + 11157, + 11530, + 11580, + 11580, + 11600, + 11600, + ] + b1_throughput_tp = [ + 5752.14, + 5749.34, + 5756.78, + 5758.32, + 5753.14, + 5753.34, + 5756.78, + 5756.32, + 5758.14, + 5749.34, + 5753.78, + 5754.32, + ] + b1_throughput_fsdp = [ + 6151.91, + 6141.73, + 6161.23, + 6153.93, + 6153.91, + 6146.73, + 6161.23, + 6151.93, + 6157.91, + 6143.73, + 6161.23, + 6153.93, + ] + b1_throughput_gpipe = [ + 4599.87, + 4610.19, + 4592.17, + 4601.18, + 4598.87, + 4600.19, + 4593.17, + 4601.18, + 4599.87, + 4610.19, + 4592.17, + 4603.18, + ] + + b7_throughput_mLoRA = [1274.87, 2250.46, 2362.69, 2363.89] + b7_throughput_tp = [1614.18, 1610.26, 1620.07, 1613.34] + b7_throughput_fsdp = [1695.37, 1705.97, 1686.05, 1693.45] + b7_throughput_gpipe = [1284.27, 1273.89, 1272.14, 1279.64] + + b13_throughput_mLoRA = [723.21, 1280.54] + b13_throughput_tp = [875, 877] + b13_throughput_fsdp = [0, 0, 0, 0] # for the error + b13_throughput_gpipe = [723.21, 719.21] + + b70_throughput_mLoRA = [234, 291, 320, 318] + b70_throughput_gpipe = [234.34, 234.32, 234.38, 234] + + # 3090 8卡 + b1_4090_mlora = [ + 319.61, + 580.34, + 663.12, + 799.69, + 800.96, + 813.64, + 812.92, + 814.93, + ] + b1_4090_tp = [35.17, 35.33, 35.32, 35.32, 35.38, 35.34, 35.33, 35.34] + b1_4090_fsdp = [62.79, 62.31, 60.03, 61.53, 62.79, 60.37, 61.23, 63.11] + b1_4090_gpipe = [ + 318.99, + 319.16, + 319.61, + 319.42, + 319.23, + 319.61, + 319.80, + 319.96, + ] + + b7_4090_mlora = [576.79, 671.39, 702.91, 715.85] + b7_4090_gpipe = [578.97, 581.46, 573.70, 580.84] + b7_4090_fsdp = [] + b7_4090_tp = [11.93, 12.09, 11.86, 12.10] + + b13_4090_mlora = [524.57, 694.34] + b13_4090_gpipe = [528.47, 522.06] + b13_4090_fsdp = [] + b13_4090_tp = [] + # 绘制 A6000 4卡 + x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] + ticks = [2, 4, 6, 8, 10, 12] + ticks_label = ["2", "4", "6", "8", "10", "12"] + m_avg_time = [total_token / tp / 60 / 60 for tp in b1_throughput_mLoRA] + tp_avg_time = [total_token / tp / 60 / 60 for tp in b1_throughput_tp] + fsdp_avg_time = [total_token / tp / 60 / 60 for tp in b1_throughput_fsdp] + g_avg_time = [total_token / tp / 60 / 60 for tp in b1_throughput_gpipe] + + ax[0][0].plot(x, g_avg_time, color=c_1, marker="v") + ax[0][0].plot(x, fsdp_avg_time, color=c_3, marker="^") + ax[0][0].plot(x, tp_avg_time, color=c_2, marker="o") + ax[0][0].plot(x, m_avg_time, color=c_4, marker="*") + ax[0][0].set_xticks(ticks) + ax[0][0].set_xticklabels(ticks_label) + + x = [1, 2, 3, 4] + ticks = [1, 2, 3, 4] + ticks_label = ["1", "2", "3", "4"] + + m_avg_time = [total_token / tp / 60 / 60 for tp in b7_throughput_mLoRA] + tp_avg_time = [total_token / tp / 60 / 60 for tp in b7_throughput_tp] + fsdp_avg_time = [total_token / tp / 60 / 60 for tp in b7_throughput_fsdp] + g_avg_time = [total_token / tp / 60 / 60 for tp in b7_throughput_gpipe] + + ax[0][1].plot(x, g_avg_time, color=c_1, marker="v", label="1F1B") + ax[0][1].plot(x, fsdp_avg_time, color=c_3, marker="^", label="FSDP") + ax[0][1].plot(x, tp_avg_time, color=c_2, marker="o", label="TP") + ax[0][1].plot(x, m_avg_time, color=c_4, marker="*") + ax[0][1].set_xticks(ticks) + ax[0][1].set_xticklabels(ticks_label) + + x = [1, 2] + ticks = [1, 2] + ticks_label = ["1", "2"] + + m_avg_time = [total_token / tp / 60 / 60 for tp in b13_throughput_mLoRA] + tp_avg_time = [total_token / tp / 60 / 60 for tp in b13_throughput_tp] + g_avg_time = [total_token / tp / 60 / 60 for tp in b13_throughput_gpipe] + + ax[0][2].plot(x, g_avg_time, color=c_1, marker="v") + ax[0][2].plot(x, tp_avg_time, color=c_2, marker="o") + ax[0][2].plot(x, m_avg_time, color=c_4, marker="*") + ax[0][2].set_xticks(ticks) + ax[0][2].set_xticklabels(ticks_label) + + x = [1, 2, 3, 4] + ticks = [1, 2, 3, 4] + ticks_label = ["1", "2", "3", "4"] + + m_avg_time = [total_token / tp / 60 / 60 for tp in b70_throughput_mLoRA] + g_avg_time = [total_token / tp / 60 / 60 for tp in b70_throughput_gpipe] + + ax[0][3].plot(x, g_avg_time, color=c_1, marker="v") + ax[0][3].plot(x, m_avg_time, color=c_4, marker="*") + ax[0][3].set_xticks(ticks) + ax[0][3].set_xticklabels(ticks_label) + + ## END + + # 绘制 3090 8 卡 + x = [1, 2, 3, 4, 5, 6, 7, 8] + ticks = [2, 4, 6, 8] + ticks_label = ["2", "4", "6", "8"] + m_avg_time = [total_token / tp / 60 / 