Updated Throughput Results

Plots/Fig_18/.ipynb_checkpoints/get_SN40L_results-checkpoint.ipynb

@@ -0,0 +1,160 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce6e6f7c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1432d29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "file_name = \"SN40L_updated_results.csv\"\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "35cfc93b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "\n",
+    "def read_csv_without_header(csv_file):\n",
+    "    with open(csv_file, \"r\", newline=\"\") as f:\n",
+    "        reader = csv.reader(f)\n",
+    "        next(reader)\n",
+    "\n",
+    "        rows = []\n",
+    "        for row in reader:\n",
+    "            rows.append(row)\n",
+    "    return rows\n",
+    "\n",
+    "\n",
+    "def get_matching_row(hardware, numhardware, framework, network, inp_out, batch):\n",
+    "    csv_file = \"../SN40L_updated_results.csv\"\n",
+    "    rows = read_csv_without_header(csv_file)\n",
+    "    \n",
+    "    for row in rows:\n",
+    "        if row != []:\n",
+    "            Hardware = str(row[0]) \n",
+    "            Num_of_Hardware = int(row[1])\n",
+    "            Framework = str(row[2])\n",
+    "            Model = str(row[3])\n",
+    "            Input_Output_Length = int(row[4]) \n",
+    "            Batch_Size = int(row[5])\n",
+    "            Throughput = float(row[6])\n",
+    "            \n",
+    "            \n",
+    "            if Hardware == hardware:\n",
+    "                if Num_of_Hardware == numhardware:\n",
+    "                    if Framework == framework:\n",
+    "                        if Model == network:\n",
+    "                            if Input_Output_Length == inp_out:\n",
+    "                                if Batch_Size == batch:\n",
+    "                                    latency = float((batch*(Input_Output_Length + Input_Output_Length))/Throughput)\n",
+    "                                    \n",
+    "                                    list_1 = [Hardware,Num_of_Hardware,Framework,Model,Input_Output_Length,Batch_Size,latency,Throughput]\n",
+    "                                    \n",
+    "                                    with open(\"SN40L_latency_throughput.csv\", 'a', newline = '') as csvfile:\n",
+    "                                        writer = csv.writer(csvfile)\n",
+    "                                        writer.writerow(list_1) \n",
+    "                        \n",
+    "                                    csvfile.close()\n",
+    "                                    \n",
+    "def get_data_rows():\n",
+    "    \n",
+    "    for framework in [\"sambaflow\"]:\n",
+    "        for hardware in [\"SambaNova SN40L\"]:\n",
+    "            for network in [\"meta-llama/Meta-Llama-3-70B\"]:\n",
+    "                inp_out_list = []\n",
+    "                for inp_out in [128, 256, 512, 1024, 2048]:\n",
+    "                    for numhardware in [8]:\n",
+    "                        for batch in [1,16]:\n",
+    "                            get_matching_row(hardware, numhardware, framework, network, inp_out, batch)\n",
+    "get_data_rows()                            "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97aab62d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbf08689",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "103697c9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bf8c083",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a35ca51",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cfc74dc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Plots/Fig_2_a/.ipynb_checkpoints/Fig 2 b-checkpoint.ipynb