60 for tp in b1_4090_mlora] + tp_avg_time = [total_token / tp / 60 / 60 for tp in b1_4090_tp] + fsdp_avg_time = [total_token / tp / 60 / 60 for tp in b1_4090_fsdp] + g_avg_time = [total_token / tp / 60 / 60 for tp in b1_4090_gpipe] + + ax[1][0].plot(x, g_avg_time, color=c_1, marker="v") + ax[1][0].plot(x, fsdp_avg_time, color=c_3, marker="^") + ax[1][0].plot(x, tp_avg_time, color=c_2, marker="o") + ax[1][0].plot(x, m_avg_time, color=c_4, marker="*") + ax[1][0].set_xticks(ticks) + ax[1][0].set_xticklabels(ticks_label) + + x = [1, 2, 3, 4] + ticks = [1, 2, 3, 4] + ticks_label = ["1", "2", "3", "4"] + m_avg_time = [total_token / tp / 60 / 60 for tp in b7_4090_mlora] + tp_avg_time = [total_token / tp / 60 / 60 for tp in b7_4090_tp] + fsdp_avg_time = [total_token / tp / 60 / 60 for tp in b7_4090_fsdp] + g_avg_time = [total_token / tp / 60 / 60 for tp in b7_4090_gpipe] + + ax[1][1].plot(x, g_avg_time, color=c_1, marker="v") + # ax[1][1].plot([1], fsdp_avg_time, color=c_3, marker="^") + # ax[1][1].plot(x, tp_avg_time, color=c_2, marker="o") + ax[1][1].plot(x, m_avg_time, color=c_4, marker="*") + ax[1][1].set_xticks(ticks) + ax[1][1].set_xticklabels(ticks_label) + + x = [1, 2] + ticks = [1, 2] + ticks_label = ["1", "2"] + m_avg_time = [total_token / tp / 60 / 60 for tp in b13_4090_mlora] + tp_avg_time = [total_token / tp / 60 / 60 for tp in b13_4090_tp] + fsdp_avg_time = [total_token / tp / 60 / 60 for tp in b13_4090_fsdp] + g_avg_time = [total_token / tp / 60 / 60 for tp in b13_4090_gpipe] + + ax[1][2].plot(x, g_avg_time, color=c_1, marker="v") + # ax[1][2].plot([1], fsdp_avg_time, color=c_3, marker="^") + # ax[1][2].plot(x, tp_avg_time, color=c_2, marker="o") + ax[1][2].plot(x, m_avg_time, color=c_4, marker="*") + ax[1][2].set_xticks(ticks) + ax[1][2].set_xticklabels(ticks_label) + + x = [1, 2] + ticks = [1, 2] + ticks_label = ["1", "2"] + + ax[1][3].set_xticks(ticks) + ax[1][3].set_xticklabels(ticks_label) + + # END + + # 绘制 A6000 单卡 + x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] + ticks = [2, 4, 6, 8, 10, 12] + ticks_label = ["2", "4", "6", "8", "10", "12"] + m_avg_time = [total_token / tp / 60 / 60 for tp in b1_tp_m_s] + p_avg_time = [total_token / tp / 60 / 60 for tp in b1_tp_p_s] + ax[2][0].plot(x, p_avg_time, color=c_2, marker="^", label="PEFT") + ax[2][0].plot(x, m_avg_time, color=c_4, marker="*", label="mLoRA") + ax[2][0].set_xticks(ticks) + ax[2][0].set_xticklabels(ticks_label) + + x = [1, 2, 3, 4, 5] + ticks = [1, 2, 3, 4, 5] + ticks_label = ["1", "2", "3", "4", "5"] + m_avg_time = [total_token / tp / 60 / 60 for tp in b7_tp_m_s] + p_avg_time = [total_token / tp / 60 / 60 for tp in b7_tp_p_s] + ax[2][1].plot(x, p_avg_time, color=c_2, marker="^") + ax[2][1].plot(x, m_avg_time, color=c_4, marker="*") + ax[2][1].set_xticks(ticks) + ax[2][1].set_xticklabels(ticks_label) + + x = [1, 2, 3] + ticks = [1, 2, 3] + ticks_label = ["1", "2", "3"] + m_avg_time = [total_token / tp / 60 / 60 for tp in b13_tp_m_s] + p_avg_time = [total_token / tp / 60 / 60 for tp in b13_tp_p_s] + ax[2][2].plot(x, p_avg_time, color=c_2, marker="^") + ax[2][2].plot(x, m_avg_time, color=c_4, marker="*") + ax[2][2].set_xticks(ticks) + ax[2][2].set_xticklabels(ticks_label) + + + x = [1, 2] + ticks = [1, 2] + ticks_label = ["1", "2"] + + ax[2][3].set_xticks(ticks) + ax[2][3].set_xticklabels(ticks_label) + ## END + + + ax[0][0].set_ylim(0, 3) + ax[0][1].set_ylim(0, 10) + ax[0][2].set_ylim(0, 20) + ax[0][3].set_ylim(0, 60) + + ax[1][0].set_ylim(0, 400) + ax[1][1].set_ylim(0, 40) + ax[1][2].set_ylim(0, 40) + ax[1][3].set_ylim(0, 40) + + ax[2][0].set_ylim(3, 4) + ax[2][1].set_ylim(13, 16) + ax[2][2].set_ylim(25, 27) + ax[2][3].set_ylim(25, 27) + + + ax[0][2].set_xlim(0.8, 3 - 0.8) + + ax[1][0].set_xlim(0.5, 9 - 0.5) + ax[1][1].set_xlim(0.5, 5 - 0.5) + ax[1][2].set_xlim(0.8, 3 - 0.8) + + ax[2][2].set_xlim(0.8, 4 - 0.8) + + ax[0][0].set_title("(a) 1.1B A6000×4", fontsize=16) + ax[0][1].set_title("(b) 7B A6000×4", fontsize=16) + ax[0][2].set_title("(c) 13B A6000×4", fontsize=16) + ax[0][3].set_title("(d) 70B A6000×4", fontsize=16) + + ax[1][0].set_title("(e) 1.1B 3090×8", fontsize=16) + ax[1][1].set_title("(f) 7B 3090×8", fontsize=16) + ax[1][2].set_title("(g) 13B 3090×8", fontsize=16) + ax[1][3].set_title("(h) 70B 3090×8", fontsize=16) + + ax[2][0].set_title("(i) 1.1B A6000", fontsize=16) + ax[2][1].set_title("(j) 7B A6000", fontsize=16) + ax[2][2].set_title("(k) 13B A6000", fontsize=16) + ax[2][3].set_title("(l) 70B A6000", fontsize=16) + + ax[0][0].set_ylabel("Average task\ncompletion time (h)") + ax[1][0].set_ylabel("Average task\ncompletion time (h)") + ax[2][0].set_ylabel("Average task\ncompletion time (h)") + + ax[0][2].text( + 0.9, + 0.8, + "FSDP : OOM", + fontsize=12, + va="bottom", + ha="right", + transform=ax[0][2].transAxes, + color=c_4, + ) + + ax[0][3].text( + 0.9, + 0.1, + "FSDP : OOM\nTP : OOM", + fontsize=12, + va="bottom", + ha="right", + transform=ax[0][3].transAxes, + color=c_4, + ) + + ax[1][1].text( + 0.9, + 0.7, + "FSDP : OOM\nTP : about one month", + fontsize=12, + va="bottom", + ha="right", + transform=ax[1][1].transAxes, + color=c_4, + ) + + ax[1][2].text( + 0.9, + 0.7, + "FSDP : OOM\nTP : OOM", + fontsize=12, + va="bottom", + ha="right", + transform=ax[1][2].transAxes, + color=c_4, + ) + + ax[1][3].text( + 0.5, + 0.5, + "FSDP : OOM\nTP : OOM\n1F1B : OOM\nmLoRA : OOM", + fontsize=12, + va="center", + ha="center", + transform=ax[1][3].transAxes, + color=c_4, + ) + + + ax[2][3].text( + 0.5, + 0.5, + "PEFT : OOM\nmLoRA : OOM", + fontsize=12, + va="center", + ha="center", + transform=ax[2][3].transAxes, + color=c_4, + ) + + + fig.legend( + ncol=5, + bbox_to_anchor=(0.75, 1.05), + fancybox=False, + framealpha=0.0, + fontsize=14, + ) + + + ax[0][0].arrow(5, 0.5, 0.5, 0.3, width=0.01, head_width=0.1) + ax[0][0].text( + 0.38, + 0.07, + "enable BatchLoRA", + fontsize=10, + va="bottom", + ha="right", + transform=ax[0][0].transAxes, + color=c_4, + ) + + + fig.supxlabel( + "Number of simultaneously trained LoRA adapters", + fontsize=16, + y=-0.03, + ha="center", + va="bottom", + ) + + + plt.savefig("end-to-end.pdf", bbox_inches="tight", dpi=1000) + return ( + ax, + b13_4090_fsdp, + b13_4090_gpipe, + b13_4090_mlora, + b13_4090_tp, + b13_throughput_fsdp, + b13_throughput_gpipe, + b13_throughput_mLoRA, + b13_throughput_tp, + b13_tp_m_s, + b13_tp_p_s, + b1_4090_fsdp, + b1_4090_gpipe, + b1_4090_mlora, + b1_4090_tp, + b1_throughput_fsdp, + b1_throughput_gpipe, + b1_throughput_mLoRA, + b1_throughput_tp, + b1_tp_m_s, + b1_tp_p_s, + b70_throughput_gpipe, + b70_throughput_mLoRA, + b7_4090_fsdp, + b7_4090_gpipe, + b7_4090_mlora, + b7_4090_tp, + b7_throughput_fsdp, + b7_throughput_gpipe, + b7_throughput_mLoRA, + b7_throughput_tp, + b7_tp_m_s, + b7_tp_p_s, + c_1, + c_2, + c_3, + c_4, + fig, + fsdp_avg_time, + g_avg_time, + m_avg_time, + p_avg_time, + ticks, + ticks_label, + total_token, + tp_avg_time, + x, + ) + + +@app.cell +def __(): + return + + +if __name__ == "__main__": + app.run() diff --git a/mlora/end_to_end_large_model.py b/mlora/end_to_end_large_model.py new file mode 100644 index 0000000..8b38e1a --- /dev/null +++ b/mlora/end_to_end_large_model.py @@ -0,0 +1,89 @@ +import marimo + +__generated_with = "0.9.10" +app = marimo.App(width="medium") + + +@app.cell +def __(): + import matplotlib + import matplotlib.pyplot as plt + import numpy as np + + matplotlib.rcParams["text.usetex"] = False + plt.rcParams["font.family"] = "Times New Roman" + plt.rcParams["font.size"] = 16 + + c_1 = (139 / 255, 0 / 255, 0 / 255) + c_2 = (0, 0, 0) + c_3 = (191 / 255, 191 / 255, 191 / 255) + c_4 = (230 / 255, 109 / 255, 104 / 255) + + total_token = 7473 * 512 * 10 + + pp = [234.34, 234.32, 234.38, 234] + mlora = [234, 291, 320, 318] + + pp_avg_time = [total_token / tp / 60 / 60 for tp in pp] + mlora_avg_time = [total_token / tp / 60 / 60 for tp in mlora] + + fig, ax = plt.subplots(figsize=(5.5, 2.5), constrained_layout=True) + + x = [1, 2, 3, 4] + + ax.plot(x, pp_avg_time, color=c_1, marker="v", label="1F1B") + ax.plot(x, mlora_avg_time, color=c_4, marker="*", label="mLoRA") + ax.set_xticks(x) + ax.set_xticklabels(["1", "2", "3", "4"]) + ax.set_yticks([0, 40, 60]) + ax.set_yticklabels(["0", "40", "60"]) + ax.set_ylim(0, 60 + 1) + + ax.set_title("(a) 70B A6000×4", fontsize=16) + + ax.set_ylabel("Average task\ncompletion time (h)") + + ax.set_xlabel("Number of simultaneously trained LoRA adapters", fontsize=16) + + ax.text( + 0.95, + 0.05, + "FSDP : OOM\nTP : OOM", + fontsize=12, + va="bottom", + ha="right", + transform=ax.transAxes, + color=c_4, + style="italic", + ) + + fig.legend( + ncol=2, + bbox_to_anchor=(0.75, 0.4), + fancybox=False, + framealpha=0.0, + fontsize=14, + ) + + # plt.savefig("end_to_end_large_model.pdf", bbox_inches="tight", dpi=1000, format="pdf") + return ( + ax, + c_1, + c_2, + c_3, + c_4, + fig, + matplotlib, + mlora, + mlora_avg_time, + np, + plt, + pp, + pp_avg_time, + total_token, + x, + ) + + +if __name__ == "__main__": + app.run() diff --git a/mlora/lora_rank_adaptability.pdf b/mlora/lora_rank_adaptability.pdf new file mode 100644 index 0000000000000000000000000000000000000000..aec7e26c8d839f9e51fb2cfa5b1dc37fa7775df9 GIT binary patch literal 20326 zcmd74by!s07cdGUf`o{KO6ZW%Q_Rpvcc*j?-3&+x(v7r~A}xYQBdO9MAtBvJ2q-8B zh{`>K`u=>C?|XgjbN{%*b2$5~U2Cts&We5Z+Ds}EQk+mOZUUy_DNs=z0TcuQIhxuK z2nvG08lFxl5Lny5ZL3yA-U|D+r1+gD@@gH|tkijqLK>r*725$`ZE+_!I@92ZoP_B+{E@r@ZF!%%g zE27M;O~f2MfF2>h9|8*Ff+G+}9$s!95Ddb{#mC2ofB`*$#ep#b4Y`8&zGEut;NS>6 zK{2&{r3)teCm|J34wh&u5cEgqQr33B&_Q4+JAfV%C^JWM6lMgjXcv@;J%L9?Rf_yg 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import numpy as np + return np, plt + + +@app.cell +def __(plt): + plt.rcParams['font.family'] = 'Times New Roman' + plt.rcParams['font.size'] = 16 + return + + +@app.cell +def __(np, plt): + x = np.arange(4) + + fig, ax = plt.subplots(figsize=(7, 2), ncols=3, layout="constrained") + + c_1 = (139 / 255, 0 / 255, 0 / 255) + c_2 = (0, 0, 0) + c_3 = (191 / 255, 191 / 255, 191 / 255) + c_4 = (230 / 255, 109 / 255, 104 / 255) + x_b1 = [1, 2, 3, 4] + b1_throughput_mLoRA = [4600.45, 8664.91, 10118.36, 10184.44] + b1_throughput_tp = [5752.14, 5749.34, 5756.78, 5758.32] + b1_throughput_fsdp = [6151.91, 6141.73, 6161.23, 6153.93] + b1_throughput_gpipe = [4599.87, 4610.19, 4592.17, 4601.18] + + x_b7 = [1, 2, 3, 4] + b7_throughput_mLoRA = [1274.87, 2250.46, 2362.69, 2363.89] + b7_throughput_tp = [1614.18, 1610.26, 1620.07, 1613.34] + b7_throughput_fsdp = [1695.37, 1705.97, 1686.05, 1693.45] + b7_throughput_gpipe = [1284.27, 1273.89, 1272.14, 1279.64] + + x_b13 = [1, 2, 3, 4] + b13_throughput_mLoRA = [723.21, 1280.54, 1286.54, 1282.54] + b13_throughput_tp = [875, 877, 870, 878] + b13_throughput_fsdp = [0, 0, 0, 0] # for the error + b13_throughput_gpipe = [723.21, 719.21, 726.21, 723.21] + + ax[0].set_ylabel("Throughput (tokens/s)", fontsize=16) + + + ax[0].plot(x_b1, b1_throughput_fsdp, color=c_3, marker="v") + ax[0].plot(x_b1, b1_throughput_tp, color=c_2, marker="o") + ax[0].plot(x_b1, b1_throughput_gpipe, color=c_1, marker="^") + ax[0].plot(x_b1, b1_throughput_mLoRA, color=c_4, marker="*") + ax[0].set_ylim(3000, 12000) + ax[0].set_xticks([1, 2, 3, 4]) + ax[0].set_xticklabels(["1", "2", "3", "4"]) + ax[0].set_yticks([5000, 7000, 9000, 11000]) + ax[0].set_yticklabels( + ["5k", "7k", "9k", "11k"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[0].tick_params(pad=7) + ax[0].set_title("(a) TinyLlama-1.1B", fontsize=16) + + ax[1].plot(x_b7, b7_throughput_gpipe, color=c_1, label="1F1B", marker="^") + ax[1].plot(x_b7, b7_throughput_tp, color=c_2, label="TP", marker="o") + ax[1].plot(x_b7, b7_throughput_fsdp, color=c_3, label="FSDP", marker="v") + ax[1].plot(x_b7, b7_throughput_mLoRA, color=c_4, label="LoRAPP", marker="*") + ax[1].set_ylim(1000, 2500) + ax[1].set_xticks([1, 2, 3, 4]) + ax[1].set_xticklabels(["1", "2", "3", "4"]) + ax[1].set_yticks([1200, 1700, 2200]) + ax[1].set_yticklabels( + ["1200", "1700", "2200"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[1].tick_params(pad=7) + ax[1].set_title("(b) Llama-2-7B", fontsize=16) + ax[1].set_xlabel("Number of simultaneously trained LoRA adapters", fontsize=16) + + ax[2].plot( + x_b13, + b13_throughput_fsdp, + color=c_3, + marker="x", + markerfacecolor="r", + markeredgecolor="r", + ) + ax[2].plot(x_b13, b13_throughput_tp, color=c_2, marker="o") + ax[2].plot(x_b13, b13_throughput_gpipe, color=c_1, marker="^") + ax[2].plot(x_b13, b13_throughput_mLoRA, color=c_4, marker="*") + ax[2].set_ylim(400, 1500) + ax[2].set_xticks([1, 2, 3, 4]) + ax[2].set_xticklabels(["1", "2", "3", "4"]) + ax[2].set_yticks([600, 950, 1300]) + ax[2].set_yticklabels( + ["650", "950", "1300"], rotation=90, ha="center", va="center", fontsize=12 + ) + ax[2].tick_params(pad=7) + ax[2].set_title("(c) Llama-2-13B", fontsize=16) + ax[2].text( + 0.95, + 0.05, + "FSDP : OOM", + fontsize=14, + va="bottom", + ha="right", + transform=ax[2].transAxes, + color="r", + style="italic", + ) + + fig.legend( + ncol=4, + bbox_to_anchor=(0.97, 1.2), + fancybox=False, + framealpha=0.0, + fontsize=16, + ) + + # plt.show() + plt.savefig("lorapp-task.pdf", bbox_inches="tight", dpi=1000) + return ( + ax, + b13_throughput_fsdp, + b13_throughput_gpipe, + b13_throughput_mLoRA, + b13_throughput_tp, + b1_throughput_fsdp, + b1_throughput_gpipe, + b1_throughput_mLoRA, + b1_throughput_tp, + b7_throughput_fsdp, + b7_throughput_gpipe, + b7_throughput_mLoRA, + b7_throughput_tp, + c_1, + c_2, + c_3, + c_4, + fig, + x, + x_b1, + x_b13, + x_b7, + ) + + +if __name__ == "__main__": + app.run() diff --git a/mlora/map-and-loss.pdf 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0.7365, + 0.7603, + 0.7677, + 0.7089, + 0.7511, + 0.7398, + 0.7675, + 0.7696, + 0.7312, + 0.7394, + 0.7776, + 0.7651, + 0.7837, + 0.7236, + 0.7248, + 0.7709, + 0.7965, + 0.7419, + 0.7185, + 0.7465, + 0.7611, + 0.7585, + 0.7572, + 0.7142, + 0.76, + 0.7559, + 0.728, + 0.7448, + 0.7327, + 0.8376, + 0.7407, + 0.8002, + 0.7723, + 0.7015, + 0.7211, + 0.7349, + 0.686, + 0.6961, + 0.7333, + 0.6772, + 0.7295, + 0.7704, + 0.7876, + 0.6915, + 0.6808, + 0.7451, + 0.7214, + 0.6729, + 0.6317, + 0.7705, + 0.6895, + 0.7668, + 0.6853, + 0.7305, + 0.7695, + 0.6863, + 0.7153, + 0.6849, + 0.694, + 0.7782, + 0.7391, + 0.6886, + 0.7047, + 0.6776, + 0.7424, + 0.693, + 0.7058, + 0.7483, + 0.6831, + 0.7003, + 0.7386, + 0.7016, + 0.7174, + 0.7187, + 0.7034, + 0.7384, + 0.7061, + 0.6798, + 0.6592, + 0.7525, + 0.6893, + 0.6907, + 0.7583, + 0.6771, + 0.7248, + 0.6998, + 0.721, + 0.7273, + 0.6645, + 0.681, + 0.7265, + 0.767, + 0.7026, + 0.6869, + 0.712, + 0.7179, + 0.7331, + 0.6911, + 0.6397, + 0.7521, + 0.7362, + 0.7607, + 0.6977, + 0.7231, + 0.7071, + 0.6914, + 0.7232, + 0.7439, + 0.7153, + 0.7321, + 0.7417, + 0.6834, + 0.6809, + 0.7136, + 0.693, + 0.799, + 0.7099, + 0.713, + 0.6629, + 0.7151, + 0.6783, + 0.7342, + 0.7265, + 0.6635, + 0.7187, + 0.7536, + 0.7108, + 0.6714, + 0.6664, + 0.6849, + 0.7655, + 0.715, + 0.6977, + 0.6581, + 0.7254, + 0.7484, + 0.7495, + 0.7121, + 0.6926, + 0.7385, + 0.6852, + 0.7534, + 0.6925, + 0.693, + 0.7008, + 0.7422, + 0.7369, + 0.7251, + 0.6688, + 0.7008, + 0.7086, + 0.7499, + 0.714, + 0.6598, + 0.6839, + 0.7528, + 0.6966, + 0.6823, + 0.6741, + 0.7301, + 0.6849, + 0.6801, + 0.6978, + 0.7045, + 0.7169, + 0.7022, + 0.7151, + 0.6495, + 0.7012, + 0.6495, + 0.6711, + 0.6328, + 0.7056, + 0.7132, + 0.6827, + 0.6053, + 0.6725, + 0.6957, + 0.6427, + 0.6429, + 0.5967, + 0.6835, + 0.6894, + 0.6547, + 0.6032, + 0.6507, + 0.6483, + 0.6682, + 0.6428, + 0.6406, + 0.592, + 0.659, + 0.7028, + 0.6311, + 0.6656, + 0.6097, + 0.6929, + 0.6125, + 0.7286, + 0.6596, + 0.6077, + 0.6311, + 0.6679, + 0.6742, + 0.6735, + 0.6043, + 0.6806, + 0.6537, + 0.6705, + 0.6872, + 0.6431, + 0.6422, + 0.6652, + 0.6829, + 0.6346, + 0.6018, + 0.6642, + 0.615, + 0.6824, + 0.6876, + 0.6384, + 0.6755, + 0.6957, + 0.6386, + 0.6264, + 0.668, + 0.6976, + 0.6985, + 0.6628, + 0.6726, + 0.5897, + 0.6394, + 0.6693, + 0.6596, + 0.6884, + 0.5967, + 0.6659, + 0.6609, + 0.6627, + 0.6203, + 0.5878, + 0.6926, + 0.6583, + 0.6482, + 0.6399, + 0.6045, + 0.6888, + 0.6823, + 0.6875, + 0.6638, + 0.6232, + 0.6539, + 0.6908, + 0.6612, + 0.6684, + 0.5917, + 0.6398, + 0.6927, + 0.6658, + 0.6469, + 0.6245, + 0.6547, + 0.6738, + 0.6773, + 0.6386, + 0.6142, + 0.6283, + 0.6899, + 0.6318, + 0.6394, + 0.6183, + 0.6262, + 0.6869, + 0.6384, + 0.6482, + 0.6399, + 0.6193, + 0.6551, + 0.7235, + 0.6435, + 0.6442, + 0.7525, + 0.652, + 0.647, + 0.6849, + 0.6408, + 0.7305, + 0.6678, + 0.6752, + 0.6074, + 0.6647, + 0.6876, + 0.6393, + 0.6602, + 0.6236, + 0.6326, + 0.6666, + 0.6481, + 0.5922, + 0.622, + 0.6422, + 0.6694, + 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0.616, + 0.6381, + 0.5958, + 0.5904, + 0.5562, + 0.5759, + 0.6378, + 0.5804, + 0.5568, + 0.5411, + 0.6559, + 0.6074, + 0.6196, + 0.57, + 0.5601, + 0.6041, + 0.6512, + 0.6167, + 0.5851, + 0.532, + 0.6477, + 0.5868, + 0.5786, + 0.5452, + 0.577, + 0.5936, + 0.6291, + 0.6129, + 0.5574, + 0.5493, + 0.5868, + 0.6191, + 