@@ -0,0 +1,206 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6527cb23",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af24bbbf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a21ad9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "import csv\n",
+    "\n",
+    "def read_csv_without_header(csv_file):\n",
+    "    with open(csv_file, \"r\", newline=\"\") as f:\n",
+    "        reader = csv.reader(f)\n",
+    "        next(reader)\n",
+    "\n",
+    "        rows = []\n",
+    "        for row in reader:\n",
+    "            rows.append(row)\n",
+    "    return rows\n",
+    "\n",
+    "\n",
+    "def get_matching_row(hardware, numhardware, framework, network, KV_block_size, batch):\n",
+    "    csv_file = \"All_results.csv\"\n",
+    "    rows = read_csv_without_header(csv_file)\n",
+    "    \n",
+    "    for row in rows:\n",
+    "        if row != []:\n",
+    "            Hardware = str(row[0]) \n",
+    "            Num_of_Hardware = int(row[1])\n",
+    "            Framework = str(row[2])\n",
+    "            Model = str(row[3])\n",
+    "            Input_Output_Length = int(row[4]) \n",
+    "            Batch_Size = int(row[5])\n",
+    "            Latency = float(row[6])\n",
+    "            Throughput = float(row[7])\n",
+    "            \n",
+    "            if Hardware == hardware:\n",
+    "                if Num_of_Hardware == numhardware:\n",
+    "                    if Framework == framework:\n",
+    "                        if Model == network:\n",
+    "                            if Input_Output_Length == inp_out:\n",
+    "                                if Batch_Size == batch:\n",
+    "                                    return Throughput\n",
+    "    return None\n",
+    "    \n",
+    "\n",
+    "def get_data_rows():\n",
+    "    \n",
+    "    results_data = []\n",
+    "    \n",
+    "    for KV_block_size in [8, 16, 32, 64, 128]:\n",
+    "        batch_row = []\n",
+    "        for batch in [1, 16, 32, 64]:\n",
+    "            for hardware in [\"Nvidia A100 GPU\"]:\n",
+    "                for numhardware in [1]:\n",
+    "                    for network in [\"meta-llama/Meta-Llama-3-8B\"]:\n",
+    "                        for framework in [\"vLLM\"]:\n",
+    "                            throughput = get_matching_row(hardware, numhardware, framework, network, KV_block_size, batch)\n",
+    "                            batch_row.append(throughput)\n",
+    "        results_data.append(batch_row)\n",
+    "                            \n",
+    "    x_axis_labels = [1, 16, 32, 64]\n",
+    "    legends       = [8, 16, 32, 64, 128]\n",
+    "\n",
+    "    colors = ['#08519c', '#a63603', '#006d2c', '#a50f15', '#54278f']\n",
+    "    symbols = ['o', 'p', 'd', 's', 'v']\n",
+    "    \n",
+    "    return results_data, x_axis_labels, legends, colors, symbols\n",
+    "\n",
+    "\n",
+    "\n",
+    "def plot_multiple_lines(lines_data, x_axis_labels, legends, colors, symbols, title, xlabel, \n",
+    "                        ylabel, linewidth, markersize, fontsize, n_cols_legend,\n",
+    "                        fig_plot_length, fig_plot_height, legend_title, save_pdf, labelspacing, columnspacing):\n",
+    "    \n",
+    "    plt.figure(figsize=(fig_plot_length, fig_plot_height))\n",
+    "    plt.rcParams['font.family'] = 'Century Schoolbook'\n",
+    "    \n",
+    "    for i, line_data in enumerate(lines_data):\n",
+    "        \n",
+    "        valid_y_axis = [x for x in line_data if x is not None]\n",
+    "        valid_x_axis = x_axis_labels[0:len(valid_y_axis)]\n",
+    "        \n",
+    "        plt.plot(valid_x_axis, valid_y_axis, color=colors[i], marker=symbols[i], \n",
+    "                 label=str(legends[i]), linewidth = linewidth, markersize = markersize)\n",
+    "    \n",
+    "    for x_value in x_axis_labels:\n",
+    "        plt.axvline(x=x_value, color='gray', linestyle='-', alpha=0.5)\n",
+    "    \n",
+    "    \n",
+    "    plt.title(title, fontsize = fontsize)\n",
+    "    \n",
+    "    plt.xlabel(xlabel, fontsize = fontsize)\n",
+    "    plt.ylabel(ylabel, fontsize = fontsize)\n",
+    "    \n",
+    "    plt.xticks(x_axis_labels, fontsize = fontsize)\n",
+    "    plt.yticks(fontsize = fontsize)\n",
+    "    \n",
+    "    def thousands_formatter(x, pos):\n",
+    "        return f'{int(x/1000)}k'\n",
+    "    \n",
+    "    plt.gca().yaxis.set_major_formatter(FuncFormatter(thousands_formatter))\n",
+    "    \n",
+    "    plt.legend(title=legend_title, loc='lower center', bbox_to_anchor=(0.5, -0.72), \n",
+    "               ncol=n_cols_legend, fontsize = fontsize, title_fontsize = fontsize, frameon = False, \n",
+    "               handlelength = 0, labelspacing = labelspacing, columnspacing = columnspacing)\n",
+    "    \n",
+    "    plt.savefig(save_pdf, dpi=300, bbox_inches='tight', pad_inches=0.03)\n",
+    "    plt.show()\n",
+    "    \n",
+    "\n",
+    "lines_data, x_axis_labels, legends, colors, symbols = get_data_rows()\n",
+    "\n",
+    "plot_title = \"vLLM: Batch Size vs Input/Output Length \\n of LLaMA-3-8B on a Single A100 GPU\"\n",
+    "x_axis_title = \"Batch Size\"\n",
+    "y_axis_title = \"Throughput (Tokens/sec)\"\n",
+    "\n",
+    "linewidth = 3\n",
+    "markersize = 8\n",
+    "fontsize = 20\n",
+    "\n",
+    "n_cols_legend = len(legends) \n",
+    "\n",
+    "fig_plot_length = 6\n",
+    "fig_plot_height = 2.8\n",
+    "\n",
+    "labelspacing = 0.2\n",
+    "columnspacing = 0.6\n",
+    "\n",
+    "legend_title = \"Input/Output Length\"\n",
+    "\n",
+    "save_pdf = \"llama_3_batch_size_vs_input_length_vLLM.pdf\"\n",
+    "\n",
+    "plot_multiple_lines(lines_data, x_axis_labels, legends, colors, symbols, \n",
+    "                    plot_title, x_axis_title, y_axis_title, \n",
+    "                    linewidth, markersize, fontsize, n_cols_legend, \n",
+    "                    fig_plot_length, fig_plot_height, legend_title, save_pdf, labelspacing, columnspacing)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b05bfb94",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e599f039",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e76e5f8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}