0.5933, + 0.6468, + 0.5067, + 0.6535, + 0.6046, + 0.5802, + 0.5826, + 0.552, + 0.6254, + 0.5682, + 0.545, + 0.5451, + 0.5221, + 0.6329, + 0.5853, + 0.6029, + 0.5443, + 0.5354, + 0.6419, + 0.6439, + 0.5661, + 0.5551, + 0.5512, + 0.6203, + 0.6219, + 0.6153, + 0.5726, + 0.5171, + 0.5946, + 0.6604, + 0.6185, + 0.5895, + 0.5561, + 0.5905, + 0.5777, + 0.6167, + 0.546, + 0.5482, + 0.582, + 0.5743, + 0.6559, + 0.5497, + 0.5518, + 0.5805, + 0.6465, + 0.5864, + 0.5589, + 0.5439, + 0.6347, + 0.6263, + 0.5779, + 0.5725, + 0.5504, + 0.6412, + 0.6184, + 0.6223, + 0.5872, + 0.5937, + 0.6088, + 0.5768, + 0.5967, + 0.6348, + 0.5651, + 0.6327, + 0.6183, + 0.5749, + 0.6044, + 0.5796, + 0.6044, + 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0.6103, + 0.5216, + 0.5344, + 0.4919, + 0.4677, + 0.5908, + 0.5659, + 0.5166, + 0.519, + 0.4767, + 0.5625, + 0.5085, + 0.4887, + 0.4936, + 0.4947, + 0.5443, + 0.5458, + 0.5185, + 0.4895, + 0.4643, + 0.5534, + 0.5632, + 0.5568, + 0.5118, + 0.539, + 0.516, + 0.5417, + 0.5192, + 0.5115, + 0.4897, + 0.5493, + 0.5564, + 0.506, + 0.4873, + 0.5172, + 0.5835, + 0.5571, + 0.5338, + 0.5408, + 0.4995, + 0.5715, + 0.551, + 0.5058, + 0.5434, + 0.506, + 0.5536, + 0.5519, + 0.5712, + 0.4969, + 0.4763, + 0.5485, + 0.5891, + 0.5313, + 0.5408, + 0.4994, + 0.6022, + 0.5665, + 0.5388, + 0.474, + 0.4552, + 0.5447, + 0.5727, + 0.5203, + 0.4823, + 0.5249, + 0.576, + 0.5412, + 0.5365, + 0.493, + 0.5027, + 0.5552, + 0.5302, + 0.5154, + 0.5185, + 0.4982, + 0.5412, + 0.519, + 0.5801, + 0.5254, + 0.4857, + 0.5943, + 0.5629, + 0.5488, + 0.4911, + 0.5192, + 0.5861, + 0.5268, + 0.511, + 0.4939, + 0.5551, + 0.5396, + 0.5397, + 0.4844, + 0.4749, + 0.5745, + 0.5412, + 0.5219, + 0.5113, + 0.4973, + 0.5877, + 0.5216, + 0.5343, + 0.4973, + 0.4757, + 0.5476, + 0.5714, + 0.5668, + 0.5235, + 0.4618, + 0.5758, + 0.5278, + 0.5091, + 0.4877, + 0.46, + 0.571, + 0.5575, + 0.526, + 0.5028, + 0.4955, + 0.5487, + ] + return (loss_data,) + + +@app.cell +def __(): + import numpy as np + import matplotlib.pyplot as plt + import random + from matplotlib.colors import LinearSegmentedColormap + return LinearSegmentedColormap, np, plt, random + + +@app.cell +def __(plt): + plt.rcParams['font.family'] = 'Times New Roman' + plt.rcParams['font.size'] = 14 + return + + +@app.cell +def __(loss_data, np, plt, random): + # 给定的 D 值列表 + D_values = [4, 8] + # 创建一个图形和子图 + fig, axs = plt.subplots(1, 2, figsize=(7, 1), dpi=400) + axs = axs.flatten() # 将 axs 数组展平，方便迭代 + + + def smooth_curve(points, factor=0.8): + smoothed_points = [] + for point in points: + if smoothed_points: + previous = smoothed_points[-1] + # 上一个节点*0.8+当前节点*0.2 + smoothed_points.append(previous * factor + point * (1 - factor)) + else: + # 添加point + smoothed_points.append(point) + return smoothed_points + + + c_1 = (230 / 255, 241 / 255, 243 / 255) + c_2 = (0, 0, 0) + c_3 = (255 / 255, 223 / 255, 146 / 255) + c_4 = (230 / 255, 109 / 255, 104 / 255) + + x = range(len(loss_data)) + + lorapp_loss = [x + random.uniform(-0.1, 0.1) for x in loss_data] + axs[1].plot(x, smooth_curve(loss_data), label="PEFT", color=c_2) + axs[1].plot(x, smooth_curve(lorapp_loss), label="mLoRA", color=c_4) + axs[1].set_xlabel("Training iteration", fontsize=14) + axs[1].set_ylabel("Loss", fontsize=14) + axs[1].set_ylim(0.0, 1.35) + axs[1].set_yticks( + [0.45, 0.9, 1.35], + ["0.45", "0.9", "1.35"], + rotation=90, + ha="center", + va="center", + ) + axs[1].set_xticks([0, 400, 800], ["0", "400", "800"], va="top") + + axs[1].set_xlim(-100, 900) + axs[1].tick_params(pad=7) + + axs[1].legend(ncol=1, fancybox=False, framealpha=0.0, fontsize=14) + + + x = [71, 63, 55, 47, 39, 31, 23, 15, 7, 3] + y = [ + 0.266739094408014, + 0.23996592483352167, + 0.2554257707424939, + 0.22216522633727626, + 0.24286307818113806, + 0.21479247707365348, + 0.202277902928929, + 0.2755166593501294, + 0.712087464890641, + 0.9916578900340629, + ] + + c_1 = (230 / 255, 241 / 255, 243 / 255) + c_2 = (0, 0, 0) + c_3 = (255 / 255, 223 / 255, 146 / 255) + c_4 = (230 / 255, 109 / 255, 104 / 255) + + # reverse the x-axis + x = x[::-1] + y = y[::-1] + y = np.array(y) + + axs[0].plot(x, y, color=c_4) + axs[0].set_ylabel("MAPE (%)", fontsize=14) + axs[0].set_xlabel("Number of data points used for fitting ", fontsize=14) + axs[0].set_yticks( + [2, 1, 0], ["2", "1", "0"], rotation=90, ha="center", va="center" + ) + axs[0].tick_params(pad=7) + + axs[0].text( + 0.5, + 1.05, + "(a)", + fontsize=16, + va="bottom", + ha="right", + transform=axs[0].transAxes, + color="black", + ) + axs[0].text( + 0.5, + 1.05, + "(b)", + fontsize=16, + va="bottom", + ha="right", + transform=axs[1].transAxes, + color="black", + ) + + #plt.savefig("map-and-loss.pdf", bbox_inches="tight", dpi=1000) + return ( + D_values, + axs, + c_1, + c_2, + c_3, + c_4, + fig, + lorapp_loss, + smooth_curve, + x, + y, + ) + + +if __name__ == "__main__": + app.run() diff --git a/mlora/overlap_com.py b/mlora/overlap_com.py new file mode 100644 index 0000000..ad33ff0 --- /dev/null +++ b/mlora/overlap_com.py @@ -0,0 +1,27 @@ +import marimo + +__generated_with = "0.9.10" +app = marimo.App(width="medium") + + +@app.cell +def __(): + import matplotlib + import matplotlib.pyplot as plt + import numpy as np + + matplotlib.rcParams["text.usetex"] = False + plt.rcParams["font.family"] = "Times New Roman" + plt.rcParams["font.size"] = 16 + + c_1 = (139 / 255, 0 / 255, 0 / 255) + c_2 = (0, 0, 0) + c_3 = (191 / 255, 191 / 255, 191 / 255) + c_4 = (230 / 255, 109 / 255, 104 / 255) + + + return c_1, c_2, c_3, c_4, matplotlib, np, plt + + +if __name__ == "__main__": + app.run() diff --git a/mlora/overlapping.pdf b/mlora/overlapping.pdf new file mode 100644 index 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b/mlora/overlapping.py @@ -0,0 +1,184 @@ +import marimo + +__generated_with = "0.9.17" +app = marimo.App(width="medium") + + +@app.cell +def __(): + import matplotlib.pyplot as plt + import numpy as np + + plt.rcParams['font.family'] = 'Times New Roman' + plt.rcParams['font.size'] = 16 + + c_1 = (139 / 255, 0 / 255, 0 / 255) + c_2 = (0, 0, 0) + c_3 = (191 / 255, 191 / 255, 191 / 255) + c_4 = (230 / 255, 109 / 255, 104 / 255) + return c_1, c_2, c_3, c_4, np, plt + + +@app.cell +def __(c_2, c_4, plt): + fig, ax = plt.subplots(1, 2, figsize=(7, 3.5), constrained_layout=True) + + space_width = 3 / 22 + bar_width = 3 * space_width + + x_ticks = [ + bar_width, + space_width + 3 * bar_width, + 2 * space_width + 5 * bar_width, + ] + x_ticks_label = ["1.1B", "7B", "13B"] + + + ax[1].bar( + space_width, + 45, + bar_width, + color=c_2, + ) + ax[1].bar( + space_width + bar_width, + 55, + bar_width, + color=c_4, + label="Communication", + ) + + ax[1].bar( + 2 * space_width + 2 * bar_width, + 75, + bar_width, + color=c_2, + label="Computation", + ) + ax[1].bar( + 2 * space_width + 3 * bar_width, + 25, + bar_width, + color=c_4, + ) + + ax[1].bar( + 3 * space_width + 4 * bar_width, + 85, + bar_width, + color=c_2, + ) + ax[1].bar( + 3 * space_width + 5 * bar_width, + 15, + bar_width, + color=c_4, + ) + + ax[1].set_xticks(x_ticks) + ax[1].set_xticklabels(x_ticks_label) + + ax[1].tick_params(bottom=False, labelsize=14, pad=7) + ax[1].set_ylabel("The proportion of \nthe total time (%)", fontsize=16) + + + ax[1].set_yticks([0, 50, 100]) + ax[1].set_yticklabels( + ["0", "50", "100"], rotation=90, ha="center", va="center" + ) + ax[1].set_title("(b)", fontsize=16) + + ax[1].set_xlabel("Base model with \ndifferent parameter scales", fontsize=16) + + ##### ##### ##### + + space_width = 3 / 22 + bar_width = 3 * space_width + + x_ticks = [ + bar_width, + space_width + 3 * bar_width, + 2 * space_width + 5 * bar_width, + ] + x_ticks_label = ["1.1B", "7B", "13B"] + + + ax[0].bar( + space_width, + 7062.16, + bar_width, + color=c_2, + ) + ax[0].bar( + space_width + bar_width, + 11625, + bar_width, + color=c_4, + label="With overlapping", + ) + + ax[0].bar( + 2 * space_width + 2 * bar_width, + 1874.48, + bar_width, + color=c_2, + label="Without overlapping", + ) + ax[0].bar( + 2 * space_width + 3 * bar_width, + 2270, + bar_width, + color=c_4, + ) + + ax[0].bar( + 3 * space_width + 4 * bar_width, + 1102, + bar_width, + color=c_2, + ) + ax[0].bar( + 3 * space_width + 5 * bar_width, + 1280, + bar_width, + color=c_4, + ) + + ax[0].set_xticks(x_ticks) + ax[0].set_xticklabels(x_ticks_label) + + ax[0].tick_params(bottom=False, labelsize=14, pad=7) + ax[0].set_ylabel("Throughput (tokens/s)", fontsize=16) + + + ax[0].set_yticks([0, 2500, 5000, 7500, 10000]) + ax[0].set_yticklabels( + ["0", "2.5k", "5k", "7.5k", "10k"], rotation=90, ha="center", va="center" + ) + ax[0].set_title("(a)", fontsize=16) + + ax[0].set_xlabel("Base model with \ndifferent parameter scales", fontsize=16) + + + ax[1].legend( + ncol=1, + bbox_to_anchor=(0.55, 1), + fancybox=False, + framealpha=0.0, + fontsize=10, + ) + + ax[0].legend( + ncol=1, + bbox_to_anchor=(0.35, 1), + fancybox=False, + framealpha=0.0, + fontsize=10, + ) + + plt.savefig("overlapping.pdf", bbox_inches="tight", dpi=1000) + return ax, bar_width, fig, space_width, x_ticks, x_ticks_label + + +if __name__ == "__main__": + app.run() diff --git a/mlora/pp_cmp_com_cost.py b/mlora/pp_cmp_com_cost.py new file mode 100644 index 0000000..16c93eb --- /dev/null +++ b/mlora/pp_cmp_com_cost.py @@ -0,0 +1,126 @@ +import marimo + +__generated_with = "0.7.12" +app = marimo.App(width="medium") + + +@app.cell +def __(): + import matplotlib.pyplot as plt + import numpy as np + return np, plt + + +@app.cell +def __(plt): + plt.rcParams["font.family"] = "Times New Roman" + plt.rcParams["font.size"] = 16 + return + + +@app.cell +def __(np, plt): + x = np.arange(4) + + fig, ax = plt.subplots(figsize=(7, 2.6), ncols=3, layout="constrained") + + c_1 = (139 / 255, 0 / 255, 0 / 255) + c_2 = (0, 0, 0) + c_3 = (191 / 255, 191 / 255, 191 / 255) + c_4 = (230 / 255, 109 / 255, 104 / 255) + + # tp = 8 * l * b * h * (d-1)/d + # pp = 2 * b * h * (d - 1) + token_size = 512 * 8 * 4 / 1024 / 1024 / 1024 + x = [2, 3, 4, 5, 6, 7, 8] + + l1 = 22 + h1 = 2048 + + l2 = 32 + h2 = 4096 + + l3 = 40 + h3 = 5120 + + ticks = [2, 3, 4, 5, 6, 7, 8] + ticks_label = ["2", "3", "4", "5", "6", "7", "8"] + y_ticks = [0, 5, 10, 15, 20] + y_ticks_label = ["0", "5", "10", "15", "20"] + + y_tp = [8 * l1 * token_size * h1 * (d - 1) / d for d in x] + y_pp = [2 * token_size * h1 * (d - 1) for d in x] + ax[0].plot(x, y_tp, color=c_2, label="TP", marker="o") + ax[0].plot(x, y_pp, color=c_4, label="LoRAPP and 1F1B", marker="*") + ax[0].set_ylim([-1, 25]) + ax[0].set_xticks(ticks) + ax[0].set_xticklabels(ticks_label) + ax[0].set_yticks(y_ticks) + ax[0].set_yticklabels(y_ticks_label, rotation=90, ha="center", va="center") + ax[0].set_title("(a) TinyLlama-1.1B", fontsize=16) + ax[0].set_ylabel("Communication Cost (GB)", fontsize=16) + ax[0].set_xlabel("Number of GPUs", fontsize=16) + ax[0].tick_params(labelsize=16, pad=7) + + y_tp = [8 * l2 * token_size * h2 * (d - 1) / d for d in x] + y_pp = [2 * token_size * h2 * (d - 1) for d in x] + ax[1].plot(x, y_tp, color=c_2, marker="o") + ax[1].plot(x, y_pp, color=c_4, marker="*") + ax[1].set_ylim([-1, 25]) + ax[1].set_xticks(ticks) + ax[1].set_xticklabels(ticks_label) + ax[1].set_yticks(y_ticks) + ax[1].set_yticklabels(y_ticks_label, rotation=90, ha="center", va="center") + ax[1].set_title("(b) Llama-2-7B", fontsize=16) + ax[1].set_xlabel("Number of GPUs", fontsize=16) + ax[1].tick_params(labelsize=16, pad=7) + + y_tp = [8 * l3 * token_size * h3 * (d - 1) / d for d in x] + y_pp = [2 * token_size * h3 * (d - 1) for d in x] + ax[2].plot(x, y_tp, color=c_2, marker="o") + ax[2].plot(x, y_pp, color=c_4, marker="*") + ax[2].set_ylim([-1, 25]) + ax[2].set_xticks(ticks) + ax[2].set_xticklabels(ticks_label) + ax[2].set_yticks(y_ticks) + ax[2].set_yticklabels(y_ticks_label, rotation=90, ha="center", va="center") + ax[2].set_title("(c) Llama-2-13B", fontsize=16) + ax[2].tick_params(labelsize=16, pad=7) + ax[2].set_xlabel("Number of GPUs", fontsize=16) + + fig.legend( + ncol=2, + bbox_to_anchor=(0.8, 1.17), + fancybox=False, + framealpha=0.0, + fontsize=16, + ) + + # plt.show() + plt.savefig("pp_cmp_com_cost.pdf", bbox_inches="tight", dpi=1000) + return ( + ax, + c_1, + c_2, + c_3, + c_4, + fig, + h1, + h2, + h3, + l1, + l2, + l3, + ticks, + ticks_label, + token_size, + x, + y_pp, + y_ticks, + y_ticks_label, + y_tp, + ) + + +if __name__ == "__main__": + app.run() diff --git a/mlora/pp_cmp_tp.pdf b/mlora/pp_cmp_tp.pdf new file mode 100644 index 0000000000000000000000000000000000000000..575a82f422167b5b86c227aa49f30d3eead6715f GIT binary patch literal 22475 zcmd74by!tR)Hf_5N+}?SQhMl)lYoSDcem7`y96oe?vO@7N)SmA1VlQN5ReW9K?M|) z6qIl8quh_T^1Qe2cYS|+aGh&ruRSws&CJ@ferxTS&8Q+F#R=s?;4_v@LrNR)p%5O3 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